System

Abstract

A system is provided.SOLUTION: The system includes means for capturing a user's face, means for analyzing the captured face image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for analyzing a gap between the user's current face and the ideal face and suggesting a specific makeup method and skin care, means for providing a real-time mirror function and allowing the user to try the suggested makeup, and means for collecting user feedback and utilizing it to improve the system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, with the spread of remote meetings and online communication, people are increasingly being seen on camera. This has led to increased interest in makeup and skincare among both men and women. However, lack of knowledge and the ability to select the right products are particularly challenging for makeup beginners and those wanting to learn how to apply makeup to different occasions. This has led to a lack of confidence in managing one's appearance. Therefore, there is a need for support that helps users achieve their ideal appearance and live their daily lives with confidence. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for capturing a user's face, analyzing the captured facial image, and generating facial feature data. It also includes a means for generating an ideal face based on a scene selected by the user, analyzing the gap between the user's current face and the ideal face, and proposing specific makeup and skin care methods. It also includes a real-time mirror function, a means for trying out the proposed makeup, and a means for collecting user feedback and using it to improve the system. In this way, the system enables users to achieve their ideal appearance and live their daily lives with confidence.

[0006] A "user" is an individual who uses the system and applications.

[0007] A "terminal" is an electrical device operated by a user, and includes a camera function and an interface.

[0008] A "server" is a central system that processes, stores, and analyzes data.

[0009] A "face capturing means" is a method and device that uses a camera to capture an image of a user's face.

[0010] A "captured face image" is image data of a user's face obtained by a face capturing means.

[0011] "Facial feature data" refers to identifying information such as eyes, nose, mouth, and contours extracted from a captured facial image.

[0012] A "scene" is a concept that indicates a specific situation or occasion, and serves as a reference point for different makeup styles and skin care methods.

[0013] The "ideal face" is a target face design generated based on the user's facial feature data and on a makeup style suited to the scene.

[0014] The "gap" is the difference that exists between the user's current face and their ideal face.

[0015] "Makeup methods" are specific steps and techniques for using cosmetics to change the impression of the face.

[0016] "Skin care" refers to the methods and processes used to care for the skin to keep it healthy.

[0017] The "real-time mirror function" is an interface that uses a camera to allow users to check their own face in real time.

[0018] "Feedback" refers to information about opinions and evaluations provided by users after using the system.

[0019] "Means for improvement" refers to the methods and processes by which collected feedback is used to improve system performance and evolve AI models. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention is a system for enabling users to feel confident about their appearance during remote meetings and online communications. The system operates as follows.

[0042] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0043] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[0044] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0045] In addition, the device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0046] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[0047] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The user launches an application and accesses the camera function.

[0051] Step 2:

[0052] The device activates its built-in camera and captures the user's face.

[0053] Step 3:

[0054] The terminal transmits the captured facial image data to the server.

[0055] Step 4:

[0056] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[0057] Step 5:

[0058] The server generates a facial feature dataset for the user based on the analysis results.

[0059] Step 6:

[0060] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[0061] Step 7:

[0062] The terminal transmits scene selection information to the server.

[0063] Step 8:

[0064] The server generates an ideal face corresponding to the scene based on the model, obtains makeup style information appropriate for the scene from the database, and combines the user's feature dataset with the ideal makeup style to generate a target facial image.

[0065] Step 9:

[0066] The server sends the generated ideal face image to the terminal.

[0067] Step 10:

[0068] The terminal displays an ideal face image to the user.

[0069] Step 11:

[0070] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care instructions, such as choosing eyeshadow colors, recommending lip colors, and recommending skin care products (moisturizing cream, lotion, etc.).

[0071] Step 12:

[0072] The server sends the proposal to the device.

[0073] Step 13:

[0074] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[0075] Step 14:

[0076] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[0077] Step 15:

[0078] After users complete the makeup or skincare process, they receive feedback within the app.

[0079] Step 16:

[0080] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[0081] Step 17:

[0082] The feedback data collected by the server is used as training data for the AI ​​model.

[0083] Step 18:

[0084] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[0085] Example 1

[0086] 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."

[0087] In recent years, remote meetings and online communication have become increasingly important, but users are unable to present their appearance with confidence. In particular, compared to face-to-face interactions, online interactions tend to be less prone to anxiety about appearance and opportunities to receive makeup advice are fewer. For this reason, there is a demand for a system that helps users achieve their ideal appearance and communicate online with confidence.

[0088] 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.

[0089] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured face image and generating facial feature data, and a means for generating an ideal face based on a scene selected by the user, thereby enabling the user to present their appearance with confidence during online communication.

[0090] A "means for capturing a face" is a device or program that has the function of recognizing a user's face and capturing it as an image.

[0091] "Means for analyzing facial images and generating facial feature data" refers to devices or programs that use artificial intelligence algorithms to analyze acquired facial images, identify facial features such as the eyes, nose, and mouth, and the condition of the skin, and convert them into data.

[0092] The "means for generating an ideal face based on a scene" refers to a device or program that has the function of obtaining makeup style information from a database and integrating it with facial feature data to create an ideal facial image in order to generate an appearance suitable for the scene selected by the user.

[0093] "Means for analyzing gaps and suggesting specific makeup and skin care methods" refers to devices or programs that analyze the differences between the current face and the ideal face and provide specific makeup techniques and skin care methods to fill those gaps.

[0094] The "real-time mirror function" refers to a device or program that has the function of providing an interface that allows a user to apply a suggested makeup method while checking their own face in real time through a camera.

[0095] "Means for collecting feedback and using it to improve the system" refers to devices or programs that have the function of collecting feedback from users after use and using it to improve the system or as learning data for artificial intelligence models.

[0096] A "database" is a system for storing, managing, searching, and retrieving necessary information.

[0097] "Artificial intelligence algorithms" are mathematical models and programs used to perform image and data analysis.

[0098] "Makeup style information" is information about makeup methods and makeup products suitable for a particular scene.

[0099] A "user" is an individual who uses this system.

[0100] This invention provides a system that allows users to feel confident about their appearance during remote meetings and online communications. A specific implementation method of this system is described below.

[0101] First, when a user launches the application, the device's camera automatically turns on and captures the user's face. At this stage, the built-in camera of a smartphone or PC is used. The facial image captured by the device is compressed, encrypted, and sent to the server.

[0102] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the received facial images. Through this analysis, the server identifies facial features (eyes, nose, mouth, contours, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[0103] Next, the user selects a scene within the application. Scenes include, for example, a business meeting or a casual date. The scene information selected by the user is sent from the device to the server. Upon receiving the scene information, the server retrieves makeup style information appropriate for the scene from a database.

[0104] The server combines the user's facial feature dataset with information on makeup styles appropriate for the occasion and generates an ideal face using a generative AI model. This generated ideal face image is sent from the server to the device and displayed to the user. For example, the server retrieves makeup styles appropriate for a business meeting from a database and combines them with the user's facial data to generate the ideal face.

[0105] Furthermore, the server analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. This includes specific makeup techniques and cosmetics to use. The suggestions are sent from the server to the user's device and displayed. For example, they include "how to use eyeshadow to accentuate the eyes" and "how to use moisturizing cream for dry skin."

[0106] Users can also use the device's real-time mirror function, which allows them to check their own face on the screen in real time and apply makeup according to instructions from the server. Specifically, instructions such as "apply eyeshadow to the right eye" and "apply blush to the cheek" are displayed on the screen.

[0107] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics felt and worked, as well as areas for improvement. The device sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model. This allows the app to provide more accurate advice that reflects user feedback.

[0108] Examples of specific prompts include:

[0109] "Please provide me with a face makeup style suitable for business meetings."

[0110] "Please analyze the gap between my current face and my ideal face and suggest specific makeup and skin care procedures."

[0111] "Analyze user feedback and use it to improve the system."

[0112] In this way, the present invention helps users achieve their ideal appearance and communicate confidently online.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] A user launches an application. The device camera automatically turns on and captures the user's face. The input of this stage is the user's face image, and the output is the captured face image. Specifically, the device camera focuses on the user's face and captures a high-resolution image.

[0116] Step 2:

[0117] The device compresses and encrypts the captured facial image and sends it to the server. The input is the captured facial image, and the output is compressed and encrypted facial image data. This data is sent to the server over the network. Specifically, the device securely protects the data using an encryption algorithm and sends it to the server over an internet connection.

[0118] Step 3:

[0119] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the facial images it receives. The input is encrypted facial image data, and the output is an analyzed facial feature dataset. The server identifies facial features (eyes, nose, mouth, face, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Specifically, the server applies image analysis algorithms to extract key facial features.

[0120] Step 4:

[0121] The user selects the desired scene within the application. The input is the user's scene selection, and the output is the selected scene information. In concrete terms, the user selects a scene, such as "business meeting" or "casual date," from a drop-down menu.

[0122] Step 5:

[0123] The terminal transmits the selected scene information to the server. The input is the selected scene information, and the output is the scene information transmitted to the server. In concrete terms, the terminal transmits the scene information as a packet to the server.

[0124] Step 6:

[0125] The server retrieves makeup style information appropriate for the scene from the database. The input is scene information, and the output is makeup style information appropriate for the scene. Specifically, the server executes a database query to access and retrieve makeup style information appropriate for the scene.

[0126] Step 7:

[0127] The server integrates the user's facial feature dataset with makeup information appropriate for the scene, and generates an ideal face using a generative AI model. The input is the facial feature dataset and makeup style information, and the output is an ideal face image. Specifically, the server runs the generative AI model and generates an ideal face image based on the integrated data.

[0128] Step 8:

[0129] The server sends the generated ideal face image to the terminal and displays it to the user. The input is the ideal face image, and the output is the ideal face image displayed on the terminal. In concrete terms, the server sends the generated ideal face image to the terminal as digital data.

[0130] Step 9:

[0131] The server analyzes the gap between the current face and the ideal face and proposes specific makeup and skin care procedures to fill the gap. The input is image data of the current face and the ideal face, and the output is specific makeup and skin care procedures. Specifically, the server uses a gap analysis algorithm to analyze the differences between the current face and the ideal face.

[0132] Step 10:

[0133] The server sends the suggestions to the device and displays them to the user. The input is the specific makeup and skin care steps, and the output is the suggestions displayed to the user. Specifically, the device receives the suggestions and displays them through the user interface.

[0134] Step 11:

[0135] The user uses the real-time mirror function on the device to try out the proposed makeup method. The input is the proposed makeup method, and the output is the makeup applied by the user. Specifically, the user applies makeup by following the instructions on the screen while checking their own face in real time using the device's camera.

[0136] Step 12:

[0137] After applying makeup or skincare, the user provides feedback within the application. The input is feedback (such as how the cosmetics used felt, their effects, and areas for improvement), and the output is feedback data. Specifically, the user fills out a feedback form within the application and submits it.

[0138] Step 13:

[0139] The device sends feedback data to the server, and the server uses the collected feedback as training data for the AI ​​model to help improve the system. The input is the collected feedback data, and the output is an improved AI model. Specifically, the server analyzes the feedback data and adds it to the training dataset for the AI ​​model.

[0140] (Application example 1)

[0141] 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."

[0142] With the current spread of remote meetings and online communication, an increasing number of users are feeling unsure about their appearance. Furthermore, in physical stores, customers who are not familiar with how to select and use appropriate makeup and skincare products have difficulty choosing the right beauty products. A system is needed to solve these problems and help users live with confidence.

[0143] 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.

[0144] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured facial image and generating facial feature data, and a means for integrating the customer's individual facial features with product information using a database and an AI model to recommend appropriate beauty products, thereby enabling users to feel confident about their appearance and easily select appropriate beauty products even in physical stores.

[0145] "User" refers to an individual who uses this system.

[0146] "Means for capturing a face" refers to a function that uses a camera or other image capture device to capture an image of a user's face as digital data.

[0147] "Means for analyzing facial images" refers to algorithms and software for detecting and recognizing facial features and characteristics based on captured facial image data.

[0148] "Facial feature data" refers to a dataset that represents each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.).

[0149] "Means for generating an ideal face" refers to a function that uses AI and database information to generate ideal facial styles and makeup based on a scene selected by the user.

[0150] "Means for analyzing the gap" refers to a function that uses AI and algorithms to compare and determine the differences between the user's current face and their ideal face.

[0151] "Means to suggest makeup methods and skin care" refers to a function that uses AI and database information to recommend specific makeup procedures and skin care products to users based on the results of gap analysis.

[0152] The "real-time mirror function" refers to a display function that allows users to try out suggested makeup while checking their own face in real time through the camera.

[0153] "Means for collecting user feedback" refers to the function that allows users to provide information about the feel, effectiveness, and areas for improvement of makeup and skincare products through the application, and collect this information as data.

[0154] "Means used to improve the system" refers to the function of using collected feedback as training data to improve the accuracy of AI models and proposed algorithms.

[0155] "Smart devices installed in physical stores" refers to electronic devices such as smartphones, tablets, and smart glasses installed in stores.

[0156] "Database and AI model" refers to data storage for accumulating information on users' facial features, product information, and makeup styles, as well as artificial intelligence algorithms for analyzing and utilizing that data.

[0157] "Means for recommending beauty products" refers to a function that integrates a user's facial feature data with database information to automatically recommend appropriate makeup and skin care products.

[0158] "Means for providing product information in real time" refers to a function that instantly provides users with product information in the store and explains how to use the product through video and text.

[0159] In this invention, first, a user launches an application using a smart device (e.g., a smartphone, tablet, smart glasses, etc.) installed in a physical store. The device's camera is activated and captures the user's face. The captured face image is then sent to a server.

[0160] The server then analyzes the received facial image data using a facial recognition model (e.g., a deep learning model using TensorFlow or PyTorch) to identify facial features (eyes, nose, mouth, contours, etc.) and skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset for the user.

[0161] The user then selects the desired scene (e.g., business meeting, casual date, etc.) within the application. The selected scene information is sent from the device to the server. The server then retrieves makeup style information appropriate for the scene from the database and combines it with the user's facial feature dataset to generate an ideal face.

[0162] The generated ideal face image is sent to the device, along with an analysis of the gap between the user's current face and the ideal face. The server then suggests specific makeup and skin care procedures to fill the gap. For example, this could include using eyeshadow to accentuate the eyes or recommending a moisturizing cream for dry skin. These suggestions are then sent to the device and displayed to the user.

[0163] The device also offers a real-time mirror function, allowing users to check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0164] After completing their makeup or skincare routine, users can provide feedback within the app, including how the cosmetics they used felt, their effectiveness, and areas for improvement. The device then sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model to help improve the system.

[0165] As a concrete example, consider the case of a 35-year-old woman using an app at a beauty salon. She launches the app and her face is captured by the camera. The server analyzes her facial features and skin condition, and based on that, it recommends a moisturizing cream for dry skin or a specific lip color. When she scans the barcode of a product sold in the store, a video or text description of how to use the product is instantly displayed within the app.

[0166] An example of an input prompt for the generative AI model is, "Please recommend the best moisturizing cream and lip color for a 35-year-old woman with dry skin." This allows users to easily choose the beauty products that are best for them.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] A user launches an application on a smart device installed in a physical store and captures their own face using the camera. The input is the user's face image, and the output is facial image data. This facial image data is sent from the device to the server.

