System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving purchase information; AI engine means for proposing a coordinate item based on the received purchase information; means for generating information of the coordinate item; and means for notifying terminals of the generated coordinate information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention relates to a system that proposes appropriate outfits when purchasing fashion items. Specifically, the system aims to solve the following problems faced by busy, fashion-conscious, and environmentally conscious consumers:

[0005] 1. The need for more efficient fashion item selection due to lack of shopping time.

[0006] 2. Lack of personalized fashion selection tailored to user preferences.

[0007] 3. Uncertainty and anxiety when choosing clothes online.

[0008] 4. Consumer demand for sustainable brand and product choices.

[0009] 5. Lack of access to new shopping experiences.

[0010] Conventional technologies have not been able to adequately address these issues, so new solutions are needed. [Means for solving the problem]

[0011] The present invention solves the above problems by the following means:

[0012] 1. Provide a means for receiving purchase information. This means obtains information about fashion items purchased by users at online shops.

[0013] 2. Provide an AI engine means for suggesting coordinated items based on the received purchase information. This means analyzes the user's past purchase history, preferences, and current fashion trends to generate optimal coordinated outfits.

[0014] 3. Provide a means for generating coordinated item information. This will generate detailed information (price, brand, etc.) about the selected items and compile it into a single coordinated suggestion.

[0015] 4. Provide a means for notifying the terminal of the generated coordination information. This means allows the coordination results to be displayed on the user's terminal, enabling efficient and personalized fashion selection.

[0016] Furthermore, the AI ​​engine means includes an algorithm that prioritizes the suggestion of items from sustainable brands, thereby realizing eco-friendly coordination suggestions, allowing users to select fashion items efficiently and in a short time while being environmentally conscious.

[0017] The "means for receiving purchase information" refers to a device or program that acquires information about fashion items purchased by a user at an online shop and transmits this information to the system.

[0018] "AI engine means" refers to a device or program that uses artificial intelligence to analyze a user's purchase history, preferences, fashion trends, etc., and generate optimal outfits.

[0019] "Coordinating items" refers primarily to other items in fashion that go well with a particular item (e.g. bottoms, shoes, accessories, etc. for a top).

[0020] "Generating means" refers to a device or program that forms analysis results and proposals based on specific data and information and provides them to users.

[0021] The "means for notifying the terminal" refers to a device or program that transmits information and data generated by the server to the user's device and displays it.

[0022] A "sustainable brand" is a brand that offers fashion items that are produced sustainably and with consideration for the environment and society.

[0023] "Analyzing" is the act of organizing data based on received information and drawing meaningful conclusions or results.

[0024] "Fashion trends" refers to the styles, colors, designs, etc. that are popular or in fashion in the current market. [Brief explanation of the drawings]

[0025] [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

[0026] 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.

[0027] First, the terms used in the following description will be explained.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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."

[0046] The present invention relates to a system for suggesting a suitable coordination for fashion items purchased by a user from an online shop. Hereinafter, an embodiment of the present invention will be specifically described.

[0047] System Configuration

[0048] The system of the present invention mainly comprises the following components:

[0049] 1. Terminal

[0050] 2. Server

[0051] 3. AI Engine

[0052] Program processing (outline)

[0053] 1. Receiving purchase information

[0054] Case study: A user purchases a white blouse from an online shop.

[0055] 1.1 User confirms purchase

[0056] A user logs into an online shop and purchases a white blouse.

[0057] 1.2 Device collects purchase information

[0058] The device will obtain detailed information about the blouse (item name, color, size, price, etc.) Specifically, this information is obtained automatically when the purchase button is clicked.

[0059] 1.3 The device sends the purchase information to the server

[0060] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[0061] 2. Coordinate Generation

[0062] The server proposes a coordination proposal using the following steps:

[0063] 2.1 The server receives the purchase information

[0064] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[0065] 2.2 The server starts the AI ​​engine

[0066] The server then activates the AI ​​engine based on the received purchase information, which then begins analysis taking into account the user's past purchase history, preferences, and current fashion trends.

[0067] 2.3 AI engine suggests coordination items

[0068] The AI ​​engine analyzes purchase history, a user's style, and trends to select the perfect accessories to go with the purchased white blouse, such as a black skirt, red heels, and gold accessories.

[0069] 2.4 Servers prioritize sustainable branded items

[0070] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[0071] 2.5 Server generates coordinate information

[0072] The server generates outfit information including detailed information about the suggested items (price, brand, etc.), which is then ready to be provided to the user.

[0073] 3. Notification and display of results

[0074] The generated coordinate information is notified to the user as follows.

[0075] 3.1 The server sends the coordination results to the terminal

[0076] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[0077] 3.2 The terminal displays the results

[0078] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[0079] 3.3 User confirms the suggested outfits

[0080] The user can check the coordination suggestions displayed on the terminal and consider additional purchases if necessary.

[0081] Specific examples

[0082] A specific scenario is shown below, where a user purchases a white blouse and the system suggests a suitable outfit.

[0083] 1. A user purchases a white blouse from an online store.

[0084] 2. The device collects purchase information and sends it to the server.

[0085] 3. The server receives the purchase information and starts the AI ​​engine.

[0086] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[0087] 5. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[0088] 6. The server generates the coordinate information and sends it to the terminal.

[0089] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[0090] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while enjoying a sustainable, brand-conscious shopping experience.

[0091] The processing flow will be explained below.

[0092] Step 1:

[0093] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[0094] Step 2:

[0095] The terminal collects purchase information. The terminal obtains detailed information about the white blouse (item name, color, size, price, etc.). This includes a program that automatically reads the data displayed on the product page.

[0096] Step 3:

[0097] The device sends the purchase information to the server. When the purchase is confirmed, the device sends the acquired purchase information to the server. HTTP or HTTPS is used as the communication protocol.

[0098] Step 4:

[0099] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the data for analysis. The purchase information is saved using a database management system (DBMS).

[0100] Step 5:

[0101] The server starts the AI ​​engine. The server passes the purchase information to the AI ​​engine and begins processing. The AI ​​engine prepares a dataset that takes into account the user's preferences, past purchase history, current fashion trends, etc.

[0102] Step 6:

[0103] The AI ​​engine analyzes the user's information. The AI ​​engine performs analysis based on a prepared dataset. This analysis uses machine learning algorithms (e.g., regression analysis, clustering) to select the best outfit items based on the user's style and trends.

[0104] Step 7:

[0105] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server maintains a list of sustainable brands and filters by comparing them with this list.

[0106] Step 8:

[0107] The server generates outfit information. The server compiles the selected outfit items (e.g., black skirt, red heels, gold accessories) and their detailed information (price, brand name) into a single outfit suggestion.

[0108] Step 9:

[0109] The server sends the coordination results to the device. The generated coordination information is packaged as data for transmission to the device and sent via a communication protocol. JSON or XML is commonly used as the data format.

[0110] Step 10:

[0111] The terminal displays the results. The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit.

[0112] Step 11:

[0113] The user checks the suggested outfits. The user checks the outfit suggestions displayed on the device and considers additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[0114] Through the above processing steps, the user can make personalized fashion choices efficiently in a short time and can also select environmentally friendly items.

[0115] Example 1

[0116] 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."

[0117] Conventional online shopping systems lacked the means to suggest appropriate outfits for the items purchased by users. As a result, users had to spend time selecting outfits that matched their tastes and trends, resulting in an inefficient shopping experience. Furthermore, systems that considered items from sustainable brands were also inadequate, which meant that the needs of environmentally conscious consumers could not be met.

[0118] 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.

[0119] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, and a means for generating information on the suggested coordinating items. This allows for prompt and appropriate coordination suggestions for items purchased by the user. Furthermore, items from sustainable brands can be preferentially suggested, making it easy for users to make environmentally conscious choices.

[0120] The "means for receiving purchase information" is a device or software for transmitting detailed information about an item to a server when a user confirms a purchase at an online shop.

[0121] "Artificial intelligence means" refers to an algorithm or system that analyzes a user's past purchase history, preferences, and fashion trends based on their purchase information, and selects appropriate coordinating items.

[0122] The "means for generating information on coordinated items" refers to a program or function for collectively generating detailed information (item names, prices, brand names, etc.) on the coordinated items selected by the artificial intelligence means.

[0123] The "means for notifying the terminal of the generated coordinate information" is a communication means for transmitting the coordinate information generated by the server to the user's terminal.

[0124] The "means for displaying the notified coordinate information on the screen" is an interface for visually displaying the coordinate information received by the terminal from the server on the user's screen.

[0125] The "means for confirming suggested coordination" is a function that allows the user to confirm the coordination suggestions displayed on the terminal and consider additional purchases as necessary.

[0126] "Past purchase history" refers to a record of items a user has previously purchased from an online shop.

[0127] "Preferences" refer to the characteristics and styles of items that a user has preferred to purchase in the past.

[0128] "Current fashion trends" refers to the trends in fashion styles and items that are popular at a particular time.

[0129] A "sustainable brand" is a brand that uses environmentally friendly materials and manufacturing methods and is socially responsible.

[0130] "Coordination information" refers to a series of suggestions that combine detailed information about items selected by the artificial intelligence means.

[0131] The present invention relates to a system for suggesting appropriate coordination for fashion items purchased by a user from an online shop. Specific embodiments for carrying out the present invention will be described below.

[0132] System Configuration

[0133] The system of the present invention mainly consists of the following components:

[0134] 1. Terminal

[0135] 2. Server

[0136] 3. AI Engine

[0137] Program processing

[0138] Receiving purchase information

[0139] First, a user confirms a purchase at an online shop. For example, suppose the user purchases a white blouse. When the purchase is confirmed, the device automatically collects detailed information about the item (item name, color, size, price, purchase date and time). The collected purchase information is sent to the server via network communication. Specifically, it is sent as an HTTP request.

[0140] Coordinate generation

[0141] The server receives the purchase information sent from the device via an HTTP request. Based on the received information, the server activates an AI engine. The AI ​​engine incorporates an algorithm that suggests coordinating items based on past purchase history, the user's preferences, and fashion trends.

[0142] The AI ​​engine analyzes a user's past purchase history and current fashion trends to select the perfect outfit, for example, a black skirt, red heels, and gold accessories to go with a white blouse. Items from sustainable brands are prioritized, and the engine suggests sustainable items whenever possible.

[0143] The server automatically organizes detailed information about the selected items and generates coordinated outfit information, which includes the item name, price, brand name, and total coordinated outfit price.

[0144] Notification and display of results

[0145] The server sends the generated coordinated outfit information to the user's device as an HTTP response. The device displays the received coordinated outfit information on the screen, allowing the user to confirm the information. Specifically, the screen displays item images, names, prices, brand names, and the total price of the coordinated outfit.

[0146] The user can check the coordination suggestions displayed on the terminal and consider purchasing additional suggested items if necessary.

[0147] Specific examples

[0148] Consider a case where a user purchases a white blouse from an online shop and is offered a suitable outfit for it.

[0149] 1. A user purchases a white blouse from an online store.

[0150] 2. The device collects purchase information and sends it to the server.

[0151] 3. The server receives the purchase information and starts the AI ​​engine.

[0152] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[0153] 5. The server will check for items from sustainable brands and prioritize sustainable items if available.

[0154] 6. The server generates the coordinate information and sends it to the terminal.

[0155] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[0156] This allows users to quickly and efficiently receive personalized fashion recommendations and easily make additional purchases as needed. Items from sustainable brands are also given priority consideration, providing a satisfying shopping experience for environmentally conscious consumers.

[0157] Prompt Sentence Examples

[0158] "When a user purchases a white blouse, please explain the program process for the system that suggests outfits based on that item."

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1:

[0161] The user confirms the purchase

[0162] The user logs into the online store and decides to purchase a white blouse by clicking the purchase button on the product detail page.

[0163] Input: User clicks

[0164] Output: Temporarily save purchase information

[0165] Step 2:

[0166] The device collects purchase information

[0167] When a purchase is confirmed, the device automatically collects detailed item information (item name, color, size, price, purchase date and time). This includes a script that uses a browser event listener to detect when the purchase button is clicked and retrieves the purchase information.

[0168] Input: Product information specified by the user

[0169] Output: Collected purchase information (item name, color, size, price, purchase date and time)

[0170] Step 3:

[0171] The device sends the purchase information to the server

[0172] The device sends the collected purchase information to the server via network communication. Specifically, the purchase information is converted into JSON format and sent as an HTTP POST request.

[0173] Input: Collected purchase information

[0174] Output: Purchase information sent to the server (JSON format)

[0175] Step 4:

[0176] The server receives the purchase information

[0177] The server receives the purchase information sent from the device via an HTTP request. Once the purchase information is received, the server parses it and prepares it for processing.

[0178] Input: Purchase information sent from the device (JSON format)

[0179] Output: Parsable purchase information

[0180] Step 5:

[0181] The server starts the AI ​​engine

[0182] The server activates an AI engine based on the received purchase information, which analyzes the user's past purchase history, preferences, and fashion trends, and begins the process of suggesting optimal coordinating items.

[0183] Input: Parsable purchase information

[0184] Output: AI engine startup and initial data setup completed

[0185] Step 6:

[0186] AI engine suggests coordination items

[0187] The AI ​​engine analyzes past purchase history, user preferences, and trend information. For example, it uses a machine learning model to analyze the data and select the most suitable items (black skirt, red heels, gold accessories).

[0188] Input: User's past purchase history, preferences, and fashion trend information

[0189] Output: A list of suggested outfit items

[0190] Step 7:

[0191] Servers prioritize sustainable branded items

[0192] The server prioritizes sustainable brand items among the suggested coordination items and adds them to the list. If a sustainable brand item is included, it is suggested as the optimal choice.

[0193] Input: A list of suggested coordinating items

[0194] Output: A list of outfits that prioritize items from sustainable brands

[0195] Step 8:

[0196] The server generates the coordinate information.

[0197] The server generates coordinate information including sustainable brand items, including item names, prices, brand names, and the total price of the coordinated items.

[0198] Input: A list of outfit items from sustainable brands

[0199] Output: Generated coordinate information

[0200] Step 9:

[0201] The server sends the coordinated results to the terminal.

[0202] The server sends the generated coordinate information to the terminal as an HTTP response.

[0203] Input: Generated coordinate information

[0204] Output: Coordinate information sent to the device

[0205] Step 10:

[0206] The terminal displays the results

[0207] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[0208] Input: Coordinate information sent from the server

[0209] Output: Coordinate information displayed on the user screen

[0210] Step 11:

[0211] The user checks the suggested outfits

[0212] The user checks the coordination suggestions displayed on the terminal and considers purchasing additional suggested items as necessary.

[0213] Input: Coordination information displayed on the screen

[0214] Output: User confirms outfit suggestions and considers purchasing

[0215] (Application example 1)

[0216] 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."

[0217] Users often have difficulty efficiently finding stylish outfits that suit them when purchasing fashion items online. Furthermore, prioritizing sustainable brand products can be time-consuming. Furthermore, if outfit suggestions are not visually easy to understand, users are less likely to accept them. There is a need for a system that can solve these issues and provide a user-friendly, sustainable shopping experience.

[0218] 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.

[0219] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, a means for generating coordinated outfits taking into consideration the user's past purchase history and preferences, a means for notifying the terminal of the generated coordinated outfit information, and a means for visually displaying the generated coordinated outfit information. This allows users to easily receive personalized coordinated outfit suggestions when shopping online, enabling them to prioritize the selection of products from sustainable brands. Furthermore, the visually easy-to-understand coordinated outfit suggestions can increase user acceptance.

[0220] "Purchase information" refers to detailed information about the product purchased by the user at the online shop (item name, color, size, price, etc.).

[0221] "Artificial intelligence" refers to algorithm technology that analyzes a user's purchase history, preferences, and current fashion trends to suggest optimal outfits.

[0222] "Past purchase history" refers to a record of products that a user has previously purchased from an online shop.

[0223] "User preferences" refers to the fashion preferences and style tendencies that the user has shown up to now.

[0224] "Fashion trends" refers to the current fashion trends and latest fashion trends in the market.

[0225] "Coordination" refers to proposing the optimal fashion style based on the items purchased by the user, combining them with other items.

[0226] A "sustainable brand" is a brand that takes the environment and society into consideration and manufactures and sells products in a sustainable manner.

[0227] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to connect to the Internet and use an online shop.

[0228] "Visually displaying" refers to displaying the generated coordinate information on the screen in a format that allows the user to understand it at a glance.

[0229] The embodiments for carrying out the present invention will be described in detail below.

[0230] The system includes means for receiving purchase information, means for suggesting coordinate items using artificial intelligence, means for notifying the generated coordinate information, and means for visually displaying it.

[0231] Program processing

[0232] 1. Receiving purchase information

[0233] The server receives detailed information (item name, color, size, price, etc.) about the product purchased by the user at the online shop. The purchase information is automatically collected and sent to the server when the user confirms the purchase of the product.

[0234] 2. Starting the AI ​​engine

[0235] Based on the received purchase information, the server launches an artificial intelligence engine (e.g., a model using TensorFlow or PyTorch). The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select the optimal coordinating items.

[0236] 3. Coordination item suggestions

[0237] The AI ​​engine generates the perfect outfit for each user based on the purchase information and analysis results, with suggested items chosen from sustainable brands whenever possible.

[0238] 4. Coordination information generation and notification

[0239] The server generates outfit information based on the detailed information of the outfit items suggested by the AI ​​engine, and this information is sent to the user's device and displayed in a visually easy-to-understand format.

[0240] Hardware and Software Use

[0241] The system uses the following hardware and software:

[0242] Hardware: Cloud servers (e.g., AWS and GCP) and user devices (e.g., smartphones, tablets, and PCs)

[0243] Software: Flask (web framework), artificial intelligence engine (e.g., TensorFlow or PyTorch-based models)

[0244] Specifically, the server uses Flask to build a web application and receives and processes purchase information. The AI ​​engine uses TensorFlow and PyTorch to analyze the user's past purchase history and current fashion trends to generate optimal outfits. The generated outfit information is sent to the device in JSON format and displayed visually on the device.

[0245] Specific examples

[0246] If the user purchases a white blouse

[0247] 1. When a user purchases a white blouse from an online shop, the purchase information is sent to the server.

[0248] 2. The server receives the purchase information and starts the AI ​​engine.

[0249] 3. The AI ​​engine analyzes past purchase history, user preferences, and current trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[0250] 4. The server generates coordinate information for the suggested items and sends it to the user's device.

[0251] 5. The user's device displays the received information on the screen, allowing the user to confirm the proposed coordination.

[0252] Example prompts for generative AI models

[0253] User has purchased a white blouse. Based on past purchase history, user preferences, and current fashion trends, generate a suitable outfit coordination proposal. Include items from sustainable brands if possible.

[0254] In this way, the user can receive optimal coordination suggestions for items purchased through online shopping in real time.

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] Receiving purchase information

[0258] Subject: Terminal

[0259] How it works: When a user purchases an item from an online store, purchase information (item name, color, size, price, etc.) is automatically collected.

[0260] Input: Item information that the user confirmed to purchase.

