System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving personal data input by a user; means for receiving consultation content related to fashion; means for analyzing data to suggest optimal fashion items based on the personal data and the consultation content; means for providing information on suggested fashion items to the user; means for generating and providing links to online sales sites of the fashion items to the user; means for recording and updating a purchase history of the fashion items; and means for improving subsequent suggestions based on the purchase history.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] Conventional fashion coordination and shopping support systems have had problems such as not being able to fully utilize the user's individual preferences and personal data, and not being able to appropriately suggest the fashion items the user desires. Furthermore, it is difficult to suggest optimal items in real time based on the user's consultation, and the integration of systems for tracking purchase history and delivery status is insufficient. Given these current circumstances, there is a need for systems that can address individual needs and improve the user experience. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system as follows. First, the system includes a means for receiving personal data entered by a user, and uses that data to understand the user's fashion preferences and sizes. Furthermore, the system also includes a means for receiving fashion-related inquiries, and obtains the user's current needs in real time. Based on this information, the system includes a means for analyzing the data to suggest optimal fashion items, thereby suggesting items that suit the user. A means for providing the user with information about the suggested items includes detailed information such as item images, prices, and stock status. Furthermore, a means for generating and providing the user with links to online sales sites for the suggested items allows the user to easily purchase the items. Finally, the system includes a means for recording and updating the purchase history of fashion items, and a means for improving subsequent suggestions based on that purchase history, thereby continuously improving the user experience. In this way, the system can meet individual needs and provide more efficient and satisfying fashion suggestions and shopping experiences.

[0006] "User" refers to an individual who uses this system to seek advice on fashion coordination.

[0007] "Personal Data" refers to information about the user, such as personal information, fashion preferences, size, brand preferences, budget, etc.

[0008] "Means for receiving" refers to the functions and interfaces for taking input and data from users into the system.

[0009] "Consultation content" refers to requests and questions about fashion that users input into the system.

[0010] "Means of analyzing data" refers to the algorithms and processes that select the most suitable fashion items based on the received personal data and consultation content.

[0011] The "optimal fashion items" refer to fashion products such as clothes and accessories that should be suggested based on the user's personal data and consultation details.

[0012] "Means for providing" refers to the interface or method for displaying the analysis results to the user.

[0013] "Link to Online Retail Site" means the URL or access to a website selling the proposed fashion item.

[0014] "Purchase History" refers to a record of fashion items purchased by a User through the System.

[0015] "Means for recording and updating" refers to the functions and processes for storing a user's purchase history and updating it as necessary.

[0016] "Means for improving subsequent suggestions" refers to algorithms or processes used to improve the accuracy of suggested fashion items based on past purchase history and user feedback. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[0039] 1. User data input

[0040] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0041] 2. Fashion consultation

[0042] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content is sent to the server and used for analysis.

[0043] 3. Data analysis and item proposal

[0044] The server analyzes the data based on the inquiry details sent by the user, the saved personal data, and past purchase history, and uses an AI engine to select the most suitable fashion items, sending this information as a list to the device.

[0045] 4. Online Search and Ordering

[0046] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0047] 5. Purchase history management and delivery support

[0048] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[0049] Specific examples

[0050] As a concrete example, consider the following scenario.

[0051] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[0052] 2. The server analyzes the inquiry and the user's saved data and suggests a "checked dress," a "brown cardigan," and "ankle boots."

[0053] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0054] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[0055] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0056] In this way, the system provides users with highly personalized fashion item suggestions, providing a comfortable and efficient shopping experience.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] When a user accesses the system, an account registration screen appears, where the user enters personal information such as name, size, preferred brand, and budget.

[0060] Step 2:

[0061] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[0062] Step 3:

[0063] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[0064] Step 4:

[0065] The content of the consultation is sent to the server, which receives it and begins preparing it for analysis along with the stored personal data and past purchase history.

[0066] Step 5:

[0067] The server uses an AI engine to perform analysis based on fashion knowledge, and selects several fashion items that are most suitable for the user.

[0068] Step 6:

[0069] The server creates a list of selected fashion items and collects images, prices, and stock status of each item, which is then sent to the device.

[0070] Step 7:

[0071] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[0072] Step 8:

[0073] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[0074] Step 9:

[0075] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[0076] Step 10:

[0077] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[0078] Step 11:

[0079] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[0080] Example 1

[0081] 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."

[0082] Conventional shopping support systems have been inadequate in proposing fashion items based on individual users' personal data and preferences, making it difficult for users to find the perfect item. Furthermore, managing purchase history and delivery information is cumbersome, resulting in inconvenience for users.

[0083] 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.

[0084] In this invention, the server includes means for receiving personal data, means for receiving fashion consultation details, means for analyzing data, means for proposing optimal fashion items using a generative AI model, means for providing information on fashion items to the user, means for generating and providing links to online sales sites to the user, means for recording and updating purchase history, means for improving subsequent proposals based on the purchase history, and means for obtaining and notifying the user of delivery information. This enables highly personalized proposals of fashion items, providing the user with an efficient and comfortable shopping experience.

[0085] 1. "Personal Data" refers to personal information such as your name, size, preferred brands, budget, etc.

[0086] 2. "Consultation content" refers to information regarding the user's desired fashion style and specific items.

[0087] 3. "Means for analyzing data" refers to technology that analyzes information and makes appropriate suggestions based on personal data and consultation details stored on the server.

[0088] 4. "Generative AI model" refers to an artificial intelligence model used for data analysis, which is used to suggest optimal fashion items based on the user's personal data and consultation details.

[0089] 5. "Fashion items" refers to fashion-related products such as clothing and accessories that users consider purchasing.

[0090] 6. "Link to Online Retail Site" refers to a web link that provides details of the proposed fashion items and allows users to easily purchase the products.

[0091] 7. "Purchase History" refers to the record of products purchased by a User in the past.

[0092] 8. "Shipping Information" refers to data used to provide users with information such as the shipping status and tracking number of purchased items.

[0093] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[0094] User Data Input

[0095] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0096] Hardware and software used

[0097] Hardware: User devices (PCs, smartphones, etc.), servers

[0098] Software: Web browser, database management system (e.g., MySQL, PostgreSQL)

[0099] Fashion consultation

[0100] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as, "I'm looking for casual clothes that are suitable for the office." The content of this consultation is sent to the server and used for analysis.

[0101] Hardware and software used

[0102] Hardware: User terminals, servers

[0103] Software: Web browser, chat system (e.g., Dialogflow, Microsoft Bot Framework)

[0104] Data analysis and item suggestions

[0105] The server analyzes the data based on the inquiry details sent by the user, the stored personal data, and past purchase history, and uses a generative AI model to select the most suitable fashion items, which are then sent to the device as a list.

[0106] Hardware and software used

[0107] Hardware: Server

[0108] Software: Data analysis engine, generative AI model (e.g., OpenAI GPT-3, BERT)

[0109] Online search and ordering

[0110] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0111] Hardware and software used

[0112] Hardware: User terminals, servers

[0113] Software: Web browser, online shop linkage system

[0114] Purchase history management and delivery support

[0115] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0116] Hardware and software used

[0117] Hardware: Server

[0118] Software: Database management system, notification system

[0119] Specific examples

[0120] As a concrete example, consider the following scenario.

[0121] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[0122] 2. The server analyzes the inquiry and the user's saved data, and uses a generative AI model to suggest a "checked dress," a "brown cardigan," and "ankle boots."

[0123] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0124] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[0125] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0126] Prompt Sentence Examples

[0127] By inputting the following prompt sentence into the generative AI model, the system can make fashion suggestions based on the user's inquiry.

[0128] "I'm 195cm tall. Can you recommend a casual yet stylish office casual look?"

[0129] This allows the system to provide users with highly personalized fashion item suggestions, enabling a comfortable and efficient shopping experience.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0133] input:

[0134] Personal data entered by the user, such as name, size, preferred brand, budget, etc.

[0135] output:

[0136] Personal data stored on the server.

[0137] Specific behavior:

[0138] The terminal uses a web browser to display a user data input form.

[0139] The user enters information into the input form and presses the submit button.

[0140] The terminal sends the input information to the server as an HTTP request.

[0141] The server stores the received information in a database (e.g. MySQL, PostgreSQL).

[0142] Step 2:

[0143] When a user starts a fashion consultation, an interactive chat window appears on the device. The user inputs their request, such as "I want casual clothes that are suitable for the office." This request is sent to the server and used for analysis.

[0144] input:

[0145] Fashion-related inquiries entered by users.

[0146] output:

[0147] Consultation details are stored on the server.

[0148] Specific behavior:

[0149] The device displays a chat window in the web browser.

[0150] The user enters the content of the consultation into the chat window and presses the send button.

[0151] The terminal sends the entered consultation details to the server as an HTTP request.

[0152] The server analyzes the consultation content using a chat system (e.g., Dialogflow, Microsoft Bot Framework).

[0153] Step 3:

[0154] The server receives the inquiry details sent by the user, as well as stored personal data and past purchase history, and performs data analysis using a generative AI model. Based on the analysis, the server selects the most suitable fashion items and sends this information as a list to the device.

[0155] input:

[0156] User's personal data, consultation details, and past purchase history.

[0157] output:

[0158] A list of optimal fashion items selected by a generative AI model.

[0159] Specific behavior:

[0160] The server retrieves user data and consultation details from the database.

[0161] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3, BERT) and selects appropriate items.

[0162] The server organizes the selected items into a list and sends it to the terminal in JSON format.

[0163] Step 4:

[0164] The device displays a list of items, each with a picture, price, and availability details. When the user selects an item from the list, a link to the online store for that item appears. By clicking the link, the user can go directly to the online store and place an order.

[0165] input:

[0166] The item list sent by the server.

[0167] output:

[0168] A link to the online retailer of the item the user selected.

[0169] Specific behavior:

[0170] The device displays the item list (images, prices, links, etc.) in a web browser.

[0171] The user selects the item they like from the list and clicks on the provided link.

[0172] The terminal redirects the user to the page of the specific product in the online shop.

[0173] The user completes the purchase process at the online store.

[0174] Step 5:

[0175] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0176] input:

[0177] Information about the item the user completed purchasing.

[0178] output:

[0179] Notifications of updated purchase history and shipping information.

[0180] Specific behavior:

[0181] After the user completes the purchase, the server receives the purchase information from the online shop via API.

[0182] The server saves and updates the purchase history in a database.

[0183] The server retrieves delivery information from the delivery company's tracking API.

[0184] The server notifies the terminal of the acquired delivery information and displays "Your ordered item has been shipped. The tracking number is XXXX."

[0185] (Application example 1)

[0186] 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."

[0187] Conventional online shopping systems lack the functionality to suggest optimal fashion items that match users' personal data and preferences. Furthermore, users are limited to a virtual shopping experience, often unable to receive the kind of interactive styling consultations found in brick-and-mortar stores. This results in users spending too much time selecting products, making it difficult to enjoy a satisfying shopping experience.

[0188] 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.

[0189] In this invention, the server includes means for receiving personal data entered by a user, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data and the consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, means for improving subsequent suggestions based on the purchase history, means for providing a virtual shopping space using a head-mounted display, and means for interactively communicating with a virtual stylist, thereby enabling the user to receive real-time styling consultation in a virtual environment and enjoy an efficient and satisfying shopping experience.

[0190] "Personal data entered by the user" refers to personal information and preferences such as the user's name, size, favorite brand, budget, etc., and is data entered into the system.

[0191] "Fashion consultation content" is information including requests and questions about fashion items and styling that the user desires.

[0192] "Means for analyzing data" refers to data processing technology for selecting the most suitable fashion items based on the user's personal data and consultation details.

[0193] "Information on suggested fashion items" refers to detailed information such as images, prices, and stock status of items selected by the system.

[0194] The "means for generating a link to an online sales site and providing it to the user" is a technology for creating a URL for an online shop where the suggested fashion item can be purchased and providing it to the user.

[0195] "Means for recording and updating purchase history of fashion items" refers to a technology that stores information about items purchased by users in a database and adds new purchase data.

[0196] "Means for improving subsequent suggestions" refers to technology that improves the accuracy of future fashion item suggestions based on past purchase history and user data.

[0197] "Means for providing a virtual shopping space using a head-mounted display" refers to a technology that allows a user to wear a head-mounted display and experience shopping in a virtual reality space.

[0198] "A means for communicating with a virtual stylist in an interactive format" is a technology that allows the virtual stylist to converse with the user in real time and provide styling advice and product suggestions.

[0199] This invention is a system that proposes optimal fashion items to users and provides efficient shopping support. This system mainly involves a server, terminals, and users, and the roles of each are clarified.

[0200] System configuration

[0201] 1. Server

[0202] The server receives personal data and fashion-related inquiries sent by users and analyzes the data. It is equipped with a data analysis engine using an AI model, and selects the most suitable fashion items based on data such as the user's preferences and past purchase history. The server also manages purchase history and improves subsequent suggestions, and is designed to always provide the latest information.

[0203] 2. Terminal

[0204] The device receives data entered by the user and sends it to the server. It also has the function of displaying information on suggested fashion items to the user. It also provides a virtual shopping space using a head-mounted display (HMD). Users can communicate interactively with a virtual stylist and receive advice in real time.

[0205] 3. Users

[0206] Users access the system and first enter their personal information (name, size, favorite brands, budget, etc.), then enter their fashion-related inquiries interactively, select suggested items based on the system's analysis results, and access the online sales site to make their purchase.

[0207] Data analysis and item suggestion flow

[0208] The server uses a generative AI model to analyze the data based on the personal data and consultation details received from the user. Based on the analysis results, it selects the fashion items that best suit the user's preferences and needs, and sends that information (images, prices, and stock status) to the device. Once the user has selected an item, the device generates a link to the online sales site and provides it to the user.

[0209] Hardware and software used

[0210] Head-mounted display (HMD): A device used to provide a virtual shopping space, allowing users to experience shopping in a virtual reality environment.

[0211] Generative AI model: An AI engine used for data analysis that suggests optimal fashion items based on the user's personal data and consultation details.

[0212] Database system: Used to store and manage users' personal data and purchase history.

[0213] Examples of concrete examples and prompts

[0214] As a concrete example, consider the following scenario.

[0215] 1. A user asks, "I'm looking for an outfit to wear on a spring date."

[0216] 2. The server analyzes the inquiry and the user's saved data and suggests a "casual dress" and a "light jacket."

[0217] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0218] 4. The user selects "Casual Dress" and clicks the provided link to complete the purchase.

[0219] 5. The server records your purchase history and notifies the device of the shipping information. The message "The dress you ordered has been shipped. The tracking number is XXXX" is displayed.

[0220] Example prompt sentence:

[0221] User: I'm looking for something to wear on a spring date.

[0222] System: We're looking for the perfect item for you. Please wait...

[0223] System: Here are the items we suggest. Please choose from "Casual Dress" or "Light Jacket."

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] User Data Input

[0227] Users enter their personal data (such as name, size, favorite brands, budget, etc.) via the device. The device receives this data and sends it to the server. The server stores the received data in a database and creates a personal profile for each user, forming the basis for future suggestions.

[0228] Input: Personal data entered by the user into the device

[0229] Output: User's personal data stored on the server

[0230] Step 2:

[0231] Fashion consultation reception

[0232] The user inputs their fashion-related inquiry via the device. The device receives the message from the user in real time and sends it to the server. The server then analyzes the received inquiry and prepares it for input into the generative AI model.