[0170] Step 2:

[0171] The server analyzes the received facial image data. This analysis uses a facial recognition model (for example, a deep learning model using TensorFlow or PyTorch). The input is the facial image data, and the output is feature data for each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.). Based on this data, the server generates a facial feature dataset for the user.

[0172] Step 3:

[0173] The user selects a desired scene (e.g., business meeting, casual date, etc.) within the application. The input is the scene information selected by the user, and the output is the scene information. This scene information is sent from the terminal to the server.

[0174] Step 4:

[0175] The server retrieves makeup style information suitable for the selected scene from the database. The input is scene information, and the output is makeup style information suitable for the scene. The server then integrates the user's facial feature dataset and makeup style information to generate an ideal facial image.

[0176] Step 5:

[0177] The server sends the generated ideal face image to the device and analyzes the gap between the user's current face and the ideal face. The input is the user's current face and ideal face, and the output is the gap analysis result. The server then proposes specific makeup and skin care procedures to close the gap.

[0178] Step 6:

[0179] The terminal displays the suggested makeup and skin care procedures to the user. The input is the suggestion from the server, and the output is the suggested display to the user. The user performs the makeup and skin care according to the displayed instructions.

[0180] Step 7:

[0181] In addition, the device provides a real-time mirror function, allowing users to check their own face in real time while applying makeup. The input is the user's face image and specific makeup instructions, and the output is a real-time display of makeup instructions.

[0182] Step 8:

[0183] After the user has completed their makeup or skin care routine, the application provides feedback. The input is the user's feedback, and the output is the collected data. The device then sends this feedback data to the server.

[0184] Step 9:

[0185] The server uses the collected feedback as training data for the AI ​​model to improve the system: the input is the feedback data, and the output is an improved AI model, which will result in more accurate advice in the future.

[0186] 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.

[0187] The present invention is a system for helping users feel confident about their appearance during remote meetings and online communications, and includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function. The system operates as follows.

[0188] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0189] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[0190] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0191] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[0192] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0193] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[0194] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence. Another distinctive feature of the present invention is that it includes a function that recognizes the user's emotions and suggests the optimal makeup and skin care procedures based on those emotions.

[0195] The processing flow will be explained below.

[0196] Step 1:

[0197] The user launches an application and accesses the camera function.

[0198] Step 2:

[0199] The device activates its built-in camera and captures the user's face.

[0200] Step 3:

[0201] The terminal transmits the captured facial image data to the server.

[0202] Step 4:

[0203] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[0204] Step 5:

[0205] The server generates a facial feature dataset for the user based on the analysis results.

[0206] Step 6:

[0207] The server applies an emotion engine that analyzes facial expressions to recognize the user's emotions and generates emotion data, which are classified into joy, sadness, anger, surprise, etc.

[0208] Step 7:

[0209] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[0210] Step 8:

[0211] The terminal transmits scene selection information to the server.

[0212] Step 9:

[0213] The server generates an ideal face based on the model for the scene. It retrieves makeup style information appropriate for the scene from a database and generates a target facial image by integrating the user's facial feature dataset and the ideal makeup style. This generated ideal face image also takes into account emotional data.

[0214] Step 10:

[0215] The server sends the generated ideal face image to the terminal.

[0216] Step 11:

[0217] The terminal displays an ideal face image to the user.

[0218] Step 12:

[0219] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care procedures. For example, it can help select eyeshadow colors, recommend lip colors, and recommend skin care products. Based on emotional data, for example, if the user is tired, it will suggest makeup and skin care that will leave them feeling refreshed.

[0220] Step 13:

[0221] The server sends the proposal to the device.

[0222] Step 14:

[0223] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[0224] Step 15:

[0225] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[0226] Step 16:

[0227] After users complete the makeup or skincare process, they receive feedback within the app.

[0228] Step 17:

[0229] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[0230] Step 18:

[0231] The feedback data collected by the server is used as training data for the AI ​​model.

[0232] Step 19:

[0233] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[0234] Example 2

[0235] 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."

[0236] In remote meetings and online communications, users often find it difficult to maintain their appearance with confidence. In particular, it is not easy to analyze facial features and receive real-time recommendations for makeup and skincare appropriate for the situation. Users also need advice that takes into account their emotional state. A concrete method is needed to solve these issues and help users achieve their ideal appearance.

[0237] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0238] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for identifying the user's emotions and suggesting makeup and skin care methods according to the emotions, means for providing a real-time mirror function and allowing the suggested makeup to be tried on, and means for collecting user feedback and using it to improve the system. This allows the user to feel confident about their appearance and receive real-time advice on the best makeup and skin care for the scene.

[0239] "User" refers to an individual who participates in a remote meeting or online communication.

[0240] The term "terminal" refers to an information processing device used by a user, such as a smartphone or tablet with a camera function.

[0241] A "server" refers to a computer system that exchanges data with terminals via the Internet and performs various processes.

[0242] "Means for capturing a face" refers to a method for capturing an image of a user's face using the camera function of the terminal.

[0243] "Facial feature data" refers to information extracted from a captured facial image, such as the eyes, nose, mouth, contours, and skin condition.

[0244] A "scene" refers to a particular situation or occasion selected by the user, including, for example, a business meeting or a casual date.

[0245] "Ideal Face" refers to the ideal state of the user's face, generated based on the selected scene.

[0246] The "gap" refers to the difference that exists between the user's current face and their ideal face.

[0247] "Makeup method" refers to the specific makeup steps and cosmetics to be used that are suggested to the user.

[0248] "Skin care" refers to the skin care methods and products recommended to the user.

[0249] "Means for identifying emotions" refers to technology that recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0250] The "real-time mirror function" refers to an interface that allows users to check and apply makeup while viewing their face in real time through the device's camera.

[0251] "Feedback" refers to opinions and impressions provided by users regarding the makeup techniques and skin care effects they used, how they felt to use, and areas for improvement.

[0252] The present invention provides a system for users to feel confident about their appearance during remote meetings and online communications. The system includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function.

[0253] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then uses a facial recognition library such as OpenCV to analyze the received image data and identify facial features (eyes, nose, mouth, contours, etc.). It also applies an AI algorithm to detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0254] Next, the user selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, and the server retrieves information from a database to generate an ideal face suitable for the scene. The server combines the user's facial feature dataset with information on makeup styles suitable for the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[0255] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care methods to close the gap. For example, it may recommend eyeshadow to accentuate the eyes, lip colors, or moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0256] The server also includes an emotion engine that analyzes the user's facial expressions to identify emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, the server will suggest a bright and glamorous makeup style. If the user looks tired, the server will recommend a skin care product that will give a refreshing feeling.

[0257] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0258] After completing their makeup or skincare routine, users provide feedback within the app. This feedback includes information on the effectiveness and feel of the cosmetics used, as well as areas for improvement. The device then sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice based on user feedback.

[0259] Specific examples

[0260] For example, if the user selects the "Business Meeting" scene, the sequence of events is as follows:

[0261] 1. The user selects a business meeting.

[0262] 2. The device sends the selection information to the server.

[0263] 3. The server retrieves makeup styles suitable for business meetings from the database and combines them with the user's facial feature dataset to generate an ideal facial image.

[0264] 4. The generated ideal face image is sent to the device and displayed to the user.

[0265] 5. The server analyzes the gap between your ideal face and your current face and suggests specific makeup methods.

[0266] 6. The device displays this suggestion to the user.

[0267] Prompt Sentence Examples

[0268] "What makeup style would you recommend for a business meeting?"

[0269] "If the emotion engine detects joy, what is an example of a recommended makeup application?"

[0270] "Give me some examples of how users provide feedback."

[0271] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

[0272] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0273] Step 1:

[0274] The user launches an application.

[0275] Specific operation: Tap the application icon on your smartphone or tablet to launch it.

[0276] Input: Application Launch Action

[0277] Output: Application launch and camera launch

[0278] Step 2:

[0279] The device's camera captures the user's face.

[0280] Specific operation: The camera app will automatically launch and the user's face will appear on the preview screen.

[0281] Input: Real-time video from the camera

[0282] Output: Captured face image

[0283] Step 3:

[0284] The captured facial image is sent to a server.

[0285] Specific operation: Facial image data is encoded and uploaded to a server via HTTPS.

[0286] Input: A captured face image

[0287] Output: Facial image data transferred to the server

[0288] Step 4:

[0289] The server analyzes the received facial image data.

[0290] Specific operation: Facial features such as eyes, nose, mouth, and contours are identified on the server using a face recognition library such as OpenCV or dlib.

[0291] Input: Facial image data

[0292] Output: Facial part information and feature data

[0293] Step 5:

[0294] The server uses an AI algorithm to detect the condition of your skin.

[0295] Specific operation: Texture analysis is performed for each patch of skin and features are extracted.

[0296] Input: Facial image data

[0297] Output: Skin condition data

[0298] Step 6:

[0299] The server generates a facial feature dataset.

[0300] Specific operation: Facial feature information and skin condition data are integrated to build a feature dataset in JSON format, etc.

[0301] Input: Facial features information and skin condition data

[0302] Output: Facial feature dataset

[0303] Step 7:

[0304] The user selects the desired scene.

[0305] Specific actions: Select a scene by tapping a drop-down menu or icon within the application.

[0306] Input: User scene selection action

[0307] Output: Selected scene information

[0308] Step 8:

[0309] The selected scene information is transmitted from the terminal to the server.

[0310] Specific operation: The selected data is sent to the server as a POST request.

[0311] Input: Selected scene information

[0312] Output: Scene information transfer to the server

[0313] Step 9:

[0314] The server retrieves information from a database to generate an ideal face suited to the scene.

[0315] Specific operation: Executes a database query that stores makeup styles and skin care procedures for each scene.

[0316] Input: Scene information

[0317] Output: Makeup style information suitable for the scene

[0318] Step 10:

[0319] The server combines the user's facial feature dataset with scene information to generate an ideal face.

[0320] Specific operation: Rendering an ideal facial image via an image generation algorithm.

[0321] Input: Facial feature dataset and scene-appropriate makeup style information

[0322] Output: Ideal face image

[0323] Step 11:

[0324] The generated ideal face image is sent to the terminal and displayed to the user.

[0325] Specific operation: The image data is downloaded to the device and displayed in the app's UI.

[0326] Input: Ideal face image

[0327] Output: What is displayed to the user

[0328] Step 12:

[0329] The server analyzes the gap between your current face and your ideal face.

[0330] Specific operation: Compare two images and calculate the difference.

[0331] Input: Current face image and ideal face image

[0332] Output: Gap data

[0333] Step 13:

[0334] The server will suggest specific makeup techniques and skin care routines to fill in the gaps.

[0335] Specific operation: A text generation algorithm generates suggestions to send to the user.

[0336] Input: Gap data

[0337] Output: Specific makeup and skin care procedures

[0338] Step 14:

[0339] The suggestions are sent to the terminal and displayed to the user.

[0340] What it does: Display the suggestion in a notification or popup format.

[0341] Input: Proposal

[0342] Output: What is displayed to the user

[0343] Step 15:

[0344] The server uses an emotion engine to identify the user's emotion.

[0345] Specific operation: Extracting emotional information from the user's facial expression data.

[0346] Input: Facial expression data

[0347] Output: Emotion data

[0348] Step 16:

[0349] Emotional data is reflected in makeup suggestions and skin care recommendations.

[0350] Specific behavior: Executes logic to dynamically change makeup style depending on emotions.

[0351] Input: Emotion data

[0352] Output: Emotion-based makeup and skincare recommendations

[0353] Step 17:

[0354] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup methods.

[0355] Specific operation: Camera images are displayed in real time on the app's UI, and operation guides are overlaid.

[0356] Input: Camera footage and proposal content

[0357] Output: Real-time mirror display

[0358] Step 18:

[0359] The user provides feedback after applying makeup or skin care.

[0360] Specific action: Fill out the feedback form within the app and press the submit button.

[0361] Input: User feedback

[0362] Output: Feedback data

[0363] Step 19:

[0364] The terminal transmits the feedback data to the server.

[0365] Specific operation: The feedback content is sent to the server via a POST request.

[0366] Input: Feedback data

[0367] Output: Feedback forwarding to the server

[0368] Step 20:

[0369] The server uses the feedback as training data for the AI ​​model to help improve the system.

[0370] Specific operation: The feedback data is stored in a database and used the next time the AI ​​model is trained.

[0371] Input: Feedback data

[0372] Output: An improved AI model

[0373] (Application example 2)

[0374] 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."

[0375] In remote meetings and online communications, it is important for users to feel confident and comfortable with their appearance. However, conventional technologies only provide simple advice on appearance, and it is difficult to provide real-time makeup application or emotional suggestions. Furthermore, support for passenger relaxation and appearance improvement in autonomous vehicles is insufficient. To solve these issues, a function that analyzes the user's facial feature data in detail and supports real-time makeup application is required.

[0376] 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.

[0377] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for analyzing the gap between the user's current face and the ideal face and suggesting specific makeup techniques and skin care, means for displaying the user's facial image in real time and providing an interface for trying out the suggested makeup, means for recognizing the user's emotions and suggesting makeup techniques and skin care according to the emotions, means for confirming and implementing the suggested makeup techniques and skin care to support the user's relaxation and appearance improvement within the self-driving vehicle, and means for collecting user feedback and using it to improve the system. This enables the user to improve their appearance while relaxing within the self-driving vehicle.

[0378] "User" refers to an individual who uses this system or their actions.

[0379] "Face capture" refers to the act of obtaining image data of a user's face using a device such as a camera.

[0380] "Facial image analysis" is the process of extracting and analyzing facial feature data using AI algorithms based on acquired facial image data.

[0381] "Facial feature data" is information that includes detailed data about each part of the user's face (e.g., eyes, nose, mouth, and contours).

[0382] A "scene" refers to an event or activity that a user is about to participate in, such as a business meeting or a casual date.

[0383] The "ideal face" refers to the facial condition that best suits the scene selected by the user, and is achieved through the proposed makeup and skin care methods.

[0384] "Gap analysis" is the process of identifying the differences between your current facial condition and your ideal face and suggesting specific steps to close those differences.

[0385] The "makeup method" refers to the makeup techniques and procedures used to make the user's face closer to their ideal face.

[0386] "Skin care" refers to a method of care used to keep a user's skin healthy and beautiful.

[0387] The "real-time mirror function" is an interface that uses a camera to display the user's face in real time and allows them to try out suggested makeup looks at the same time.

[0388] "Emotion recognition" is a technology that analyzes a user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise from those expressions.

[0389] "Feedback collection" is the process of collecting information provided by users about their experience with the product, its effectiveness, areas for improvement, and so on.

[0390] "Relaxation" refers to the user relaxing and refreshing inside the self-driving vehicle.

[0391] "Autonomous vehicle" refers to a vehicle that operates autonomously without driver intervention.

[0392] The present invention is a system that helps users improve their appearance while relaxing in an autonomous vehicle. This system has the following functions: First, when a user launches an application, the device's camera is activated and captures the user's face. This captured facial image is sent to a server. Next, the server analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, contours, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[0393] The user then selects a desired scene (e.g., a business meeting) within the application. The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The server then combines the user's facial feature dataset with information on makeup styles suited to the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[0394] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0395] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[0396] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0397] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice that reflects user feedback.

