System

Abstract

A system is provided.SOLUTION: A system comprising: means for registering personal information of a user; means for inputting stored product features as text; natural language processing means for analyzing the text and extracting product features; means for searching a product database and listing similar products based on the extracted features; means for filtering the listed products based on the personal information of the user; means for generating styling suggestions for the filtered products; and means for transmitting a final product list and styling suggestions to a user terminal.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] Consumers interested in fashion and interior design often see items they like on the street or on TV, but don't end up purchasing them because they lack specific product information. They also worry about whether the product they want to buy will suit them or how to style it. Traditional online shopping requires a lot of effort when searching for similar products, and the means for receiving styling suggestions are limited, resulting in an inconvenient consumer experience. The present invention aims to solve these problems by providing a system that allows users to easily and efficiently find suitable products and receive advice on styling them. [Means for solving the problem]

[0005] The present invention provides a means for registering a user's personal information and a means for inputting memorized product features as text. The system also includes a natural language processing means for analyzing the text information and extracting product features, a means for searching a product database, and a means for listing similar products based on the extracted features. The system further includes a means for filtering the listed products based on the user's personal information, a means for generating styling suggestions for the filtered products, and a means for transmitting the final product list and styling suggestions to the user's terminal. This allows users to easily find products that suit them and receive advice on how to incorporate those products into their daily lives.

[0006] "User personal information" means information such as the user's name, gender, age, size, preferences, and lifestyle.

[0007] "Means for inputting as text" means means for providing an interface for the user to input the characteristics of the product they want in text form.

[0008] "Natural language processing means" refers to technology that analyzes text information and extracts product features and keywords from it.

[0009] "Product database" refers to a database that stores information on various products.

[0010] The "means for listing similar products" refers to a means for searching a product database for similar products based on the extracted features and creating a list of such products.

[0011] "Filtering means" refers to a means for sorting the listed products based on the user's personal information and selecting the most suitable product.

[0012] The term "means for generating styling suggestions" refers to means for suggesting styling methods for selected products based on the user's profile information.

[0013] "User terminal" means a device used by a user, such as a computer, smartphone, or tablet.

[0014] "Means for transmitting the final product list and styling suggestions" refers to a technique for transmitting the selected product list and styling suggestions to a user terminal. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention provides a system that allows a user to easily search for similar products to an item that the user has seen on the street or on television and that also provides styling suggestions for the item. Specific embodiments of the system are described below.

[0037] Registering user information

[0038] 1. User:

[0039] Users create an account on a dedicated application or website, and when they log in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0040] 2. Terminal:

[0041] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[0042] 3. Server:

[0043] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[0044] Enter product information

[0045] 1. User:

[0046] The user enters the product characteristics they remember in the text input field, for example, entering a specific characteristic such as "red leather jacket."

[0047] 2. Terminal:

[0048] The terminal receives the entered text and sends it to the server when the send button is pressed.

[0049] 3. Server:

[0050] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords.

[0051] Search for similar products

[0052] 1. Server:

[0053] The server uses the keywords obtained from the analysis to search a product database, for example, to create a list of similar products based on keywords such as "red," "leather," and "jacket."

[0054] 2. Server:

[0055] The server filters the listed products based on the user's registration information, for example, selecting only products that match the user's size or style preferences.

[0056] Styling suggestions

[0057] 1. Server:

[0058] The server then sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account past purchase history and preferences to suggest optimal outfits.

[0059] 2. Server:

[0060] The server generates a final product list including styling suggestions and sends it to the user's terminal.

[0061] User Visibility

[0062] 1. Device:

[0063] The device parses the received JSON data and displays the listed product information and styling suggestions to the user in an easy-to-understand format, including product images, product names, prices, and links to sellers.

[0064] 2. User:

[0065] The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[0066] Specific examples

[0067] Example 1:

[0068] If a user enters the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that match the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good paired with a denim jacket," and displays the final product list to the user.

[0069] Example 2:

[0070] If a user enters the characteristic "silver earrings," the server analyzes the keywords "silver" and "earrings" and searches and filters for products that match. Along with the filtered list of products, the AI ​​stylist generates styling advice, such as "This would go well with a simple dress." This information is displayed on the user's device, allowing the user to review the products and suggested styling.

[0071] This system allows users to easily find products that meet their needs and also provides appropriate advice on styling those products, significantly improving the online shopping experience and increasing user satisfaction.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] User: The user creates an account on a dedicated application or website and enters the necessary personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0075] Step 2:

[0076] Terminal: The terminal displays the information entered by the user in a dedicated form, collects the input, converts the input information into JSON format, and sends it to the server.

[0077] Step 3:

[0078] Server: The server parses the received JSON data, creates a new record in the user profile table, and saves the personal information to the database. The server generates a registration completion message and sends it to the device.

[0079] Step 4:

[0080] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[0081] Step 5:

[0082] User: The user enters the characteristics of a product they see on the street or on TV into a text input field in a dedicated application or website, for example, describing the product's characteristics as "a red leather jacket."

[0083] Step 6:

[0084] Terminal: The terminal receives the text information entered by the user and, when the send button is pressed, sends the text information to the server.

[0085] Step 7:

[0086] Server: The server passes the received text information to an NLP engine, which analyzes the product's characteristics, extracting keywords such as "red," "leather," and "jacket."

[0087] Step 8:

[0088] Server: The server searches the product database based on the extracted keywords and lists similar products that match the keywords.

[0089] Step 9:

[0090] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.). For example, it selects products that fit the user's size or preferred style.

[0091] Step 10:

[0092] Server: The server sends the filtered product list to the AI ​​stylist engine to generate styling suggestions. The AI ​​stylist takes into account the user's past purchase history and preferences to suggest the best outfits.

[0093] Step 11:

[0094] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[0095] Step 12:

[0096] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user, such as product images, product names, prices, seller links, styling suggestions, etc.

[0097] Step 13:

[0098] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a purchase link opens the corresponding online store or seller page in a new tab or window.

[0099] Step 14:

[0100] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[0101] Example 1

[0102] 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."

[0103] In today's online shopping environment, it is difficult for users to efficiently search for similar products to those they see on the street or on TV. Furthermore, there is a lack of systems that can not only find products but also provide users with optimal styling suggestions. There is a need for a system that can find products that match a user's interests and even suggest how to coordinate those products.

[0104] 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.

[0105] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for displaying the generated product information and styling suggestions on the user terminal, and means for providing a purchase link for the user. This enables a user to efficiently find products similar to a product and receive optimal styling suggestions for that product.

[0106] "User" refers to a person who uses this system, registers personal information, searches for products, and receives styling suggestions.

[0107] "User information registration means" refers to a mechanism that allows users to input and save personal information on a dedicated application or website.

[0108] The "text input means" refers to an interface for inputting memorized product features by the user in text format.

[0109] "Natural language processing means" refers to a processing engine for extracting product features from text. Specifically, it can analyze keywords.

[0110] The "product database search means" refers to a process for searching a stored product database based on the extracted keywords and listing similar products.

[0111] "Filtering means" refers to a process for narrowing down the listed products based on the user's personal information (size, preferences, lifestyle, etc.).

[0112] The term "styling suggestion generating means" refers to a process for suggesting optimal styling for filtered products, taking into consideration the user's past purchase history and preferences.

[0113] "User terminal transmission means" refers to the process for transmitting the final product list and styling suggestions to the user's terminal.

[0114] "Display means" refers to an interface for displaying the generated product information and styling suggestions on the user's terminal screen.

[0115] The "purchase link providing means" refers to a process of providing a link for a user to purchase a product that the user likes.

[0116] The present invention provides a system that allows a user to easily search for similar products to an item that catches their eye and that they have seen on the street or on television, and also provides styling suggestions for the item. Specific embodiments of the system are described in detail below.

[0117] First, a user creates an account using a dedicated application or website. When the user logs in for the first time, they enter personal information such as name, gender, age, size, preferences, and lifestyle. This information is converted from the device into JSON format and sent to the server. The server analyzes the received information, creates a new record in the user profile table, and saves it in the database.

[0118] Next, the user enters the product characteristics they remember into the text input field. For example, they enter specific characteristics such as "red leather jacket." The device receives this input information and when they press the send button, the text data is sent to the server. The server passes the received text information to an NLP engine, which analyzes the product characteristics and extracts keywords.

[0119] The server searches a product database using the extracted keywords (e.g., "red," "leather," "jacket") to list related products, and then filters the results based on the user's profile information (size, preferred style, etc.) to select the most suitable product.

[0120] Once filtering is complete, the server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account the user's past purchase history and preferences to suggest optimal outfits.

[0121] Finally, the server sends the generated product information and styling suggestions to the user's device. The device parses the received JSON data and displays the product information and styling suggestions in an easy-to-understand manner. This includes product images, product names, prices, and seller links. The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[0122] For example, if a user inputs the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that fit the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good with a denim jacket," and displays the final product list to the user. This system allows users to easily find products that meet their needs and receive appropriate advice on styling those products.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] User account creation

[0126] The user opens a dedicated application or website and accesses the account creation screen, where they enter personal information such as name, gender, age, size, preferences, lifestyle, etc. This information is collected as input.

[0127] Step 2:

[0128] Information collection and transmission by devices

[0129] The terminal displays the information entered by the user on a dedicated form, and after all information is entered correctly by the user, it converts the information into JSON format, which is generated as output and sent to the server.

[0130] Step 3:

[0131] Data analysis and storage by server

[0132] The server receives the JSON data from the device as input, parses it, creates a new record in the user profile table, and saves the parsed personal information to the database. An output message indicating that registration is complete is generated and sent to the device.

[0133] Step 4:

[0134] User input of product features

[0135] The user enters the product characteristics they remember into a text input field, for example, a specific characteristic such as "red leather jacket," and this is collected as input data.

[0136] Step 5:

[0137] Sending text via device

[0138] The terminal receives the text information entered by the user, and when the send button is pressed, the text data is sent to the server as input data.

[0139] Step 6:

[0140] Server-based text analysis

[0141] The server passes the text information received from the device to the NLP engine, which takes the text as input. It analyzes the product's features and extracts keywords. For example, keywords such as "red," "leather," and "jacket" are generated as output.

[0142] Step 7:

[0143] Searching the product database by the server

[0144] The server receives the keywords extracted through the analysis (e.g., "red," "leather," "jacket") as input and searches the product database. A list of related products is generated as output.

[0145] Step 8:

[0146] Server-based filtering

[0147] The server receives the list of products as input and filters them based on the user's registered information (size, preferred style, etc.) to narrow down the products that best suit the user and generate a list of those products as output.

[0148] Step 9:

[0149] Server-generated styling suggestions

[0150] The server passes the filtered product list as input to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist engine considers the user's past purchase history and preferences to suggest optimal outfits. Specific styling suggestions are generated as output.

[0151] Step 10:

[0152] Server generates final list

[0153] The server receives as input the final product list including styling suggestions and generates data for transmission to the user terminal, which is generated as output and transmitted to the terminal.

[0154] Step 11:

[0155] Displaying results on a terminal

[0156] The device receives JSON data from the server, parses it, and displays product information and styling suggestions in an easy-to-understand format to the user. Specifically, the device displays product images, product names, prices, and links to sellers.

[0157] Step 12:

[0158] User review and purchase

[0159] The user checks the information displayed on the device as input, and if they find a product they like, they click the purchase link to complete the purchase. This is the user's purchasing behavior as output.

[0160] (Application example 1)

[0161] 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."

[0162] In current online shopping, it is difficult for users to easily search for similar products to those they see on the street or on television, and to receive styling suggestions for those products. As a result, users spend a lot of time and effort finding products that suit their preferences and needs. Furthermore, the lack of styling suggestions leaves them unsure about how to coordinate their outfits after purchase. A system that can solve these issues is needed.

[0163] 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.

[0164] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for specifying the styling suggestions using a generative AI model, and means for displaying the generated product list and styling suggestions to the user as prompt sentences. This allows a user to easily search for products similar to a product they have seen and also receive styling suggestions for the product.

[0165] "Means for registering user personal information" refers to the function of entering personal information such as the user's name, gender, age, size, preferences, and lifestyle, and storing it in a database.

[0166] "Means for inputting memorized product features as text" refers to a function that allows a user to input the features of a product that they have seen on the street or on television in text form.

[0167] "Natural language processing means for analyzing text and extracting product features" refers to a function for analyzing input text and automatically extracting product features.

[0168] "Means for searching a product database and listing similar products based on extracted features" refers to a function for searching a database based on extracted product features and listing similar products.

[0169] "Means for filtering listed products based on the user's personal information" refers to a function for selecting the product that best matches the user's personal information from among the similar products listed.

[0170] The "means for generating styling suggestions for filtered products" refers to a function for generating styling suggestions for selected products.

[0171] "Means for transmitting the final product list and styling suggestions to the user terminal" refers to a function for transmitting the generated product list and styling suggestions to the user terminal.

[0172] "Means for realizing styling suggestions using a generative AI model" refers to the function of realizing styling suggestions using an AI model and proposing specific outfits.

[0173] The "means for displaying the generated product list and styling suggestions to the user as prompt sentences" refers to a function for displaying the product list and coordination suggestions to the user in a prompt format.

[0174] The present invention is a system for searching for similar products to a product that a user has seen and providing styling suggestions for the product. A detailed description will be given of specific embodiments of the present invention.

[0175] System Program

[0176] This system consists of a server and user terminals (mainly smartphone applications). The main hardware and software used include:

[0177] Hardware: Servers (cloud servers, etc.), smartphones

[0178] Software: Python, Flask, JSON, Natural Language Processing Engine (NLP Engine)

[0179] Program processing

[0180] The server performs the following process.

[0181] A means of registering user personal information: Users enter personal information such as name, gender, age, size, preferences, and lifestyle, and the server stores this in a database.

[0182] A method for inputting memorized product features as text: The user inputs the features of products they have seen on the street or on TV in text format and sends them to the server.

[0183] Natural language processing means for analyzing text and extracting product features: The server analyzes the received text using a natural language processing engine and extracts product features.

[0184] A means for searching a product database and listing similar products based on the extracted features: The server searches a database based on the extracted features and lists similar products.

[0185] Means for filtering listed products based on user's personal information: The server filters listed products based on the user's personal information and selects suitable products.

[0186] Means for generating styling suggestions for filtered products: The server generates styling suggestions for selected products using a generative AI model.

[0187] Means for transmitting the final product list and styling suggestions to the user terminal: The server transmits the generated product list and styling suggestions to the user terminal.

[0188] Means of using generative AI models to concretize styling suggestions: Using AI models to concretize styling suggestions and propose specific outfits.

[0189] A means for displaying the generated product list and styling suggestions to the user as prompt sentences: The product list and coordination suggestions are displayed to the user in a prompt format.

[0190] Data processing / calculation

[0191] The server performs the following data processing and calculations:

[0192] Registering personal information: Convert the entered data into JSON format and save it in the database.

[0193] Product feature analysis: The provided text is analyzed using a natural language processing engine (NLP engine) to extract keywords.

[0194] Product search: Based on the extracted keywords, the product database is searched and the products that best match the registered information are listed.

[0195] Generate styling suggestions: Based on the filtered products, a generative AI model generates styling suggestions.

[0196] Sending the results: The generated product list and styling suggestions are sent back to the user's device in JSON format and displayed as a prompt.

[0197] Specific examples

[0198] Here are some examples of input prompts:

[0199] Input prompt (text format):

[0200] User Information:

[0201] Name: Sato

[0202] Gender: Male

[0203] Age: 28

[0204] Size: Medium

[0205] Preference: Casual

[0206] Lifestyle: Active

[0207] Product information:

[0208] black leather backpack

[0209] By inputting specific examples like this, users can easily search for similar products to the one they saw and receive appropriate styling suggestions for that product. Furthermore, based on the information presented as prompts, the generative AI model suggests outfits to support users' financial decisions.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] A user creates an account on a dedicated application or website. When logging in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.). The entered information is converted into JSON format and sent from the device to the server. This information is saved as a new record in the user profile table.

[0213] Step 2:

[0214] The user enters the characteristics of a product they have seen on the street or on TV in a text input field. For example, they enter specific characteristics such as "black leather backpack." The entered information is received by the device and sent to the server.

[0215] Step 3:

[0216] The server passes the received text information to a natural language processing engine (NLP engine) to analyze the product's features. Specifically, it extracts the keywords "black," "leather," and "backpack." The extracted keywords are used in the next search process.

[0217] Step 4:

[0218] The server then searches the product database using the extracted keywords. This search generates a list of products that match the keywords. For example, a list of products that match the keyword "black leather backpack" is generated.

[0219] Step 5:

[0220] The server filters the listed products based on the user's personal information, for example, selecting only products that match the user's size and preferences, resulting in a list of products that best fit the user's profile information.

[0221] Step 6:

[0222] The server uses a generative AI model to generate styling suggestions based on the filtered products. Specifically, it suggests outfits such as "pairing a selected backpack with a casual shirt and jeans." These styling suggestions are generated taking into account past purchase history and preferences.

[0223] Step 7:

[0224] The server sends the final product list and styling suggestions in JSON format to the device, which then parses the information and displays the product information and styling suggestions to the user, including product images, product names, prices, vendor links, and styling suggestions.

[0225] Input and Output

[0226] Input: User's personal information, product features (text input)

[0227] Output: Filtered product list, styling suggestions

[0228] Data processing or data calculation

[0229] Converting to JSON format and saving to storage

[0230] Keyword extraction using natural language processing

[0231] Database Search and Filtering

[0232] Styling suggestion generation using generative AI models

[0233] Generate and display prompt statements

[0234] This allows users to easily search for similar products to the one they see and receive appropriate styling suggestions for that product.

[0235] 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.

[0236] The present invention is a system that allows a user to easily input the characteristics of a product that interests them, and searches for and suggests similar products. This system incorporates an emotion engine that recognizes the user's emotions and includes a function that makes suggestions based on the user's psychological state. Specific embodiments of the present invention are described below.

[0237] Registering user information

[0238] 1. User:

[0239] Users create an account on a dedicated application or website and enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0240] 2. Terminal:

[0241] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[0242] 3. Server:

[0243] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[0244] Product information input and emotion recognition

[0245] 1. User:

[0246] The user enters the characteristics of the product they see in a text input field, for example, entering a specific characteristic such as "red leather jacket."