[0233] Input: Fashion consultation details entered by the user into the device

[0234] Output: Consultation details sent to the server

[0235] Step 3:

[0236] Data analysis and item suggestions

[0237] The server uses a generative AI model to analyze the user's personal data and the details of their inquiry. The AI ​​model selects the fashion items that best fit the user's preferences and the details of their inquiry, and generates information about them (images, prices, stock status, etc.). This information is sent to the device and displayed to the user.

[0238] Input: User's personal data and consultation details input into the generative AI model

[0239] Output: Information about suggested fashion items sent to the device

[0240] Step 4:

[0241] Viewing and selecting items from the list

[0242] The device receives the fashion item information sent from the server and displays it to the user. The user selects an item from the displayed item list. The device then sends the user's selection to the server.

[0243] Input: Fashion item information sent from the server to the device

[0244] Output: Information about the items selected by the user

[0245] Step 5:

[0246] Online purchase process

[0247] The terminal generates a link to an online sales site based on the user's selection and provides it to the user. The user clicks the link to access the online sales site and purchases the product. The terminal then sends information about the completion of the purchase to the server.

[0248] Input: Information about the item selected by the user

[0249] Output: Online retailer link and purchase completion information

[0250] Step 6:

[0251] Purchase history management and notifications

[0252] The server records and updates the user's purchase history information in a database. It also obtains delivery information and sends a notification to the terminal. The terminal displays the delivery information (e.g., tracking number) to the user.

[0253] Input: Purchase completion information and shipping information

[0254] Output: Purchase history recorded in the database and shipping information sent to the user

[0255] Step 7:

[0256] Subsequent proposal improvements

[0257] The server uses the stored personal data and purchase history to improve future suggestions, and the generative AI model learns from new data to make more accurate fashion item suggestions.

[0258] Input: Updated personal data and purchase history

[0259] Output: Improved fashion item suggestions

[0260] 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.

[0261] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below in natural language.

[0262] 1. User data input

[0263] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[0264] 2. Emotion recognition

[0265] The device is equipped with an emotion engine. Before the user starts a consultation, it uses a facial recognition camera to collect the user's facial expression data and analyzes their emotions. This analysis recognizes the user's current emotional state (e.g., joy, sadness, stress, etc.).

[0266] 3. Fashion consultation

[0267] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[0268] 4. Data analysis and item proposal

[0269] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[0270] 5. Online Search and Ordering

[0271] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0272] 6. Purchase history management and delivery support

[0273] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[0274] Specific examples

[0275] As a concrete example, consider the following scenario.

[0276] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[0277] 2. The device's emotion engine detects the user's stress level.

[0278] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[0279] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0280] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[0281] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0282] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[0283] The processing flow will be explained below.

[0284] Step 1:

[0285] A user accesses the system and is presented with an account registration screen, where they enter personal data such as their name, size, preferred brand, budget, and permission to use the facial recognition camera.

[0286] Step 2:

[0287] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[0288] Step 3:

[0289] The device activates the emotion engine and uses the face recognition camera to collect the user's facial expression data, which is then analyzed by the emotion engine to recognize the user's current emotional state.

[0290] Step 4:

[0291] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[0292] Step 5:

[0293] The consultation content and emotional data generated by the emotion engine are sent to the server, which receives this data and begins preparing for analysis.

[0294] Step 6:

[0295] The server analyzes the data based on the inquiry details sent by the user, stored personal data, past purchase history, and current emotional state, and uses an AI engine to select the most suitable fashion items.

[0296] Step 7:

[0297] The server creates a list of selected fashion items and collects images, prices, and availability of each item, which is then sent to the terminal and displayed to the user.

[0298] Step 8:

[0299] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[0300] Step 9:

[0301] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[0302] Step 10:

[0303] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[0304] Step 11:

[0305] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[0306] Step 12:

[0307] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[0308] Example 2

[0309] 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."

[0310] Conventional fashion suggestion systems make suggestions based on a user's personal data and past purchase history, but because they do not take the user's emotional state into account, they have difficulty suggesting items that suit the user's current psychological state and mood. Furthermore, the selection of suggested items is limited, making it difficult for users to have a satisfying shopping experience. This reduces users' motivation to purchase, and creates the problem of not being able to provide an optimal shopping experience.

[0311] 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.

[0312] In this invention, the server includes means for receiving personal data input by a user, means for analyzing the user's current emotional state, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data, emotional state data, and consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, and means for improving subsequent suggestions based on the purchase history. This enables personalized fashion item suggestions that take the user's current emotional state into consideration, providing a comfortable and efficient shopping experience.

[0313] "User Data" is personal information about the user that the user enters, such as name, size, preferred brand, budget, and facial recognition camera permissions.

[0314] "Emotional state" refers to the user's psychological state, such as joy, sadness, or stress, obtained by analyzing the user's facial expression data.

[0315] "Fashion consultation" refers to specific requests or inquiries about fashion that users make to the system.

[0316] "Data analysis" refers to the process of using an AI engine to select the most suitable fashion items using user data, emotional state data, and fashion consultation details.

[0317] "Suggested fashion items" are candidate fashion items that are provided to the user as a result of data analysis.

[0318] "Online Retail Link" means a URL to a website where the suggested fashion item can be purchased.

[0319] "Purchase history" is a record of fashion items purchased by the user in the past, and includes the purchase date and time, item name, price, delivery information, and the like.

[0320] "Subsequent suggestions" are improved candidates for the next fashion item to be suggested based on previous purchase history and user data.

[0321] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below.

[0322] This system has three main components: the user, the terminal, and the server. Specifically, it consists of the terminal part that receives input from the user, the server part that performs emotion recognition and data analysis, and the user interface that provides fashion suggestions and online shopping support to the user.

[0323] User Data Input

[0324] When a user accesses the system for the first time, a user data entry screen appears on the terminal. Here, the user enters their user data. This user data includes their name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. The specific hardware used is a PC or smartphone, and the software used is a web browser or dedicated application.

[0325] emotion recognition

[0326] The device is equipped with an emotion engine, which uses a facial recognition camera to collect the user's facial expression data before the user begins a consultation. This facial expression data is analyzed in real time by the emotion engine to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.). The analysis results are sent to a server and stored in a database. Specifically, an AI model (e.g., a facial expression recognition model) is used for emotion recognition.

[0327] Fashion consultation

[0328] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server for analysis. The software used for this is a conversational agent based on natural language processing (NLP).

[0329] Data analysis and item suggestions

[0330] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine (e.g., machine learning algorithm) to select the most suitable fashion items and sends this information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[0331] Online search and ordering

[0332] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0333] Purchase history management and delivery support

[0334] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. Specific information displayed may include, "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0335] Specific examples

[0336] For example, if a user says, "I want clothes for a date that fit the autumn trends," and the device's emotion engine detects the user's stress level, the server analyzes the content of the request, emotional data, and stored personal data. As a result, the server suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots." These suggestions are displayed on the device, and the user selects the "calm-colored cardigan" and clicks the provided link to complete the purchase. The server then records the purchase history and notifies the device of delivery information.

[0337] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Step 1:

[0340] When a user accesses the system for the first time, a user data entry screen appears on the terminal.

[0341] What it does: The user enters personal data such as name, size, preferred brands, budget, and facial recognition camera permissions.

[0342] Input: User data (e.g., name "Yamada Taro", size "M", favorite brand "Uniqlo", budget "10,000 yen", "Allow" to use face recognition camera).

[0343] Output: User data is saved in the database and sent to the server.

[0344] Step 2:

[0345] The device's built-in emotion engine collects the user's facial expression data in real time.

[0346] Specific operation: When a user stands in front of the screen, the facial recognition camera captures facial expression data.

[0347] Input: Real-time facial expression data.

[0348] Data processing: The emotion engine analyzes facial expression data and determines the user's emotional state (e.g., joy, sadness, stress, etc.).

[0349] Output: The emotional state data is sent to the server and stored in a database.

[0350] Step 3:

[0351] When a user starts a fashion consultation, an interactive chat window will appear on the device.

[0352] Specific operation: The user inputs a question such as "I want some casual clothes that are suitable for the office."

[0353] Input: User's consultation content.

[0354] Output: The consultation content is sent to the server and stored together with the emotional state data.

[0355] Step 4:

[0356] The server performs data analysis using user data, emotional state data, user inquiries, and past purchase history.

[0357] Specific operation: The AI ​​engine integrates this data and selects the most suitable fashion items.

[0358] Input: User data, emotional state data, consultation details, past purchase history.

[0359] Data calculation: The AI ​​engine analyzes using machine learning algorithms and selects items.

[0360] Output: The list of suggested fashion items is sent to the terminal.

[0361] Step 5:

[0362] A list of fashion items will appear on your device.

[0363] Specific behavior: The user selects the item they like from the list.

[0364] Input: A list of suggested fashion items.

[0365] Output: A link to the online retailer of the selected item is generated and provided to the user.

[0366] Step 6:

[0367] Users click on a link to an online retailer of the selected item and begin shopping.

[0368] Specific operation: A user goes through the process of purchasing an item on an online sales site.

[0369] Input: The user's item selection.

[0370] Output: When the purchase process is completed, the user's purchase history is recorded and updated on the server.

[0371] Step 7:

[0372] It obtains delivery information based on purchase history and sends a notification to the device.

[0373] Specific operation: Obtains delivery status from the delivery company and notifies the user.

[0374] Input: Purchase history, delivery status.

[0375] Output: The terminal displays specific information such as "Your order has been shipped. Here is your tracking number."

[0376] Following this step, a system incorporating an emotion engine can provide a more comfortable and efficient shopping experience by making personalized fashion recommendations based on the user's emotional state.

[0377] (Application example 2)

[0378] 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."

[0379] Conventional fashion suggestion and shopping support systems make suggestions without considering the user's emotional state, making it difficult to provide items that suit the user's current mental state. Furthermore, when shopping in a physical store, users have difficulty locating suggested items. Another issue is that users may feel stressed while shopping in the store.

[0380] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving personal information entered by the user, means for receiving fashion consultation details, means for analyzing data to suggest optimal clothing based on the personal information and consultation details, means for providing information on the suggested clothing to the user, means for generating and providing a link to an online clothing sales site for the user, means for recording and updating the purchase history, means for improving subsequent suggestions based on the purchase history, emotion analysis means for recognizing the user's emotional state and suggesting clothing that matches the emotional state, and means for guiding the user to the location of the suggested clothing in the store. This makes it possible to suggest fashion items that suit the user's emotional state, allowing the user to comfortably search for products without feeling stressed even when shopping in a physical store.

[0381] A "user" is a user of the system and an individual who provides personal information and consultation details.

[0382] "Personal Information" is data entered by the user, including personal information such as name, size, preferred brands, budget, etc.

[0383] The "contents of consultation" are information about requests and wishes that the user inputs to the system, including specific requests such as "I want casual clothes that are suitable for the office."

[0384] "Clothing" refers to fashion items suggested to users, including clothes, shoes, accessories, etc.

[0385] The "means for analyzing data" refers to a method and device that performs calculations and evaluations to select optimal clothing based on personal information, consultation details, and past purchase history entered by the user.

[0386] "Link to Online Retail Site" means a web address provided to allow a user to purchase the suggested clothing item over the Internet.

[0387] "Means for recording and updating purchase history" refers to a method and device for storing information about items purchased by a user and updating that information as necessary.

[0388] The "means for improving subsequent suggestions" refers to a method and apparatus that improves the system's suggestion method to provide more relevant suggestions based on the user's past purchasing history.

[0389] "Emotion analysis means" refers to a method and apparatus that analyzes a user's facial expressions and other data to recognize the user's current emotional state.

[0390] "Store location guidance" refers to methods and devices that provide the location of suggested clothing items to help a user find them in a store.

[0391] The present invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the emotions of a user. An embodiment of this system will be described in detail below.

[0392] 1. User data input

[0393] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[0394] 2. Emotion recognition

[0395] The device is equipped with an emotion engine that uses a facial recognition camera to collect facial expression data and analyze the user's emotions before the user starts a consultation. This analysis allows the device to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.).

[0396] 3. Fashion consultation

[0397] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[0398] 4. Data analysis and item proposal

[0399] The server analyzes data based on the user's inquiry, saved personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[0400] 5. Online Search and Ordering

[0401] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0402] 6. Purchase history management and delivery support

[0403] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[0404] Specific examples

[0405] As a concrete example, consider the following scenario.

[0406] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[0407] 2. The device's emotion engine detects the user's stress level.

[0408] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[0409] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0410] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[0411] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0412] Example prompts for generative AI models

[0413] Using the user's facial expression data and preference data as input, suggest relaxing fashion items. Since the current emotional state is stressed, comfortable clothing is best. Also display the product location in the store.

[0414] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[0415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0416] Step 1:

[0417] When a user accesses the system for the first time, a user data entry screen appears on the terminal. The user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. Based on this input data, the server generates a personal profile for the user. The main input here is the user's personal information, and the output is the personal profile.

[0418] Step 2:

[0419] Before starting a consultation, the device's on-board emotion engine uses a facial recognition camera to collect the user's facial expression data and analyze their emotions. Specifically, a deep learning model (e.g., a TensorFlow-based model) analyzes the facial expression data and identifies the user's emotional state (e.g., joy, sadness, stress, etc.). The input of this step is the user's facial expression data, and the output is the user's current emotional state.

[0420] Step 3:

[0421] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I want casual clothes that are suitable for the office." This consultation is sent to the server, which uses it for analysis along with a stored personal profile and current emotional state. The input for this step is the user's consultation, and the output is data ready for analysis.

[0422] Step 4:

[0423] The server analyzes data based on the inquiry details sent by the user, stored personal information, past purchase history, and current emotional state. The server uses an AI engine (e.g., a generative AI model) to select the most suitable fashion items. Specifically, if the user's stress level is high, it will suggest clothing made of relaxing materials, and if an emotion of joy is detected, it will select items with a gorgeous design. The input here is the user's various profile information and emotional state, and the output is a list of suggested items.

[0424] Step 5:

[0425] A list of suggested items is displayed on the terminal. Each item includes details such as an image, price, and availability. When the user selects an item they like, a link to the online sales site for that item is provided. By clicking the link, the user can access the online shop directly and proceed with the product order. The input of this step is the item list sent from the server, and the output is the purchase page link selected by the user.

[0426] Step 6:

[0427] When a user completes a purchase, the server records and updates the purchase history. In addition, the server retrieves delivery information and sends a notification to the terminal. Specific information is provided, such as "Your order has been shipped. Here is the tracking number." The input of this step is the user's purchase information and the corresponding delivery information, and the output is an updated purchase history and delivery notification.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] [Second embodiment]

[0432] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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."

[0444] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[0445] 1. User data input

[0446] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0447] 2. Fashion consultation

[0448] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content is sent to the server and used for analysis.

[0449] 3. Data analysis and item proposal

[0450] The server analyzes the data based on the inquiry details sent by the user, the saved personal data, and past purchase history, and uses an AI engine to select the most suitable fashion items, sending this information as a list to the device.

