System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving basic data from a user; generation and AI means for analyzing characteristics of the user based on the received basic data of the user; means for selecting personalized fashion items and hairstyles based on an analysis result of the generation and AI means; means for presenting the selected fashion items and hairstyles to the user; virtual fitting means for allowing the user to virtually try the selected fashion items and hairstyles; and means for storing a final styling based on the user's selection.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] Men in their 40s and older who are looking for a partner have access to a lot of information about fashion and hairstyles, but they face the challenge of finding the style that best suits them. Furthermore, their busy lifestyles make it difficult for them to find time for stylists and hairdressers. These factors contribute to a lack of confidence and a reluctance to pursue a marriage search. Therefore, it is necessary to provide an environment where users can easily find the style that best suits them and approach their marriage search with confidence. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving basic information from a user, a generating AI means for analyzing the user's characteristics based on the input basic information, a means for selecting personalized fashion items and hairstyles based on the analysis results of the generating AI means, a means for presenting the selected fashion items and hairstyles to the user, a virtual try-on means for allowing the user to virtually try on the selected fashion items and hairstyles, and a means for saving the final styling based on the user's selection. The system also includes a means for providing feedback from an expert and a means for saving the analyzed information and the selected fashion items and hairstyles in a database. In this way, the system creates an environment in which users can easily find the style that best suits them and approach their marriage search with confidence.

[0006] "User" refers to an individual who uses the system to receive styling suggestions.

[0007] "Basic information" refers to information that indicates the user's basic attributes, such as age, occupation, and lifestyle, which are necessary for styling suggestions.

[0008] "Generative AI" refers to artificial intelligence technology that analyzes a user's body shape and facial features based on their photo and basic information they enter.

[0009] "Analysis results" refers to the data obtained after the generating AI analyzes the user's photo and basic information.

[0010] "Personalized fashion items and hairstyles" refers to fashion items and hairstyles suggested to suit a user's individual body type and characteristics.

[0011] "Means for selection" refers to the technology or system for selecting the most suitable fashion items and hairstyles based on the analysis results.

[0012] "Presentation means" refers to the technology or method for displaying selected fashion items and hairstyles to the user.

[0013] "Virtual try-on means" refers to technologies and systems that allow users to virtually try on suggested fashion items and hairstyles.

[0014] "Means for saving final styling based on selection" refers to technology or systems for recording and saving styling information selected by a user.

[0015] "Expert feedback" refers to additional opinions or advice provided by stylists, hairdressers, or other professionals in response to styling suggestions.

[0016] "Database" refers to the system or technology for storing analytical results and selected styling information. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. This system includes the following elements:

[0039] Enter your user information and upload a photo

[0040] Users enter basic information such as their age, occupation, and lifestyle through a dedicated website or application, and upload their own photos. At this stage, users can also enter their wishes and preferences.

[0041] Receiving and analyzing user information and photos

[0042] Based on the user information and photos received by the server, the generation AI analyzes the user's body type and facial features, accurately determining the user's face shape, body type, skin color, etc., and generates data to suggest styling accordingly.

[0043] Generate styling suggestions

[0044] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database, taking into account the user's preferences and lifestyle. The selected fashion items and hairstyles are presented as the optimal combination for the user.

[0045] Presentation of proposal content

[0046] The device will present the selected fashion items and hairstyles to the user, who can then review the suggestions through the application.

[0047] Virtual try-on

[0048] Users can virtually try on suggested fashion items and hairstyles using the virtual try-on feature. This feature allows users to select the style they want to try and see how it will look on them in a virtual mirror. This allows them to see in advance whether the suggested styling will suit them.

[0049] Expert feedback

[0050] The server receives feedback from experts (stylists and hairdressers) and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[0051] Select and save your final styling

[0052] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[0053] Specific examples

[0054] For example, say the user is a 42-year-old engineer who enjoys a casual lifestyle and the outdoors. The user enters a photo and basic information. The server receives this, and the generative AI analyzes facial features and body type, resulting in a result such as "thin and long face." The server then suggests slim-fitting shirts, denim jeans, and short hairstyles from its database. The user then uses the virtual try-on feature to virtually try these on. The expert then provides feedback, saying, "A brightly colored shirt would suit you better." The user finally accepts and saves the suggestions. The user can then refer to the saved styles and actually purchase the clothing at a later date.

[0055] In this way, the present invention provides users with an environment in which they can approach their matchmaking with confidence and simplifies the process.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The device then sends the entered data and photo to the server.

[0059] Step 2:

[0060] The server receives the user information and photo, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photo and basic information.

[0061] Step 3:

[0062] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The analysis results are then sent back to the server.

[0063] Step 4:

[0064] The server receives the analysis results and extracts a list of suitable fashion items and hairstyles from the database, which is personalized based on the user's characteristics and preferences.

[0065] Step 5:

[0066] The server filters the extracted list of fashion items and hairstyles to generate optimal combinations, and the generated styling suggestions are notified to the user.

[0067] Step 6:

[0068] The user receives a notification and sees styling suggestions within the app, and the device displays details of the fashion item and hairstyle.

[0069] Step 7:

[0070] The user uses the virtual try-on feature to virtually try on the suggested styles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try on different styles.

[0071] Step 8:

[0072] The server sends styling suggestions to experts (stylists or hairdressers) and asks for feedback. The experts review the suggestions and provide additional advice.

[0073] Step 9:

[0074] The expert's feedback is sent back to the server, which notifies the user of the feedback information, and the user confirms the feedback and finalizes the styling.

[0075] Step 10:

[0076] The user selects the final styling. The device sends the selection to the server. The server stores the selection in a database.

[0077] Step 11:

[0078] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[0079] Example 1

[0080] 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."

[0081] Conventional styling suggestion systems were unable to accurately grasp a user's basic information and characteristics, making it difficult to provide personalized styling suggestions. Furthermore, users were unable to actually try out the suggested styling and were unable to receive real-time feedback from experts. Furthermore, it was difficult to provide styling that reflected the user's preferences and wishes, and data storage and reuse were insufficient.

[0082] 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.

[0083] In this invention, the server includes means for receiving basic information from a user, means for receiving the basic information and a photo and analyzing the user's body type and facial features using a generative AI model, means for selecting personalized fashion items and hairstyles based on the analysis results by the generative AI means, means for presenting the selected fashion items and hairstyles to the user, means for a virtual try-on that allows the user to virtually try on the selected fashion items and hairstyles, means for receiving feedback from an expert and providing it to the user, and means for saving the final styling based on the user's selection. This allows the server to accurately reflect the user's features and preferences, make optimal styling suggestions based on the virtual try-on and expert feedback, and also allows the data to be saved and reused.

[0084] "Basic user information" refers to basic information about the user, such as age, occupation, lifestyle, and preferences.

[0085] A "generative AI model" refers to an artificial intelligence model that uses machine learning technology to analyze images and data and extract user characteristics.

[0086] "Analyzing the user's body shape and facial features" means that the generative AI model analyzes the user's photo to identify detailed features such as body shape, facial shape, and skin color.

[0087] "Personalized fashion items and hairstyles" refers to individually tailored fashion items and hairstyles selected based on the user's characteristics and preferences.

[0088] "Virtual Try-On Facility" means a technological facility that allows a user to try on selected fashion items and hairstyles in a virtual environment.

[0089] "Expert feedback" refers to advice and evaluations provided to users by professionals such as stylists and hairdressers.

[0090] "Means for saving final styling" refers to a technical means for saving the styling selected by the user in a database or the like, so that it can be reused or referenced later.

[0091] A "database" refers to an information system that systematically stores large amounts of information and allows it to be searched and used as needed.

[0092] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system inputs the user's basic information and photo, analyzes the user's body type and facial features using a generative AI model, and suggests personalized fashion items and hairstyles based on that information. Furthermore, users can virtually try on suggested styles using a virtual try-on feature and receive feedback from experts.

[0093] 1. Enter your user information and upload a photo

[0094] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, and preferences. They also upload photos of their face or their entire body. The hardware used is a PC or smartphone, and the software is a web browser or dedicated application.

[0095] 2. Receiving and analyzing user information and photos

[0096] The server passes the basic information and photo data received from the user to the generative AI model. The generative AI model uses OpenAI's image analysis technology, for example, to boldly analyze the user's body shape and facial features. It obtains a detailed understanding of the user's facial shape, body type, skin color, and other characteristics and generates analytical data.

[0097] 3. Generating styling suggestions

[0098] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database that includes general fashion and beauty-related data. Specifically, it suggests slim-fitting shirts, denim jeans, and short hairstyles.

[0099] 4. Presentation of proposal

[0100] The server sends the generated styling suggestions to the device, which then visually displays them to the user, who can then view and confirm the suggestions through a dedicated application or web browser.

[0101] 5. Virtual try-on

[0102] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles. This feature is realized using technology from Modiface, for example. Users can use the camera function of their smartphone or computer to see how the proposed items will look on them in real time.

[0103] 6. Expert feedback

[0104] The server receives feedback from stylists, hairdressers, and other experts and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[0105] 7. Select and save your final styling

[0106] The user reviews the suggestions and expert feedback and selects the final styling. The device sends the selection to the server, which stores it in a database that the user can access and review at any time.

[0107] Specific examples

[0108] For example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. When the user enters a photo and basic information, the server receives it, and the generative AI model analyzes facial features and body type, obtaining an analysis result such as "thin and long face." The server then uses its database to suggest items such as slim-fitting shirts, denim jeans, and short hairstyles. The user can then use the virtual try-on feature to virtually try on these items, and an expert will provide feedback such as "bright-colored shirts would look better on you." The user can then accept and save the suggestions.

[0109] Prompt Sentence Examples

[0110] "I'm a 42-year-old engineer. I like a casual lifestyle and often spend time outdoors. I'm thin with a long face. Can you suggest some fashion items and hairstyles that would suit me?"

[0111] In this way, the system can make styling suggestions based on the user's characteristics and preferences, providing the optimal style through virtual try-ons and expert feedback.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, preferences, etc. Users then upload a photo.

[0115] Input: User's basic information (age, occupation, lifestyle, preferences) and photo image.

[0116] Output: The user's basic information and photo are sent to the server.

[0117] What happens: The user fills out the form and clicks the upload button to submit the photo.

[0118] Step 2:

[0119] The server analyzes the basic information and photo data received from the user, and processes the information using a generative AI model to analyze the user's body shape and facial features.

[0120] Input: User basic information and photo data.

[0121] Output: Analysis of the user's body shape and facial features by the generative AI.

[0122] Specific operation: The server passes the received data to the generative AI model and performs image analysis, for example, using OpenAI's image analysis technology to determine face shape and body type.

[0123] Step 3:

[0124] The server selects personalized fashion items and hairstyles from a database based on the analysis results.

[0125] Input: Analysis results and user preference information.

[0126] Output: A list of selected fashion items and hairstyles.

[0127] What it does: The server searches a database and selects the items that best suit the user, such as a slim-fitting shirt, denim jeans, and a short hairstyle.

[0128] Step 4:

[0129] The server sends the generated styling suggestions to the terminal, which then visually displays them to the user.

[0130] Input: Selected fashion item and hairstyle data.

[0131] Output: Visual styling suggestions displayed on the user's device.

[0132] Specific operation: The server sends data to the terminal in JSON format, etc., and the terminal displays the data.

[0133] Step 5:

[0134] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles.

[0135] Input: Selected fashion item and hairstyle data.

[0136] Output: A display of the results of the user virtually trying on the garment.

[0137] Specific operation: Using the device's camera and technologies such as Modiface, the suggestions are reflected on the user's appearance in real time.

[0138] Step 6:

[0139] The server receives the feedback from the experts and provides it to the user.

[0140] Input: Expert feedback information.

[0141] Output: Any additional advice or rating that is displayed to the user.

[0142] How it works: The expert reviews the suggestion and sends the advice to the server, which then forwards it to the user's device, where it displays the advice.

[0143] Step 7:

[0144] The user reviews the suggestions and expert feedback and selects the final styling.

[0145] Input: The final selection made by the user.

[0146] Output: The selected styling is saved to the server.

[0147] What happens: The user makes a final selection and clicks the submit button to send the selection to the server, which records it in a database.

[0148] (Application example 1)

[0149] 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."

[0150] In today's modern lifestyle, choosing the right diet to maintain good health is difficult, especially for busy men in their 40s and older. Personalizing meals based on individual health conditions and food preferences is time-consuming, and achieving this with expert feedback is even more difficult. There are also limited ways to confirm in advance whether the meal you choose is suitable for you. Therefore, there is a need for a system that allows users to easily select the optimal meal menu from the comfort of their own home and check it in advance.

[0151] 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.

[0152] In this invention, the server includes means for receiving basic information from a user, a generating AI means for analyzing user characteristics based on the input basic information, a means for selecting a personalized meal menu based on the analysis results by the generating AI means, a means for presenting the selected meal menu to the user, a virtual tasting means for allowing the user to virtually check the nutritional components and calorie information of the selected meal menu, a means for providing feedback from an expert, and a means for saving the final meal menu based on the user's selection. This allows the user to easily receive healthy meal menus that are optimal for them from the comfort of their own home, and to select and save a final meal plan while reviewing the contents in advance and receiving advice from an expert.

[0153] "User" refers to an individual who uses this system to receive meal menu suggestions and feedback.

[0154] "Basic information" refers to information about the user's age, occupation, lifestyle, body type, food preferences, etc.

[0155] "Generative AI means" refers to the artificial intelligence function that analyzes the user's basic information and generates a personalized meal menu.

[0156] A "meal menu" is a specific meal plan or recipe suggested based on the user's health and preferences.

[0157] "Virtual tasting tool" refers to a function that allows users to virtually check the nutritional content and calorie information of the proposed meal menu.

[0158] An "expert" is someone with the expertise to provide advice on a user's health and diet, such as a registered dietitian or health coach.

[0159] "Feedback" refers to additional advice or opinions provided by experts based on the user's condition and preferences.

[0160] "Meal Plan" means a set of specific meal plans saved based on a User's selections.

[0161] A "database" is an electronic data storage system for storing analyzed information and selected meal menus.

[0162] This invention relates to a system that allows male users in their 40s or older to receive healthy and optimal meal menu suggestions from the comfort of their own homes. The embodiments for carrying out the invention are as follows.

[0163] The system consists of the following main functions: inputting user information and uploading photos, suggesting meal menus based on analysis results, a virtual tasting function, feedback from experts, saving the final meal plan, and creating a database to manage this information.

[0164] Enter your user information and upload a photo

[0165] Users enter basic information such as age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application. In addition, users can upload photos of their body type and face. This information and photos are received by the server.

[0166] Receiving and analyzing user information and photos

[0167] The server uses OpenAI's generative AI model to analyze the uploaded information and photos, providing a detailed understanding of the user's health, body type, lifestyle, and dietary preferences. The analysis results serve as the basis for generating a personalized meal menu.

[0168] Generate a meal menu

[0169] Based on the analysis results, the generative AI selects an appropriate meal plan from the database, taking into account the user's health status and dietary preferences. For example, it may suggest low-carb meals or meals using vitamin-rich ingredients.

[0170] Presentation of proposal content

[0171] The device presents the user with a selection of meal options, which can then be viewed through a smartphone app.

[0172] Virtual Tasting

[0173] Users can virtually check the nutritional content and calorie information of the proposed meal menu using the virtual tasting function, allowing them to check in advance whether the proposed menu is compatible with their lifestyle and health condition.

[0174] Expert feedback

[0175] The server receives feedback from experts such as registered dietitians and health coaches and provides it to the user, who then provides additional advice based on the user's characteristics and preferences.

[0176] Select and save your final meal plan

[0177] Users review the suggestions and expert feedback and select a final meal plan, which is then saved in a database that users can access and review at any time.

[0178] Specific examples

[0179] For example, let's say the user is a 45-year-old engineer with an active lifestyle and a desire to follow a low-carb diet. The user enters their age, occupation, lifestyle, body type, and food preferences, and uploads a photo. The server receives this information, and the generative AI analyzes the user's health and body type. An example of a prompt to be input to the generative AI model is as follows:

[0180] Please suggest a meal plan that is ideal for Age: 45, Occupation: Engineer, Lifestyle: Active, Build: Medium, Food Preference: Low Carbohydrate.

[0181] Based on this, a low-carb meal menu is suggested, and experts provide feedback that a meal with a moderate amount of protein would be even better. The user then selects and saves the final suggested menu.

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1: Enter your user information and upload a photo

[0184] Users enter basic information such as their age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application, and upload photos of their body and face. The input data and uploaded photos are sent to the server. The input in this step is the user's basic information and photo, and the output is the transmission of data to the server.