[0247] 2. Terminal:

[0248] The device receives the input text and simultaneously collects emotional data (input speed, input style, emotional icons, etc.) of the user while inputting the text. This information is then sent to the server.

[0249] 3. Server:

[0250] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data and determines the user's current psychological state.

[0251] Search for similar products

[0252] 1. Server:

[0253] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords.

[0254] 2. Server:

[0255] The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data, and selects the product that best suits the user's current psychological state.

[0256] Styling suggestions

[0257] 1. Server:

[0258] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest optimal outfits.

[0259] 2. Server:

[0260] The server generates a final product list including styling suggestions and sends this information to the user's terminal.

[0261] User Visibility

[0262] 1. Device:

[0263] The device analyzes the received information and displays a list of product information and styling suggestions to the user, including product images, product names, prices, seller links, and styling suggestions.

[0264] 2. User:

[0265] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[0266] Specific examples

[0267] Example 1:

[0268] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and strong typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on this. Furthermore, taking into account the user's state of excitement, it creates a list of stylish and latest backpack designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[0269] Example 2:

[0270] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[0271] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[0272] The processing flow will be explained below.

[0273] Step 1:

[0274] User: The user accesses a dedicated application or website, creates an account, and enters personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0275] Step 2:

[0276] Terminal: The terminal displays the information entered by the user in a dedicated form and collects the input. The collected information is converted into JSON format and sent to the server.

[0277] Step 3:

[0278] Server: The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[0279] Step 4:

[0280] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[0281] Step 5:

[0282] User: The user enters the characteristics of the product they see into a text input field, for example, "red leather jacket."

[0283] Step 6:

[0284] Terminal: The terminal receives the input text information and simultaneously collects the user's emotional data (input speed, input style, emotional icon, etc.) of the text input, and transmits the text information and emotional data to the server.

[0285] Step 7:

[0286] Server: The server passes the received text information to the NLP engine, which analyzes the product's features and extracts keywords. In parallel, the emotion engine analyzes the emotion data and determines the user's current psychological state.

[0287] Step 8:

[0288] Server: The server compares the keywords extracted by the NLP engine (e.g., "red," "leather," "jacket") with a product database and lists similar products.

[0289] Step 9:

[0290] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data. It selects the product that best suits the user's psychological state (e.g., "excited" or "calm").

[0291] Step 10:

[0292] Server: The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest the optimal outfit.

[0293] Step 11:

[0294] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[0295] Step 12:

[0296] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user. Product images, product names, prices, seller links, styling suggestions, etc. are displayed.

[0297] Step 13:

[0298] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a link opens the corresponding online store or seller's page in a new tab or window.

[0299] Step 14:

[0300] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[0301] Example 2

[0302] 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."

[0303] Current online shopping systems often suggest products without considering the user's psychological state or emotions. As a result, the suggested products often do not match the user's current needs or preferences. Furthermore, styling suggestions do not reflect the individual user's emotions or lifestyle, resulting in an inconsistent experience. This leads to unsatisfying shopping experiences for users.

[0304] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for registering personal data of a user, means for inputting stored product characteristics as text, natural language processing means for analyzing the text and extracting product characteristics, means for searching a database storing product information and listing similar products based on the extracted characteristics, means for filtering the listed products based on the user's personal data and emotional data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user device. This enables a more personalized shopping experience that takes into account the user's psychological state and emotions.

[0305] "User's personal data" refers to information about the user himself / herself, such as the user's name, gender, age, size, preferences, and lifestyle.

[0306] "Product characteristics" are specific characteristics and attributes of a product, such as its color, material, design, and use.

[0307] "Natural language processing means" refers to technology that analyzes input text and extracts specific keywords and information, and generally uses an NLP engine.

[0308] A "database storing product information" is a data storage that stores information about multiple products and allows searching and filtering.

[0309] "Similar products" refer to other products with similar characteristics that are selected based on the input product characteristics.

[0310] "Emotion data" is information about the user's psychological state obtained from the user's input speed, input style, emotion icons, and the like.

[0311] "Filtering means" refers to technology that selects appropriate products from the listed items based on extracted keywords and emotional data.

[0312] "Means for generating styling suggestions" refers to technology that uses AI and machine learning models to coordinate and suggest outfits based on the user's preferences, past purchase history, and current psychological state.

[0313] "User device" refers to an electronic device that can connect to the Internet and is used by a user, such as a smartphone, tablet, or PC.

[0314] This invention provides a system for providing a personalized shopping experience that takes into account a user's emotions. The system has a series of means for analyzing the user's input information and emotion data and generating optimal product and styling suggestions.

[0315] System Configuration

[0316] Registering user information

[0317] User:

[0318] Users access a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle using text boxes, drop-down menus, and check boxes.

[0319] Device:

[0320] The device collects the information entered by the user, converts it into JSON format, and sends it to the server. This transmission process is performed using JavaScript or HTML form submission functions.

[0321] server:

[0322] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, back-end languages ​​such as Python and Java are used, and the database is MySQL or PostgreSQL.

[0323] Product information input and emotion recognition

[0324] User:

[0325] The user enters the product characteristics they see into a text entry field, for example, entering a specific characteristic such as "red leather jacket."

[0326] Device:

[0327] Along with the text entered, the device simultaneously collects input speed, input style, and emotional data such as emoticons, which are then converted into JSON format and sent to the server.

[0328] server:

[0329] The server passes the received text information to a natural language processing (NLP) engine, which analyzes the product's features and extracts keywords. For example, NLP engines such as spaCy or NLTK are used. It also analyzes emotional data using emotion engines such as the Emotion API to determine the user's current psychological state.

[0330] Search and filter similar products

[0331] server:

[0332] The server uses the keywords extracted by the NLP engine to search the product database and list similar products, using SQL queries or full-text search engines like Elasticsearch.

[0333] server:

[0334] The products listed are filtered based on the user's personal data and analyzed emotional data, using an AI model to select the products that best fit the user's current emotional state.

[0335] Styling suggestions

[0336] server:

[0337] The filtered product list is then sent to the AI ​​Stylist engine, which generates styling suggestions using machine learning frameworks such as TensorFlow and PyTorch, taking into account the user's past purchase history, preferences, and psychological state.

[0338] server:

[0339] A final product list including styling suggestions is generated in JSON format and sent to the user's device.

[0340] User Visibility

[0341] Device:

[0342] The device analyzes the received information and displays the optimal product information and styling suggestions for the user. Specifically, it uses HTML and CSS to display product images, product names, prices, seller links, and styling suggestions. It also uses JavaScript to dynamically update the displayed content.

[0343] User:

[0344] The user checks the displayed product information and, if they find something they like, clicks on the purchase link, which opens the corresponding online shopping site or seller's page in a new tab or window.

[0345] Specific examples

[0346] Example 1:

[0347] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and forceful typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on these. Furthermore, taking into account the user's state of excitement, it lists stylish backpacks with the latest designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[0348] Example prompt:

[0349] "Black leather backpack"

[0350] Example 2:

[0351] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[0352] Example prompt:

[0353] "Silver earrings"

[0354] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356] Step 1:

[0357] The user enters personal information

[0358] Example of how it works:

[0359] Users access a registration form on a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle. For example, they enter their name in a TextBox, select their gender from a DropDownList, and select their preferences using a CheckBox.

[0360] input:

[0361] Personal information such as name, gender, age, size, preferences, and lifestyle.

[0362] output:

[0363] The entered personal information will be displayed on the terminal, ready for the next processing step.

[0364] Step 2:

[0365] The device collects information and sends it to the server

[0366] Example of how it works:

[0367] The device converts the information entered by the user into JSON format and sends it to the server. Specifically, it uses the AJAX function of JavaScript and uses an HTTP POST request.

[0368] input:

[0369] Personal information entered by the user.

[0370] output:

[0371] Sends JSON format data from the terminal to the server.

[0372] Step 3:

[0373] The server analyzes the data and stores it in a database

[0374] Example of how it works:

[0375] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, it uses Python frameworks such as Flask or Django to execute SQL queries and stores the data in MySQL or PostgreSQL.

[0376] input:

[0377] Personal information data in JSON format sent from the device.

[0378] output:

[0379] The user's personal information stored in the database and a message indicating completion of registration are generated and sent to the terminal.

[0380] Step 4:

[0381] The user inputs the product's features

[0382] Example of how it works:

[0383] The user enters the product characteristics they see into a text entry field, for example, "red leather jacket."

[0384] input:

[0385] User-supplied text about product features.

[0386] output:

[0387] The product characteristic text entered on the terminal is displayed and the terminal is ready to proceed to the next processing step.

[0388] Step 5:

[0389] The device collects feature data and emotion data and sends it to the server.

[0390] Example of how it works:

[0391] The device simultaneously collects input data such as typing speed, typing style, and emotional icons along with the input text. This information is converted into JSON format and sent to the server. One example is the implementation of JavaScript code to measure keystroke speed.

[0392] input:

[0393] Text data and sentiment data (typing speed, typing style, emoticons) of product characteristics by users.

[0394] output:

[0395] Sending feature data and emotion data in JSON format from the device to the server.

[0396] Step 6:

[0397] The server analyzes the text and sentiment data

[0398] Example of how it works:

[0399] The server passes the received text information to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. Examples of NLP engines used include spaCy and NLTK. The server also analyzes the emotional data using an emotion engine to determine the user's current psychological state.

[0400] input:

[0401] Text data and sentiment data of product characteristics sent from the device.

[0402] output:

[0403] Extracted product keywords and analyzed user emotional states.

[0404] Step 7:

[0405] The server searches the product database and lists similar products

[0406] Example of how it works:

[0407] The server uses the keywords extracted by the NLP engine to search the product database, using SQL queries and full-text search engines such as Elasticsearch.

[0408] input:

[0409] Extracted product keywords.

[0410] output:

[0411] A list of similar products that match your keywords.

[0412] Step 8:

[0413] The server performs filtering to select the best product

[0414] Example of how it works:

[0415] The server filters the listed products based on the user's personal and emotional data, using an AI model to select the products that best fit the user's current emotional state.

[0416] input:

[0417] A list of similar products, personal data of the user, and emotional data.

[0418] output:

[0419] A filtered list of the best products.

[0420] Step 9:

[0421] The server generates styling suggestions using the AI ​​stylist engine

[0422] Example of how it works:

[0423] The server sends the filtered product list to an AI stylist engine that generates styling suggestions, using machine learning frameworks such as TensorFlow and PyTorch.

[0424] input:

[0425] A filtered list of the best products.

[0426] output:

[0427] Generated styling suggestions.

[0428] Step 10:

[0429] The server generates the final product list and sends it to the terminal.

[0430] Example of how it works:

[0431] The server generates the final product list, including styling suggestions, in JSON format and sends it to the user's device. Data is sent using a RESTful API or GraphQL.

[0432] input:

[0433] Generated styling suggestions and a list of the best products.

[0434] output:

[0435] JSON data of the final product list and styling suggestions.

[0436] Step 11:

[0437] The device displays product information and styling suggestions to the user.

[0438] Example of how it works:

[0439] The device analyzes the received information and displays the product image, product name, price, seller link, and styling suggestions using HTML and CSS. The display content is dynamically updated using JavaScript.

[0440] input:

[0441] Final product list and styling suggestions sent from the server in JSON format.

[0442] output:

[0443] Product information and styling suggestions displayed to users.

[0444] Step 12:

[0445] User reviews the product and clicks the purchase link

[0446] Example of how it works:

[0447] The user checks the displayed product information, and if they find something they like, they click the purchase link. This action opens the online shopping site or seller's page in a new tab or window, and the purchase process proceeds.

[0448] input:

[0449] User expresses intent to purchase a product (clicks a button).

[0450] output:

[0451] The page of the corresponding online shopping site or seller will open and the purchase process will begin.

[0452] (Application example 2)

[0453] 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."

[0454] In today's online shopping environment, users face difficulties in finding the product that best suits their preferences and current state of mind from the wide variety of products available. Another problem is the lack of personalized recommendations that appropriately reflect different users' feelings and preferences regarding the same product. Therefore, there is a need for a system that can efficiently search for and recommend products that meet users' needs.

[0455] The identification processing 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 registering the user's personal information, means for inputting memorized product features as text, means for collecting emotion data at the time of input, natural language processing means for analyzing the text and extracting product features, means for searching the product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information and emotion data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user terminal. This enables personalized product suggestions and styling suggestions based on the user's personal information and emotion data.

[0456] "Means for registering user's personal information" includes a function for registering personal information such as the user's name, gender, age, size, preferences, and lifestyle in a database.

[0457] The "means for inputting memorized product characteristics as text" includes a function for providing an interface that allows the user to input the characteristics of products that the user has come across in text form.

[0458] The "means for collecting emotional data during input" includes a function for collecting the emotional state of the user when inputting product features, including input speed, input style, and voice tone.

[0459] "Natural language processing means for analyzing text and extracting product features" includes a natural language processing engine for analyzing and extracting product features from input text.

[0460] "Means for searching a product database and listing similar products based on extracted features" includes a function for searching and listing similar products in a database based on features extracted by natural language processing.

[0461] The "means for filtering listed products based on the user's personal information and emotional data" includes a function for re-sorting products that have already been listed based on the user's registered information and current emotional data.

[0462] The "means for generating styling suggestions for filtered products" includes an artificial intelligence stylist engine that suggests appropriate outfits for the re-sorted products.

[0463] The "means for transmitting the final product list and styling suggestions to the user terminal" includes a function for transmitting the final selected product list and styling suggestions to the user terminal for display.

[0464] The present invention provides a system that makes personalized product and styling suggestions based on a user's personal information and emotional data. Specific embodiments for carrying out the invention are described below.

[0465] System Configuration

[0466] User terminal

[0467] The user terminal is equipped with a means for the user to input personal information, a means for inputting the characteristics of the products they see by text or voice, and a means for collecting the user's emotional data (input speed, input style, voice tone).The user terminal can be a smartphone or smart glasses.

[0468] server

[0469] The server includes the following functions:

[0470] 1. Personal information registration:

[0471] The personal information received from the user is sent to the server in JSON format and saved in the database.

[0472] 2. Product feature and sentiment data analysis:

[0473] The input text is analyzed using a natural language processing engine (NLP engine) to extract keywords. The emotion engine also analyzes the emotional data to determine the user's emotional state. The NLP engine uses SpaCy and the Google NLP API, while the emotion engine uses the Google Cloud Speech-to-Text API.

[0474] 3. Search and filter similar products:

[0475] The product database is searched based on keywords extracted by the NLP engine, and similar products are listed. Furthermore, filtering is performed based on the user's personal information and emotional data.

[0476] 4. Styling suggestions:

[0477] For the filtered products, the AI ​​stylist engine generates optimal styling suggestions, taking into account the user's profile and emotional state.

[0478] 5. Sending and viewing results:

[0479] The final product list and styling suggestions are sent to the user's terminal and displayed to the user.

[0480] Specific examples

[0481] If a user inputs the characteristic "black leather backpack" and the emotional data indicates "excitement," the NLP engine extracts keywords such as "black," "leather," and "backpack." The emotional engine identifies the user's excitement level based on the emotional data. Based on this information, the server filters and lists stylish backpacks with the latest designs. The AI ​​stylist engine suggests how to coordinate them with a denim jacket. The final product list and styling suggestions are displayed on the user's device.

[0482] Prompt Sentence Examples

[0483] User comment: "Black leather backpack"

[0484] Emotion data: {"input speed": 1.5, "input style": "enthusiastic"}

[0485] Keywords: ["black", "leather", "backpack"]

[0486] Emotion analysis result: "Excited"

[0487] User profile: {"Name": "Taro Yamada", "Age": 30, "Gender": "Male", "Size": "L", "Preferences": ["Casual", "Outdoors"], "Lifestyle": ["Weekend camping"]}

[0488] Question: Please list the best products and styling suggestions.

[0489] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0490] Step 1:

[0491] The user terminal provides an interface for inputting the user's personal information (name, gender, age, size, preferences, lifestyle, etc.), and the user inputs this information. The input personal information is converted into JSON format through a dedicated form and sent to the server.

[0492] Step 2:

[0493] The server analyzes the received JSON format personal information, creates a new record in the user profile table, saves it in the database, and generates a registration completion message and sends it to the user's device.

[0494] Step 3:

[0495] The user inputs the characteristics of the product they see into the device in text or voice format. For example, they input a specific characteristic such as "red leather jacket." The device collects the input text, voice, and related emotional data (e.g., input speed, input style, and voice tone).

[0496] Step 4:

[0497] The device sends the collected text, voice, and emotion data to a server. The server passes the text data to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data to determine the user's current psychological state.

[0498] Step 5:

[0499] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords. For example, if the keywords "red," "leather," and "jacket" are extracted, the server searches the database for products that match these keywords.

[0500] Step 6:

[0501] The server filters the listed products based on the user's personal information (size, preferences, lifestyle, etc.) and emotional data (current mental state). For example, if the user's emotional state is "excited," it will prioritize products with stylish and latest designs.

[0502] Step 7:

[0503] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and emotional state to suggest optimal outfits.

[0504] Step 8:

[0505] The server generates a final product list and styling suggestions and sends this information to the user's terminal. The user terminal analyzes the received information and displays the listed product information and styling suggestions to the user, including product images, product names, prices, vendor links, styling suggestions, etc.

[0506] Step 9:

[0507] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] [Second embodiment]

[0512] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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).

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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."

[0524] The present invention provides a system that allows a user to easily search for similar products to an item that the user has seen on the street or on television and that also provides styling suggestions for the item. Specific embodiments of the system are described below.

[0525] Registering user information

[0526] 1. User:

[0527] Users create an account on a dedicated application or website, and when they log in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0528] 2. Terminal:

[0529] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[0530] 3. Server:

[0531] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[0532] Enter product information

[0533] 1. User:

[0534] The user enters the product characteristics they remember in the text input field, for example, entering a specific characteristic such as "red leather jacket."

[0535] 2. Terminal:

[0536] The terminal receives the entered text and sends it to the server when the send button is pressed.

[0537] 3. Server:

[0538] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords.