[0451] 4. Online Search and Ordering

[0452] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0453] 5. Purchase history management and delivery support

[0454] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[0455] Specific examples

[0456] As a concrete example, consider the following scenario.

[0457] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[0458] 2. The server analyzes the inquiry and the user's saved data and suggests a "checked dress," a "brown cardigan," and "ankle boots."

[0459] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0460] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[0461] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0462] In this way, the system provides users with highly personalized fashion item suggestions, providing a comfortable and efficient shopping experience.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] When a user accesses the system, an account registration screen appears, where the user enters personal information such as name, size, preferred brand, and budget.

[0466] Step 2:

[0467] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[0468] Step 3:

[0469] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[0470] Step 4:

[0471] The content of the consultation is sent to the server, which receives it and begins preparing it for analysis along with the stored personal data and past purchase history.

[0472] Step 5:

[0473] The server uses an AI engine to perform analysis based on fashion knowledge, and selects several fashion items that are most suitable for the user.

[0474] Step 6:

[0475] The server creates a list of selected fashion items and collects images, prices, and stock status of each item, which is then sent to the device.

[0476] Step 7:

[0477] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[0478] Step 8:

[0479] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[0480] Step 9:

[0481] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[0482] Step 10:

[0483] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[0484] Step 11:

[0485] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[0486] Example 1

[0487] 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."

[0488] Conventional shopping support systems have been inadequate in proposing fashion items based on individual users' personal data and preferences, making it difficult for users to find the perfect item. Furthermore, managing purchase history and delivery information is cumbersome, resulting in inconvenience for users.

[0489] 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.

[0490] In this invention, the server includes means for receiving personal data, means for receiving fashion consultation details, means for analyzing data, means for proposing optimal fashion items using a generative AI model, means for providing information on fashion items to the user, means for generating and providing links to online sales sites to the user, means for recording and updating purchase history, means for improving subsequent proposals based on the purchase history, and means for obtaining and notifying the user of delivery information. This enables highly personalized proposals of fashion items, providing the user with an efficient and comfortable shopping experience.

[0491] 1. "Personal Data" refers to personal information such as your name, size, preferred brands, budget, etc.

[0492] 2. "Consultation content" refers to information regarding the user's desired fashion style and specific items.

[0493] 3. "Means for analyzing data" refers to technology that analyzes information and makes appropriate suggestions based on personal data and consultation details stored on the server.

[0494] 4. "Generative AI model" refers to an artificial intelligence model used for data analysis, which is used to suggest optimal fashion items based on the user's personal data and consultation details.

[0495] 5. "Fashion items" refers to fashion-related products such as clothing and accessories that users consider purchasing.

[0496] 6. "Link to Online Retail Site" refers to a web link that provides details of the proposed fashion items and allows users to easily purchase the products.

[0497] 7. "Purchase History" refers to the record of products purchased by a User in the past.

[0498] 8. "Shipping Information" refers to data used to provide users with information such as the shipping status and tracking number of purchased items.

[0499] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[0500] User Data Input

[0501] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0502] Hardware and software used

[0503] Hardware: User devices (PCs, smartphones, etc.), servers

[0504] Software: Web browser, database management system (e.g., MySQL, PostgreSQL)

[0505] Fashion consultation

[0506] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as, "I'm looking for casual clothes that are suitable for the office." The content of this consultation is sent to the server and used for analysis.

[0507] Hardware and software used

[0508] Hardware: User terminals, servers

[0509] Software: Web browser, chat system (e.g., Dialogflow, Microsoft Bot Framework)

[0510] Data analysis and item suggestions

[0511] The server analyzes the data based on the inquiry details sent by the user, the stored personal data, and past purchase history, and uses a generative AI model to select the most suitable fashion items, which are then sent to the device as a list.

[0512] Hardware and software used

[0513] Hardware: Server

[0514] Software: Data analysis engine, generative AI model (e.g., OpenAI GPT-3, BERT)

[0515] Online search and ordering

[0516] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0517] Hardware and software used

[0518] Hardware: User terminals, servers

[0519] Software: Web browser, online shop linkage system

[0520] Purchase history management and delivery support

[0521] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0522] Hardware and software used

[0523] Hardware: Server

[0524] Software: Database management system, notification system

[0525] Specific examples

[0526] As a concrete example, consider the following scenario.

[0527] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[0528] 2. The server analyzes the inquiry and the user's saved data, and uses a generative AI model to suggest a "checked dress," a "brown cardigan," and "ankle boots."

[0529] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0530] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[0531] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0532] Prompt Sentence Examples

[0533] By inputting the following prompt sentence into the generative AI model, the system can make fashion suggestions based on the user's inquiry.

[0534] "I'm 195cm tall. Can you recommend a casual yet stylish office casual look?"

[0535] This allows the system to provide users with highly personalized fashion item suggestions, enabling a comfortable and efficient shopping experience.

[0536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0537] Step 1:

[0538] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0539] input:

[0540] Personal data entered by the user, such as name, size, preferred brand, budget, etc.

[0541] output:

[0542] Personal data stored on the server.

[0543] Specific behavior:

[0544] The terminal uses a web browser to display a user data input form.

[0545] The user enters information into the input form and presses the submit button.

[0546] The terminal sends the input information to the server as an HTTP request.

[0547] The server stores the received information in a database (e.g. MySQL, PostgreSQL).

[0548] Step 2:

[0549] When a user starts a fashion consultation, an interactive chat window appears on the device. The user inputs their request, such as "I want casual clothes that are suitable for the office." This request is sent to the server and used for analysis.

[0550] input:

[0551] Fashion-related inquiries entered by users.

[0552] output:

[0553] Consultation details are stored on the server.

[0554] Specific behavior:

[0555] The device displays a chat window in the web browser.

[0556] The user enters the content of the consultation into the chat window and presses the send button.

[0557] The terminal sends the entered consultation details to the server as an HTTP request.

[0558] The server analyzes the consultation content using a chat system (e.g., Dialogflow, Microsoft Bot Framework).

[0559] Step 3:

[0560] The server receives the inquiry details sent by the user, as well as stored personal data and past purchase history, and performs data analysis using a generative AI model. Based on the analysis, the server selects the most suitable fashion items and sends this information as a list to the device.

[0561] input:

[0562] User's personal data, consultation details, and past purchase history.

[0563] output:

[0564] A list of optimal fashion items selected by a generative AI model.

[0565] Specific behavior:

[0566] The server retrieves user data and consultation details from the database.

[0567] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3, BERT) and selects appropriate items.

[0568] The server organizes the selected items into a list and sends it to the terminal in JSON format.

[0569] Step 4:

[0570] The device displays a list of items, each with a picture, price, and availability details. When the user selects an item from the list, a link to the online store for that item appears. By clicking the link, the user can go directly to the online store and place an order.

[0571] input:

[0572] The item list sent by the server.

[0573] output:

[0574] A link to the online retailer of the item the user selected.

[0575] Specific behavior:

[0576] The device displays the item list (images, prices, links, etc.) in a web browser.

[0577] The user selects the item they like from the list and clicks on the provided link.

[0578] The terminal redirects the user to the page of the specific product in the online shop.

[0579] The user completes the purchase process at the online store.

[0580] Step 5:

[0581] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0582] input:

[0583] Information about the item the user completed purchasing.

[0584] output:

[0585] Notifications of updated purchase history and shipping information.

[0586] Specific behavior:

[0587] After the user completes the purchase, the server receives the purchase information from the online shop via API.

[0588] The server saves and updates the purchase history in a database.

[0589] The server retrieves delivery information from the delivery company's tracking API.

[0590] The server notifies the terminal of the acquired delivery information and displays "Your ordered item has been shipped. The tracking number is XXXX."

[0591] (Application example 1)

[0592] 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."

[0593] Conventional online shopping systems lack the functionality to suggest optimal fashion items that match users' personal data and preferences. Furthermore, users are limited to a virtual shopping experience, often unable to receive the kind of interactive styling consultations found in brick-and-mortar stores. This results in users spending too much time selecting products, making it difficult to enjoy a satisfying shopping experience.

[0594] 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.

[0595] In this invention, the server includes means for receiving personal data entered by a user, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data and the consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, means for improving subsequent suggestions based on the purchase history, means for providing a virtual shopping space using a head-mounted display, and means for interactively communicating with a virtual stylist, thereby enabling the user to receive real-time styling consultation in a virtual environment and enjoy an efficient and satisfying shopping experience.

[0596] "Personal data entered by the user" refers to personal information and preferences such as the user's name, size, favorite brand, budget, etc., and is data entered into the system.

[0597] "Fashion consultation content" is information including requests and questions about fashion items and styling that the user desires.

[0598] "Means for analyzing data" refers to data processing technology for selecting the most suitable fashion items based on the user's personal data and consultation details.

[0599] "Information on suggested fashion items" refers to detailed information such as images, prices, and stock status of items selected by the system.

[0600] The "means for generating a link to an online sales site and providing it to the user" is a technology for creating a URL for an online shop where the suggested fashion item can be purchased and providing it to the user.

[0601] "Means for recording and updating purchase history of fashion items" refers to a technology that stores information about items purchased by users in a database and adds new purchase data.

[0602] "Means for improving subsequent suggestions" refers to technology that improves the accuracy of future fashion item suggestions based on past purchase history and user data.

[0603] "Means for providing a virtual shopping space using a head-mounted display" refers to a technology that allows a user to wear a head-mounted display and experience shopping in a virtual reality space.

[0604] "A means for communicating with a virtual stylist in an interactive format" is a technology that allows the virtual stylist to converse with the user in real time and provide styling advice and product suggestions.

[0605] This invention is a system that proposes optimal fashion items to users and provides efficient shopping support. This system mainly involves a server, terminals, and users, and the roles of each are clarified.

[0606] System configuration

[0607] 1. Server

[0608] The server receives personal data and fashion-related inquiries sent by users and analyzes the data. It is equipped with a data analysis engine using an AI model, and selects the most suitable fashion items based on data such as the user's preferences and past purchase history. The server also manages purchase history and improves subsequent suggestions, and is designed to always provide the latest information.

[0609] 2. Terminal

[0610] The device receives data entered by the user and sends it to the server. It also has the function of displaying information on suggested fashion items to the user. It also provides a virtual shopping space using a head-mounted display (HMD). Users can communicate interactively with a virtual stylist and receive advice in real time.

[0611] 3. Users

[0612] Users access the system and first enter their personal information (name, size, favorite brands, budget, etc.), then enter their fashion-related inquiries interactively, select suggested items based on the system's analysis results, and access the online sales site to make their purchase.

[0613] Data analysis and item suggestion flow

[0614] The server uses a generative AI model to analyze the data based on the personal data and consultation details received from the user. Based on the analysis results, it selects the fashion items that best suit the user's preferences and needs, and sends that information (images, prices, and stock status) to the device. Once the user has selected an item, the device generates a link to the online sales site and provides it to the user.

[0615] Hardware and software used

[0616] Head-mounted display (HMD): A device used to provide a virtual shopping space, allowing users to experience shopping in a virtual reality environment.

[0617] Generative AI model: An AI engine used for data analysis that suggests optimal fashion items based on the user's personal data and consultation details.

[0618] Database system: Used to store and manage users' personal data and purchase history.

[0619] Examples of concrete examples and prompts

[0620] As a concrete example, consider the following scenario.

[0621] 1. A user asks, "I'm looking for an outfit to wear on a spring date."

[0622] 2. The server analyzes the inquiry and the user's saved data and suggests a "casual dress" and a "light jacket."

[0623] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0624] 4. The user selects "Casual Dress" and clicks the provided link to complete the purchase.

[0625] 5. The server records your purchase history and notifies the device of the shipping information. The message "The dress you ordered has been shipped. The tracking number is XXXX" is displayed.

[0626] Example prompt sentence:

[0627] User: I'm looking for something to wear on a spring date.

[0628] System: We're looking for the perfect item for you. Please wait...

[0629] System: Here are the items we suggest. Please choose from "Casual Dress" or "Light Jacket."

[0630] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0631] Step 1:

[0632] User Data Input

[0633] Users enter their personal data (such as name, size, favorite brands, budget, etc.) via the device. The device receives this data and sends it to the server. The server stores the received data in a database and creates a personal profile for each user, forming the basis for future suggestions.

[0634] Input: Personal data entered by the user into the device

[0635] Output: User's personal data stored on the server

[0636] Step 2:

[0637] Fashion consultation reception

[0638] The user inputs their fashion-related inquiry via the device. The device receives the message from the user in real time and sends it to the server. The server then analyzes the received inquiry and prepares it for input into the generative AI model.

[0639] Input: Fashion consultation details entered by the user into the device

[0640] Output: Consultation details sent to the server

[0641] Step 3:

[0642] Data analysis and item suggestions

[0643] The server uses a generative AI model to analyze the user's personal data and the details of their inquiry. The AI ​​model selects the fashion items that best fit the user's preferences and the details of their inquiry, and generates information about them (images, prices, stock status, etc.). This information is sent to the device and displayed to the user.

[0644] Input: User's personal data and consultation details input into the generative AI model

[0645] Output: Information about suggested fashion items sent to the device

[0646] Step 4:

[0647] Viewing and selecting items from the list

[0648] The device receives the fashion item information sent from the server and displays it to the user. The user selects an item from the displayed item list. The device then sends the user's selection to the server.

[0649] Input: Fashion item information sent from the server to the device

[0650] Output: Information about the items selected by the user

[0651] Step 5:

[0652] Online purchase process

[0653] The terminal generates a link to an online sales site based on the user's selection and provides it to the user. The user clicks the link to access the online sales site and purchases the product. The terminal then sends information about the completion of the purchase to the server.

[0654] Input: Information about the item selected by the user

[0655] Output: Online retailer link and purchase completion information

[0656] Step 6:

[0657] Purchase history management and notifications

[0658] The server records and updates the user's purchase history information in a database. It also obtains delivery information and sends a notification to the terminal. The terminal displays the delivery information (e.g., tracking number) to the user.

[0659] Input: Purchase completion information and shipping information

[0660] Output: Purchase history recorded in the database and shipping information sent to the user

[0661] Step 7:

[0662] Subsequent proposal improvements

[0663] The server uses the stored personal data and purchase history to improve future suggestions, and the generative AI model learns from new data to make more accurate fashion item suggestions.

[0664] Input: Updated personal data and purchase history

[0665] Output: Improved fashion item suggestions

[0666] 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.

[0667] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below in natural language.

[0668] 1. User data input

[0669] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[0670] 2. Emotion recognition

[0671] The device is equipped with an emotion engine. Before the user starts a consultation, it uses a facial recognition camera to collect the user's facial expression data and analyzes their emotions. This analysis recognizes the user's current emotional state (e.g., joy, sadness, stress, etc.).

[0672] 3. Fashion consultation

[0673] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[0674] 4. Data analysis and item proposal

[0675] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[0676] 5. Online Search and Ordering

[0677] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0678] 6. Purchase history management and delivery support

[0679] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[0680] Specific examples

[0681] As a concrete example, consider the following scenario.

[0682] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[0683] 2. The device's emotion engine detects the user's stress level.

[0684] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[0685] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0686] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[0687] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0688] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[0689] The processing flow will be explained below.

[0690] Step 1:

[0691] A user accesses the system and is presented with an account registration screen, where they enter personal data such as their name, size, preferred brand, budget, and permission to use the facial recognition camera.