[0185] Step 2: Receive and analyze user information and photos

[0186] The server uses generative AI to perform a detailed analysis based on the received user's basic information and photo. Specifically, it analyzes the user's facial features, body type, lifestyle, etc. to understand their health condition and dietary preferences. The input for this step is the user's basic information and photo, and the output is the analyzed data.

[0187] Step 3: Generate the meal menu

[0188] The generative AI selects an appropriate meal menu from a database based on the analysis results. For example, low-carb menus or menus rich in vitamins are suggested based on the user's characteristics. The input for this step is the analysis data, and the output is a personalized meal menu.

[0189] Step 4: Present your proposal

[0190] The terminal presents the user with a meal menu selected by the generative AI. The user can review these suggestions through a smartphone app. The input of this step is a personalized meal menu, and the output is a display of the suggestions to the user.

[0191] Step 5: Virtual Tasting

[0192] The virtual tasting feature allows users to virtually check the nutritional and calorie information of the proposed meal menu. This feature allows users to consider whether the menu fits their lifestyle and health status. The input of this step is the nutritional information of the meal menu, and the output is the information displayed to the user.

[0193] Step 6: Expert feedback

[0194] The server receives feedback from experts, such as registered dietitians and health coaches, who provide additional advice based on the user's characteristics and preferences. The input of this step is the expert feedback, and the output is a reflection of that feedback to the user.

[0195] Step 7: Select and save your final meal plan

[0196] The user reviews the suggestions and expert feedback and selects the final meal plan. The selected meal plan is saved in a database and can be accessed and reviewed by the user at any time. The input of this step is the final meal plan, and the output is saving it to the database.

[0197] 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.

[0198] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized experience. This system includes the following elements.

[0199] Enter your user information and upload a photo

[0200] Users enter basic information such as their age, occupation, and lifestyle, and upload their own photos through a dedicated website or application. They can also enter emotional information based on self-diagnosis. The device then sends this data to a server.

[0201] Receiving, analyzing, and recognizing emotions in user information and photos

[0202] The server receives user information, photos, and emotional information. The server uses the generative AI to analyze the user's body shape and facial features. The emotion engine then analyzes emotional information from the user's facial expressions using facial recognition technology and combines this with the generative AI's analysis data.

[0203] Generate styling suggestions

[0204] The server selects personalized fashion items and hairstyles from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[0205] Presentation of proposal content

[0206] The device will present the user with selected fashion items and hairstyles, which the user can review through the app. The suggestions are tailored to the user's emotions, providing a more comfortable experience.

[0207] Virtual try-on

[0208] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[0209] Expert feedback

[0210] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[0211] Select and save your final styling

[0212] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[0213] Specific examples

[0214] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[0215] In this way, the present invention provides users with personalized styling suggestions based on their emotions, providing an environment in which they can approach their search for a partner with confidence.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The user then completes a simple self-diagnosis and enters their emotional state. The device then sends the entered data, photo, and emotional information to the server.

[0219] Step 2:

[0220] The server receives user information, photos, and emotion information, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photos and basic information.

[0221] Step 3:

[0222] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The results of this analysis are sent back to the server.

[0223] Step 4:

[0224] The server calls the emotion engine, analyzes the facial expressions in the user's photo, and parses the emotional information. The emotion engine recognizes the user's current emotional state (e.g., happy, nervous, sad) based on the user's facial expressions. The analysis results are also sent back to the server.

[0225] Step 5:

[0226] The server combines the analysis results of the generation AI and the emotion engine, and based on this combined data, extracts personalized fashion items and hairstyles from the database.

[0227] Step 6:

[0228] The server selects the extracted fashion items and hairstyles from a list and generates the optimal combination. The server takes into account the user's emotional state to provide a more suitable styling. The generated styling suggestions are notified to the user.

[0229] Step 7:

[0230] The user receives a notification and confirms the styling suggestion within the application. The device displays details of the fashion item and hairstyle. The user confirms the suggested style and views the details.

[0231] Step 8:

[0232] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[0233] Step 9:

[0234] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[0235] Step 10:

[0236] The user reviews the suggestions and expert feedback and selects the final styling. The final styling is selected taking into account the user's emotional state and the expert's advice. The selected styling is sent from the device to the server and stored in a database.

[0237] Step 11:

[0238] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[0239] As a concrete example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. The user uploads a photo of themselves in the emotional state of "fun" to the system. The server receives the photo and basic information, and the generation AI analyzes their facial features and body type. At the same time, the emotion engine analyzes their facial expressions and recognizes the emotion of "fun." The server then selects from its database a casual, light-colored slim-fit shirt, denim jeans, and a short hairstyle. The user virtually tries these on using the virtual try-on function, and the expert gives feedback that "the light-colored shirt suits you." The user then finally selects and saves this style. In this way, the present invention takes the user's emotional state into account to provide more personalized styling suggestions.

[0240] Example 2

[0241] 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."

[0242] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. Conventional methods have the drawback of making it difficult for users to select appropriate items themselves, and suggestions do not take emotions into account, resulting in low user satisfaction. Furthermore, it is difficult to receive timely feedback from experts, making it difficult to make optimal choices. Therefore, there is a need for a system that provides users with more personalized suggestions and incorporates expert opinions.

[0243] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving basic information from a user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo of the user, a means for analyzing emotional information using face recognition technology, a means for selecting personalized fashion items and hairstyles based on the analysis results of the generation AI means and the emotional information analysis means, a means for presenting the selected fashion items and hairstyles to the user, a virtual try-on means for allowing the user to virtually try on the selected fashion items and hairstyles, and a means for saving the final styling based on the user's selection. This allows the user to not only receive appropriate styling suggestions from the comfort of their own home, but also enable suggestions that take emotions into consideration, and further allows them to make optimal selections by receiving expert feedback.

[0244] "Basic information" refers to data such as the user's age, occupation, and lifestyle.

[0245] "Photo" refers to an image file showing the user's face and body shape.

[0246] "Generative AI means" refers to the artificial intelligence techniques used to analyze user characteristics.

[0247] "Facial recognition technology" refers to technology for analyzing emotional information from a user's facial expressions.

[0248] "Emotional information" refers to the emotional state analyzed from the user's facial expressions.

[0249] "Personalized fashion items" refer to clothing and accessories selected based on the user's characteristics, preferences, and emotional state.

[0250] "Hairstyle" refers to the hairstyle suggested to the user.

[0251] "Virtual Try-On Facility" refers to a system feature that allows a user to virtually try on selected fashion items and hairstyles.

[0252] "Expert feedback" refers to advice and evaluations provided by professionals such as stylists and hairdressers.

[0253] "Database" refers to an information management system for storing analyzed information and selected fashion items and hairstyles.

[0254] "Server" refers to a computer system that receives, processes, and analyzes user input.

[0255] "Terminal" refers to a device such as a computer or smartphone that is operated by a user.

[0256] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system aims to provide a more personalized experience by combining an emotion engine that recognizes the user's emotions. The system includes the following elements:

[0257] Enter your user information and upload a photo

[0258] The user accesses a dedicated website or application. They enter basic information such as their age, occupation, and lifestyle, and upload their own photos. They can also enter their own emotional information through self-diagnosis. This information is sent from the device to the server.

[0259] Receiving, analyzing, and recognizing emotions in user information and photos

[0260] The server receives the user's basic information, photo, and emotional information. Next, the server uses a generative AI (e.g., publicly available artificial intelligence technology) to analyze the user's body shape and facial features. In addition, it uses an emotional engine (e.g., software using facial recognition technology) to analyze emotional information from the user's facial expressions. The results of this analysis are combined with the generative AI's analysis data and processed.

[0261] Example prompt sentence:

[0262] "Analyze this user's facial and body features."

[0263] Generate styling suggestions

[0264] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from its database, taking into account the user's preferences, lifestyle, and emotional state.

[0265] Example prompt sentence:

[0266] "42-year-old engineer, casual lifestyle, emotional state: 'fun'"

[0267] Presentation of proposal content

[0268] The server sends the selected fashion items and hairstyles to the device, which then presents them to the user, who can then confirm the suggestions through the application.

[0269] Virtual try-on

[0270] When a user selects the virtual try-on feature, the device generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try on different styles.

[0271] Expert feedback

[0272] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server then notifies the user of this feedback.

[0273] Example prompt sentence:

[0274] "A lighter colored shirt would fit that sentiment."

[0275] Select and save your final styling

[0276] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling information is sent from the device to a server and stored in a database. The saved information can be accessed and reviewed by the user as many times as needed.

[0277] Examples:

[0278] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotion information, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then suggests a slim-fit shirt, denim jeans, and a short hairstyle from its database. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves this suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[0279] In this way, the present invention provides users with styling suggestions based on their personalized emotions, providing an environment in which they can approach their search for a partner with confidence.

[0280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0281] Step 1:

[0282] Users access a dedicated website or application and enter basic information such as age, occupation, and lifestyle. Next, they upload their own photos and enter their emotional information through self-diagnosis. The device then sends this information to a server.

[0283] Input: Basic information such as age, occupation, lifestyle, user photo, emotional information

[0284] Output: Basic information, photo, and emotional information sent to the server

[0285] Step 2:

[0286] The server processes the received basic information, photos, and emotional information, and uses a generative AI model (e.g., general artificial intelligence technology) to analyze the user's body shape and facial features.

[0287] Input: User's basic information, photo

[0288] Output: Analysis results for body shape and facial features

[0289] Step 3:

[0290] The server uses facial recognition technology to analyze emotional information from the user's photo. It then uses an emotion engine (e.g., general emotion recognition software) to analyze the user's emotions and combines them with the analysis results of the generative AI.

[0291] Input: User photo

[0292] Output: Parsed emotion information

[0293] Step 4:

[0294] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from the database, taking into account the user's preferences, lifestyle, and emotional state.

[0295] Input: Analysis results of body shape and facial features, emotional information

[0296] Output: Selected fashion items and hairstyles

[0297] Step 5:

[0298] The server transmits the selected fashion items and hairstyles to the terminal, which then presents them to the user.

[0299] Input: Selected fashion items and hairstyles

[0300] Output: Styling suggestions presented to the user

[0301] Step 6:

[0302] When the user selects the virtual try-on function, the terminal generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try out different styles.

[0303] Input: Selected fashion items and hairstyles

[0304] Output: Virtual try-on screen

[0305] Step 7:

[0306] The server sends styling suggestions to experts for feedback, the experts review the suggestions and provide additional advice, and the server notifies the user of this feedback information.

[0307] Input: Styling suggestions

[0308] Output: Expert feedback

[0309] Step 8:

[0310] The user reviews the suggestions and expert feedback and selects the final styling. The device then sends the selected styling information to the server, which stores it in a database.

[0311] Input: Final styling selection

[0312] Output: Styling information stored in a database

[0313] Each step is designed to be easy for users to operate at home, and the server and device are linked to provide a smooth experience. This system allows users to receive optimal styling suggestions to help them approach their marriage search with confidence.

[0314] (Application example 2)

[0315] 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."

[0316] Conventional styling systems are limited to making suggestions based on basic user information and lack personalized suggestions that take into account the user's emotions and momentary moods. Furthermore, trying on suggested fashions and hairstyles requires a user to visit a physical store, making it difficult to try them on. The present invention aims to solve these issues by providing a system that offers personalized styling suggestions based on the user's emotions and includes a virtual try-on function.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0318] In this invention, the server includes a means for receiving basic information and a photo from the user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo, and an emotion engine means for recognizing emotional information from the user's facial photo, thereby enabling personalized styling suggestions and virtual try-ons based on the user's emotions.

[0319] "Basic user information" refers to information about basic attributes such as age, occupation, and lifestyle provided by the user.

[0320] A "photo" is image data that includes the user's face and body shape.

[0321] "Generative AI" is an artificial intelligence that analyzes basic information and photos provided by users and extracts features.

[0322] The "Emotion Engine" is a technology that recognizes and analyzes emotional information from a user's facial photograph.

[0323] "Personalized fashion items" are clothing and accessories that are individually selected based on a user's characteristics and emotional information.

[0324] A "personalized hairstyle" is a hairstyle that is individually selected based on the user's characteristics and emotional information.

[0325] "Virtual Try-On" is a feature that allows users to try on selected fashion items and hairstyles in a virtual space.

[0326] "Expert feedback" is additional advice or opinion provided by professionals such as stylists or hairdressers.

[0327] A "database" is a repository of analyzed information and selected fashion items and hairstyles.

[0328] This invention is a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine, it provides a more personalized experience. As a specific embodiment, we will explain a system that includes the following elements.

[0329] 1. Enter your user information and upload a photo

[0330] Users enter basic information such as age, occupation, and lifestyle, and upload their own photos via their device. They can also enter emotional information based on self-diagnosis. This data is sent from the device to the server.

[0331] 2. Receiving, analyzing, and recognizing emotions in user information and photos

[0332] The server receives the user's information and photo, and uses generative AI to analyze the user's body shape and facial features. The emotion engine uses facial recognition technology to analyze the user's emotional information from their facial expressions. This allows the server to obtain the user's characteristics and current emotional state.

[0333] 3. Generating styling suggestions

[0334] The server selects personalized fashion items and hairstyles for the user from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[0335] 4. Presentation of proposal

[0336] The suggested fashion items and hairstyles are presented to the user via the device, and the user can check the suggestions through the application.

[0337] 5. Virtual try-on

[0338] Users can virtually try on suggested fashion items and hairstyles using the device's virtual try-on feature, which allows users to try out different styles and see how they look.

[0339] 6. Expert feedback

[0340] The server sends styling suggestions to experts (stylists and hairdressers) and requests feedback. Feedback from the experts is sent to the server and notified to the user.

[0341] 7. Select and save your final styling

[0342] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is saved on the server and can be accessed and reviewed by the user at any time.

[0343] The hardware required is the user's device (smartphone or PC) and a server, while the software uses OpenCV, FER, and TensorFlow (a generative AI model) to perform image analysis and emotion recognition.

[0344] Specific examples

[0345] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[0346] Prompt Sentence Examples

[0347] "I'm a 42-year-old engineer with a casual lifestyle, loves the outdoors, and am in a fun mood right now. Based on this information, the suggestions were a slim-fit shirt, denim jeans, and a short hairstyle. Further expert feedback suggested that a lighter-colored shirt would fit that sentiment."

[0348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0349] Step 1:

[0350] Users input basic information such as age, occupation, and lifestyle, as well as their own photos, into the application via their device, which then sends the data to the server.

[0351] Input: User's basic information (age, occupation, lifestyle, etc.), user photo

[0352] Output: Basic information and photo data sent

[0353] Step 2:

[0354] The server inputs the user information and photo data received from the device into a generative AI model for analysis.

[0355] Input: User's basic information and photo

[0356] Output: Analyzed user characteristics information (body type, facial features, etc.)

[0357] How it works: Uses generative AI models for facial and body feature extraction

[0358] Step 3:

[0359] The server analyzes facial expressions from the user's photograph and obtains emotion information using an emotion engine.

[0360] Input: User's face photo

[0361] Output: User's emotional information (e.g., happy, sad)

[0362] How it works: The emotion engine analyzes images using facial expression recognition technology to estimate emotional states.

[0363] Step 4:

[0364] The server combines the analysis results of the generative AI model and the emotion engine to generate personalized fashion item and hairstyle suggestions.

[0365] Input: Analyzed user characteristics and emotion information

[0366] Output: Suggested fashion items and hairstyles

[0367] How it works: Selects items from a database based on the user's characteristics and emotions and generates a list

[0368] Step 5:

[0369] The terminal presents the suggestions provided by the server to the user, who then confirms them.

[0370] Input: Suggested fashion items and hairstyles

[0371] Output: Fashion item and hairstyle information presented to the user

[0372] Action: The user reviews the proposal through the application.

[0373] Step 6:

[0374] Users can use the virtual try-on function on their device to virtually try on suggested fashion items and hairstyles.

[0375] Input: Fashion items and hairstyles the user wants to try on

[0376] Output: Virtual try-on images and videos

[0377] Action: Applying items to the user avatar using the virtual try-on feature

[0378] Step 7:

[0379] The server collects expert feedback on the proposed styling and notifies the user.

[0380] Input: Suggested fashion items and hairstyles

[0381] Output: Expert feedback

[0382] How it works: The server collects opinions and advice from stylists and hairdressers and delivers them to the user.

[0383] Step 8:

[0384] The user selects the final styling and sends the results from the device to the server, which stores the results in a database.

[0385] Input: Final styling selected by the user

[0386] Output: Final styling information saved

[0387] What happens: The server receives the user's final selection and stores it in the database

[0388] 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.

[0389] 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.

[0390] 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.

[0391] [Second embodiment]

[0392] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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."

[0404] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. This system includes the following elements:

[0405] Enter your user information and upload a photo

[0406] Users enter basic information such as their age, occupation, and lifestyle through a dedicated website or application, and upload their own photos. At this stage, users can also enter their wishes and preferences.