[0261] Output: Purchase information is obtained and sent from the device to the server.

[0262] Specific operation: When the user clicks the "Purchase" button, the device obtains detailed information about the item and sends a POST request to the server.

[0263] Step 2:

[0264] Receive and store purchase information on our server

[0265] Subject: Server

[0266] Operation: The server receives the purchase information sent from the terminal and stores it in a database.

[0267] Input: Purchase information sent from the device.

[0268] Output: Purchase information stored in a database.

[0269] Specific operation: The server receives purchase information via the API endpoint of the Flask server and performs the process of saving it in a database (e.g., MySQL or MongoDB).

[0270] Step 3:

[0271] AI engine startup and analysis

[0272] Subject: Server

[0273] How it works: The server launches an AI engine to perform analysis based on the saved purchase information.

[0274] Input: Purchase information stored in a database, as well as your purchasing history and preferences.

[0275] Output: Coordinate candidates as the analysis result.

[0276] How it works: The server calls an AI engine (such as a model implemented in TensorFlow or PyTorch) and inputs and analyzes purchase information, past purchase history, user preferences, and fashion trends. The model then generates appropriate outfit items.

[0277] Step 4:

[0278] Coordination information generation

[0279] Subject: Server

[0280] Operation: The server generates specific coordination information based on the analysis results of the AI ​​engine.

[0281] Input: Analysis results from the AI ​​engine.

[0282] Output: Coordinate information for presentation to the user.

[0283] Specific operation: The server formats the outfit suggestions obtained from the AI ​​engine, prioritizes items from sustainable brands, and generates the final outfit information (a series of item names, brands, prices, etc.).

[0284] Step 5:

[0285] Notification and display of coordination information

[0286] Subject: Server and Terminal

[0287] Operation: The server sends the generated coordinate information to the terminal, which then visually displays this information.

[0288] Input: Generated coordinate information.

[0289] Output: Coordination suggestions displayed on the user's device.

[0290] Specific operation: The server sends the generated outfit information (JSON format) to the device. Based on the received information, the device displays the item images, detailed information (brand name, price, etc.), and the total price of the outfit on the user's screen.

[0291] This processing flow allows the user to receive optimal coordination suggestions for the items they have purchased.

[0292] 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.

[0293] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[0294] System Configuration

[0295] The system of the present invention mainly comprises the following components:

[0296] 1. Terminal

[0297] 2. Server

[0298] 3. AI Engine

[0299] 4. Emotion Engine

[0300] Program processing (outline)

[0301] 1. Receiving purchase information

[0302] Case study: A user purchases a white blouse from an online shop.

[0303] 1.1 User confirms purchase

[0304] A user logs in to an online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[0305] 1.2 Device collects purchase information

[0306] The device retrieves detailed information about the white blouse (item name, color, size, price, etc.). Specifically, it includes a program to automatically read the data displayed on the product page.

[0307] 1.3 The device sends the purchase information to the server

[0308] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[0309] 2. Coordinate Generation

[0310] The server proposes a coordination proposal using the following steps:

[0311] 2.1 The server receives the purchase information

[0312] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[0313] 2.2 The server starts the AI ​​engine and emotion engine

[0314] The server then activates the AI ​​engine and emotion engine based on the received purchase information. The AI ​​engine then prepares a dataset to take into account the user's preferences, past purchase history, current fashion trends, etc.

[0315] 2.3 Emotion engine analyzes user emotions

[0316] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, analyzes that information, and determines the user's current emotional state based on the analysis results.

[0317] 2.4 The AI ​​engine analyzes user information

[0318] The AI ​​engine selects the optimal outfit items based on the user's past purchase history, preferences, and current fashion trend data, while also taking into account emotional information provided by the emotion engine.

[0319] 2.5 Server selects sustainable brands

[0320] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[0321] 2.6 Server generates coordinate information

[0322] The server then compiles detailed information (price, brand, etc.) about the suggested items into a single outfit suggestion, which may include adjusting colors and styles based on the user's emotional state.

[0323] 3. Notification and display of results

[0324] The generated coordinate information is notified to the user as follows.

[0325] 3.1 The server sends the coordination results to the terminal

[0326] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[0327] 3.2 The terminal displays the results

[0328] The terminal displays the received coordinated information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[0329] 3.3 User confirms the suggested outfits

[0330] The user can check the outfit suggestions displayed on the device and consider additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[0331] Specific examples

[0332] A specific scenario is shown below, where a user purchases a white blouse and the system uses the emotion engine to suggest a suitable outfit.

[0333] 1. A user purchases a white blouse from an online store.

[0334] 2. The device collects purchase information and sends it to the server.

[0335] 3. The server receives the purchase information and activates the AI ​​engine and emotion engine.

[0336] 4. The emotion engine determines from the user's facial expression that the current emotional state is "happy."

[0337] 5. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse. Because the emotion is "happiness," the AI ​​engine prioritizes coordinating items in vibrant colors.

[0338] 6. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[0339] 7. The server generates the coordinate information and sends it to the terminal.

[0340] 8. The terminal displays the received information to the user, who then confirms the proposed coordination.

[0341] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while also enjoying a shopping experience that takes into account their emotional state.

[0342] The processing flow will be explained below.

[0343] Step 1:

[0344] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[0345] Step 2:

[0346] The device collects purchase information. The device obtains detailed information about the white blouse (item name, color, size, price, etc.). Specifically, a script runs that automatically reads the data listed on the product page.

[0347] Step 3:

[0348] The terminal sends the purchase information to the server. After collecting the purchase information, the terminal sends it to the server. HTTP or HTTPS is used as the communication protocol.

[0349] Step 4:

[0350] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the information in a database. The stored information is used as a data set for subsequent analysis.

[0351] Step 5:

[0352] The server starts the AI ​​engine and emotion engine. The server starts the AI ​​engine and emotion engine simultaneously based on the received purchase information.

[0353] Step 6:

[0354] The emotion engine analyzes the user's emotions. The emotion engine acquires the user's facial expressions, voice, or text input data and analyzes the user's emotions based on them. The analysis results are output as emotional states such as "happiness," "sadness," and "surprise."

[0355] Step 7:

[0356] The AI ​​engine analyzes the user's information. It analyzes the user's past purchase history, preferences, current fashion trends, etc., and selects the optimal coordination items. At this time, the analysis results of the emotion engine are also used as input data.

[0357] Step 8:

[0358] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server compares the results with the sustainable brand list and selects items.

[0359] Step 9:

[0360] The server generates coordination information based on the selected coordination items (e.g., black skirt, red heels, gold accessories) and detailed information (price, brand, color and style corresponding to the emotion, etc.).

[0361] Step 10:

[0362] The server sends the coordinated results to the device. The generated coordinated information is packaged in JSON or XML format and sent to the device, which then receives the information.

[0363] Step 11:

[0364] The terminal displays the results. The terminal displays the coordinated information received from the server to the user. The displayed content includes an image of each item, its name, price, brand name, and emotionally appropriate comments and descriptions.

[0365] Step 12:

[0366] The user can then review the suggested outfits. The user can then review the outfit suggestions displayed on their device and purchase additional items as needed. They will also be given the option to add items directly to their shopping cart.

[0367] Through the above processing steps, the user can make personalized fashion choices quickly and efficiently, and enjoy a shopping experience that also takes into account their emotional state.

[0368] Example 2

[0369] 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."

[0370] Conventional coordination suggestion systems have the problem of not being able to sufficiently increase user satisfaction because they make suggestions based solely on past purchase history and trend data without taking into account the user's emotional state. Additionally, they lack the functionality to prioritize recommendations for products from sustainable brands, which means they do not provide an environmentally conscious shopping experience.

[0371] 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.

[0372] In this invention, the server includes means for receiving purchase information, artificial intelligence engine means for suggesting coordinating items based on the received purchase information, emotion engine means for analyzing the user's emotions, means for checking whether the suggested items are from sustainable brands, and means for generating information on the coordinating items, thereby enabling personalized coordination suggestions that take into account the user's emotional state and preferentially suggesting products from sustainable brands.

[0373] "Purchase information" refers to detailed information such as product name, color, size, and price that is generated when a user purchases a product from an online shop or a physical store.

[0374] An "artificial intelligence engine" refers to a program or algorithm that analyzes a user's past purchase history, preferences, and current fashion trend data to select the optimal coordinating items.

[0375] An "emotion engine" refers to a program or algorithm that recognizes emotions from a user's facial expressions, voice, text input, etc., and determines the user's current emotional state based on the analysis results.

[0376] A "sustainable brand" refers to a company or brand that places importance on environmentally friendly product development and ethical working conditions.

[0377] "Coordination information" refers to information about the user's fashion coordination, including detailed information about the suggested fashion items (product name, price, brand name, etc.).

[0378] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[0379] The main components of this system are the terminal, server, AI engine, and emotion engine. Each element has the following functions and works together to propose coordination.

[0380] 1. Terminal

[0381] The terminals include personal computers, smartphones, tablets, etc. used by users. When a user purchases a product from an online shop, the terminal has the function of collecting purchase information and sending it to a server. Specifically, this information includes the product name, color, size, price, etc.

[0382] 2. Server

[0383] The server receives purchase information sent from the device and activates an artificial intelligence engine and emotion engine, which selects coordinating items and generates coordination information. The server also checks for the availability of sustainable brand products and prioritizes their suggestions.

[0384] 3. Artificial Intelligence Engine

[0385] The AI ​​engine inputs data such as the user's past purchase history, preferences, and current fashion trends. By analyzing this data, it selects the optimal outfit items. For example, it uses deep learning frameworks such as TensorFlow and PyTorch.

[0386] 4. Emotion Engine

[0387] The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. It uses this information to determine the user's current emotional state, for example, using a generative AI model such as OpenAI's GPT-3.

[0388] As a concrete example, consider the case where a user purchases a white blouse from an online store. The following steps are performed:

[0389] A user purchases a white blouse from an online shop. After the purchase is completed, the device collects the purchase information and sends it to the server.

[0390] The server receives the purchase information and activates the artificial intelligence engine and emotion engine.

[0391] The emotion engine determines the emotion result "happiness" from the analysis of the user's facial expression.

[0392] The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories that go perfectly with a white blouse. Bright colors are selected because they represent the emotion "happiness."

[0393] The server checks for the availability of sustainable branded products and prioritizes sustainable items whenever possible.

[0394] Finally, the server transmits the generated coordinate information to the terminal, which displays it to the user.

[0395] In this way, users can receive personalized outfit suggestions that take their emotions into consideration, and as sustainable brands are prioritized in the suggestions, they can enjoy an environmentally conscious shopping experience.

[0396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0397] Step 1:

[0398] The user confirms the purchase

[0399] A user logs in to an online shop, selects a product (e.g., a white blouse) and completes the purchase procedure, which causes the terminal to acquire purchase information (product name, color, size, price, etc.) as input.

[0400] Step 2:

[0401] The device collects purchase information

[0402] The device runs a program (e.g., JavaScript) to automatically retrieve product information from the online shop's purchase page. The retrieved details (product name, color, size, price, etc.) are collected as data. This data becomes the input for the next processing step.

[0403] Step 3:

[0404] The device sends the purchase information to the server

[0405] The device sends the collected purchase information in JSON format to the server. The data is sent when the purchase procedure is completed. This sent purchase information becomes the starting point for processing on the server.

[0406] Step 4:

[0407] The server receives the purchase information

[0408] The server receives the purchase information sent from the terminal and stores it in a database. The received data includes detailed information such as the product name, color, size, and price. This data becomes the input for the next processing step.

[0409] Step 5:

[0410] The server runs the artificial intelligence engine and emotion engine.

[0411] The server launches an AI engine and an emotion engine based on the received purchase information. The AI ​​engine preprocesses the input data to prepare data on the user's preferences, past purchase history, and the latest fashion trends. The emotion engine loads a model (e.g., OpenAI's GPT-3) to analyze the user's facial expressions and text input.

[0412] Step 6:

[0413] Emotion engine analyzes user emotions

[0414] The emotion engine processes emotions based on the user's facial expressions, voice, and text input information. This determines the user's current emotional state (e.g., "happiness"). The analysis results are output as data, which is then input into the artificial intelligence engine.

[0415] Step 7:

[0416] An artificial intelligence engine analyzes user information

[0417] The AI ​​engine analyzes the user's purchase information, as well as past purchase history, preferences, and current fashion trend data. Based on this data, it selects the optimal coordinating items. This selection process also takes into account the analysis results of the emotion engine. The selected coordinating items are output as data.

[0418] Step 8:

[0419] Server selects sustainable brands

[0420] The server refers to a sustainability database to check whether the coordinated items selected by the AI ​​engine are from sustainable brands. The server adjusts the selection results so that items from sustainable brands are prioritized. Coordination information including sustainable items is output as data.

[0421] Step 9:

[0422] The server generates the coordinate information.

[0423] The server then compiles detailed information (price, brand name, etc.) about the selected coordinate items into a single proposal and generates coordinate information. This coordinate information is then output as data to be sent to the terminal.

[0424] Step 10:

[0425] The server sends the coordinate information to the terminal.

[0426] The server sends the generated coordinate information in JSON format to the user's device. This sent data becomes the input for the next processing step.

[0427] Step 11:

[0428] The terminal displays the results

[0429] The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit. The user can review this information and consider actions such as additional purchases.

[0430] (Application example 2)

[0431] 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."

[0432] Conventional outfit suggestion systems do not take into account the user's current emotional state, and the outfits they suggest do not always satisfy the user. Furthermore, the items suggested are not uniformly from sustainable brands, and they are not sufficiently environmentally friendly. There is a need to solve these problems and realize more personalized and environmentally friendly outfit suggestions for users.

[0433] 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.

[0434] In this invention, the server includes means for receiving purchase information, artificial intelligence means for suggesting coordinating items based on the received purchase information, emotion analysis means for analyzing the emotional state of the user, means for generating information on coordinating items, and means for notifying the communication terminal of the generated coordinate information. This enables personalized coordination suggestions that take the emotional state of the user into consideration, and further enables environmentally friendly suggestions by prioritizing items from sustainable brands.

[0435] The "means for receiving purchase information" is a function for receiving information about products purchased by a user from a communication terminal.

[0436] The "artificial intelligence means" is a system that has the function of analyzing the user's past purchase history, preferences, and current fashion trends based on the received purchase information, and selecting the most suitable coordinating items.

[0437] The "emotion analysis means" is a system that has the function of analyzing the user's emotional state from facial expressions, voice, text input, and the like.

[0438] The "means for generating information on coordinated items" is a function for generating detailed information on optimal coordinated items based on data obtained from the artificial intelligence means and the emotion analysis means.

[0439] The "means for notifying the communication terminal of the generated coordinate information" is a system having a function for transmitting the information of the generated coordinate items to the user's communication terminal and displaying it.

[0440] A "sustainable brand" is a brand that places importance on environmental protection and social responsibility and offers products that are produced in a sustainable manner.

[0441] In this invention, we will develop a system that proposes suitable outfits for fashion items purchased by a user. This system has a function of taking into account the user's current emotional state and preferentially proposing items from sustainable brands. Specific embodiments will be described below.

[0442] System Configuration

[0443] The system of the present invention comprises the following components:

[0444] Terminal

[0445] server

[0446] Artificial Intelligence Engine

[0447] Sentiment Analysis Engine

[0448] Hardware and software used

[0449] Hardware: Smartphone (iOS or Android), built-in camera

[0450] Software: Public sentiment analysis libraries (e.g., Microsoft Azure Emotion API), artificial intelligence engines (e.g., TensorFlow), cloud services (e.g., Amazon Web Services, Google Cloud Platform), data processing programs (e.g., Python, JavaScript)

[0451] Program processing overview

[0452] 1. Receiving purchase information:

[0453] When a user purchases an item online or at a virtual store, the purchase information (product name, color, size, price, etc.) is sent to the device, which then sends this information to the server.

[0454] 2. Emotional state analysis:

[0455] The device's camera is used to capture the user's face, and an emotion analysis library is used to analyze their current emotional state, which is then sent to the server.

[0456] 3. Coordinate generation:

[0457] The server launches an AI engine based on the received purchase information and sentiment analysis results. The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[0458] In addition, the system takes into account the user's emotional state obtained from the emotion analysis engine to suggest more appropriate outfits.

[0459] We prioritize selecting items from sustainable brands and offer environmentally friendly suggestions.

[0460] 4. Displaying the results:

[0461] The generated coordinated information is sent from the server to the terminal and displayed to the user, who can then check the suggested coordinated items and consider additional purchases if necessary.

[0462] Specific examples

[0463] For example, if a user purchases a blue dress in a virtual store, the device receives the purchase information and sends it to the server. The server then activates an emotion analysis engine to analyze the user's emotional state from the face captured by the smartphone camera. In this case, the server determines that the user's current emotion is "calm." The server then uses an artificial intelligence engine to analyze the user's past purchase history, preferences, and current fashion trends, and suggests a gray cardigan, silver accessories, and white sneakers that are appropriate for a calm emotion. Items from sustainable brands are prioritized, and the server sends the outfit information to the device and displays it to the user. The user then checks the outfit and makes additional purchases if necessary.

[0464] Prompt Sentence Examples

[0465] A user has purchased a blue dress. Suggest outfits to go with the dress. Also, consider that the user's current emotional state is "calm." Include sustainable brands in your suggestions.

[0466] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0467] Step 1:

[0468] A user purchases an item online or in a virtual store. The device receives this purchase information and sends the details (product name, color, size, price, etc.) to the server. This purchase information is important because it is used in subsequent processing.

[0469] Input: Purchase information (product name, color, size, price, etc.)

[0470] Output: Purchase information sent to the server

[0471] Step 2:

[0472] The server starts an emotion analysis engine to analyze the user's facial expressions based on the purchase information received from the device, captures the user's face using the device's camera, and sends the image to the server.

[0473] Input: Purchase information sent from the device, captured user face image

[0474] Output: Face image sent to the server

[0475] Step 3:

[0476] The server utilizes an emotion analysis engine to analyze the user's emotional state from the captured facial image. An emotion analysis library (e.g., Microsoft Azure Emotion API) is used to determine the user's current emotional state.

[0477] Input: User's face image

[0478] Output: Parsed emotional state (e.g. happy, calm, sad, etc.)

[0479] Step 4:

[0480] The server activates an AI engine based on the purchase information and emotional state, which analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[0481] Input: Purchase information, analyzed emotional state, past purchase history, user preferences, fashion trend data

[0482] Output: Information on the best coordinated items (product name, color, size, price, etc.)

[0483] Step 5:

[0484] The server preferentially selects items from sustainable brands from the generated coordination information.

[0485] Input: Information on the best coordinated items

[0486] Output: Sustainable brand item information

[0487] Step 6:

[0488] The server then proposes outfits to the user based on the selected item information, including item images, names, prices, brand names, and the total price of the outfit.

[0489] Input: Sustainable brand item information

[0490] Output: Coordination suggestion information (item image, name, price, brand name, total price, etc.)