[0692] Step 2:

[0693] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[0694] Step 3:

[0695] The device activates the emotion engine and uses the face recognition camera to collect the user's facial expression data, which is then analyzed by the emotion engine to recognize the user's current emotional state.

[0696] Step 4:

[0697] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[0698] Step 5:

[0699] The consultation content and emotional data generated by the emotion engine are sent to the server, which receives this data and begins preparing for analysis.

[0700] Step 6:

[0701] The server analyzes the data based on the inquiry details sent by the user, stored personal data, past purchase history, and current emotional state, and uses an AI engine to select the most suitable fashion items.

[0702] Step 7:

[0703] The server creates a list of selected fashion items and collects images, prices, and availability of each item, which is then sent to the terminal and displayed to the user.

[0704] Step 8:

[0705] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[0706] Step 9:

[0707] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[0708] Step 10:

[0709] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[0710] Step 11:

[0711] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[0712] Step 12:

[0713] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[0714] Example 2

[0715] 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."

[0716] Conventional fashion suggestion systems make suggestions based on a user's personal data and past purchase history, but because they do not take the user's emotional state into account, they have difficulty suggesting items that suit the user's current psychological state and mood. Furthermore, the selection of suggested items is limited, making it difficult for users to have a satisfying shopping experience. This reduces users' motivation to purchase, and creates the problem of not being able to provide an optimal shopping experience.

[0717] 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.

[0718] In this invention, the server includes means for receiving personal data input by a user, means for analyzing the user's current emotional state, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data, emotional state data, and consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, and means for improving subsequent suggestions based on the purchase history. This enables personalized fashion item suggestions that take the user's current emotional state into consideration, providing a comfortable and efficient shopping experience.

[0719] "User Data" is personal information about the user that the user enters, such as name, size, preferred brand, budget, and facial recognition camera permissions.

[0720] "Emotional state" refers to the user's psychological state, such as joy, sadness, or stress, obtained by analyzing the user's facial expression data.

[0721] "Fashion consultation" refers to specific requests or inquiries about fashion that users make to the system.

[0722] "Data analysis" refers to the process of using an AI engine to select the most suitable fashion items using user data, emotional state data, and fashion consultation details.

[0723] "Suggested fashion items" are candidate fashion items that are provided to the user as a result of data analysis.

[0724] "Online Retail Link" means a URL to a website where the suggested fashion item can be purchased.

[0725] "Purchase history" is a record of fashion items purchased by the user in the past, and includes the purchase date and time, item name, price, delivery information, and the like.

[0726] "Subsequent suggestions" are improved candidates for the next fashion item to be suggested based on previous purchase history and user data.

[0727] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below.

[0728] This system has three main components: the user, the terminal, and the server. Specifically, it consists of the terminal part that receives input from the user, the server part that performs emotion recognition and data analysis, and the user interface that provides fashion suggestions and online shopping support to the user.

[0729] User Data Input

[0730] When a user accesses the system for the first time, a user data entry screen appears on the terminal. Here, the user enters their user data. This user data includes their name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. The specific hardware used is a PC or smartphone, and the software used is a web browser or dedicated application.

[0731] emotion recognition

[0732] The device is equipped with an emotion engine, which uses a facial recognition camera to collect the user's facial expression data before the user begins a consultation. This facial expression data is analyzed in real time by the emotion engine to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.). The analysis results are sent to a server and stored in a database. Specifically, an AI model (e.g., a facial expression recognition model) is used for emotion recognition.

[0733] Fashion consultation

[0734] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server for analysis. The software used for this is a conversational agent based on natural language processing (NLP).

[0735] Data analysis and item suggestions

[0736] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine (e.g., machine learning algorithm) to select the most suitable fashion items and sends this information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[0737] Online search and ordering

[0738] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0739] Purchase history management and delivery support

[0740] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. Specific information displayed may include, "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0741] Specific examples

[0742] For example, if a user says, "I want clothes for a date that fit the autumn trends," and the device's emotion engine detects the user's stress level, the server analyzes the content of the request, emotional data, and stored personal data. As a result, the server suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots." These suggestions are displayed on the device, and the user selects the "calm-colored cardigan" and clicks the provided link to complete the purchase. The server then records the purchase history and notifies the device of delivery information.

[0743] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[0744] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0745] Step 1:

[0746] When a user accesses the system for the first time, a user data entry screen appears on the terminal.

[0747] What it does: The user enters personal data such as name, size, preferred brands, budget, and facial recognition camera permissions.

[0748] Input: User data (e.g., name "Yamada Taro", size "M", favorite brand "Uniqlo", budget "10,000 yen", "Allow" to use face recognition camera).

[0749] Output: User data is saved in the database and sent to the server.

[0750] Step 2:

[0751] The device's built-in emotion engine collects the user's facial expression data in real time.

[0752] Specific operation: When a user stands in front of the screen, the facial recognition camera captures facial expression data.

[0753] Input: Real-time facial expression data.

[0754] Data processing: The emotion engine analyzes facial expression data and determines the user's emotional state (e.g., joy, sadness, stress, etc.).

[0755] Output: The emotional state data is sent to the server and stored in a database.

[0756] Step 3:

[0757] When a user starts a fashion consultation, an interactive chat window will appear on the device.

[0758] Specific operation: The user inputs a question such as "I want some casual clothes that are suitable for the office."

[0759] Input: User's consultation content.

[0760] Output: The consultation content is sent to the server and stored together with the emotional state data.

[0761] Step 4:

[0762] The server performs data analysis using user data, emotional state data, user inquiries, and past purchase history.

[0763] Specific operation: The AI ​​engine integrates this data and selects the most suitable fashion items.

[0764] Input: User data, emotional state data, consultation details, past purchase history.

[0765] Data calculation: The AI ​​engine analyzes using machine learning algorithms and selects items.

[0766] Output: The list of suggested fashion items is sent to the terminal.

[0767] Step 5:

[0768] A list of fashion items will appear on your device.

[0769] Specific behavior: The user selects the item they like from the list.

[0770] Input: A list of suggested fashion items.

[0771] Output: A link to the online retailer of the selected item is generated and provided to the user.

[0772] Step 6:

[0773] Users click on a link to an online retailer of the selected item and begin shopping.

[0774] Specific operation: A user goes through the process of purchasing an item on an online sales site.

[0775] Input: The user's item selection.

[0776] Output: When the purchase process is completed, the user's purchase history is recorded and updated on the server.

[0777] Step 7:

[0778] It obtains delivery information based on purchase history and sends a notification to the device.

[0779] Specific operation: Obtains delivery status from the delivery company and notifies the user.

[0780] Input: Purchase history, delivery status.

[0781] Output: The terminal displays specific information such as "Your order has been shipped. Here is your tracking number."

[0782] Following this step, a system incorporating an emotion engine can provide a more comfortable and efficient shopping experience by making personalized fashion recommendations based on the user's emotional state.

[0783] (Application example 2)

[0784] 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."

[0785] Conventional fashion suggestion and shopping support systems make suggestions without considering the user's emotional state, making it difficult to provide items that suit the user's current mental state. Furthermore, when shopping in a physical store, users have difficulty locating suggested items. Another issue is that users may feel stressed while shopping in the store.

[0786] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving personal information entered by the user, means for receiving fashion consultation details, means for analyzing data to suggest optimal clothing based on the personal information and consultation details, means for providing information on the suggested clothing to the user, means for generating and providing a link to an online clothing sales site for the user, means for recording and updating the purchase history, means for improving subsequent suggestions based on the purchase history, emotion analysis means for recognizing the user's emotional state and suggesting clothing that matches the emotional state, and means for guiding the user to the location of the suggested clothing in the store. This makes it possible to suggest fashion items that suit the user's emotional state, allowing the user to comfortably search for products without feeling stressed even when shopping in a physical store.

[0787] A "user" is a user of the system and an individual who provides personal information and consultation details.

[0788] "Personal Information" is data entered by the user, including personal information such as name, size, preferred brands, budget, etc.

[0789] The "contents of consultation" are information about requests and wishes that the user inputs to the system, including specific requests such as "I want casual clothes that are suitable for the office."

[0790] "Clothing" refers to fashion items suggested to users, including clothes, shoes, accessories, etc.

[0791] The "means for analyzing data" refers to a method and device that performs calculations and evaluations to select optimal clothing based on personal information, consultation details, and past purchase history entered by the user.

[0792] "Link to Online Retail Site" means a web address provided to allow a user to purchase the suggested clothing item over the Internet.

[0793] "Means for recording and updating purchase history" refers to a method and device for storing information about items purchased by a user and updating that information as necessary.

[0794] The "means for improving subsequent suggestions" refers to a method and apparatus that improves the system's suggestion method to provide more relevant suggestions based on the user's past purchasing history.

[0795] "Emotion analysis means" refers to a method and apparatus that analyzes a user's facial expressions and other data to recognize the user's current emotional state.

[0796] "Store location guidance" refers to methods and devices that provide the location of suggested clothing items to help a user find them in a store.

[0797] The present invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the emotions of a user. An embodiment of this system will be described in detail below.

[0798] 1. User data input

[0799] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[0800] 2. Emotion recognition

[0801] The device is equipped with an emotion engine that uses a facial recognition camera to collect facial expression data and analyze the user's emotions before the user starts a consultation. This analysis allows the device to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.).

[0802] 3. Fashion consultation

[0803] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[0804] 4. Data analysis and item proposal

[0805] The server analyzes data based on the user's inquiry, saved personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[0806] 5. Online Search and Ordering

[0807] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0808] 6. Purchase history management and delivery support

[0809] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[0810] Specific examples

[0811] As a concrete example, consider the following scenario.

[0812] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[0813] 2. The device's emotion engine detects the user's stress level.

[0814] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[0815] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0816] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[0817] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0818] Example prompts for generative AI models

[0819] Using the user's facial expression data and preference data as input, suggest relaxing fashion items. Since the current emotional state is stressed, comfortable clothing is best. Also display the product location in the store.

[0820] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[0821] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0822] Step 1:

[0823] When a user accesses the system for the first time, a user data entry screen appears on the terminal. The user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. Based on this input data, the server generates a personal profile for the user. The main input here is the user's personal information, and the output is the personal profile.

[0824] Step 2:

[0825] Before starting a consultation, the device's on-board emotion engine uses a facial recognition camera to collect the user's facial expression data and analyze their emotions. Specifically, a deep learning model (e.g., a TensorFlow-based model) analyzes the facial expression data and identifies the user's emotional state (e.g., joy, sadness, stress, etc.). The input of this step is the user's facial expression data, and the output is the user's current emotional state.

[0826] Step 3:

[0827] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I want casual clothes that are suitable for the office." This consultation is sent to the server, which uses it for analysis along with a stored personal profile and current emotional state. The input for this step is the user's consultation, and the output is data ready for analysis.

[0828] Step 4:

[0829] The server analyzes data based on the inquiry details sent by the user, stored personal information, past purchase history, and current emotional state. The server uses an AI engine (e.g., a generative AI model) to select the most suitable fashion items. Specifically, if the user's stress level is high, it will suggest clothing made of relaxing materials, and if an emotion of joy is detected, it will select items with a gorgeous design. The input here is the user's various profile information and emotional state, and the output is a list of suggested items.

[0830] Step 5:

[0831] A list of suggested items is displayed on the terminal. Each item includes details such as an image, price, and availability. When the user selects an item they like, a link to the online sales site for that item is provided. By clicking the link, the user can access the online shop directly and proceed with the product order. The input of this step is the item list sent from the server, and the output is the purchase page link selected by the user.

[0832] Step 6:

[0833] When a user completes a purchase, the server records and updates the purchase history. In addition, the server retrieves delivery information and sends a notification to the terminal. Specific information is provided, such as "Your order has been shipped. Here is the tracking number." The input of this step is the user's purchase information and the corresponding delivery information, and the output is an updated purchase history and delivery notification.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] [Third embodiment]

[0838] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0839] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0840] 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).

[0841] 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.

[0842] 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.

[0843] 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).

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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."

[0850] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[0851] 1. User data input

[0852] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0853] 2. Fashion consultation

[0854] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content is sent to the server and used for analysis.

[0855] 3. Data analysis and item proposal

[0856] The server analyzes the data based on the inquiry details sent by the user, the saved personal data, and past purchase history, and uses an AI engine to select the most suitable fashion items, sending this information as a list to the device.

[0857] 4. Online Search and Ordering

[0858] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0859] 5. Purchase history management and delivery support

[0860] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[0861] Specific examples

[0862] As a concrete example, consider the following scenario.

[0863] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[0864] 2. The server analyzes the inquiry and the user's saved data and suggests a "checked dress," a "brown cardigan," and "ankle boots."

[0865] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0866] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[0867] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0868] In this way, the system provides users with highly personalized fashion item suggestions, providing a comfortable and efficient shopping experience.

[0869] The processing flow will be explained below.

[0870] Step 1:

[0871] When a user accesses the system, an account registration screen appears, where the user enters personal information such as name, size, preferred brand, and budget.

[0872] Step 2:

[0873] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[0874] Step 3:

[0875] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[0876] Step 4:

[0877] The content of the consultation is sent to the server, which receives it and begins preparing it for analysis along with the stored personal data and past purchase history.

[0878] Step 5:

[0879] The server uses an AI engine to perform analysis based on fashion knowledge, and selects several fashion items that are most suitable for the user.

[0880] Step 6:

[0881] The server creates a list of selected fashion items and collects images, prices, and stock status of each item, which is then sent to the device.

[0882] Step 7:

[0883] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[0884] Step 8:

[0885] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[0886] Step 9:

[0887] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[0888] Step 10:

[0889] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[0890] Step 11:

[0891] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[0892] Example 1

[0893] 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."

[0894] Conventional shopping support systems have been inadequate in proposing fashion items based on individual users' personal data and preferences, making it difficult for users to find the perfect item. Furthermore, managing purchase history and delivery information is cumbersome, resulting in inconvenience for users.

[0895] 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.

[0896] In this invention, the server includes means for receiving personal data, means for receiving fashion consultation details, means for analyzing data, means for proposing optimal fashion items using a generative AI model, means for providing information on fashion items to the user, means for generating and providing links to online sales sites to the user, means for recording and updating purchase history, means for improving subsequent proposals based on the purchase history, and means for obtaining and notifying the user of delivery information. This enables highly personalized proposals of fashion items, providing the user with an efficient and comfortable shopping experience.

[0897] 1. "Personal Data" refers to personal information such as your name, size, preferred brands, budget, etc.

[0898] 2. "Consultation content" refers to information regarding the user's desired fashion style and specific items.

[0899] 3. "Means for analyzing data" refers to technology that analyzes information and makes appropriate suggestions based on personal data and consultation details stored on the server.

[0900] 4. "Generative AI model" refers to an artificial intelligence model used for data analysis, which is used to suggest optimal fashion items based on the user's personal data and consultation details.

[0901] 5. "Fashion items" refers to fashion-related products such as clothing and accessories that users consider purchasing.

[0902] 6. "Link to Online Retail Site" refers to a web link that provides details of the proposed fashion items and allows users to easily purchase the products.

[0903] 7. "Purchase History" refers to the record of products purchased by a User in the past.

[0904] 8. "Shipping Information" refers to data used to provide users with information such as the shipping status and tracking number of purchased items.