[0398] The hardware used includes cameras, smartphones, head-mounted displays (HMDs), and displays inside autonomous vehicles. The software uses OpenCV for facial and emotion recognition, AI algorithms for analysis and proposal generation, and OpenCV for real-time video display.

[0399] Specific examples

[0400] For example, consider a passenger relaxing in the car while also getting ready for a business meeting. To do this, the user uses a smartphone or head-mounted display to capture their face with a camera. The captured facial image is sent to a server for analysis and suggestions. The passenger can then check and implement makeup and skin care procedures in real time.

[0401] Prompt Sentence Examples

[0402] "A relaxation and appearance improvement assistance system in a self-driving car. Describe an application that captures the passenger's face with a camera and provides makeup suggestions."

[0403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0404] Processing steps of the system that realizes the application example

[0405] Step 1:

[0406] An application is launched on the device. When a user launches the application, the device's camera is activated and captures the user's face. The input at this point is the user's face image. The output is the captured face image data.

[0407] Step 2:

[0408] Send face image data to the server. The terminal sends the captured face image data to the server. The input is the face image data, and the output is the data sent to the server. The server receives this image data.

[0409] Step 3:

[0410] The facial image is analyzed and facial feature data is generated. The server analyzes the received facial image data, identifies each part of the face such as eyes, nose, mouth, and face contours, and applies AI algorithms to detect skin conditions (dryness, oiliness, acne, etc.). The input is facial image data, and the output is facial feature data.

[0411] Step 4:

[0412] The user selects a desired scene within the application. The user operates the application and inputs a scene selection, such as a business meeting. The output is the selected scene information.

[0413] Step 5:

[0414] Send scene information to the server. The terminal sends selected scene information to the server. The input is the scene information and the output is the data sent to the server. The server receives this information.

[0415] Step 6:

[0416] The information for generating an ideal face is obtained from a database. The server obtains information on makeup styles appropriate for the scene from the database and integrates it with the user's facial feature dataset. The input is the facial feature dataset and scene information, and the output is an ideal face image.

[0417] Step 7:

[0418] The server analyzes the gap between the current face and the ideal face and generates specific makeup and skin care procedures to fill the gap. The input is the current face data and the ideal face data, and the output is the makeup and skin care procedures.

[0419] Step 8:

[0420] Recognizes emotions and generates suggestions based on those emotions. The server uses an emotion engine to analyze the user's facial expressions and identify emotions such as joy, sadness, anger, and surprise. This emotion data is used to adjust makeup suggestions and skin care recommendations. The input is facial feature data and current expression data, and the output is makeup suggestions and skin care procedures based on the emotion.

[0421] Step 9:

[0422] It provides a real-time mirror function. The device uses a camera to display the user's face in real time, and simultaneously overlays makeup suggestions from the server on the screen. The input is a real-time facial image and suggested makeup instructions, and the output is the image presented to the user.

[0423] Step 10:

[0424] Feedback is collected and sent to the server. After applying makeup or skin care, the user provides feedback within the application. This feedback data is sent from the device to the server. The input is the user's feedback, and the output is the data sent to the server. The server uses this feedback as training data for the AI ​​model to help improve the system.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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."

[0441] The present invention is a system for enabling users to feel confident about their appearance during remote meetings and online communications. The system operates as follows.

[0442] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0443] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[0444] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0445] In addition, the device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0446] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[0447] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] The user launches an application and accesses the camera function.

[0451] Step 2:

[0452] The device activates its built-in camera and captures the user's face.

[0453] Step 3:

[0454] The terminal transmits the captured facial image data to the server.

[0455] Step 4:

[0456] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[0457] Step 5:

[0458] The server generates a facial feature dataset for the user based on the analysis results.

[0459] Step 6:

[0460] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[0461] Step 7:

[0462] The terminal transmits scene selection information to the server.

[0463] Step 8:

[0464] The server generates an ideal face corresponding to the scene based on the model, obtains makeup style information appropriate for the scene from the database, and combines the user's feature dataset with the ideal makeup style to generate a target facial image.

[0465] Step 9:

[0466] The server sends the generated ideal face image to the terminal.

[0467] Step 10:

[0468] The terminal displays an ideal face image to the user.

[0469] Step 11:

[0470] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care instructions, such as choosing eyeshadow colors, recommending lip colors, and recommending skin care products (moisturizing cream, lotion, etc.).

[0471] Step 12:

[0472] The server sends the proposal to the device.

[0473] Step 13:

[0474] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[0475] Step 14:

[0476] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[0477] Step 15:

[0478] After users complete the makeup or skincare process, they receive feedback within the app.

[0479] Step 16:

[0480] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[0481] Step 17:

[0482] The feedback data collected by the server is used as training data for the AI ​​model.

[0483] Step 18:

[0484] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[0485] Example 1

[0486] 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."

[0487] In recent years, remote meetings and online communication have become increasingly important, but users are unable to present their appearance with confidence. In particular, compared to face-to-face interactions, online interactions tend to be less prone to anxiety about appearance and opportunities to receive makeup advice are fewer. For this reason, there is a demand for a system that helps users achieve their ideal appearance and communicate online with confidence.

[0488] 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.

[0489] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured face image and generating facial feature data, and a means for generating an ideal face based on a scene selected by the user, thereby enabling the user to present their appearance with confidence during online communication.

[0490] A "means for capturing a face" is a device or program that has the function of recognizing a user's face and capturing it as an image.

[0491] "Means for analyzing facial images and generating facial feature data" refers to devices or programs that use artificial intelligence algorithms to analyze acquired facial images, identify facial features such as the eyes, nose, and mouth, and the condition of the skin, and convert them into data.

[0492] The "means for generating an ideal face based on a scene" refers to a device or program that has the function of obtaining makeup style information from a database and integrating it with facial feature data to create an ideal facial image in order to generate an appearance suitable for the scene selected by the user.

[0493] "Means for analyzing gaps and suggesting specific makeup and skin care methods" refers to devices or programs that analyze the differences between the current face and the ideal face and provide specific makeup techniques and skin care methods to fill those gaps.

[0494] The "real-time mirror function" refers to a device or program that has the function of providing an interface that allows a user to apply a suggested makeup method while checking their own face in real time through a camera.

[0495] "Means for collecting feedback and using it to improve the system" refers to devices or programs that have the function of collecting feedback from users after use and using it to improve the system or as learning data for artificial intelligence models.

[0496] A "database" is a system for storing, managing, searching, and retrieving necessary information.

[0497] "Artificial intelligence algorithms" are mathematical models and programs used to perform image and data analysis.

[0498] "Makeup style information" is information about makeup methods and makeup products suitable for a particular scene.

[0499] A "user" is an individual who uses this system.

[0500] This invention provides a system that allows users to feel confident about their appearance during remote meetings and online communications. A specific implementation method of this system is described below.

[0501] First, when a user launches the application, the device's camera automatically turns on and captures the user's face. At this stage, the built-in camera of a smartphone or PC is used. The facial image captured by the device is compressed, encrypted, and sent to the server.

[0502] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the received facial images. Through this analysis, the server identifies facial features (eyes, nose, mouth, contours, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[0503] Next, the user selects a scene within the application. Scenes include, for example, a business meeting or a casual date. The scene information selected by the user is sent from the device to the server. Upon receiving the scene information, the server retrieves makeup style information appropriate for the scene from a database.

[0504] The server combines the user's facial feature dataset with information on makeup styles appropriate for the occasion and generates an ideal face using a generative AI model. This generated ideal face image is sent from the server to the device and displayed to the user. For example, the server retrieves makeup styles appropriate for a business meeting from a database and combines them with the user's facial data to generate the ideal face.

[0505] Furthermore, the server analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. This includes specific makeup techniques and cosmetics to use. The suggestions are sent from the server to the user's device and displayed. For example, they include "how to use eyeshadow to accentuate the eyes" and "how to use moisturizing cream for dry skin."

[0506] Users can also use the device's real-time mirror function, which allows them to check their own face on the screen in real time and apply makeup according to instructions from the server. Specifically, instructions such as "apply eyeshadow to the right eye" and "apply blush to the cheek" are displayed on the screen.

[0507] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics felt and worked, as well as areas for improvement. The device sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model. This allows the app to provide more accurate advice that reflects user feedback.

[0508] Examples of specific prompts include:

[0509] "Please provide me with a face makeup style suitable for business meetings."

[0510] "Please analyze the gap between my current face and my ideal face and suggest specific makeup and skin care procedures."

[0511] "Analyze user feedback and use it to improve the system."

[0512] In this way, the present invention helps users achieve their ideal appearance and communicate confidently online.

[0513] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0514] Step 1:

[0515] A user launches an application. The device camera automatically turns on and captures the user's face. The input of this stage is the user's face image, and the output is the captured face image. Specifically, the device camera focuses on the user's face and captures a high-resolution image.

[0516] Step 2:

[0517] The device compresses and encrypts the captured facial image and sends it to the server. The input is the captured facial image, and the output is compressed and encrypted facial image data. This data is sent to the server over the network. Specifically, the device securely protects the data using an encryption algorithm and sends it to the server over an internet connection.

[0518] Step 3:

[0519] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the facial images it receives. The input is encrypted facial image data, and the output is an analyzed facial feature dataset. The server identifies facial features (eyes, nose, mouth, face, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Specifically, the server applies image analysis algorithms to extract key facial features.

[0520] Step 4:

[0521] The user selects the desired scene within the application. The input is the user's scene selection, and the output is the selected scene information. In concrete terms, the user selects a scene, such as "business meeting" or "casual date," from a drop-down menu.

[0522] Step 5:

[0523] The terminal transmits the selected scene information to the server. The input is the selected scene information, and the output is the scene information transmitted to the server. In concrete terms, the terminal transmits the scene information as a packet to the server.

[0524] Step 6:

[0525] The server retrieves makeup style information appropriate for the scene from the database. The input is scene information, and the output is makeup style information appropriate for the scene. Specifically, the server executes a database query to access and retrieve makeup style information appropriate for the scene.

[0526] Step 7:

[0527] The server integrates the user's facial feature dataset with makeup information appropriate for the scene, and generates an ideal face using a generative AI model. The input is the facial feature dataset and makeup style information, and the output is an ideal face image. Specifically, the server runs the generative AI model and generates an ideal face image based on the integrated data.

[0528] Step 8:

[0529] The server sends the generated ideal face image to the terminal and displays it to the user. The input is the ideal face image, and the output is the ideal face image displayed on the terminal. In concrete terms, the server sends the generated ideal face image to the terminal as digital data.

[0530] Step 9:

[0531] The server analyzes the gap between the current face and the ideal face and proposes specific makeup and skin care procedures to fill the gap. The input is image data of the current face and the ideal face, and the output is specific makeup and skin care procedures. Specifically, the server uses a gap analysis algorithm to analyze the differences between the current face and the ideal face.

[0532] Step 10:

[0533] The server sends the suggestions to the device and displays them to the user. The input is the specific makeup and skin care steps, and the output is the suggestions displayed to the user. Specifically, the device receives the suggestions and displays them through the user interface.

[0534] Step 11:

[0535] The user uses the real-time mirror function on the device to try out the proposed makeup method. The input is the proposed makeup method, and the output is the makeup applied by the user. Specifically, the user applies makeup by following the instructions on the screen while checking their own face in real time using the device's camera.

[0536] Step 12:

[0537] After applying makeup or skincare, the user provides feedback within the application. The input is feedback (such as how the cosmetics used felt, their effects, and areas for improvement), and the output is feedback data. Specifically, the user fills out a feedback form within the application and submits it.

[0538] Step 13:

[0539] The device sends feedback data to the server, and the server uses the collected feedback as training data for the AI ​​model to help improve the system. The input is the collected feedback data, and the output is an improved AI model. Specifically, the server analyzes the feedback data and adds it to the training dataset for the AI ​​model.

[0540] (Application example 1)

[0541] 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."

[0542] With the current spread of remote meetings and online communication, an increasing number of users are feeling unsure about their appearance. Furthermore, in physical stores, customers who are not familiar with how to select and use appropriate makeup and skincare products have difficulty choosing the right beauty products. A system is needed to solve these problems and help users live with confidence.

[0543] 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.

[0544] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured facial image and generating facial feature data, and a means for integrating the customer's individual facial features with product information using a database and an AI model to recommend appropriate beauty products, thereby enabling users to feel confident about their appearance and easily select appropriate beauty products even in physical stores.

[0545] "User" refers to an individual who uses this system.

[0546] "Means for capturing a face" refers to a function that uses a camera or other image capture device to capture an image of a user's face as digital data.

[0547] "Means for analyzing facial images" refers to algorithms and software for detecting and recognizing facial features and characteristics based on captured facial image data.

[0548] "Facial feature data" refers to a dataset that represents each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.).

[0549] "Means for generating an ideal face" refers to a function that uses AI and database information to generate ideal facial styles and makeup based on a scene selected by the user.

[0550] "Means for analyzing the gap" refers to a function that uses AI and algorithms to compare and determine the differences between the user's current face and their ideal face.

[0551] "Means to suggest makeup methods and skin care" refers to a function that uses AI and database information to recommend specific makeup procedures and skin care products to users based on the results of gap analysis.

[0552] The "real-time mirror function" refers to a display function that allows users to try out suggested makeup while checking their own face in real time through the camera.

[0553] "Means for collecting user feedback" refers to the function that allows users to provide information about the feel, effectiveness, and areas for improvement of makeup and skincare products through the application, and collect this information as data.

[0554] "Means used to improve the system" refers to the function of using collected feedback as training data to improve the accuracy of AI models and proposed algorithms.

[0555] "Smart devices installed in physical stores" refers to electronic devices such as smartphones, tablets, and smart glasses installed in stores.

[0556] "Database and AI model" refers to data storage for accumulating information on users' facial features, product information, and makeup styles, as well as artificial intelligence algorithms for analyzing and utilizing that data.

[0557] "Means for recommending beauty products" refers to a function that integrates a user's facial feature data with database information to automatically recommend appropriate makeup and skin care products.

[0558] "Means for providing product information in real time" refers to a function that instantly provides users with product information in the store and explains how to use the product through video and text.

[0559] In this invention, first, a user launches an application using a smart device (e.g., a smartphone, tablet, smart glasses, etc.) installed in a physical store. The device's camera is activated and captures the user's face. The captured face image is then sent to a server.

[0560] The server then analyzes the received facial image data using a facial recognition model (e.g., a deep learning model using TensorFlow or PyTorch) to identify facial features (eyes, nose, mouth, contours, etc.) and skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset for the user.

[0561] The user then selects the desired scene (e.g., business meeting, casual date, etc.) within the application. The selected scene information is sent from the device to the server. The server then retrieves makeup style information appropriate for the scene from the database and combines it with the user's facial feature dataset to generate an ideal face.

[0562] The generated ideal face image is sent to the device, along with an analysis of the gap between the user's current face and the ideal face. The server then suggests specific makeup and skin care procedures to fill the gap. For example, this could include using eyeshadow to accentuate the eyes or recommending a moisturizing cream for dry skin. These suggestions are then sent to the device and displayed to the user.