[0491] Step 7:

[0492] The terminal receives the coordinated outfit suggestion information sent from the server and displays it on the user's screen. The user can check it and consider additional purchases if necessary.

[0493] Input: Coordination suggestion information

[0494] Output: Coordination suggestions displayed on the user's screen

[0495] The above is a specific process flow for implementing the invention. The system considers the user's emotional state and suggests items from sustainable brands, thereby increasing user satisfaction and providing an environmentally friendly shopping experience.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] [Second embodiment]

[0500] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0501] 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.

[0502] 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).

[0503] 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.

[0504] 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.

[0505] 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).

[0506] 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.

[0507] 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.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] 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."

[0512] The present invention relates to a system for suggesting a suitable coordination for fashion items purchased by a user from an online shop. Hereinafter, an embodiment of the present invention will be specifically described.

[0513] System Configuration

[0514] The system of the present invention mainly comprises the following components:

[0515] 1. Terminal

[0516] 2. Server

[0517] 3. AI Engine

[0518] Program processing (outline)

[0519] 1. Receiving purchase information

[0520] Case study: A user purchases a white blouse from an online shop.

[0521] 1.1 User confirms purchase

[0522] A user logs into an online shop and purchases a white blouse.

[0523] 1.2 Device collects purchase information

[0524] The device will obtain detailed information about the blouse (item name, color, size, price, etc.) Specifically, this information is obtained automatically when the purchase button is clicked.

[0525] 1.3 The device sends the purchase information to the server

[0526] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[0527] 2. Coordinate Generation

[0528] The server proposes a coordination proposal using the following steps:

[0529] 2.1 The server receives the purchase information

[0530] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[0531] 2.2 The server starts the AI ​​engine

[0532] The server then activates the AI ​​engine based on the received purchase information, which then begins analysis taking into account the user's past purchase history, preferences, and current fashion trends.

[0533] 2.3 AI engine suggests coordination items

[0534] The AI ​​engine analyzes purchase history, a user's style, and trends to select the perfect accessories to go with the purchased white blouse, such as a black skirt, red heels, and gold accessories.

[0535] 2.4 Servers prioritize sustainable branded items

[0536] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[0537] 2.5 Server generates coordinate information

[0538] The server generates outfit information including detailed information about the suggested items (price, brand, etc.), which is then ready to be provided to the user.

[0539] 3. Notification and display of results

[0540] The generated coordinate information is notified to the user as follows.

[0541] 3.1 The server sends the coordination results to the terminal

[0542] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[0543] 3.2 The terminal displays the results

[0544] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[0545] 3.3 User confirms the suggested outfits

[0546] The user can check the coordination suggestions displayed on the terminal and consider additional purchases if necessary.

[0547] Specific examples

[0548] A specific scenario is shown below, where a user purchases a white blouse and the system suggests a suitable outfit.

[0549] 1. A user purchases a white blouse from an online store.

[0550] 2. The device collects purchase information and sends it to the server.

[0551] 3. The server receives the purchase information and starts the AI ​​engine.

[0552] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[0553] 5. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[0554] 6. The server generates the coordinate information and sends it to the terminal.

[0555] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[0556] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while enjoying a sustainable, brand-conscious shopping experience.

[0557] The processing flow will be explained below.

[0558] Step 1:

[0559] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[0560] Step 2:

[0561] The terminal collects purchase information. The terminal obtains detailed information about the white blouse (item name, color, size, price, etc.). This includes a program that automatically reads the data displayed on the product page.

[0562] Step 3:

[0563] The device sends the purchase information to the server. When the purchase is confirmed, the device sends the acquired purchase information to the server. HTTP or HTTPS is used as the communication protocol.

[0564] Step 4:

[0565] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the data for analysis. The purchase information is saved using a database management system (DBMS).

[0566] Step 5:

[0567] The server starts the AI ​​engine. The server passes the purchase information to the AI ​​engine and begins processing. The AI ​​engine prepares a dataset that takes into account the user's preferences, past purchase history, current fashion trends, etc.

[0568] Step 6:

[0569] The AI ​​engine analyzes the user's information. The AI ​​engine performs analysis based on a prepared dataset. This analysis uses machine learning algorithms (e.g., regression analysis, clustering) to select the best outfit items based on the user's style and trends.

[0570] Step 7:

[0571] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server maintains a list of sustainable brands and filters by comparing them with this list.

[0572] Step 8:

[0573] The server generates outfit information. The server compiles the selected outfit items (e.g., black skirt, red heels, gold accessories) and their detailed information (price, brand name) into a single outfit suggestion.

[0574] Step 9:

[0575] The server sends the coordination results to the device. The generated coordination information is packaged as data for transmission to the device and sent via a communication protocol. JSON or XML is commonly used as the data format.

[0576] Step 10:

[0577] The terminal displays the results. The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit.

[0578] Step 11:

[0579] The user checks the suggested outfits. The user checks the outfit suggestions displayed on the device and considers additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[0580] Through the above processing steps, the user can make personalized fashion choices efficiently in a short time and can also select environmentally friendly items.

[0581] Example 1

[0582] 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."

[0583] Conventional online shopping systems lacked the means to suggest appropriate outfits for the items purchased by users. As a result, users had to spend time selecting outfits that matched their tastes and trends, resulting in an inefficient shopping experience. Furthermore, systems that considered items from sustainable brands were also inadequate, which meant that the needs of environmentally conscious consumers could not be met.

[0584] 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.

[0585] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, and a means for generating information on the suggested coordinating items. This allows for prompt and appropriate coordination suggestions for items purchased by the user. Furthermore, items from sustainable brands can be preferentially suggested, making it easy for users to make environmentally conscious choices.

[0586] The "means for receiving purchase information" is a device or software for transmitting detailed information about an item to a server when a user confirms a purchase at an online shop.

[0587] "Artificial intelligence means" refers to an algorithm or system that analyzes a user's past purchase history, preferences, and fashion trends based on their purchase information, and selects appropriate coordinating items.

[0588] The "means for generating information on coordinated items" refers to a program or function for collectively generating detailed information (item names, prices, brand names, etc.) on the coordinated items selected by the artificial intelligence means.

[0589] The "means for notifying the terminal of the generated coordinate information" is a communication means for transmitting the coordinate information generated by the server to the user's terminal.

[0590] The "means for displaying the notified coordinate information on the screen" is an interface for visually displaying the coordinate information received by the terminal from the server on the user's screen.

[0591] The "means for confirming suggested coordination" is a function that allows the user to confirm the coordination suggestions displayed on the terminal and consider additional purchases as necessary.

[0592] "Past purchase history" refers to a record of items a user has previously purchased from an online shop.

[0593] "Preferences" refer to the characteristics and styles of items that a user has preferred to purchase in the past.

[0594] "Current fashion trends" refers to the trends in fashion styles and items that are popular at a particular time.

[0595] A "sustainable brand" is a brand that uses environmentally friendly materials and manufacturing methods and is socially responsible.

[0596] "Coordination information" refers to a series of suggestions that combine detailed information about items selected by the artificial intelligence means.

[0597] The present invention relates to a system for suggesting appropriate coordination for fashion items purchased by a user from an online shop. Specific embodiments for carrying out the present invention will be described below.

[0598] System Configuration

[0599] The system of the present invention mainly consists of the following components:

[0600] 1. Terminal

[0601] 2. Server

[0602] 3. AI Engine

[0603] Program processing

[0604] Receiving purchase information

[0605] First, a user confirms a purchase at an online shop. For example, suppose the user purchases a white blouse. When the purchase is confirmed, the device automatically collects detailed information about the item (item name, color, size, price, purchase date and time). The collected purchase information is sent to the server via network communication. Specifically, it is sent as an HTTP request.

[0606] Coordinate generation

[0607] The server receives the purchase information sent from the device via an HTTP request. Based on the received information, the server activates an AI engine. The AI ​​engine incorporates an algorithm that suggests coordinating items based on past purchase history, the user's preferences, and fashion trends.

[0608] The AI ​​engine analyzes a user's past purchase history and current fashion trends to select the perfect outfit, for example, a black skirt, red heels, and gold accessories to go with a white blouse. Items from sustainable brands are prioritized, and the engine suggests sustainable items whenever possible.

[0609] The server automatically organizes detailed information about the selected items and generates coordinated outfit information, which includes the item name, price, brand name, and total coordinated outfit price.

[0610] Notification and display of results

[0611] The server sends the generated coordinated outfit information to the user's device as an HTTP response. The device displays the received coordinated outfit information on the screen, allowing the user to confirm the information. Specifically, the screen displays item images, names, prices, brand names, and the total price of the coordinated outfit.

[0612] The user can check the coordination suggestions displayed on the terminal and consider purchasing additional suggested items if necessary.

[0613] Specific examples

[0614] Consider a case where a user purchases a white blouse from an online shop and is offered a suitable outfit for it.

[0615] 1. A user purchases a white blouse from an online store.

[0616] 2. The device collects purchase information and sends it to the server.

[0617] 3. The server receives the purchase information and starts the AI ​​engine.

[0618] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[0619] 5. The server will check for items from sustainable brands and prioritize sustainable items if available.

[0620] 6. The server generates the coordinate information and sends it to the terminal.

[0621] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[0622] This allows users to quickly and efficiently receive personalized fashion recommendations and easily make additional purchases as needed. Items from sustainable brands are also given priority consideration, providing a satisfying shopping experience for environmentally conscious consumers.

[0623] Prompt Sentence Examples

[0624] "When a user purchases a white blouse, please explain the program process for the system that suggests outfits based on that item."

[0625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0626] Step 1:

[0627] The user confirms the purchase

[0628] The user logs into the online store and decides to purchase a white blouse by clicking the purchase button on the product detail page.

[0629] Input: User clicks

[0630] Output: Temporarily save purchase information

[0631] Step 2:

[0632] The device collects purchase information

[0633] When a purchase is confirmed, the device automatically collects detailed item information (item name, color, size, price, purchase date and time). This includes a script that uses a browser event listener to detect when the purchase button is clicked and retrieves the purchase information.

[0634] Input: Product information specified by the user

[0635] Output: Collected purchase information (item name, color, size, price, purchase date and time)

[0636] Step 3:

[0637] The device sends the purchase information to the server

[0638] The device sends the collected purchase information to the server via network communication. Specifically, the purchase information is converted into JSON format and sent as an HTTP POST request.

[0639] Input: Collected purchase information

[0640] Output: Purchase information sent to the server (JSON format)

[0641] Step 4:

[0642] The server receives the purchase information

[0643] The server receives the purchase information sent from the device via an HTTP request. Once the purchase information is received, the server parses it and prepares it for processing.

[0644] Input: Purchase information sent from the device (JSON format)

[0645] Output: Parsable purchase information

[0646] Step 5:

[0647] The server starts the AI ​​engine

[0648] The server activates an AI engine based on the received purchase information, which analyzes the user's past purchase history, preferences, and fashion trends, and begins the process of suggesting optimal coordinating items.

[0649] Input: Parsable purchase information

[0650] Output: AI engine startup and initial data setup completed

[0651] Step 6:

[0652] AI engine suggests coordination items

[0653] The AI ​​engine analyzes past purchase history, user preferences, and trend information. For example, it uses a machine learning model to analyze the data and select the most suitable items (black skirt, red heels, gold accessories).

[0654] Input: User's past purchase history, preferences, and fashion trend information

[0655] Output: A list of suggested outfit items

[0656] Step 7:

[0657] Servers prioritize sustainable branded items

[0658] The server prioritizes sustainable brand items among the suggested coordination items and adds them to the list. If a sustainable brand item is included, it is suggested as the optimal choice.

[0659] Input: A list of suggested coordinating items

[0660] Output: A list of outfits that prioritize items from sustainable brands

[0661] Step 8:

[0662] The server generates the coordinate information.

[0663] The server generates coordinate information including sustainable brand items, including item names, prices, brand names, and the total price of the coordinated items.

[0664] Input: A list of outfit items from sustainable brands

[0665] Output: Generated coordinate information

[0666] Step 9:

[0667] The server sends the coordinated results to the terminal.

[0668] The server sends the generated coordinate information to the terminal as an HTTP response.

[0669] Input: Generated coordinate information

[0670] Output: Coordinate information sent to the device

[0671] Step 10:

[0672] The terminal displays the results

[0673] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[0674] Input: Coordinate information sent from the server

[0675] Output: Coordinate information displayed on the user screen

[0676] Step 11:

[0677] The user checks the suggested outfits

[0678] The user checks the coordination suggestions displayed on the terminal and considers purchasing additional suggested items as necessary.

[0679] Input: Coordination information displayed on the screen

[0680] Output: User confirms outfit suggestions and considers purchasing

[0681] (Application example 1)

[0682] 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."

[0683] Users often have difficulty efficiently finding stylish outfits that suit them when purchasing fashion items online. Furthermore, prioritizing sustainable brand products can be time-consuming. Furthermore, if outfit suggestions are not visually easy to understand, users are less likely to accept them. There is a need for a system that can solve these issues and provide a user-friendly, sustainable shopping experience.

[0684] 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.

[0685] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, a means for generating coordinated outfits taking into consideration the user's past purchase history and preferences, a means for notifying the terminal of the generated coordinated outfit information, and a means for visually displaying the generated coordinated outfit information. This allows users to easily receive personalized coordinated outfit suggestions when shopping online, enabling them to prioritize the selection of products from sustainable brands. Furthermore, the visually easy-to-understand coordinated outfit suggestions can increase user acceptance.

[0686] "Purchase information" refers to detailed information about the product purchased by the user at the online shop (item name, color, size, price, etc.).

[0687] "Artificial intelligence" refers to algorithm technology that analyzes a user's purchase history, preferences, and current fashion trends to suggest optimal outfits.

[0688] "Past purchase history" refers to a record of products that a user has previously purchased from an online shop.

[0689] "User preferences" refers to the fashion preferences and style tendencies that the user has shown up to now.

[0690] "Fashion trends" refers to the current fashion trends and latest fashion trends in the market.

[0691] "Coordination" refers to proposing the optimal fashion style based on the items purchased by the user, combining them with other items.

[0692] A "sustainable brand" is a brand that takes the environment and society into consideration and manufactures and sells products in a sustainable manner.

[0693] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to connect to the Internet and use an online shop.

[0694] "Visually displaying" refers to displaying the generated coordinate information on the screen in a format that allows the user to understand it at a glance.

[0695] The embodiments for carrying out the present invention will be described in detail below.

[0696] The system includes means for receiving purchase information, means for suggesting coordinate items using artificial intelligence, means for notifying the generated coordinate information, and means for visually displaying it.

[0697] Program processing

[0698] 1. Receiving purchase information

[0699] The server receives detailed information (item name, color, size, price, etc.) about the product purchased by the user at the online shop. The purchase information is automatically collected and sent to the server when the user confirms the purchase of the product.

[0700] 2. Starting the AI ​​engine

[0701] Based on the received purchase information, the server launches an artificial intelligence engine (e.g., a model using TensorFlow or PyTorch). The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select the optimal coordinating items.

[0702] 3. Coordination item suggestions

[0703] The AI ​​engine generates the perfect outfit for each user based on the purchase information and analysis results, with suggested items chosen from sustainable brands whenever possible.

[0704] 4. Coordination information generation and notification

[0705] The server generates outfit information based on the detailed information of the outfit items suggested by the AI ​​engine, and this information is sent to the user's device and displayed in a visually easy-to-understand format.

[0706] Hardware and Software Use

[0707] The system uses the following hardware and software:

[0708] Hardware: Cloud servers (e.g., AWS and GCP) and user devices (e.g., smartphones, tablets, and PCs)

[0709] Software: Flask (web framework), artificial intelligence engine (e.g., TensorFlow or PyTorch-based models)

[0710] Specifically, the server uses Flask to build a web application and receives and processes purchase information. The AI ​​engine uses TensorFlow and PyTorch to analyze the user's past purchase history and current fashion trends to generate optimal outfits. The generated outfit information is sent to the device in JSON format and displayed visually on the device.

[0711] Specific examples

[0712] If the user purchases a white blouse

[0713] 1. When a user purchases a white blouse from an online shop, the purchase information is sent to the server.

[0714] 2. The server receives the purchase information and starts the AI ​​engine.

[0715] 3. The AI ​​engine analyzes past purchase history, user preferences, and current trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[0716] 4. The server generates coordinate information for the suggested items and sends it to the user's device.

[0717] 5. The user's device displays the received information on the screen, allowing the user to confirm the proposed coordination.

[0718] Example prompts for generative AI models

[0719] User has purchased a white blouse. Based on past purchase history, user preferences, and current fashion trends, generate a suitable outfit coordination proposal. Include items from sustainable brands if possible.

[0720] In this way, the user can receive optimal coordination suggestions for items purchased through online shopping in real time.

[0721] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0722] Step 1:

[0723] Receiving purchase information

[0724] Subject: Terminal

[0725] How it works: When a user purchases an item from an online store, purchase information (item name, color, size, price, etc.) is automatically collected.

[0726] Input: Item information that the user confirmed to purchase.

[0727] Output: Purchase information is obtained and sent from the device to the server.

[0728] Specific operation: When the user clicks the "Purchase" button, the device obtains detailed information about the item and sends a POST request to the server.

[0729] Step 2:

[0730] Receive and store purchase information on our server

[0731] Subject: Server

[0732] Operation: The server receives the purchase information sent from the terminal and stores it in a database.

[0733] Input: Purchase information sent from the device.

[0734] Output: Purchase information stored in a database.

[0735] Specific operation: The server receives purchase information via the API endpoint of the Flask server and performs the process of saving it in a database (e.g., MySQL or MongoDB).

[0736] Step 3:

[0737] AI engine startup and analysis

[0738] Subject: Server

[0739] How it works: The server launches an AI engine to perform analysis based on the saved purchase information.

[0740] Input: Purchase information stored in a database, as well as your purchasing history and preferences.

[0741] Output: Coordinate candidates as the analysis result.

[0742] How it works: The server calls an AI engine (such as a model implemented in TensorFlow or PyTorch) and inputs and analyzes purchase information, past purchase history, user preferences, and fashion trends. The model then generates appropriate outfit items.

[0743] Step 4:

[0744] Coordination information generation

[0745] Subject: Server

[0746] Operation: The server generates specific coordination information based on the analysis results of the AI ​​engine.

[0747] Input: Analysis results from the AI ​​engine.

[0748] Output: Coordinate information for presentation to the user.

[0749] Specific operation: The server formats the outfit suggestions obtained from the AI ​​engine, prioritizes items from sustainable brands, and generates the final outfit information (a series of item names, brands, prices, etc.).

[0750] Step 5:

[0751] Notification and display of coordination information

[0752] Subject: Server and Terminal

[0753] Operation: The server sends the generated coordinate information to the terminal, which then visually displays this information.

[0754] Input: Generated coordinate information.

[0755] Output: Coordination suggestions displayed on the user's device.

[0756] Specific operation: The server sends the generated outfit information (JSON format) to the device. Based on the received information, the device displays the item images, detailed information (brand name, price, etc.), and the total price of the outfit on the user's screen.