[0905] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[0906] User Data Input

[0907] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0908] Hardware and software used

[0909] Hardware: User devices (PCs, smartphones, etc.), servers

[0910] Software: Web browser, database management system (e.g., MySQL, PostgreSQL)

[0911] Fashion consultation

[0912] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as, "I'm looking for casual clothes that are suitable for the office." The content of this consultation is sent to the server and used for analysis.

[0913] Hardware and software used

[0914] Hardware: User terminals, servers

[0915] Software: Web browser, chat system (e.g., Dialogflow, Microsoft Bot Framework)

[0916] Data analysis and item suggestions

[0917] The server analyzes the data based on the inquiry details sent by the user, the stored personal data, and past purchase history, and uses a generative AI model to select the most suitable fashion items, which are then sent to the device as a list.

[0918] Hardware and software used

[0919] Hardware: Server

[0920] Software: Data analysis engine, generative AI model (e.g., OpenAI GPT-3, BERT)

[0921] Online search and ordering

[0922] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[0923] Hardware and software used

[0924] Hardware: User terminals, servers

[0925] Software: Web browser, online shop linkage system

[0926] Purchase history management and delivery support

[0927] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0928] Hardware and software used

[0929] Hardware: Server

[0930] Software: Database management system, notification system

[0931] Specific examples

[0932] As a concrete example, consider the following scenario.

[0933] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[0934] 2. The server analyzes the inquiry and the user's saved data, and uses a generative AI model to suggest a "checked dress," a "brown cardigan," and "ankle boots."

[0935] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[0936] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[0937] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[0938] Prompt Sentence Examples

[0939] By inputting the following prompt sentence into the generative AI model, the system can make fashion suggestions based on the user's inquiry.

[0940] "I'm 195cm tall. Can you recommend a casual yet stylish office casual look?"

[0941] This allows the system to provide users with highly personalized fashion item suggestions, enabling a comfortable and efficient shopping experience.

[0942] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0943] Step 1:

[0944] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[0945] input:

[0946] Personal data entered by the user, such as name, size, preferred brand, budget, etc.

[0947] output:

[0948] Personal data stored on the server.

[0949] Specific behavior:

[0950] The terminal uses a web browser to display a user data input form.

[0951] The user enters information into the input form and presses the submit button.

[0952] The terminal sends the input information to the server as an HTTP request.

[0953] The server stores the received information in a database (e.g. MySQL, PostgreSQL).

[0954] Step 2:

[0955] When a user starts a fashion consultation, an interactive chat window appears on the device. The user inputs their request, such as "I want casual clothes that are suitable for the office." This request is sent to the server and used for analysis.

[0956] input:

[0957] Fashion-related inquiries entered by users.

[0958] output:

[0959] Consultation details are stored on the server.

[0960] Specific behavior:

[0961] The device displays a chat window in the web browser.

[0962] The user enters the content of the consultation into the chat window and presses the send button.

[0963] The terminal sends the entered consultation details to the server as an HTTP request.

[0964] The server analyzes the consultation content using a chat system (e.g., Dialogflow, Microsoft Bot Framework).

[0965] Step 3:

[0966] The server receives the inquiry details sent by the user, as well as stored personal data and past purchase history, and performs data analysis using a generative AI model. Based on the analysis, the server selects the most suitable fashion items and sends this information as a list to the device.

[0967] input:

[0968] User's personal data, consultation details, and past purchase history.

[0969] output:

[0970] A list of optimal fashion items selected by a generative AI model.

[0971] Specific behavior:

[0972] The server retrieves user data and consultation details from the database.

[0973] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3, BERT) and selects appropriate items.

[0974] The server organizes the selected items into a list and sends it to the terminal in JSON format.

[0975] Step 4:

[0976] The device displays a list of items, each with a picture, price, and availability details. When the user selects an item from the list, a link to the online store for that item appears. By clicking the link, the user can go directly to the online store and place an order.

[0977] input:

[0978] The item list sent by the server.

[0979] output:

[0980] A link to the online retailer of the item the user selected.

[0981] Specific behavior:

[0982] The device displays the item list (images, prices, links, etc.) in a web browser.

[0983] The user selects the item they like from the list and clicks on the provided link.

[0984] The terminal redirects the user to the page of the specific product in the online shop.

[0985] The user completes the purchase process at the online store.

[0986] Step 5:

[0987] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[0988] input:

[0989] Information about the item the user completed purchasing.

[0990] output:

[0991] Notifications of updated purchase history and shipping information.

[0992] Specific behavior:

[0993] After the user completes the purchase, the server receives the purchase information from the online shop via API.

[0994] The server saves and updates the purchase history in a database.

[0995] The server retrieves delivery information from the delivery company's tracking API.

[0996] The server notifies the terminal of the acquired delivery information and displays "Your ordered item has been shipped. The tracking number is XXXX."

[0997] (Application example 1)

[0998] 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."

[0999] Conventional online shopping systems lack the functionality to suggest optimal fashion items that match users' personal data and preferences. Furthermore, users are limited to a virtual shopping experience, often unable to receive the kind of interactive styling consultations found in brick-and-mortar stores. This results in users spending too much time selecting products, making it difficult to enjoy a satisfying shopping experience.

[1000] 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.

[1001] In this invention, the server includes means for receiving personal data entered by a user, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data and the consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, means for improving subsequent suggestions based on the purchase history, means for providing a virtual shopping space using a head-mounted display, and means for interactively communicating with a virtual stylist, thereby enabling the user to receive real-time styling consultation in a virtual environment and enjoy an efficient and satisfying shopping experience.

[1002] "Personal data entered by the user" refers to personal information and preferences such as the user's name, size, favorite brand, budget, etc., and is data entered into the system.

[1003] "Fashion consultation content" is information including requests and questions about fashion items and styling that the user desires.

[1004] "Means for analyzing data" refers to data processing technology for selecting the most suitable fashion items based on the user's personal data and consultation details.

[1005] "Information on suggested fashion items" refers to detailed information such as images, prices, and stock status of items selected by the system.

[1006] The "means for generating a link to an online sales site and providing it to the user" is a technology for creating a URL for an online shop where the suggested fashion item can be purchased and providing it to the user.

[1007] "Means for recording and updating purchase history of fashion items" refers to a technology that stores information about items purchased by users in a database and adds new purchase data.

[1008] "Means for improving subsequent suggestions" refers to technology that improves the accuracy of future fashion item suggestions based on past purchase history and user data.

[1009] "Means for providing a virtual shopping space using a head-mounted display" refers to a technology that allows a user to wear a head-mounted display and experience shopping in a virtual reality space.

[1010] "A means for communicating with a virtual stylist in an interactive format" is a technology that allows the virtual stylist to converse with the user in real time and provide styling advice and product suggestions.

[1011] This invention is a system that proposes optimal fashion items to users and provides efficient shopping support. This system mainly involves a server, terminals, and users, and the roles of each are clarified.

[1012] System configuration

[1013] 1. Server

[1014] The server receives personal data and fashion-related inquiries sent by users and analyzes the data. It is equipped with a data analysis engine using an AI model, and selects the most suitable fashion items based on data such as the user's preferences and past purchase history. The server also manages purchase history and improves subsequent suggestions, and is designed to always provide the latest information.

[1015] 2. Terminal

[1016] The device receives data entered by the user and sends it to the server. It also has the function of displaying information on suggested fashion items to the user. It also provides a virtual shopping space using a head-mounted display (HMD). Users can communicate interactively with a virtual stylist and receive advice in real time.

[1017] 3. Users

[1018] Users access the system and first enter their personal information (name, size, favorite brands, budget, etc.), then enter their fashion-related inquiries interactively, select suggested items based on the system's analysis results, and access the online sales site to make their purchase.

[1019] Data analysis and item suggestion flow

[1020] The server uses a generative AI model to analyze the data based on the personal data and consultation details received from the user. Based on the analysis results, it selects the fashion items that best suit the user's preferences and needs, and sends that information (images, prices, and stock status) to the device. Once the user has selected an item, the device generates a link to the online sales site and provides it to the user.

[1021] Hardware and software used

[1022] Head-mounted display (HMD): A device used to provide a virtual shopping space, allowing users to experience shopping in a virtual reality environment.

[1023] Generative AI model: An AI engine used for data analysis that suggests optimal fashion items based on the user's personal data and consultation details.

[1024] Database system: Used to store and manage users' personal data and purchase history.

[1025] Examples of concrete examples and prompts

[1026] As a concrete example, consider the following scenario.

[1027] 1. A user asks, "I'm looking for an outfit to wear on a spring date."

[1028] 2. The server analyzes the inquiry and the user's saved data and suggests a "casual dress" and a "light jacket."

[1029] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1030] 4. The user selects "Casual Dress" and clicks the provided link to complete the purchase.

[1031] 5. The server records your purchase history and notifies the device of the shipping information. The message "The dress you ordered has been shipped. The tracking number is XXXX" is displayed.

[1032] Example prompt sentence:

[1033] User: I'm looking for something to wear on a spring date.

[1034] System: We're looking for the perfect item for you. Please wait...

[1035] System: Here are the items we suggest. Please choose from "Casual Dress" or "Light Jacket."

[1036] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1037] Step 1:

[1038] User Data Input

[1039] Users enter their personal data (such as name, size, favorite brands, budget, etc.) via the device. The device receives this data and sends it to the server. The server stores the received data in a database and creates a personal profile for each user, forming the basis for future suggestions.

[1040] Input: Personal data entered by the user into the device

[1041] Output: User's personal data stored on the server

[1042] Step 2:

[1043] Fashion consultation reception

[1044] The user inputs their fashion-related inquiry via the device. The device receives the message from the user in real time and sends it to the server. The server then analyzes the received inquiry and prepares it for input into the generative AI model.

[1045] Input: Fashion consultation details entered by the user into the device

[1046] Output: Consultation details sent to the server

[1047] Step 3:

[1048] Data analysis and item suggestions

[1049] The server uses a generative AI model to analyze the user's personal data and the details of their inquiry. The AI ​​model selects the fashion items that best fit the user's preferences and the details of their inquiry, and generates information about them (images, prices, stock status, etc.). This information is sent to the device and displayed to the user.

[1050] Input: User's personal data and consultation details input into the generative AI model

[1051] Output: Information about suggested fashion items sent to the device

[1052] Step 4:

[1053] Viewing and selecting items from the list

[1054] The device receives the fashion item information sent from the server and displays it to the user. The user selects an item from the displayed item list. The device then sends the user's selection to the server.

[1055] Input: Fashion item information sent from the server to the device

[1056] Output: Information about the items selected by the user

[1057] Step 5:

[1058] Online purchase process

[1059] The terminal generates a link to an online sales site based on the user's selection and provides it to the user. The user clicks the link to access the online sales site and purchases the product. The terminal then sends information about the completion of the purchase to the server.

[1060] Input: Information about the item selected by the user

[1061] Output: Online retailer link and purchase completion information

[1062] Step 6:

[1063] Purchase history management and notifications

[1064] The server records and updates the user's purchase history information in a database. It also obtains delivery information and sends a notification to the terminal. The terminal displays the delivery information (e.g., tracking number) to the user.

[1065] Input: Purchase completion information and shipping information

[1066] Output: Purchase history recorded in the database and shipping information sent to the user

[1067] Step 7:

[1068] Subsequent proposal improvements

[1069] The server uses the stored personal data and purchase history to improve future suggestions, and the generative AI model learns from new data to make more accurate fashion item suggestions.

[1070] Input: Updated personal data and purchase history

[1071] Output: Improved fashion item suggestions

[1072] 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.

[1073] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below in natural language.

[1074] 1. User data input

[1075] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[1076] 2. Emotion recognition

[1077] The device is equipped with an emotion engine. Before the user starts a consultation, it uses a facial recognition camera to collect the user's facial expression data and analyzes their emotions. This analysis recognizes the user's current emotional state (e.g., joy, sadness, stress, etc.).

[1078] 3. Fashion consultation

[1079] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[1080] 4. Data analysis and item proposal

[1081] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[1082] 5. Online Search and Ordering

[1083] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1084] 6. Purchase history management and delivery support

[1085] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[1086] Specific examples

[1087] As a concrete example, consider the following scenario.

[1088] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[1089] 2. The device's emotion engine detects the user's stress level.

[1090] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[1091] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1092] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[1093] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[1094] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[1095] The processing flow will be explained below.

[1096] Step 1:

[1097] A user accesses the system and is presented with an account registration screen, where they enter personal data such as their name, size, preferred brand, budget, and permission to use the facial recognition camera.

[1098] Step 2:

[1099] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[1100] Step 3:

[1101] The device activates the emotion engine and uses the face recognition camera to collect the user's facial expression data, which is then analyzed by the emotion engine to recognize the user's current emotional state.

[1102] Step 4:

[1103] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[1104] Step 5:

[1105] The consultation content and emotional data generated by the emotion engine are sent to the server, which receives this data and begins preparing for analysis.

[1106] Step 6:

[1107] The server analyzes the data based on the inquiry details sent by the user, stored personal data, past purchase history, and current emotional state, and uses an AI engine to select the most suitable fashion items.

[1108] Step 7:

[1109] The server creates a list of selected fashion items and collects images, prices, and availability of each item, which is then sent to the terminal and displayed to the user.

[1110] Step 8:

[1111] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[1112] Step 9:

[1113] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[1114] Step 10:

[1115] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[1116] Step 11:

[1117] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[1118] Step 12:

[1119] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[1120] Example 2

[1121] 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."

[1122] Conventional fashion suggestion systems make suggestions based on a user's personal data and past purchase history, but because they do not take the user's emotional state into account, they have difficulty suggesting items that suit the user's current psychological state and mood. Furthermore, the selection of suggested items is limited, making it difficult for users to have a satisfying shopping experience. This reduces users' motivation to purchase, and creates the problem of not being able to provide an optimal shopping experience.

[1123] 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.

[1124] In this invention, the server includes means for receiving personal data input by a user, means for analyzing the user's current emotional state, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data, emotional state data, and consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, and means for improving subsequent suggestions based on the purchase history. This enables personalized fashion item suggestions that take the user's current emotional state into consideration, providing a comfortable and efficient shopping experience.

[1125] "User Data" is personal information about the user that the user enters, such as name, size, preferred brand, budget, and facial recognition camera permissions.

[1126] "Emotional state" refers to the user's psychological state, such as joy, sadness, or stress, obtained by analyzing the user's facial expression data.

[1127] "Fashion consultation" refers to specific requests or inquiries about fashion that users make to the system.

[1128] "Data analysis" refers to the process of using an AI engine to select the most suitable fashion items using user data, emotional state data, and fashion consultation details.

[1129] "Suggested fashion items" are candidate fashion items that are provided to the user as a result of data analysis.

[1130] "Online Retail Link" means a URL to a website where the suggested fashion item can be purchased.

[1131] "Purchase history" is a record of fashion items purchased by the user in the past, and includes the purchase date and time, item name, price, delivery information, and the like.

[1132] "Subsequent suggestions" are improved candidates for the next fashion item to be suggested based on previous purchase history and user data.

[1133] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below.

[1134] This system has three main components: the user, the terminal, and the server. Specifically, it consists of the terminal part that receives input from the user, the server part that performs emotion recognition and data analysis, and the user interface that provides fashion suggestions and online shopping support to the user.