[0539] Search for similar products

[0540] 1. Server:

[0541] The server uses the keywords obtained from the analysis to search a product database, for example, to create a list of similar products based on keywords such as "red," "leather," and "jacket."

[0542] 2. Server:

[0543] The server filters the listed products based on the user's registration information, for example, selecting only products that match the user's size or style preferences.

[0544] Styling suggestions

[0545] 1. Server:

[0546] The server then sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account past purchase history and preferences to suggest optimal outfits.

[0547] 2. Server:

[0548] The server generates a final product list including styling suggestions and sends it to the user's terminal.

[0549] User Visibility

[0550] 1. Device:

[0551] The device parses the received JSON data and displays the listed product information and styling suggestions to the user in an easy-to-understand format, including product images, product names, prices, and links to sellers.

[0552] 2. User:

[0553] The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[0554] Specific examples

[0555] Example 1:

[0556] If a user enters the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that match the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good paired with a denim jacket," and displays the final product list to the user.

[0557] Example 2:

[0558] If a user enters the characteristic "silver earrings," the server analyzes the keywords "silver" and "earrings" and searches and filters for products that match. Along with the filtered list of products, the AI ​​stylist generates styling advice, such as "This would go well with a simple dress." This information is displayed on the user's device, allowing the user to review the products and suggested styling.

[0559] This system allows users to easily find products that meet their needs and also provides appropriate advice on styling those products, significantly improving the online shopping experience and increasing user satisfaction.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] User: The user creates an account on a dedicated application or website and enters the necessary personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0563] Step 2:

[0564] Terminal: The terminal displays the information entered by the user in a dedicated form, collects the input, converts the input information into JSON format, and sends it to the server.

[0565] Step 3:

[0566] Server: The server parses the received JSON data, creates a new record in the user profile table, and saves the personal information to the database. The server generates a registration completion message and sends it to the device.

[0567] Step 4:

[0568] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[0569] Step 5:

[0570] User: The user enters the characteristics of a product they see on the street or on TV into a text input field in a dedicated application or website, for example, describing the product's characteristics as "a red leather jacket."

[0571] Step 6:

[0572] Terminal: The terminal receives the text information entered by the user and, when the send button is pressed, sends the text information to the server.

[0573] Step 7:

[0574] Server: The server passes the received text information to an NLP engine, which analyzes the product's characteristics, extracting keywords such as "red," "leather," and "jacket."

[0575] Step 8:

[0576] Server: The server searches the product database based on the extracted keywords and lists similar products that match the keywords.

[0577] Step 9:

[0578] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.). For example, it selects products that fit the user's size or preferred style.

[0579] Step 10:

[0580] Server: The server sends the filtered product list to the AI ​​stylist engine to generate styling suggestions. The AI ​​stylist takes into account the user's past purchase history and preferences to suggest the best outfits.

[0581] Step 11:

[0582] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[0583] Step 12:

[0584] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user, such as product images, product names, prices, seller links, styling suggestions, etc.

[0585] Step 13:

[0586] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a purchase link opens the corresponding online store or seller page in a new tab or window.

[0587] Step 14:

[0588] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[0589] Example 1

[0590] 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."

[0591] In today's online shopping environment, it is difficult for users to efficiently search for similar products to those they see on the street or on TV. Furthermore, there is a lack of systems that can not only find products but also provide users with optimal styling suggestions. There is a need for a system that can find products that match a user's interests and even suggest how to coordinate those products.

[0592] 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.

[0593] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for displaying the generated product information and styling suggestions on the user terminal, and means for providing a purchase link for the user. This enables a user to efficiently find products similar to a product and receive optimal styling suggestions for that product.

[0594] "User" refers to a person who uses this system, registers personal information, searches for products, and receives styling suggestions.

[0595] "User information registration means" refers to a mechanism that allows users to input and save personal information on a dedicated application or website.

[0596] The "text input means" refers to an interface for inputting memorized product features by the user in text format.

[0597] "Natural language processing means" refers to a processing engine for extracting product features from text. Specifically, it can analyze keywords.

[0598] The "product database search means" refers to a process for searching a stored product database based on the extracted keywords and listing similar products.

[0599] "Filtering means" refers to a process for narrowing down the listed products based on the user's personal information (size, preferences, lifestyle, etc.).

[0600] The term "styling suggestion generating means" refers to a process for suggesting optimal styling for filtered products, taking into consideration the user's past purchase history and preferences.

[0601] "User terminal transmission means" refers to the process for transmitting the final product list and styling suggestions to the user's terminal.

[0602] "Display means" refers to an interface for displaying the generated product information and styling suggestions on the user's terminal screen.

[0603] The "purchase link providing means" refers to a process of providing a link for a user to purchase a product that the user likes.

[0604] The present invention provides a system that allows a user to easily search for similar products to an item that catches their eye and that they have seen on the street or on television, and also provides styling suggestions for the item. Specific embodiments of the system are described in detail below.

[0605] First, a user creates an account using a dedicated application or website. When the user logs in for the first time, they enter personal information such as name, gender, age, size, preferences, and lifestyle. This information is converted from the device into JSON format and sent to the server. The server analyzes the received information, creates a new record in the user profile table, and saves it in the database.

[0606] Next, the user enters the product characteristics they remember into the text input field. For example, they enter specific characteristics such as "red leather jacket." The device receives this input information and when they press the send button, the text data is sent to the server. The server passes the received text information to an NLP engine, which analyzes the product characteristics and extracts keywords.

[0607] The server searches a product database using the extracted keywords (e.g., "red," "leather," "jacket") to list related products, and then filters the results based on the user's profile information (size, preferred style, etc.) to select the most suitable product.

[0608] Once filtering is complete, the server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account the user's past purchase history and preferences to suggest optimal outfits.

[0609] Finally, the server sends the generated product information and styling suggestions to the user's device. The device parses the received JSON data and displays the product information and styling suggestions in an easy-to-understand manner. This includes product images, product names, prices, and seller links. The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[0610] For example, if a user inputs the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that fit the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good with a denim jacket," and displays the final product list to the user. This system allows users to easily find products that meet their needs and receive appropriate advice on styling those products.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1:

[0613] User account creation

[0614] The user opens a dedicated application or website and accesses the account creation screen, where they enter personal information such as name, gender, age, size, preferences, lifestyle, etc. This information is collected as input.

[0615] Step 2:

[0616] Information collection and transmission by devices

[0617] The terminal displays the information entered by the user on a dedicated form, and after all information is entered correctly by the user, it converts the information into JSON format, which is generated as output and sent to the server.

[0618] Step 3:

[0619] Data analysis and storage by server

[0620] The server receives the JSON data from the device as input, parses it, creates a new record in the user profile table, and saves the parsed personal information to the database. An output message indicating that registration is complete is generated and sent to the device.

[0621] Step 4:

[0622] User input of product features

[0623] The user enters the product characteristics they remember into a text input field, for example, a specific characteristic such as "red leather jacket," and this is collected as input data.

[0624] Step 5:

[0625] Sending text via device

[0626] The terminal receives the text information entered by the user, and when the send button is pressed, the text data is sent to the server as input data.

[0627] Step 6:

[0628] Server-based text analysis

[0629] The server passes the text information received from the device to the NLP engine, which takes the text as input. It analyzes the product's features and extracts keywords. For example, keywords such as "red," "leather," and "jacket" are generated as output.

[0630] Step 7:

[0631] Searching the product database by the server

[0632] The server receives the keywords extracted through the analysis (e.g., "red," "leather," "jacket") as input and searches the product database. A list of related products is generated as output.

[0633] Step 8:

[0634] Server-based filtering

[0635] The server receives the list of products as input and filters them based on the user's registered information (size, preferred style, etc.) to narrow down the products that best suit the user and generate a list of those products as output.

[0636] Step 9:

[0637] Server-generated styling suggestions

[0638] The server passes the filtered product list as input to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist engine considers the user's past purchase history and preferences to suggest optimal outfits. Specific styling suggestions are generated as output.

[0639] Step 10:

[0640] Server generates final list

[0641] The server receives as input the final product list including styling suggestions and generates data for transmission to the user terminal, which is generated as output and transmitted to the terminal.

[0642] Step 11:

[0643] Displaying results on a terminal

[0644] The device receives JSON data from the server, parses it, and displays product information and styling suggestions in an easy-to-understand format to the user. Specifically, the device displays product images, product names, prices, and links to sellers.

[0645] Step 12:

[0646] User review and purchase

[0647] The user checks the information displayed on the device as input, and if they find a product they like, they click the purchase link to complete the purchase. This is the user's purchasing behavior as output.

[0648] (Application example 1)

[0649] 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."

[0650] In current online shopping, it is difficult for users to easily search for similar products to those they see on the street or on television, and to receive styling suggestions for those products. As a result, users spend a lot of time and effort finding products that suit their preferences and needs. Furthermore, the lack of styling suggestions leaves them unsure about how to coordinate their outfits after purchase. A system that can solve these issues is needed.

[0651] 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.

[0652] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for specifying the styling suggestions using a generative AI model, and means for displaying the generated product list and styling suggestions to the user as prompt sentences. This allows a user to easily search for products similar to a product they have seen and also receive styling suggestions for the product.

[0653] "Means for registering user personal information" refers to the function of entering personal information such as the user's name, gender, age, size, preferences, and lifestyle, and storing it in a database.

[0654] "Means for inputting memorized product features as text" refers to a function that allows a user to input the features of a product that they have seen on the street or on television in text form.

[0655] "Natural language processing means for analyzing text and extracting product features" refers to a function for analyzing input text and automatically extracting product features.

[0656] "Means for searching a product database and listing similar products based on extracted features" refers to a function for searching a database based on extracted product features and listing similar products.

[0657] "Means for filtering listed products based on the user's personal information" refers to a function for selecting the product that best matches the user's personal information from among the similar products listed.

[0658] The "means for generating styling suggestions for filtered products" refers to a function for generating styling suggestions for selected products.

[0659] "Means for transmitting the final product list and styling suggestions to the user terminal" refers to a function for transmitting the generated product list and styling suggestions to the user terminal.

[0660] "Means for realizing styling suggestions using a generative AI model" refers to the function of realizing styling suggestions using an AI model and proposing specific outfits.

[0661] The "means for displaying the generated product list and styling suggestions to the user as prompt sentences" refers to a function for displaying the product list and coordination suggestions to the user in a prompt format.

[0662] The present invention is a system for searching for similar products to a product that a user has seen and providing styling suggestions for the product. A detailed description will be given of specific embodiments of the present invention.

[0663] System Program

[0664] This system consists of a server and user terminals (mainly smartphone applications). The main hardware and software used include:

[0665] Hardware: Servers (cloud servers, etc.), smartphones

[0666] Software: Python, Flask, JSON, Natural Language Processing Engine (NLP Engine)

[0667] Program processing

[0668] The server performs the following process.

[0669] A means of registering user personal information: Users enter personal information such as name, gender, age, size, preferences, and lifestyle, and the server stores this in a database.

[0670] A method for inputting memorized product features as text: The user inputs the features of products they have seen on the street or on TV in text format and sends them to the server.

[0671] Natural language processing means for analyzing text and extracting product features: The server analyzes the received text using a natural language processing engine and extracts product features.

[0672] A means for searching a product database and listing similar products based on the extracted features: The server searches a database based on the extracted features and lists similar products.

[0673] Means for filtering listed products based on user's personal information: The server filters listed products based on the user's personal information and selects suitable products.

[0674] Means for generating styling suggestions for filtered products: The server generates styling suggestions for selected products using a generative AI model.

[0675] Means for transmitting the final product list and styling suggestions to the user terminal: The server transmits the generated product list and styling suggestions to the user terminal.

[0676] Means of using generative AI models to concretize styling suggestions: Using AI models to concretize styling suggestions and propose specific outfits.

[0677] A means for displaying the generated product list and styling suggestions to the user as prompt sentences: The product list and coordination suggestions are displayed to the user in a prompt format.

[0678] Data processing / calculation

[0679] The server performs the following data processing and calculations:

[0680] Registering personal information: Convert the entered data into JSON format and save it in the database.

[0681] Product feature analysis: The provided text is analyzed using a natural language processing engine (NLP engine) to extract keywords.

[0682] Product search: Based on the extracted keywords, the product database is searched and the products that best match the registered information are listed.

[0683] Generate styling suggestions: Based on the filtered products, a generative AI model generates styling suggestions.

[0684] Sending the results: The generated product list and styling suggestions are sent back to the user's device in JSON format and displayed as a prompt.

[0685] Specific examples

[0686] Here are some examples of input prompts:

[0687] Input prompt (text format):

[0688] User Information:

[0689] Name: Sato

[0690] Gender: Male

[0691] Age: 28

[0692] Size: Medium

[0693] Preference: Casual

[0694] Lifestyle: Active

[0695] Product information:

[0696] black leather backpack

[0697] By inputting specific examples like this, users can easily search for similar products to the one they saw and receive appropriate styling suggestions for that product. Furthermore, based on the information presented as prompts, the generative AI model suggests outfits to support users' financial decisions.

[0698] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0699] Step 1:

[0700] A user creates an account on a dedicated application or website. When logging in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.). The entered information is converted into JSON format and sent from the device to the server. This information is saved as a new record in the user profile table.

[0701] Step 2:

[0702] The user enters the characteristics of a product they have seen on the street or on TV in a text input field. For example, they enter specific characteristics such as "black leather backpack." The entered information is received by the device and sent to the server.

[0703] Step 3:

[0704] The server passes the received text information to a natural language processing engine (NLP engine) to analyze the product's features. Specifically, it extracts the keywords "black," "leather," and "backpack." The extracted keywords are used in the next search process.

[0705] Step 4:

[0706] The server then searches the product database using the extracted keywords. This search generates a list of products that match the keywords. For example, a list of products that match the keyword "black leather backpack" is generated.

[0707] Step 5:

[0708] The server filters the listed products based on the user's personal information, for example, selecting only products that match the user's size and preferences, resulting in a list of products that best fit the user's profile information.

[0709] Step 6:

[0710] The server uses a generative AI model to generate styling suggestions based on the filtered products. Specifically, it suggests outfits such as "pairing a selected backpack with a casual shirt and jeans." These styling suggestions are generated taking into account past purchase history and preferences.

[0711] Step 7:

[0712] The server sends the final product list and styling suggestions in JSON format to the device, which then parses the information and displays the product information and styling suggestions to the user, including product images, product names, prices, vendor links, and styling suggestions.

[0713] Input and Output

[0714] Input: User's personal information, product features (text input)

[0715] Output: Filtered product list, styling suggestions

[0716] Data processing or data calculation

[0717] Converting to JSON format and saving to storage

[0718] Keyword extraction using natural language processing

[0719] Database Search and Filtering

[0720] Styling suggestion generation using generative AI models

[0721] Generate and display prompt statements

[0722] This allows users to easily search for similar products to the one they see and receive appropriate styling suggestions for that product.

[0723] 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.

[0724] The present invention is a system that allows a user to easily input the characteristics of a product that interests them, and searches for and suggests similar products. This system incorporates an emotion engine that recognizes the user's emotions and includes a function that makes suggestions based on the user's psychological state. Specific embodiments of the present invention are described below.

[0725] Registering user information

[0726] 1. User:

[0727] Users create an account on a dedicated application or website and enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0728] 2. Terminal:

[0729] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[0730] 3. Server:

[0731] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[0732] Product information input and emotion recognition

[0733] 1. User:

[0734] The user enters the characteristics of the product they see in a text input field, for example, entering a specific characteristic such as "red leather jacket."

[0735] 2. Terminal:

[0736] The device receives the input text and simultaneously collects emotional data (input speed, input style, emotional icons, etc.) of the user while inputting the text. This information is then sent to the server.

[0737] 3. Server:

[0738] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data and determines the user's current psychological state.

[0739] Search for similar products

[0740] 1. Server:

[0741] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords.

[0742] 2. Server:

[0743] The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data, and selects the product that best suits the user's current psychological state.

[0744] Styling suggestions

[0745] 1. Server:

[0746] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest optimal outfits.

[0747] 2. Server:

[0748] The server generates a final product list including styling suggestions and sends this information to the user's terminal.

[0749] User Visibility

[0750] 1. Device:

[0751] The device analyzes the received information and displays a list of product information and styling suggestions to the user, including product images, product names, prices, seller links, and styling suggestions.

[0752] 2. User:

[0753] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[0754] Specific examples

[0755] Example 1:

[0756] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and strong typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on this. Furthermore, taking into account the user's state of excitement, it creates a list of stylish and latest backpack designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[0757] Example 2:

[0758] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[0759] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[0760] The processing flow will be explained below.

[0761] Step 1:

[0762] User: The user accesses a dedicated application or website, creates an account, and enters personal information (name, gender, age, size, preferences, lifestyle, etc.).

[0763] Step 2:

[0764] Terminal: The terminal displays the information entered by the user in a dedicated form and collects the input. The collected information is converted into JSON format and sent to the server.

[0765] Step 3:

[0766] Server: The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[0767] Step 4:

[0768] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[0769] Step 5:

[0770] User: The user enters the characteristics of the product they see into a text input field, for example, "red leather jacket."

[0771] Step 6:

[0772] Terminal: The terminal receives the input text information and simultaneously collects the user's emotional data (input speed, input style, emotional icon, etc.) of the text input, and transmits the text information and emotional data to the server.

[0773] Step 7:

[0774] Server: The server passes the received text information to the NLP engine, which analyzes the product's features and extracts keywords. In parallel, the emotion engine analyzes the emotion data and determines the user's current psychological state.

[0775] Step 8:

[0776] Server: The server compares the keywords extracted by the NLP engine (e.g., "red," "leather," "jacket") with a product database and lists similar products.

[0777] Step 9:

[0778] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data. It selects the product that best suits the user's psychological state (e.g., "excited" or "calm").

[0779] Step 10:

[0780] Server: The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest the optimal outfit.

[0781] Step 11:

[0782] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[0783] Step 12:

[0784] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user. Product images, product names, prices, seller links, styling suggestions, etc. are displayed.

[0785] Step 13:

[0786] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a link opens the corresponding online store or seller's page in a new tab or window.