[0563] The device also offers a real-time mirror function, allowing users to check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0564] After completing their makeup or skincare routine, users can provide feedback within the app, including how the cosmetics they used felt, their effectiveness, and areas for improvement. The device then sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model to help improve the system.

[0565] As a concrete example, consider the case of a 35-year-old woman using an app at a beauty salon. She launches the app and her face is captured by the camera. The server analyzes her facial features and skin condition, and based on that, it recommends a moisturizing cream for dry skin or a specific lip color. When she scans the barcode of a product sold in the store, a video or text description of how to use the product is instantly displayed within the app.

[0566] An example of an input prompt for the generative AI model is, "Please recommend the best moisturizing cream and lip color for a 35-year-old woman with dry skin." This allows users to easily choose the beauty products that are best for them.

[0567] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0568] Step 1:

[0569] A user launches an application on a smart device installed in a physical store and captures their own face using the camera. The input is the user's face image, and the output is facial image data. This facial image data is sent from the device to the server.

[0570] Step 2:

[0571] The server analyzes the received facial image data. This analysis uses a facial recognition model (for example, a deep learning model using TensorFlow or PyTorch). The input is the facial image data, and the output is feature data for each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.). Based on this data, the server generates a facial feature dataset for the user.

[0572] Step 3:

[0573] The user selects a desired scene (e.g., business meeting, casual date, etc.) within the application. The input is the scene information selected by the user, and the output is the scene information. This scene information is sent from the terminal to the server.

[0574] Step 4:

[0575] The server retrieves makeup style information suitable for the selected scene from the database. The input is scene information, and the output is makeup style information suitable for the scene. The server then integrates the user's facial feature dataset and makeup style information to generate an ideal facial image.

[0576] Step 5:

[0577] The server sends the generated ideal face image to the device and analyzes the gap between the user's current face and the ideal face. The input is the user's current face and ideal face, and the output is the gap analysis result. The server then proposes specific makeup and skin care procedures to close the gap.

[0578] Step 6:

[0579] The terminal displays the suggested makeup and skin care procedures to the user. The input is the suggestion from the server, and the output is the suggested display to the user. The user performs the makeup and skin care according to the displayed instructions.

[0580] Step 7:

[0581] In addition, the device provides a real-time mirror function, allowing users to check their own face in real time while applying makeup. The input is the user's face image and specific makeup instructions, and the output is a real-time display of makeup instructions.

[0582] Step 8:

[0583] After the user has completed their makeup or skin care routine, the application provides feedback. The input is the user's feedback, and the output is the collected data. The device then sends this feedback data to the server.

[0584] Step 9:

[0585] The server uses the collected feedback as training data for the AI ​​model to improve the system: the input is the feedback data, and the output is an improved AI model, which will result in more accurate advice in the future.

[0586] 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.

[0587] The present invention is a system for helping users feel confident about their appearance during remote meetings and online communications, and includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function. The system operates as follows.

[0588] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0589] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[0590] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0591] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[0592] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0593] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[0594] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence. Another distinctive feature of the present invention is that it includes a function that recognizes the user's emotions and suggests the optimal makeup and skin care procedures based on those emotions.

[0595] The processing flow will be explained below.

[0596] Step 1:

[0597] The user launches an application and accesses the camera function.

[0598] Step 2:

[0599] The device activates its built-in camera and captures the user's face.

[0600] Step 3:

[0601] The terminal transmits the captured facial image data to the server.

[0602] Step 4:

[0603] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[0604] Step 5:

[0605] The server generates a facial feature dataset for the user based on the analysis results.

[0606] Step 6:

[0607] The server applies an emotion engine that analyzes facial expressions to recognize the user's emotions and generates emotion data, which are classified into joy, sadness, anger, surprise, etc.

[0608] Step 7:

[0609] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[0610] Step 8:

[0611] The terminal transmits scene selection information to the server.

[0612] Step 9:

[0613] The server generates an ideal face based on the model for the scene. It retrieves makeup style information appropriate for the scene from a database and generates a target facial image by integrating the user's facial feature dataset and the ideal makeup style. This generated ideal face image also takes into account emotional data.

[0614] Step 10:

[0615] The server sends the generated ideal face image to the terminal.

[0616] Step 11:

[0617] The terminal displays an ideal face image to the user.

[0618] Step 12:

[0619] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care procedures. For example, it can help select eyeshadow colors, recommend lip colors, and recommend skin care products. Based on emotional data, for example, if the user is tired, it will suggest makeup and skin care that will leave them feeling refreshed.

[0620] Step 13:

[0621] The server sends the proposal to the device.

[0622] Step 14:

[0623] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[0624] Step 15:

[0625] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[0626] Step 16:

[0627] After users complete the makeup or skincare process, they receive feedback within the app.

[0628] Step 17:

[0629] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[0630] Step 18:

[0631] The feedback data collected by the server is used as training data for the AI ​​model.

[0632] Step 19:

[0633] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[0634] Example 2

[0635] 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."

[0636] In remote meetings and online communications, users often find it difficult to maintain their appearance with confidence. In particular, it is not easy to analyze facial features and receive real-time recommendations for makeup and skincare appropriate for the situation. Users also need advice that takes into account their emotional state. A concrete method is needed to solve these issues and help users achieve their ideal appearance.

[0637] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0638] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for identifying the user's emotions and suggesting makeup and skin care methods according to the emotions, means for providing a real-time mirror function and allowing the suggested makeup to be tried on, and means for collecting user feedback and using it to improve the system. This allows the user to feel confident about their appearance and receive real-time advice on the best makeup and skin care for the scene.

[0639] "User" refers to an individual who participates in a remote meeting or online communication.

[0640] The term "terminal" refers to an information processing device used by a user, such as a smartphone or tablet with a camera function.

[0641] A "server" refers to a computer system that exchanges data with terminals via the Internet and performs various processes.

[0642] "Means for capturing a face" refers to a method for capturing an image of a user's face using the camera function of the terminal.

[0643] "Facial feature data" refers to information extracted from a captured facial image, such as the eyes, nose, mouth, contours, and skin condition.

[0644] A "scene" refers to a particular situation or occasion selected by the user, including, for example, a business meeting or a casual date.

[0645] "Ideal Face" refers to the ideal state of the user's face, generated based on the selected scene.

[0646] The "gap" refers to the difference that exists between the user's current face and their ideal face.

[0647] "Makeup method" refers to the specific makeup steps and cosmetics to be used that are suggested to the user.

[0648] "Skin care" refers to the skin care methods and products recommended to the user.

[0649] "Means for identifying emotions" refers to technology that recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0650] The "real-time mirror function" refers to an interface that allows users to check and apply makeup while viewing their face in real time through the device's camera.

[0651] "Feedback" refers to opinions and impressions provided by users regarding the makeup techniques and skin care effects they used, how they felt to use, and areas for improvement.

[0652] The present invention provides a system for users to feel confident about their appearance during remote meetings and online communications. The system includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function.

[0653] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then uses a facial recognition library such as OpenCV to analyze the received image data and identify facial features (eyes, nose, mouth, contours, etc.). It also applies an AI algorithm to detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0654] Next, the user selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, and the server retrieves information from a database to generate an ideal face suitable for the scene. The server combines the user's facial feature dataset with information on makeup styles suitable for the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[0655] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care methods to close the gap. For example, it may recommend eyeshadow to accentuate the eyes, lip colors, or moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0656] The server also includes an emotion engine that analyzes the user's facial expressions to identify emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, the server will suggest a bright and glamorous makeup style. If the user looks tired, the server will recommend a skin care product that will give a refreshing feeling.

[0657] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0658] After completing their makeup or skincare routine, users provide feedback within the app. This feedback includes information on the effectiveness and feel of the cosmetics used, as well as areas for improvement. The device then sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice based on user feedback.

[0659] Specific examples

[0660] For example, if the user selects the "Business Meeting" scene, the sequence of events is as follows:

[0661] 1. The user selects a business meeting.

[0662] 2. The device sends the selection information to the server.

[0663] 3. The server retrieves makeup styles suitable for business meetings from the database and combines them with the user's facial feature dataset to generate an ideal facial image.

[0664] 4. The generated ideal face image is sent to the device and displayed to the user.

[0665] 5. The server analyzes the gap between your ideal face and your current face and suggests specific makeup methods.

[0666] 6. The device displays this suggestion to the user.

[0667] Prompt Sentence Examples

[0668] "What makeup style would you recommend for a business meeting?"

[0669] "If the emotion engine detects joy, what is an example of a recommended makeup application?"

[0670] "Give me some examples of how users provide feedback."

[0671] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

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

[0673] Step 1:

[0674] The user launches an application.

[0675] Specific operation: Tap the application icon on your smartphone or tablet to launch it.

[0676] Input: Application Launch Action

[0677] Output: Application launch and camera launch

[0678] Step 2:

[0679] The device's camera captures the user's face.

[0680] Specific operation: The camera app will automatically launch and the user's face will appear on the preview screen.

[0681] Input: Real-time video from the camera

[0682] Output: Captured face image

[0683] Step 3:

[0684] The captured facial image is sent to a server.

[0685] Specific operation: Facial image data is encoded and uploaded to a server via HTTPS.

[0686] Input: A captured face image

[0687] Output: Facial image data transferred to the server

[0688] Step 4:

[0689] The server analyzes the received facial image data.

[0690] Specific operation: Facial features such as eyes, nose, mouth, and contours are identified on the server using a face recognition library such as OpenCV or dlib.

[0691] Input: Facial image data

[0692] Output: Facial part information and feature data

[0693] Step 5:

[0694] The server uses an AI algorithm to detect the condition of your skin.

[0695] Specific operation: Texture analysis is performed for each patch of skin and features are extracted.

[0696] Input: Facial image data

[0697] Output: Skin condition data

[0698] Step 6:

[0699] The server generates a facial feature dataset.

[0700] Specific operation: Facial feature information and skin condition data are integrated to build a feature dataset in JSON format, etc.

[0701] Input: Facial features information and skin condition data

[0702] Output: Facial feature dataset

[0703] Step 7:

[0704] The user selects the desired scene.

[0705] Specific actions: Select a scene by tapping a drop-down menu or icon within the application.

[0706] Input: User scene selection action

[0707] Output: Selected scene information

[0708] Step 8:

[0709] The selected scene information is transmitted from the terminal to the server.

[0710] Specific operation: The selected data is sent to the server as a POST request.

[0711] Input: Selected scene information

[0712] Output: Scene information transfer to the server

[0713] Step 9:

[0714] The server retrieves information from a database to generate an ideal face suited to the scene.

[0715] Specific operation: Executes a database query that stores makeup styles and skin care procedures for each scene.

[0716] Input: Scene information

[0717] Output: Makeup style information suitable for the scene

[0718] Step 10:

[0719] The server combines the user's facial feature dataset with scene information to generate an ideal face.

[0720] Specific operation: Rendering an ideal facial image via an image generation algorithm.

[0721] Input: Facial feature dataset and scene-appropriate makeup style information

[0722] Output: Ideal face image

[0723] Step 11:

[0724] The generated ideal face image is sent to the terminal and displayed to the user.

[0725] Specific operation: The image data is downloaded to the device and displayed in the app's UI.

[0726] Input: Ideal face image

[0727] Output: What is displayed to the user

[0728] Step 12:

[0729] The server analyzes the gap between your current face and your ideal face.

[0730] Specific operation: Compare two images and calculate the difference.

[0731] Input: Current face image and ideal face image

[0732] Output: Gap data

[0733] Step 13:

[0734] The server will suggest specific makeup techniques and skin care routines to fill in the gaps.

[0735] Specific operation: A text generation algorithm generates suggestions to send to the user.

[0736] Input: Gap data

[0737] Output: Specific makeup and skin care procedures

[0738] Step 14:

[0739] The suggestions are sent to the terminal and displayed to the user.

[0740] What it does: Display the suggestion in a notification or popup format.

[0741] Input: Proposal

[0742] Output: What is displayed to the user

[0743] Step 15:

[0744] The server uses an emotion engine to identify the user's emotion.

[0745] Specific operation: Extracting emotional information from the user's facial expression data.

[0746] Input: Facial expression data

[0747] Output: Emotion data

[0748] Step 16:

[0749] Emotional data is reflected in makeup suggestions and skin care recommendations.

[0750] Specific behavior: Executes logic to dynamically change makeup style depending on emotions.

[0751] Input: Emotion data

[0752] Output: Emotion-based makeup and skincare recommendations

[0753] Step 17:

[0754] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup methods.

[0755] Specific operation: Camera images are displayed in real time on the app's UI, and operation guides are overlaid.

[0756] Input: Camera footage and proposal content

[0757] Output: Real-time mirror display

[0758] Step 18:

[0759] The user provides feedback after applying makeup or skin care.

[0760] Specific action: Fill out the feedback form within the app and press the submit button.

[0761] Input: User feedback

[0762] Output: Feedback data

[0763] Step 19:

[0764] The terminal transmits the feedback data to the server.

[0765] Specific operation: The feedback content is sent to the server via a POST request.

[0766] Input: Feedback data

[0767] Output: Feedback forwarding to the server

[0768] Step 20:

[0769] The server uses the feedback as training data for the AI ​​model to help improve the system.

[0770] Specific operation: The feedback data is stored in a database and used the next time the AI ​​model is trained.

[0771] Input: Feedback data

[0772] Output: An improved AI model

[0773] (Application example 2)

[0774] 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."

[0775] In remote meetings and online communications, it is important for users to feel confident and comfortable with their appearance. However, conventional technologies only provide simple advice on appearance, and it is difficult to provide real-time makeup application or emotional suggestions. Furthermore, support for passenger relaxation and appearance improvement in autonomous vehicles is insufficient. To solve these issues, a function that analyzes the user's facial feature data in detail and supports real-time makeup application is required.

[0776] 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.

[0777] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for analyzing the gap between the user's current face and the ideal face and suggesting specific makeup techniques and skin care, means for displaying the user's facial image in real time and providing an interface for trying out the suggested makeup, means for recognizing the user's emotions and suggesting makeup techniques and skin care according to the emotions, means for confirming and implementing the suggested makeup techniques and skin care to support the user's relaxation and appearance improvement within the self-driving vehicle, and means for collecting user feedback and using it to improve the system. This enables the user to improve their appearance while relaxing within the self-driving vehicle.

[0778] "User" refers to an individual who uses this system or their actions.

[0779] "Face capture" refers to the act of obtaining image data of a user's face using a device such as a camera.

[0780] "Facial image analysis" is the process of extracting and analyzing facial feature data using AI algorithms based on acquired facial image data.

[0781] "Facial feature data" is information that includes detailed data about each part of the user's face (e.g., eyes, nose, mouth, and contours).

[0782] A "scene" refers to an event or activity that a user is about to participate in, such as a business meeting or a casual date.

[0783] The "ideal face" refers to the facial condition that best suits the scene selected by the user, and is achieved through the proposed makeup and skin care methods.

[0784] "Gap analysis" is the process of identifying the differences between your current facial condition and your ideal face and suggesting specific steps to close those differences.