[1135] User Data Input

[1136] When a user accesses the system for the first time, a user data entry screen appears on the terminal. Here, the user enters their user data. This user data includes their name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. The specific hardware used is a PC or smartphone, and the software used is a web browser or dedicated application.

[1137] emotion recognition

[1138] The device is equipped with an emotion engine, which uses a facial recognition camera to collect the user's facial expression data before the user begins a consultation. This facial expression data is analyzed in real time by the emotion engine to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.). The analysis results are sent to a server and stored in a database. Specifically, an AI model (e.g., a facial expression recognition model) is used for emotion recognition.

[1139] Fashion consultation

[1140] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server for analysis. The software used for this is a conversational agent based on natural language processing (NLP).

[1141] Data analysis and item suggestions

[1142] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine (e.g., machine learning algorithm) to select the most suitable fashion items and sends this information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[1143] Online search and ordering

[1144] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1145] Purchase history management and delivery support

[1146] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. Specific information displayed may include, "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[1147] Specific examples

[1148] For example, if a user says, "I want clothes for a date that fit the autumn trends," and the device's emotion engine detects the user's stress level, the server analyzes the content of the request, emotional data, and stored personal data. As a result, the server suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots." These suggestions are displayed on the device, and the user selects the "calm-colored cardigan" and clicks the provided link to complete the purchase. The server then records the purchase history and notifies the device of delivery information.

[1149] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[1150] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1151] Step 1:

[1152] When a user accesses the system for the first time, a user data entry screen appears on the terminal.

[1153] What it does: The user enters personal data such as name, size, preferred brands, budget, and facial recognition camera permissions.

[1154] Input: User data (e.g., name "Yamada Taro", size "M", favorite brand "Uniqlo", budget "10,000 yen", "Allow" to use face recognition camera).

[1155] Output: User data is saved in the database and sent to the server.

[1156] Step 2:

[1157] The device's built-in emotion engine collects the user's facial expression data in real time.

[1158] Specific operation: When a user stands in front of the screen, the facial recognition camera captures facial expression data.

[1159] Input: Real-time facial expression data.

[1160] Data processing: The emotion engine analyzes facial expression data and determines the user's emotional state (e.g., joy, sadness, stress, etc.).

[1161] Output: The emotional state data is sent to the server and stored in a database.

[1162] Step 3:

[1163] When a user starts a fashion consultation, an interactive chat window will appear on the device.

[1164] Specific operation: The user inputs a question such as "I want some casual clothes that are suitable for the office."

[1165] Input: User's consultation content.

[1166] Output: The consultation content is sent to the server and stored together with the emotional state data.

[1167] Step 4:

[1168] The server performs data analysis using user data, emotional state data, user inquiries, and past purchase history.

[1169] Specific operation: The AI ​​engine integrates this data and selects the most suitable fashion items.

[1170] Input: User data, emotional state data, consultation details, past purchase history.

[1171] Data calculation: The AI ​​engine analyzes using machine learning algorithms and selects items.

[1172] Output: The list of suggested fashion items is sent to the terminal.

[1173] Step 5:

[1174] A list of fashion items will appear on your device.

[1175] Specific behavior: The user selects the item they like from the list.

[1176] Input: A list of suggested fashion items.

[1177] Output: A link to the online retailer of the selected item is generated and provided to the user.

[1178] Step 6:

[1179] Users click on a link to an online retailer of the selected item and begin shopping.

[1180] Specific operation: A user goes through the process of purchasing an item on an online sales site.

[1181] Input: The user's item selection.

[1182] Output: When the purchase process is completed, the user's purchase history is recorded and updated on the server.

[1183] Step 7:

[1184] It obtains delivery information based on purchase history and sends a notification to the device.

[1185] Specific operation: Obtains delivery status from the delivery company and notifies the user.

[1186] Input: Purchase history, delivery status.

[1187] Output: The terminal displays specific information such as "Your order has been shipped. Here is your tracking number."

[1188] Following this step, a system incorporating an emotion engine can provide a more comfortable and efficient shopping experience by making personalized fashion recommendations based on the user's emotional state.

[1189] (Application example 2)

[1190] 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."

[1191] Conventional fashion suggestion and shopping support systems make suggestions without considering the user's emotional state, making it difficult to provide items that suit the user's current mental state. Furthermore, when shopping in a physical store, users have difficulty locating suggested items. Another issue is that users may feel stressed while shopping in the store.

[1192] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving personal information entered by the user, means for receiving fashion consultation details, means for analyzing data to suggest optimal clothing based on the personal information and consultation details, means for providing information on the suggested clothing to the user, means for generating and providing a link to an online clothing sales site for the user, means for recording and updating the purchase history, means for improving subsequent suggestions based on the purchase history, emotion analysis means for recognizing the user's emotional state and suggesting clothing that matches the emotional state, and means for guiding the user to the location of the suggested clothing in the store. This makes it possible to suggest fashion items that suit the user's emotional state, allowing the user to comfortably search for products without feeling stressed even when shopping in a physical store.

[1193] A "user" is a user of the system and an individual who provides personal information and consultation details.

[1194] "Personal Information" is data entered by the user, including personal information such as name, size, preferred brands, budget, etc.

[1195] The "contents of consultation" are information about requests and wishes that the user inputs to the system, including specific requests such as "I want casual clothes that are suitable for the office."

[1196] "Clothing" refers to fashion items suggested to users, including clothes, shoes, accessories, etc.

[1197] The "means for analyzing data" refers to a method and device that performs calculations and evaluations to select optimal clothing based on personal information, consultation details, and past purchase history entered by the user.

[1198] "Link to Online Retail Site" means a web address provided to allow a user to purchase the suggested clothing item over the Internet.

[1199] "Means for recording and updating purchase history" refers to a method and device for storing information about items purchased by a user and updating that information as necessary.

[1200] The "means for improving subsequent suggestions" refers to a method and apparatus that improves the system's suggestion method to provide more relevant suggestions based on the user's past purchasing history.

[1201] "Emotion analysis means" refers to a method and apparatus that analyzes a user's facial expressions and other data to recognize the user's current emotional state.

[1202] "Store location guidance" refers to methods and devices that provide the location of suggested clothing items to help a user find them in a store.

[1203] The present invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the emotions of a user. An embodiment of this system will be described in detail below.

[1204] 1. User data input

[1205] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[1206] 2. Emotion recognition

[1207] The device is equipped with an emotion engine that uses a facial recognition camera to collect facial expression data and analyze the user's emotions before the user starts a consultation. This analysis allows the device to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.).

[1208] 3. Fashion consultation

[1209] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[1210] 4. Data analysis and item proposal

[1211] The server analyzes data based on the user's inquiry, saved personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[1212] 5. Online Search and Ordering

[1213] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1214] 6. Purchase history management and delivery support

[1215] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[1216] Specific examples

[1217] As a concrete example, consider the following scenario.

[1218] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[1219] 2. The device's emotion engine detects the user's stress level.

[1220] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[1221] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1222] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[1223] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[1224] Example prompts for generative AI models

[1225] Using the user's facial expression data and preference data as input, suggest relaxing fashion items. Since the current emotional state is stressed, comfortable clothing is best. Also display the product location in the store.

[1226] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[1227] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1228] Step 1:

[1229] When a user accesses the system for the first time, a user data entry screen appears on the terminal. The user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. Based on this input data, the server generates a personal profile for the user. The main input here is the user's personal information, and the output is the personal profile.

[1230] Step 2:

[1231] Before starting a consultation, the device's on-board emotion engine uses a facial recognition camera to collect the user's facial expression data and analyze their emotions. Specifically, a deep learning model (e.g., a TensorFlow-based model) analyzes the facial expression data and identifies the user's emotional state (e.g., joy, sadness, stress, etc.). The input of this step is the user's facial expression data, and the output is the user's current emotional state.

[1232] Step 3:

[1233] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I want casual clothes that are suitable for the office." This consultation is sent to the server, which uses it for analysis along with a stored personal profile and current emotional state. The input for this step is the user's consultation, and the output is data ready for analysis.

[1234] Step 4:

[1235] The server analyzes data based on the inquiry details sent by the user, stored personal information, past purchase history, and current emotional state. The server uses an AI engine (e.g., a generative AI model) to select the most suitable fashion items. Specifically, if the user's stress level is high, it will suggest clothing made of relaxing materials, and if an emotion of joy is detected, it will select items with a gorgeous design. The input here is the user's various profile information and emotional state, and the output is a list of suggested items.

[1236] Step 5:

[1237] A list of suggested items is displayed on the terminal. Each item includes details such as an image, price, and availability. When the user selects an item they like, a link to the online sales site for that item is provided. By clicking the link, the user can access the online shop directly and proceed with the product order. The input of this step is the item list sent from the server, and the output is the purchase page link selected by the user.

[1238] Step 6:

[1239] When a user completes a purchase, the server records and updates the purchase history. In addition, the server retrieves delivery information and sends a notification to the terminal. Specific information is provided, such as "Your order has been shipped. Here is the tracking number." The input of this step is the user's purchase information and the corresponding delivery information, and the output is an updated purchase history and delivery notification.

[1240] 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.

[1241] 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.

[1242] 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.

[1243] [Fourth embodiment]

[1244] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1245] 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.

[1246] 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).

[1247] 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.

[1248] 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.

[1249] 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).

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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.

[1255] 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.

[1256] 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."

[1257] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[1258] 1. User data input

[1259] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[1260] 2. Fashion consultation

[1261] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content is sent to the server and used for analysis.

[1262] 3. Data analysis and item proposal

[1263] The server analyzes the data based on the inquiry details sent by the user, the saved personal data, and past purchase history, and uses an AI engine to select the most suitable fashion items, sending this information as a list to the device.

[1264] 4. Online Search and Ordering

[1265] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1266] 5. Purchase history management and delivery support

[1267] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[1268] Specific examples

[1269] As a concrete example, consider the following scenario.

[1270] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[1271] 2. The server analyzes the inquiry and the user's saved data and suggests a "checked dress," a "brown cardigan," and "ankle boots."

[1272] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1273] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[1274] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[1275] In this way, the system provides users with highly personalized fashion item suggestions, providing a comfortable and efficient shopping experience.

[1276] The processing flow will be explained below.

[1277] Step 1:

[1278] When a user accesses the system, an account registration screen appears, where the user enters personal information such as name, size, preferred brand, and budget.

[1279] Step 2:

[1280] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[1281] Step 3:

[1282] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[1283] Step 4:

[1284] The content of the consultation is sent to the server, which receives it and begins preparing it for analysis along with the stored personal data and past purchase history.

[1285] Step 5:

[1286] The server uses an AI engine to perform analysis based on fashion knowledge, and selects several fashion items that are most suitable for the user.

[1287] Step 6:

[1288] The server creates a list of selected fashion items and collects images, prices, and stock status of each item, which is then sent to the device.

[1289] Step 7:

[1290] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[1291] Step 8:

[1292] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[1293] Step 9:

[1294] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[1295] Step 10:

[1296] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[1297] Step 11:

[1298] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[1299] Example 1

[1300] 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."

[1301] Conventional shopping support systems have been inadequate in proposing fashion items based on individual users' personal data and preferences, making it difficult for users to find the perfect item. Furthermore, managing purchase history and delivery information is cumbersome, resulting in inconvenience for users.

[1302] 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.

[1303] In this invention, the server includes means for receiving personal data, means for receiving fashion consultation details, means for analyzing data, means for proposing optimal fashion items using a generative AI model, means for providing information on fashion items to the user, means for generating and providing links to online sales sites to the user, means for recording and updating purchase history, means for improving subsequent proposals based on the purchase history, and means for obtaining and notifying the user of delivery information. This enables highly personalized proposals of fashion items, providing the user with an efficient and comfortable shopping experience.

[1304] 1. "Personal Data" refers to personal information such as your name, size, preferred brands, budget, etc.

[1305] 2. "Consultation content" refers to information regarding the user's desired fashion style and specific items.

[1306] 3. "Means for analyzing data" refers to technology that analyzes information and makes appropriate suggestions based on personal data and consultation details stored on the server.

[1307] 4. "Generative AI model" refers to an artificial intelligence model used for data analysis, which is used to suggest optimal fashion items based on the user's personal data and consultation details.

[1308] 5. "Fashion items" refers to fashion-related products such as clothing and accessories that users consider purchasing.

[1309] 6. "Link to Online Retail Site" refers to a web link that provides details of the proposed fashion items and allows users to easily purchase the products.

[1310] 7. "Purchase History" refers to the record of products purchased by a User in the past.

[1311] 8. "Shipping Information" refers to data used to provide users with information such as the shipping status and tracking number of purchased items.

[1312] This invention relates to a system that proposes optimal fashion items to users and provides efficient shopping support. The program processing of this system will be explained below in natural language.

[1313] User Data Input

[1314] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[1315] Hardware and software used

[1316] Hardware: User devices (PCs, smartphones, etc.), servers

[1317] Software: Web browser, database management system (e.g., MySQL, PostgreSQL)

[1318] Fashion consultation

[1319] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as, "I'm looking for casual clothes that are suitable for the office." The content of this consultation is sent to the server and used for analysis.

[1320] Hardware and software used

[1321] Hardware: User terminals, servers

[1322] Software: Web browser, chat system (e.g., Dialogflow, Microsoft Bot Framework)

[1323] Data analysis and item suggestions

[1324] The server analyzes the data based on the inquiry details sent by the user, the stored personal data, and past purchase history, and uses a generative AI model to select the most suitable fashion items, which are then sent to the device as a list.

[1325] Hardware and software used

[1326] Hardware: Server

[1327] Software: Data analysis engine, generative AI model (e.g., OpenAI GPT-3, BERT)

[1328] Online search and ordering

[1329] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1330] Hardware and software used

[1331] Hardware: User terminals, servers

[1332] Software: Web browser, online shop linkage system

[1333] Purchase history management and delivery support

[1334] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[1335] Hardware and software used

[1336] Hardware: Server

[1337] Software: Database management system, notification system

[1338] Specific examples

[1339] As a concrete example, consider the following scenario.

[1340] 1. A user asks, "I want to find some clothes for a date that fit the fall trends."

[1341] 2. The server analyzes the inquiry and the user's saved data, and uses a generative AI model to suggest a "checked dress," a "brown cardigan," and "ankle boots."

[1342] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1343] 4. The user selects the "brown cardigan" and clicks the provided link to check out.

[1344] 5. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[1345] Prompt Sentence Examples

[1346] By inputting the following prompt sentence into the generative AI model, the system can make fashion suggestions based on the user's inquiry.

[1347] "I'm 195cm tall. Can you recommend a casual yet stylish office casual look?"

[1348] This allows the system to provide users with highly personalized fashion item suggestions, enabling a comfortable and efficient shopping experience.

[1349] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1350] Step 1:

[1351] When a user accesses the system for the first time, a user data entry screen appears on the terminal, where the user enters personal data such as name, size, preferred brand, budget, etc. The entered data is sent to the server and stored in a database.

[1352] input:

[1353] Personal data entered by the user, such as name, size, preferred brand, budget, etc.

[1354] output:

[1355] Personal data stored on the server.

[1356] Specific behavior:

[1357] The terminal uses a web browser to display a user data input form.