[0757] This processing flow allows the user to receive optimal coordination suggestions for the items they have purchased.

[0758] 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.

[0759] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[0760] System Configuration

[0761] The system of the present invention mainly comprises the following components:

[0762] 1. Terminal

[0763] 2. Server

[0764] 3. AI Engine

[0765] 4. Emotion Engine

[0766] Program processing (outline)

[0767] 1. Receiving purchase information

[0768] Case study: A user purchases a white blouse from an online shop.

[0769] 1.1 User confirms purchase

[0770] A user logs in to an online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[0771] 1.2 Device collects purchase information

[0772] The device retrieves detailed information about the white blouse (item name, color, size, price, etc.). Specifically, it includes a program to automatically read the data displayed on the product page.

[0773] 1.3 The device sends the purchase information to the server

[0774] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[0775] 2. Coordinate Generation

[0776] The server proposes a coordination proposal using the following steps:

[0777] 2.1 The server receives the purchase information

[0778] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[0779] 2.2 The server starts the AI ​​engine and emotion engine

[0780] The server then activates the AI ​​engine and emotion engine based on the received purchase information. The AI ​​engine then prepares a dataset to take into account the user's preferences, past purchase history, current fashion trends, etc.

[0781] 2.3 Emotion engine analyzes user emotions

[0782] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, analyzes that information, and determines the user's current emotional state based on the analysis results.

[0783] 2.4 The AI ​​engine analyzes user information

[0784] The AI ​​engine selects the optimal outfit items based on the user's past purchase history, preferences, and current fashion trend data, while also taking into account emotional information provided by the emotion engine.

[0785] 2.5 Server selects sustainable brands

[0786] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[0787] 2.6 Server generates coordinate information

[0788] The server then compiles detailed information (price, brand, etc.) about the suggested items into a single outfit suggestion, which may include adjusting colors and styles based on the user's emotional state.

[0789] 3. Notification and display of results

[0790] The generated coordinate information is notified to the user as follows.

[0791] 3.1 The server sends the coordination results to the terminal

[0792] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[0793] 3.2 The terminal displays the results

[0794] The terminal displays the received coordinated information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[0795] 3.3 User confirms the suggested outfits

[0796] The user can check the outfit suggestions displayed on the device and consider additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[0797] Specific examples

[0798] A specific scenario is shown below, where a user purchases a white blouse and the system uses the emotion engine to suggest a suitable outfit.

[0799] 1. A user purchases a white blouse from an online store.

[0800] 2. The device collects purchase information and sends it to the server.

[0801] 3. The server receives the purchase information and activates the AI ​​engine and emotion engine.

[0802] 4. The emotion engine determines from the user's facial expression that the current emotional state is "happy."

[0803] 5. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse. Because the emotion is "happiness," the AI ​​engine prioritizes coordinating items in vibrant colors.

[0804] 6. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[0805] 7. The server generates the coordinate information and sends it to the terminal.

[0806] 8. The terminal displays the received information to the user, who then confirms the proposed coordination.

[0807] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while also enjoying a shopping experience that takes into account their emotional state.

[0808] The processing flow will be explained below.

[0809] Step 1:

[0810] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[0811] Step 2:

[0812] The device collects purchase information. The device obtains detailed information about the white blouse (item name, color, size, price, etc.). Specifically, a script runs that automatically reads the data listed on the product page.

[0813] Step 3:

[0814] The terminal sends the purchase information to the server. After collecting the purchase information, the terminal sends it to the server. HTTP or HTTPS is used as the communication protocol.

[0815] Step 4:

[0816] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the information in a database. The stored information is used as a data set for subsequent analysis.

[0817] Step 5:

[0818] The server starts the AI ​​engine and emotion engine. The server starts the AI ​​engine and emotion engine simultaneously based on the received purchase information.

[0819] Step 6:

[0820] The emotion engine analyzes the user's emotions. The emotion engine acquires the user's facial expressions, voice, or text input data and analyzes the user's emotions based on them. The analysis results are output as emotional states such as "happiness," "sadness," and "surprise."

[0821] Step 7:

[0822] The AI ​​engine analyzes the user's information. It analyzes the user's past purchase history, preferences, current fashion trends, etc., and selects the optimal coordination items. At this time, the analysis results of the emotion engine are also used as input data.

[0823] Step 8:

[0824] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server compares the results with the sustainable brand list and selects items.

[0825] Step 9:

[0826] The server generates coordination information based on the selected coordination items (e.g., black skirt, red heels, gold accessories) and detailed information (price, brand, color and style corresponding to the emotion, etc.).

[0827] Step 10:

[0828] The server sends the coordinated results to the device. The generated coordinated information is packaged in JSON or XML format and sent to the device, which then receives the information.

[0829] Step 11:

[0830] The terminal displays the results. The terminal displays the coordinated information received from the server to the user. The displayed content includes an image of each item, its name, price, brand name, and emotionally appropriate comments and descriptions.

[0831] Step 12:

[0832] The user can then review the suggested outfits. The user can then review the outfit suggestions displayed on their device and purchase additional items as needed. They will also be given the option to add items directly to their shopping cart.

[0833] Through the above processing steps, the user can make personalized fashion choices quickly and efficiently, and enjoy a shopping experience that also takes into account their emotional state.

[0834] Example 2

[0835] 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."

[0836] Conventional coordination suggestion systems have the problem of not being able to sufficiently increase user satisfaction because they make suggestions based solely on past purchase history and trend data without taking into account the user's emotional state. Additionally, they lack the functionality to prioritize recommendations for products from sustainable brands, which means they do not provide an environmentally conscious shopping experience.

[0837] 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.

[0838] In this invention, the server includes means for receiving purchase information, artificial intelligence engine means for suggesting coordinating items based on the received purchase information, emotion engine means for analyzing the user's emotions, means for checking whether the suggested items are from sustainable brands, and means for generating information on the coordinating items, thereby enabling personalized coordination suggestions that take into account the user's emotional state and preferentially suggesting products from sustainable brands.

[0839] "Purchase information" refers to detailed information such as product name, color, size, and price that is generated when a user purchases a product from an online shop or a physical store.

[0840] An "artificial intelligence engine" refers to a program or algorithm that analyzes a user's past purchase history, preferences, and current fashion trend data to select the optimal coordinating items.

[0841] An "emotion engine" refers to a program or algorithm that recognizes emotions from a user's facial expressions, voice, text input, etc., and determines the user's current emotional state based on the analysis results.

[0842] A "sustainable brand" refers to a company or brand that places importance on environmentally friendly product development and ethical working conditions.

[0843] "Coordination information" refers to information about the user's fashion coordination, including detailed information about the suggested fashion items (product name, price, brand name, etc.).

[0844] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[0845] The main components of this system are the terminal, server, AI engine, and emotion engine. Each element has the following functions and works together to propose coordination.

[0846] 1. Terminal

[0847] The terminals include personal computers, smartphones, tablets, etc. used by users. When a user purchases a product from an online shop, the terminal has the function of collecting purchase information and sending it to a server. Specifically, this information includes the product name, color, size, price, etc.

[0848] 2. Server

[0849] The server receives purchase information sent from the device and activates an artificial intelligence engine and emotion engine, which selects coordinating items and generates coordination information. The server also checks for the availability of sustainable brand products and prioritizes their suggestions.

[0850] 3. Artificial Intelligence Engine

[0851] The AI ​​engine inputs data such as the user's past purchase history, preferences, and current fashion trends. By analyzing this data, it selects the optimal outfit items. For example, it uses deep learning frameworks such as TensorFlow and PyTorch.

[0852] 4. Emotion Engine

[0853] The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. It uses this information to determine the user's current emotional state, for example, using a generative AI model such as OpenAI's GPT-3.

[0854] As a concrete example, consider the case where a user purchases a white blouse from an online store. The following steps are performed:

[0855] A user purchases a white blouse from an online shop. After the purchase is completed, the device collects the purchase information and sends it to the server.

[0856] The server receives the purchase information and activates the artificial intelligence engine and emotion engine.

[0857] The emotion engine determines the emotion result "happiness" from the analysis of the user's facial expression.

[0858] The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories that go perfectly with a white blouse. Bright colors are selected because they represent the emotion "happiness."

[0859] The server checks for the availability of sustainable branded products and prioritizes sustainable items whenever possible.

[0860] Finally, the server transmits the generated coordinate information to the terminal, which displays it to the user.

[0861] In this way, users can receive personalized outfit suggestions that take their emotions into consideration, and as sustainable brands are prioritized in the suggestions, they can enjoy an environmentally conscious shopping experience.

[0862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0863] Step 1:

[0864] The user confirms the purchase

[0865] A user logs in to an online shop, selects a product (e.g., a white blouse) and completes the purchase procedure, which causes the terminal to acquire purchase information (product name, color, size, price, etc.) as input.

[0866] Step 2:

[0867] The device collects purchase information

[0868] The device runs a program (e.g., JavaScript) to automatically retrieve product information from the online shop's purchase page. The retrieved details (product name, color, size, price, etc.) are collected as data. This data becomes the input for the next processing step.

[0869] Step 3:

[0870] The device sends the purchase information to the server

[0871] The device sends the collected purchase information in JSON format to the server. The data is sent when the purchase procedure is completed. This sent purchase information becomes the starting point for processing on the server.

[0872] Step 4:

[0873] The server receives the purchase information

[0874] The server receives the purchase information sent from the terminal and stores it in a database. The received data includes detailed information such as the product name, color, size, and price. This data becomes the input for the next processing step.

[0875] Step 5:

[0876] The server runs the artificial intelligence engine and emotion engine.

[0877] The server launches an AI engine and an emotion engine based on the received purchase information. The AI ​​engine preprocesses the input data to prepare data on the user's preferences, past purchase history, and the latest fashion trends. The emotion engine loads a model (e.g., OpenAI's GPT-3) to analyze the user's facial expressions and text input.

[0878] Step 6:

[0879] Emotion engine analyzes user emotions

[0880] The emotion engine processes emotions based on the user's facial expressions, voice, and text input information. This determines the user's current emotional state (e.g., "happiness"). The analysis results are output as data, which is then input into the artificial intelligence engine.

[0881] Step 7:

[0882] An artificial intelligence engine analyzes user information

[0883] The AI ​​engine analyzes the user's purchase information, as well as past purchase history, preferences, and current fashion trend data. Based on this data, it selects the optimal coordinating items. This selection process also takes into account the analysis results of the emotion engine. The selected coordinating items are output as data.

[0884] Step 8:

[0885] Server selects sustainable brands

[0886] The server refers to a sustainability database to check whether the coordinated items selected by the AI ​​engine are from sustainable brands. The server adjusts the selection results so that items from sustainable brands are prioritized. Coordination information including sustainable items is output as data.

[0887] Step 9:

[0888] The server generates the coordinate information.

[0889] The server then compiles detailed information (price, brand name, etc.) about the selected coordinate items into a single proposal and generates coordinate information. This coordinate information is then output as data to be sent to the terminal.

[0890] Step 10:

[0891] The server sends the coordinate information to the terminal.

[0892] The server sends the generated coordinate information in JSON format to the user's device. This sent data becomes the input for the next processing step.

[0893] Step 11:

[0894] The terminal displays the results

[0895] The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit. The user can review this information and consider actions such as additional purchases.

[0896] (Application example 2)

[0897] 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."

[0898] Conventional outfit suggestion systems do not take into account the user's current emotional state, and the outfits they suggest do not always satisfy the user. Furthermore, the items suggested are not uniformly from sustainable brands, and they are not sufficiently environmentally friendly. There is a need to solve these problems and realize more personalized and environmentally friendly outfit suggestions for users.

[0899] 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.

[0900] In this invention, the server includes means for receiving purchase information, artificial intelligence means for suggesting coordinating items based on the received purchase information, emotion analysis means for analyzing the emotional state of the user, means for generating information on coordinating items, and means for notifying the communication terminal of the generated coordinate information. This enables personalized coordination suggestions that take the emotional state of the user into consideration, and further enables environmentally friendly suggestions by prioritizing items from sustainable brands.

[0901] The "means for receiving purchase information" is a function for receiving information about products purchased by a user from a communication terminal.

[0902] The "artificial intelligence means" is a system that has the function of analyzing the user's past purchase history, preferences, and current fashion trends based on the received purchase information, and selecting the most suitable coordinating items.

[0903] The "emotion analysis means" is a system that has the function of analyzing the user's emotional state from facial expressions, voice, text input, and the like.

[0904] The "means for generating information on coordinated items" is a function for generating detailed information on optimal coordinated items based on data obtained from the artificial intelligence means and the emotion analysis means.

[0905] The "means for notifying the communication terminal of the generated coordinate information" is a system having a function for transmitting the information of the generated coordinate items to the user's communication terminal and displaying it.

[0906] A "sustainable brand" is a brand that places importance on environmental protection and social responsibility and offers products that are produced in a sustainable manner.

[0907] In this invention, we will develop a system that proposes suitable outfits for fashion items purchased by a user. This system has a function of taking into account the user's current emotional state and preferentially proposing items from sustainable brands. Specific embodiments will be described below.

[0908] System Configuration

[0909] The system of the present invention comprises the following components:

[0910] Terminal

[0911] server

[0912] Artificial Intelligence Engine

[0913] Sentiment Analysis Engine

[0914] Hardware and software used

[0915] Hardware: Smartphone (iOS or Android), built-in camera

[0916] Software: Public sentiment analysis libraries (e.g., Microsoft Azure Emotion API), artificial intelligence engines (e.g., TensorFlow), cloud services (e.g., Amazon Web Services, Google Cloud Platform), data processing programs (e.g., Python, JavaScript)

[0917] Program processing overview

[0918] 1. Receiving purchase information:

[0919] When a user purchases an item online or at a virtual store, the purchase information (product name, color, size, price, etc.) is sent to the device, which then sends this information to the server.

[0920] 2. Emotional state analysis:

[0921] The device's camera is used to capture the user's face, and an emotion analysis library is used to analyze their current emotional state, which is then sent to the server.

[0922] 3. Coordinate generation:

[0923] The server launches an AI engine based on the received purchase information and sentiment analysis results. The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[0924] In addition, the system takes into account the user's emotional state obtained from the emotion analysis engine to suggest more appropriate outfits.

[0925] We prioritize selecting items from sustainable brands and offer environmentally friendly suggestions.

[0926] 4. Displaying the results:

[0927] The generated coordinated information is sent from the server to the terminal and displayed to the user, who can then check the suggested coordinated items and consider additional purchases if necessary.

[0928] Specific examples

[0929] For example, if a user purchases a blue dress in a virtual store, the device receives the purchase information and sends it to the server. The server then activates an emotion analysis engine to analyze the user's emotional state from the face captured by the smartphone camera. In this case, the server determines that the user's current emotion is "calm." The server then uses an artificial intelligence engine to analyze the user's past purchase history, preferences, and current fashion trends, and suggests a gray cardigan, silver accessories, and white sneakers that are appropriate for a calm emotion. Items from sustainable brands are prioritized, and the server sends the outfit information to the device and displays it to the user. The user then checks the outfit and makes additional purchases if necessary.

[0930] Prompt Sentence Examples

[0931] A user has purchased a blue dress. Suggest outfits to go with the dress. Also, consider that the user's current emotional state is "calm." Include sustainable brands in your suggestions.

[0932] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0933] Step 1:

[0934] A user purchases an item online or in a virtual store. The device receives this purchase information and sends the details (product name, color, size, price, etc.) to the server. This purchase information is important because it is used in subsequent processing.

[0935] Input: Purchase information (product name, color, size, price, etc.)

[0936] Output: Purchase information sent to the server

[0937] Step 2:

[0938] The server starts an emotion analysis engine to analyze the user's facial expressions based on the purchase information received from the device, captures the user's face using the device's camera, and sends the image to the server.

[0939] Input: Purchase information sent from the device, captured user face image

[0940] Output: Face image sent to the server

[0941] Step 3:

[0942] The server utilizes an emotion analysis engine to analyze the user's emotional state from the captured facial image. An emotion analysis library (e.g., Microsoft Azure Emotion API) is used to determine the user's current emotional state.

[0943] Input: User's face image

[0944] Output: Parsed emotional state (e.g. happy, calm, sad, etc.)

[0945] Step 4:

[0946] The server activates an AI engine based on the purchase information and emotional state, which analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[0947] Input: Purchase information, analyzed emotional state, past purchase history, user preferences, fashion trend data

[0948] Output: Information on the best coordinated items (product name, color, size, price, etc.)

[0949] Step 5:

[0950] The server preferentially selects items from sustainable brands from the generated coordination information.

[0951] Input: Information on the best coordinated items

[0952] Output: Sustainable brand item information

[0953] Step 6:

[0954] The server then proposes outfits to the user based on the selected item information, including item images, names, prices, brand names, and the total price of the outfit.

[0955] Input: Sustainable brand item information

[0956] Output: Coordination suggestion information (item image, name, price, brand name, total price, etc.)

[0957] Step 7:

[0958] The terminal receives the coordinated outfit suggestion information sent from the server and displays it on the user's screen. The user can check it and consider additional purchases if necessary.

[0959] Input: Coordination suggestion information

[0960] Output: Coordination suggestions displayed on the user's screen

[0961] The above is a specific process flow for implementing the invention. The system considers the user's emotional state and suggests items from sustainable brands, thereby increasing user satisfaction and providing an environmentally friendly shopping experience.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] [Third embodiment]

[0966] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0967] 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.

[0968] 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).

[0969] 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.

[0970] 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.

[0971] 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).

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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.

[0977] 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."

[0978] The present invention relates to a system for suggesting a suitable coordination for fashion items purchased by a user from an online shop. Hereinafter, an embodiment of the present invention will be specifically described.

[0979] System Configuration

[0980] The system of the present invention mainly comprises the following components:

[0981] 1. Terminal

[0982] 2. Server

[0983] 3. AI Engine

[0984] Program processing (outline)

[0985] 1. Receiving purchase information

[0986] Case study: A user purchases a white blouse from an online shop.

[0987] 1.1 User confirms purchase

[0988] A user logs into an online shop and purchases a white blouse.

[0989] 1.2 Device collects purchase information

[0990] The device will obtain detailed information about the blouse (item name, color, size, price, etc.) Specifically, this information is obtained automatically when the purchase button is clicked.

[0991] 1.3 The device sends the purchase information to the server

[0992] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[0993] 2. Coordinate Generation

[0994] The server proposes a coordination proposal using the following steps:

[0995] 2.1 The server receives the purchase information

[0996] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[0997] 2.2 The server starts the AI ​​engine

[0998] The server then activates the AI ​​engine based on the received purchase information, which then begins analysis taking into account the user's past purchase history, preferences, and current fashion trends.

[0999] 2.3 AI engine suggests coordination items

[1000] The AI ​​engine analyzes purchase history, a user's style, and trends to select the perfect accessories to go with the purchased white blouse, such as a black skirt, red heels, and gold accessories.

[1001] 2.4 Servers prioritize sustainable branded items

[1002] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[1003] 2.5 Server generates coordinate information

[1004] The server generates outfit information including detailed information about the suggested items (price, brand, etc.), which is then ready to be provided to the user.