[0407] Receiving and analyzing user information and photos

[0408] Based on the user information and photos received by the server, the generation AI analyzes the user's body type and facial features, accurately determining the user's face shape, body type, skin color, etc., and generates data to suggest styling accordingly.

[0409] Generate styling suggestions

[0410] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database, taking into account the user's preferences and lifestyle. The selected fashion items and hairstyles are presented as the optimal combination for the user.

[0411] Presentation of proposal content

[0412] The device will present the selected fashion items and hairstyles to the user, who can then review the suggestions through the application.

[0413] Virtual try-on

[0414] Users can virtually try on suggested fashion items and hairstyles using the virtual try-on feature. This feature allows users to select the style they want to try and see how it will look on them in a virtual mirror. This allows them to see in advance whether the suggested styling will suit them.

[0415] Expert feedback

[0416] The server receives feedback from experts (stylists and hairdressers) and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[0417] Select and save your final styling

[0418] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[0419] Specific examples

[0420] For example, say the user is a 42-year-old engineer who enjoys a casual lifestyle and the outdoors. The user enters a photo and basic information. The server receives this, and the generative AI analyzes facial features and body type, resulting in a result such as "thin and long face." The server then suggests slim-fitting shirts, denim jeans, and short hairstyles from its database. The user then uses the virtual try-on feature to virtually try these on. The expert then provides feedback, saying, "A brightly colored shirt would suit you better." The user finally accepts and saves the suggestions. The user can then refer to the saved styles and actually purchase the clothing at a later date.

[0421] In this way, the present invention provides users with an environment in which they can approach their matchmaking with confidence and simplifies the process.

[0422] The processing flow will be explained below.

[0423] Step 1:

[0424] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The device then sends the entered data and photo to the server.

[0425] Step 2:

[0426] The server receives the user information and photo, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photo and basic information.

[0427] Step 3:

[0428] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The analysis results are then sent back to the server.

[0429] Step 4:

[0430] The server receives the analysis results and extracts a list of suitable fashion items and hairstyles from the database, which is personalized based on the user's characteristics and preferences.

[0431] Step 5:

[0432] The server filters the extracted list of fashion items and hairstyles to generate optimal combinations, and the generated styling suggestions are notified to the user.

[0433] Step 6:

[0434] The user receives a notification and sees styling suggestions within the app, and the device displays details of the fashion item and hairstyle.

[0435] Step 7:

[0436] The user uses the virtual try-on feature to virtually try on the suggested styles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try on different styles.

[0437] Step 8:

[0438] The server sends styling suggestions to experts (stylists or hairdressers) and asks for feedback. The experts review the suggestions and provide additional advice.

[0439] Step 9:

[0440] The expert's feedback is sent back to the server, which notifies the user of the feedback information, and the user confirms the feedback and finalizes the styling.

[0441] Step 10:

[0442] The user selects the final styling. The device sends the selection to the server. The server stores the selection in a database.

[0443] Step 11:

[0444] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[0445] Example 1

[0446] 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."

[0447] Conventional styling suggestion systems were unable to accurately grasp a user's basic information and characteristics, making it difficult to provide personalized styling suggestions. Furthermore, users were unable to actually try out the suggested styling and were unable to receive real-time feedback from experts. Furthermore, it was difficult to provide styling that reflected the user's preferences and wishes, and data storage and reuse were insufficient.

[0448] 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.

[0449] In this invention, the server includes means for receiving basic information from a user, means for receiving the basic information and a photo and analyzing the user's body type and facial features using a generative AI model, means for selecting personalized fashion items and hairstyles based on the analysis results by the generative AI means, means for presenting the selected fashion items and hairstyles to the user, means for a virtual try-on that allows the user to virtually try on the selected fashion items and hairstyles, means for receiving feedback from an expert and providing it to the user, and means for saving the final styling based on the user's selection. This allows the server to accurately reflect the user's features and preferences, make optimal styling suggestions based on the virtual try-on and expert feedback, and also allows the data to be saved and reused.

[0450] "Basic user information" refers to basic information about the user, such as age, occupation, lifestyle, and preferences.

[0451] A "generative AI model" refers to an artificial intelligence model that uses machine learning technology to analyze images and data and extract user characteristics.

[0452] "Analyzing the user's body shape and facial features" means that the generative AI model analyzes the user's photo to identify detailed features such as body shape, facial shape, and skin color.

[0453] "Personalized fashion items and hairstyles" refers to individually tailored fashion items and hairstyles selected based on the user's characteristics and preferences.

[0454] "Virtual Try-On Facility" means a technological facility that allows a user to try on selected fashion items and hairstyles in a virtual environment.

[0455] "Expert feedback" refers to advice and evaluations provided to users by professionals such as stylists and hairdressers.

[0456] "Means for saving final styling" refers to a technical means for saving the styling selected by the user in a database or the like, so that it can be reused or referenced later.

[0457] A "database" refers to an information system that systematically stores large amounts of information and allows it to be searched and used as needed.

[0458] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system inputs the user's basic information and photo, analyzes the user's body type and facial features using a generative AI model, and suggests personalized fashion items and hairstyles based on that information. Furthermore, users can virtually try on suggested styles using a virtual try-on feature and receive feedback from experts.

[0459] 1. Enter your user information and upload a photo

[0460] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, and preferences. They also upload photos of their face or their entire body. The hardware used is a PC or smartphone, and the software is a web browser or dedicated application.

[0461] 2. Receiving and analyzing user information and photos

[0462] The server passes the basic information and photo data received from the user to the generative AI model. The generative AI model uses OpenAI's image analysis technology, for example, to boldly analyze the user's body shape and facial features. It obtains a detailed understanding of the user's facial shape, body type, skin color, and other characteristics and generates analytical data.

[0463] 3. Generating styling suggestions

[0464] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database that includes general fashion and beauty-related data. Specifically, it suggests slim-fitting shirts, denim jeans, and short hairstyles.

[0465] 4. Presentation of proposal

[0466] The server sends the generated styling suggestions to the device, which then visually displays them to the user, who can then view and confirm the suggestions through a dedicated application or web browser.

[0467] 5. Virtual try-on

[0468] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles. This feature is realized using technology from Modiface, for example. Users can use the camera function of their smartphone or computer to see how the proposed items will look on them in real time.

[0469] 6. Expert feedback

[0470] The server receives feedback from stylists, hairdressers, and other experts and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[0471] 7. Select and save your final styling

[0472] The user reviews the suggestions and expert feedback and selects the final styling. The device sends the selection to the server, which stores it in a database that the user can access and review at any time.

[0473] Specific examples

[0474] For example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. When the user enters a photo and basic information, the server receives it, and the generative AI model analyzes facial features and body type, obtaining an analysis result such as "thin and long face." The server then uses its database to suggest items such as slim-fitting shirts, denim jeans, and short hairstyles. The user can then use the virtual try-on feature to virtually try on these items, and an expert will provide feedback such as "bright-colored shirts would look better on you." The user can then accept and save the suggestions.

[0475] Prompt Sentence Examples

[0476] "I'm a 42-year-old engineer. I like a casual lifestyle and often spend time outdoors. I'm thin with a long face. Can you suggest some fashion items and hairstyles that would suit me?"

[0477] In this way, the system can make styling suggestions based on the user's characteristics and preferences, providing the optimal style through virtual try-ons and expert feedback.

[0478] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0479] Step 1:

[0480] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, preferences, etc. Users then upload a photo.

[0481] Input: User's basic information (age, occupation, lifestyle, preferences) and photo image.

[0482] Output: The user's basic information and photo are sent to the server.

[0483] What happens: The user fills out the form and clicks the upload button to submit the photo.

[0484] Step 2:

[0485] The server analyzes the basic information and photo data received from the user, and processes the information using a generative AI model to analyze the user's body shape and facial features.

[0486] Input: User basic information and photo data.

[0487] Output: Analysis of the user's body shape and facial features by the generative AI.

[0488] Specific operation: The server passes the received data to the generative AI model and performs image analysis, for example, using OpenAI's image analysis technology to determine face shape and body type.

[0489] Step 3:

[0490] The server selects personalized fashion items and hairstyles from a database based on the analysis results.

[0491] Input: Analysis results and user preference information.

[0492] Output: A list of selected fashion items and hairstyles.

[0493] What it does: The server searches a database and selects the items that best suit the user, such as a slim-fitting shirt, denim jeans, and a short hairstyle.

[0494] Step 4:

[0495] The server sends the generated styling suggestions to the terminal, which then visually displays them to the user.

[0496] Input: Selected fashion item and hairstyle data.

[0497] Output: Visual styling suggestions displayed on the user's device.

[0498] Specific operation: The server sends data to the terminal in JSON format, etc., and the terminal displays the data.

[0499] Step 5:

[0500] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles.

[0501] Input: Selected fashion item and hairstyle data.

[0502] Output: A display of the results of the user virtually trying on the garment.

[0503] Specific operation: Using the device's camera and technologies such as Modiface, the suggestions are reflected on the user's appearance in real time.

[0504] Step 6:

[0505] The server receives the feedback from the experts and provides it to the user.

[0506] Input: Expert feedback information.

[0507] Output: Any additional advice or rating that is displayed to the user.

[0508] How it works: The expert reviews the suggestion and sends the advice to the server, which then forwards it to the user's device, where it displays the advice.

[0509] Step 7:

[0510] The user reviews the suggestions and expert feedback and selects the final styling.

[0511] Input: The final selection made by the user.

[0512] Output: The selected styling is saved to the server.

[0513] What happens: The user makes a final selection and clicks the submit button to send the selection to the server, which records it in a database.

[0514] (Application example 1)

[0515] 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."

[0516] In today's modern lifestyle, choosing the right diet to maintain good health is difficult, especially for busy men in their 40s and older. Personalizing meals based on individual health conditions and food preferences is time-consuming, and achieving this with expert feedback is even more difficult. There are also limited ways to confirm in advance whether the meal you choose is suitable for you. Therefore, there is a need for a system that allows users to easily select the optimal meal menu from the comfort of their own home and check it in advance.

[0517] 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.

[0518] In this invention, the server includes means for receiving basic information from a user, a generating AI means for analyzing user characteristics based on the input basic information, a means for selecting a personalized meal menu based on the analysis results by the generating AI means, a means for presenting the selected meal menu to the user, a virtual tasting means for allowing the user to virtually check the nutritional components and calorie information of the selected meal menu, a means for providing feedback from an expert, and a means for saving the final meal menu based on the user's selection. This allows the user to easily receive healthy meal menus that are optimal for them from the comfort of their own home, and to select and save a final meal plan while reviewing the contents in advance and receiving advice from an expert.

[0519] "User" refers to an individual who uses this system to receive meal menu suggestions and feedback.

[0520] "Basic information" refers to information about the user's age, occupation, lifestyle, body type, food preferences, etc.

[0521] "Generative AI means" refers to the artificial intelligence function that analyzes the user's basic information and generates a personalized meal menu.

[0522] A "meal menu" is a specific meal plan or recipe suggested based on the user's health and preferences.

[0523] "Virtual tasting tool" refers to a function that allows users to virtually check the nutritional content and calorie information of the proposed meal menu.

[0524] An "expert" is someone with the expertise to provide advice on a user's health and diet, such as a registered dietitian or health coach.

[0525] "Feedback" refers to additional advice or opinions provided by experts based on the user's condition and preferences.

[0526] "Meal Plan" means a set of specific meal plans saved based on a User's selections.

[0527] A "database" is an electronic data storage system for storing analyzed information and selected meal menus.

[0528] This invention relates to a system that allows male users in their 40s or older to receive healthy and optimal meal menu suggestions from the comfort of their own homes. The embodiments for carrying out the invention are as follows.

[0529] The system consists of the following main functions: inputting user information and uploading photos, suggesting meal menus based on analysis results, a virtual tasting function, feedback from experts, saving the final meal plan, and creating a database to manage this information.

[0530] Enter your user information and upload a photo

[0531] Users enter basic information such as age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application. In addition, users can upload photos of their body type and face. This information and photos are received by the server.

[0532] Receiving and analyzing user information and photos

[0533] The server uses OpenAI's generative AI model to analyze the uploaded information and photos, providing a detailed understanding of the user's health, body type, lifestyle, and dietary preferences. The analysis results serve as the basis for generating a personalized meal menu.

[0534] Generate a meal menu

[0535] Based on the analysis results, the generative AI selects an appropriate meal plan from the database, taking into account the user's health status and dietary preferences. For example, it may suggest low-carb meals or meals using vitamin-rich ingredients.

[0536] Presentation of proposal content

[0537] The device presents the user with a selection of meal options, which can then be viewed through a smartphone app.

[0538] Virtual Tasting

[0539] Users can virtually check the nutritional content and calorie information of the proposed meal menu using the virtual tasting function, allowing them to check in advance whether the proposed menu is compatible with their lifestyle and health condition.

[0540] Expert feedback

[0541] The server receives feedback from experts such as registered dietitians and health coaches and provides it to the user, who then provides additional advice based on the user's characteristics and preferences.

[0542] Select and save your final meal plan

[0543] Users review the suggestions and expert feedback and select a final meal plan, which is then saved in a database that users can access and review at any time.

[0544] Specific examples

[0545] For example, let's say the user is a 45-year-old engineer with an active lifestyle and a desire to follow a low-carb diet. The user enters their age, occupation, lifestyle, body type, and food preferences, and uploads a photo. The server receives this information, and the generative AI analyzes the user's health and body type. An example of a prompt to be input to the generative AI model is as follows:

[0546] Please suggest a meal plan that is ideal for Age: 45, Occupation: Engineer, Lifestyle: Active, Build: Medium, Food Preference: Low Carbohydrate.

[0547] Based on this, a low-carb meal menu is suggested, and experts provide feedback that a meal with a moderate amount of protein would be even better. The user then selects and saves the final suggested menu.

[0548] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0549] Step 1: Enter your user information and upload a photo

[0550] Users enter basic information such as their age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application, and upload photos of their body and face. The input data and uploaded photos are sent to the server. The input in this step is the user's basic information and photo, and the output is the transmission of data to the server.

[0551] Step 2: Receive and analyze user information and photos

[0552] The server uses generative AI to perform a detailed analysis based on the received user's basic information and photo. Specifically, it analyzes the user's facial features, body type, lifestyle, etc. to understand their health condition and dietary preferences. The input for this step is the user's basic information and photo, and the output is the analyzed data.

[0553] Step 3: Generate the meal menu

[0554] The generative AI selects an appropriate meal menu from a database based on the analysis results. For example, low-carb menus or menus rich in vitamins are suggested based on the user's characteristics. The input for this step is the analysis data, and the output is a personalized meal menu.

[0555] Step 4: Present your proposal

[0556] The terminal presents the user with a meal menu selected by the generative AI. The user can review these suggestions through a smartphone app. The input of this step is a personalized meal menu, and the output is a display of the suggestions to the user.

[0557] Step 5: Virtual Tasting

[0558] The virtual tasting feature allows users to virtually check the nutritional and calorie information of the proposed meal menu. This feature allows users to consider whether the menu fits their lifestyle and health status. The input of this step is the nutritional information of the meal menu, and the output is the information displayed to the user.

[0559] Step 6: Expert feedback

[0560] The server receives feedback from experts, such as registered dietitians and health coaches, who provide additional advice based on the user's characteristics and preferences. The input of this step is the expert feedback, and the output is a reflection of that feedback to the user.

[0561] Step 7: Select and save your final meal plan

[0562] The user reviews the suggestions and expert feedback and selects the final meal plan. The selected meal plan is saved in a database and can be accessed and reviewed by the user at any time. The input of this step is the final meal plan, and the output is saving it to the database.

[0563] 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.

[0564] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized experience. This system includes the following elements.

[0565] Enter your user information and upload a photo

[0566] Users enter basic information such as their age, occupation, and lifestyle, and upload their own photos through a dedicated website or application. They can also enter emotional information based on self-diagnosis. The device then sends this data to a server.

[0567] Receiving, analyzing, and recognizing emotions in user information and photos

[0568] The server receives user information, photos, and emotional information. The server uses the generative AI to analyze the user's body shape and facial features. The emotion engine then analyzes emotional information from the user's facial expressions using facial recognition technology and combines this with the generative AI's analysis data.

[0569] Generate styling suggestions

[0570] The server selects personalized fashion items and hairstyles from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[0571] Presentation of proposal content

[0572] The device will present the user with selected fashion items and hairstyles, which the user can review through the app. The suggestions are tailored to the user's emotions, providing a more comfortable experience.

[0573] Virtual try-on

[0574] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[0575] Expert feedback

[0576] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[0577] Select and save your final styling

[0578] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[0579] Specific examples

[0580] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[0581] In this way, the present invention provides users with personalized styling suggestions based on their emotions, providing an environment in which they can approach their search for a partner with confidence.