[0787] Step 14:

[0788] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[0789] Example 2

[0790] 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."

[0791] Current online shopping systems often suggest products without considering the user's psychological state or emotions. As a result, the suggested products often do not match the user's current needs or preferences. Furthermore, styling suggestions do not reflect the individual user's emotions or lifestyle, resulting in an inconsistent experience. This leads to unsatisfying shopping experiences for users.

[0792] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for registering personal data of a user, means for inputting stored product characteristics as text, natural language processing means for analyzing the text and extracting product characteristics, means for searching a database storing product information and listing similar products based on the extracted characteristics, means for filtering the listed products based on the user's personal data and emotional data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user device. This enables a more personalized shopping experience that takes into account the user's psychological state and emotions.

[0793] "User's personal data" refers to information about the user himself / herself, such as the user's name, gender, age, size, preferences, and lifestyle.

[0794] "Product characteristics" are specific characteristics and attributes of a product, such as its color, material, design, and use.

[0795] "Natural language processing means" refers to technology that analyzes input text and extracts specific keywords and information, and generally uses an NLP engine.

[0796] A "database storing product information" is a data storage that stores information about multiple products and allows searching and filtering.

[0797] "Similar products" refer to other products with similar characteristics that are selected based on the input product characteristics.

[0798] "Emotion data" is information about the user's psychological state obtained from the user's input speed, input style, emotion icons, and the like.

[0799] "Filtering means" refers to technology that selects appropriate products from the listed items based on extracted keywords and emotional data.

[0800] "Means for generating styling suggestions" refers to technology that uses AI and machine learning models to coordinate and suggest outfits based on the user's preferences, past purchase history, and current psychological state.

[0801] "User device" refers to an electronic device that can connect to the Internet and is used by a user, such as a smartphone, tablet, or PC.

[0802] This invention provides a system for providing a personalized shopping experience that takes into account a user's emotions. The system has a series of means for analyzing the user's input information and emotion data and generating optimal product and styling suggestions.

[0803] System Configuration

[0804] Registering user information

[0805] User:

[0806] Users access a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle using text boxes, drop-down menus, and check boxes.

[0807] Device:

[0808] The device collects the information entered by the user, converts it into JSON format, and sends it to the server. This transmission process is performed using JavaScript or HTML form submission functions.

[0809] server:

[0810] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, back-end languages ​​such as Python and Java are used, and the database is MySQL or PostgreSQL.

[0811] Product information input and emotion recognition

[0812] User:

[0813] The user enters the product characteristics they see into a text entry field, for example, entering a specific characteristic such as "red leather jacket."

[0814] Device:

[0815] Along with the text entered, the device simultaneously collects input speed, input style, and emotional data such as emoticons, which are then converted into JSON format and sent to the server.

[0816] server:

[0817] The server passes the received text information to a natural language processing (NLP) engine, which analyzes the product's features and extracts keywords. For example, NLP engines such as spaCy or NLTK are used. It also analyzes emotional data using emotion engines such as the Emotion API to determine the user's current psychological state.

[0818] Search and filter similar products

[0819] server:

[0820] The server uses the keywords extracted by the NLP engine to search the product database and list similar products, using SQL queries or full-text search engines like Elasticsearch.

[0821] server:

[0822] The products listed are filtered based on the user's personal data and analyzed emotional data, using an AI model to select the products that best fit the user's current emotional state.

[0823] Styling suggestions

[0824] server:

[0825] The filtered product list is then sent to the AI ​​Stylist engine, which generates styling suggestions using machine learning frameworks such as TensorFlow and PyTorch, taking into account the user's past purchase history, preferences, and psychological state.

[0826] server:

[0827] A final product list including styling suggestions is generated in JSON format and sent to the user's device.

[0828] User Visibility

[0829] Device:

[0830] The device analyzes the received information and displays the optimal product information and styling suggestions for the user. Specifically, it uses HTML and CSS to display product images, product names, prices, seller links, and styling suggestions. It also uses JavaScript to dynamically update the displayed content.

[0831] User:

[0832] The user checks the displayed product information and, if they find something they like, clicks on the purchase link, which opens the corresponding online shopping site or seller's page in a new tab or window.

[0833] Specific examples

[0834] Example 1:

[0835] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and forceful typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on these. Furthermore, taking into account the user's state of excitement, it lists stylish backpacks with the latest designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[0836] Example prompt:

[0837] "Black leather backpack"

[0838] Example 2:

[0839] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[0840] Example prompt:

[0841] "Silver earrings"

[0842] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[0843] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0844] Step 1:

[0845] The user enters personal information

[0846] Example of how it works:

[0847] Users access a registration form on a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle. For example, they enter their name in a TextBox, select their gender from a DropDownList, and select their preferences using a CheckBox.

[0848] input:

[0849] Personal information such as name, gender, age, size, preferences, and lifestyle.

[0850] output:

[0851] The entered personal information will be displayed on the terminal, ready for the next processing step.

[0852] Step 2:

[0853] The device collects information and sends it to the server

[0854] Example of how it works:

[0855] The device converts the information entered by the user into JSON format and sends it to the server. Specifically, it uses the AJAX function of JavaScript and uses an HTTP POST request.

[0856] input:

[0857] Personal information entered by the user.

[0858] output:

[0859] Sends JSON format data from the terminal to the server.

[0860] Step 3:

[0861] The server analyzes the data and stores it in a database

[0862] Example of how it works:

[0863] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, it uses Python frameworks such as Flask or Django to execute SQL queries and stores the data in MySQL or PostgreSQL.

[0864] input:

[0865] Personal information data in JSON format sent from the device.

[0866] output:

[0867] The user's personal information stored in the database and a message indicating completion of registration are generated and sent to the terminal.

[0868] Step 4:

[0869] The user inputs the product's features

[0870] Example of how it works:

[0871] The user enters the product characteristics they see into a text entry field, for example, "red leather jacket."

[0872] input:

[0873] User-supplied text about product features.

[0874] output:

[0875] The product characteristic text entered on the terminal is displayed and the terminal is ready to proceed to the next processing step.

[0876] Step 5:

[0877] The device collects feature data and emotion data and sends it to the server.

[0878] Example of how it works:

[0879] The device simultaneously collects input data such as typing speed, typing style, and emotional icons along with the input text. This information is converted into JSON format and sent to the server. One example is the implementation of JavaScript code to measure keystroke speed.

[0880] input:

[0881] Text data and sentiment data (typing speed, typing style, emoticons) of product characteristics by users.

[0882] output:

[0883] Sending feature data and emotion data in JSON format from the device to the server.

[0884] Step 6:

[0885] The server analyzes the text and sentiment data

[0886] Example of how it works:

[0887] The server passes the received text information to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. Examples of NLP engines used include spaCy and NLTK. The server also analyzes the emotional data using an emotion engine to determine the user's current psychological state.

[0888] input:

[0889] Text data and sentiment data of product characteristics sent from the device.

[0890] output:

[0891] Extracted product keywords and analyzed user emotional states.

[0892] Step 7:

[0893] The server searches the product database and lists similar products

[0894] Example of how it works:

[0895] The server uses the keywords extracted by the NLP engine to search the product database, using SQL queries and full-text search engines such as Elasticsearch.

[0896] input:

[0897] Extracted product keywords.

[0898] output:

[0899] A list of similar products that match your keywords.

[0900] Step 8:

[0901] The server performs filtering to select the best product

[0902] Example of how it works:

[0903] The server filters the listed products based on the user's personal and emotional data, using an AI model to select the products that best fit the user's current emotional state.

[0904] input:

[0905] A list of similar products, personal data of the user, and emotional data.

[0906] output:

[0907] A filtered list of the best products.

[0908] Step 9:

[0909] The server generates styling suggestions using the AI ​​stylist engine

[0910] Example of how it works:

[0911] The server sends the filtered product list to an AI stylist engine that generates styling suggestions, using machine learning frameworks such as TensorFlow and PyTorch.

[0912] input:

[0913] A filtered list of the best products.

[0914] output:

[0915] Generated styling suggestions.

[0916] Step 10:

[0917] The server generates the final product list and sends it to the terminal.

[0918] Example of how it works:

[0919] The server generates the final product list, including styling suggestions, in JSON format and sends it to the user's device. Data is sent using a RESTful API or GraphQL.

[0920] input:

[0921] Generated styling suggestions and a list of the best products.

[0922] output:

[0923] JSON data of the final product list and styling suggestions.

[0924] Step 11:

[0925] The device displays product information and styling suggestions to the user.

[0926] Example of how it works:

[0927] The device analyzes the received information and displays the product image, product name, price, seller link, and styling suggestions using HTML and CSS. The display content is dynamically updated using JavaScript.

[0928] input:

[0929] Final product list and styling suggestions sent from the server in JSON format.

[0930] output:

[0931] Product information and styling suggestions displayed to users.

[0932] Step 12:

[0933] User reviews the product and clicks the purchase link

[0934] Example of how it works:

[0935] The user checks the displayed product information, and if they find something they like, they click the purchase link. This action opens the online shopping site or seller's page in a new tab or window, and the purchase process proceeds.

[0936] input:

[0937] User expresses intent to purchase a product (clicks a button).

[0938] output:

[0939] The page of the corresponding online shopping site or seller will open and the purchase process will begin.

[0940] (Application example 2)

[0941] 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."

[0942] In today's online shopping environment, users face difficulties in finding the product that best suits their preferences and current state of mind from the wide variety of products available. Another problem is the lack of personalized recommendations that appropriately reflect different users' feelings and preferences regarding the same product. Therefore, there is a need for a system that can efficiently search for and recommend products that meet users' needs.

[0943] The identification processing 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 registering the user's personal information, means for inputting memorized product features as text, means for collecting emotion data at the time of input, natural language processing means for analyzing the text and extracting product features, means for searching the product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information and emotion data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user terminal. This enables personalized product suggestions and styling suggestions based on the user's personal information and emotion data.

[0944] "Means for registering user's personal information" includes a function for registering personal information such as the user's name, gender, age, size, preferences, and lifestyle in a database.

[0945] The "means for inputting memorized product characteristics as text" includes a function for providing an interface that allows the user to input the characteristics of products that the user has come across in text form.

[0946] The "means for collecting emotional data during input" includes a function for collecting the emotional state of the user when inputting product features, including input speed, input style, and voice tone.

[0947] "Natural language processing means for analyzing text and extracting product features" includes a natural language processing engine for analyzing and extracting product features from input text.

[0948] "Means for searching a product database and listing similar products based on extracted features" includes a function for searching and listing similar products in a database based on features extracted by natural language processing.

[0949] The "means for filtering listed products based on the user's personal information and emotional data" includes a function for re-sorting products that have already been listed based on the user's registered information and current emotional data.

[0950] The "means for generating styling suggestions for filtered products" includes an artificial intelligence stylist engine that suggests appropriate outfits for the re-sorted products.

[0951] The "means for transmitting the final product list and styling suggestions to the user terminal" includes a function for transmitting the final selected product list and styling suggestions to the user terminal for display.

[0952] The present invention provides a system that makes personalized product and styling suggestions based on a user's personal information and emotional data. Specific embodiments for carrying out the invention are described below.

[0953] System Configuration

[0954] User terminal

[0955] The user terminal is equipped with a means for the user to input personal information, a means for inputting the characteristics of the products they see by text or voice, and a means for collecting the user's emotional data (input speed, input style, voice tone).The user terminal can be a smartphone or smart glasses.

[0956] server

[0957] The server includes the following functions:

[0958] 1. Personal information registration:

[0959] The personal information received from the user is sent to the server in JSON format and saved in the database.

[0960] 2. Product feature and sentiment data analysis:

[0961] The input text is analyzed using a natural language processing engine (NLP engine) to extract keywords. The emotion engine also analyzes the emotional data to determine the user's emotional state. The NLP engine uses SpaCy and the Google NLP API, while the emotion engine uses the Google Cloud Speech-to-Text API.

[0962] 3. Search and filter similar products:

[0963] The product database is searched based on keywords extracted by the NLP engine, and similar products are listed. Furthermore, filtering is performed based on the user's personal information and emotional data.

[0964] 4. Styling suggestions:

[0965] For the filtered products, the AI ​​stylist engine generates optimal styling suggestions, taking into account the user's profile and emotional state.

[0966] 5. Sending and viewing results:

[0967] The final product list and styling suggestions are sent to the user's terminal and displayed to the user.

[0968] Specific examples

[0969] If a user inputs the characteristic "black leather backpack" and the emotional data indicates "excitement," the NLP engine extracts keywords such as "black," "leather," and "backpack." The emotional engine identifies the user's excitement level based on the emotional data. Based on this information, the server filters and lists stylish backpacks with the latest designs. The AI ​​stylist engine suggests how to coordinate them with a denim jacket. The final product list and styling suggestions are displayed on the user's device.

[0970] Prompt Sentence Examples

[0971] User comment: "Black leather backpack"

[0972] Emotion data: {"input speed": 1.5, "input style": "enthusiastic"}

[0973] Keywords: ["black", "leather", "backpack"]

[0974] Emotion analysis result: "Excited"

[0975] User profile: {"Name": "Taro Yamada", "Age": 30, "Gender": "Male", "Size": "L", "Preferences": ["Casual", "Outdoors"], "Lifestyle": ["Weekend camping"]}

[0976] Question: Please list the best products and styling suggestions.

[0977] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0978] Step 1:

[0979] The user terminal provides an interface for inputting the user's personal information (name, gender, age, size, preferences, lifestyle, etc.), and the user inputs this information. The input personal information is converted into JSON format through a dedicated form and sent to the server.

[0980] Step 2:

[0981] The server analyzes the received JSON format personal information, creates a new record in the user profile table, saves it in the database, and generates a registration completion message and sends it to the user's device.

[0982] Step 3:

[0983] The user inputs the characteristics of the product they see into the device in text or voice format. For example, they input a specific characteristic such as "red leather jacket." The device collects the input text, voice, and related emotional data (e.g., input speed, input style, and voice tone).

[0984] Step 4:

[0985] The device sends the collected text, voice, and emotion data to a server. The server passes the text data to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data to determine the user's current psychological state.

[0986] Step 5:

[0987] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords. For example, if the keywords "red," "leather," and "jacket" are extracted, the server searches the database for products that match these keywords.

[0988] Step 6:

[0989] The server filters the listed products based on the user's personal information (size, preferences, lifestyle, etc.) and emotional data (current mental state). For example, if the user's emotional state is "excited," it will prioritize products with stylish and latest designs.

[0990] Step 7:

[0991] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and emotional state to suggest optimal outfits.

[0992] Step 8:

[0993] The server generates a final product list and styling suggestions and sends this information to the user's terminal. The user terminal analyzes the received information and displays the listed product information and styling suggestions to the user, including product images, product names, prices, vendor links, styling suggestions, etc.

[0994] Step 9:

[0995] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] [Third embodiment]

[1000] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1001] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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."

[1012] The present invention provides a system that allows a user to easily search for similar products to an item that the user has seen on the street or on television and that also provides styling suggestions for the item. Specific embodiments of the system are described below.

[1013] Registering user information

[1014] 1. User:

[1015] Users create an account on a dedicated application or website, and when they log in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1016] 2. Terminal:

[1017] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[1018] 3. Server:

[1019] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[1020] Enter product information

[1021] 1. User:

[1022] The user enters the product characteristics they remember in the text input field, for example, entering a specific characteristic such as "red leather jacket."

[1023] 2. Terminal:

[1024] The terminal receives the entered text and sends it to the server when the send button is pressed.

[1025] 3. Server:

[1026] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords.

[1027] Search for similar products

[1028] 1. Server:

[1029] The server uses the keywords obtained from the analysis to search a product database, for example, to create a list of similar products based on keywords such as "red," "leather," and "jacket."

[1030] 2. Server:

[1031] The server filters the listed products based on the user's registration information, for example, selecting only products that match the user's size or style preferences.

[1032] Styling suggestions

[1033] 1. Server:

[1034] The server then sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account past purchase history and preferences to suggest optimal outfits.

[1035] 2. Server:

[1036] The server generates a final product list including styling suggestions and sends it to the user's terminal.

[1037] User Visibility

[1038] 1. Device:

[1039] The device parses the received JSON data and displays the listed product information and styling suggestions to the user in an easy-to-understand format, including product images, product names, prices, and links to sellers.

[1040] 2. User:

[1041] The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[1042] Specific examples

[1043] Example 1:

[1044] If a user enters the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that match the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good paired with a denim jacket," and displays the final product list to the user.

[1045] Example 2:

[1046] If a user enters the characteristic "silver earrings," the server analyzes the keywords "silver" and "earrings" and searches and filters for products that match. Along with the filtered list of products, the AI ​​stylist generates styling advice, such as "This would go well with a simple dress." This information is displayed on the user's device, allowing the user to review the products and suggested styling.

[1047] This system allows users to easily find products that meet their needs and also provides appropriate advice on styling those products, significantly improving the online shopping experience and increasing user satisfaction.

[1048] The processing flow will be explained below.

[1049] Step 1:

[1050] User: The user creates an account on a dedicated application or website and enters the necessary personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1051] Step 2:

[1052] Terminal: The terminal displays the information entered by the user in a dedicated form, collects the input, converts the input information into JSON format, and sends it to the server.

[1053] Step 3:

[1054] Server: The server parses the received JSON data, creates a new record in the user profile table, and saves the personal information to the database. The server generates a registration completion message and sends it to the device.

[1055] Step 4:

[1056] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[1057] Step 5:

[1058] User: The user enters the characteristics of a product they see on the street or on TV into a text input field in a dedicated application or website, for example, describing the product's characteristics as "a red leather jacket."

[1059] Step 6:

[1060] Terminal: The terminal receives the text information entered by the user and, when the send button is pressed, sends the text information to the server.

[1061] Step 7:

[1062] Server: The server passes the received text information to an NLP engine, which analyzes the product's characteristics, extracting keywords such as "red," "leather," and "jacket."

[1063] Step 8:

[1064] Server: The server searches the product database based on the extracted keywords and lists similar products that match the keywords.

[1065] Step 9:

[1066] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.). For example, it selects products that fit the user's size or preferred style.