[1005] 3. Notification and display of results

[1006] The generated coordinate information is notified to the user as follows.

[1007] 3.1 The server sends the coordination results to the terminal

[1008] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[1009] 3.2 The terminal displays the results

[1010] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[1011] 3.3 User confirms the suggested outfits

[1012] The user can check the coordination suggestions displayed on the terminal and consider additional purchases if necessary.

[1013] Specific examples

[1014] A specific scenario is shown below, where a user purchases a white blouse and the system suggests a suitable outfit.

[1015] 1. A user purchases a white blouse from an online store.

[1016] 2. The device collects purchase information and sends it to the server.

[1017] 3. The server receives the purchase information and starts the AI ​​engine.

[1018] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[1019] 5. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[1020] 6. The server generates the coordinate information and sends it to the terminal.

[1021] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[1022] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while enjoying a sustainable, brand-conscious shopping experience.

[1023] The processing flow will be explained below.

[1024] Step 1:

[1025] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[1026] Step 2:

[1027] The terminal collects purchase information. The terminal obtains detailed information about the white blouse (item name, color, size, price, etc.). This includes a program that automatically reads the data displayed on the product page.

[1028] Step 3:

[1029] The device sends the purchase information to the server. When the purchase is confirmed, the device sends the acquired purchase information to the server. HTTP or HTTPS is used as the communication protocol.

[1030] Step 4:

[1031] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the data for analysis. The purchase information is saved using a database management system (DBMS).

[1032] Step 5:

[1033] The server starts the AI ​​engine. The server passes the purchase information to the AI ​​engine and begins processing. The AI ​​engine prepares a dataset that takes into account the user's preferences, past purchase history, current fashion trends, etc.

[1034] Step 6:

[1035] The AI ​​engine analyzes the user's information. The AI ​​engine performs analysis based on a prepared dataset. This analysis uses machine learning algorithms (e.g., regression analysis, clustering) to select the best outfit items based on the user's style and trends.

[1036] Step 7:

[1037] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server maintains a list of sustainable brands and filters by comparing them with this list.

[1038] Step 8:

[1039] The server generates outfit information. The server compiles the selected outfit items (e.g., black skirt, red heels, gold accessories) and their detailed information (price, brand name) into a single outfit suggestion.

[1040] Step 9:

[1041] The server sends the coordination results to the device. The generated coordination information is packaged as data for transmission to the device and sent via a communication protocol. JSON or XML is commonly used as the data format.

[1042] Step 10:

[1043] The terminal displays the results. The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit.

[1044] Step 11:

[1045] The user checks the suggested outfits. The user checks the outfit suggestions displayed on the device and considers additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[1046] Through the above processing steps, the user can make personalized fashion choices efficiently in a short time and can also select environmentally friendly items.

[1047] Example 1

[1048] 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."

[1049] Conventional online shopping systems lacked the means to suggest appropriate outfits for the items purchased by users. As a result, users had to spend time selecting outfits that matched their tastes and trends, resulting in an inefficient shopping experience. Furthermore, systems that considered items from sustainable brands were also inadequate, which meant that the needs of environmentally conscious consumers could not be met.

[1050] 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.

[1051] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, and a means for generating information on the suggested coordinating items. This allows for prompt and appropriate coordination suggestions for items purchased by the user. Furthermore, items from sustainable brands can be preferentially suggested, making it easy for users to make environmentally conscious choices.

[1052] The "means for receiving purchase information" is a device or software for transmitting detailed information about an item to a server when a user confirms a purchase at an online shop.

[1053] "Artificial intelligence means" refers to an algorithm or system that analyzes a user's past purchase history, preferences, and fashion trends based on their purchase information, and selects appropriate coordinating items.

[1054] The "means for generating information on coordinated items" refers to a program or function for collectively generating detailed information (item names, prices, brand names, etc.) on the coordinated items selected by the artificial intelligence means.

[1055] The "means for notifying the terminal of the generated coordinate information" is a communication means for transmitting the coordinate information generated by the server to the user's terminal.

[1056] The "means for displaying the notified coordinate information on the screen" is an interface for visually displaying the coordinate information received by the terminal from the server on the user's screen.

[1057] The "means for confirming suggested coordination" is a function that allows the user to confirm the coordination suggestions displayed on the terminal and consider additional purchases as necessary.

[1058] "Past purchase history" refers to a record of items a user has previously purchased from an online shop.

[1059] "Preferences" refer to the characteristics and styles of items that a user has preferred to purchase in the past.

[1060] "Current fashion trends" refers to the trends in fashion styles and items that are popular at a particular time.

[1061] A "sustainable brand" is a brand that uses environmentally friendly materials and manufacturing methods and is socially responsible.

[1062] "Coordination information" refers to a series of suggestions that combine detailed information about items selected by the artificial intelligence means.

[1063] The present invention relates to a system for suggesting appropriate coordination for fashion items purchased by a user from an online shop. Specific embodiments for carrying out the present invention will be described below.

[1064] System Configuration

[1065] The system of the present invention mainly consists of the following components:

[1066] 1. Terminal

[1067] 2. Server

[1068] 3. AI Engine

[1069] Program processing

[1070] Receiving purchase information

[1071] First, a user confirms a purchase at an online shop. For example, suppose the user purchases a white blouse. When the purchase is confirmed, the device automatically collects detailed information about the item (item name, color, size, price, purchase date and time). The collected purchase information is sent to the server via network communication. Specifically, it is sent as an HTTP request.

[1072] Coordinate generation

[1073] The server receives the purchase information sent from the device via an HTTP request. Based on the received information, the server activates an AI engine. The AI ​​engine incorporates an algorithm that suggests coordinating items based on past purchase history, the user's preferences, and fashion trends.

[1074] The AI ​​engine analyzes a user's past purchase history and current fashion trends to select the perfect outfit, for example, a black skirt, red heels, and gold accessories to go with a white blouse. Items from sustainable brands are prioritized, and the engine suggests sustainable items whenever possible.

[1075] The server automatically organizes detailed information about the selected items and generates coordinated outfit information, which includes the item name, price, brand name, and total coordinated outfit price.

[1076] Notification and display of results

[1077] The server sends the generated coordinated outfit information to the user's device as an HTTP response. The device displays the received coordinated outfit information on the screen, allowing the user to confirm the information. Specifically, the screen displays item images, names, prices, brand names, and the total price of the coordinated outfit.

[1078] The user can check the coordination suggestions displayed on the terminal and consider purchasing additional suggested items if necessary.

[1079] Specific examples

[1080] Consider a case where a user purchases a white blouse from an online shop and is offered a suitable outfit for it.

[1081] 1. A user purchases a white blouse from an online store.

[1082] 2. The device collects purchase information and sends it to the server.

[1083] 3. The server receives the purchase information and starts the AI ​​engine.

[1084] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[1085] 5. The server will check for items from sustainable brands and prioritize sustainable items if available.

[1086] 6. The server generates the coordinate information and sends it to the terminal.

[1087] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[1088] This allows users to quickly and efficiently receive personalized fashion recommendations and easily make additional purchases as needed. Items from sustainable brands are also given priority consideration, providing a satisfying shopping experience for environmentally conscious consumers.

[1089] Prompt Sentence Examples

[1090] "When a user purchases a white blouse, please explain the program process for the system that suggests outfits based on that item."

[1091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1092] Step 1:

[1093] The user confirms the purchase

[1094] The user logs into the online store and decides to purchase a white blouse by clicking the purchase button on the product detail page.

[1095] Input: User clicks

[1096] Output: Temporarily save purchase information

[1097] Step 2:

[1098] The device collects purchase information

[1099] When a purchase is confirmed, the device automatically collects detailed item information (item name, color, size, price, purchase date and time). This includes a script that uses a browser event listener to detect when the purchase button is clicked and retrieves the purchase information.

[1100] Input: Product information specified by the user

[1101] Output: Collected purchase information (item name, color, size, price, purchase date and time)

[1102] Step 3:

[1103] The device sends the purchase information to the server

[1104] The device sends the collected purchase information to the server via network communication. Specifically, the purchase information is converted into JSON format and sent as an HTTP POST request.

[1105] Input: Collected purchase information

[1106] Output: Purchase information sent to the server (JSON format)

[1107] Step 4:

[1108] The server receives the purchase information

[1109] The server receives the purchase information sent from the device via an HTTP request. Once the purchase information is received, the server parses it and prepares it for processing.

[1110] Input: Purchase information sent from the device (JSON format)

[1111] Output: Parsable purchase information

[1112] Step 5:

[1113] The server starts the AI ​​engine

[1114] The server activates an AI engine based on the received purchase information, which analyzes the user's past purchase history, preferences, and fashion trends, and begins the process of suggesting optimal coordinating items.

[1115] Input: Parsable purchase information

[1116] Output: AI engine startup and initial data setup completed

[1117] Step 6:

[1118] AI engine suggests coordination items

[1119] The AI ​​engine analyzes past purchase history, user preferences, and trend information. For example, it uses a machine learning model to analyze the data and select the most suitable items (black skirt, red heels, gold accessories).

[1120] Input: User's past purchase history, preferences, and fashion trend information

[1121] Output: A list of suggested outfit items

[1122] Step 7:

[1123] Servers prioritize sustainable branded items

[1124] The server prioritizes sustainable brand items among the suggested coordination items and adds them to the list. If a sustainable brand item is included, it is suggested as the optimal choice.

[1125] Input: A list of suggested coordinating items

[1126] Output: A list of outfits that prioritize items from sustainable brands

[1127] Step 8:

[1128] The server generates the coordinate information.

[1129] The server generates coordinate information including sustainable brand items, including item names, prices, brand names, and the total price of the coordinated items.

[1130] Input: A list of outfit items from sustainable brands

[1131] Output: Generated coordinate information

[1132] Step 9:

[1133] The server sends the coordinated results to the terminal.

[1134] The server sends the generated coordinate information to the terminal as an HTTP response.

[1135] Input: Generated coordinate information

[1136] Output: Coordinate information sent to the device

[1137] Step 10:

[1138] The terminal displays the results

[1139] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[1140] Input: Coordinate information sent from the server

[1141] Output: Coordinate information displayed on the user screen

[1142] Step 11:

[1143] The user checks the suggested outfits

[1144] The user checks the coordination suggestions displayed on the terminal and considers purchasing additional suggested items as necessary.

[1145] Input: Coordination information displayed on the screen

[1146] Output: User confirms outfit suggestions and considers purchasing

[1147] (Application example 1)

[1148] 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."

[1149] Users often have difficulty efficiently finding stylish outfits that suit them when purchasing fashion items online. Furthermore, prioritizing sustainable brand products can be time-consuming. Furthermore, if outfit suggestions are not visually easy to understand, users are less likely to accept them. There is a need for a system that can solve these issues and provide a user-friendly, sustainable shopping experience.

[1150] 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.

[1151] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, a means for generating coordinated outfits taking into consideration the user's past purchase history and preferences, a means for notifying the terminal of the generated coordinated outfit information, and a means for visually displaying the generated coordinated outfit information. This allows users to easily receive personalized coordinated outfit suggestions when shopping online, enabling them to prioritize the selection of products from sustainable brands. Furthermore, the visually easy-to-understand coordinated outfit suggestions can increase user acceptance.

[1152] "Purchase information" refers to detailed information about the product purchased by the user at the online shop (item name, color, size, price, etc.).

[1153] "Artificial intelligence" refers to algorithm technology that analyzes a user's purchase history, preferences, and current fashion trends to suggest optimal outfits.

[1154] "Past purchase history" refers to a record of products that a user has previously purchased from an online shop.

[1155] "User preferences" refers to the fashion preferences and style tendencies that the user has shown up to now.

[1156] "Fashion trends" refers to the current fashion trends and latest fashion trends in the market.

[1157] "Coordination" refers to proposing the optimal fashion style based on the items purchased by the user, combining them with other items.

[1158] A "sustainable brand" is a brand that takes the environment and society into consideration and manufactures and sells products in a sustainable manner.

[1159] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to connect to the Internet and use an online shop.

[1160] "Visually displaying" refers to displaying the generated coordinate information on the screen in a format that allows the user to understand it at a glance.

[1161] The embodiments for carrying out the present invention will be described in detail below.

[1162] The system includes means for receiving purchase information, means for suggesting coordinate items using artificial intelligence, means for notifying the generated coordinate information, and means for visually displaying it.

[1163] Program processing

[1164] 1. Receiving purchase information

[1165] The server receives detailed information (item name, color, size, price, etc.) about the product purchased by the user at the online shop. The purchase information is automatically collected and sent to the server when the user confirms the purchase of the product.

[1166] 2. Starting the AI ​​engine

[1167] Based on the received purchase information, the server launches an artificial intelligence engine (e.g., a model using TensorFlow or PyTorch). The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select the optimal coordinating items.

[1168] 3. Coordination item suggestions

[1169] The AI ​​engine generates the perfect outfit for each user based on the purchase information and analysis results, with suggested items chosen from sustainable brands whenever possible.

[1170] 4. Coordination information generation and notification

[1171] The server generates outfit information based on the detailed information of the outfit items suggested by the AI ​​engine, and this information is sent to the user's device and displayed in a visually easy-to-understand format.

[1172] Hardware and Software Use

[1173] The system uses the following hardware and software:

[1174] Hardware: Cloud servers (e.g., AWS and GCP) and user devices (e.g., smartphones, tablets, and PCs)

[1175] Software: Flask (web framework), artificial intelligence engine (e.g., TensorFlow or PyTorch-based models)

[1176] Specifically, the server uses Flask to build a web application and receives and processes purchase information. The AI ​​engine uses TensorFlow and PyTorch to analyze the user's past purchase history and current fashion trends to generate optimal outfits. The generated outfit information is sent to the device in JSON format and displayed visually on the device.

[1177] Specific examples

[1178] If the user purchases a white blouse

[1179] 1. When a user purchases a white blouse from an online shop, the purchase information is sent to the server.

[1180] 2. The server receives the purchase information and starts the AI ​​engine.

[1181] 3. The AI ​​engine analyzes past purchase history, user preferences, and current trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[1182] 4. The server generates coordinate information for the suggested items and sends it to the user's device.

[1183] 5. The user's device displays the received information on the screen, allowing the user to confirm the proposed coordination.

[1184] Example prompts for generative AI models

[1185] User has purchased a white blouse. Based on past purchase history, user preferences, and current fashion trends, generate a suitable outfit coordination proposal. Include items from sustainable brands if possible.

[1186] In this way, the user can receive optimal coordination suggestions for items purchased through online shopping in real time.

[1187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1188] Step 1:

[1189] Receiving purchase information

[1190] Subject: Terminal

[1191] How it works: When a user purchases an item from an online store, purchase information (item name, color, size, price, etc.) is automatically collected.

[1192] Input: Item information that the user confirmed to purchase.

[1193] Output: Purchase information is obtained and sent from the device to the server.

[1194] Specific operation: When the user clicks the "Purchase" button, the device obtains detailed information about the item and sends a POST request to the server.

[1195] Step 2:

[1196] Receive and store purchase information on our server

[1197] Subject: Server

[1198] Operation: The server receives the purchase information sent from the terminal and stores it in a database.

[1199] Input: Purchase information sent from the device.

[1200] Output: Purchase information stored in a database.

[1201] Specific operation: The server receives purchase information via the API endpoint of the Flask server and performs the process of saving it in a database (e.g., MySQL or MongoDB).

[1202] Step 3:

[1203] AI engine startup and analysis

[1204] Subject: Server

[1205] How it works: The server launches an AI engine to perform analysis based on the saved purchase information.

[1206] Input: Purchase information stored in a database, as well as your purchasing history and preferences.

[1207] Output: Coordinate candidates as the analysis result.

[1208] How it works: The server calls an AI engine (such as a model implemented in TensorFlow or PyTorch) and inputs and analyzes purchase information, past purchase history, user preferences, and fashion trends. The model then generates appropriate outfit items.

[1209] Step 4:

[1210] Coordination information generation

[1211] Subject: Server

[1212] Operation: The server generates specific coordination information based on the analysis results of the AI ​​engine.

[1213] Input: Analysis results from the AI ​​engine.

[1214] Output: Coordinate information for presentation to the user.

[1215] Specific operation: The server formats the outfit suggestions obtained from the AI ​​engine, prioritizes items from sustainable brands, and generates the final outfit information (a series of item names, brands, prices, etc.).

[1216] Step 5:

[1217] Notification and display of coordination information

[1218] Subject: Server and Terminal

[1219] Operation: The server sends the generated coordinate information to the terminal, which then visually displays this information.

[1220] Input: Generated coordinate information.

[1221] Output: Coordination suggestions displayed on the user's device.

[1222] Specific operation: The server sends the generated outfit information (JSON format) to the device. Based on the received information, the device displays the item images, detailed information (brand name, price, etc.), and the total price of the outfit on the user's screen.

[1223] This processing flow allows the user to receive optimal coordination suggestions for the items they have purchased.

[1224] 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.

[1225] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[1226] System Configuration

[1227] The system of the present invention mainly comprises the following components:

[1228] 1. Terminal

[1229] 2. Server

[1230] 3. AI Engine

[1231] 4. Emotion Engine

[1232] Program processing (outline)

[1233] 1. Receiving purchase information

[1234] Case study: A user purchases a white blouse from an online shop.

[1235] 1.1 User confirms purchase

[1236] A user logs in to an online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[1237] 1.2 Device collects purchase information

[1238] The device retrieves detailed information about the white blouse (item name, color, size, price, etc.). Specifically, it includes a program to automatically read the data displayed on the product page.

[1239] 1.3 The device sends the purchase information to the server

[1240] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[1241] 2. Coordinate Generation

[1242] The server proposes a coordination proposal using the following steps:

[1243] 2.1 The server receives the purchase information

[1244] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[1245] 2.2 The server starts the AI ​​engine and emotion engine

[1246] The server then activates the AI ​​engine and emotion engine based on the received purchase information. The AI ​​engine then prepares a dataset to take into account the user's preferences, past purchase history, current fashion trends, etc.

[1247] 2.3 Emotion engine analyzes user emotions

[1248] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, analyzes that information, and determines the user's current emotional state based on the analysis results.

[1249] 2.4 The AI ​​engine analyzes user information

[1250] The AI ​​engine selects the optimal outfit items based on the user's past purchase history, preferences, and current fashion trend data, while also taking into account emotional information provided by the emotion engine.

[1251] 2.5 Server selects sustainable brands

[1252] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[1253] 2.6 Server generates coordinate information

[1254] The server then compiles detailed information (price, brand, etc.) about the suggested items into a single outfit suggestion, which may include adjusting colors and styles based on the user's emotional state.

[1255] 3. Notification and display of results

[1256] The generated coordinate information is notified to the user as follows.

[1257] 3.1 The server sends the coordination results to the terminal

[1258] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[1259] 3.2 The terminal displays the results

[1260] The terminal displays the received coordinated information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[1261] 3.3 User confirms the suggested outfits

[1262] The user can check the outfit suggestions displayed on the device and consider additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[1263] Specific examples

[1264] A specific scenario is shown below, where a user purchases a white blouse and the system uses the emotion engine to suggest a suitable outfit.