[0582] The processing flow will be explained below.

[0583] Step 1:

[0584] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The user then completes a simple self-diagnosis and enters their emotional state. The device then sends the entered data, photo, and emotional information to the server.

[0585] Step 2:

[0586] The server receives user information, photos, and emotion information, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photos and basic information.

[0587] Step 3:

[0588] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The results of this analysis are sent back to the server.

[0589] Step 4:

[0590] The server calls the emotion engine, analyzes the facial expressions in the user's photo, and parses the emotional information. The emotion engine recognizes the user's current emotional state (e.g., happy, nervous, sad) based on the user's facial expressions. The analysis results are also sent back to the server.

[0591] Step 5:

[0592] The server combines the analysis results of the generation AI and the emotion engine, and based on this combined data, extracts personalized fashion items and hairstyles from the database.

[0593] Step 6:

[0594] The server selects the extracted fashion items and hairstyles from a list and generates the optimal combination. The server takes into account the user's emotional state to provide a more suitable styling. The generated styling suggestions are notified to the user.

[0595] Step 7:

[0596] The user receives a notification and confirms the styling suggestion within the application. The device displays details of the fashion item and hairstyle. The user confirms the suggested style and views the details.

[0597] Step 8:

[0598] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[0599] Step 9:

[0600] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[0601] Step 10:

[0602] The user reviews the suggestions and expert feedback and selects the final styling. The final styling is selected taking into account the user's emotional state and the expert's advice. The selected styling is sent from the device to the server and stored in a database.

[0603] Step 11:

[0604] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[0605] As a concrete example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. The user uploads a photo of themselves in the emotional state of "fun" to the system. The server receives the photo and basic information, and the generation AI analyzes their facial features and body type. At the same time, the emotion engine analyzes their facial expressions and recognizes the emotion of "fun." The server then selects from its database a casual, light-colored slim-fit shirt, denim jeans, and a short hairstyle. The user virtually tries these on using the virtual try-on function, and the expert gives feedback that "the light-colored shirt suits you." The user then finally selects and saves this style. In this way, the present invention takes the user's emotional state into account to provide more personalized styling suggestions.

[0606] Example 2

[0607] 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."

[0608] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. Conventional methods have the drawback of making it difficult for users to select appropriate items themselves, and suggestions do not take emotions into account, resulting in low user satisfaction. Furthermore, it is difficult to receive timely feedback from experts, making it difficult to make optimal choices. Therefore, there is a need for a system that provides users with more personalized suggestions and incorporates expert opinions.

[0609] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving basic information from a user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo of the user, a means for analyzing emotional information using face recognition technology, a means for selecting personalized fashion items and hairstyles based on the analysis results of the generation AI means and the emotional information analysis means, a means for presenting the selected fashion items and hairstyles to the user, a virtual try-on means for allowing the user to virtually try on the selected fashion items and hairstyles, and a means for saving the final styling based on the user's selection. This allows the user to not only receive appropriate styling suggestions from the comfort of their own home, but also enable suggestions that take emotions into consideration, and further allows them to make optimal selections by receiving expert feedback.

[0610] "Basic information" refers to data such as the user's age, occupation, and lifestyle.

[0611] "Photo" refers to an image file showing the user's face and body shape.

[0612] "Generative AI means" refers to the artificial intelligence techniques used to analyze user characteristics.

[0613] "Facial recognition technology" refers to technology for analyzing emotional information from a user's facial expressions.

[0614] "Emotional information" refers to the emotional state analyzed from the user's facial expressions.

[0615] "Personalized fashion items" refer to clothing and accessories selected based on the user's characteristics, preferences, and emotional state.

[0616] "Hairstyle" refers to the hairstyle suggested to the user.

[0617] "Virtual Try-On Facility" refers to a system feature that allows a user to virtually try on selected fashion items and hairstyles.

[0618] "Expert feedback" refers to advice and evaluations provided by professionals such as stylists and hairdressers.

[0619] "Database" refers to an information management system for storing analyzed information and selected fashion items and hairstyles.

[0620] "Server" refers to a computer system that receives, processes, and analyzes user input.

[0621] "Terminal" refers to a device such as a computer or smartphone that is operated by a user.

[0622] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system aims to provide a more personalized experience by combining an emotion engine that recognizes the user's emotions. The system includes the following elements:

[0623] Enter your user information and upload a photo

[0624] The user accesses a dedicated website or application. They enter basic information such as their age, occupation, and lifestyle, and upload their own photos. They can also enter their own emotional information through self-diagnosis. This information is sent from the device to the server.

[0625] Receiving, analyzing, and recognizing emotions in user information and photos

[0626] The server receives the user's basic information, photo, and emotional information. Next, the server uses a generative AI (e.g., publicly available artificial intelligence technology) to analyze the user's body shape and facial features. In addition, it uses an emotional engine (e.g., software using facial recognition technology) to analyze emotional information from the user's facial expressions. The results of this analysis are combined with the generative AI's analysis data and processed.

[0627] Example prompt sentence:

[0628] "Analyze this user's facial and body features."

[0629] Generate styling suggestions

[0630] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from its database, taking into account the user's preferences, lifestyle, and emotional state.

[0631] Example prompt sentence:

[0632] "42-year-old engineer, casual lifestyle, emotional state: 'fun'"

[0633] Presentation of proposal content

[0634] The server sends the selected fashion items and hairstyles to the device, which then presents them to the user, who can then confirm the suggestions through the application.

[0635] Virtual try-on

[0636] When a user selects the virtual try-on feature, the device generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try on different styles.

[0637] Expert feedback

[0638] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server then notifies the user of this feedback.

[0639] Example prompt sentence:

[0640] "A lighter colored shirt would fit that sentiment."

[0641] Select and save your final styling

[0642] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling information is sent from the device to a server and stored in a database. The saved information can be accessed and reviewed by the user as many times as needed.

[0643] Examples:

[0644] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotion information, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then suggests a slim-fit shirt, denim jeans, and a short hairstyle from its database. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves this suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[0645] In this way, the present invention provides users with styling suggestions based on their personalized emotions, providing an environment in which they can approach their search for a partner with confidence.

[0646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0647] Step 1:

[0648] Users access a dedicated website or application and enter basic information such as age, occupation, and lifestyle. Next, they upload their own photos and enter their emotional information through self-diagnosis. The device then sends this information to a server.

[0649] Input: Basic information such as age, occupation, lifestyle, user photo, emotional information

[0650] Output: Basic information, photo, and emotional information sent to the server

[0651] Step 2:

[0652] The server processes the received basic information, photos, and emotional information, and uses a generative AI model (e.g., general artificial intelligence technology) to analyze the user's body shape and facial features.

[0653] Input: User's basic information, photo

[0654] Output: Analysis results for body shape and facial features

[0655] Step 3:

[0656] The server uses facial recognition technology to analyze emotional information from the user's photo. It then uses an emotion engine (e.g., general emotion recognition software) to analyze the user's emotions and combines them with the analysis results of the generative AI.

[0657] Input: User photo

[0658] Output: Parsed emotion information

[0659] Step 4:

[0660] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from the database, taking into account the user's preferences, lifestyle, and emotional state.

[0661] Input: Analysis results of body shape and facial features, emotional information

[0662] Output: Selected fashion items and hairstyles

[0663] Step 5:

[0664] The server transmits the selected fashion items and hairstyles to the terminal, which then presents them to the user.

[0665] Input: Selected fashion items and hairstyles

[0666] Output: Styling suggestions presented to the user

[0667] Step 6:

[0668] When the user selects the virtual try-on function, the terminal generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try out different styles.

[0669] Input: Selected fashion items and hairstyles

[0670] Output: Virtual try-on screen

[0671] Step 7:

[0672] The server sends styling suggestions to experts for feedback, the experts review the suggestions and provide additional advice, and the server notifies the user of this feedback information.

[0673] Input: Styling suggestions

[0674] Output: Expert feedback

[0675] Step 8:

[0676] The user reviews the suggestions and expert feedback and selects the final styling. The device then sends the selected styling information to the server, which stores it in a database.

[0677] Input: Final styling selection

[0678] Output: Styling information stored in a database

[0679] Each step is designed to be easy for users to operate at home, and the server and device are linked to provide a smooth experience. This system allows users to receive optimal styling suggestions to help them approach their marriage search with confidence.

[0680] (Application example 2)

[0681] 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."

[0682] Conventional styling systems are limited to making suggestions based on basic user information and lack personalized suggestions that take into account the user's emotions and momentary moods. Furthermore, trying on suggested fashions and hairstyles requires a user to visit a physical store, making it difficult to try them on. The present invention aims to solve these issues by providing a system that offers personalized styling suggestions based on the user's emotions and includes a virtual try-on function.

[0683] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0684] In this invention, the server includes a means for receiving basic information and a photo from the user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo, and an emotion engine means for recognizing emotional information from the user's facial photo, thereby enabling personalized styling suggestions and virtual try-ons based on the user's emotions.

[0685] "Basic user information" refers to information about basic attributes such as age, occupation, and lifestyle provided by the user.

[0686] A "photo" is image data that includes the user's face and body shape.

[0687] "Generative AI" is an artificial intelligence that analyzes basic information and photos provided by users and extracts features.

[0688] The "Emotion Engine" is a technology that recognizes and analyzes emotional information from a user's facial photograph.

[0689] "Personalized fashion items" are clothing and accessories that are individually selected based on a user's characteristics and emotional information.

[0690] A "personalized hairstyle" is a hairstyle that is individually selected based on the user's characteristics and emotional information.

[0691] "Virtual Try-On" is a feature that allows users to try on selected fashion items and hairstyles in a virtual space.

[0692] "Expert feedback" is additional advice or opinion provided by professionals such as stylists or hairdressers.

[0693] A "database" is a repository of analyzed information and selected fashion items and hairstyles.

[0694] This invention is a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine, it provides a more personalized experience. As a specific embodiment, we will explain a system that includes the following elements.

[0695] 1. Enter your user information and upload a photo

[0696] Users enter basic information such as age, occupation, and lifestyle, and upload their own photos via their device. They can also enter emotional information based on self-diagnosis. This data is sent from the device to the server.

[0697] 2. Receiving, analyzing, and recognizing emotions in user information and photos

[0698] The server receives the user's information and photo, and uses generative AI to analyze the user's body shape and facial features. The emotion engine uses facial recognition technology to analyze the user's emotional information from their facial expressions. This allows the server to obtain the user's characteristics and current emotional state.

[0699] 3. Generating styling suggestions

[0700] The server selects personalized fashion items and hairstyles for the user from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[0701] 4. Presentation of proposal

[0702] The suggested fashion items and hairstyles are presented to the user via the device, and the user can check the suggestions through the application.

[0703] 5. Virtual try-on

[0704] Users can virtually try on suggested fashion items and hairstyles using the device's virtual try-on feature, which allows users to try out different styles and see how they look.

[0705] 6. Expert feedback

[0706] The server sends styling suggestions to experts (stylists and hairdressers) and requests feedback. Feedback from the experts is sent to the server and notified to the user.

[0707] 7. Select and save your final styling

[0708] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is saved on the server and can be accessed and reviewed by the user at any time.

[0709] The hardware required is the user's device (smartphone or PC) and a server, while the software uses OpenCV, FER, and TensorFlow (a generative AI model) to perform image analysis and emotion recognition.

[0710] Specific examples

[0711] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[0712] Prompt Sentence Examples

[0713] "I'm a 42-year-old engineer with a casual lifestyle, loves the outdoors, and am in a fun mood right now. Based on this information, the suggestions were a slim-fit shirt, denim jeans, and a short hairstyle. Further expert feedback suggested that a lighter-colored shirt would fit that sentiment."

[0714] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0715] Step 1:

[0716] Users input basic information such as age, occupation, and lifestyle, as well as their own photos, into the application via their device, which then sends the data to the server.

[0717] Input: User's basic information (age, occupation, lifestyle, etc.), user photo

[0718] Output: Basic information and photo data sent

[0719] Step 2:

[0720] The server inputs the user information and photo data received from the device into a generative AI model for analysis.

[0721] Input: User's basic information and photo

[0722] Output: Analyzed user characteristics information (body type, facial features, etc.)

[0723] How it works: Uses generative AI models for facial and body feature extraction

[0724] Step 3:

[0725] The server analyzes facial expressions from the user's photograph and obtains emotion information using an emotion engine.

[0726] Input: User's face photo

[0727] Output: User's emotional information (e.g., happy, sad)

[0728] How it works: The emotion engine analyzes images using facial expression recognition technology to estimate emotional states.

[0729] Step 4:

[0730] The server combines the analysis results of the generative AI model and the emotion engine to generate personalized fashion item and hairstyle suggestions.

[0731] Input: Analyzed user characteristics and emotion information

[0732] Output: Suggested fashion items and hairstyles

[0733] How it works: Selects items from a database based on the user's characteristics and emotions and generates a list

[0734] Step 5:

[0735] The terminal presents the suggestions provided by the server to the user, who then confirms them.

[0736] Input: Suggested fashion items and hairstyles

[0737] Output: Fashion item and hairstyle information presented to the user

[0738] Action: The user reviews the proposal through the application.

[0739] Step 6:

[0740] Users can use the virtual try-on function on their device to virtually try on suggested fashion items and hairstyles.

[0741] Input: Fashion items and hairstyles the user wants to try on

[0742] Output: Virtual try-on images and videos

[0743] Action: Applying items to the user avatar using the virtual try-on feature

[0744] Step 7:

[0745] The server collects expert feedback on the proposed styling and notifies the user.

[0746] Input: Suggested fashion items and hairstyles

[0747] Output: Expert feedback

[0748] How it works: The server collects opinions and advice from stylists and hairdressers and delivers them to the user.

[0749] Step 8:

[0750] The user selects the final styling and sends the results from the device to the server, which stores the results in a database.

[0751] Input: Final styling selected by the user

[0752] Output: Final styling information saved

[0753] What happens: The server receives the user's final selection and stores it in the database

[0754] 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.

[0755] 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.

[0756] 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.

[0757] [Third embodiment]

[0758] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0759] 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.

[0760] 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).

[0761] 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.

[0762] 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.

[0763] 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).

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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."

[0770] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. This system includes the following elements:

[0771] Enter your user information and upload a photo

[0772] Users enter basic information such as their age, occupation, and lifestyle through a dedicated website or application, and upload their own photos. At this stage, users can also enter their wishes and preferences.

[0773] Receiving and analyzing user information and photos

[0774] Based on the user information and photos received by the server, the generation AI analyzes the user's body type and facial features, accurately determining the user's face shape, body type, skin color, etc., and generates data to suggest styling accordingly.

[0775] Generate styling suggestions

[0776] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database, taking into account the user's preferences and lifestyle. The selected fashion items and hairstyles are presented as the optimal combination for the user.

[0777] Presentation of proposal content

[0778] The device will present the selected fashion items and hairstyles to the user, who can then review the suggestions through the application.

[0779] Virtual try-on

[0780] Users can virtually try on suggested fashion items and hairstyles using the virtual try-on feature. This feature allows users to select the style they want to try and see how it will look on them in a virtual mirror. This allows them to see in advance whether the suggested styling will suit them.

[0781] Expert feedback

[0782] The server receives feedback from experts (stylists and hairdressers) and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[0783] Select and save your final styling

[0784] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[0785] Specific examples

[0786] For example, say the user is a 42-year-old engineer who enjoys a casual lifestyle and the outdoors. The user enters a photo and basic information. The server receives this, and the generative AI analyzes facial features and body type, resulting in a result such as "thin and long face." The server then suggests slim-fitting shirts, denim jeans, and short hairstyles from its database. The user then uses the virtual try-on feature to virtually try these on. The expert then provides feedback, saying, "A brightly colored shirt would suit you better." The user finally accepts and saves the suggestions. The user can then refer to the saved styles and actually purchase the clothing at a later date.

[0787] In this way, the present invention provides users with an environment in which they can approach their matchmaking with confidence and simplifies the process.

[0788] The processing flow will be explained below.

[0789] Step 1:

[0790] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The device then sends the entered data and photo to the server.

[0791] Step 2:

[0792] The server receives the user information and photo, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photo and basic information.

[0793] Step 3:

[0794] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The analysis results are then sent back to the server.

[0795] Step 4:

[0796] The server receives the analysis results and extracts a list of suitable fashion items and hairstyles from the database, which is personalized based on the user's characteristics and preferences.

[0797] Step 5:

[0798] The server filters the extracted list of fashion items and hairstyles to generate optimal combinations, and the generated styling suggestions are notified to the user.

[0799] Step 6:

[0800] The user receives a notification and sees styling suggestions within the app, and the device displays details of the fashion item and hairstyle.