[0785] The "makeup method" refers to the makeup techniques and procedures used to make the user's face closer to their ideal face.

[0786] "Skin care" refers to a method of care used to keep a user's skin healthy and beautiful.

[0787] The "real-time mirror function" is an interface that uses a camera to display the user's face in real time and allows them to try out suggested makeup looks at the same time.

[0788] "Emotion recognition" is a technology that analyzes a user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise from those expressions.

[0789] "Feedback collection" is the process of collecting information provided by users about their experience with the product, its effectiveness, areas for improvement, and so on.

[0790] "Relaxation" refers to the user relaxing and refreshing inside the self-driving vehicle.

[0791] "Autonomous vehicle" refers to a vehicle that operates autonomously without driver intervention.

[0792] The present invention is a system that helps users improve their appearance while relaxing in an autonomous vehicle. This system has the following functions: First, when a user launches an application, the device's camera is activated and captures the user's face. This captured facial image is sent to a server. Next, the server analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, contours, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[0793] The user then selects a desired scene (e.g., a business meeting) within the application. The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The server then combines the user's facial feature dataset with information on makeup styles suited to the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[0794] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0795] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[0796] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0797] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice that reflects user feedback.

[0798] The hardware used includes cameras, smartphones, head-mounted displays (HMDs), and displays inside autonomous vehicles. The software uses OpenCV for facial and emotion recognition, AI algorithms for analysis and proposal generation, and OpenCV for real-time video display.

[0799] Specific examples

[0800] For example, consider a passenger relaxing in the car while also getting ready for a business meeting. To do this, the user uses a smartphone or head-mounted display to capture their face with a camera. The captured facial image is sent to a server for analysis and suggestions. The passenger can then check and implement makeup and skin care procedures in real time.

[0801] Prompt Sentence Examples

[0802] "A relaxation and appearance improvement assistance system in a self-driving car. Describe an application that captures the passenger's face with a camera and provides makeup suggestions."

[0803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0804] Processing steps of the system that realizes the application example

[0805] Step 1:

[0806] An application is launched on the device. When a user launches the application, the device's camera is activated and captures the user's face. The input at this point is the user's face image. The output is the captured face image data.

[0807] Step 2:

[0808] Send face image data to the server. The terminal sends the captured face image data to the server. The input is the face image data, and the output is the data sent to the server. The server receives this image data.

[0809] Step 3:

[0810] The facial image is analyzed and facial feature data is generated. The server analyzes the received facial image data, identifies each part of the face such as eyes, nose, mouth, and face contours, and applies AI algorithms to detect skin conditions (dryness, oiliness, acne, etc.). The input is facial image data, and the output is facial feature data.

[0811] Step 4:

[0812] The user selects a desired scene within the application. The user operates the application and inputs a scene selection, such as a business meeting. The output is the selected scene information.

[0813] Step 5:

[0814] Send scene information to the server. The terminal sends selected scene information to the server. The input is the scene information and the output is the data sent to the server. The server receives this information.

[0815] Step 6:

[0816] The information for generating an ideal face is obtained from a database. The server obtains information on makeup styles appropriate for the scene from the database and integrates it with the user's facial feature dataset. The input is the facial feature dataset and scene information, and the output is an ideal face image.

[0817] Step 7:

[0818] The server analyzes the gap between the current face and the ideal face and generates specific makeup and skin care procedures to fill the gap. The input is the current face data and the ideal face data, and the output is the makeup and skin care procedures.

[0819] Step 8:

[0820] Recognizes emotions and generates suggestions based on those emotions. The server uses an emotion engine to analyze the user's facial expressions and identify emotions such as joy, sadness, anger, and surprise. This emotion data is used to adjust makeup suggestions and skin care recommendations. The input is facial feature data and current expression data, and the output is makeup suggestions and skin care procedures based on the emotion.

[0821] Step 9:

[0822] It provides a real-time mirror function. The device uses a camera to display the user's face in real time, and simultaneously overlays makeup suggestions from the server on the screen. The input is a real-time facial image and suggested makeup instructions, and the output is the image presented to the user.

[0823] Step 10:

[0824] Feedback is collected and sent to the server. After applying makeup or skin care, the user provides feedback within the application. This feedback data is sent from the device to the server. The input is the user's feedback, and the output is the data sent to the server. The server uses this feedback as training data for the AI ​​model to help improve the system.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] [Third embodiment]

[0829] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0830] 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.

[0831] 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).

[0832] 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.

[0833] 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.

[0834] 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).

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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."

[0841] The present invention is a system for enabling users to feel confident about their appearance during remote meetings and online communications. The system operates as follows.

[0842] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0843] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[0844] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0845] In addition, the device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0846] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[0847] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

[0848] The processing flow will be explained below.

[0849] Step 1:

[0850] The user launches an application and accesses the camera function.

[0851] Step 2:

[0852] The device activates its built-in camera and captures the user's face.

[0853] Step 3:

[0854] The terminal transmits the captured facial image data to the server.

[0855] Step 4:

[0856] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[0857] Step 5:

[0858] The server generates a facial feature dataset for the user based on the analysis results.

[0859] Step 6:

[0860] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[0861] Step 7:

[0862] The terminal transmits scene selection information to the server.

[0863] Step 8:

[0864] The server generates an ideal face corresponding to the scene based on the model, obtains makeup style information appropriate for the scene from the database, and combines the user's feature dataset with the ideal makeup style to generate a target facial image.

[0865] Step 9:

[0866] The server sends the generated ideal face image to the terminal.

[0867] Step 10:

[0868] The terminal displays an ideal face image to the user.

[0869] Step 11:

[0870] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care instructions, such as choosing eyeshadow colors, recommending lip colors, and recommending skin care products (moisturizing cream, lotion, etc.).

[0871] Step 12:

[0872] The server sends the proposal to the device.

[0873] Step 13:

[0874] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[0875] Step 14:

[0876] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[0877] Step 15:

[0878] After users complete the makeup or skincare process, they receive feedback within the app.

[0879] Step 16:

[0880] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[0881] Step 17:

[0882] The feedback data collected by the server is used as training data for the AI ​​model.

[0883] Step 18:

[0884] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[0885] Example 1

[0886] 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."

[0887] In recent years, remote meetings and online communication have become increasingly important, but users are unable to present their appearance with confidence. In particular, compared to face-to-face interactions, online interactions tend to be less prone to anxiety about appearance and opportunities to receive makeup advice are fewer. For this reason, there is a demand for a system that helps users achieve their ideal appearance and communicate online with confidence.

[0888] 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.

[0889] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured face image and generating facial feature data, and a means for generating an ideal face based on a scene selected by the user, thereby enabling the user to present their appearance with confidence during online communication.

[0890] A "means for capturing a face" is a device or program that has the function of recognizing a user's face and capturing it as an image.

[0891] "Means for analyzing facial images and generating facial feature data" refers to devices or programs that use artificial intelligence algorithms to analyze acquired facial images, identify facial features such as the eyes, nose, and mouth, and the condition of the skin, and convert them into data.

[0892] The "means for generating an ideal face based on a scene" refers to a device or program that has the function of obtaining makeup style information from a database and integrating it with facial feature data to create an ideal facial image in order to generate an appearance suitable for the scene selected by the user.

[0893] "Means for analyzing gaps and suggesting specific makeup and skin care methods" refers to devices or programs that analyze the differences between the current face and the ideal face and provide specific makeup techniques and skin care methods to fill those gaps.

[0894] The "real-time mirror function" refers to a device or program that has the function of providing an interface that allows a user to apply a suggested makeup method while checking their own face in real time through a camera.

[0895] "Means for collecting feedback and using it to improve the system" refers to devices or programs that have the function of collecting feedback from users after use and using it to improve the system or as learning data for artificial intelligence models.

[0896] A "database" is a system for storing, managing, searching, and retrieving necessary information.

[0897] "Artificial intelligence algorithms" are mathematical models and programs used to perform image and data analysis.

[0898] "Makeup style information" is information about makeup methods and makeup products suitable for a particular scene.

[0899] A "user" is an individual who uses this system.

[0900] This invention provides a system that allows users to feel confident about their appearance during remote meetings and online communications. A specific implementation method of this system is described below.

[0901] First, when a user launches the application, the device's camera automatically turns on and captures the user's face. At this stage, the built-in camera of a smartphone or PC is used. The facial image captured by the device is compressed, encrypted, and sent to the server.

[0902] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the received facial images. Through this analysis, the server identifies facial features (eyes, nose, mouth, contours, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[0903] Next, the user selects a scene within the application. Scenes include, for example, a business meeting or a casual date. The scene information selected by the user is sent from the device to the server. Upon receiving the scene information, the server retrieves makeup style information appropriate for the scene from a database.

[0904] The server combines the user's facial feature dataset with information on makeup styles appropriate for the occasion and generates an ideal face using a generative AI model. This generated ideal face image is sent from the server to the device and displayed to the user. For example, the server retrieves makeup styles appropriate for a business meeting from a database and combines them with the user's facial data to generate the ideal face.

[0905] Furthermore, the server analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. This includes specific makeup techniques and cosmetics to use. The suggestions are sent from the server to the user's device and displayed. For example, they include "how to use eyeshadow to accentuate the eyes" and "how to use moisturizing cream for dry skin."

[0906] Users can also use the device's real-time mirror function, which allows them to check their own face on the screen in real time and apply makeup according to instructions from the server. Specifically, instructions such as "apply eyeshadow to the right eye" and "apply blush to the cheek" are displayed on the screen.

[0907] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics felt and worked, as well as areas for improvement. The device sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model. This allows the app to provide more accurate advice that reflects user feedback.

[0908] Examples of specific prompts include:

[0909] "Please provide me with a face makeup style suitable for business meetings."

[0910] "Please analyze the gap between my current face and my ideal face and suggest specific makeup and skin care procedures."

[0911] "Analyze user feedback and use it to improve the system."

[0912] In this way, the present invention helps users achieve their ideal appearance and communicate confidently online.

[0913] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0914] Step 1:

[0915] A user launches an application. The device camera automatically turns on and captures the user's face. The input of this stage is the user's face image, and the output is the captured face image. Specifically, the device camera focuses on the user's face and captures a high-resolution image.

[0916] Step 2:

[0917] The device compresses and encrypts the captured facial image and sends it to the server. The input is the captured facial image, and the output is compressed and encrypted facial image data. This data is sent to the server over the network. Specifically, the device securely protects the data using an encryption algorithm and sends it to the server over an internet connection.

[0918] Step 3:

[0919] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the facial images it receives. The input is encrypted facial image data, and the output is an analyzed facial feature dataset. The server identifies facial features (eyes, nose, mouth, face, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Specifically, the server applies image analysis algorithms to extract key facial features.

[0920] Step 4:

[0921] The user selects the desired scene within the application. The input is the user's scene selection, and the output is the selected scene information. In concrete terms, the user selects a scene, such as "business meeting" or "casual date," from a drop-down menu.

[0922] Step 5:

[0923] The terminal transmits the selected scene information to the server. The input is the selected scene information, and the output is the scene information transmitted to the server. In concrete terms, the terminal transmits the scene information as a packet to the server.

[0924] Step 6:

[0925] The server retrieves makeup style information appropriate for the scene from the database. The input is scene information, and the output is makeup style information appropriate for the scene. Specifically, the server executes a database query to access and retrieve makeup style information appropriate for the scene.

[0926] Step 7:

[0927] The server integrates the user's facial feature dataset with makeup information appropriate for the scene, and generates an ideal face using a generative AI model. The input is the facial feature dataset and makeup style information, and the output is an ideal face image. Specifically, the server runs the generative AI model and generates an ideal face image based on the integrated data.

[0928] Step 8:

[0929] The server sends the generated ideal face image to the terminal and displays it to the user. The input is the ideal face image, and the output is the ideal face image displayed on the terminal. In concrete terms, the server sends the generated ideal face image to the terminal as digital data.

[0930] Step 9:

[0931] The server analyzes the gap between the current face and the ideal face and proposes specific makeup and skin care procedures to fill the gap. The input is image data of the current face and the ideal face, and the output is specific makeup and skin care procedures. Specifically, the server uses a gap analysis algorithm to analyze the differences between the current face and the ideal face.

[0932] Step 10:

[0933] The server sends the suggestions to the device and displays them to the user. The input is the specific makeup and skin care steps, and the output is the suggestions displayed to the user. Specifically, the device receives the suggestions and displays them through the user interface.

[0934] Step 11:

[0935] The user uses the real-time mirror function on the device to try out the proposed makeup method. The input is the proposed makeup method, and the output is the makeup applied by the user. Specifically, the user applies makeup by following the instructions on the screen while checking their own face in real time using the device's camera.

[0936] Step 12:

[0937] After applying makeup or skincare, the user provides feedback within the application. The input is feedback (such as how the cosmetics used felt, their effects, and areas for improvement), and the output is feedback data. Specifically, the user fills out a feedback form within the application and submits it.

[0938] Step 13:

[0939] The device sends feedback data to the server, and the server uses the collected feedback as training data for the AI ​​model to help improve the system. The input is the collected feedback data, and the output is an improved AI model. Specifically, the server analyzes the feedback data and adds it to the training dataset for the AI ​​model.

[0940] (Application example 1)

[0941] 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."

[0942] With the current spread of remote meetings and online communication, an increasing number of users are feeling unsure about their appearance. Furthermore, in physical stores, customers who are not familiar with how to select and use appropriate makeup and skincare products have difficulty choosing the right beauty products. A system is needed to solve these problems and help users live with confidence.

[0943] 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.

[0944] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured facial image and generating facial feature data, and a means for integrating the customer's individual facial features with product information using a database and an AI model to recommend appropriate beauty products, thereby enabling users to feel confident about their appearance and easily select appropriate beauty products even in physical stores.

[0945] "User" refers to an individual who uses this system.

[0946] "Means for capturing a face" refers to a function that uses a camera or other image capture device to capture an image of a user's face as digital data.

[0947] "Means for analyzing facial images" refers to algorithms and software for detecting and recognizing facial features and characteristics based on captured facial image data.

[0948] "Facial feature data" refers to a dataset that represents each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.).

[0949] "Means for generating an ideal face" refers to a function that uses AI and database information to generate ideal facial styles and makeup based on a scene selected by the user.

[0950] "Means for analyzing the gap" refers to a function that uses AI and algorithms to compare and determine the differences between the user's current face and their ideal face.

[0951] "Means to suggest makeup methods and skin care" refers to a function that uses AI and database information to recommend specific makeup procedures and skin care products to users based on the results of gap analysis.

[0952] The "real-time mirror function" refers to a display function that allows users to try out suggested makeup while checking their own face in real time through the camera.

[0953] "Means for collecting user feedback" refers to the function that allows users to provide information about the feel, effectiveness, and areas for improvement of makeup and skincare products through the application, and collect this information as data.

[0954] "Means used to improve the system" refers to the function of using collected feedback as training data to improve the accuracy of AI models and proposed algorithms.

[0955] "Smart devices installed in physical stores" refers to electronic devices such as smartphones, tablets, and smart glasses installed in stores.