[1265] 1. A user purchases a white blouse from an online store.

[1266] 2. The device collects purchase information and sends it to the server.

[1267] 3. The server receives the purchase information and activates the AI ​​engine and emotion engine.

[1268] 4. The emotion engine determines from the user's facial expression that the current emotional state is "happy."

[1269] 5. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse. Because the emotion is "happiness," the AI ​​engine prioritizes coordinating items in vibrant colors.

[1270] 6. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[1271] 7. The server generates the coordinate information and sends it to the terminal.

[1272] 8. The terminal displays the received information to the user, who then confirms the proposed coordination.

[1273] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while also enjoying a shopping experience that takes into account their emotional state.

[1274] The processing flow will be explained below.

[1275] Step 1:

[1276] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[1277] Step 2:

[1278] The device collects purchase information. The device obtains detailed information about the white blouse (item name, color, size, price, etc.). Specifically, a script runs that automatically reads the data listed on the product page.

[1279] Step 3:

[1280] The terminal sends the purchase information to the server. After collecting the purchase information, the terminal sends it to the server. HTTP or HTTPS is used as the communication protocol.

[1281] Step 4:

[1282] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the information in a database. The stored information is used as a data set for subsequent analysis.

[1283] Step 5:

[1284] The server starts the AI ​​engine and emotion engine. The server starts the AI ​​engine and emotion engine simultaneously based on the received purchase information.

[1285] Step 6:

[1286] The emotion engine analyzes the user's emotions. The emotion engine acquires the user's facial expressions, voice, or text input data and analyzes the user's emotions based on them. The analysis results are output as emotional states such as "happiness," "sadness," and "surprise."

[1287] Step 7:

[1288] The AI ​​engine analyzes the user's information. It analyzes the user's past purchase history, preferences, current fashion trends, etc., and selects the optimal coordination items. At this time, the analysis results of the emotion engine are also used as input data.

[1289] Step 8:

[1290] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server compares the results with the sustainable brand list and selects items.

[1291] Step 9:

[1292] The server generates coordination information based on the selected coordination items (e.g., black skirt, red heels, gold accessories) and detailed information (price, brand, color and style corresponding to the emotion, etc.).

[1293] Step 10:

[1294] The server sends the coordinated results to the device. The generated coordinated information is packaged in JSON or XML format and sent to the device, which then receives the information.

[1295] Step 11:

[1296] The terminal displays the results. The terminal displays the coordinated information received from the server to the user. The displayed content includes an image of each item, its name, price, brand name, and emotionally appropriate comments and descriptions.

[1297] Step 12:

[1298] The user can then review the suggested outfits. The user can then review the outfit suggestions displayed on their device and purchase additional items as needed. They will also be given the option to add items directly to their shopping cart.

[1299] Through the above processing steps, the user can make personalized fashion choices quickly and efficiently, and enjoy a shopping experience that also takes into account their emotional state.

[1300] Example 2

[1301] 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."

[1302] Conventional coordination suggestion systems have the problem of not being able to sufficiently increase user satisfaction because they make suggestions based solely on past purchase history and trend data without taking into account the user's emotional state. Additionally, they lack the functionality to prioritize recommendations for products from sustainable brands, which means they do not provide an environmentally conscious shopping experience.

[1303] 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.

[1304] In this invention, the server includes means for receiving purchase information, artificial intelligence engine means for suggesting coordinating items based on the received purchase information, emotion engine means for analyzing the user's emotions, means for checking whether the suggested items are from sustainable brands, and means for generating information on the coordinating items, thereby enabling personalized coordination suggestions that take into account the user's emotional state and preferentially suggesting products from sustainable brands.

[1305] "Purchase information" refers to detailed information such as product name, color, size, and price that is generated when a user purchases a product from an online shop or a physical store.

[1306] An "artificial intelligence engine" refers to a program or algorithm that analyzes a user's past purchase history, preferences, and current fashion trend data to select the optimal coordinating items.

[1307] An "emotion engine" refers to a program or algorithm that recognizes emotions from a user's facial expressions, voice, text input, etc., and determines the user's current emotional state based on the analysis results.

[1308] A "sustainable brand" refers to a company or brand that places importance on environmentally friendly product development and ethical working conditions.

[1309] "Coordination information" refers to information about the user's fashion coordination, including detailed information about the suggested fashion items (product name, price, brand name, etc.).

[1310] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[1311] The main components of this system are the terminal, server, AI engine, and emotion engine. Each element has the following functions and works together to propose coordination.

[1312] 1. Terminal

[1313] The terminals include personal computers, smartphones, tablets, etc. used by users. When a user purchases a product from an online shop, the terminal has the function of collecting purchase information and sending it to a server. Specifically, this information includes the product name, color, size, price, etc.

[1314] 2. Server

[1315] The server receives purchase information sent from the device and activates an artificial intelligence engine and emotion engine, which selects coordinating items and generates coordination information. The server also checks for the availability of sustainable brand products and prioritizes their suggestions.

[1316] 3. Artificial Intelligence Engine

[1317] The AI ​​engine inputs data such as the user's past purchase history, preferences, and current fashion trends. By analyzing this data, it selects the optimal outfit items. For example, it uses deep learning frameworks such as TensorFlow and PyTorch.

[1318] 4. Emotion Engine

[1319] The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. It uses this information to determine the user's current emotional state, for example, using a generative AI model such as OpenAI's GPT-3.

[1320] As a concrete example, consider the case where a user purchases a white blouse from an online store. The following steps are performed:

[1321] A user purchases a white blouse from an online shop. After the purchase is completed, the device collects the purchase information and sends it to the server.

[1322] The server receives the purchase information and activates the artificial intelligence engine and emotion engine.

[1323] The emotion engine determines the emotion result "happiness" from the analysis of the user's facial expression.

[1324] The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories that go perfectly with a white blouse. Bright colors are selected because they represent the emotion "happiness."

[1325] The server checks for the availability of sustainable branded products and prioritizes sustainable items whenever possible.

[1326] Finally, the server transmits the generated coordinate information to the terminal, which displays it to the user.

[1327] In this way, users can receive personalized outfit suggestions that take their emotions into consideration, and as sustainable brands are prioritized in the suggestions, they can enjoy an environmentally conscious shopping experience.

[1328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1329] Step 1:

[1330] The user confirms the purchase

[1331] A user logs in to an online shop, selects a product (e.g., a white blouse) and completes the purchase procedure, which causes the terminal to acquire purchase information (product name, color, size, price, etc.) as input.

[1332] Step 2:

[1333] The device collects purchase information

[1334] The device runs a program (e.g., JavaScript) to automatically retrieve product information from the online shop's purchase page. The retrieved details (product name, color, size, price, etc.) are collected as data. This data becomes the input for the next processing step.

[1335] Step 3:

[1336] The device sends the purchase information to the server

[1337] The device sends the collected purchase information in JSON format to the server. The data is sent when the purchase procedure is completed. This sent purchase information becomes the starting point for processing on the server.

[1338] Step 4:

[1339] The server receives the purchase information

[1340] The server receives the purchase information sent from the terminal and stores it in a database. The received data includes detailed information such as the product name, color, size, and price. This data becomes the input for the next processing step.

[1341] Step 5:

[1342] The server runs the artificial intelligence engine and emotion engine.

[1343] The server launches an AI engine and an emotion engine based on the received purchase information. The AI ​​engine preprocesses the input data to prepare data on the user's preferences, past purchase history, and the latest fashion trends. The emotion engine loads a model (e.g., OpenAI's GPT-3) to analyze the user's facial expressions and text input.

[1344] Step 6:

[1345] Emotion engine analyzes user emotions

[1346] The emotion engine processes emotions based on the user's facial expressions, voice, and text input information. This determines the user's current emotional state (e.g., "happiness"). The analysis results are output as data, which is then input into the artificial intelligence engine.

[1347] Step 7:

[1348] An artificial intelligence engine analyzes user information

[1349] The AI ​​engine analyzes the user's purchase information, as well as past purchase history, preferences, and current fashion trend data. Based on this data, it selects the optimal coordinating items. This selection process also takes into account the analysis results of the emotion engine. The selected coordinating items are output as data.

[1350] Step 8:

[1351] Server selects sustainable brands

[1352] The server refers to a sustainability database to check whether the coordinated items selected by the AI ​​engine are from sustainable brands. The server adjusts the selection results so that items from sustainable brands are prioritized. Coordination information including sustainable items is output as data.

[1353] Step 9:

[1354] The server generates the coordinate information.

[1355] The server then compiles detailed information (price, brand name, etc.) about the selected coordinate items into a single proposal and generates coordinate information. This coordinate information is then output as data to be sent to the terminal.

[1356] Step 10:

[1357] The server sends the coordinate information to the terminal.

[1358] The server sends the generated coordinate information in JSON format to the user's device. This sent data becomes the input for the next processing step.

[1359] Step 11:

[1360] The terminal displays the results

[1361] The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit. The user can review this information and consider actions such as additional purchases.

[1362] (Application example 2)

[1363] 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."

[1364] Conventional outfit suggestion systems do not take into account the user's current emotional state, and the outfits they suggest do not always satisfy the user. Furthermore, the items suggested are not uniformly from sustainable brands, and they are not sufficiently environmentally friendly. There is a need to solve these problems and realize more personalized and environmentally friendly outfit suggestions for users.

[1365] 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.

[1366] In this invention, the server includes means for receiving purchase information, artificial intelligence means for suggesting coordinating items based on the received purchase information, emotion analysis means for analyzing the emotional state of the user, means for generating information on coordinating items, and means for notifying the communication terminal of the generated coordinate information. This enables personalized coordination suggestions that take the emotional state of the user into consideration, and further enables environmentally friendly suggestions by prioritizing items from sustainable brands.

[1367] The "means for receiving purchase information" is a function for receiving information about products purchased by a user from a communication terminal.

[1368] The "artificial intelligence means" is a system that has the function of analyzing the user's past purchase history, preferences, and current fashion trends based on the received purchase information, and selecting the most suitable coordinating items.

[1369] The "emotion analysis means" is a system that has the function of analyzing the user's emotional state from facial expressions, voice, text input, and the like.

[1370] The "means for generating information on coordinated items" is a function for generating detailed information on optimal coordinated items based on data obtained from the artificial intelligence means and the emotion analysis means.

[1371] The "means for notifying the communication terminal of the generated coordinate information" is a system having a function for transmitting the information of the generated coordinate items to the user's communication terminal and displaying it.

[1372] A "sustainable brand" is a brand that places importance on environmental protection and social responsibility and offers products that are produced in a sustainable manner.

[1373] In this invention, we will develop a system that proposes suitable outfits for fashion items purchased by a user. This system has a function of taking into account the user's current emotional state and preferentially proposing items from sustainable brands. Specific embodiments will be described below.

[1374] System Configuration

[1375] The system of the present invention comprises the following components:

[1376] Terminal

[1377] server

[1378] Artificial Intelligence Engine

[1379] Sentiment Analysis Engine

[1380] Hardware and software used

[1381] Hardware: Smartphone (iOS or Android), built-in camera

[1382] Software: Public sentiment analysis libraries (e.g., Microsoft Azure Emotion API), artificial intelligence engines (e.g., TensorFlow), cloud services (e.g., Amazon Web Services, Google Cloud Platform), data processing programs (e.g., Python, JavaScript)

[1383] Program processing overview

[1384] 1. Receiving purchase information:

[1385] When a user purchases an item online or at a virtual store, the purchase information (product name, color, size, price, etc.) is sent to the device, which then sends this information to the server.

[1386] 2. Emotional state analysis:

[1387] The device's camera is used to capture the user's face, and an emotion analysis library is used to analyze their current emotional state, which is then sent to the server.

[1388] 3. Coordinate generation:

[1389] The server launches an AI engine based on the received purchase information and sentiment analysis results. The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[1390] In addition, the system takes into account the user's emotional state obtained from the emotion analysis engine to suggest more appropriate outfits.

[1391] We prioritize selecting items from sustainable brands and offer environmentally friendly suggestions.

[1392] 4. Displaying the results:

[1393] The generated coordinated information is sent from the server to the terminal and displayed to the user, who can then check the suggested coordinated items and consider additional purchases if necessary.

[1394] Specific examples

[1395] For example, if a user purchases a blue dress in a virtual store, the device receives the purchase information and sends it to the server. The server then activates an emotion analysis engine to analyze the user's emotional state from the face captured by the smartphone camera. In this case, the server determines that the user's current emotion is "calm." The server then uses an artificial intelligence engine to analyze the user's past purchase history, preferences, and current fashion trends, and suggests a gray cardigan, silver accessories, and white sneakers that are appropriate for a calm emotion. Items from sustainable brands are prioritized, and the server sends the outfit information to the device and displays it to the user. The user then checks the outfit and makes additional purchases if necessary.

[1396] Prompt Sentence Examples

[1397] A user has purchased a blue dress. Suggest outfits to go with the dress. Also, consider that the user's current emotional state is "calm." Include sustainable brands in your suggestions.

[1398] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1399] Step 1:

[1400] A user purchases an item online or in a virtual store. The device receives this purchase information and sends the details (product name, color, size, price, etc.) to the server. This purchase information is important because it is used in subsequent processing.

[1401] Input: Purchase information (product name, color, size, price, etc.)

[1402] Output: Purchase information sent to the server

[1403] Step 2:

[1404] The server starts an emotion analysis engine to analyze the user's facial expressions based on the purchase information received from the device, captures the user's face using the device's camera, and sends the image to the server.

[1405] Input: Purchase information sent from the device, captured user face image

[1406] Output: Face image sent to the server

[1407] Step 3:

[1408] The server utilizes an emotion analysis engine to analyze the user's emotional state from the captured facial image. An emotion analysis library (e.g., Microsoft Azure Emotion API) is used to determine the user's current emotional state.

[1409] Input: User's face image

[1410] Output: Parsed emotional state (e.g. happy, calm, sad, etc.)

[1411] Step 4:

[1412] The server activates an AI engine based on the purchase information and emotional state, which analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[1413] Input: Purchase information, analyzed emotional state, past purchase history, user preferences, fashion trend data

[1414] Output: Information on the best coordinated items (product name, color, size, price, etc.)

[1415] Step 5:

[1416] The server preferentially selects items from sustainable brands from the generated coordination information.

[1417] Input: Information on the best coordinated items

[1418] Output: Sustainable brand item information

[1419] Step 6:

[1420] The server then proposes outfits to the user based on the selected item information, including item images, names, prices, brand names, and the total price of the outfit.

[1421] Input: Sustainable brand item information

[1422] Output: Coordination suggestion information (item image, name, price, brand name, total price, etc.)

[1423] Step 7:

[1424] The terminal receives the coordinated outfit suggestion information sent from the server and displays it on the user's screen. The user can check it and consider additional purchases if necessary.

[1425] Input: Coordination suggestion information

[1426] Output: Coordination suggestions displayed on the user's screen

[1427] The above is a specific process flow for implementing the invention. The system considers the user's emotional state and suggests items from sustainable brands, thereby increasing user satisfaction and providing an environmentally friendly shopping experience.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] [Fourth embodiment]

[1432] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1433] 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.

[1434] 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).

[1435] 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.

[1436] 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.

[1437] 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).

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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."

[1445] The present invention relates to a system for suggesting a suitable coordination for fashion items purchased by a user from an online shop. Hereinafter, an embodiment of the present invention will be specifically described.

[1446] System Configuration

[1447] The system of the present invention mainly comprises the following components:

[1448] 1. Terminal

[1449] 2. Server

[1450] 3. AI Engine

[1451] Program processing (outline)

[1452] 1. Receiving purchase information

[1453] Case study: A user purchases a white blouse from an online shop.

[1454] 1.1 User confirms purchase

[1455] A user logs into an online shop and purchases a white blouse.

[1456] 1.2 Device collects purchase information

[1457] The device will obtain detailed information about the blouse (item name, color, size, price, etc.) Specifically, this information is obtained automatically when the purchase button is clicked.

[1458] 1.3 The device sends the purchase information to the server

[1459] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[1460] 2. Coordinate Generation

[1461] The server proposes a coordination proposal using the following steps:

[1462] 2.1 The server receives the purchase information

[1463] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[1464] 2.2 The server starts the AI ​​engine

[1465] The server then activates the AI ​​engine based on the received purchase information, which then begins analysis taking into account the user's past purchase history, preferences, and current fashion trends.

[1466] 2.3 AI engine suggests coordination items

[1467] The AI ​​engine analyzes purchase history, a user's style, and trends to select the perfect accessories to go with the purchased white blouse, such as a black skirt, red heels, and gold accessories.

[1468] 2.4 Servers prioritize sustainable branded items

[1469] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[1470] 2.5 Server generates coordinate information

[1471] The server generates outfit information including detailed information about the suggested items (price, brand, etc.), which is then ready to be provided to the user.

[1472] 3. Notification and display of results

[1473] The generated coordinate information is notified to the user as follows.

[1474] 3.1 The server sends the coordination results to the terminal

[1475] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[1476] 3.2 The terminal displays the results

[1477] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[1478] 3.3 User confirms the suggested outfits

[1479] The user can check the coordination suggestions displayed on the terminal and consider additional purchases if necessary.

[1480] Specific examples

[1481] A specific scenario is shown below, where a user purchases a white blouse and the system suggests a suitable outfit.

[1482] 1. A user purchases a white blouse from an online store.

[1483] 2. The device collects purchase information and sends it to the server.

[1484] 3. The server receives the purchase information and starts the AI ​​engine.

[1485] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[1486] 5. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[1487] 6. The server generates the coordinate information and sends it to the terminal.

[1488] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[1489] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while enjoying a sustainable, brand-conscious shopping experience.

[1490] The processing flow will be explained below.

[1491] Step 1:

[1492] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[1493] Step 2:

[1494] The terminal collects purchase information. The terminal obtains detailed information about the white blouse (item name, color, size, price, etc.). This includes a program that automatically reads the data displayed on the product page.

[1495] Step 3:

[1496] The device sends the purchase information to the server. When the purchase is confirmed, the device sends the acquired purchase information to the server. HTTP or HTTPS is used as the communication protocol.

[1497] Step 4:

[1498] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the data for analysis. The purchase information is saved using a database management system (DBMS).

[1499] Step 5:

[1500] The server starts the AI ​​engine. The server passes the purchase information to the AI ​​engine and begins processing. The AI ​​engine prepares a dataset that takes into account the user's preferences, past purchase history, current fashion trends, etc.

[1501] Step 6:

[1502] The AI ​​engine analyzes the user's information. The AI ​​engine performs analysis based on a prepared dataset. This analysis uses machine learning algorithms (e.g., regression analysis, clustering) to select the best outfit items based on the user's style and trends.

[1503] Step 7:

[1504] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server maintains a list of sustainable brands and filters by comparing them with this list.