[1358] The user enters information into the input form and presses the submit button.

[1359] The terminal sends the input information to the server as an HTTP request.

[1360] The server stores the received information in a database (e.g. MySQL, PostgreSQL).

[1361] Step 2:

[1362] When a user starts a fashion consultation, an interactive chat window appears on the device. The user inputs their request, such as "I want casual clothes that are suitable for the office." This request is sent to the server and used for analysis.

[1363] input:

[1364] Fashion-related inquiries entered by users.

[1365] output:

[1366] Consultation details are stored on the server.

[1367] Specific behavior:

[1368] The device displays a chat window in the web browser.

[1369] The user enters the content of the consultation into the chat window and presses the send button.

[1370] The terminal sends the entered consultation details to the server as an HTTP request.

[1371] The server analyzes the consultation content using a chat system (e.g., Dialogflow, Microsoft Bot Framework).

[1372] Step 3:

[1373] The server receives the inquiry details sent by the user, as well as stored personal data and past purchase history, and performs data analysis using a generative AI model. Based on the analysis, the server selects the most suitable fashion items and sends this information as a list to the device.

[1374] input:

[1375] User's personal data, consultation details, and past purchase history.

[1376] output:

[1377] A list of optimal fashion items selected by a generative AI model.

[1378] Specific behavior:

[1379] The server retrieves user data and consultation details from the database.

[1380] The server analyzes the data using a generative AI model (e.g., OpenAI GPT-3, BERT) and selects appropriate items.

[1381] The server organizes the selected items into a list and sends it to the terminal in JSON format.

[1382] Step 4:

[1383] The device displays a list of items, each with a picture, price, and availability details. When the user selects an item from the list, a link to the online store for that item appears. By clicking the link, the user can go directly to the online store and place an order.

[1384] input:

[1385] The item list sent by the server.

[1386] output:

[1387] A link to the online retailer of the item the user selected.

[1388] Specific behavior:

[1389] The device displays the item list (images, prices, links, etc.) in a web browser.

[1390] The user selects the item they like from the list and clicks on the provided link.

[1391] The terminal redirects the user to the page of the specific product in the online shop.

[1392] The user completes the purchase process at the online store.

[1393] Step 5:

[1394] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends specific information to the terminal, such as "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[1395] input:

[1396] Information about the item the user completed purchasing.

[1397] output:

[1398] Notifications of updated purchase history and shipping information.

[1399] Specific behavior:

[1400] After the user completes the purchase, the server receives the purchase information from the online shop via API.

[1401] The server saves and updates the purchase history in a database.

[1402] The server retrieves delivery information from the delivery company's tracking API.

[1403] The server notifies the terminal of the acquired delivery information and displays "Your ordered item has been shipped. The tracking number is XXXX."

[1404] (Application example 1)

[1405] 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."

[1406] Conventional online shopping systems lack the functionality to suggest optimal fashion items that match users' personal data and preferences. Furthermore, users are limited to a virtual shopping experience, often unable to receive the kind of interactive styling consultations found in brick-and-mortar stores. This results in users spending too much time selecting products, making it difficult to enjoy a satisfying shopping experience.

[1407] 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.

[1408] In this invention, the server includes means for receiving personal data entered by a user, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data and the consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, means for improving subsequent suggestions based on the purchase history, means for providing a virtual shopping space using a head-mounted display, and means for interactively communicating with a virtual stylist, thereby enabling the user to receive real-time styling consultation in a virtual environment and enjoy an efficient and satisfying shopping experience.

[1409] "Personal data entered by the user" refers to personal information and preferences such as the user's name, size, favorite brand, budget, etc., and is data entered into the system.

[1410] "Fashion consultation content" is information including requests and questions about fashion items and styling that the user desires.

[1411] "Means for analyzing data" refers to data processing technology for selecting the most suitable fashion items based on the user's personal data and consultation details.

[1412] "Information on suggested fashion items" refers to detailed information such as images, prices, and stock status of items selected by the system.

[1413] The "means for generating a link to an online sales site and providing it to the user" is a technology for creating a URL for an online shop where the suggested fashion item can be purchased and providing it to the user.

[1414] "Means for recording and updating purchase history of fashion items" refers to a technology that stores information about items purchased by users in a database and adds new purchase data.

[1415] "Means for improving subsequent suggestions" refers to technology that improves the accuracy of future fashion item suggestions based on past purchase history and user data.

[1416] "Means for providing a virtual shopping space using a head-mounted display" refers to a technology that allows a user to wear a head-mounted display and experience shopping in a virtual reality space.

[1417] "A means for communicating with a virtual stylist in an interactive format" is a technology that allows the virtual stylist to converse with the user in real time and provide styling advice and product suggestions.

[1418] This invention is a system that proposes optimal fashion items to users and provides efficient shopping support. This system mainly involves a server, terminals, and users, and the roles of each are clarified.

[1419] System configuration

[1420] 1. Server

[1421] The server receives personal data and fashion-related inquiries sent by users and analyzes the data. It is equipped with a data analysis engine using an AI model, and selects the most suitable fashion items based on data such as the user's preferences and past purchase history. The server also manages purchase history and improves subsequent suggestions, and is designed to always provide the latest information.

[1422] 2. Terminal

[1423] The device receives data entered by the user and sends it to the server. It also has the function of displaying information on suggested fashion items to the user. It also provides a virtual shopping space using a head-mounted display (HMD). Users can communicate interactively with a virtual stylist and receive advice in real time.

[1424] 3. Users

[1425] Users access the system and first enter their personal information (name, size, favorite brands, budget, etc.), then enter their fashion-related inquiries interactively, select suggested items based on the system's analysis results, and access the online sales site to make their purchase.

[1426] Data analysis and item suggestion flow

[1427] The server uses a generative AI model to analyze the data based on the personal data and consultation details received from the user. Based on the analysis results, it selects the fashion items that best suit the user's preferences and needs, and sends that information (images, prices, and stock status) to the device. Once the user has selected an item, the device generates a link to the online sales site and provides it to the user.

[1428] Hardware and software used

[1429] Head-mounted display (HMD): A device used to provide a virtual shopping space, allowing users to experience shopping in a virtual reality environment.

[1430] Generative AI model: An AI engine used for data analysis that suggests optimal fashion items based on the user's personal data and consultation details.

[1431] Database system: Used to store and manage users' personal data and purchase history.

[1432] Examples of concrete examples and prompts

[1433] As a concrete example, consider the following scenario.

[1434] 1. A user asks, "I'm looking for an outfit to wear on a spring date."

[1435] 2. The server analyzes the inquiry and the user's saved data and suggests a "casual dress" and a "light jacket."

[1436] 3. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1437] 4. The user selects "Casual Dress" and clicks the provided link to complete the purchase.

[1438] 5. The server records your purchase history and notifies the device of the shipping information. The message "The dress you ordered has been shipped. The tracking number is XXXX" is displayed.

[1439] Example prompt sentence:

[1440] User: I'm looking for something to wear on a spring date.

[1441] System: We're looking for the perfect item for you. Please wait...

[1442] System: Here are the items we suggest. Please choose from "Casual Dress" or "Light Jacket."

[1443] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1444] Step 1:

[1445] User Data Input

[1446] Users enter their personal data (such as name, size, favorite brands, budget, etc.) via the device. The device receives this data and sends it to the server. The server stores the received data in a database and creates a personal profile for each user, forming the basis for future suggestions.

[1447] Input: Personal data entered by the user into the device

[1448] Output: User's personal data stored on the server

[1449] Step 2:

[1450] Fashion consultation reception

[1451] The user inputs their fashion-related inquiry via the device. The device receives the message from the user in real time and sends it to the server. The server then analyzes the received inquiry and prepares it for input into the generative AI model.

[1452] Input: Fashion consultation details entered by the user into the device

[1453] Output: Consultation details sent to the server

[1454] Step 3:

[1455] Data analysis and item suggestions

[1456] The server uses a generative AI model to analyze the user's personal data and the details of their inquiry. The AI ​​model selects the fashion items that best fit the user's preferences and the details of their inquiry, and generates information about them (images, prices, stock status, etc.). This information is sent to the device and displayed to the user.

[1457] Input: User's personal data and consultation details input into the generative AI model

[1458] Output: Information about suggested fashion items sent to the device

[1459] Step 4:

[1460] Viewing and selecting items from the list

[1461] The device receives the fashion item information sent from the server and displays it to the user. The user selects an item from the displayed item list. The device then sends the user's selection to the server.

[1462] Input: Fashion item information sent from the server to the device

[1463] Output: Information about the items selected by the user

[1464] Step 5:

[1465] Online purchase process

[1466] The terminal generates a link to an online sales site based on the user's selection and provides it to the user. The user clicks the link to access the online sales site and purchases the product. The terminal then sends information about the completion of the purchase to the server.

[1467] Input: Information about the item selected by the user

[1468] Output: Online retailer link and purchase completion information

[1469] Step 6:

[1470] Purchase history management and notifications

[1471] The server records and updates the user's purchase history information in a database. It also obtains delivery information and sends a notification to the terminal. The terminal displays the delivery information (e.g., tracking number) to the user.

[1472] Input: Purchase completion information and shipping information

[1473] Output: Purchase history recorded in the database and shipping information sent to the user

[1474] Step 7:

[1475] Subsequent proposal improvements

[1476] The server uses the stored personal data and purchase history to improve future suggestions, and the generative AI model learns from new data to make more accurate fashion item suggestions.

[1477] Input: Updated personal data and purchase history

[1478] Output: Improved fashion item suggestions

[1479] 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.

[1480] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below in natural language.

[1481] 1. User data input

[1482] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[1483] 2. Emotion recognition

[1484] The device is equipped with an emotion engine. Before the user starts a consultation, it uses a facial recognition camera to collect the user's facial expression data and analyzes their emotions. This analysis recognizes the user's current emotional state (e.g., joy, sadness, stress, etc.).

[1485] 3. Fashion consultation

[1486] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[1487] 4. Data analysis and item proposal

[1488] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[1489] 5. Online Search and Ordering

[1490] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1491] 6. Purchase history management and delivery support

[1492] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[1493] Specific examples

[1494] As a concrete example, consider the following scenario.

[1495] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[1496] 2. The device's emotion engine detects the user's stress level.

[1497] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[1498] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1499] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[1500] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[1501] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[1502] The processing flow will be explained below.

[1503] Step 1:

[1504] A user accesses the system and is presented with an account registration screen, where they enter personal data such as their name, size, preferred brand, budget, and permission to use the facial recognition camera.

[1505] Step 2:

[1506] The entered personal data is sent to the server, which receives this data and stores it in a database, thereby creating a user profile.

[1507] Step 3:

[1508] The device activates the emotion engine and uses the face recognition camera to collect the user's facial expression data, which is then analyzed by the emotion engine to recognize the user's current emotional state.

[1509] Step 4:

[1510] To start a fashion consultation, the user opens a chat window on the system and enters the details of the consultation, such as "I'm looking for casual clothes that are suitable for the office."

[1511] Step 5:

[1512] The consultation content and emotional data generated by the emotion engine are sent to the server, which receives this data and begins preparing for analysis.

[1513] Step 6:

[1514] The server analyzes the data based on the inquiry details sent by the user, stored personal data, past purchase history, and current emotional state, and uses an AI engine to select the most suitable fashion items.

[1515] Step 7:

[1516] The server creates a list of selected fashion items and collects images, prices, and availability of each item, which is then sent to the terminal and displayed to the user.

[1517] Step 8:

[1518] A list of suggested fashion items will be displayed on the device, from which the user can select the items they like.

[1519] Step 9:

[1520] When a user selects a specific item from the list, a link to the online retailer for that item will be displayed on the device for quick access.

[1521] Step 10:

[1522] The user clicks the link to go to the online shop and completes the purchase of the selected item. Once the purchase is complete, the sales site notifies the server of the purchase.

[1523] Step 11:

[1524] The server updates the purchase history and adds the new item to the user's profile, which allows for more personalized recommendations next time.

[1525] Step 12:

[1526] After the purchase is complete, the server checks the delivery tracking information and sends a notification to the device, which displays "Your order has been shipped. Here is your tracking number," and the user can check the tracking number.

[1527] Example 2

[1528] 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."

[1529] Conventional fashion suggestion systems make suggestions based on a user's personal data and past purchase history, but because they do not take the user's emotional state into account, they have difficulty suggesting items that suit the user's current psychological state and mood. Furthermore, the selection of suggested items is limited, making it difficult for users to have a satisfying shopping experience. This reduces users' motivation to purchase, and creates the problem of not being able to provide an optimal shopping experience.

[1530] 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.

[1531] In this invention, the server includes means for receiving personal data input by a user, means for analyzing the user's current emotional state, means for receiving fashion consultation details, means for analyzing the data to suggest optimal fashion items based on the personal data, emotional state data, and consultation details, means for providing information on the suggested fashion items to the user, means for generating and providing links to online sales sites for the fashion items to the user, means for recording and updating a purchase history of the fashion items, and means for improving subsequent suggestions based on the purchase history. This enables personalized fashion item suggestions that take the user's current emotional state into consideration, providing a comfortable and efficient shopping experience.

[1532] "User Data" is personal information about the user that the user enters, such as name, size, preferred brand, budget, and facial recognition camera permissions.

[1533] "Emotional state" refers to the user's psychological state, such as joy, sadness, or stress, obtained by analyzing the user's facial expression data.

[1534] "Fashion consultation" refers to specific requests or inquiries about fashion that users make to the system.

[1535] "Data analysis" refers to the process of using an AI engine to select the most suitable fashion items using user data, emotional state data, and fashion consultation details.

[1536] "Suggested fashion items" are candidate fashion items that are provided to the user as a result of data analysis.

[1537] "Online Retail Link" means a URL to a website where the suggested fashion item can be purchased.

[1538] "Purchase history" is a record of fashion items purchased by the user in the past, and includes the purchase date and time, item name, price, delivery information, and the like.

[1539] "Subsequent suggestions" are improved candidates for the next fashion item to be suggested based on previous purchase history and user data.

[1540] This invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the user's emotions. The program processing of this system will be explained below.

[1541] This system has three main components: the user, the terminal, and the server. Specifically, it consists of the terminal part that receives input from the user, the server part that performs emotion recognition and data analysis, and the user interface that provides fashion suggestions and online shopping support to the user.

[1542] User Data Input

[1543] When a user accesses the system for the first time, a user data entry screen appears on the terminal. Here, the user enters their user data. This user data includes their name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. The specific hardware used is a PC or smartphone, and the software used is a web browser or dedicated application.

[1544] emotion recognition

[1545] The device is equipped with an emotion engine, which uses a facial recognition camera to collect the user's facial expression data before the user begins a consultation. This facial expression data is analyzed in real time by the emotion engine to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.). The analysis results are sent to a server and stored in a database. Specifically, an AI model (e.g., a facial expression recognition model) is used for emotion recognition.

[1546] Fashion consultation

[1547] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server for analysis. The software used for this is a conversational agent based on natural language processing (NLP).

[1548] Data analysis and item suggestions

[1549] The server analyzes data based on the user's inquiry, stored personal data, past purchase history, and current emotional state. It uses an AI engine (e.g., machine learning algorithm) to select the most suitable fashion items and sends this information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[1550] Online search and ordering

[1551] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1552] Purchase history management and delivery support

[1553] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. Specific information displayed may include, "The item you ordered has been shipped. Here is the tracking number." This allows the user to confirm the arrival of the item.