[0956] "Database and AI model" refers to data storage for accumulating information on users' facial features, product information, and makeup styles, as well as artificial intelligence algorithms for analyzing and utilizing that data.

[0957] "Means for recommending beauty products" refers to a function that integrates a user's facial feature data with database information to automatically recommend appropriate makeup and skin care products.

[0958] "Means for providing product information in real time" refers to a function that instantly provides users with product information in the store and explains how to use the product through video and text.

[0959] In this invention, first, a user launches an application using a smart device (e.g., a smartphone, tablet, smart glasses, etc.) installed in a physical store. The device's camera is activated and captures the user's face. The captured face image is then sent to a server.

[0960] The server then analyzes the received facial image data using a facial recognition model (e.g., a deep learning model using TensorFlow or PyTorch) to identify facial features (eyes, nose, mouth, contours, etc.) and skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset for the user.

[0961] The user then selects the desired scene (e.g., business meeting, casual date, etc.) within the application. The selected scene information is sent from the device to the server. The server then retrieves makeup style information appropriate for the scene from the database and combines it with the user's facial feature dataset to generate an ideal face.

[0962] The generated ideal face image is sent to the device, along with an analysis of the gap between the user's current face and the ideal face. The server then suggests specific makeup and skin care procedures to fill the gap. For example, this could include using eyeshadow to accentuate the eyes or recommending a moisturizing cream for dry skin. These suggestions are then sent to the device and displayed to the user.

[0963] The device also offers a real-time mirror function, allowing users to check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0964] After completing their makeup or skincare routine, users can provide feedback within the app, including how the cosmetics they used felt, their effectiveness, and areas for improvement. The device then sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model to help improve the system.

[0965] As a concrete example, consider the case of a 35-year-old woman using an app at a beauty salon. She launches the app and her face is captured by the camera. The server analyzes her facial features and skin condition, and based on that, it recommends a moisturizing cream for dry skin or a specific lip color. When she scans the barcode of a product sold in the store, a video or text description of how to use the product is instantly displayed within the app.

[0966] An example of an input prompt for the generative AI model is, "Please recommend the best moisturizing cream and lip color for a 35-year-old woman with dry skin." This allows users to easily choose the beauty products that are best for them.

[0967] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0968] Step 1:

[0969] A user launches an application on a smart device installed in a physical store and captures their own face using the camera. The input is the user's face image, and the output is facial image data. This facial image data is sent from the device to the server.

[0970] Step 2:

[0971] The server analyzes the received facial image data. This analysis uses a facial recognition model (for example, a deep learning model using TensorFlow or PyTorch). The input is the facial image data, and the output is feature data for each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.). Based on this data, the server generates a facial feature dataset for the user.

[0972] Step 3:

[0973] The user selects a desired scene (e.g., business meeting, casual date, etc.) within the application. The input is the scene information selected by the user, and the output is the scene information. This scene information is sent from the terminal to the server.

[0974] Step 4:

[0975] The server retrieves makeup style information suitable for the selected scene from the database. The input is scene information, and the output is makeup style information suitable for the scene. The server then integrates the user's facial feature dataset and makeup style information to generate an ideal facial image.

[0976] Step 5:

[0977] The server sends the generated ideal face image to the device and analyzes the gap between the user's current face and the ideal face. The input is the user's current face and ideal face, and the output is the gap analysis result. The server then proposes specific makeup and skin care procedures to close the gap.

[0978] Step 6:

[0979] The terminal displays the suggested makeup and skin care procedures to the user. The input is the suggestion from the server, and the output is the suggested display to the user. The user performs the makeup and skin care according to the displayed instructions.

[0980] Step 7:

[0981] In addition, the device provides a real-time mirror function, allowing users to check their own face in real time while applying makeup. The input is the user's face image and specific makeup instructions, and the output is a real-time display of makeup instructions.

[0982] Step 8:

[0983] After the user has completed their makeup or skin care routine, the application provides feedback. The input is the user's feedback, and the output is the collected data. The device then sends this feedback data to the server.

[0984] Step 9:

[0985] The server uses the collected feedback as training data for the AI ​​model to improve the system: the input is the feedback data, and the output is an improved AI model, which will result in more accurate advice in the future.

[0986] 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.

[0987] The present invention is a system for helping users feel confident about their appearance during remote meetings and online communications, and includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function. The system operates as follows.

[0988] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[0989] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[0990] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[0991] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[0992] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[0993] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[0994] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence. Another distinctive feature of the present invention is that it includes a function that recognizes the user's emotions and suggests the optimal makeup and skin care procedures based on those emotions.

[0995] The processing flow will be explained below.

[0996] Step 1:

[0997] The user launches an application and accesses the camera function.

[0998] Step 2:

[0999] The device activates its built-in camera and captures the user's face.

[1000] Step 3:

[1001] The terminal transmits the captured facial image data to the server.

[1002] Step 4:

[1003] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[1004] Step 5:

[1005] The server generates a facial feature dataset for the user based on the analysis results.

[1006] Step 6:

[1007] The server applies an emotion engine that analyzes facial expressions to recognize the user's emotions and generates emotion data, which are classified into joy, sadness, anger, surprise, etc.

[1008] Step 7:

[1009] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[1010] Step 8:

[1011] The terminal transmits scene selection information to the server.

[1012] Step 9:

[1013] The server generates an ideal face based on the model for the scene. It retrieves makeup style information appropriate for the scene from a database and generates a target facial image by integrating the user's facial feature dataset and the ideal makeup style. This generated ideal face image also takes into account emotional data.

[1014] Step 10:

[1015] The server sends the generated ideal face image to the terminal.

[1016] Step 11:

[1017] The terminal displays an ideal face image to the user.

[1018] Step 12:

[1019] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care procedures. For example, it can help select eyeshadow colors, recommend lip colors, and recommend skin care products. Based on emotional data, for example, if the user is tired, it will suggest makeup and skin care that will leave them feeling refreshed.

[1020] Step 13:

[1021] The server sends the proposal to the device.

[1022] Step 14:

[1023] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[1024] Step 15:

[1025] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[1026] Step 16:

[1027] After users complete the makeup or skincare process, they receive feedback within the app.

[1028] Step 17:

[1029] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[1030] Step 18:

[1031] The feedback data collected by the server is used as training data for the AI ​​model.

[1032] Step 19:

[1033] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[1034] Example 2

[1035] 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."

[1036] In remote meetings and online communications, users often find it difficult to maintain their appearance with confidence. In particular, it is not easy to analyze facial features and receive real-time recommendations for makeup and skincare appropriate for the situation. Users also need advice that takes into account their emotional state. A concrete method is needed to solve these issues and help users achieve their ideal appearance.

[1037] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1038] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for identifying the user's emotions and suggesting makeup and skin care methods according to the emotions, means for providing a real-time mirror function and allowing the suggested makeup to be tried on, and means for collecting user feedback and using it to improve the system. This allows the user to feel confident about their appearance and receive real-time advice on the best makeup and skin care for the scene.

[1039] "User" refers to an individual who participates in a remote meeting or online communication.

[1040] The term "terminal" refers to an information processing device used by a user, such as a smartphone or tablet with a camera function.

[1041] A "server" refers to a computer system that exchanges data with terminals via the Internet and performs various processes.

[1042] "Means for capturing a face" refers to a method for capturing an image of a user's face using the camera function of the terminal.

[1043] "Facial feature data" refers to information extracted from a captured facial image, such as the eyes, nose, mouth, contours, and skin condition.

[1044] A "scene" refers to a particular situation or occasion selected by the user, including, for example, a business meeting or a casual date.

[1045] "Ideal Face" refers to the ideal state of the user's face, generated based on the selected scene.

[1046] The "gap" refers to the difference that exists between the user's current face and their ideal face.

[1047] "Makeup method" refers to the specific makeup steps and cosmetics to be used that are suggested to the user.

[1048] "Skin care" refers to the skin care methods and products recommended to the user.

[1049] "Means for identifying emotions" refers to technology that recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[1050] The "real-time mirror function" refers to an interface that allows users to check and apply makeup while viewing their face in real time through the device's camera.

[1051] "Feedback" refers to opinions and impressions provided by users regarding the makeup techniques and skin care effects they used, how they felt to use, and areas for improvement.

[1052] The present invention provides a system for users to feel confident about their appearance during remote meetings and online communications. The system includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function.

[1053] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then uses a facial recognition library such as OpenCV to analyze the received image data and identify facial features (eyes, nose, mouth, contours, etc.). It also applies an AI algorithm to detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[1054] Next, the user selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, and the server retrieves information from a database to generate an ideal face suitable for the scene. The server combines the user's facial feature dataset with information on makeup styles suitable for the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[1055] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care methods to close the gap. For example, it may recommend eyeshadow to accentuate the eyes, lip colors, or moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[1056] The server also includes an emotion engine that analyzes the user's facial expressions to identify emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, the server will suggest a bright and glamorous makeup style. If the user looks tired, the server will recommend a skin care product that will give a refreshing feeling.

[1057] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[1058] After completing their makeup or skincare routine, users provide feedback within the app. This feedback includes information on the effectiveness and feel of the cosmetics used, as well as areas for improvement. The device then sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice based on user feedback.

[1059] Specific examples

[1060] For example, if the user selects the "Business Meeting" scene, the sequence of events is as follows:

[1061] 1. The user selects a business meeting.

[1062] 2. The device sends the selection information to the server.

[1063] 3. The server retrieves makeup styles suitable for business meetings from the database and combines them with the user's facial feature dataset to generate an ideal facial image.

[1064] 4. The generated ideal face image is sent to the device and displayed to the user.

[1065] 5. The server analyzes the gap between your ideal face and your current face and suggests specific makeup methods.

[1066] 6. The device displays this suggestion to the user.

[1067] Prompt Sentence Examples

[1068] "What makeup style would you recommend for a business meeting?"

[1069] "If the emotion engine detects joy, what is an example of a recommended makeup application?"

[1070] "Give me some examples of how users provide feedback."

[1071] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

[1072] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] The user launches an application.

[1075] Specific operation: Tap the application icon on your smartphone or tablet to launch it.

[1076] Input: Application Launch Action

[1077] Output: Application launch and camera launch

[1078] Step 2:

[1079] The device's camera captures the user's face.

[1080] Specific operation: The camera app will automatically launch and the user's face will appear on the preview screen.

[1081] Input: Real-time video from the camera

[1082] Output: Captured face image

[1083] Step 3:

[1084] The captured facial image is sent to a server.

[1085] Specific operation: Facial image data is encoded and uploaded to a server via HTTPS.

[1086] Input: A captured face image

[1087] Output: Facial image data transferred to the server

[1088] Step 4:

[1089] The server analyzes the received facial image data.

[1090] Specific operation: Facial features such as eyes, nose, mouth, and contours are identified on the server using a face recognition library such as OpenCV or dlib.

[1091] Input: Facial image data

[1092] Output: Facial part information and feature data

[1093] Step 5:

[1094] The server uses an AI algorithm to detect the condition of your skin.

[1095] Specific operation: Texture analysis is performed for each patch of skin and features are extracted.

[1096] Input: Facial image data

[1097] Output: Skin condition data

[1098] Step 6:

[1099] The server generates a facial feature dataset.

[1100] Specific operation: Facial feature information and skin condition data are integrated to build a feature dataset in JSON format, etc.

[1101] Input: Facial features information and skin condition data

[1102] Output: Facial feature dataset

[1103] Step 7:

[1104] The user selects the desired scene.

[1105] Specific actions: Select a scene by tapping a drop-down menu or icon within the application.

[1106] Input: User scene selection action

[1107] Output: Selected scene information

[1108] Step 8:

[1109] The selected scene information is transmitted from the terminal to the server.

[1110] Specific operation: The selected data is sent to the server as a POST request.

[1111] Input: Selected scene information

[1112] Output: Scene information transfer to the server

[1113] Step 9:

[1114] The server retrieves information from a database to generate an ideal face suited to the scene.

[1115] Specific operation: Executes a database query that stores makeup styles and skin care procedures for each scene.

[1116] Input: Scene information

[1117] Output: Makeup style information suitable for the scene

[1118] Step 10:

[1119] The server combines the user's facial feature dataset with scene information to generate an ideal face.

[1120] Specific operation: Rendering an ideal facial image via an image generation algorithm.

[1121] Input: Facial feature dataset and scene-appropriate makeup style information

[1122] Output: Ideal face image

[1123] Step 11:

[1124] The generated ideal face image is sent to the terminal and displayed to the user.

[1125] Specific operation: The image data is downloaded to the device and displayed in the app's UI.

[1126] Input: Ideal face image

[1127] Output: What is displayed to the user

[1128] Step 12:

[1129] The server analyzes the gap between your current face and your ideal face.

[1130] Specific operation: Compare two images and calculate the difference.

[1131] Input: Current face image and ideal face image

[1132] Output: Gap data

[1133] Step 13:

[1134] The server will suggest specific makeup techniques and skin care routines to fill in the gaps.

[1135] Specific operation: A text generation algorithm generates suggestions to send to the user.

[1136] Input: Gap data

[1137] Output: Specific makeup and skin care procedures

[1138] Step 14:

[1139] The suggestions are sent to the terminal and displayed to the user.

[1140] What it does: Display the suggestion in a notification or popup format.

[1141] Input: Proposal

[1142] Output: What is displayed to the user

[1143] Step 15:

[1144] The server uses an emotion engine to identify the user's emotion.

[1145] Specific operation: Extracting emotional information from the user's facial expression data.

[1146] Input: Facial expression data

[1147] Output: Emotion data

[1148] Step 16:

[1149] Emotional data is reflected in makeup suggestions and skin care recommendations.

[1150] Specific behavior: Executes logic to dynamically change makeup style depending on emotions.

[1151] Input: Emotion data

[1152] Output: Emotion-based makeup and skincare recommendations

[1153] Step 17:

[1154] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup methods.

[1155] Specific operation: Camera images are displayed in real time on the app's UI, and operation guides are overlaid.

[1156] Input: Camera footage and proposal content

[1157] Output: Real-time mirror display

[1158] Step 18:

[1159] The user provides feedback after applying makeup or skin care.

[1160] Specific action: Fill out the feedback form within the app and press the submit button.

[1161] Input: User feedback

[1162] Output: Feedback data

[1163] Step 19:

[1164] The terminal transmits the feedback data to the server.

[1165] Specific operation: The feedback content is sent to the server via a POST request.

[1166] Input: Feedback data

[1167] Output: Feedback forwarding to the server

[1168] Step 20:

[1169] The server uses the feedback as training data for the AI ​​model to help improve the system.

[1170] Specific operation: The feedback data is stored in a database and used the next time the AI ​​model is trained.

[1171] Input: Feedback data

[1172] Output: An improved AI model

[1173] (Application example 2)

[1174] 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."

[1175] In remote meetings and online communications, it is important for users to feel confident and comfortable with their appearance. However, conventional technologies only provide simple advice on appearance, and it is difficult to provide real-time makeup application or emotional suggestions. Furthermore, support for passenger relaxation and appearance improvement in autonomous vehicles is insufficient. To solve these issues, a function that analyzes the user's facial feature data in detail and supports real-time makeup application is required.

[1176] 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.