[1505] Step 8:

[1506] The server generates outfit information. The server compiles the selected outfit items (e.g., black skirt, red heels, gold accessories) and their detailed information (price, brand name) into a single outfit suggestion.

[1507] Step 9:

[1508] The server sends the coordination results to the device. The generated coordination information is packaged as data for transmission to the device and sent via a communication protocol. JSON or XML is commonly used as the data format.

[1509] Step 10:

[1510] The terminal displays the results. The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit.

[1511] Step 11:

[1512] The user checks the suggested outfits. The user checks the outfit suggestions displayed on the device and considers additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[1513] Through the above processing steps, the user can make personalized fashion choices efficiently in a short time and can also select environmentally friendly items.

[1514] Example 1

[1515] 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."

[1516] Conventional online shopping systems lacked the means to suggest appropriate outfits for the items purchased by users. As a result, users had to spend time selecting outfits that matched their tastes and trends, resulting in an inefficient shopping experience. Furthermore, systems that considered items from sustainable brands were also inadequate, which meant that the needs of environmentally conscious consumers could not be met.

[1517] 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.

[1518] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, and a means for generating information on the suggested coordinating items. This allows for prompt and appropriate coordination suggestions for items purchased by the user. Furthermore, items from sustainable brands can be preferentially suggested, making it easy for users to make environmentally conscious choices.

[1519] The "means for receiving purchase information" is a device or software for transmitting detailed information about an item to a server when a user confirms a purchase at an online shop.

[1520] "Artificial intelligence means" refers to an algorithm or system that analyzes a user's past purchase history, preferences, and fashion trends based on their purchase information, and selects appropriate coordinating items.

[1521] The "means for generating information on coordinated items" refers to a program or function for collectively generating detailed information (item names, prices, brand names, etc.) on the coordinated items selected by the artificial intelligence means.

[1522] The "means for notifying the terminal of the generated coordinate information" is a communication means for transmitting the coordinate information generated by the server to the user's terminal.

[1523] The "means for displaying the notified coordinate information on the screen" is an interface for visually displaying the coordinate information received by the terminal from the server on the user's screen.

[1524] The "means for confirming suggested coordination" is a function that allows the user to confirm the coordination suggestions displayed on the terminal and consider additional purchases as necessary.

[1525] "Past purchase history" refers to a record of items a user has previously purchased from an online shop.

[1526] "Preferences" refer to the characteristics and styles of items that a user has preferred to purchase in the past.

[1527] "Current fashion trends" refers to the trends in fashion styles and items that are popular at a particular time.

[1528] A "sustainable brand" is a brand that uses environmentally friendly materials and manufacturing methods and is socially responsible.

[1529] "Coordination information" refers to a series of suggestions that combine detailed information about items selected by the artificial intelligence means.

[1530] The present invention relates to a system for suggesting appropriate coordination for fashion items purchased by a user from an online shop. Specific embodiments for carrying out the present invention will be described below.

[1531] System Configuration

[1532] The system of the present invention mainly consists of the following components:

[1533] 1. Terminal

[1534] 2. Server

[1535] 3. AI Engine

[1536] Program processing

[1537] Receiving purchase information

[1538] First, a user confirms a purchase at an online shop. For example, suppose the user purchases a white blouse. When the purchase is confirmed, the device automatically collects detailed information about the item (item name, color, size, price, purchase date and time). The collected purchase information is sent to the server via network communication. Specifically, it is sent as an HTTP request.

[1539] Coordinate generation

[1540] The server receives the purchase information sent from the device via an HTTP request. Based on the received information, the server activates an AI engine. The AI ​​engine incorporates an algorithm that suggests coordinating items based on past purchase history, the user's preferences, and fashion trends.

[1541] The AI ​​engine analyzes a user's past purchase history and current fashion trends to select the perfect outfit, for example, a black skirt, red heels, and gold accessories to go with a white blouse. Items from sustainable brands are prioritized, and the engine suggests sustainable items whenever possible.

[1542] The server automatically organizes detailed information about the selected items and generates coordinated outfit information, which includes the item name, price, brand name, and total coordinated outfit price.

[1543] Notification and display of results

[1544] The server sends the generated coordinated outfit information to the user's device as an HTTP response. The device displays the received coordinated outfit information on the screen, allowing the user to confirm the information. Specifically, the screen displays item images, names, prices, brand names, and the total price of the coordinated outfit.

[1545] The user can check the coordination suggestions displayed on the terminal and consider purchasing additional suggested items if necessary.

[1546] Specific examples

[1547] Consider a case where a user purchases a white blouse from an online shop and is offered a suitable outfit for it.

[1548] 1. A user purchases a white blouse from an online store.

[1549] 2. The device collects purchase information and sends it to the server.

[1550] 3. The server receives the purchase information and starts the AI ​​engine.

[1551] 4. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[1552] 5. The server will check for items from sustainable brands and prioritize sustainable items if available.

[1553] 6. The server generates the coordinate information and sends it to the terminal.

[1554] 7. The terminal displays the received information to the user, who then confirms the proposed coordination.

[1555] This allows users to quickly and efficiently receive personalized fashion recommendations and easily make additional purchases as needed. Items from sustainable brands are also given priority consideration, providing a satisfying shopping experience for environmentally conscious consumers.

[1556] Prompt Sentence Examples

[1557] "When a user purchases a white blouse, please explain the program process for the system that suggests outfits based on that item."

[1558] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1559] Step 1:

[1560] The user confirms the purchase

[1561] The user logs into the online store and decides to purchase a white blouse by clicking the purchase button on the product detail page.

[1562] Input: User clicks

[1563] Output: Temporarily save purchase information

[1564] Step 2:

[1565] The device collects purchase information

[1566] When a purchase is confirmed, the device automatically collects detailed item information (item name, color, size, price, purchase date and time). This includes a script that uses a browser event listener to detect when the purchase button is clicked and retrieves the purchase information.

[1567] Input: Product information specified by the user

[1568] Output: Collected purchase information (item name, color, size, price, purchase date and time)

[1569] Step 3:

[1570] The device sends the purchase information to the server

[1571] The device sends the collected purchase information to the server via network communication. Specifically, the purchase information is converted into JSON format and sent as an HTTP POST request.

[1572] Input: Collected purchase information

[1573] Output: Purchase information sent to the server (JSON format)

[1574] Step 4:

[1575] The server receives the purchase information

[1576] The server receives the purchase information sent from the device via an HTTP request. Once the purchase information is received, the server parses it and prepares it for processing.

[1577] Input: Purchase information sent from the device (JSON format)

[1578] Output: Parsable purchase information

[1579] Step 5:

[1580] The server starts the AI ​​engine

[1581] The server activates an AI engine based on the received purchase information, which analyzes the user's past purchase history, preferences, and fashion trends, and begins the process of suggesting optimal coordinating items.

[1582] Input: Parsable purchase information

[1583] Output: AI engine startup and initial data setup completed

[1584] Step 6:

[1585] AI engine suggests coordination items

[1586] The AI ​​engine analyzes past purchase history, user preferences, and trend information. For example, it uses a machine learning model to analyze the data and select the most suitable items (black skirt, red heels, gold accessories).

[1587] Input: User's past purchase history, preferences, and fashion trend information

[1588] Output: A list of suggested outfit items

[1589] Step 7:

[1590] Servers prioritize sustainable branded items

[1591] The server prioritizes sustainable brand items among the suggested coordination items and adds them to the list. If a sustainable brand item is included, it is suggested as the optimal choice.

[1592] Input: A list of suggested coordinating items

[1593] Output: A list of outfits that prioritize items from sustainable brands

[1594] Step 8:

[1595] The server generates the coordinate information.

[1596] The server generates coordinate information including sustainable brand items, including item names, prices, brand names, and the total price of the coordinated items.

[1597] Input: A list of outfit items from sustainable brands

[1598] Output: Generated coordinate information

[1599] Step 9:

[1600] The server sends the coordinated results to the terminal.

[1601] The server sends the generated coordinate information to the terminal as an HTTP response.

[1602] Input: Generated coordinate information

[1603] Output: Coordinate information sent to the device

[1604] Step 10:

[1605] The terminal displays the results

[1606] The terminal displays the received coordinated outfit information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[1607] Input: Coordinate information sent from the server

[1608] Output: Coordinate information displayed on the user screen

[1609] Step 11:

[1610] The user checks the suggested outfits

[1611] The user checks the coordination suggestions displayed on the terminal and considers purchasing additional suggested items as necessary.

[1612] Input: Coordination information displayed on the screen

[1613] Output: User confirms outfit suggestions and considers purchasing

[1614] (Application example 1)

[1615] 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."

[1616] Users often have difficulty efficiently finding stylish outfits that suit them when purchasing fashion items online. Furthermore, prioritizing sustainable brand products can be time-consuming. Furthermore, if outfit suggestions are not visually easy to understand, users are less likely to accept them. There is a need for a system that can solve these issues and provide a user-friendly, sustainable shopping experience.

[1617] 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.

[1618] In this invention, the server includes a means for receiving purchase information, an artificial intelligence means for suggesting coordinating items based on the received purchase information, a means for generating coordinated outfits taking into consideration the user's past purchase history and preferences, a means for notifying the terminal of the generated coordinated outfit information, and a means for visually displaying the generated coordinated outfit information. This allows users to easily receive personalized coordinated outfit suggestions when shopping online, enabling them to prioritize the selection of products from sustainable brands. Furthermore, the visually easy-to-understand coordinated outfit suggestions can increase user acceptance.

[1619] "Purchase information" refers to detailed information about the product purchased by the user at the online shop (item name, color, size, price, etc.).

[1620] "Artificial intelligence" refers to algorithm technology that analyzes a user's purchase history, preferences, and current fashion trends to suggest optimal outfits.

[1621] "Past purchase history" refers to a record of products that a user has previously purchased from an online shop.

[1622] "User preferences" refers to the fashion preferences and style tendencies that the user has shown up to now.

[1623] "Fashion trends" refers to the current fashion trends and latest fashion trends in the market.

[1624] "Coordination" refers to proposing the optimal fashion style based on the items purchased by the user, combining them with other items.

[1625] A "sustainable brand" is a brand that takes the environment and society into consideration and manufactures and sells products in a sustainable manner.

[1626] "Terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to connect to the Internet and use an online shop.

[1627] "Visually displaying" refers to displaying the generated coordinate information on the screen in a format that allows the user to understand it at a glance.

[1628] The embodiments for carrying out the present invention will be described in detail below.

[1629] The system includes means for receiving purchase information, means for suggesting coordinate items using artificial intelligence, means for notifying the generated coordinate information, and means for visually displaying it.

[1630] Program processing

[1631] 1. Receiving purchase information

[1632] The server receives detailed information (item name, color, size, price, etc.) about the product purchased by the user at the online shop. The purchase information is automatically collected and sent to the server when the user confirms the purchase of the product.

[1633] 2. Starting the AI ​​engine

[1634] Based on the received purchase information, the server launches an artificial intelligence engine (e.g., a model using TensorFlow or PyTorch). The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select the optimal coordinating items.

[1635] 3. Coordination item suggestions

[1636] The AI ​​engine generates the perfect outfit for each user based on the purchase information and analysis results, with suggested items chosen from sustainable brands whenever possible.

[1637] 4. Coordination information generation and notification

[1638] The server generates outfit information based on the detailed information of the outfit items suggested by the AI ​​engine, and this information is sent to the user's device and displayed in a visually easy-to-understand format.

[1639] Hardware and Software Use

[1640] The system uses the following hardware and software:

[1641] Hardware: Cloud servers (e.g., AWS and GCP) and user devices (e.g., smartphones, tablets, and PCs)

[1642] Software: Flask (web framework), artificial intelligence engine (e.g., TensorFlow or PyTorch-based models)

[1643] Specifically, the server uses Flask to build a web application and receives and processes purchase information. The AI ​​engine uses TensorFlow and PyTorch to analyze the user's past purchase history and current fashion trends to generate optimal outfits. The generated outfit information is sent to the device in JSON format and displayed visually on the device.

[1644] Specific examples

[1645] If the user purchases a white blouse

[1646] 1. When a user purchases a white blouse from an online shop, the purchase information is sent to the server.

[1647] 2. The server receives the purchase information and starts the AI ​​engine.

[1648] 3. The AI ​​engine analyzes past purchase history, user preferences, and current trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse.

[1649] 4. The server generates coordinate information for the suggested items and sends it to the user's device.

[1650] 5. The user's device displays the received information on the screen, allowing the user to confirm the proposed coordination.

[1651] Example prompts for generative AI models

[1652] User has purchased a white blouse. Based on past purchase history, user preferences, and current fashion trends, generate a suitable outfit coordination proposal. Include items from sustainable brands if possible.

[1653] In this way, the user can receive optimal coordination suggestions for items purchased through online shopping in real time.

[1654] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1655] Step 1:

[1656] Receiving purchase information

[1657] Subject: Terminal

[1658] How it works: When a user purchases an item from an online store, purchase information (item name, color, size, price, etc.) is automatically collected.

[1659] Input: Item information that the user confirmed to purchase.

[1660] Output: Purchase information is obtained and sent from the device to the server.

[1661] Specific operation: When the user clicks the "Purchase" button, the device obtains detailed information about the item and sends a POST request to the server.

[1662] Step 2:

[1663] Receive and store purchase information on our server

[1664] Subject: Server

[1665] Operation: The server receives the purchase information sent from the terminal and stores it in a database.

[1666] Input: Purchase information sent from the device.

[1667] Output: Purchase information stored in a database.

[1668] Specific operation: The server receives purchase information via the API endpoint of the Flask server and performs the process of saving it in a database (e.g., MySQL or MongoDB).

[1669] Step 3:

[1670] AI engine startup and analysis

[1671] Subject: Server

[1672] How it works: The server launches an AI engine to perform analysis based on the saved purchase information.

[1673] Input: Purchase information stored in a database, as well as your purchasing history and preferences.

[1674] Output: Coordinate candidates as the analysis result.

[1675] How it works: The server calls an AI engine (such as a model implemented in TensorFlow or PyTorch) and inputs and analyzes purchase information, past purchase history, user preferences, and fashion trends. The model then generates appropriate outfit items.

[1676] Step 4:

[1677] Coordination information generation

[1678] Subject: Server

[1679] Operation: The server generates specific coordination information based on the analysis results of the AI ​​engine.

[1680] Input: Analysis results from the AI ​​engine.

[1681] Output: Coordinate information for presentation to the user.

[1682] Specific operation: The server formats the outfit suggestions obtained from the AI ​​engine, prioritizes items from sustainable brands, and generates the final outfit information (a series of item names, brands, prices, etc.).

[1683] Step 5:

[1684] Notification and display of coordination information

[1685] Subject: Server and Terminal

[1686] Operation: The server sends the generated coordinate information to the terminal, which then visually displays this information.

[1687] Input: Generated coordinate information.

[1688] Output: Coordination suggestions displayed on the user's device.

[1689] Specific operation: The server sends the generated outfit information (JSON format) to the device. Based on the received information, the device displays the item images, detailed information (brand name, price, etc.), and the total price of the outfit on the user's screen.

[1690] This processing flow allows the user to receive optimal coordination suggestions for the items they have purchased.

[1691] 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.

[1692] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[1693] System Configuration

[1694] The system of the present invention mainly comprises the following components:

[1695] 1. Terminal

[1696] 2. Server

[1697] 3. AI Engine

[1698] 4. Emotion Engine

[1699] Program processing (outline)

[1700] 1. Receiving purchase information

[1701] Case study: A user purchases a white blouse from an online shop.

[1702] 1.1 User confirms purchase

[1703] A user logs in to an online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[1704] 1.2 Device collects purchase information

[1705] The device retrieves detailed information about the white blouse (item name, color, size, price, etc.). Specifically, it includes a program to automatically read the data displayed on the product page.

[1706] 1.3 The device sends the purchase information to the server

[1707] The device sends the collected information to the server, for example, the moment a purchase is confirmed.

[1708] 2. Coordinate Generation

[1709] The server proposes a coordination proposal using the following steps:

[1710] 2.1 The server receives the purchase information

[1711] The server receives the purchase information sent from the terminal, allowing the system to know exactly which items have been purchased.

[1712] 2.2 The server starts the AI ​​engine and emotion engine

[1713] The server then activates the AI ​​engine and emotion engine based on the received purchase information. The AI ​​engine then prepares a dataset to take into account the user's preferences, past purchase history, current fashion trends, etc.

[1714] 2.3 Emotion engine analyzes user emotions

[1715] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, analyzes that information, and determines the user's current emotional state based on the analysis results.

[1716] 2.4 The AI ​​engine analyzes user information

[1717] The AI ​​engine selects the optimal outfit items based on the user's past purchase history, preferences, and current fashion trend data, while also taking into account emotional information provided by the emotion engine.

[1718] 2.5 Server selects sustainable brands

[1719] We check whether the selected items are from sustainable brands and prioritize products from sustainable brands whenever possible.

[1720] 2.6 Server generates coordinate information

[1721] The server then compiles detailed information (price, brand, etc.) about the suggested items into a single outfit suggestion, which may include adjusting colors and styles based on the user's emotional state.

[1722] 3. Notification and display of results

[1723] The generated coordinate information is notified to the user as follows.

[1724] 3.1 The server sends the coordination results to the terminal

[1725] The server sends the generated coordinate information to the user's device, typically as JSON format data.

[1726] 3.2 The terminal displays the results

[1727] The terminal displays the received coordinated information on the user's screen, including item images, names, prices, brand names, and the total price of the coordinated outfit.

[1728] 3.3 User confirms the suggested outfits

[1729] The user can check the outfit suggestions displayed on the device and consider additional purchases if necessary. In this case, the user can add the items to their shopping cart.

[1730] Specific examples

[1731] A specific scenario is shown below, where a user purchases a white blouse and the system uses the emotion engine to suggest a suitable outfit.

[1732] 1. A user purchases a white blouse from an online store.

[1733] 2. The device collects purchase information and sends it to the server.

[1734] 3. The server receives the purchase information and activates the AI ​​engine and emotion engine.

[1735] 4. The emotion engine determines from the user's facial expression that the current emotional state is "happy."

[1736] 5. The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories to go with the white blouse. Because the emotion is "happiness," the AI ​​engine prioritizes coordinating items in vibrant colors.

[1737] 6. Servers check for sustainable branded items and prioritize sustainable items whenever possible.

[1738] 7. The server generates the coordinate information and sends it to the terminal.

[1739] 8. The terminal displays the received information to the user, who then confirms the proposed coordination.

[1740] Thus, the present invention allows users to make personalized fashion choices quickly and efficiently, while also enjoying a shopping experience that takes into account their emotional state.

[1741] The processing flow will be explained below.

[1742] Step 1:

[1743] The user confirms the purchase. The user logs in to the online shop and purchases a white blouse. At this time, the user clicks the purchase button to complete the purchase process.

[1744] Step 2:

[1745] The device collects purchase information. The device obtains detailed information about the white blouse (item name, color, size, price, etc.). Specifically, a script runs that automatically reads the data listed on the product page.