[1067] Step 10:

[1068] Server: The server sends the filtered product list to the AI ​​stylist engine to generate styling suggestions. The AI ​​stylist takes into account the user's past purchase history and preferences to suggest the best outfits.

[1069] Step 11:

[1070] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[1071] Step 12:

[1072] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user, such as product images, product names, prices, seller links, styling suggestions, etc.

[1073] Step 13:

[1074] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a purchase link opens the corresponding online store or seller page in a new tab or window.

[1075] Step 14:

[1076] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[1077] Example 1

[1078] 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."

[1079] In today's online shopping environment, it is difficult for users to efficiently search for similar products to those they see on the street or on TV. Furthermore, there is a lack of systems that can not only find products but also provide users with optimal styling suggestions. There is a need for a system that can find products that match a user's interests and even suggest how to coordinate those products.

[1080] 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.

[1081] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for displaying the generated product information and styling suggestions on the user terminal, and means for providing a purchase link for the user. This enables a user to efficiently find products similar to a product and receive optimal styling suggestions for that product.

[1082] "User" refers to a person who uses this system, registers personal information, searches for products, and receives styling suggestions.

[1083] "User information registration means" refers to a mechanism that allows users to input and save personal information on a dedicated application or website.

[1084] The "text input means" refers to an interface for inputting memorized product features by the user in text format.

[1085] "Natural language processing means" refers to a processing engine for extracting product features from text. Specifically, it can analyze keywords.

[1086] The "product database search means" refers to a process for searching a stored product database based on the extracted keywords and listing similar products.

[1087] "Filtering means" refers to a process for narrowing down the listed products based on the user's personal information (size, preferences, lifestyle, etc.).

[1088] The term "styling suggestion generating means" refers to a process for suggesting optimal styling for filtered products, taking into consideration the user's past purchase history and preferences.

[1089] "User terminal transmission means" refers to the process for transmitting the final product list and styling suggestions to the user's terminal.

[1090] "Display means" refers to an interface for displaying the generated product information and styling suggestions on the user's terminal screen.

[1091] The "purchase link providing means" refers to a process of providing a link for a user to purchase a product that the user likes.

[1092] The present invention provides a system that allows a user to easily search for similar products to an item that catches their eye and that they have seen on the street or on television, and also provides styling suggestions for the item. Specific embodiments of the system are described in detail below.

[1093] First, a user creates an account using a dedicated application or website. When the user logs in for the first time, they enter personal information such as name, gender, age, size, preferences, and lifestyle. This information is converted from the device into JSON format and sent to the server. The server analyzes the received information, creates a new record in the user profile table, and saves it in the database.

[1094] Next, the user enters the product characteristics they remember into the text input field. For example, they enter specific characteristics such as "red leather jacket." The device receives this input information and when they press the send button, the text data is sent to the server. The server passes the received text information to an NLP engine, which analyzes the product characteristics and extracts keywords.

[1095] The server searches a product database using the extracted keywords (e.g., "red," "leather," "jacket") to list related products, and then filters the results based on the user's profile information (size, preferred style, etc.) to select the most suitable product.

[1096] Once filtering is complete, the server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account the user's past purchase history and preferences to suggest optimal outfits.

[1097] Finally, the server sends the generated product information and styling suggestions to the user's device. The device parses the received JSON data and displays the product information and styling suggestions in an easy-to-understand manner. This includes product images, product names, prices, and seller links. The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[1098] For example, if a user inputs the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that fit the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good with a denim jacket," and displays the final product list to the user. This system allows users to easily find products that meet their needs and receive appropriate advice on styling those products.

[1099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1100] Step 1:

[1101] User account creation

[1102] The user opens a dedicated application or website and accesses the account creation screen, where they enter personal information such as name, gender, age, size, preferences, lifestyle, etc. This information is collected as input.

[1103] Step 2:

[1104] Information collection and transmission by devices

[1105] The terminal displays the information entered by the user on a dedicated form, and after all information is entered correctly by the user, it converts the information into JSON format, which is generated as output and sent to the server.

[1106] Step 3:

[1107] Data analysis and storage by server

[1108] The server receives the JSON data from the device as input, parses it, creates a new record in the user profile table, and saves the parsed personal information to the database. An output message indicating that registration is complete is generated and sent to the device.

[1109] Step 4:

[1110] User input of product features

[1111] The user enters the product characteristics they remember into a text input field, for example, a specific characteristic such as "red leather jacket," and this is collected as input data.

[1112] Step 5:

[1113] Sending text via device

[1114] The terminal receives the text information entered by the user, and when the send button is pressed, the text data is sent to the server as input data.

[1115] Step 6:

[1116] Server-based text analysis

[1117] The server passes the text information received from the device to the NLP engine, which takes the text as input. It analyzes the product's features and extracts keywords. For example, keywords such as "red," "leather," and "jacket" are generated as output.

[1118] Step 7:

[1119] Searching the product database by the server

[1120] The server receives the keywords extracted through the analysis (e.g., "red," "leather," "jacket") as input and searches the product database. A list of related products is generated as output.

[1121] Step 8:

[1122] Server-based filtering

[1123] The server receives the list of products as input and filters them based on the user's registered information (size, preferred style, etc.) to narrow down the products that best suit the user and generate a list of those products as output.

[1124] Step 9:

[1125] Server-generated styling suggestions

[1126] The server passes the filtered product list as input to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist engine considers the user's past purchase history and preferences to suggest optimal outfits. Specific styling suggestions are generated as output.

[1127] Step 10:

[1128] Server generates final list

[1129] The server receives as input the final product list including styling suggestions and generates data for transmission to the user terminal, which is generated as output and transmitted to the terminal.

[1130] Step 11:

[1131] Displaying results on a terminal

[1132] The device receives JSON data from the server, parses it, and displays product information and styling suggestions in an easy-to-understand format to the user. Specifically, the device displays product images, product names, prices, and links to sellers.

[1133] Step 12:

[1134] User review and purchase

[1135] The user checks the information displayed on the device as input, and if they find a product they like, they click the purchase link to complete the purchase. This is the user's purchasing behavior as output.

[1136] (Application example 1)

[1137] 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."

[1138] In current online shopping, it is difficult for users to easily search for similar products to those they see on the street or on television, and to receive styling suggestions for those products. As a result, users spend a lot of time and effort finding products that suit their preferences and needs. Furthermore, the lack of styling suggestions leaves them unsure about how to coordinate their outfits after purchase. A system that can solve these issues is needed.

[1139] 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.

[1140] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for specifying the styling suggestions using a generative AI model, and means for displaying the generated product list and styling suggestions to the user as prompt sentences. This allows a user to easily search for products similar to a product they have seen and also receive styling suggestions for the product.

[1141] "Means for registering user personal information" refers to the function of entering personal information such as the user's name, gender, age, size, preferences, and lifestyle, and storing it in a database.

[1142] "Means for inputting memorized product features as text" refers to a function that allows a user to input the features of a product that they have seen on the street or on television in text form.

[1143] "Natural language processing means for analyzing text and extracting product features" refers to a function for analyzing input text and automatically extracting product features.

[1144] "Means for searching a product database and listing similar products based on extracted features" refers to a function for searching a database based on extracted product features and listing similar products.

[1145] "Means for filtering listed products based on the user's personal information" refers to a function for selecting the product that best matches the user's personal information from among the similar products listed.

[1146] The "means for generating styling suggestions for filtered products" refers to a function for generating styling suggestions for selected products.

[1147] "Means for transmitting the final product list and styling suggestions to the user terminal" refers to a function for transmitting the generated product list and styling suggestions to the user terminal.

[1148] "Means for realizing styling suggestions using a generative AI model" refers to the function of realizing styling suggestions using an AI model and proposing specific outfits.

[1149] The "means for displaying the generated product list and styling suggestions to the user as prompt sentences" refers to a function for displaying the product list and coordination suggestions to the user in a prompt format.

[1150] The present invention is a system for searching for similar products to a product that a user has seen and providing styling suggestions for the product. A detailed description will be given of specific embodiments of the present invention.

[1151] System Program

[1152] This system consists of a server and user terminals (mainly smartphone applications). The main hardware and software used include:

[1153] Hardware: Servers (cloud servers, etc.), smartphones

[1154] Software: Python, Flask, JSON, Natural Language Processing Engine (NLP Engine)

[1155] Program processing

[1156] The server performs the following process.

[1157] A means of registering user personal information: Users enter personal information such as name, gender, age, size, preferences, and lifestyle, and the server stores this in a database.

[1158] A method for inputting memorized product features as text: The user inputs the features of products they have seen on the street or on TV in text format and sends them to the server.

[1159] Natural language processing means for analyzing text and extracting product features: The server analyzes the received text using a natural language processing engine and extracts product features.

[1160] A means for searching a product database and listing similar products based on the extracted features: The server searches a database based on the extracted features and lists similar products.

[1161] Means for filtering listed products based on user's personal information: The server filters listed products based on the user's personal information and selects suitable products.

[1162] Means for generating styling suggestions for filtered products: The server generates styling suggestions for selected products using a generative AI model.

[1163] Means for transmitting the final product list and styling suggestions to the user terminal: The server transmits the generated product list and styling suggestions to the user terminal.

[1164] Means of using generative AI models to concretize styling suggestions: Using AI models to concretize styling suggestions and propose specific outfits.

[1165] A means for displaying the generated product list and styling suggestions to the user as prompt sentences: The product list and coordination suggestions are displayed to the user in a prompt format.

[1166] Data processing / calculation

[1167] The server performs the following data processing and calculations:

[1168] Registering personal information: Convert the entered data into JSON format and save it in the database.

[1169] Product feature analysis: The provided text is analyzed using a natural language processing engine (NLP engine) to extract keywords.

[1170] Product search: Based on the extracted keywords, the product database is searched and the products that best match the registered information are listed.

[1171] Generate styling suggestions: Based on the filtered products, a generative AI model generates styling suggestions.

[1172] Sending the results: The generated product list and styling suggestions are sent back to the user's device in JSON format and displayed as a prompt.

[1173] Specific examples

[1174] Here are some examples of input prompts:

[1175] Input prompt (text format):

[1176] User Information:

[1177] Name: Sato

[1178] Gender: Male

[1179] Age: 28

[1180] Size: Medium

[1181] Preference: Casual

[1182] Lifestyle: Active

[1183] Product information:

[1184] black leather backpack

[1185] By inputting specific examples like this, users can easily search for similar products to the one they saw and receive appropriate styling suggestions for that product. Furthermore, based on the information presented as prompts, the generative AI model suggests outfits to support users' financial decisions.

[1186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1187] Step 1:

[1188] A user creates an account on a dedicated application or website. When logging in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.). The entered information is converted into JSON format and sent from the device to the server. This information is saved as a new record in the user profile table.

[1189] Step 2:

[1190] The user enters the characteristics of a product they have seen on the street or on TV in a text input field. For example, they enter specific characteristics such as "black leather backpack." The entered information is received by the device and sent to the server.

[1191] Step 3:

[1192] The server passes the received text information to a natural language processing engine (NLP engine) to analyze the product's features. Specifically, it extracts the keywords "black," "leather," and "backpack." The extracted keywords are used in the next search process.

[1193] Step 4:

[1194] The server then searches the product database using the extracted keywords. This search generates a list of products that match the keywords. For example, a list of products that match the keyword "black leather backpack" is generated.

[1195] Step 5:

[1196] The server filters the listed products based on the user's personal information, for example, selecting only products that match the user's size and preferences, resulting in a list of products that best fit the user's profile information.

[1197] Step 6:

[1198] The server uses a generative AI model to generate styling suggestions based on the filtered products. Specifically, it suggests outfits such as "pairing a selected backpack with a casual shirt and jeans." These styling suggestions are generated taking into account past purchase history and preferences.

[1199] Step 7:

[1200] The server sends the final product list and styling suggestions in JSON format to the device, which then parses the information and displays the product information and styling suggestions to the user, including product images, product names, prices, vendor links, and styling suggestions.

[1201] Input and Output

[1202] Input: User's personal information, product features (text input)

[1203] Output: Filtered product list, styling suggestions

[1204] Data processing or data calculation

[1205] Converting to JSON format and saving to storage

[1206] Keyword extraction using natural language processing

[1207] Database Search and Filtering

[1208] Styling suggestion generation using generative AI models

[1209] Generate and display prompt statements

[1210] This allows users to easily search for similar products to the one they see and receive appropriate styling suggestions for that product.

[1211] 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.

[1212] The present invention is a system that allows a user to easily input the characteristics of a product that interests them, and searches for and suggests similar products. This system incorporates an emotion engine that recognizes the user's emotions and includes a function that makes suggestions based on the user's psychological state. Specific embodiments of the present invention are described below.

[1213] Registering user information

[1214] 1. User:

[1215] Users create an account on a dedicated application or website and enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1216] 2. Terminal:

[1217] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[1218] 3. Server:

[1219] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[1220] Product information input and emotion recognition

[1221] 1. User:

[1222] The user enters the characteristics of the product they see in a text input field, for example, entering a specific characteristic such as "red leather jacket."

[1223] 2. Terminal:

[1224] The device receives the input text and simultaneously collects emotional data (input speed, input style, emotional icons, etc.) of the user while inputting the text. This information is then sent to the server.

[1225] 3. Server:

[1226] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data and determines the user's current psychological state.

[1227] Search for similar products

[1228] 1. Server:

[1229] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords.

[1230] 2. Server:

[1231] The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data, and selects the product that best suits the user's current psychological state.

[1232] Styling suggestions

[1233] 1. Server:

[1234] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest optimal outfits.

[1235] 2. Server:

[1236] The server generates a final product list including styling suggestions and sends this information to the user's terminal.

[1237] User Visibility

[1238] 1. Device:

[1239] The device analyzes the received information and displays a list of product information and styling suggestions to the user, including product images, product names, prices, seller links, and styling suggestions.

[1240] 2. User:

[1241] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[1242] Specific examples

[1243] Example 1:

[1244] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and strong typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on this. Furthermore, taking into account the user's state of excitement, it creates a list of stylish and latest backpack designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[1245] Example 2:

[1246] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[1247] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[1248] The processing flow will be explained below.

[1249] Step 1:

[1250] User: The user accesses a dedicated application or website, creates an account, and enters personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1251] Step 2:

[1252] Terminal: The terminal displays the information entered by the user in a dedicated form and collects the input. The collected information is converted into JSON format and sent to the server.

[1253] Step 3:

[1254] Server: The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[1255] Step 4:

[1256] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[1257] Step 5:

[1258] User: The user enters the characteristics of the product they see into a text input field, for example, "red leather jacket."

[1259] Step 6:

[1260] Terminal: The terminal receives the input text information and simultaneously collects the user's emotional data (input speed, input style, emotional icon, etc.) of the text input, and transmits the text information and emotional data to the server.

[1261] Step 7:

[1262] Server: The server passes the received text information to the NLP engine, which analyzes the product's features and extracts keywords. In parallel, the emotion engine analyzes the emotion data and determines the user's current psychological state.

[1263] Step 8:

[1264] Server: The server compares the keywords extracted by the NLP engine (e.g., "red," "leather," "jacket") with a product database and lists similar products.

[1265] Step 9:

[1266] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data. It selects the product that best suits the user's psychological state (e.g., "excited" or "calm").

[1267] Step 10:

[1268] Server: The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest the optimal outfit.

[1269] Step 11:

[1270] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[1271] Step 12:

[1272] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user. Product images, product names, prices, seller links, styling suggestions, etc. are displayed.

[1273] Step 13:

[1274] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a link opens the corresponding online store or seller's page in a new tab or window.

[1275] Step 14:

[1276] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[1277] Example 2

[1278] 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."

[1279] Current online shopping systems often suggest products without considering the user's psychological state or emotions. As a result, the suggested products often do not match the user's current needs or preferences. Furthermore, styling suggestions do not reflect the individual user's emotions or lifestyle, resulting in an inconsistent experience. This leads to unsatisfying shopping experiences for users.

[1280] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for registering personal data of a user, means for inputting stored product characteristics as text, natural language processing means for analyzing the text and extracting product characteristics, means for searching a database storing product information and listing similar products based on the extracted characteristics, means for filtering the listed products based on the user's personal data and emotional data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user device. This enables a more personalized shopping experience that takes into account the user's psychological state and emotions.

[1281] "User's personal data" refers to information about the user himself / herself, such as the user's name, gender, age, size, preferences, and lifestyle.

[1282] "Product characteristics" are specific characteristics and attributes of a product, such as its color, material, design, and use.

[1283] "Natural language processing means" refers to technology that analyzes input text and extracts specific keywords and information, and generally uses an NLP engine.

[1284] A "database storing product information" is a data storage that stores information about multiple products and allows searching and filtering.

[1285] "Similar products" refer to other products with similar characteristics that are selected based on the input product characteristics.

[1286] "Emotion data" is information about the user's psychological state obtained from the user's input speed, input style, emotion icons, and the like.

[1287] "Filtering means" refers to technology that selects appropriate products from the listed items based on extracted keywords and emotional data.

[1288] "Means for generating styling suggestions" refers to technology that uses AI and machine learning models to coordinate and suggest outfits based on the user's preferences, past purchase history, and current psychological state.

[1289] "User device" refers to an electronic device that can connect to the Internet and is used by a user, such as a smartphone, tablet, or PC.

[1290] This invention provides a system for providing a personalized shopping experience that takes into account a user's emotions. The system has a series of means for analyzing the user's input information and emotion data and generating optimal product and styling suggestions.

[1291] System Configuration

[1292] Registering user information

[1293] User:

[1294] Users access a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle using text boxes, drop-down menus, and check boxes.

[1295] Device:

[1296] The device collects the information entered by the user, converts it into JSON format, and sends it to the server. This transmission process is performed using JavaScript or HTML form submission functions.

[1297] server:

[1298] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, back-end languages ​​such as Python and Java are used, and the database is MySQL or PostgreSQL.

[1299] Product information input and emotion recognition

[1300] User:

[1301] The user enters the product characteristics they see into a text entry field, for example, entering a specific characteristic such as "red leather jacket."

[1302] Device:

[1303] Along with the text entered, the device simultaneously collects input speed, input style, and emotional data such as emoticons, which are then converted into JSON format and sent to the server.