[1554] Specific examples

[1555] For example, if a user says, "I want clothes for a date that fit the autumn trends," and the device's emotion engine detects the user's stress level, the server analyzes the content of the request, emotional data, and stored personal data. As a result, the server suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots." These suggestions are displayed on the device, and the user selects the "calm-colored cardigan" and clicks the provided link to complete the purchase. The server then records the purchase history and notifies the device of delivery information.

[1556] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[1557] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1558] Step 1:

[1559] When a user accesses the system for the first time, a user data entry screen appears on the terminal.

[1560] What it does: The user enters personal data such as name, size, preferred brands, budget, and facial recognition camera permissions.

[1561] Input: User data (e.g., name "Yamada Taro", size "M", favorite brand "Uniqlo", budget "10,000 yen", "Allow" to use face recognition camera).

[1562] Output: User data is saved in the database and sent to the server.

[1563] Step 2:

[1564] The device's built-in emotion engine collects the user's facial expression data in real time.

[1565] Specific operation: When a user stands in front of the screen, the facial recognition camera captures facial expression data.

[1566] Input: Real-time facial expression data.

[1567] Data processing: The emotion engine analyzes facial expression data and determines the user's emotional state (e.g., joy, sadness, stress, etc.).

[1568] Output: The emotional state data is sent to the server and stored in a database.

[1569] Step 3:

[1570] When a user starts a fashion consultation, an interactive chat window will appear on the device.

[1571] Specific operation: The user inputs a question such as "I want some casual clothes that are suitable for the office."

[1572] Input: User's consultation content.

[1573] Output: The consultation content is sent to the server and stored together with the emotional state data.

[1574] Step 4:

[1575] The server performs data analysis using user data, emotional state data, user inquiries, and past purchase history.

[1576] Specific operation: The AI ​​engine integrates this data and selects the most suitable fashion items.

[1577] Input: User data, emotional state data, consultation details, past purchase history.

[1578] Data calculation: The AI ​​engine analyzes using machine learning algorithms and selects items.

[1579] Output: The list of suggested fashion items is sent to the terminal.

[1580] Step 5:

[1581] A list of fashion items will appear on your device.

[1582] Specific behavior: The user selects the item they like from the list.

[1583] Input: A list of suggested fashion items.

[1584] Output: A link to the online retailer of the selected item is generated and provided to the user.

[1585] Step 6:

[1586] Users click on a link to an online retailer of the selected item and begin shopping.

[1587] Specific operation: A user goes through the process of purchasing an item on an online sales site.

[1588] Input: The user's item selection.

[1589] Output: When the purchase process is completed, the user's purchase history is recorded and updated on the server.

[1590] Step 7:

[1591] It obtains delivery information based on purchase history and sends a notification to the device.

[1592] Specific operation: Obtains delivery status from the delivery company and notifies the user.

[1593] Input: Purchase history, delivery status.

[1594] Output: The terminal displays specific information such as "Your order has been shipped. Here is your tracking number."

[1595] Following this step, a system incorporating an emotion engine can provide a more comfortable and efficient shopping experience by making personalized fashion recommendations based on the user's emotional state.

[1596] (Application example 2)

[1597] 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."

[1598] Conventional fashion suggestion and shopping support systems make suggestions without considering the user's emotional state, making it difficult to provide items that suit the user's current mental state. Furthermore, when shopping in a physical store, users have difficulty locating suggested items. Another issue is that users may feel stressed while shopping in the store.

[1599] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving personal information entered by the user, means for receiving fashion consultation details, means for analyzing data to suggest optimal clothing based on the personal information and consultation details, means for providing information on the suggested clothing to the user, means for generating and providing a link to an online clothing sales site for the user, means for recording and updating the purchase history, means for improving subsequent suggestions based on the purchase history, emotion analysis means for recognizing the user's emotional state and suggesting clothing that matches the emotional state, and means for guiding the user to the location of the suggested clothing in the store. This makes it possible to suggest fashion items that suit the user's emotional state, allowing the user to comfortably search for products without feeling stressed even when shopping in a physical store.

[1600] A "user" is a user of the system and an individual who provides personal information and consultation details.

[1601] "Personal Information" is data entered by the user, including personal information such as name, size, preferred brands, budget, etc.

[1602] The "contents of consultation" are information about requests and wishes that the user inputs to the system, including specific requests such as "I want casual clothes that are suitable for the office."

[1603] "Clothing" refers to fashion items suggested to users, including clothes, shoes, accessories, etc.

[1604] The "means for analyzing data" refers to a method and device that performs calculations and evaluations to select optimal clothing based on personal information, consultation details, and past purchase history entered by the user.

[1605] "Link to Online Retail Site" means a web address provided to allow a user to purchase the suggested clothing item over the Internet.

[1606] "Means for recording and updating purchase history" refers to a method and device for storing information about items purchased by a user and updating that information as necessary.

[1607] The "means for improving subsequent suggestions" refers to a method and apparatus that improves the system's suggestion method to provide more relevant suggestions based on the user's past purchasing history.

[1608] "Emotion analysis means" refers to a method and apparatus that analyzes a user's facial expressions and other data to recognize the user's current emotional state.

[1609] "Store location guidance" refers to methods and devices that provide the location of suggested clothing items to help a user find them in a store.

[1610] The present invention relates to a fashion suggestion and shopping support system incorporating an emotion engine that recognizes the emotions of a user. An embodiment of this system will be described in detail below.

[1611] 1. User data input

[1612] When a user first accesses the system, a user data entry screen appears on the terminal, where the user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database.

[1613] 2. Emotion recognition

[1614] The device is equipped with an emotion engine that uses a facial recognition camera to collect facial expression data and analyze the user's emotions before the user starts a consultation. This analysis allows the device to recognize the user's current emotional state (e.g., joy, sadness, stress, etc.).

[1615] 3. Fashion consultation

[1616] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I'm looking for casual clothes that are suitable for the office." This consultation content and emotional state data generated by the emotion engine are sent to the server and used for analysis.

[1617] 4. Data analysis and item proposal

[1618] The server analyzes data based on the user's inquiry, saved personal data, past purchase history, and current emotional state. It uses an AI engine to select the most suitable fashion items and sends that information as a list to the device. Depending on the user's emotional state, it suggests relaxed casual styles or glamorous items that will lift their spirits.

[1619] 5. Online Search and Ordering

[1620] The device displays a list of items, including images, prices, and availability details for each item. When the user selects an item, a link to the online store for that item is provided. By clicking the link, the user can go directly to the online store and place an order.

[1621] 6. Purchase history management and delivery support

[1622] When a user completes a purchase, the server records and updates the purchase history. It also obtains delivery information and sends a notification to the device. By providing specific information such as "The item you ordered has been shipped. Here is the tracking number," the user can confirm the arrival of the item.

[1623] Specific examples

[1624] As a concrete example, consider the following scenario.

[1625] 1. The user is busy and stressed at work and asks, "I want to find some clothes for a date that are in line with the fall trends."

[1626] 2. The device's emotion engine detects the user's stress level.

[1627] 3. The server analyzes the consultation content, emotional data, and saved personal data, and suggests "a checked dress made of a relaxing material," "a cardigan in a muted color," and "comfortable ankle boots."

[1628] 4. Your device will display a list of suggested items, including detailed information about each item (image, price, link).

[1629] 5. The user selects the "calm cardigan" and clicks the provided link to check out.

[1630] 6. The server records the purchase history and notifies the device of the delivery information. The message "The cardigan you ordered has been shipped. The tracking number is XXXX" is displayed.

[1631] Example prompts for generative AI models

[1632] Using the user's facial expression data and preference data as input, suggest relaxing fashion items. Since the current emotional state is stressed, comfortable clothing is best. Also display the product location in the store.

[1633] In this way, the system, which incorporates an emotion engine, makes highly personalized fashion item suggestions that take into account the user's emotional state, providing a comfortable and efficient shopping experience.

[1634] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1635] Step 1:

[1636] When a user accesses the system for the first time, a user data entry screen appears on the terminal. The user enters personal information such as name, size, preferred brand, budget, and permission for the facial recognition camera. The entered data is sent to the server and stored in a database. Based on this input data, the server generates a personal profile for the user. The main input here is the user's personal information, and the output is the personal profile.

[1637] Step 2:

[1638] Before starting a consultation, the device's on-board emotion engine uses a facial recognition camera to collect the user's facial expression data and analyze their emotions. Specifically, a deep learning model (e.g., a TensorFlow-based model) analyzes the facial expression data and identifies the user's emotional state (e.g., joy, sadness, stress, etc.). The input of this step is the user's facial expression data, and the output is the user's current emotional state.

[1639] Step 3:

[1640] When a user starts a fashion consultation, an interactive chat window appears on the device. The user enters information such as "I want casual clothes that are suitable for the office." This consultation is sent to the server, which uses it for analysis along with a stored personal profile and current emotional state. The input for this step is the user's consultation, and the output is data ready for analysis.

[1641] Step 4:

[1642] The server analyzes data based on the inquiry details sent by the user, stored personal information, past purchase history, and current emotional state. The server uses an AI engine (e.g., a generative AI model) to select the most suitable fashion items. Specifically, if the user's stress level is high, it will suggest clothing made of relaxing materials, and if an emotion of joy is detected, it will select items with a gorgeous design. The input here is the user's various profile information and emotional state, and the output is a list of suggested items.

[1643] Step 5:

[1644] A list of suggested items is displayed on the terminal. Each item includes details such as an image, price, and availability. When the user selects an item they like, a link to the online sales site for that item is provided. By clicking the link, the user can access the online shop directly and proceed with the product order. The input of this step is the item list sent from the server, and the output is the purchase page link selected by the user.

[1645] Step 6:

[1646] When a user completes a purchase, the server records and updates the purchase history. In addition, the server retrieves delivery information and sends a notification to the terminal. Specific information is provided, such as "Your order has been shipped. Here is the tracking number." The input of this step is the user's purchase information and the corresponding delivery information, and the output is an updated purchase history and delivery notification.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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).

[1654] 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.

[1655] 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."

[1656] 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.

[1657] 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).

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] The following is further disclosed regarding the above embodiment.

[1669] (Claim 1)

[1670] means for receiving personal data input by a user;

[1671] A means for receiving fashion-related consultations;

[1672] A means for analyzing data to propose optimal fashion items based on the personal data and consultation content;

[1673] means for providing information on suggested fashion items to a user;

[1674] means for generating a link to an online sales site for the fashion item and providing it to the user;

[1675] A means for recording and updating the purchase history of said fashion items;

[1676] means for refining subsequent recommendations based on said purchase history;

[1677] A system including:

[1678] (Claim 2)

[1679] 2. The system according to claim 1, wherein the means for analyzing the data operates based on the user's past purchase history and stored personal data.

[1680] (Claim 3)

[1681] 2. The system of claim 1, wherein the fashion item information includes an image of the item, a price, and availability.

[1682] "Example 1"

[1683] (Claim 1)

[1684] means for receiving personal data input by a user;

[1685] A means for receiving fashion-related consultations;

[1686] A means for analyzing data based on the personal data and the consultation content;

[1687] A method to suggest optimal fashion items using generative AI models, and

[1688] means for providing information on suggested fashion items to a user;

[1689] means for generating a link to an online sales site for the fashion item and providing it to the user;

[1690] A means for recording and updating the purchase history of said fashion items;

[1691] means for refining subsequent recommendations based on said purchase history;

[1692] A means for obtaining delivery information and notifying the user;

[1693] A system including:

[1694] (Claim 2)

[1695] 2. The system according to claim 1, wherein the means for analyzing the data operates based on the user's past purchase history and stored personal data.

[1696] (Claim 3)

[1697] 2. The system of claim 1, wherein the fashion item information includes an image of the item, a price, and availability.

[1698] "Application Example 1"

[1699] (Claim 1)

[1700] means for receiving personal data input by a user;

[1701] A means for receiving fashion-related consultations;

[1702] A means for analyzing data to propose optimal fashion items based on the personal data and consultation content;

[1703] means for providing information on suggested fashion items to a user;

[1704] means for generating a link to an online sales site for the fashion item and providing it to the user;

[1705] A means for recording and updating the purchase history of said fashion items;

[1706] means for refining subsequent recommendations based on said purchase history;

[1707] A means for providing a virtual shopping space using a head-mounted display;

[1708] A means to communicate interactively with a virtual stylist;

[1709] A system including:

[1710] (Claim 2)

[1711] 2. The system according to claim 1, wherein the means for analyzing the data operates based on the user's past purchase history and stored personal data.

[1712] (Claim 3)

[1713] 2. The system of claim 1, wherein the fashion item information includes an image of the item, a price, and availability.

[1714] "Example 2: Combining Emotion Engines"

[1715] (Claim 1)

[1716] means for receiving personal data input by a user;

[1717] means for analyzing a user's current emotional state;

[1718] A means for receiving fashion-related consultations;

[1719] A means for analyzing data to propose optimal fashion items based on the personal data, emotional state data, and consultation content;

[1720] means for providing information on suggested fashion items to a user;

[1721] means for generating a link to an online sales site for the fashion item and providing it to the user;

[1722] A means for recording and updating the purchase history of said fashion items;

[1723] means for refining subsequent recommendations based on said purchase history;

[1724] A system including:

[1725] (Claim 2)

[1726] 2. The system according to claim 1, wherein the means for analyzing the data operates based on the user's past purchase history and stored personal data.

[1727] (Claim 3)

[1728] 2. The system of claim 1, wherein the fashion item information includes an image of the item, a price, and availability.

[1729] "Application example 2 when combining emotion engines"

[1730] (Claim 1)

[1731] means for receiving personal information input by a user;

[1732] A means for receiving fashion-related consultations;

[1733] A means for analyzing data to propose optimal clothing based on the personal information and consultation content;

[1734] means for providing suggested clothing information to a user;

[1735] means for generating a link to an online clothing sales site and providing it to a user;

[1736] A means for recording and updating said purchase history;

[1737] means for refining subsequent recommendations based on said purchase history;

[1738] an emotion analysis means for recognizing an emotional state of a user and suggesting clothing items according to the emotional state;

[1739] means for providing guidance to the location of the suggested garment in the store;

[1740] A system including:

[1741] (Claim 2)

[1742] 2. The system according to claim 1, wherein the means for analyzing the data operates based on the user's past purchase history and stored personal information.

[1743] (Claim 3)

[1744] 10. The system of claim 1, wherein the clothing information includes an image of the item, a price, and availability. [Explanation of symbols]

[1745] 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 personal data input by a user; A means for receiving fashion-related consultations; A means for analyzing data to propose optimal fashion items based on the personal data and consultation content; means for providing information on suggested fashion items to a user; means for generating a link to an online sales site for the fashion item and providing it to the user; a means for recording and updating the purchase history of said fashion items; means for refining subsequent recommendations based on said purchase history; A system including:

2. 2. The system according to claim 1, wherein the means for analyzing the data operates based on the user's past purchase history and stored personal data.

3. 2. The system of claim 1, wherein the fashion item information includes an image of the item, a price, and availability.

Images