[1746] Step 3:

[1747] The terminal sends the purchase information to the server. After collecting the purchase information, the terminal sends it to the server. HTTP or HTTPS is used as the communication protocol.

[1748] Step 4:

[1749] The server receives the purchase information. The server receives the purchase information sent from the terminal and stores the information in a database. The stored information is used as a data set for subsequent analysis.

[1750] Step 5:

[1751] The server starts the AI ​​engine and emotion engine. The server starts the AI ​​engine and emotion engine simultaneously based on the received purchase information.

[1752] Step 6:

[1753] The emotion engine analyzes the user's emotions. The emotion engine acquires the user's facial expressions, voice, or text input data and analyzes the user's emotions based on them. The analysis results are output as emotional states such as "happiness," "sadness," and "surprise."

[1754] Step 7:

[1755] The AI ​​engine analyzes the user's information. It analyzes the user's past purchase history, preferences, current fashion trends, etc., and selects the optimal coordination items. At this time, the analysis results of the emotion engine are also used as input data.

[1756] Step 8:

[1757] The server selects sustainable brands. The output of the AI ​​engine is filtered to prioritize items from sustainable brands. The server compares the results with the sustainable brand list and selects items.

[1758] Step 9:

[1759] The server generates coordination information based on the selected coordination items (e.g., black skirt, red heels, gold accessories) and detailed information (price, brand, color and style corresponding to the emotion, etc.).

[1760] Step 10:

[1761] The server sends the coordinated results to the device. The generated coordinated information is packaged in JSON or XML format and sent to the device, which then receives the information.

[1762] Step 11:

[1763] The terminal displays the results. The terminal displays the coordinated information received from the server to the user. The displayed content includes an image of each item, its name, price, brand name, and emotionally appropriate comments and descriptions.

[1764] Step 12:

[1765] The user can then review the suggested outfits. The user can then review the outfit suggestions displayed on their device and purchase additional items as needed. They will also be given the option to add items directly to their shopping cart.

[1766] Through the above processing steps, the user can make personalized fashion choices quickly and efficiently, and enjoy a shopping experience that also takes into account their emotional state.

[1767] Example 2

[1768] 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."

[1769] Conventional coordination suggestion systems have the problem of not being able to sufficiently increase user satisfaction because they make suggestions based solely on past purchase history and trend data without taking into account the user's emotional state. Additionally, they lack the functionality to prioritize recommendations for products from sustainable brands, which means they do not provide an environmentally conscious shopping experience.

[1770] 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.

[1771] In this invention, the server includes means for receiving purchase information, artificial intelligence engine means for suggesting coordinating items based on the received purchase information, emotion engine means for analyzing the user's emotions, means for checking whether the suggested items are from sustainable brands, and means for generating information on the coordinating items, thereby enabling personalized coordination suggestions that take into account the user's emotional state and preferentially suggesting products from sustainable brands.

[1772] "Purchase information" refers to detailed information such as product name, color, size, and price that is generated when a user purchases a product from an online shop or a physical store.

[1773] An "artificial intelligence engine" refers to a program or algorithm that analyzes a user's past purchase history, preferences, and current fashion trend data to select the optimal coordinating items.

[1774] An "emotion engine" refers to a program or algorithm that recognizes emotions from a user's facial expressions, voice, text input, etc., and determines the user's current emotional state based on the analysis results.

[1775] A "sustainable brand" refers to a company or brand that places importance on environmentally friendly product development and ethical working conditions.

[1776] "Coordination information" refers to information about the user's fashion coordination, including detailed information about the suggested fashion items (product name, price, brand name, etc.).

[1777] The present invention relates to a system for suggesting coordinated outfits suitable for fashion items purchased by a user, and in particular to a system for suggesting coordinated outfits that take into account the emotional state of the user by combining an emotion engine. Specific embodiments will be described below.

[1778] The main components of this system are the terminal, server, AI engine, and emotion engine. Each element has the following functions and works together to propose coordination.

[1779] 1. Terminal

[1780] The terminals include personal computers, smartphones, tablets, etc. used by users. When a user purchases a product from an online shop, the terminal has the function of collecting purchase information and sending it to a server. Specifically, this information includes the product name, color, size, price, etc.

[1781] 2. Server

[1782] The server receives purchase information sent from the device and activates an artificial intelligence engine and emotion engine, which selects coordinating items and generates coordination information. The server also checks for the availability of sustainable brand products and prioritizes their suggestions.

[1783] 3. Artificial Intelligence Engine

[1784] The AI ​​engine inputs data such as the user's past purchase history, preferences, and current fashion trends. By analyzing this data, it selects the optimal outfit items. For example, it uses deep learning frameworks such as TensorFlow and PyTorch.

[1785] 4. Emotion Engine

[1786] The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. It uses this information to determine the user's current emotional state, for example, using a generative AI model such as OpenAI's GPT-3.

[1787] As a concrete example, consider the case where a user purchases a white blouse from an online store. The following steps are performed:

[1788] A user purchases a white blouse from an online shop. After the purchase is completed, the device collects the purchase information and sends it to the server.

[1789] The server receives the purchase information and activates the artificial intelligence engine and emotion engine.

[1790] The emotion engine determines the emotion result "happiness" from the analysis of the user's facial expression.

[1791] The AI ​​engine analyzes past purchase history, preferences, and trends to suggest a black skirt, red heels, and gold accessories that go perfectly with a white blouse. Bright colors are selected because they represent the emotion "happiness."

[1792] The server checks for the availability of sustainable branded products and prioritizes sustainable items whenever possible.

[1793] Finally, the server transmits the generated coordinate information to the terminal, which displays it to the user.

[1794] In this way, users can receive personalized outfit suggestions that take their emotions into consideration, and as sustainable brands are prioritized in the suggestions, they can enjoy an environmentally conscious shopping experience.

[1795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1796] Step 1:

[1797] The user confirms the purchase

[1798] A user logs in to an online shop, selects a product (e.g., a white blouse) and completes the purchase procedure, which causes the terminal to acquire purchase information (product name, color, size, price, etc.) as input.

[1799] Step 2:

[1800] The device collects purchase information

[1801] The device runs a program (e.g., JavaScript) to automatically retrieve product information from the online shop's purchase page. The retrieved details (product name, color, size, price, etc.) are collected as data. This data becomes the input for the next processing step.

[1802] Step 3:

[1803] The device sends the purchase information to the server

[1804] The device sends the collected purchase information in JSON format to the server. The data is sent when the purchase procedure is completed. This sent purchase information becomes the starting point for processing on the server.

[1805] Step 4:

[1806] The server receives the purchase information

[1807] The server receives the purchase information sent from the terminal and stores it in a database. The received data includes detailed information such as the product name, color, size, and price. This data becomes the input for the next processing step.

[1808] Step 5:

[1809] The server runs the artificial intelligence engine and emotion engine.

[1810] The server launches an AI engine and an emotion engine based on the received purchase information. The AI ​​engine preprocesses the input data to prepare data on the user's preferences, past purchase history, and the latest fashion trends. The emotion engine loads a model (e.g., OpenAI's GPT-3) to analyze the user's facial expressions and text input.

[1811] Step 6:

[1812] Emotion engine analyzes user emotions

[1813] The emotion engine processes emotions based on the user's facial expressions, voice, and text input information. This determines the user's current emotional state (e.g., "happiness"). The analysis results are output as data, which is then input into the artificial intelligence engine.

[1814] Step 7:

[1815] An artificial intelligence engine analyzes user information

[1816] The AI ​​engine analyzes the user's purchase information, as well as past purchase history, preferences, and current fashion trend data. Based on this data, it selects the optimal coordinating items. This selection process also takes into account the analysis results of the emotion engine. The selected coordinating items are output as data.

[1817] Step 8:

[1818] Server selects sustainable brands

[1819] The server refers to a sustainability database to check whether the coordinated items selected by the AI ​​engine are from sustainable brands. The server adjusts the selection results so that items from sustainable brands are prioritized. Coordination information including sustainable items is output as data.

[1820] Step 9:

[1821] The server generates the coordinate information.

[1822] The server then compiles detailed information (price, brand name, etc.) about the selected coordinate items into a single proposal and generates coordinate information. This coordinate information is then output as data to be sent to the terminal.

[1823] Step 10:

[1824] The server sends the coordinate information to the terminal.

[1825] The server sends the generated coordinate information in JSON format to the user's device. This sent data becomes the input for the next processing step.

[1826] Step 11:

[1827] The terminal displays the results

[1828] The terminal displays the received coordinated outfit information on the user's screen. The displayed content includes item images, names, prices, brand names, and the total price of the coordinated outfit. The user can review this information and consider actions such as additional purchases.

[1829] (Application example 2)

[1830] 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."

[1831] Conventional outfit suggestion systems do not take into account the user's current emotional state, and the outfits they suggest do not always satisfy the user. Furthermore, the items suggested are not uniformly from sustainable brands, and they are not sufficiently environmentally friendly. There is a need to solve these problems and realize more personalized and environmentally friendly outfit suggestions for users.

[1832] 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.

[1833] In this invention, the server includes means for receiving purchase information, artificial intelligence means for suggesting coordinating items based on the received purchase information, emotion analysis means for analyzing the emotional state of the user, means for generating information on coordinating items, and means for notifying the communication terminal of the generated coordinate information. This enables personalized coordination suggestions that take the emotional state of the user into consideration, and further enables environmentally friendly suggestions by prioritizing items from sustainable brands.

[1834] The "means for receiving purchase information" is a function for receiving information about products purchased by a user from a communication terminal.

[1835] The "artificial intelligence means" is a system that has the function of analyzing the user's past purchase history, preferences, and current fashion trends based on the received purchase information, and selecting the most suitable coordinating items.

[1836] The "emotion analysis means" is a system that has the function of analyzing the user's emotional state from facial expressions, voice, text input, and the like.

[1837] The "means for generating information on coordinated items" is a function for generating detailed information on optimal coordinated items based on data obtained from the artificial intelligence means and the emotion analysis means.

[1838] The "means for notifying the communication terminal of the generated coordinate information" is a system having a function for transmitting the information of the generated coordinate items to the user's communication terminal and displaying it.

[1839] A "sustainable brand" is a brand that places importance on environmental protection and social responsibility and offers products that are produced in a sustainable manner.

[1840] In this invention, we will develop a system that proposes suitable outfits for fashion items purchased by a user. This system has a function of taking into account the user's current emotional state and preferentially proposing items from sustainable brands. Specific embodiments will be described below.

[1841] System Configuration

[1842] The system of the present invention comprises the following components:

[1843] Terminal

[1844] server

[1845] Artificial Intelligence Engine

[1846] Sentiment Analysis Engine

[1847] Hardware and software used

[1848] Hardware: Smartphone (iOS or Android), built-in camera

[1849] Software: Public sentiment analysis libraries (e.g., Microsoft Azure Emotion API), artificial intelligence engines (e.g., TensorFlow), cloud services (e.g., Amazon Web Services, Google Cloud Platform), data processing programs (e.g., Python, JavaScript)

[1850] Program processing overview

[1851] 1. Receiving purchase information:

[1852] When a user purchases an item online or at a virtual store, the purchase information (product name, color, size, price, etc.) is sent to the device, which then sends this information to the server.

[1853] 2. Emotional state analysis:

[1854] The device's camera is used to capture the user's face, and an emotion analysis library is used to analyze their current emotional state, which is then sent to the server.

[1855] 3. Coordinate generation:

[1856] The server launches an AI engine based on the received purchase information and sentiment analysis results. The AI ​​engine analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[1857] In addition, the system takes into account the user's emotional state obtained from the emotion analysis engine to suggest more appropriate outfits.

[1858] We prioritize selecting items from sustainable brands and offer environmentally friendly suggestions.

[1859] 4. Displaying the results:

[1860] The generated coordinated information is sent from the server to the terminal and displayed to the user, who can then check the suggested coordinated items and consider additional purchases if necessary.

[1861] Specific examples

[1862] For example, if a user purchases a blue dress in a virtual store, the device receives the purchase information and sends it to the server. The server then activates an emotion analysis engine to analyze the user's emotional state from the face captured by the smartphone camera. In this case, the server determines that the user's current emotion is "calm." The server then uses an artificial intelligence engine to analyze the user's past purchase history, preferences, and current fashion trends, and suggests a gray cardigan, silver accessories, and white sneakers that are appropriate for a calm emotion. Items from sustainable brands are prioritized, and the server sends the outfit information to the device and displays it to the user. The user then checks the outfit and makes additional purchases if necessary.

[1863] Prompt Sentence Examples

[1864] A user has purchased a blue dress. Suggest outfits to go with the dress. Also, consider that the user's current emotional state is "calm." Include sustainable brands in your suggestions.

[1865] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1866] Step 1:

[1867] A user purchases an item online or in a virtual store. The device receives this purchase information and sends the details (product name, color, size, price, etc.) to the server. This purchase information is important because it is used in subsequent processing.

[1868] Input: Purchase information (product name, color, size, price, etc.)

[1869] Output: Purchase information sent to the server

[1870] Step 2:

[1871] The server starts an emotion analysis engine to analyze the user's facial expressions based on the purchase information received from the device, captures the user's face using the device's camera, and sends the image to the server.

[1872] Input: Purchase information sent from the device, captured user face image

[1873] Output: Face image sent to the server

[1874] Step 3:

[1875] The server utilizes an emotion analysis engine to analyze the user's emotional state from the captured facial image. An emotion analysis library (e.g., Microsoft Azure Emotion API) is used to determine the user's current emotional state.

[1876] Input: User's face image

[1877] Output: Parsed emotional state (e.g. happy, calm, sad, etc.)

[1878] Step 4:

[1879] The server activates an AI engine based on the purchase information and emotional state, which analyzes the user's past purchase history, preferences, and current fashion trends to select optimal coordinating items.

[1880] Input: Purchase information, analyzed emotional state, past purchase history, user preferences, fashion trend data

[1881] Output: Information on the best coordinated items (product name, color, size, price, etc.)

[1882] Step 5:

[1883] The server preferentially selects items from sustainable brands from the generated coordination information.

[1884] Input: Information on the best coordinated items

[1885] Output: Sustainable brand item information

[1886] Step 6:

[1887] The server then proposes outfits to the user based on the selected item information, including item images, names, prices, brand names, and the total price of the outfit.

[1888] Input: Sustainable brand item information

[1889] Output: Coordination suggestion information (item image, name, price, brand name, total price, etc.)

[1890] Step 7:

[1891] The terminal receives the coordinated outfit suggestion information sent from the server and displays it on the user's screen. The user can check it and consider additional purchases if necessary.

[1892] Input: Coordination suggestion information

[1893] Output: Coordination suggestions displayed on the user's screen

[1894] The above is a specific process flow for implementing the invention. The system considers the user's emotional state and suggests items from sustainable brands, thereby increasing user satisfaction and providing an environmentally friendly shopping experience.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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).

[1902] 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.

[1903] 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."

[1904] 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.

[1905] 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).

[1906] 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.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] 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.

[1914] 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.

[1915] 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.

[1916] The following is further disclosed regarding the above embodiment.

[1917] (Claim 1)

[1918] means for receiving purchase information;

[1919] an AI engine means for suggesting coordinated items based on the received purchase information;

[1920] A means for generating coordinate item information;

[1921] The system includes a means for notifying the terminal of the generated coordinate information.

[1922] (Claim 2)

[1923] 2. The system according to claim 1, wherein the AI ​​engine means analyzes the user's past purchase history and preferences, and selects coordinating items taking into consideration current fashion trends.

[1924] (Claim 3)

[1925] The system of claim 1, wherein the generated coordination information preferentially suggests items from sustainable brands.

[1926] "Example 1"

[1927] (Claim 1)

[1928] means for receiving purchase information;

[1929] an artificial intelligence means for suggesting coordinated items based on the received purchase information;

[1930] A means for generating information on suggested coordination items;

[1931] means for notifying the terminal of the generated coordinate information;

[1932] means for displaying the notified coordinate information on a screen;

[1933] A way to check suggested outfits

[1934] A system including:

[1935] (Claim 2)

[1936] 2. The system according to claim 1, wherein the artificial intelligence means analyzes the user's past purchase history, preferences, and current fashion trends to select coordinating items.

[1937] (Claim 3)

[1938] The system of claim 1, wherein the generated coordination information preferentially suggests items from sustainable brands.

[1939] "Application Example 1"

[1940] (Claim 1)

[1941] means for receiving purchase information;

[1942] an artificial intelligence means for suggesting coordinated items based on the received purchase information;

[1943] A means for generating a coordinated outfit in consideration of a user's past purchase history and preferences;

[1944] means for notifying the terminal of the generated coordinate information;

[1945] The system includes a means for visually displaying the generated coordinate information.

[1946] (Claim 2)

[1947] 2. The system according to claim 1, wherein the artificial intelligence means analyzes the user's past purchase history and preferences, and selects coordinating items taking into consideration current fashion trends.

[1948] (Claim 3)

[1949] The system according to claim 1, wherein the generated coordination information preferentially suggests items from sustainable brands, and the means for displaying the coordination information visually presents the information to the user.

[1950] "Example 2: Combining Emotion Engines"

[1951] (Claim 1)

[1952] means for receiving purchase information;

[1953] an artificial intelligence engine means for suggesting coordinate items based on the received purchase information;

[1954] emotion engine means for analyzing the emotion of a user;

[1955] A way to check whether the proposed item is from a sustainable brand, and

[1956] A means for generating coordinate item information;

[1957] The system includes a means for notifying the terminal of the generated coordinate information.

[1958] (Claim 2)

[1959] 2. The system according to claim 1, wherein the artificial intelligence engine means analyzes the user's past purchase history and preferences, and selects coordinate items taking into consideration current fashion trends and the user's emotional state.

[1960] (Claim 3)

[1961] The system of claim 1, wherein the generated coordination information preferentially suggests items from sustainable brands.

[1962] "Application example 2 when combining emotion engines"

[1963] (Claim 1)

[1964] means for receiving purchase information;

[1965] an artificial intelligence means for suggesting coordinated items based on the received purchase information;

[1966] emotion analysis means for analyzing the emotional state of a user;

[1967] A means for generating coordinate item information;

[1968] The system includes a means for notifying the communication terminal of the generated coordinate information.

[1969] (Claim 2)

[1970] 2. The system of claim 1, wherein the artificial intelligence means analyzes the user's past purchase history and preferences, takes into consideration current fashion trends, and selects coordinating items based on the user's emotional state analyzed by the emotion analysis means.

[1971] (Claim 3)

[1972] The system of claim 1, wherein the generated coordination information preferentially suggests items from sustainable brands. [Explanation of symbols]

[1973] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving purchase information; an AI engine means for suggesting coordinated items based on the received purchase information; A means for generating coordinate item information; The system includes a means for notifying the terminal of the generated coordinate information.

2. 2. The system according to claim 1, wherein the AI ​​engine means analyzes the user's past purchase history and preferences and selects coordinate items taking into consideration current fashion trends.

3. The system according to claim 1 , wherein the generated coordination information preferentially suggests items from sustainable brands.

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