[1304] server:

[1305] The server passes the received text information to a natural language processing (NLP) engine, which analyzes the product's features and extracts keywords. For example, NLP engines such as spaCy or NLTK are used. It also analyzes emotional data using emotion engines such as the Emotion API to determine the user's current psychological state.

[1306] Search and filter similar products

[1307] server:

[1308] The server uses the keywords extracted by the NLP engine to search the product database and list similar products, using SQL queries or full-text search engines like Elasticsearch.

[1309] server:

[1310] The products listed are filtered based on the user's personal data and analyzed emotional data, using an AI model to select the products that best fit the user's current emotional state.

[1311] Styling suggestions

[1312] server:

[1313] The filtered product list is then sent to the AI ​​Stylist engine, which generates styling suggestions using machine learning frameworks such as TensorFlow and PyTorch, taking into account the user's past purchase history, preferences, and psychological state.

[1314] server:

[1315] A final product list including styling suggestions is generated in JSON format and sent to the user's device.

[1316] User Visibility

[1317] Device:

[1318] The device analyzes the received information and displays the optimal product information and styling suggestions for the user. Specifically, it uses HTML and CSS to display product images, product names, prices, seller links, and styling suggestions. It also uses JavaScript to dynamically update the displayed content.

[1319] User:

[1320] The user checks the displayed product information and, if they find something they like, clicks on the purchase link, which opens the corresponding online shopping site or seller's page in a new tab or window.

[1321] Specific examples

[1322] Example 1:

[1323] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and forceful typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on these. Furthermore, taking into account the user's state of excitement, it lists stylish backpacks with the latest designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[1324] Example prompt:

[1325] "Black leather backpack"

[1326] Example 2:

[1327] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[1328] Example prompt:

[1329] "Silver earrings"

[1330] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[1331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1332] Step 1:

[1333] The user enters personal information

[1334] Example of how it works:

[1335] Users access a registration form on a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle. For example, they enter their name in a TextBox, select their gender from a DropDownList, and select their preferences using a CheckBox.

[1336] input:

[1337] Personal information such as name, gender, age, size, preferences, and lifestyle.

[1338] output:

[1339] The entered personal information will be displayed on the terminal, ready for the next processing step.

[1340] Step 2:

[1341] The device collects information and sends it to the server

[1342] Example of how it works:

[1343] The device converts the information entered by the user into JSON format and sends it to the server. Specifically, it uses the AJAX function of JavaScript and uses an HTTP POST request.

[1344] input:

[1345] Personal information entered by the user.

[1346] output:

[1347] Sends JSON format data from the terminal to the server.

[1348] Step 3:

[1349] The server analyzes the data and stores it in a database

[1350] Example of how it works:

[1351] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, it uses Python frameworks such as Flask or Django to execute SQL queries and stores the data in MySQL or PostgreSQL.

[1352] input:

[1353] Personal information data in JSON format sent from the device.

[1354] output:

[1355] The user's personal information stored in the database and a message indicating completion of registration are generated and sent to the terminal.

[1356] Step 4:

[1357] The user inputs the product's features

[1358] Example of how it works:

[1359] The user enters the product characteristics they see into a text entry field, for example, "red leather jacket."

[1360] input:

[1361] User-supplied text about product features.

[1362] output:

[1363] The product characteristic text entered on the terminal is displayed and the terminal is ready to proceed to the next processing step.

[1364] Step 5:

[1365] The device collects feature data and emotion data and sends it to the server.

[1366] Example of how it works:

[1367] The device simultaneously collects input data such as typing speed, typing style, and emotional icons along with the input text. This information is converted into JSON format and sent to the server. One example is the implementation of JavaScript code to measure keystroke speed.

[1368] input:

[1369] Text data and sentiment data (typing speed, typing style, emoticons) of product characteristics by users.

[1370] output:

[1371] Sending feature data and emotion data in JSON format from the device to the server.

[1372] Step 6:

[1373] The server analyzes the text and sentiment data

[1374] Example of how it works:

[1375] The server passes the received text information to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. Examples of NLP engines used include spaCy and NLTK. The server also analyzes the emotional data using an emotion engine to determine the user's current psychological state.

[1376] input:

[1377] Text data and sentiment data of product characteristics sent from the device.

[1378] output:

[1379] Extracted product keywords and analyzed user emotional states.

[1380] Step 7:

[1381] The server searches the product database and lists similar products

[1382] Example of how it works:

[1383] The server uses the keywords extracted by the NLP engine to search the product database, using SQL queries and full-text search engines such as Elasticsearch.

[1384] input:

[1385] Extracted product keywords.

[1386] output:

[1387] A list of similar products that match your keywords.

[1388] Step 8:

[1389] The server performs filtering to select the best product

[1390] Example of how it works:

[1391] The server filters the listed products based on the user's personal and emotional data, using an AI model to select the products that best fit the user's current emotional state.

[1392] input:

[1393] A list of similar products, personal data of the user, and emotional data.

[1394] output:

[1395] A filtered list of the best products.

[1396] Step 9:

[1397] The server generates styling suggestions using the AI ​​stylist engine

[1398] Example of how it works:

[1399] The server sends the filtered product list to an AI stylist engine that generates styling suggestions, using machine learning frameworks such as TensorFlow and PyTorch.

[1400] input:

[1401] A filtered list of the best products.

[1402] output:

[1403] Generated styling suggestions.

[1404] Step 10:

[1405] The server generates the final product list and sends it to the terminal.

[1406] Example of how it works:

[1407] The server generates the final product list, including styling suggestions, in JSON format and sends it to the user's device. Data is sent using a RESTful API or GraphQL.

[1408] input:

[1409] Generated styling suggestions and a list of the best products.

[1410] output:

[1411] JSON data of the final product list and styling suggestions.

[1412] Step 11:

[1413] The device displays product information and styling suggestions to the user.

[1414] Example of how it works:

[1415] The device analyzes the received information and displays the product image, product name, price, seller link, and styling suggestions using HTML and CSS. The display content is dynamically updated using JavaScript.

[1416] input:

[1417] Final product list and styling suggestions sent from the server in JSON format.

[1418] output:

[1419] Product information and styling suggestions displayed to users.

[1420] Step 12:

[1421] User reviews the product and clicks the purchase link

[1422] Example of how it works:

[1423] The user checks the displayed product information, and if they find something they like, they click the purchase link. This action opens the online shopping site or seller's page in a new tab or window, and the purchase process proceeds.

[1424] input:

[1425] User expresses intent to purchase a product (clicks a button).

[1426] output:

[1427] The page of the corresponding online shopping site or seller will open and the purchase process will begin.

[1428] (Application example 2)

[1429] 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."

[1430] In today's online shopping environment, users face difficulties in finding the product that best suits their preferences and current state of mind from the wide variety of products available. Another problem is the lack of personalized recommendations that appropriately reflect different users' feelings and preferences regarding the same product. Therefore, there is a need for a system that can efficiently search for and recommend products that meet users' needs.

[1431] The identification processing 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 registering the user's personal information, means for inputting memorized product features as text, means for collecting emotion data at the time of input, natural language processing means for analyzing the text and extracting product features, means for searching the product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information and emotion data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user terminal. This enables personalized product suggestions and styling suggestions based on the user's personal information and emotion data.

[1432] "Means for registering user's personal information" includes a function for registering personal information such as the user's name, gender, age, size, preferences, and lifestyle in a database.

[1433] The "means for inputting memorized product characteristics as text" includes a function for providing an interface that allows the user to input the characteristics of products that the user has come across in text form.

[1434] The "means for collecting emotional data during input" includes a function for collecting the emotional state of the user when inputting product features, including input speed, input style, and voice tone.

[1435] "Natural language processing means for analyzing text and extracting product features" includes a natural language processing engine for analyzing and extracting product features from input text.

[1436] "Means for searching a product database and listing similar products based on extracted features" includes a function for searching and listing similar products in a database based on features extracted by natural language processing.

[1437] The "means for filtering listed products based on the user's personal information and emotional data" includes a function for re-sorting products that have already been listed based on the user's registered information and current emotional data.

[1438] The "means for generating styling suggestions for filtered products" includes an artificial intelligence stylist engine that suggests appropriate outfits for the re-sorted products.

[1439] The "means for transmitting the final product list and styling suggestions to the user terminal" includes a function for transmitting the final selected product list and styling suggestions to the user terminal for display.

[1440] The present invention provides a system that makes personalized product and styling suggestions based on a user's personal information and emotional data. Specific embodiments for carrying out the invention are described below.

[1441] System Configuration

[1442] User terminal

[1443] The user terminal is equipped with a means for the user to input personal information, a means for inputting the characteristics of the products they see by text or voice, and a means for collecting the user's emotional data (input speed, input style, voice tone).The user terminal can be a smartphone or smart glasses.

[1444] server

[1445] The server includes the following functions:

[1446] 1. Personal information registration:

[1447] The personal information received from the user is sent to the server in JSON format and saved in the database.

[1448] 2. Product feature and sentiment data analysis:

[1449] The input text is analyzed using a natural language processing engine (NLP engine) to extract keywords. The emotion engine also analyzes the emotional data to determine the user's emotional state. The NLP engine uses SpaCy and the Google NLP API, while the emotion engine uses the Google Cloud Speech-to-Text API.

[1450] 3. Search and filter similar products:

[1451] The product database is searched based on keywords extracted by the NLP engine, and similar products are listed. Furthermore, filtering is performed based on the user's personal information and emotional data.

[1452] 4. Styling suggestions:

[1453] For the filtered products, the AI ​​stylist engine generates optimal styling suggestions, taking into account the user's profile and emotional state.

[1454] 5. Sending and viewing results:

[1455] The final product list and styling suggestions are sent to the user's terminal and displayed to the user.

[1456] Specific examples

[1457] If a user inputs the characteristic "black leather backpack" and the emotional data indicates "excitement," the NLP engine extracts keywords such as "black," "leather," and "backpack." The emotional engine identifies the user's excitement level based on the emotional data. Based on this information, the server filters and lists stylish backpacks with the latest designs. The AI ​​stylist engine suggests how to coordinate them with a denim jacket. The final product list and styling suggestions are displayed on the user's device.

[1458] Prompt Sentence Examples

[1459] User comment: "Black leather backpack"

[1460] Emotion data: {"input speed": 1.5, "input style": "enthusiastic"}

[1461] Keywords: ["black", "leather", "backpack"]

[1462] Emotion analysis result: "Excited"

[1463] User profile: {"Name": "Taro Yamada", "Age": 30, "Gender": "Male", "Size": "L", "Preferences": ["Casual", "Outdoors"], "Lifestyle": ["Weekend camping"]}

[1464] Question: Please list the best products and styling suggestions.

[1465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1466] Step 1:

[1467] The user terminal provides an interface for inputting the user's personal information (name, gender, age, size, preferences, lifestyle, etc.), and the user inputs this information. The input personal information is converted into JSON format through a dedicated form and sent to the server.

[1468] Step 2:

[1469] The server analyzes the received JSON format personal information, creates a new record in the user profile table, saves it in the database, and generates a registration completion message and sends it to the user's device.

[1470] Step 3:

[1471] The user inputs the characteristics of the product they see into the device in text or voice format. For example, they input a specific characteristic such as "red leather jacket." The device collects the input text, voice, and related emotional data (e.g., input speed, input style, and voice tone).

[1472] Step 4:

[1473] The device sends the collected text, voice, and emotion data to a server. The server passes the text data to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data to determine the user's current psychological state.

[1474] Step 5:

[1475] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords. For example, if the keywords "red," "leather," and "jacket" are extracted, the server searches the database for products that match these keywords.

[1476] Step 6:

[1477] The server filters the listed products based on the user's personal information (size, preferences, lifestyle, etc.) and emotional data (current mental state). For example, if the user's emotional state is "excited," it will prioritize products with stylish and latest designs.

[1478] Step 7:

[1479] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and emotional state to suggest optimal outfits.

[1480] Step 8:

[1481] The server generates a final product list and styling suggestions and sends this information to the user's terminal. The user terminal analyzes the received information and displays the listed product information and styling suggestions to the user, including product images, product names, prices, vendor links, styling suggestions, etc.

[1482] Step 9:

[1483] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] [Fourth embodiment]

[1488] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1489] 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.

[1490] 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).

[1491] 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.

[1492] 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.

[1493] 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).

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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.

[1500] 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."

[1501] The present invention provides a system that allows a user to easily search for similar products to an item that the user has seen on the street or on television and that also provides styling suggestions for the item. Specific embodiments of the system are described below.

[1502] Registering user information

[1503] 1. User:

[1504] Users create an account on a dedicated application or website, and when they log in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1505] 2. Terminal:

[1506] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[1507] 3. Server:

[1508] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[1509] Enter product information

[1510] 1. User:

[1511] The user enters the product characteristics they remember in the text input field, for example, entering a specific characteristic such as "red leather jacket."

[1512] 2. Terminal:

[1513] The terminal receives the entered text and sends it to the server when the send button is pressed.

[1514] 3. Server:

[1515] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords.

[1516] Search for similar products

[1517] 1. Server:

[1518] The server uses the keywords obtained from the analysis to search a product database, for example, to create a list of similar products based on keywords such as "red," "leather," and "jacket."

[1519] 2. Server:

[1520] The server filters the listed products based on the user's registration information, for example, selecting only products that match the user's size or style preferences.

[1521] Styling suggestions

[1522] 1. Server:

[1523] The server then sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account past purchase history and preferences to suggest optimal outfits.

[1524] 2. Server:

[1525] The server generates a final product list including styling suggestions and sends it to the user's terminal.

[1526] User Visibility

[1527] 1. Device:

[1528] The device parses the received JSON data and displays the listed product information and styling suggestions to the user in an easy-to-understand format, including product images, product names, prices, and links to sellers.

[1529] 2. User:

[1530] The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[1531] Specific examples

[1532] Example 1:

[1533] If a user enters the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that match the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good paired with a denim jacket," and displays the final product list to the user.

[1534] Example 2:

[1535] If a user enters the characteristic "silver earrings," the server analyzes the keywords "silver" and "earrings" and searches and filters for products that match. Along with the filtered list of products, the AI ​​stylist generates styling advice, such as "This would go well with a simple dress." This information is displayed on the user's device, allowing the user to review the products and suggested styling.

[1536] This system allows users to easily find products that meet their needs and also provides appropriate advice on styling those products, significantly improving the online shopping experience and increasing user satisfaction.

[1537] The processing flow will be explained below.

[1538] Step 1:

[1539] User: The user creates an account on a dedicated application or website and enters the necessary personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1540] Step 2:

[1541] Terminal: The terminal displays the information entered by the user in a dedicated form, collects the input, converts the input information into JSON format, and sends it to the server.

[1542] Step 3:

[1543] Server: The server parses the received JSON data, creates a new record in the user profile table, and saves the personal information to the database. The server generates a registration completion message and sends it to the device.

[1544] Step 4:

[1545] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[1546] Step 5:

[1547] User: The user enters the characteristics of a product they see on the street or on TV into a text input field in a dedicated application or website, for example, describing the product's characteristics as "a red leather jacket."

[1548] Step 6:

[1549] Terminal: The terminal receives the text information entered by the user and, when the send button is pressed, sends the text information to the server.

[1550] Step 7:

[1551] Server: The server passes the received text information to an NLP engine, which analyzes the product's characteristics, extracting keywords such as "red," "leather," and "jacket."

[1552] Step 8:

[1553] Server: The server searches the product database based on the extracted keywords and lists similar products that match the keywords.

[1554] Step 9:

[1555] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.). For example, it selects products that fit the user's size or preferred style.

[1556] Step 10:

[1557] Server: The server sends the filtered product list to the AI ​​stylist engine to generate styling suggestions. The AI ​​stylist takes into account the user's past purchase history and preferences to suggest the best outfits.

[1558] Step 11:

[1559] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[1560] Step 12:

[1561] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user, such as product images, product names, prices, seller links, styling suggestions, etc.

[1562] Step 13:

[1563] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a purchase link opens the corresponding online store or seller page in a new tab or window.

[1564] Step 14:

[1565] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[1566] Example 1

[1567] 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."

[1568] In today's online shopping environment, it is difficult for users to efficiently search for similar products to those they see on the street or on TV. Furthermore, there is a lack of systems that can not only find products but also provide users with optimal styling suggestions. There is a need for a system that can find products that match a user's interests and even suggest how to coordinate those products.

[1569] 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.

[1570] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for displaying the generated product information and styling suggestions on the user terminal, and means for providing a purchase link for the user. This enables a user to efficiently find products similar to a product and receive optimal styling suggestions for that product.

[1571] "User" refers to a person who uses this system, registers personal information, searches for products, and receives styling suggestions.

[1572] "User information registration means" refers to a mechanism that allows users to input and save personal information on a dedicated application or website.

[1573] The "text input means" refers to an interface for inputting memorized product features by the user in text format.

[1574] "Natural language processing means" refers to a processing engine for extracting product features from text. Specifically, it can analyze keywords.

[1575] The "product database search means" refers to a process for searching a stored product database based on the extracted keywords and listing similar products.

[1576] "Filtering means" refers to a process for narrowing down the listed products based on the user's personal information (size, preferences, lifestyle, etc.).

[1577] The term "styling suggestion generating means" refers to a process for suggesting optimal styling for filtered products, taking into consideration the user's past purchase history and preferences.

[1578] "User terminal transmission means" refers to the process for transmitting the final product list and styling suggestions to the user's terminal.

[1579] "Display means" refers to an interface for displaying the generated product information and styling suggestions on the user's terminal screen.

[1580] The "purchase link providing means" refers to a process of providing a link for a user to purchase a product that the user likes.

[1581] The present invention provides a system that allows a user to easily search for similar products to an item that catches their eye and that they have seen on the street or on television, and also provides styling suggestions for the item. Specific embodiments of the system are described in detail below.

[1582] First, a user creates an account using a dedicated application or website. When the user logs in for the first time, they enter personal information such as name, gender, age, size, preferences, and lifestyle. This information is converted from the device into JSON format and sent to the server. The server analyzes the received information, creates a new record in the user profile table, and saves it in the database.