[1177] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for analyzing the gap between the user's current face and the ideal face and suggesting specific makeup techniques and skin care, means for displaying the user's facial image in real time and providing an interface for trying out the suggested makeup, means for recognizing the user's emotions and suggesting makeup techniques and skin care according to the emotions, means for confirming and implementing the suggested makeup techniques and skin care to support the user's relaxation and appearance improvement within the self-driving vehicle, and means for collecting user feedback and using it to improve the system. This enables the user to improve their appearance while relaxing within the self-driving vehicle.

[1178] "User" refers to an individual who uses this system or their actions.

[1179] "Face capture" refers to the act of obtaining image data of a user's face using a device such as a camera.

[1180] "Facial image analysis" is the process of extracting and analyzing facial feature data using AI algorithms based on acquired facial image data.

[1181] "Facial feature data" is information that includes detailed data about each part of the user's face (e.g., eyes, nose, mouth, and contours).

[1182] A "scene" refers to an event or activity that a user is about to participate in, such as a business meeting or a casual date.

[1183] The "ideal face" refers to the facial condition that best suits the scene selected by the user, and is achieved through the proposed makeup and skin care methods.

[1184] "Gap analysis" is the process of identifying the differences between your current facial condition and your ideal face and suggesting specific steps to close those differences.

[1185] The "makeup method" refers to the makeup techniques and procedures used to make the user's face closer to their ideal face.

[1186] "Skin care" refers to a method of care used to keep a user's skin healthy and beautiful.

[1187] The "real-time mirror function" is an interface that uses a camera to display the user's face in real time and allows them to try out suggested makeup looks at the same time.

[1188] "Emotion recognition" is a technology that analyzes a user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise from those expressions.

[1189] "Feedback collection" is the process of collecting information provided by users about their experience with the product, its effectiveness, areas for improvement, and so on.

[1190] "Relaxation" refers to the user relaxing and refreshing inside the self-driving vehicle.

[1191] "Autonomous vehicle" refers to a vehicle that operates autonomously without driver intervention.

[1192] The present invention is a system that helps users improve their appearance while relaxing in an autonomous vehicle. This system has the following functions: First, when a user launches an application, the device's camera is activated and captures the user's face. This captured facial image is sent to a server. Next, the server analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, contours, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[1193] The user then selects a desired scene (e.g., a business meeting) within the application. The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The server then combines the user's facial feature dataset with information on makeup styles suited to the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[1194] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[1195] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[1196] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[1197] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice that reflects user feedback.

[1198] The hardware used includes cameras, smartphones, head-mounted displays (HMDs), and displays inside autonomous vehicles. The software uses OpenCV for facial and emotion recognition, AI algorithms for analysis and proposal generation, and OpenCV for real-time video display.

[1199] Specific examples

[1200] For example, consider a passenger relaxing in the car while also getting ready for a business meeting. To do this, the user uses a smartphone or head-mounted display to capture their face with a camera. The captured facial image is sent to a server for analysis and suggestions. The passenger can then check and implement makeup and skin care procedures in real time.

[1201] Prompt Sentence Examples

[1202] "A relaxation and appearance improvement assistance system in a self-driving car. Describe an application that captures the passenger's face with a camera and provides makeup suggestions."

[1203] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1204] Processing steps of the system that realizes the application example

[1205] Step 1:

[1206] An application is launched on the device. When a user launches the application, the device's camera is activated and captures the user's face. The input at this point is the user's face image. The output is the captured face image data.

[1207] Step 2:

[1208] Send face image data to the server. The terminal sends the captured face image data to the server. The input is the face image data, and the output is the data sent to the server. The server receives this image data.

[1209] Step 3:

[1210] The facial image is analyzed and facial feature data is generated. The server analyzes the received facial image data, identifies each part of the face such as eyes, nose, mouth, and face contours, and applies AI algorithms to detect skin conditions (dryness, oiliness, acne, etc.). The input is facial image data, and the output is facial feature data.

[1211] Step 4:

[1212] The user selects a desired scene within the application. The user operates the application and inputs a scene selection, such as a business meeting. The output is the selected scene information.

[1213] Step 5:

[1214] Send scene information to the server. The terminal sends selected scene information to the server. The input is the scene information and the output is the data sent to the server. The server receives this information.

[1215] Step 6:

[1216] The information for generating an ideal face is obtained from a database. The server obtains information on makeup styles appropriate for the scene from the database and integrates it with the user's facial feature dataset. The input is the facial feature dataset and scene information, and the output is an ideal face image.

[1217] Step 7:

[1218] The server analyzes the gap between the current face and the ideal face and generates specific makeup and skin care procedures to fill the gap. The input is the current face data and the ideal face data, and the output is the makeup and skin care procedures.

[1219] Step 8:

[1220] Recognizes emotions and generates suggestions based on those emotions. The server uses an emotion engine to analyze the user's facial expressions and identify emotions such as joy, sadness, anger, and surprise. This emotion data is used to adjust makeup suggestions and skin care recommendations. The input is facial feature data and current expression data, and the output is makeup suggestions and skin care procedures based on the emotion.

[1221] Step 9:

[1222] It provides a real-time mirror function. The device uses a camera to display the user's face in real time, and simultaneously overlays makeup suggestions from the server on the screen. The input is a real-time facial image and suggested makeup instructions, and the output is the image presented to the user.

[1223] Step 10:

[1224] Feedback is collected and sent to the server. After applying makeup or skin care, the user provides feedback within the application. This feedback data is sent from the device to the server. The input is the user's feedback, and the output is the data sent to the server. The server uses this feedback as training data for the AI ​​model to help improve the system.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] [Fourth embodiment]

[1229] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1230] 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.

[1231] 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).

[1232] 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.

[1233] 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.

[1234] 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).

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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.

[1241] 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."

[1242] The present invention is a system for enabling users to feel confident about their appearance during remote meetings and online communications. The system operates as follows.

[1243] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[1244] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[1245] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[1246] In addition, the device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[1247] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[1248] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

[1249] The processing flow will be explained below.

[1250] Step 1:

[1251] The user launches an application and accesses the camera function.

[1252] Step 2:

[1253] The device activates its built-in camera and captures the user's face.

[1254] Step 3:

[1255] The terminal transmits the captured facial image data to the server.

[1256] Step 4:

[1257] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[1258] Step 5:

[1259] The server generates a facial feature dataset for the user based on the analysis results.

[1260] Step 6:

[1261] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[1262] Step 7:

[1263] The terminal transmits scene selection information to the server.

[1264] Step 8:

[1265] The server generates an ideal face corresponding to the scene based on the model, obtains makeup style information appropriate for the scene from the database, and combines the user's feature dataset with the ideal makeup style to generate a target facial image.

[1266] Step 9:

[1267] The server sends the generated ideal face image to the terminal.

[1268] Step 10:

[1269] The terminal displays an ideal face image to the user.

[1270] Step 11:

[1271] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care instructions, such as choosing eyeshadow colors, recommending lip colors, and recommending skin care products (moisturizing cream, lotion, etc.).

[1272] Step 12:

[1273] The server sends the proposal to the device.

[1274] Step 13:

[1275] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[1276] Step 14:

[1277] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[1278] Step 15:

[1279] After users complete the makeup or skincare process, they receive feedback within the app.

[1280] Step 16:

[1281] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[1282] Step 17:

[1283] The feedback data collected by the server is used as training data for the AI ​​model.

[1284] Step 18:

[1285] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[1286] Example 1

[1287] 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."

[1288] In recent years, remote meetings and online communication have become increasingly important, but users are unable to present their appearance with confidence. In particular, compared to face-to-face interactions, online interactions tend to be less prone to anxiety about appearance and opportunities to receive makeup advice are fewer. For this reason, there is a demand for a system that helps users achieve their ideal appearance and communicate online with confidence.

[1289] 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.

[1290] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured face image and generating facial feature data, and a means for generating an ideal face based on a scene selected by the user, thereby enabling the user to present their appearance with confidence during online communication.

[1291] A "means for capturing a face" is a device or program that has the function of recognizing a user's face and capturing it as an image.

[1292] "Means for analyzing facial images and generating facial feature data" refers to devices or programs that use artificial intelligence algorithms to analyze acquired facial images, identify facial features such as the eyes, nose, and mouth, and the condition of the skin, and convert them into data.

[1293] The "means for generating an ideal face based on a scene" refers to a device or program that has the function of obtaining makeup style information from a database and integrating it with facial feature data to create an ideal facial image in order to generate an appearance suitable for the scene selected by the user.

[1294] "Means for analyzing gaps and suggesting specific makeup and skin care methods" refers to devices or programs that analyze the differences between the current face and the ideal face and provide specific makeup techniques and skin care methods to fill those gaps.

[1295] The "real-time mirror function" refers to a device or program that has the function of providing an interface that allows a user to apply a suggested makeup method while checking their own face in real time through a camera.

[1296] "Means for collecting feedback and using it to improve the system" refers to devices or programs that have the function of collecting feedback from users after use and using it to improve the system or as learning data for artificial intelligence models.

[1297] A "database" is a system for storing, managing, searching, and retrieving necessary information.

[1298] "Artificial intelligence algorithms" are mathematical models and programs used to perform image and data analysis.

[1299] "Makeup style information" is information about makeup methods and makeup products suitable for a particular scene.

[1300] A "user" is an individual who uses this system.

[1301] This invention provides a system that allows users to feel confident about their appearance during remote meetings and online communications. A specific implementation method of this system is described below.

[1302] First, when a user launches the application, the device's camera automatically turns on and captures the user's face. At this stage, the built-in camera of a smartphone or PC is used. The facial image captured by the device is compressed, encrypted, and sent to the server.

[1303] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the received facial images. Through this analysis, the server identifies facial features (eyes, nose, mouth, contours, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[1304] Next, the user selects a scene within the application. Scenes include, for example, a business meeting or a casual date. The scene information selected by the user is sent from the device to the server. Upon receiving the scene information, the server retrieves makeup style information appropriate for the scene from a database.

[1305] The server combines the user's facial feature dataset with information on makeup styles appropriate for the occasion and generates an ideal face using a generative AI model. This generated ideal face image is sent from the server to the device and displayed to the user. For example, the server retrieves makeup styles appropriate for a business meeting from a database and combines them with the user's facial data to generate the ideal face.

[1306] Furthermore, the server analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. This includes specific makeup techniques and cosmetics to use. The suggestions are sent from the server to the user's device and displayed. For example, they include "how to use eyeshadow to accentuate the eyes" and "how to use moisturizing cream for dry skin."

[1307] Users can also use the device's real-time mirror function, which allows them to check their own face on the screen in real time and apply makeup according to instructions from the server. Specifically, instructions such as "apply eyeshadow to the right eye" and "apply blush to the cheek" are displayed on the screen.

[1308] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics felt and worked, as well as areas for improvement. The device sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model. This allows the app to provide more accurate advice that reflects user feedback.

[1309] Examples of specific prompts include:

[1310] "Please provide me with a face makeup style suitable for business meetings."

[1311] "Please analyze the gap between my current face and my ideal face and suggest specific makeup and skin care procedures."

[1312] "Analyze user feedback and use it to improve the system."

[1313] In this way, the present invention helps users achieve their ideal appearance and communicate confidently online.

[1314] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1315] Step 1:

[1316] A user launches an application. The device camera automatically turns on and captures the user's face. The input of this stage is the user's face image, and the output is the captured face image. Specifically, the device camera focuses on the user's face and captures a high-resolution image.

[1317] Step 2:

[1318] The device compresses and encrypts the captured facial image and sends it to the server. The input is the captured facial image, and the output is compressed and encrypted facial image data. This data is sent to the server over the network. Specifically, the device securely protects the data using an encryption algorithm and sends it to the server over an internet connection.

[1319] Step 3:

[1320] The server uses artificial intelligence algorithms such as TensorFlow and OpenCV to analyze the facial images it receives. The input is encrypted facial image data, and the output is an analyzed facial feature dataset. The server identifies facial features (eyes, nose, mouth, face, etc.) and detects skin conditions (dryness, oiliness, acne, etc.). Specifically, the server applies image analysis algorithms to extract key facial features.

[1321] Step 4:

[1322] The user selects the desired scene within the application. The input is the user's scene selection, and the output is the selected scene information. In concrete terms, the user selects a scene, such as "business meeting" or "casual date," from a drop-down menu.

[1323] Step 5:

[1324] The terminal transmits the selected scene information to the server. The input is the selected scene information, and the output is the scene information transmitted to the server. In concrete terms, the terminal transmits the scene information as a packet to the server.

[1325] Step 6:

[1326] The server retrieves makeup style information appropriate for the scene from the database. The input is scene information, and the output is makeup style information appropriate for the scene. Specifically, the server executes a database query to access and retrieve makeup style information appropriate for the scene.

[1327] Step 7:

[1328] The server integrates the user's facial feature dataset with makeup information appropriate for the scene, and generates an ideal face using a generative AI model. The input is the facial feature dataset and makeup style information, and the output is an ideal face image. Specifically, the server runs the generative AI model and generates an ideal face image based on the integrated data.

[1329] Step 8:

[1330] The server sends the generated ideal face image to the terminal and displays it to the user. The input is the ideal face image, and the output is the ideal face image displayed on the terminal. In concrete terms, the server sends the generated ideal face image to the terminal as digital data.

[1331] Step 9:

[1332] The server analyzes the gap between the current face and the ideal face and proposes specific makeup and skin care procedures to fill the gap. The input is image data of the current face and the ideal face, and the output is specific makeup and skin care procedures. Specifically, the server uses a gap analysis algorithm to analyze the differences between the current face and the ideal face.

[1333] Step 10:

[1334] The server sends the suggestions to the device and displays them to the user. The input is the specific makeup and skin care steps, and the output is the suggestions displayed to the user. Specifically, the device receives the suggestions and displays them through the user interface.

[1335] Step 11:

[1336] The user uses the real-time mirror function on the device to try out the proposed makeup method. The input is the proposed makeup method, and the output is the makeup applied by the user. Specifically, the user applies makeup by following the instructions on the screen while checking their own face in real time using the device's camera.

[1337] Step 12:

[1338] After applying makeup or skincare, the user provides feedback within the application. The input is feedback (such as how the cosmetics used felt, their effects, and areas for improvement), and the output is feedback data. Specifically, the user fills out a feedback form within the application and submits it.

[1339] Step 13:

[1340] The device sends feedback data to the server, and the server uses the collected feedback as training data for the AI ​​model to help improve the system. The input is the collected feedback data, and the output is an improved AI model. Specifically, the server analyzes the feedback data and adds it to the training dataset for the AI ​​model.

[1341] (Application example 1)

[1342] 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."

[1343] With the current spread of remote meetings and online communication, an increasing number of users are feeling unsure about their appearance. Furthermore, in physical stores, customers who are not familiar with how to select and use appropriate makeup and skincare products have difficulty choosing the right beauty products. A system is needed to solve these problems and help users live with confidence.

[1344] 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.

[1345] In this invention, the server includes a means for capturing a user's face, a means for analyzing the captured facial image and generating facial feature data, and a means for integrating the customer's individual facial features with product information using a database and an AI model to recommend appropriate beauty products, thereby enabling users to feel confident about their appearance and easily select appropriate beauty products even in physical stores.