[0801] Step 7:

[0802] The user uses the virtual try-on feature to virtually try on the suggested styles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try on different styles.

[0803] Step 8:

[0804] The server sends styling suggestions to experts (stylists or hairdressers) and asks for feedback. The experts review the suggestions and provide additional advice.

[0805] Step 9:

[0806] The expert's feedback is sent back to the server, which notifies the user of the feedback information, and the user confirms the feedback and finalizes the styling.

[0807] Step 10:

[0808] The user selects the final styling. The device sends the selection to the server. The server stores the selection in a database.

[0809] Step 11:

[0810] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[0811] Example 1

[0812] 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."

[0813] Conventional styling suggestion systems were unable to accurately grasp a user's basic information and characteristics, making it difficult to provide personalized styling suggestions. Furthermore, users were unable to actually try out the suggested styling and were unable to receive real-time feedback from experts. Furthermore, it was difficult to provide styling that reflected the user's preferences and wishes, and data storage and reuse were insufficient.

[0814] 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.

[0815] In this invention, the server includes means for receiving basic information from a user, means for receiving the basic information and a photo and analyzing the user's body type and facial features using a generative AI model, means for selecting personalized fashion items and hairstyles based on the analysis results by the generative AI means, means for presenting the selected fashion items and hairstyles to the user, means for a virtual try-on that allows the user to virtually try on the selected fashion items and hairstyles, means for receiving feedback from an expert and providing it to the user, and means for saving the final styling based on the user's selection. This allows the server to accurately reflect the user's features and preferences, make optimal styling suggestions based on the virtual try-on and expert feedback, and also allows the data to be saved and reused.

[0816] "Basic user information" refers to basic information about the user, such as age, occupation, lifestyle, and preferences.

[0817] A "generative AI model" refers to an artificial intelligence model that uses machine learning technology to analyze images and data and extract user characteristics.

[0818] "Analyzing the user's body shape and facial features" means that the generative AI model analyzes the user's photo to identify detailed features such as body shape, facial shape, and skin color.

[0819] "Personalized fashion items and hairstyles" refers to individually tailored fashion items and hairstyles selected based on the user's characteristics and preferences.

[0820] "Virtual Try-On Facility" means a technological facility that allows a user to try on selected fashion items and hairstyles in a virtual environment.

[0821] "Expert feedback" refers to advice and evaluations provided to users by professionals such as stylists and hairdressers.

[0822] "Means for saving final styling" refers to a technical means for saving the styling selected by the user in a database or the like, so that it can be reused or referenced later.

[0823] A "database" refers to an information system that systematically stores large amounts of information and allows it to be searched and used as needed.

[0824] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system inputs the user's basic information and photo, analyzes the user's body type and facial features using a generative AI model, and suggests personalized fashion items and hairstyles based on that information. Furthermore, users can virtually try on suggested styles using a virtual try-on feature and receive feedback from experts.

[0825] 1. Enter your user information and upload a photo

[0826] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, and preferences. They also upload photos of their face or their entire body. The hardware used is a PC or smartphone, and the software is a web browser or dedicated application.

[0827] 2. Receiving and analyzing user information and photos

[0828] The server passes the basic information and photo data received from the user to the generative AI model. The generative AI model uses OpenAI's image analysis technology, for example, to boldly analyze the user's body shape and facial features. It obtains a detailed understanding of the user's facial shape, body type, skin color, and other characteristics and generates analytical data.

[0829] 3. Generating styling suggestions

[0830] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database that includes general fashion and beauty-related data. Specifically, it suggests slim-fitting shirts, denim jeans, and short hairstyles.

[0831] 4. Presentation of proposal

[0832] The server sends the generated styling suggestions to the device, which then visually displays them to the user, who can then view and confirm the suggestions through a dedicated application or web browser.

[0833] 5. Virtual try-on

[0834] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles. This feature is realized using technology from Modiface, for example. Users can use the camera function of their smartphone or computer to see how the proposed items will look on them in real time.

[0835] 6. Expert feedback

[0836] The server receives feedback from stylists, hairdressers, and other experts and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[0837] 7. Select and save your final styling

[0838] The user reviews the suggestions and expert feedback and selects the final styling. The device sends the selection to the server, which stores it in a database that the user can access and review at any time.

[0839] Specific examples

[0840] For example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. When the user enters a photo and basic information, the server receives it, and the generative AI model analyzes facial features and body type, obtaining an analysis result such as "thin and long face." The server then uses its database to suggest items such as slim-fitting shirts, denim jeans, and short hairstyles. The user can then use the virtual try-on feature to virtually try on these items, and an expert will provide feedback such as "bright-colored shirts would look better on you." The user can then accept and save the suggestions.

[0841] Prompt Sentence Examples

[0842] "I'm a 42-year-old engineer. I like a casual lifestyle and often spend time outdoors. I'm thin with a long face. Can you suggest some fashion items and hairstyles that would suit me?"

[0843] In this way, the system can make styling suggestions based on the user's characteristics and preferences, providing the optimal style through virtual try-ons and expert feedback.

[0844] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0845] Step 1:

[0846] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, preferences, etc. Users then upload a photo.

[0847] Input: User's basic information (age, occupation, lifestyle, preferences) and photo image.

[0848] Output: The user's basic information and photo are sent to the server.

[0849] What happens: The user fills out the form and clicks the upload button to submit the photo.

[0850] Step 2:

[0851] The server analyzes the basic information and photo data received from the user, and processes the information using a generative AI model to analyze the user's body shape and facial features.

[0852] Input: User basic information and photo data.

[0853] Output: Analysis of the user's body shape and facial features by the generative AI.

[0854] Specific operation: The server passes the received data to the generative AI model and performs image analysis, for example, using OpenAI's image analysis technology to determine face shape and body type.

[0855] Step 3:

[0856] The server selects personalized fashion items and hairstyles from a database based on the analysis results.

[0857] Input: Analysis results and user preference information.

[0858] Output: A list of selected fashion items and hairstyles.

[0859] What it does: The server searches a database and selects the items that best suit the user, such as a slim-fitting shirt, denim jeans, and a short hairstyle.

[0860] Step 4:

[0861] The server sends the generated styling suggestions to the terminal, which then visually displays them to the user.

[0862] Input: Selected fashion item and hairstyle data.

[0863] Output: Visual styling suggestions displayed on the user's device.

[0864] Specific operation: The server sends data to the terminal in JSON format, etc., and the terminal displays the data.

[0865] Step 5:

[0866] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles.

[0867] Input: Selected fashion item and hairstyle data.

[0868] Output: A display of the results of the user virtually trying on the garment.

[0869] Specific operation: Using the device's camera and technologies such as Modiface, the suggestions are reflected on the user's appearance in real time.

[0870] Step 6:

[0871] The server receives the feedback from the experts and provides it to the user.

[0872] Input: Expert feedback information.

[0873] Output: Any additional advice or rating that is displayed to the user.

[0874] How it works: The expert reviews the suggestion and sends the advice to the server, which then forwards it to the user's device, where it displays the advice.

[0875] Step 7:

[0876] The user reviews the suggestions and expert feedback and selects the final styling.

[0877] Input: The final selection made by the user.

[0878] Output: The selected styling is saved to the server.

[0879] What happens: The user makes a final selection and clicks the submit button to send the selection to the server, which records it in a database.

[0880] (Application example 1)

[0881] 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."

[0882] In today's modern lifestyle, choosing the right diet to maintain good health is difficult, especially for busy men in their 40s and older. Personalizing meals based on individual health conditions and food preferences is time-consuming, and achieving this with expert feedback is even more difficult. There are also limited ways to confirm in advance whether the meal you choose is suitable for you. Therefore, there is a need for a system that allows users to easily select the optimal meal menu from the comfort of their own home and check it in advance.

[0883] 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.

[0884] In this invention, the server includes means for receiving basic information from a user, a generating AI means for analyzing user characteristics based on the input basic information, a means for selecting a personalized meal menu based on the analysis results by the generating AI means, a means for presenting the selected meal menu to the user, a virtual tasting means for allowing the user to virtually check the nutritional components and calorie information of the selected meal menu, a means for providing feedback from an expert, and a means for saving the final meal menu based on the user's selection. This allows the user to easily receive healthy meal menus that are optimal for them from the comfort of their own home, and to select and save a final meal plan while reviewing the contents in advance and receiving advice from an expert.

[0885] "User" refers to an individual who uses this system to receive meal menu suggestions and feedback.

[0886] "Basic information" refers to information about the user's age, occupation, lifestyle, body type, food preferences, etc.

[0887] "Generative AI means" refers to the artificial intelligence function that analyzes the user's basic information and generates a personalized meal menu.

[0888] A "meal menu" is a specific meal plan or recipe suggested based on the user's health and preferences.

[0889] "Virtual tasting tool" refers to a function that allows users to virtually check the nutritional content and calorie information of the proposed meal menu.

[0890] An "expert" is someone with the expertise to provide advice on a user's health and diet, such as a registered dietitian or health coach.

[0891] "Feedback" refers to additional advice or opinions provided by experts based on the user's condition and preferences.

[0892] "Meal Plan" means a set of specific meal plans saved based on a User's selections.

[0893] A "database" is an electronic data storage system for storing analyzed information and selected meal menus.

[0894] This invention relates to a system that allows male users in their 40s or older to receive healthy and optimal meal menu suggestions from the comfort of their own homes. The embodiments for carrying out the invention are as follows.

[0895] The system consists of the following main functions: inputting user information and uploading photos, suggesting meal menus based on analysis results, a virtual tasting function, feedback from experts, saving the final meal plan, and configuring a database to manage this information.

[0896] Enter your user information and upload a photo

[0897] Users enter basic information such as age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application. In addition, users can upload photos of their body type and face. This information and photos are received by the server.

[0898] Receiving and analyzing user information and photos

[0899] The server uses OpenAI's generative AI model to analyze the uploaded information and photos, providing a detailed understanding of the user's health, body type, lifestyle, and dietary preferences. The analysis results serve as the basis for generating a personalized meal menu.

[0900] Generate a meal menu

[0901] Based on the analysis results, the generative AI selects an appropriate meal plan from the database, taking into account the user's health status and dietary preferences. For example, it may suggest low-carb meals or meals using vitamin-rich ingredients.

[0902] Presentation of proposal content

[0903] The device presents the user with a selection of meal options, which can then be viewed through a smartphone app.

[0904] Virtual Tasting

[0905] Users can virtually check the nutritional content and calorie information of the proposed meal menu using the virtual tasting function, allowing them to check in advance whether the proposed menu is compatible with their lifestyle and health condition.

[0906] Expert feedback

[0907] The server receives feedback from experts such as registered dietitians and health coaches and provides it to the user, who then provides additional advice based on the user's characteristics and preferences.

[0908] Select and save your final meal plan

[0909] Users review the suggestions and expert feedback and select a final meal plan, which is then saved in a database that users can access and review at any time.

[0910] Specific examples

[0911] For example, let's say the user is a 45-year-old engineer with an active lifestyle and a desire to follow a low-carb diet. The user enters their age, occupation, lifestyle, body type, and food preferences, and uploads a photo. The server receives this information, and the generative AI analyzes the user's health and body type. An example of a prompt to be input to the generative AI model is as follows:

[0912] Please suggest a meal plan that is ideal for Age: 45, Occupation: Engineer, Lifestyle: Active, Build: Medium, Food Preference: Low Carbohydrate.

[0913] Based on this, a low-carb meal menu is suggested, and experts provide feedback that a meal with a moderate amount of protein would be even better. The user then selects and saves the final suggested menu.

[0914] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0915] Step 1: Enter your user information and upload a photo

[0916] Users enter basic information such as their age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application, and upload photos of their body and face. The input data and uploaded photos are sent to the server. The input in this step is the user's basic information and photo, and the output is the transmission of data to the server.

[0917] Step 2: Receive and analyze user information and photos

[0918] The server uses generative AI to perform a detailed analysis based on the received user's basic information and photo. Specifically, it analyzes the user's facial features, body type, lifestyle, etc. to understand their health condition and dietary preferences. The input for this step is the user's basic information and photo, and the output is the analyzed data.

[0919] Step 3: Generate the meal menu

[0920] The generative AI selects an appropriate meal menu from a database based on the analysis results. For example, low-carb menus or menus rich in vitamins are suggested based on the user's characteristics. The input for this step is the analysis data, and the output is a personalized meal menu.

[0921] Step 4: Present your proposal

[0922] The terminal presents the user with a meal menu selected by the generative AI. The user can review these suggestions through a smartphone app. The input of this step is a personalized meal menu, and the output is a display of the suggestions to the user.

[0923] Step 5: Virtual Tasting

[0924] The virtual tasting feature allows users to virtually check the nutritional and calorie information of the proposed meal menu. This feature allows users to consider whether the menu fits their lifestyle and health status. The input of this step is the nutritional information of the meal menu, and the output is the information displayed to the user.

[0925] Step 6: Expert feedback

[0926] The server receives feedback from experts, such as registered dietitians and health coaches, who provide additional advice based on the user's characteristics and preferences. The input of this step is the expert feedback, and the output is a reflection of that feedback to the user.

[0927] Step 7: Select and save your final meal plan

[0928] The user reviews the suggestions and expert feedback and selects the final meal plan. The selected meal plan is saved in a database and can be accessed and reviewed by the user at any time. The input of this step is the final meal plan, and the output is saving it to the database.

[0929] 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.

[0930] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized experience. This system includes the following elements.

[0931] Enter your user information and upload a photo

[0932] Users enter basic information such as their age, occupation, and lifestyle, and upload their own photos through a dedicated website or application. They can also enter emotional information based on self-diagnosis. The device then sends this data to a server.

[0933] Receiving, analyzing, and recognizing emotions in user information and photos

[0934] The server receives user information, photos, and emotional information. The server uses the generative AI to analyze the user's body shape and facial features. The emotion engine then analyzes emotional information from the user's facial expressions using facial recognition technology and combines this with the generative AI's analysis data.

[0935] Generate styling suggestions

[0936] The server selects personalized fashion items and hairstyles from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[0937] Presentation of proposal content

[0938] The device will present the user with selected fashion items and hairstyles, which the user can review through the app. The suggestions are tailored to the user's emotions, providing a more comfortable experience.

[0939] Virtual try-on

[0940] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[0941] Expert feedback

[0942] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[0943] Select and save your final styling

[0944] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[0945] Specific examples

[0946] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[0947] In this way, the present invention provides users with personalized styling suggestions based on their emotions, providing an environment in which they can approach their search for a partner with confidence.

[0948] The processing flow will be explained below.

[0949] Step 1:

[0950] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The user then completes a simple self-diagnosis and enters their emotional state. The device then sends the entered data, photo, and emotional information to the server.

[0951] Step 2:

[0952] The server receives user information, photos, and emotion information, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photos and basic information.

[0953] Step 3:

[0954] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The results of this analysis are sent back to the server.

[0955] Step 4:

[0956] The server calls the emotion engine, analyzes the facial expressions in the user's photo, and parses the emotional information. The emotion engine recognizes the user's current emotional state (e.g., happy, nervous, sad) based on the user's facial expressions. The analysis results are also sent back to the server.

[0957] Step 5:

[0958] The server combines the analysis results of the generation AI and the emotion engine, and based on this combined data, extracts personalized fashion items and hairstyles from the database.

[0959] Step 6:

[0960] The server selects the extracted fashion items and hairstyles from a list and generates the optimal combination. The server takes into account the user's emotional state to provide a more suitable styling. The generated styling suggestions are notified to the user.

[0961] Step 7:

[0962] The user receives a notification and confirms the styling suggestion within the application. The device displays details of the fashion item and hairstyle. The user confirms the suggested style and views the details.

[0963] Step 8:

[0964] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[0965] Step 9:

[0966] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[0967] Step 10:

[0968] The user reviews the suggestions and expert feedback and selects the final styling. The final styling is selected taking into account the user's emotional state and the expert's advice. The selected styling is sent from the device to the server and stored in a database.

[0969] Step 11:

[0970] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[0971] As a concrete example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. The user uploads a photo of themselves in the emotional state of "fun" to the system. The server receives the photo and basic information, and the generation AI analyzes their facial features and body type. At the same time, the emotion engine analyzes their facial expressions and recognizes the emotion of "fun." The server then selects from its database a casual, light-colored slim-fit shirt, denim jeans, and a short hairstyle. The user virtually tries these on using the virtual try-on function, and the expert gives feedback that "the light-colored shirt suits you." The user then finally selects and saves this style. In this way, the present invention takes the user's emotional state into account to provide more personalized styling suggestions.

[0972] Example 2

[0973] 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."