[1583] Next, the user enters the product characteristics they remember into the text input field. For example, they enter specific characteristics such as "red leather jacket." The device receives this input information and when they press the send button, the text data is sent to the server. The server passes the received text information to an NLP engine, which analyzes the product characteristics and extracts keywords.

[1584] The server searches a product database using the extracted keywords (e.g., "red," "leather," "jacket") to list related products, and then filters the results based on the user's profile information (size, preferred style, etc.) to select the most suitable product.

[1585] Once filtering is complete, the server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions, taking into account the user's past purchase history and preferences to suggest optimal outfits.

[1586] Finally, the server sends the generated product information and styling suggestions to the user's device. The device parses the received JSON data and displays the product information and styling suggestions in an easy-to-understand manner. This includes product images, product names, prices, and seller links. The user checks the displayed information, and if they find a product they like, they click the purchase link to proceed with the purchase.

[1587] For example, if a user inputs the characteristics of a "black leather backpack," the server extracts the keywords "black," "leather," and "backpack." Based on this, the server searches the product database and lists products that fit the user's profile information (e.g., preferences for casual fashion). The AI ​​stylist then makes styling suggestions, such as "It would look good with a denim jacket," and displays the final product list to the user. This system allows users to easily find products that meet their needs and receive appropriate advice on styling those products.

[1588] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1589] Step 1:

[1590] User account creation

[1591] The user opens a dedicated application or website and accesses the account creation screen, where they enter personal information such as name, gender, age, size, preferences, lifestyle, etc. This information is collected as input.

[1592] Step 2:

[1593] Information collection and transmission by devices

[1594] The terminal displays the information entered by the user on a dedicated form, and after all information is entered correctly by the user, it converts the information into JSON format, which is generated as output and sent to the server.

[1595] Step 3:

[1596] Data analysis and storage by server

[1597] The server receives the JSON data from the device as input, parses it, creates a new record in the user profile table, and saves the parsed personal information to the database. An output message indicating that registration is complete is generated and sent to the device.

[1598] Step 4:

[1599] User input of product features

[1600] The user enters the product characteristics they remember into a text input field, for example, a specific characteristic such as "red leather jacket," and this is collected as input data.

[1601] Step 5:

[1602] Sending text via device

[1603] The terminal receives the text information entered by the user, and when the send button is pressed, the text data is sent to the server as input data.

[1604] Step 6:

[1605] Server-based text analysis

[1606] The server passes the text information received from the device to the NLP engine, which takes the text as input. It analyzes the product's features and extracts keywords. For example, keywords such as "red," "leather," and "jacket" are generated as output.

[1607] Step 7:

[1608] Searching the product database by the server

[1609] The server receives the keywords extracted through the analysis (e.g., "red," "leather," "jacket") as input and searches the product database. A list of related products is generated as output.

[1610] Step 8:

[1611] Server-based filtering

[1612] The server receives the list of products as input and filters them based on the user's registered information (size, preferred style, etc.) to narrow down the products that best suit the user and generate a list of those products as output.

[1613] Step 9:

[1614] Server-generated styling suggestions

[1615] The server passes the filtered product list as input to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist engine considers the user's past purchase history and preferences to suggest optimal outfits. Specific styling suggestions are generated as output.

[1616] Step 10:

[1617] Server generates final list

[1618] The server receives as input the final product list including styling suggestions and generates data for transmission to the user terminal, which is generated as output and transmitted to the terminal.

[1619] Step 11:

[1620] Displaying results on a terminal

[1621] The device receives JSON data from the server, parses it, and displays product information and styling suggestions in an easy-to-understand format to the user. Specifically, the device displays product images, product names, prices, and links to sellers.

[1622] Step 12:

[1623] User review and purchase

[1624] The user checks the information displayed on the device as input, and if they find a product they like, they click the purchase link to complete the purchase. This is the user's purchasing behavior as output.

[1625] (Application example 1)

[1626] 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."

[1627] In current online shopping, it is difficult for users to easily search for similar products to those they see on the street or on television, and to receive styling suggestions for those products. As a result, users spend a lot of time and effort finding products that suit their preferences and needs. Furthermore, the lack of styling suggestions leaves them unsure about how to coordinate their outfits after purchase. A system that can solve these issues is needed.

[1628] 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.

[1629] In this invention, the server includes means for registering a user's personal information, means for inputting memorized product features as text, natural language processing means for analyzing the text and extracting product features, means for searching a product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information, means for generating styling suggestions for the filtered products, means for transmitting the final product list and styling suggestions to a user terminal, means for specifying the styling suggestions using a generative AI model, and means for displaying the generated product list and styling suggestions to the user as prompt sentences. This allows a user to easily search for products similar to a product they have seen and also receive styling suggestions for the product.

[1630] "Means for registering user personal information" refers to the function of entering personal information such as the user's name, gender, age, size, preferences, and lifestyle, and storing it in a database.

[1631] "Means for inputting memorized product features as text" refers to a function that allows a user to input the features of a product that they have seen on the street or on television in text form.

[1632] "Natural language processing means for analyzing text and extracting product features" refers to a function for analyzing input text and automatically extracting product features.

[1633] "Means for searching a product database and listing similar products based on extracted features" refers to a function for searching a database based on extracted product features and listing similar products.

[1634] "Means for filtering listed products based on the user's personal information" refers to a function for selecting the product that best matches the user's personal information from among the similar products listed.

[1635] The "means for generating styling suggestions for filtered products" refers to a function for generating styling suggestions for selected products.

[1636] "Means for transmitting the final product list and styling suggestions to the user terminal" refers to a function for transmitting the generated product list and styling suggestions to the user terminal.

[1637] "Means for realizing styling suggestions using a generative AI model" refers to the function of realizing styling suggestions using an AI model and proposing specific outfits.

[1638] The "means for displaying the generated product list and styling suggestions to the user as prompt sentences" refers to a function for displaying the product list and coordination suggestions to the user in a prompt format.

[1639] The present invention is a system for searching for similar products to a product that a user has seen and providing styling suggestions for the product. A detailed description will be given of specific embodiments of the present invention.

[1640] System Program

[1641] This system consists of a server and user terminals (mainly smartphone applications). The main hardware and software used include:

[1642] Hardware: Servers (cloud servers, etc.), smartphones

[1643] Software: Python, Flask, JSON, Natural Language Processing Engine (NLP Engine)

[1644] Program processing

[1645] The server performs the following process.

[1646] A means of registering user personal information: Users enter personal information such as name, gender, age, size, preferences, and lifestyle, and the server stores this in a database.

[1647] A method for inputting memorized product features as text: The user inputs the features of products they have seen on the street or on TV in text format and sends them to the server.

[1648] Natural language processing means for analyzing text and extracting product features: The server analyzes the received text using a natural language processing engine and extracts product features.

[1649] A means for searching a product database and listing similar products based on the extracted features: The server searches a database based on the extracted features and lists similar products.

[1650] Means for filtering listed products based on user's personal information: The server filters listed products based on the user's personal information and selects suitable products.

[1651] Means for generating styling suggestions for filtered products: The server generates styling suggestions for selected products using a generative AI model.

[1652] Means for transmitting the final product list and styling suggestions to the user terminal: The server transmits the generated product list and styling suggestions to the user terminal.

[1653] Means of using generative AI models to concretize styling suggestions: Using AI models to concretize styling suggestions and propose specific outfits.

[1654] A means for displaying the generated product list and styling suggestions to the user as prompt sentences: The product list and coordination suggestions are displayed to the user in a prompt format.

[1655] Data processing / calculation

[1656] The server performs the following data processing and calculations:

[1657] Registering personal information: Convert the entered data into JSON format and save it in the database.

[1658] Product feature analysis: The provided text is analyzed using a natural language processing engine (NLP engine) to extract keywords.

[1659] Product search: Based on the extracted keywords, the product database is searched and the products that best match the registered information are listed.

[1660] Generate styling suggestions: Based on the filtered products, a generative AI model generates styling suggestions.

[1661] Sending the results: The generated product list and styling suggestions are sent back to the user's device in JSON format and displayed as a prompt.

[1662] Specific examples

[1663] Here are some examples of input prompts:

[1664] Input prompt (text format):

[1665] User Information:

[1666] Name: Sato

[1667] Gender: Male

[1668] Age: 28

[1669] Size: Medium

[1670] Preference: Casual

[1671] Lifestyle: Active

[1672] Product information:

[1673] black leather backpack

[1674] By inputting specific examples like this, users can easily search for similar products to the one they saw and receive appropriate styling suggestions for that product. Furthermore, based on the information presented as prompts, the generative AI model suggests outfits to support users' financial decisions.

[1675] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1676] Step 1:

[1677] A user creates an account on a dedicated application or website. When logging in for the first time, they enter their personal information (name, gender, age, size, preferences, lifestyle, etc.). The entered information is converted into JSON format and sent from the device to the server. This information is saved as a new record in the user profile table.

[1678] Step 2:

[1679] The user enters the characteristics of a product they have seen on the street or on TV in a text input field. For example, they enter specific characteristics such as "black leather backpack." The entered information is received by the device and sent to the server.

[1680] Step 3:

[1681] The server passes the received text information to a natural language processing engine (NLP engine) to analyze the product's features. Specifically, it extracts the keywords "black," "leather," and "backpack." The extracted keywords are used in the next search process.

[1682] Step 4:

[1683] The server then searches the product database using the extracted keywords. This search generates a list of products that match the keywords. For example, a list of products that match the keyword "black leather backpack" is generated.

[1684] Step 5:

[1685] The server filters the listed products based on the user's personal information, for example, selecting only products that match the user's size and preferences, resulting in a list of products that best fit the user's profile information.

[1686] Step 6:

[1687] The server uses a generative AI model to generate styling suggestions based on the filtered products. Specifically, it suggests outfits such as "pairing a selected backpack with a casual shirt and jeans." These styling suggestions are generated taking into account past purchase history and preferences.

[1688] Step 7:

[1689] The server sends the final product list and styling suggestions in JSON format to the device, which then parses the information and displays the product information and styling suggestions to the user, including product images, product names, prices, vendor links, and styling suggestions.

[1690] Input and Output

[1691] Input: User's personal information, product features (text input)

[1692] Output: Filtered product list, styling suggestions

[1693] Data processing or data calculation

[1694] Converting to JSON format and saving to storage

[1695] Keyword extraction using natural language processing

[1696] Database Search and Filtering

[1697] Styling suggestion generation using generative AI models

[1698] Generate and display prompt statements

[1699] This allows users to easily search for similar products to the one they see and receive appropriate styling suggestions for that product.

[1700] 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.

[1701] The present invention is a system that allows a user to easily input the characteristics of a product that interests them, and searches for and suggests similar products. This system incorporates an emotion engine that recognizes the user's emotions and includes a function that makes suggestions based on the user's psychological state. Specific embodiments of the present invention are described below.

[1702] Registering user information

[1703] 1. User:

[1704] Users create an account on a dedicated application or website and enter their personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1705] 2. Terminal:

[1706] The device displays the information the user enters in a dedicated form, collects the input, converts the collected information into JSON format, and sends it to the server.

[1707] 3. Server:

[1708] The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[1709] Product information input and emotion recognition

[1710] 1. User:

[1711] The user enters the characteristics of the product they see in a text input field, for example, entering a specific characteristic such as "red leather jacket."

[1712] 2. Terminal:

[1713] The device receives the input text and simultaneously collects emotional data (input speed, input style, emotional icons, etc.) of the user while inputting the text. This information is then sent to the server.

[1714] 3. Server:

[1715] The server passes the received text information to an NLP engine, which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data and determines the user's current psychological state.

[1716] Search for similar products

[1717] 1. Server:

[1718] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords.

[1719] 2. Server:

[1720] The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data, and selects the product that best suits the user's current psychological state.

[1721] Styling suggestions

[1722] 1. Server:

[1723] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest optimal outfits.

[1724] 2. Server:

[1725] The server generates a final product list including styling suggestions and sends this information to the user's terminal.

[1726] User Visibility

[1727] 1. Device:

[1728] The device analyzes the received information and displays a list of product information and styling suggestions to the user, including product images, product names, prices, seller links, and styling suggestions.

[1729] 2. User:

[1730] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[1731] Specific examples

[1732] Example 1:

[1733] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and strong typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on this. Furthermore, taking into account the user's state of excitement, it creates a list of stylish and latest backpack designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[1734] Example 2:

[1735] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[1736] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[1737] The processing flow will be explained below.

[1738] Step 1:

[1739] User: The user accesses a dedicated application or website, creates an account, and enters personal information (name, gender, age, size, preferences, lifestyle, etc.).

[1740] Step 2:

[1741] Terminal: The terminal displays the information entered by the user in a dedicated form and collects the input. The collected information is converted into JSON format and sent to the server.

[1742] Step 3:

[1743] Server: The server parses the received JSON data, creates a new record in the user profile table, saves the personal information to the database, and generates a registration completion message and sends it to the device.

[1744] Step 4:

[1745] Terminal: The terminal receives the registration completion message from the server and displays a notification to the user that registration is complete.

[1746] Step 5:

[1747] User: The user enters the characteristics of the product they see into a text input field, for example, "red leather jacket."

[1748] Step 6:

[1749] Terminal: The terminal receives the input text information and simultaneously collects the user's emotional data (input speed, input style, emotional icon, etc.) of the text input, and transmits the text information and emotional data to the server.

[1750] Step 7:

[1751] Server: The server passes the received text information to the NLP engine, which analyzes the product's features and extracts keywords. In parallel, the emotion engine analyzes the emotion data and determines the user's current psychological state.

[1752] Step 8:

[1753] Server: The server compares the keywords extracted by the NLP engine (e.g., "red," "leather," "jacket") with a product database and lists similar products.

[1754] Step 9:

[1755] Server: The server filters the listed products based on the user's registered information (size, preferences, lifestyle, etc.) and emotional data. It selects the product that best suits the user's psychological state (e.g., "excited" or "calm").

[1756] Step 10:

[1757] Server: The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and current psychological state to suggest the optimal outfit.

[1758] Step 11:

[1759] Server: The server generates the final product list including styling suggestions and sends this information to the user's device.

[1760] Step 12:

[1761] Terminal: The terminal analyzes the received information and displays the listed product information and styling suggestions to the user. Product images, product names, prices, seller links, styling suggestions, etc. are displayed.

[1762] Step 13:

[1763] User: The user reviews the displayed information and, if they like the product, clicks on a link to purchase it. Clicking on a link opens the corresponding online store or seller's page in a new tab or window.

[1764] Step 14:

[1765] Device: Based on the link the user clicks, the device will open the corresponding online shopping site or seller page, allowing the user to proceed with the purchase.

[1766] Example 2

[1767] 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."

[1768] Current online shopping systems often suggest products without considering the user's psychological state or emotions. As a result, the suggested products often do not match the user's current needs or preferences. Furthermore, styling suggestions do not reflect the individual user's emotions or lifestyle, resulting in an inconsistent experience. This leads to unsatisfying shopping experiences for users.

[1769] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for registering personal data of a user, means for inputting stored product characteristics as text, natural language processing means for analyzing the text and extracting product characteristics, means for searching a database storing product information and listing similar products based on the extracted characteristics, means for filtering the listed products based on the user's personal data and emotional data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user device. This enables a more personalized shopping experience that takes into account the user's psychological state and emotions.

[1770] "User's personal data" refers to information about the user himself / herself, such as the user's name, gender, age, size, preferences, and lifestyle.

[1771] "Product characteristics" are specific characteristics and attributes of a product, such as its color, material, design, and use.

[1772] "Natural language processing means" refers to technology that analyzes input text and extracts specific keywords and information, and generally uses an NLP engine.

[1773] A "database storing product information" is a data storage that stores information about multiple products and allows searching and filtering.

[1774] "Similar products" refer to other products with similar characteristics that are selected based on the input product characteristics.

[1775] "Emotion data" is information about the user's psychological state obtained from the user's input speed, input style, emotion icons, and the like.

[1776] "Filtering means" refers to technology that selects appropriate products from the listed items based on extracted keywords and emotional data.

[1777] "Means for generating styling suggestions" refers to technology that uses AI and machine learning models to coordinate and suggest outfits based on the user's preferences, past purchase history, and current psychological state.

[1778] "User device" refers to an electronic device that can connect to the Internet and is used by a user, such as a smartphone, tablet, or PC.

[1779] This invention provides a system for providing a personalized shopping experience that takes into account a user's emotions. The system has a series of means for analyzing the user's input information and emotion data and generating optimal product and styling suggestions.

[1780] System Configuration

[1781] Registering user information

[1782] User:

[1783] Users access a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle using text boxes, drop-down menus, and check boxes.

[1784] Device:

[1785] The device collects the information entered by the user, converts it into JSON format, and sends it to the server. This transmission process is performed using JavaScript or HTML form submission functions.

[1786] server:

[1787] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, back-end languages ​​such as Python and Java are used, and the database is MySQL or PostgreSQL.

[1788] Product information input and emotion recognition

[1789] User:

[1790] The user enters the product characteristics they see into a text entry field, for example, entering a specific characteristic such as "red leather jacket."

[1791] Device:

[1792] Along with the text entered, the device simultaneously collects input speed, input style, and emotional data such as emoticons, which are then converted into JSON format and sent to the server.

[1793] server:

[1794] The server passes the received text information to a natural language processing (NLP) engine, which analyzes the product's features and extracts keywords. For example, NLP engines such as spaCy or NLTK are used. It also analyzes emotional data using emotion engines such as the Emotion API to determine the user's current psychological state.

[1795] Search and filter similar products

[1796] server:

[1797] The server uses the keywords extracted by the NLP engine to search the product database and list similar products, using SQL queries or full-text search engines like Elasticsearch.

[1798] server:

[1799] The products listed are filtered based on the user's personal data and analyzed emotional data, using an AI model to select the products that best fit the user's current emotional state.

[1800] Styling suggestions

[1801] server:

[1802] The filtered product list is then sent to the AI ​​Stylist engine, which generates styling suggestions using machine learning frameworks such as TensorFlow and PyTorch, taking into account the user's past purchase history, preferences, and psychological state.

[1803] server:

[1804] A final product list including styling suggestions is generated in JSON format and sent to the user's device.