[0974] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. Conventional methods have the drawback of making it difficult for users to select appropriate items themselves, and suggestions do not take emotions into account, resulting in low user satisfaction. Furthermore, it is difficult to receive timely feedback from experts, making it difficult to make optimal choices. Therefore, there is a need for a system that provides users with more personalized suggestions and incorporates expert opinions.

[0975] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving basic information from a user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo of the user, a means for analyzing emotional information using face recognition technology, a means for selecting personalized fashion items and hairstyles based on the analysis results of the generation AI means and the emotional information analysis means, a means for presenting the selected fashion items and hairstyles to the user, a virtual try-on means for allowing the user to virtually try on the selected fashion items and hairstyles, and a means for saving the final styling based on the user's selection. This allows the user to not only receive appropriate styling suggestions from the comfort of their own home, but also enable suggestions that take emotions into consideration, and further allows them to make optimal selections by receiving expert feedback.

[0976] "Basic information" refers to data such as the user's age, occupation, and lifestyle.

[0977] "Photo" refers to an image file showing the user's face and body shape.

[0978] "Generative AI means" refers to the artificial intelligence techniques used to analyze user characteristics.

[0979] "Facial recognition technology" refers to technology for analyzing emotional information from a user's facial expressions.

[0980] "Emotional information" refers to the emotional state analyzed from the user's facial expressions.

[0981] "Personalized fashion items" refer to clothing and accessories selected based on the user's characteristics, preferences, and emotional state.

[0982] "Hairstyle" refers to the hairstyle suggested to the user.

[0983] "Virtual Try-On Facility" refers to a system feature that allows a user to virtually try on selected fashion items and hairstyles.

[0984] "Expert feedback" refers to advice and evaluations provided by professionals such as stylists and hairdressers.

[0985] "Database" refers to an information management system for storing analyzed information and selected fashion items and hairstyles.

[0986] "Server" refers to a computer system that receives, processes, and analyzes user input.

[0987] "Terminal" refers to a device such as a computer or smartphone that is operated by a user.

[0988] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system aims to provide a more personalized experience by combining an emotion engine that recognizes the user's emotions. The system includes the following elements:

[0989] Enter your user information and upload a photo

[0990] The user accesses a dedicated website or application. They enter basic information such as their age, occupation, and lifestyle, and upload their own photos. They can also enter their own emotional information through self-diagnosis. This information is sent from the device to the server.

[0991] Receiving, analyzing, and recognizing emotions in user information and photos

[0992] The server receives the user's basic information, photo, and emotional information. Next, the server uses a generative AI (e.g., publicly available artificial intelligence technology) to analyze the user's body shape and facial features. In addition, it uses an emotional engine (e.g., software using facial recognition technology) to analyze emotional information from the user's facial expressions. The results of this analysis are combined with the generative AI's analysis data and processed.

[0993] Example prompt sentence:

[0994] "Analyze this user's facial and body features."

[0995] Generate styling suggestions

[0996] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from its database, taking into account the user's preferences, lifestyle, and emotional state.

[0997] Example prompt sentence:

[0998] "42-year-old engineer, casual lifestyle, emotional state: 'fun'"

[0999] Presentation of proposal content

[1000] The server sends the selected fashion items and hairstyles to the device, which then presents them to the user, who can then confirm the suggestions through the application.

[1001] Virtual try-on

[1002] When a user selects the virtual try-on feature, the device generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try on different styles.

[1003] Expert feedback

[1004] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server then notifies the user of this feedback.

[1005] Example prompt sentence:

[1006] "A lighter colored shirt would fit that sentiment."

[1007] Select and save your final styling

[1008] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling information is sent from the device to a server and stored in a database. The saved information can be accessed and reviewed by the user as many times as needed.

[1009] Examples:

[1010] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotion information, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then suggests a slim-fit shirt, denim jeans, and a short hairstyle from its database. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves this suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[1011] In this way, the present invention provides users with styling suggestions based on their personalized emotions, providing an environment in which they can approach their search for a partner with confidence.

[1012] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1013] Step 1:

[1014] Users access a dedicated website or application and enter basic information such as age, occupation, and lifestyle. Next, they upload their own photos and enter their emotional information through self-diagnosis. The device then sends this information to a server.

[1015] Input: Basic information such as age, occupation, lifestyle, user photo, emotional information

[1016] Output: Basic information, photo, and emotional information sent to the server

[1017] Step 2:

[1018] The server processes the received basic information, photos, and emotional information, and uses a generative AI model (e.g., general artificial intelligence technology) to analyze the user's body shape and facial features.

[1019] Input: User's basic information, photo

[1020] Output: Analysis results for body shape and facial features

[1021] Step 3:

[1022] The server uses facial recognition technology to analyze emotional information from the user's photo. It then uses an emotion engine (e.g., general emotion recognition software) to analyze the user's emotions and combines them with the analysis results of the generative AI.

[1023] Input: User photo

[1024] Output: Parsed emotion information

[1025] Step 4:

[1026] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from the database, taking into account the user's preferences, lifestyle, and emotional state.

[1027] Input: Analysis results of body shape and facial features, emotional information

[1028] Output: Selected fashion items and hairstyles

[1029] Step 5:

[1030] The server transmits the selected fashion items and hairstyles to the terminal, which then presents them to the user.

[1031] Input: Selected fashion items and hairstyles

[1032] Output: Styling suggestions presented to the user

[1033] Step 6:

[1034] When the user selects the virtual try-on function, the terminal generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try out different styles.

[1035] Input: Selected fashion items and hairstyles

[1036] Output: Virtual try-on screen

[1037] Step 7:

[1038] The server sends styling suggestions to experts for feedback, the experts review the suggestions and provide additional advice, and the server notifies the user of this feedback information.

[1039] Input: Styling suggestions

[1040] Output: Expert feedback

[1041] Step 8:

[1042] The user reviews the suggestions and expert feedback and selects the final styling. The device then sends the selected styling information to the server, which stores it in a database.

[1043] Input: Final styling selection

[1044] Output: Styling information stored in a database

[1045] Each step is designed to be easy for users to operate at home, and the server and device are linked to provide a smooth experience. This system allows users to receive optimal styling suggestions to help them approach their marriage search with confidence.

[1046] (Application example 2)

[1047] 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."

[1048] Conventional styling systems are limited to making suggestions based on basic user information and lack personalized suggestions that take into account the user's emotions and momentary moods. Furthermore, trying on suggested fashions and hairstyles requires a user to visit a physical store, making it difficult to try them on. The present invention aims to solve these issues by providing a system that offers personalized styling suggestions based on the user's emotions and includes a virtual try-on function.

[1049] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1050] In this invention, the server includes a means for receiving basic information and a photo from the user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo, and an emotion engine means for recognizing emotional information from the user's facial photo, thereby enabling personalized styling suggestions and virtual try-ons based on the user's emotions.

[1051] "Basic user information" refers to information about basic attributes such as age, occupation, and lifestyle provided by the user.

[1052] A "photo" is image data that includes the user's face and body shape.

[1053] "Generative AI" is an artificial intelligence that analyzes basic information and photos provided by users and extracts features.

[1054] The "Emotion Engine" is a technology that recognizes and analyzes emotional information from a user's facial photograph.

[1055] "Personalized fashion items" are clothing and accessories that are individually selected based on a user's characteristics and emotional information.

[1056] A "personalized hairstyle" is a hairstyle that is individually selected based on the user's characteristics and emotional information.

[1057] "Virtual Try-On" is a feature that allows users to try on selected fashion items and hairstyles in a virtual space.

[1058] "Expert feedback" is additional advice or opinion provided by professionals such as stylists or hairdressers.

[1059] A "database" is a repository of analyzed information and selected fashion items and hairstyles.

[1060] This invention is a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine, it provides a more personalized experience. As a specific embodiment, we will explain a system that includes the following elements.

[1061] 1. Enter your user information and upload a photo

[1062] Users enter basic information such as age, occupation, and lifestyle, and upload their own photos via their device. They can also enter emotional information based on self-diagnosis. This data is sent from the device to the server.

[1063] 2. Receiving, analyzing, and recognizing emotions in user information and photos

[1064] The server receives the user's information and photo, and uses generative AI to analyze the user's body shape and facial features. The emotion engine uses facial recognition technology to analyze the user's emotional information from their facial expressions. This allows the server to obtain the user's characteristics and current emotional state.

[1065] 3. Generating styling suggestions

[1066] The server selects personalized fashion items and hairstyles for the user from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[1067] 4. Presentation of proposal

[1068] The suggested fashion items and hairstyles are presented to the user via the device, and the user can check the suggestions through the application.

[1069] 5. Virtual try-on

[1070] Users can virtually try on suggested fashion items and hairstyles using the device's virtual try-on feature, which allows users to try out different styles and see how they look.

[1071] 6. Expert feedback

[1072] The server sends styling suggestions to experts (stylists and hairdressers) and requests feedback. Feedback from the experts is sent to the server and notified to the user.

[1073] 7. Select and save your final styling

[1074] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is saved on the server and can be accessed and reviewed by the user at any time.

[1075] The hardware required is the user's device (smartphone or PC) and a server, while the software uses OpenCV, FER, and TensorFlow (a generative AI model) to perform image analysis and emotion recognition.

[1076] Specific examples

[1077] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[1078] Prompt Sentence Examples

[1079] "I'm a 42-year-old engineer with a casual lifestyle, loves the outdoors, and am in a fun mood right now. Based on this information, the suggestions were a slim-fit shirt, denim jeans, and a short hairstyle. Further expert feedback suggested that a lighter-colored shirt would fit that sentiment."

[1080] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1081] Step 1:

[1082] Users input basic information such as age, occupation, and lifestyle, as well as their own photos, into the application via their device, which then sends the data to the server.

[1083] Input: User's basic information (age, occupation, lifestyle, etc.), user photo

[1084] Output: Basic information and photo data sent

[1085] Step 2:

[1086] The server inputs the user information and photo data received from the device into a generative AI model for analysis.

[1087] Input: User's basic information and photo

[1088] Output: Analyzed user characteristics information (body type, facial features, etc.)

[1089] How it works: Uses generative AI models for facial and body feature extraction

[1090] Step 3:

[1091] The server analyzes facial expressions from the user's photograph and obtains emotion information using an emotion engine.

[1092] Input: User's face photo

[1093] Output: User's emotional information (e.g., happy, sad)

[1094] How it works: The emotion engine analyzes images using facial expression recognition technology to estimate emotional states.

[1095] Step 4:

[1096] The server combines the analysis results of the generative AI model and the emotion engine to generate personalized fashion item and hairstyle suggestions.

[1097] Input: Analyzed user characteristics and emotion information

[1098] Output: Suggested fashion items and hairstyles

[1099] How it works: Selects items from a database based on the user's characteristics and emotions and generates a list

[1100] Step 5:

[1101] The terminal presents the suggestions provided by the server to the user, who then confirms them.

[1102] Input: Suggested fashion items and hairstyles

[1103] Output: Fashion item and hairstyle information presented to the user

[1104] Action: The user reviews the proposal through the application.

[1105] Step 6:

[1106] Users can use the virtual try-on function on their device to virtually try on suggested fashion items and hairstyles.

[1107] Input: Fashion items and hairstyles the user wants to try on

[1108] Output: Virtual try-on images and videos

[1109] Action: Applying items to the user avatar using the virtual try-on feature

[1110] Step 7:

[1111] The server collects expert feedback on the proposed styling and notifies the user.

[1112] Input: Suggested fashion items and hairstyles

[1113] Output: Expert feedback

[1114] How it works: The server collects opinions and advice from stylists and hairdressers and delivers them to the user.

[1115] Step 8:

[1116] The user selects the final styling and sends the results from the device to the server, which stores the results in a database.

[1117] Input: Final styling selected by the user

[1118] Output: Final styling information saved

[1119] What happens: The server receives the user's final selection and stores it in the database

[1120] 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.

[1121] 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.

[1122] 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.

[1123] [Fourth embodiment]

[1124] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1125] 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.

[1126] 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).

[1127] 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.

[1128] 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.

[1129] 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).

[1130] 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.

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] 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.

[1136] 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."

[1137] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. This system includes the following elements:

[1138] Enter your user information and upload a photo

[1139] Users enter basic information such as their age, occupation, and lifestyle through a dedicated website or application, and upload their own photos. At this stage, users can also enter their wishes and preferences.

[1140] Receiving and analyzing user information and photos

[1141] Based on the user information and photos received by the server, the generation AI analyzes the user's body type and facial features, accurately determining the user's face shape, body type, skin color, etc., and generates data to suggest styling accordingly.

[1142] Generate styling suggestions

[1143] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database, taking into account the user's preferences and lifestyle. The selected fashion items and hairstyles are presented as the optimal combination for the user.

[1144] Presentation of proposal content

[1145] The device will present the selected fashion items and hairstyles to the user, who can then review the suggestions through the application.

[1146] Virtual try-on

[1147] Users can virtually try on suggested fashion items and hairstyles using the virtual try-on feature. This feature allows users to select the style they want to try and see how it will look on them in a virtual mirror. This allows them to see in advance whether the suggested styling will suit them.

[1148] Expert feedback

[1149] The server receives feedback from experts (stylists and hairdressers) and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[1150] Select and save your final styling

[1151] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[1152] Specific examples

[1153] For example, say the user is a 42-year-old engineer who enjoys a casual lifestyle and the outdoors. The user enters a photo and basic information. The server receives this, and the generative AI analyzes facial features and body type, resulting in a result such as "thin and long face." The server then suggests slim-fitting shirts, denim jeans, and short hairstyles from its database. The user then uses the virtual try-on feature to virtually try these on. The expert then provides feedback, saying, "A brightly colored shirt would suit you better." The user finally accepts and saves the suggestions. The user can then refer to the saved styles and actually purchase the clothing at a later date.

[1154] In this way, the present invention provides users with an environment in which they can approach their matchmaking with confidence and simplifies the process.

[1155] The processing flow will be explained below.

[1156] Step 1:

[1157] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The device then sends the entered data and photo to the server.

[1158] Step 2:

[1159] The server receives the user information and photo, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photo and basic information.

[1160] Step 3:

[1161] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The analysis results are then sent back to the server.

[1162] Step 4:

[1163] The server receives the analysis results and extracts a list of suitable fashion items and hairstyles from the database, which is personalized based on the user's characteristics and preferences.

[1164] Step 5:

[1165] The server filters the extracted list of fashion items and hairstyles to generate optimal combinations, and the generated styling suggestions are notified to the user.

[1166] Step 6:

[1167] The user receives a notification and sees styling suggestions within the app, and the device displays details of the fashion item and hairstyle.

[1168] Step 7:

[1169] The user uses the virtual try-on feature to virtually try on the suggested styles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try on different styles.

[1170] Step 8:

[1171] The server sends styling suggestions to experts (stylists or hairdressers) and asks for feedback. The experts review the suggestions and provide additional advice.

[1172] Step 9:

[1173] The expert's feedback is sent back to the server, which notifies the user of the feedback information, and the user confirms the feedback and finalizes the styling.

[1174] Step 10:

[1175] The user selects the final styling. The device sends the selection to the server. The server stores the selection in a database.

[1176] Step 11:

[1177] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[1178] Example 1

[1179] 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."

[1180] Conventional styling suggestion systems were unable to accurately grasp a user's basic information and characteristics, making it difficult to provide personalized styling suggestions. Furthermore, users were unable to actually try out the suggested styling and were unable to receive real-time feedback from experts. Furthermore, it was difficult to provide styling that reflected the user's preferences and wishes, and data storage and reuse were insufficient.

[1181] 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.

[1182] In this invention, the server includes means for receiving basic information from a user, means for receiving the basic information and a photo and analyzing the user's body type and facial features using a generative AI model, means for selecting personalized fashion items and hairstyles based on the analysis results by the generative AI means, means for presenting the selected fashion items and hairstyles to the user, means for a virtual try-on that allows the user to virtually try on the selected fashion items and hairstyles, means for receiving feedback from an expert and providing it to the user, and means for saving the final styling based on the user's selection. This allows the server to accurately reflect the user's features and preferences, make optimal styling suggestions based on the virtual try-on and expert feedback, and also allows the data to be saved and reused.

[1183] "Basic user information" refers to basic information about the user, such as age, occupation, lifestyle, and preferences.

[1184] A "generative AI model" refers to an artificial intelligence model that uses machine learning technology to analyze images and data and extract user characteristics.

[1185] "Analyzing the user's body shape and facial features" means that the generative AI model analyzes the user's photo to identify detailed features such as body shape, facial shape, and skin color.

[1186] "Personalized fashion items and hairstyles" refers to individually tailored fashion items and hairstyles selected based on the user's characteristics and preferences.

[1187] "Virtual Try-On Facility" means a technological facility that allows a user to try on selected fashion items and hairstyles in a virtual environment.

[1188] "Expert feedback" refers to advice and evaluations provided to users by professionals such as stylists and hairdressers.

[1189] "Means for saving final styling" refers to a technical means for saving the styling selected by the user in a database or the like, so that it can be reused or referenced later.

[1190] A "database" refers to an information system that systematically stores large amounts of information and allows it to be searched and used as needed.

[1191] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system inputs the user's basic information and photo, analyzes the user's body type and facial features using a generative AI model, and suggests personalized fashion items and hairstyles based on that information. Furthermore, users can virtually try on suggested styles using a virtual try-on feature and receive feedback from experts.

[1192] 1. Enter your user information and upload a photo

[1193] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, and preferences. They also upload photos of their face or their entire body. The hardware used is a PC or smartphone, and the software is a web browser or dedicated application.

[1194] 2. Receiving and analyzing user information and photos

[1195] The server passes the basic information and photo data received from the user to the generative AI model. The generative AI model uses OpenAI's image analysis technology, for example, to boldly analyze the user's body shape and facial features. It obtains a detailed understanding of the user's facial shape, body type, skin color, and other characteristics and generates analytical data.

[1196] 3. Generating styling suggestions

[1197] Based on the analysis results, the server selects personalized fashion items and hairstyles from a database that includes general fashion and beauty-related data. Specifically, it suggests slim-fitting shirts, denim jeans, and short hairstyles.

[1198] 4. Presentation of proposal

[1199] The server sends the generated styling suggestions to the device, which then visually displays them to the user, who can then view and confirm the suggestions through a dedicated application or web browser.

[1200] 5. Virtual try-on

[1201] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles. This feature is realized using technology from Modiface, for example. Users can use the camera function of their smartphone or computer to see how the proposed items will look on them in real time.

[1202] 6. Expert feedback

[1203] The server receives feedback from stylists, hairdressers, and other experts and provides it to the user. The experts then provide additional advice based on the user's characteristics and preferences, providing more personalized suggestions to the user.

[1204] 7. Select and save your final styling

[1205] The user reviews the suggestions and expert feedback and selects the final styling. The device sends the selection to the server, which stores it in a database that the user can access and review at any time.

[1206] Specific examples

[1207] For example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. When the user enters a photo and basic information, the server receives it, and the generative AI model analyzes facial features and body type, obtaining an analysis result such as "thin and long face." The server then uses its database to suggest items such as slim-fitting shirts, denim jeans, and short hairstyles. The user can then use the virtual try-on feature to virtually try on these items, and an expert will provide feedback such as "bright-colored shirts would look better on you." The user can then accept and save the suggestions.

[1208] Prompt Sentence Examples

[1209] "I'm a 42-year-old engineer. I like a casual lifestyle and often spend time outdoors. I'm thin with a long face. Can you suggest some fashion items and hairstyles that would suit me?"

[1210] In this way, the system can make styling suggestions based on the user's characteristics and preferences, providing the optimal style through virtual try-ons and expert feedback.

[1211] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1212] Step 1:

[1213] Users access a dedicated website or application and enter basic information such as their age, occupation, lifestyle, preferences, etc. Users then upload a photo.

[1214] Input: User's basic information (age, occupation, lifestyle, preferences) and photo image.

[1215] Output: The user's basic information and photo are sent to the server.

[1216] What happens: The user fills out the form and clicks the upload button to submit the photo.

[1217] Step 2:

[1218] The server analyzes the basic information and photo data received from the user, and processes the information using a generative AI model to analyze the user's body shape and facial features.

[1219] Input: User basic information and photo data.

[1220] Output: Analysis of the user's body shape and facial features by the generative AI.

[1221] Specific operation: The server passes the received data to the generative AI model and performs image analysis, for example, using OpenAI's image analysis technology to determine face shape and body type.

[1222] Step 3:

[1223] The server selects personalized fashion items and hairstyles from a database based on the analysis results.

[1224] Input: Analysis results and user preference information.

[1225] Output: A list of selected fashion items and hairstyles.

[1226] What it does: The server searches a database and selects the items that best suit the user, such as a slim-fitting shirt, denim jeans, and a short hairstyle.

[1227] Step 4:

[1228] The server sends the generated styling suggestions to the terminal, which then visually displays them to the user.

[1229] Input: Selected fashion item and hairstyle data.

[1230] Output: Visual styling suggestions displayed on the user's device.

[1231] Specific operation: The server sends data to the terminal in JSON format, etc., and the terminal displays the data.

[1232] Step 5:

[1233] Users can use the virtual try-on feature to virtually try on suggested fashion items and hairstyles.

[1234] Input: Selected fashion item and hairstyle data.

[1235] Output: A display of the results of the user virtually trying on the garment.

[1236] Specific operation: Using the device's camera and technologies such as Modiface, the suggestions are reflected on the user's appearance in real time.

[1237] Step 6:

[1238] The server receives the feedback from the experts and provides it to the user.

[1239] Input: Expert feedback information.

[1240] Output: Any additional advice or rating that is displayed to the user.

[1241] How it works: The expert reviews the suggestion and sends the advice to the server, which then forwards it to the user's device, where it displays the advice.

[1242] Step 7:

[1243] The user reviews the suggestions and expert feedback and selects the final styling.

[1244] Input: The final selection made by the user.

[1245] Output: The selected styling is saved to the server.

[1246] What happens: The user makes a final selection and clicks the submit button to send the selection to the server, which records it in a database.

[1247] (Application example 1)

[1248] 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."

[1249] In today's modern lifestyle, choosing the right diet to maintain good health is difficult, especially for busy men in their 40s and older. Personalizing meals based on individual health conditions and food preferences is time-consuming, and achieving this with expert feedback is even more difficult. There are also limited ways to confirm in advance whether the meal you choose is suitable for you. Therefore, there is a need for a system that allows users to easily select the optimal meal menu from the comfort of their own home and check it in advance.

[1250] 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.

[1251] In this invention, the server includes means for receiving basic information from a user, a generating AI means for analyzing user characteristics based on the input basic information, a means for selecting a personalized meal menu based on the analysis results by the generating AI means, a means for presenting the selected meal menu to the user, a virtual tasting means for allowing the user to virtually check the nutritional components and calorie information of the selected meal menu, a means for providing feedback from an expert, and a means for saving the final meal menu based on the user's selection. This allows the user to easily receive healthy meal menus that are optimal for them from the comfort of their own home, and to select and save a final meal plan while reviewing the contents in advance and receiving advice from an expert.

[1252] "User" refers to an individual who uses this system to receive meal menu suggestions and feedback.

[1253] "Basic information" refers to information about the user's age, occupation, lifestyle, body type, food preferences, etc.

[1254] "Generative AI means" refers to the artificial intelligence function that analyzes the user's basic information and generates a personalized meal menu.

[1255] A "meal menu" is a specific meal plan or recipe suggested based on the user's health and preferences.

[1256] "Virtual tasting tool" refers to a function that allows users to virtually check the nutritional content and calorie information of the proposed meal menu.

[1257] An "expert" is someone with the expertise to provide advice on a user's health and diet, such as a registered dietitian or health coach.

[1258] "Feedback" refers to additional advice or opinions provided by experts based on the user's condition and preferences.

[1259] "Meal Plan" means a set of specific meal plans saved based on a User's selections.

[1260] A "database" is an electronic data storage system for storing analyzed information and selected meal menus.

[1261] This invention relates to a system that allows male users in their 40s or older to receive healthy and optimal meal menu suggestions from the comfort of their own homes. The embodiments for carrying out the invention are as follows.

[1262] The system consists of the following main functions: inputting user information and uploading photos, suggesting meal menus based on analysis results, a virtual tasting function, feedback from experts, saving the final meal plan, and creating a database to manage this information.

[1263] Enter your user information and upload a photo

[1264] Users enter basic information such as age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application. In addition, users can upload photos of their body type and face. This information and photos are received by the server.

[1265] Receiving and analyzing user information and photos

[1266] The server uses OpenAI's generative AI model to analyze the uploaded information and photos, providing a detailed understanding of the user's health, body type, lifestyle, and dietary preferences. The analysis results serve as the basis for generating a personalized meal menu.

[1267] Generate a meal menu

[1268] Based on the analysis results, the generative AI selects an appropriate meal plan from the database, taking into account the user's health status and dietary preferences. For example, it may suggest low-carb meals or meals using vitamin-rich ingredients.

[1269] Presentation of proposal content

[1270] The device presents the user with a selection of meal options, which can then be viewed through a smartphone app.

[1271] Virtual Tasting

[1272] Users can virtually check the nutritional content and calorie information of the proposed meal menu using the virtual tasting function, allowing them to check in advance whether the proposed menu is compatible with their lifestyle and health condition.

[1273] Expert feedback

[1274] The server receives feedback from experts such as registered dietitians and health coaches and provides it to the user, who then provides additional advice based on the user's characteristics and preferences.

[1275] Select and save your final meal plan

[1276] Users review the suggestions and expert feedback and select a final meal plan, which is then saved in a database that users can access and review at any time.

[1277] Specific examples

[1278] For example, let's say the user is a 45-year-old engineer with an active lifestyle and a desire to follow a low-carb diet. The user enters their age, occupation, lifestyle, body type, and food preferences, and uploads a photo. The server receives this information, and the generative AI analyzes the user's health and body type. An example of a prompt to be input to the generative AI model is as follows:

[1279] Please suggest a meal plan that is ideal for Age: 45, Occupation: Engineer, Lifestyle: Active, Build: Medium, Food Preference: Low Carbohydrate.

[1280] Based on this, a low-carb meal menu is suggested, and experts provide feedback that a meal with a moderate amount of protein would be even better. The user then selects and saves the final suggested menu.

[1281] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1282] Step 1: Enter your user information and upload a photo

[1283] Users enter basic information such as their age, occupation, lifestyle, body type, and food preferences through a dedicated website or smartphone application, and upload photos of their body and face. The input data and uploaded photos are sent to the server. The input in this step is the user's basic information and photo, and the output is the transmission of data to the server.

[1284] Step 2: Receive and analyze user information and photos

[1285] The server uses generative AI to perform a detailed analysis based on the received user's basic information and photo. Specifically, it analyzes the user's facial features, body type, lifestyle, etc. to understand their health condition and dietary preferences. The input for this step is the user's basic information and photo, and the output is the analyzed data.

[1286] Step 3: Generate the meal menu

[1287] The generative AI selects an appropriate meal menu from a database based on the analysis results. For example, low-carb menus or menus rich in vitamins are suggested based on the user's characteristics. The input for this step is the analysis data, and the output is a personalized meal menu.

[1288] Step 4: Present your proposal

[1289] The terminal presents the user with a meal menu selected by the generative AI. The user can review these suggestions through a smartphone app. The input of this step is a personalized meal menu, and the output is a display of the suggestions to the user.

[1290] Step 5: Virtual Tasting

[1291] The virtual tasting feature allows users to virtually check the nutritional and calorie information of the proposed meal menu. This feature allows users to consider whether the menu fits their lifestyle and health status. The input of this step is the nutritional information of the meal menu, and the output is the information displayed to the user.

[1292] Step 6: Expert feedback

[1293] The server receives feedback from experts, such as registered dietitians and health coaches, who provide additional advice based on the user's characteristics and preferences. The input of this step is the expert feedback, and the output is a reflection of that feedback to the user.

[1294] Step 7: Select and save your final meal plan

[1295] The user reviews the suggestions and expert feedback and selects the final meal plan. The selected meal plan is saved in a database and can be accessed and reviewed by the user at any time. The input of this step is the final meal plan, and the output is saving it to the database.

[1296] 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.

[1297] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine that recognizes the user's emotions, it provides a more personalized experience. This system includes the following elements.

[1298] Enter your user information and upload a photo

[1299] Users enter basic information such as their age, occupation, and lifestyle, and upload their own photos through a dedicated website or application. They can also enter emotional information based on self-diagnosis. The device then sends this data to a server.

[1300] Receiving, analyzing, and recognizing emotions in user information and photos

[1301] The server receives user information, photos, and emotional information. The server uses the generative AI to analyze the user's body shape and facial features. The emotion engine then analyzes emotional information from the user's facial expressions using facial recognition technology and combines this with the generative AI's analysis data.

[1302] Generate styling suggestions

[1303] The server selects personalized fashion items and hairstyles from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[1304] Presentation of proposal content

[1305] The device will present the user with selected fashion items and hairstyles, which the user can review through the app. The suggestions are tailored to the user's emotions, providing a more comfortable experience.

[1306] Virtual try-on

[1307] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[1308] Expert feedback

[1309] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[1310] Select and save your final styling

[1311] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is sent from the device to the server and stored in a database. This stored information can be accessed and reviewed by the user as many times as needed.

[1312] Specific examples

[1313] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[1314] In this way, the present invention provides users with personalized styling suggestions based on their emotions, providing an environment in which they can approach their search for a partner with confidence.

[1315] The processing flow will be explained below.

[1316] Step 1:

[1317] A user accesses a website or application and creates an account. They enter basic information (age, occupation, lifestyle) and upload a photo. The user then completes a simple self-diagnosis and enters their emotional state. The device then sends the entered data, photo, and emotional information to the server.

[1318] Step 2:

[1319] The server receives user information, photos, and emotion information, which are temporarily stored in a database. The server then calls the generation AI module to analyze the user's photos and basic information.

[1320] Step 3:

[1321] The generative AI analyzes the user's body type and facial features, specifically identifying face shape, skin color, body proportions, etc. The results of this analysis are sent back to the server.

[1322] Step 4:

[1323] The server calls the emotion engine, analyzes the facial expressions in the user's photo, and parses the emotional information. The emotion engine recognizes the user's current emotional state (e.g., happy, nervous, sad) based on the user's facial expressions. The analysis results are also sent back to the server.

[1324] Step 5:

[1325] The server combines the analysis results of the generation AI and the emotion engine, and based on this combined data, extracts personalized fashion items and hairstyles from the database.

[1326] Step 6:

[1327] The server selects the extracted fashion items and hairstyles from a list and generates the optimal combination. The server takes into account the user's emotional state to provide a more suitable styling. The generated styling suggestions are notified to the user.

[1328] Step 7:

[1329] The user receives a notification and confirms the styling suggestion within the application. The device displays details of the fashion item and hairstyle. The user confirms the suggested style and views the details.

[1330] Step 8:

[1331] The user uses the virtual try-on feature to virtually try on suggested fashion items and hairstyles. The device generates a virtual try-on screen and displays it to the user, allowing the user to try out different styles.

[1332] Step 9:

[1333] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server notifies the user of the feedback from the experts.

[1334] Step 10:

[1335] The user reviews the suggestions and expert feedback and selects the final styling. The final styling is selected taking into account the user's emotional state and the expert's advice. The selected styling is sent from the device to the server and stored in a database.

[1336] Step 11:

[1337] The server will send a confirmation message to the user informing them that the selected styling has been saved, and the user will be able to refer to the saved styling at any time.

[1338] As a concrete example, consider a user who is a 42-year-old engineer with a casual lifestyle and loves the outdoors. The user uploads a photo of themselves in the emotional state of "fun" to the system. The server receives the photo and basic information, and the generation AI analyzes their facial features and body type. At the same time, the emotion engine analyzes their facial expressions and recognizes the emotion of "fun." The server then selects from its database a casual, light-colored slim-fit shirt, denim jeans, and a short hairstyle. The user virtually tries these on using the virtual try-on function, and the expert gives feedback that "the light-colored shirt suits you." The user then finally selects and saves this style. In this way, the present invention takes the user's emotional state into account to provide more personalized styling suggestions.

[1339] Example 2

[1340] 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."

[1341] This invention relates to a system that allows men in their 40s and older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. Conventional methods have the drawback of making it difficult for users to select appropriate items themselves, and suggestions do not take emotions into account, resulting in low user satisfaction. Furthermore, it is difficult to receive timely feedback from experts, making it difficult to make optimal choices. Therefore, there is a need for a system that provides users with more personalized suggestions and incorporates expert opinions.

[1342] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving basic information from a user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo of the user, a means for analyzing emotional information using face recognition technology, a means for selecting personalized fashion items and hairstyles based on the analysis results of the generation AI means and the emotional information analysis means, a means for presenting the selected fashion items and hairstyles to the user, a virtual try-on means for allowing the user to virtually try on the selected fashion items and hairstyles, and a means for saving the final styling based on the user's selection. This allows the user to not only receive appropriate styling suggestions from the comfort of their own home, but also enable suggestions that take emotions into consideration, and further allows them to make optimal selections by receiving expert feedback.

[1343] "Basic information" refers to data such as the user's age, occupation, and lifestyle.

[1344] "Photo" refers to an image file showing the user's face and body shape.

[1345] "Generative AI means" refers to the artificial intelligence techniques used to analyze user characteristics.

[1346] "Facial recognition technology" refers to technology for analyzing emotional information from a user's facial expressions.