[1805] User Visibility

[1806] Device:

[1807] The device analyzes the received information and displays the optimal product information and styling suggestions for the user. Specifically, it uses HTML and CSS to display product images, product names, prices, seller links, and styling suggestions. It also uses JavaScript to dynamically update the displayed content.

[1808] User:

[1809] The user checks the displayed product information and, if they find something they like, clicks on the purchase link, which opens the corresponding online shopping site or seller's page in a new tab or window.

[1810] Specific examples

[1811] Example 1:

[1812] When a user inputs the characteristics of a "black leather backpack," the emotion engine recognizes the "excited state" due to the fast and forceful typing. The server extracts the keywords "black," "leather," and "backpack" and searches the product database based on these. Furthermore, taking into account the user's state of excitement, it lists stylish backpacks with the latest designs. The AI ​​stylist suggests combinations with denim jackets, and the final product list is displayed to the user.

[1813] Example prompt:

[1814] "Black leather backpack"

[1815] Example 2:

[1816] If a user inputs the characteristic "silver earrings" and the emotion engine recognizes the user's "calm state" because the input is slow and careful, the server analyzes the keywords "silver" and "earrings" and searches for products based on this. Taking into account the user's calm state, earrings with simple and elegant designs are filtered out. The AI ​​stylist suggests combinations with simple dresses, and this information is displayed to the user.

[1817] Example prompt:

[1818] "Silver earrings"

[1819] This system allows users to receive product and styling suggestions that best suit their current emotional state, providing a more personalized shopping experience.

[1820] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1821] Step 1:

[1822] The user enters personal information

[1823] Example of how it works:

[1824] Users access a registration form on a dedicated application or website and enter personal data such as name, gender, age, size, preferences, and lifestyle. For example, they enter their name in a TextBox, select their gender from a DropDownList, and select their preferences using a CheckBox.

[1825] input:

[1826] Personal information such as name, gender, age, size, preferences, and lifestyle.

[1827] output:

[1828] The entered personal information will be displayed on the terminal, ready for the next processing step.

[1829] Step 2:

[1830] The device collects information and sends it to the server

[1831] Example of how it works:

[1832] The device converts the information entered by the user into JSON format and sends it to the server. Specifically, it uses the AJAX function of JavaScript and uses an HTTP POST request.

[1833] input:

[1834] Personal information entered by the user.

[1835] output:

[1836] Sends JSON format data from the terminal to the server.

[1837] Step 3:

[1838] The server analyzes the data and stores it in a database

[1839] Example of how it works:

[1840] The server parses the received JSON data, creates a new record in the database, and stores the personal data. Specifically, it uses Python frameworks such as Flask or Django to execute SQL queries and stores the data in MySQL or PostgreSQL.

[1841] input:

[1842] Personal information data in JSON format sent from the device.

[1843] output:

[1844] The user's personal information stored in the database and a message indicating completion of registration are generated and sent to the terminal.

[1845] Step 4:

[1846] The user inputs the product's features

[1847] Example of how it works:

[1848] The user enters the product characteristics they see into a text entry field, for example, "red leather jacket."

[1849] input:

[1850] User-supplied text about product features.

[1851] output:

[1852] The product characteristic text entered on the terminal is displayed and the terminal is ready to proceed to the next processing step.

[1853] Step 5:

[1854] The device collects feature data and emotion data and sends it to the server.

[1855] Example of how it works:

[1856] The device simultaneously collects input data such as typing speed, typing style, and emotional icons along with the input text. This information is converted into JSON format and sent to the server. One example is the implementation of JavaScript code to measure keystroke speed.

[1857] input:

[1858] Text data and sentiment data (typing speed, typing style, emoticons) of product characteristics by users.

[1859] output:

[1860] Sending feature data and emotion data in JSON format from the device to the server.

[1861] Step 6:

[1862] The server analyzes the text and sentiment data

[1863] Example of how it works:

[1864] The server passes the received text information to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. Examples of NLP engines used include spaCy and NLTK. The server also analyzes the emotional data using an emotion engine to determine the user's current psychological state.

[1865] input:

[1866] Text data and sentiment data of product characteristics sent from the device.

[1867] output:

[1868] Extracted product keywords and analyzed user emotional states.

[1869] Step 7:

[1870] The server searches the product database and lists similar products

[1871] Example of how it works:

[1872] The server uses the keywords extracted by the NLP engine to search the product database, using SQL queries and full-text search engines such as Elasticsearch.

[1873] input:

[1874] Extracted product keywords.

[1875] output:

[1876] A list of similar products that match your keywords.

[1877] Step 8:

[1878] The server performs filtering to select the best product

[1879] Example of how it works:

[1880] The server filters the listed products based on the user's personal and emotional data, using an AI model to select the products that best fit the user's current emotional state.

[1881] input:

[1882] A list of similar products, personal data of the user, and emotional data.

[1883] output:

[1884] A filtered list of the best products.

[1885] Step 9:

[1886] The server generates styling suggestions using the AI ​​stylist engine

[1887] Example of how it works:

[1888] The server sends the filtered product list to an AI stylist engine that generates styling suggestions, using machine learning frameworks such as TensorFlow and PyTorch.

[1889] input:

[1890] A filtered list of the best products.

[1891] output:

[1892] Generated styling suggestions.

[1893] Step 10:

[1894] The server generates the final product list and sends it to the terminal.

[1895] Example of how it works:

[1896] The server generates the final product list, including styling suggestions, in JSON format and sends it to the user's device. Data is sent using a RESTful API or GraphQL.

[1897] input:

[1898] Generated styling suggestions and a list of the best products.

[1899] output:

[1900] JSON data of the final product list and styling suggestions.

[1901] Step 11:

[1902] The device displays product information and styling suggestions to the user.

[1903] Example of how it works:

[1904] The device analyzes the received information and displays the product image, product name, price, seller link, and styling suggestions using HTML and CSS. The display content is dynamically updated using JavaScript.

[1905] input:

[1906] Final product list and styling suggestions sent from the server in JSON format.

[1907] output:

[1908] Product information and styling suggestions displayed to users.

[1909] Step 12:

[1910] User reviews the product and clicks the purchase link

[1911] Example of how it works:

[1912] The user checks the displayed product information, and if they find something they like, they click the purchase link. This action opens the online shopping site or seller's page in a new tab or window, and the purchase process proceeds.

[1913] input:

[1914] User expresses intent to purchase a product (clicks a button).

[1915] output:

[1916] The page of the corresponding online shopping site or seller will open and the purchase process will begin.

[1917] (Application example 2)

[1918] 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."

[1919] In today's online shopping environment, users face difficulties in finding the product that best suits their preferences and current state of mind from the wide variety of products available. Another problem is the lack of personalized recommendations that appropriately reflect different users' feelings and preferences regarding the same product. Therefore, there is a need for a system that can efficiently search for and recommend products that meet users' needs.

[1920] The identification processing 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 registering the user's personal information, means for inputting memorized product features as text, means for collecting emotion data at the time of input, natural language processing means for analyzing the text and extracting product features, means for searching the product database and listing similar products based on the extracted features, means for filtering the listed products based on the user's personal information and emotion data, means for generating styling suggestions for the filtered products, and means for transmitting the final product list and styling suggestions to the user terminal. This enables personalized product suggestions and styling suggestions based on the user's personal information and emotion data.

[1921] "Means for registering user's personal information" includes a function for registering personal information such as the user's name, gender, age, size, preferences, and lifestyle in a database.

[1922] The "means for inputting memorized product characteristics as text" includes a function for providing an interface that allows the user to input the characteristics of products that the user has come across in text form.

[1923] The "means for collecting emotional data during input" includes a function for collecting the emotional state of the user when inputting product features, including input speed, input style, and voice tone.

[1924] "Natural language processing means for analyzing text and extracting product features" includes a natural language processing engine for analyzing and extracting product features from input text.

[1925] "Means for searching a product database and listing similar products based on extracted features" includes a function for searching and listing similar products in a database based on features extracted by natural language processing.

[1926] The "means for filtering listed products based on the user's personal information and emotional data" includes a function for re-sorting products that have already been listed based on the user's registered information and current emotional data.

[1927] The "means for generating styling suggestions for filtered products" includes an artificial intelligence stylist engine that suggests appropriate outfits for the re-sorted products.

[1928] The "means for transmitting the final product list and styling suggestions to the user terminal" includes a function for transmitting the final selected product list and styling suggestions to the user terminal for display.

[1929] The present invention provides a system that makes personalized product and styling suggestions based on a user's personal information and emotional data. Specific embodiments for carrying out the invention are described below.

[1930] System Configuration

[1931] User terminal

[1932] The user terminal is equipped with a means for the user to input personal information, a means for inputting the characteristics of the products they see by text or voice, and a means for collecting the user's emotional data (input speed, input style, voice tone).The user terminal can be a smartphone or smart glasses.

[1933] server

[1934] The server includes the following functions:

[1935] 1. Personal information registration:

[1936] The personal information received from the user is sent to the server in JSON format and saved in the database.

[1937] 2. Product feature and sentiment data analysis:

[1938] The input text is analyzed using a natural language processing engine (NLP engine) to extract keywords. The emotion engine also analyzes the emotional data to determine the user's emotional state. The NLP engine uses SpaCy and the Google NLP API, while the emotion engine uses the Google Cloud Speech-to-Text API.

[1939] 3. Search and filter similar products:

[1940] The product database is searched based on keywords extracted by the NLP engine, and similar products are listed. Furthermore, filtering is performed based on the user's personal information and emotional data.

[1941] 4. Styling suggestions:

[1942] For the filtered products, the AI ​​stylist engine generates optimal styling suggestions, taking into account the user's profile and emotional state.

[1943] 5. Sending and viewing results:

[1944] The final product list and styling suggestions are sent to the user's terminal and displayed to the user.

[1945] Specific examples

[1946] If a user inputs the characteristic "black leather backpack" and the emotional data indicates "excitement," the NLP engine extracts keywords such as "black," "leather," and "backpack." The emotional engine identifies the user's excitement level based on the emotional data. Based on this information, the server filters and lists stylish backpacks with the latest designs. The AI ​​stylist engine suggests how to coordinate them with a denim jacket. The final product list and styling suggestions are displayed on the user's device.

[1947] Prompt Sentence Examples

[1948] User comment: "Black leather backpack"

[1949] Emotion data: {"input speed": 1.5, "input style": "enthusiastic"}

[1950] Keywords: ["black", "leather", "backpack"]

[1951] Emotion analysis result: "Excited"

[1952] User profile: {"Name": "Taro Yamada", "Age": 30, "Gender": "Male", "Size": "L", "Preferences": ["Casual", "Outdoors"], "Lifestyle": ["Weekend camping"]}

[1953] Question: Please list the best products and styling suggestions.

[1954] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1955] Step 1:

[1956] The user terminal provides an interface for inputting the user's personal information (name, gender, age, size, preferences, lifestyle, etc.), and the user inputs this information. The input personal information is converted into JSON format through a dedicated form and sent to the server.

[1957] Step 2:

[1958] The server analyzes the received JSON format personal information, creates a new record in the user profile table, saves it in the database, and generates a registration completion message and sends it to the user's device.

[1959] Step 3:

[1960] The user inputs the characteristics of the product they see into the device in text or voice format. For example, they input a specific characteristic such as "red leather jacket." The device collects the input text, voice, and related emotional data (e.g., input speed, input style, and voice tone).

[1961] Step 4:

[1962] The device sends the collected text, voice, and emotion data to a server. The server passes the text data to a natural language processing engine (NLP engine), which analyzes the product's features and extracts keywords. The emotion engine also analyzes the emotion data to determine the user's current psychological state.

[1963] Step 5:

[1964] The server searches a product database using the keywords extracted by the NLP engine and lists similar products that match the keywords. For example, if the keywords "red," "leather," and "jacket" are extracted, the server searches the database for products that match these keywords.

[1965] Step 6:

[1966] The server filters the listed products based on the user's personal information (size, preferences, lifestyle, etc.) and emotional data (current mental state). For example, if the user's emotional state is "excited," it will prioritize products with stylish and latest designs.

[1967] Step 7:

[1968] The server sends the filtered product list to the AI ​​stylist engine, which generates styling suggestions. The AI ​​stylist takes into account the user's past purchase history, preferences, and emotional state to suggest optimal outfits.

[1969] Step 8:

[1970] The server generates a final product list and styling suggestions and sends this information to the user's terminal. The user terminal analyzes the received information and displays the listed product information and styling suggestions to the user, including product images, product names, prices, vendor links, styling suggestions, etc.

[1971] Step 9:

[1972] Users can check the displayed information and, if they find a product they like, click a link to purchase it. Clicking on the purchase link opens the corresponding online shopping site or seller's page in a new tab or window.

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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).

[1980] 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.

[1981] 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."

[1982] 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.

[1983] 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).

[1984] 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.

[1985] 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.

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] The following is further disclosed regarding the above embodiment.

[1995] (Claim 1)

[1996] A means for registering personal information of a user;

[1997] A means for inputting memorized product features as text;

[1998] a natural language processing means for analyzing text and extracting product features;

[1999] a means for searching a product database and listing similar products based on the extracted features;

[2000] means for filtering the listed products based on the user's personal information;

[2001] means for generating styling suggestions for the filtered products;

[2002] The system includes means for transmitting the final product list and styling suggestions to a user terminal.

[2003] (Claim 2)

[2004] 2. The system of claim 1, wherein the natural language processing means includes a process for extracting a plurality of keywords from the text.

[2005] (Claim 3)

[2006] 2. The system of claim 1, wherein the means for searching the product database based on the extracted keywords includes a process for filtering based on user profile information.

[2007] (Claim 4)

[2008] 10. The system of claim 1, further comprising means for taking into account a user's past purchasing history and preferences when generating styling suggestions.

[2009] (Claim 5)

[2010] 10. The system of claim 1, further comprising: means for generating and displaying a link for purchasing the product selected by the user.

[2011] "Example 1"

[2012] (Claim 1)

[2013] A means for registering personal information of a user;

[2014] A means for inputting memorized product features as text;

[2015] a natural language processing means for analyzing text and extracting product features;

[2016] a means for searching a product database and listing similar products based on the extracted features;

[2017] means for filtering the listed products based on the user's personal information;

[2018] means for generating styling suggestions for the filtered products;

[2019] means for transmitting the final product list and styling suggestions to a user terminal;

[2020] a means for displaying the generated product information and styling suggestions on a user's terminal;

[2021] The system includes a means for providing a user purchase link.

[2022] (Claim 2)

[2023] 2. The system according to claim 1, wherein the natural language processing means includes a process for extracting a plurality of keywords from the text and a process for proposing an optimal coordination based on the generated styling suggestions.

[2024] (Claim 3)

[2025] 2. The system of claim 1, wherein the means for searching the product database based on the extracted keywords includes a process for filtering based on the user's profile information and past purchase history.

[2026] "Application Example 1"

[2027] (Claim 1)

[2028] A means for registering personal information of a user;

[2029] A means for inputting memorized product features as text;

[2030] a natural language processing means for analyzing text and extracting product features;

[2031] a means for searching a product database and listing similar products based on the extracted features;

[2032] means for filtering the listed products based on the user's personal information;

[2033] means for generating styling suggestions for the filtered products;

[2034] means for transmitting the final product list and styling suggestions to a user terminal;

[2035] A means of using generative AI models to materialize styling suggestions; and

[2036] A means to display the generated product list and styling suggestions to the user as prompts

[2037] A system including:

[2038] (Claim 2)

[2039] 2. The system of claim 1, wherein the natural language processing means includes a process for extracting a plurality of keywords from the text.

[2040] (Claim 3)

[2041] 2. The system of claim 1, wherein the means for searching the product database based on the extracted keywords includes a process for filtering based on user profile information.

[2042] "Example 2: Combining Emotion Engines"

[2043] (Claim 1)

[2044] means for registering personal data of users;

[2045] A means for inputting memorized product characteristics as text;

[2046] natural language processing means for analyzing text and extracting product characteristics;

[2047] A means for searching a database storing product information and listing similar products based on the extracted characteristics;

[2048] means for filtering the listed products based on the user's personal data and emotional data;

[2049] means for generating styling suggestions for the filtered products;

[2050] The system includes means for transmitting the final product list and styling suggestions to a user device.

[2051] (Claim 2)

[2052] The natural language processing means includes a process of extracting a plurality of keywords from the text.

[2053] means for analyzing user emotion data;

[2054] 10. The system of claim 1.

[2055] (Claim 3)

[2056] The means for searching a database storing product information based on the extracted keywords includes a process for filtering based on user profile information and emotion data.

[2057] 10. The system of claim 1.

[2058] "Application example 2 when combining emotion engines"

[2059] (Claim 1)

[2060] A means for registering personal information of a user;

[2061] A means for inputting memorized product features as text;

[2062] a means for collecting input emotion data;

[2063] a natural language processing means for analyzing text and extracting product features;

[2064] a means for searching a product database and listing similar products based on the extracted features;

[2065] means for filtering the listed products based on the user's personal information and sentiment data;

[2066] means for generating styling suggestions for the filtered products;

[2067] The system includes means for transmitting the final product list and styling suggestions to a user terminal.

[2068] (Claim 2)

[2069] 2. The system of claim 1, wherein the natural language processing means includes a process for extracting a plurality of keywords from the text.

[2070] (Claim 3)

[2071] 10. The system of claim 1, wherein the means for searching the product database based on the extracted keywords includes filtering based on user profile information and emotion data. [Explanation of symbols]

[2072] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for registering personal information of a user; A means for inputting memorized product features as text; a natural language processing means for analyzing text and extracting product features; a means for searching a product database and listing similar products based on the extracted features; means for filtering the listed products based on the user's personal information; means for generating styling suggestions for the filtered products; The system includes means for transmitting the final product list and styling suggestions to a user terminal.

2. The system according to claim 1 , wherein the natural language processing means includes a process for extracting a plurality of keywords from the text.

3. 2. The system of claim 1, wherein the means for searching the product database based on the extracted keywords includes a process for filtering based on user profile information.

4. The system of claim 1 , further comprising means for taking into account a user's past purchasing history and preferences when generating styling suggestions.

5. 10. The system of claim 1, further comprising: means for generating and displaying a link for purchasing the product selected by the user.

Images