[1347] "Emotional information" refers to the emotional state analyzed from the user's facial expressions.

[1348] "Personalized fashion items" refer to clothing and accessories selected based on the user's characteristics, preferences, and emotional state.

[1349] "Hairstyle" refers to the hairstyle suggested to the user.

[1350] "Virtual Try-On Facility" refers to a system feature that allows a user to virtually try on selected fashion items and hairstyles.

[1351] "Expert feedback" refers to advice and evaluations provided by professionals such as stylists and hairdressers.

[1352] "Database" refers to an information management system for storing analyzed information and selected fashion items and hairstyles.

[1353] "Server" refers to a computer system that receives, processes, and analyzes user input.

[1354] "Terminal" refers to a device such as a computer or smartphone that is operated by a user.

[1355] This invention relates to a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes. The system aims to provide a more personalized experience by combining an emotion engine that recognizes the user's emotions. The system includes the following elements:

[1356] Enter your user information and upload a photo

[1357] The user accesses a dedicated website or application. They enter basic information such as their age, occupation, and lifestyle, and upload their own photos. They can also enter their own emotional information through self-diagnosis. This information is sent from the device to the server.

[1358] Receiving, analyzing, and recognizing emotions in user information and photos

[1359] The server receives the user's basic information, photo, and emotional information. Next, the server uses a generative AI (e.g., publicly available artificial intelligence technology) to analyze the user's body shape and facial features. In addition, it uses an emotional engine (e.g., software using facial recognition technology) to analyze emotional information from the user's facial expressions. The results of this analysis are combined with the generative AI's analysis data and processed.

[1360] Example prompt sentence:

[1361] "Analyze this user's facial and body features."

[1362] Generate styling suggestions

[1363] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from its database, taking into account the user's preferences, lifestyle, and emotional state.

[1364] Example prompt sentence:

[1365] "42-year-old engineer, casual lifestyle, emotional state: 'fun'"

[1366] Presentation of proposal content

[1367] The server sends the selected fashion items and hairstyles to the device, which then presents them to the user, who can then confirm the suggestions through the application.

[1368] Virtual try-on

[1369] When a user selects the virtual try-on feature, the device generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try on different styles.

[1370] Expert feedback

[1371] The server sends styling suggestions to experts (stylists or hairdressers) and requests feedback. The experts review the suggestions and provide additional advice. The server then notifies the user of this feedback.

[1372] Example prompt sentence:

[1373] "A lighter colored shirt would fit that sentiment."

[1374] Select and save your final styling

[1375] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling information is sent from the device to a server and stored in a database. The saved information can be accessed and reviewed by the user as many times as needed.

[1376] Examples:

[1377] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotion information, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then suggests a slim-fit shirt, denim jeans, and a short hairstyle from its database. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves this suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[1378] In this way, the present invention provides users with styling suggestions based on their personalized emotions, providing an environment in which they can approach their search for a partner with confidence.

[1379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1380] Step 1:

[1381] Users access a dedicated website or application and enter basic information such as age, occupation, and lifestyle. Next, they upload their own photos and enter their emotional information through self-diagnosis. The device then sends this information to a server.

[1382] Input: Basic information such as age, occupation, lifestyle, user photo, emotional information

[1383] Output: Basic information, photo, and emotional information sent to the server

[1384] Step 2:

[1385] The server processes the received basic information, photos, and emotional information, and uses a generative AI model (e.g., general artificial intelligence technology) to analyze the user's body shape and facial features.

[1386] Input: User's basic information, photo

[1387] Output: Analysis results for body shape and facial features

[1388] Step 3:

[1389] The server uses facial recognition technology to analyze emotional information from the user's photo. It then uses an emotion engine (e.g., general emotion recognition software) to analyze the user's emotions and combines them with the analysis results of the generative AI.

[1390] Input: User photo

[1391] Output: Parsed emotion information

[1392] Step 4:

[1393] Based on the analysis results, the server selects the most suitable fashion items and hairstyles for the user from the database, taking into account the user's preferences, lifestyle, and emotional state.

[1394] Input: Analysis results of body shape and facial features, emotional information

[1395] Output: Selected fashion items and hairstyles

[1396] Step 5:

[1397] The server transmits the selected fashion items and hairstyles to the terminal, which then presents them to the user.

[1398] Input: Selected fashion items and hairstyles

[1399] Output: Styling suggestions presented to the user

[1400] Step 6:

[1401] When the user selects the virtual try-on function, the terminal generates a virtual try-on screen, allowing the user to virtually try on the suggested fashion items and hairstyles, allowing the user to try out different styles.

[1402] Input: Selected fashion items and hairstyles

[1403] Output: Virtual try-on screen

[1404] Step 7:

[1405] The server sends styling suggestions to experts for feedback, the experts review the suggestions and provide additional advice, and the server notifies the user of this feedback information.

[1406] Input: Styling suggestions

[1407] Output: Expert feedback

[1408] Step 8:

[1409] The user reviews the suggestions and expert feedback and selects the final styling. The device then sends the selected styling information to the server, which stores it in a database.

[1410] Input: Final styling selection

[1411] Output: Styling information stored in a database

[1412] Each step is designed to be easy for users to operate at home, and the server and device are linked to provide a smooth experience. This system allows users to receive optimal styling suggestions to help them approach their marriage search with confidence.

[1413] (Application example 2)

[1414] 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."

[1415] Conventional styling systems are limited to making suggestions based on basic user information and lack personalized suggestions that take into account the user's emotions and momentary moods. Furthermore, trying on suggested fashions and hairstyles requires a user to visit a physical store, making it difficult to try them on. The present invention aims to solve these issues by providing a system that offers personalized styling suggestions based on the user's emotions and includes a virtual try-on function.

[1416] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1417] In this invention, the server includes a means for receiving basic information and a photo from the user, a generation AI means for analyzing the user's characteristics based on the input basic information and photo, and an emotion engine means for recognizing emotional information from the user's facial photo, thereby enabling personalized styling suggestions and virtual try-ons based on the user's emotions.

[1418] "Basic user information" refers to information about basic attributes such as age, occupation, and lifestyle provided by the user.

[1419] A "photo" is image data that includes the user's face and body shape.

[1420] "Generative AI" is an artificial intelligence that analyzes basic information and photos provided by users and extracts features.

[1421] The "Emotion Engine" is a technology that recognizes and analyzes emotional information from a user's facial photograph.

[1422] "Personalized fashion items" are clothing and accessories that are individually selected based on a user's characteristics and emotional information.

[1423] A "personalized hairstyle" is a hairstyle that is individually selected based on the user's characteristics and emotional information.

[1424] "Virtual Try-On" is a feature that allows users to try on selected fashion items and hairstyles in a virtual space.

[1425] "Expert feedback" is additional advice or opinion provided by professionals such as stylists or hairdressers.

[1426] A "database" is a repository of analyzed information and selected fashion items and hairstyles.

[1427] This invention is a system that allows men in their 40s or older looking for a partner to easily receive optimal styling suggestions from the comfort of their own homes, and by combining it with an emotion engine, it provides a more personalized experience. As a specific embodiment, we will explain a system that includes the following elements.

[1428] 1. Enter your user information and upload a photo

[1429] Users enter basic information such as age, occupation, and lifestyle, and upload their own photos via their device. They can also enter emotional information based on self-diagnosis. This data is sent from the device to the server.

[1430] 2. Receiving, analyzing, and recognizing emotions in user information and photos

[1431] The server receives the user's information and photo, and uses generative AI to analyze the user's body shape and facial features. The emotion engine uses facial recognition technology to analyze the user's emotional information from their facial expressions. This allows the server to obtain the user's characteristics and current emotional state.

[1432] 3. Generating styling suggestions

[1433] The server selects personalized fashion items and hairstyles for the user from a database based on the analysis results, including emotional information, taking into account the user's preferences, lifestyle, and current emotional state.

[1434] 4. Presentation of proposal

[1435] The suggested fashion items and hairstyles are presented to the user via the device, and the user can check the suggestions through the application.

[1436] 5. Virtual try-on

[1437] Users can virtually try on suggested fashion items and hairstyles using the device's virtual try-on feature, which allows users to try out different styles and see how they look.

[1438] 6. Expert feedback

[1439] The server sends styling suggestions to experts (stylists and hairdressers) and requests feedback. Feedback from the experts is sent to the server and notified to the user.

[1440] 7. Select and save your final styling

[1441] The user reviews the suggestions and expert feedback and selects the final styling. The selected styling is saved on the server and can be accessed and reviewed by the user at any time.

[1442] The hardware required is the user's device (smartphone or PC) and a server, while the software uses OpenCV, FER, and TensorFlow (a generative AI model) to perform image analysis and emotion recognition.

[1443] Specific examples

[1444] For example, suppose the user is a 42-year-old engineer who enjoys a casual lifestyle and loves the outdoors. If the user enters "fun" as their emotional state, the server receives their photo and basic information, and the generation AI analyzes their facial features and body type. The emotion engine analyzes the user's facial expressions and confirms the emotion "fun." The server then uses its database to suggest a slim-fit shirt, denim jeans, and a short hairstyle that match the user's body type and emotion. The user then uses the virtual try-on feature to virtually try on these items. The expert then provides feedback, suggesting that "a lighter-colored shirt would suit that emotion." The user ultimately accepts and saves the suggestion. The user can then refer to the saved style and actually purchase the clothing at a later date.

[1445] Prompt Sentence Examples

[1446] "I'm a 42-year-old engineer with a casual lifestyle, loves the outdoors, and am in a fun mood right now. Based on this information, the suggestions were a slim-fit shirt, denim jeans, and a short hairstyle. Further expert feedback suggested that a lighter-colored shirt would fit that sentiment."

[1447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1448] Step 1:

[1449] Users input basic information such as age, occupation, and lifestyle, as well as their own photos, into the application via their device, which then sends the data to the server.

[1450] Input: User's basic information (age, occupation, lifestyle, etc.), user photo

[1451] Output: Basic information and photo data sent

[1452] Step 2:

[1453] The server inputs the user information and photo data received from the device into a generative AI model for analysis.

[1454] Input: User's basic information and photo

[1455] Output: Analyzed user characteristics information (body type, facial features, etc.)

[1456] How it works: Uses generative AI models for facial and body feature extraction

[1457] Step 3:

[1458] The server analyzes facial expressions from the user's photograph and obtains emotion information using an emotion engine.

[1459] Input: User's face photo

[1460] Output: User's emotional information (e.g., happy, sad)

[1461] How it works: The emotion engine analyzes images using facial expression recognition technology to estimate emotional states.

[1462] Step 4:

[1463] The server combines the analysis results of the generative AI model and the emotion engine to generate personalized fashion item and hairstyle suggestions.

[1464] Input: Analyzed user characteristics and emotion information

[1465] Output: Suggested fashion items and hairstyles

[1466] How it works: Selects items from a database based on the user's characteristics and emotions and generates a list

[1467] Step 5:

[1468] The terminal presents the suggestions provided by the server to the user, who then confirms them.

[1469] Input: Suggested fashion items and hairstyles

[1470] Output: Fashion item and hairstyle information presented to the user

[1471] Action: The user reviews the proposal through the application.

[1472] Step 6:

[1473] Users can use the virtual try-on function on their device to virtually try on suggested fashion items and hairstyles.

[1474] Input: Fashion items and hairstyles the user wants to try on

[1475] Output: Virtual try-on images and videos

[1476] Action: Applying items to the user avatar using the virtual try-on feature

[1477] Step 7:

[1478] The server collects expert feedback on the proposed styling and notifies the user.

[1479] Input: Suggested fashion items and hairstyles

[1480] Output: Expert feedback

[1481] How it works: The server collects opinions and advice from stylists and hairdressers and delivers them to the user.

[1482] Step 8:

[1483] The user selects the final styling and sends the results from the device to the server, which stores the results in a database.

[1484] Input: Final styling selected by the user

[1485] Output: Final styling information saved

[1486] What happens: The server receives the user's final selection and stores it in the database

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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).

[1494] 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.

[1495] 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."

[1496] 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.

[1497] 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).

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] The following is further disclosed regarding the above embodiment.

[1509] (Claim 1)

[1510] A means for users to enter basic information;

[1511] A generation AI means that analyzes the user's characteristics based on the basic information entered by the user, and

[1512] A means for selecting personalized fashion items and hairstyles based on the analysis results of the generating AI means;

[1513] means for presenting the selected fashion items and hairstyles to the user;

[1514] a virtual try-on means for allowing a user to virtually try on selected fashion items and hairstyles;

[1515] A means to save final styling based on user selections

[1516] A system including:

[1517] (Claim 2)

[1518] 10. The system of claim 1, further comprising means for providing expert feedback.

[1519] (Claim 3)

[1520] 10. The system of claim 1, further comprising means for storing the analyzed information and the selected fashion items and hairstyles in a database.

[1521] "Example 1"

[1522] (Claim 1)

[1523] A means for users to enter basic information;

[1524] A means for receiving basic information and a photo and analyzing the user's body type and facial features using a generative AI model;

[1525] A means for selecting personalized fashion items and hairstyles based on the analysis results of the generating AI means;

[1526] means for presenting selected fashion items and hairstyles to a user;

[1527] a virtual try-on means for allowing a user to virtually try on selected fashion items and hairstyles;

[1528] A means of receiving expert feedback and providing it to users;

[1529] A means to save final styling based on user selections

[1530] A system including:

[1531] (Claim 2)

[1532] 10. The system of claim 1, further comprising means for storing the analyzed information and the selected fashion items and hairstyles in a database.

[1533] (Claim 3)

[1534] The system of claim 1, further comprising means for suggesting styling taking into account preferences and wishes input by the user.

[1535] "Application Example 1"

[1536] (Claim 1)

[1537] A means for users to enter basic information;

[1538] A generation AI means that analyzes the user's characteristics based on the basic information entered by the user, and

[1539] A means for selecting a personalized meal menu based on the analysis results by the generating AI means;

[1540] means for presenting the selected meal menu to the user;

[1541] A virtual tasting method that allows users to virtually check the nutritional content and calorie information of the selected meal menu, and

[1542] a means of providing expert feedback;

[1543] A means to save the final meal menu based on the user's selections

[1544] A system including:

[1545] (Claim 2)

[1546] 10. The system of claim 1, further comprising means for storing the parsed information and the selected meal menu in a database.

[1547] (Claim 3)

[1548] 10. The system of claim 1, further comprising means for providing feedback from a registered dietitian or health coach.

[1549] "Example 2: Combining Emotion Engines"

[1550] (Claim 1)

[1551] A means for users to enter basic information;

[1552] A generation AI means that analyzes the user's characteristics based on the user's basic information and photo entered;

[1553] A means for analyzing emotional information using facial recognition technology;

[1554] a means for selecting personalized fashion items and hairstyles based on the analysis results of the generating AI means and the emotion information analyzing means;

[1555] means for presenting the selected fashion items and hairstyles to the user;

[1556] a virtual try-on means for allowing a user to virtually try on selected fashion items and hairstyles;

[1557] A means to save final styling based on user selections

[1558] A system including:

[1559] (Claim 2)

[1560] 10. The system of claim 1, further comprising means for providing expert feedback.

[1561] (Claim 3)

[1562] 10. The system of claim 1, further comprising means for storing the analyzed information and the selected fashion items and hairstyles in a database.

[1563] "Application example 2 when combining emotion engines"

[1564] (Claim 1)

[1565] A way for users to enter basic information and photos,

[1566] A generative AI means for analyzing user characteristics based on the basic information and photos of the user entered;

[1567] an emotion engine means for recognizing emotion information from a facial photograph of a user;

[1568] a means for selecting personalized fashion items and hairstyles based on the analysis results by the generating AI means and the emotion engine means;

[1569] means for presenting the selected fashion items and hairstyles to the user;

[1570] a virtual try-on means for allowing a user to virtually try on selected fashion items and hairstyles;

[1571] A means to save final styling based on user selections

[1572] A system including:

[1573] (Claim 2)

[1574] 10. The system of claim 1, further comprising means for providing expert feedback.

[1575] (Claim 3)

[1576] 10. The system of claim 1, further comprising means for storing the analyzed information and the selected fashion items and hairstyles in a database. [Explanation of symbols]

[1577] 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 users to enter basic information; A generation AI means that analyzes the user's characteristics based on the basic information entered by the user, and A means for selecting personalized fashion items and hairstyles based on the analysis results of the generating AI means; means for presenting the selected fashion items and hairstyles to the user; a virtual try-on means for allowing a user to virtually try on selected fashion items and hairstyles; A means to save final styling based on user selections A system including:

2. The system of claim 1 further comprising means for providing expert feedback.

3. The system of claim 1 , further comprising means for storing the analyzed information and the selected fashion items and hairstyles in a database.

Images