[1346] "User" refers to an individual who uses this system.

[1347] "Means for capturing a face" refers to a function that uses a camera or other image capture device to capture an image of a user's face as digital data.

[1348] "Means for analyzing facial images" refers to algorithms and software for detecting and recognizing facial features and characteristics based on captured facial image data.

[1349] "Facial feature data" refers to a dataset that represents each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.).

[1350] "Means for generating an ideal face" refers to a function that uses AI and database information to generate ideal facial styles and makeup based on a scene selected by the user.

[1351] "Means for analyzing the gap" refers to a function that uses AI and algorithms to compare and determine the differences between the user's current face and their ideal face.

[1352] "Means to suggest makeup methods and skin care" refers to a function that uses AI and database information to recommend specific makeup procedures and skin care products to users based on the results of gap analysis.

[1353] The "real-time mirror function" refers to a display function that allows users to try out suggested makeup while checking their own face in real time through the camera.

[1354] "Means for collecting user feedback" refers to the function that allows users to provide information about the feel, effectiveness, and areas for improvement of makeup and skincare products through the application, and collect this information as data.

[1355] "Means used to improve the system" refers to the function of using collected feedback as training data to improve the accuracy of AI models and proposed algorithms.

[1356] "Smart devices installed in physical stores" refers to electronic devices such as smartphones, tablets, and smart glasses installed in stores.

[1357] "Database and AI model" refers to data storage for accumulating information on users' facial features, product information, and makeup styles, as well as artificial intelligence algorithms for analyzing and utilizing that data.

[1358] "Means for recommending beauty products" refers to a function that integrates a user's facial feature data with database information to automatically recommend appropriate makeup and skin care products.

[1359] "Means for providing product information in real time" refers to a function that instantly provides users with product information in the store and explains how to use the product through video and text.

[1360] In this invention, first, a user launches an application using a smart device (e.g., a smartphone, tablet, smart glasses, etc.) installed in a physical store. The device's camera is activated and captures the user's face. The captured face image is then sent to a server.

[1361] The server then analyzes the received facial image data using a facial recognition model (e.g., a deep learning model using TensorFlow or PyTorch) to identify facial features (eyes, nose, mouth, contours, etc.) and skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset for the user.

[1362] The user then selects the desired scene (e.g., business meeting, casual date, etc.) within the application. The selected scene information is sent from the device to the server. The server then retrieves makeup style information appropriate for the scene from the database and combines it with the user's facial feature dataset to generate an ideal face.

[1363] The generated ideal face image is sent to the device, along with an analysis of the gap between the user's current face and the ideal face. The server then suggests specific makeup and skin care procedures to fill the gap. For example, this could include using eyeshadow to accentuate the eyes or recommending a moisturizing cream for dry skin. These suggestions are then sent to the device and displayed to the user.

[1364] The device also offers a real-time mirror function, allowing users to check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[1365] After completing their makeup or skincare routine, users can provide feedback within the app, including how the cosmetics they used felt, their effectiveness, and areas for improvement. The device then sends this feedback data to the server, which then uses the collected feedback as training data for the AI ​​model to help improve the system.

[1366] As a concrete example, consider the case of a 35-year-old woman using an app at a beauty salon. She launches the app and her face is captured by the camera. The server analyzes her facial features and skin condition, and based on that, it recommends a moisturizing cream for dry skin or a specific lip color. When she scans the barcode of a product sold in the store, a video or text description of how to use the product is instantly displayed within the app.

[1367] An example of an input prompt for the generative AI model is, "Please recommend the best moisturizing cream and lip color for a 35-year-old woman with dry skin." This allows users to easily choose the beauty products that are best for them.

[1368] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1369] Step 1:

[1370] A user launches an application on a smart device installed in a physical store and captures their own face using the camera. The input is the user's face image, and the output is facial image data. This facial image data is sent from the device to the server.

[1371] Step 2:

[1372] The server analyzes the received facial image data. This analysis uses a facial recognition model (for example, a deep learning model using TensorFlow or PyTorch). The input is the facial image data, and the output is feature data for each part of the face (eyes, nose, mouth, contours, etc.) and skin condition (dryness, oiliness, acne, etc.). Based on this data, the server generates a facial feature dataset for the user.

[1373] Step 3:

[1374] The user selects a desired scene (e.g., business meeting, casual date, etc.) within the application. The input is the scene information selected by the user, and the output is the scene information. This scene information is sent from the terminal to the server.

[1375] Step 4:

[1376] The server retrieves makeup style information suitable for the selected scene from the database. The input is scene information, and the output is makeup style information suitable for the scene. The server then integrates the user's facial feature dataset and makeup style information to generate an ideal facial image.

[1377] Step 5:

[1378] The server sends the generated ideal face image to the device and analyzes the gap between the user's current face and the ideal face. The input is the user's current face and ideal face, and the output is the gap analysis result. The server then proposes specific makeup and skin care procedures to close the gap.

[1379] Step 6:

[1380] The terminal displays the suggested makeup and skin care procedures to the user. The input is the suggestion from the server, and the output is the suggested display to the user. The user performs the makeup and skin care according to the displayed instructions.

[1381] Step 7:

[1382] In addition, the device provides a real-time mirror function, allowing users to check their own face in real time while applying makeup. The input is the user's face image and specific makeup instructions, and the output is a real-time display of makeup instructions.

[1383] Step 8:

[1384] After the user has completed their makeup or skin care routine, the application provides feedback. The input is the user's feedback, and the output is the collected data. The device then sends this feedback data to the server.

[1385] Step 9:

[1386] The server uses the collected feedback as training data for the AI ​​model to improve the system: the input is the feedback data, and the output is an improved AI model, which will result in more accurate advice in the future.

[1387] 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.

[1388] The present invention is a system for helping users feel confident about their appearance during remote meetings and online communications, and includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function. The system operates as follows.

[1389] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, face, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[1390] The user then selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The ideal face is generated by combining the user's facial feature dataset with information on makeup styles suited to the scene. This generated ideal face image is sent to the device and displayed to the user.

[1391] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[1392] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[1393] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[1394] After completing their makeup or skincare routine, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the system to provide more accurate advice that reflects user feedback.

[1395] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence. Another distinctive feature of the present invention is that it includes a function that recognizes the user's emotions and suggests the optimal makeup and skin care procedures based on those emotions.

[1396] The processing flow will be explained below.

[1397] Step 1:

[1398] The user launches an application and accesses the camera function.

[1399] Step 2:

[1400] The device activates its built-in camera and captures the user's face.

[1401] Step 3:

[1402] The terminal transmits the captured facial image data to the server.

[1403] Step 4:

[1404] The server applies AI algorithms to analyze the image data received, identifying and extracting facial features (eyes, nose, mouth, contours, etc.) and detecting skin conditions (dryness, oiliness, acne, etc.).

[1405] Step 5:

[1406] The server generates a facial feature dataset for the user based on the analysis results.

[1407] Step 6:

[1408] The server applies an emotion engine that analyzes facial expressions to recognize the user's emotions and generates emotion data, which are classified into joy, sadness, anger, surprise, etc.

[1409] Step 7:

[1410] The user selects a scene (e.g., business meeting, casual date, etc.) on the app screen.

[1411] Step 8:

[1412] The terminal transmits scene selection information to the server.

[1413] Step 9:

[1414] The server generates an ideal face based on the model for the scene. It retrieves makeup style information appropriate for the scene from a database and generates a target facial image by integrating the user's facial feature dataset and the ideal makeup style. This generated ideal face image also takes into account emotional data.

[1415] Step 10:

[1416] The server sends the generated ideal face image to the terminal.

[1417] Step 11:

[1418] The terminal displays an ideal face image to the user.

[1419] Step 12:

[1420] The server analyzes the gap between the ideal face and the current face and automatically generates specific makeup and skin care procedures. For example, it can help select eyeshadow colors, recommend lip colors, and recommend skin care products. Based on emotional data, for example, if the user is tired, it will suggest makeup and skin care that will leave them feeling refreshed.

[1421] Step 13:

[1422] The server sends the proposal to the device.

[1423] Step 14:

[1424] The device will then activate a real-time mirror function, allowing the user to try on the suggested makeup.

[1425] Step 15:

[1426] The user uses the real-time mirror function to follow makeup and skincare instructions from the server, such as "apply eyeshadow to the right eye" or "apply blush to the cheek."

[1427] Step 16:

[1428] After users complete the makeup or skincare process, they receive feedback within the app.

[1429] Step 17:

[1430] The device sends the user's feedback data to the server. The feedback includes "impressions of the items used," "ease of applying makeup," "areas for improvement," etc.

[1431] Step 18:

[1432] The feedback data collected by the server is used as training data for the AI ​​model.

[1433] Step 19:

[1434] The server uses the feedback data to improve the AI ​​model and reflects new knowledge in the system. Based on user opinions and requests, the system updates its suggestions for new makeup techniques and skincare product recommendations.

[1435] Example 2

[1436] 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."

[1437] In remote meetings and online communications, users often find it difficult to maintain their appearance with confidence. In particular, it is not easy to analyze facial features and receive real-time recommendations for makeup and skincare appropriate for the situation. Users also need advice that takes into account their emotional state. A concrete method is needed to solve these issues and help users achieve their ideal appearance.

[1438] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1439] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for identifying the user's emotions and suggesting makeup and skin care methods according to the emotions, means for providing a real-time mirror function and allowing the suggested makeup to be tried on, and means for collecting user feedback and using it to improve the system. This allows the user to feel confident about their appearance and receive real-time advice on the best makeup and skin care for the scene.

[1440] "User" refers to an individual who participates in a remote meeting or online communication.

[1441] The term "terminal" refers to an information processing device used by a user, such as a smartphone or tablet with a camera function.

[1442] A "server" refers to a computer system that exchanges data with terminals via the Internet and performs various processes.

[1443] "Means for capturing a face" refers to a method for capturing an image of a user's face using the camera function of the terminal.

[1444] "Facial feature data" refers to information extracted from a captured facial image, such as the eyes, nose, mouth, contours, and skin condition.

[1445] A "scene" refers to a particular situation or occasion selected by the user, including, for example, a business meeting or a casual date.

[1446] "Ideal Face" refers to the ideal state of the user's face, generated based on the selected scene.

[1447] The "gap" refers to the difference that exists between the user's current face and their ideal face.

[1448] "Makeup method" refers to the specific makeup steps and cosmetics to be used that are suggested to the user.

[1449] "Skin care" refers to the skin care methods and products recommended to the user.

[1450] "Means for identifying emotions" refers to technology that recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[1451] The "real-time mirror function" refers to an interface that allows users to check and apply makeup while viewing their face in real time through the device's camera.

[1452] "Feedback" refers to opinions and impressions provided by users regarding the makeup techniques and skin care effects they used, how they felt to use, and areas for improvement.

[1453] The present invention provides a system for users to feel confident about their appearance during remote meetings and online communications. The system includes face capture, emotion engine, makeup suggestions, real-time mirror function, and feedback collection function.

[1454] First, when a user launches the application, the device's camera activates and captures the user's face. The captured facial image is sent to the server. The server then uses a facial recognition library such as OpenCV to analyze the received image data and identify facial features (eyes, nose, mouth, contours, etc.). It also applies an AI algorithm to detect skin conditions (dryness, oiliness, acne, etc.). Based on the results of this analysis, the server generates a facial feature dataset.

[1455] Next, the user selects the desired scene within the application (e.g., business meeting, casual date, etc.). The selected scene information is sent from the device to the server, and the server retrieves information from a database to generate an ideal face suitable for the scene. The server combines the user's facial feature dataset with information on makeup styles suitable for the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[1456] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care methods to close the gap. For example, it may recommend eyeshadow to accentuate the eyes, lip colors, or moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[1457] The server also includes an emotion engine that analyzes the user's facial expressions to identify emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, the server will suggest a bright and glamorous makeup style. If the user looks tired, the server will recommend a skin care product that will give a refreshing feeling.

[1458] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[1459] After completing their makeup or skincare routine, users provide feedback within the app. This feedback includes information on the effectiveness and feel of the cosmetics used, as well as areas for improvement. The device then sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice based on user feedback.

[1460] Specific examples

[1461] For example, if the user selects the "Business Meeting" scene, the sequence of events is as follows:

[1462] 1. The user selects a business meeting.

[1463] 2. The device sends the selection information to the server.

[1464] 3. The server retrieves makeup styles suitable for business meetings from the database and combines them with the user's facial feature dataset to generate an ideal facial image.

[1465] 4. The generated ideal face image is sent to the device and displayed to the user.

[1466] 5. The server analyzes the gap between your ideal face and your current face and suggests specific makeup methods.

[1467] 6. The device displays this suggestion to the user.

[1468] Prompt Sentence Examples

[1469] "What makeup style would you recommend for a business meeting?"

[1470] "If the emotion engine detects joy, what is an example of a recommended makeup application?"

[1471] "Give me some examples of how users provide feedback."

[1472] In this way, the present invention is a system that helps users achieve their ideal appearance and live their daily lives with confidence.

[1473] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1474] Step 1:

[1475] The user launches an application.

[1476] Specific operation: Tap the application icon on your smartphone or tablet to launch it.

[1477] Input: Application Launch Action

[1478] Output: Application launch and camera launch

[1479] Step 2:

[1480] The device's camera captures the user's face.

[1481] Specific operation: The camera app will automatically launch and the user's face will appear on the preview screen.

[1482] Input: Real-time video from the camera

[1483] Output: Captured face image

[1484] Step 3:

[1485] The captured facial image is sent to a server.

[1486] Specific operation: Facial image data is encoded and uploaded to a server via HTTPS.

[1487] Input: A captured face image

[1488] Output: Facial image data transferred to the server

[1489] Step 4:

[1490] The server analyzes the received facial image data.

[1491] Specific operation: Facial features such as eyes, nose, mouth, and contours are identified on the server using a face recognition library such as OpenCV or dlib.

[1492] Input: Facial image data

[1493] Output: Facial part information and feature data

[1494] Step 5:

[1495] The server uses an AI algorithm to detect the condition of your skin.

[1496] Specific operation: Texture analysis is performed for each patch of skin and features are extracted.

[1497] Input: Facial image data

[1498] Output: Skin condition data

[1499] Step 6:

[1500] The server generates a facial feature dataset.

[1501] Specific operation: Facial feature information and skin condition data are integrated to build a feature dataset in JSON format, etc.

[1502] Input: Facial features information and skin condition data

[1503] Output: Facial feature dataset

[1504] Step 7:

[1505] The user selects the desired scene.

[1506] Specific actions: Select a scene by tapping a drop-down menu or icon within the application.

[1507] Input: User scene selection action

[1508] Output: Selected scene information

[1509] Step 8:

[1510] The selected scene information is transmitted from the terminal to the server.

[1511] Specific operation: The selected data is sent to the server as a POST request.

[1512] Input: Selected scene information

[1513] Output: Scene information transfer to the server

[1514] Step 9:

[1515] The server retrieves information from a database to generate an ideal face suited to the scene.

[1516] Specific operation: Executes a database query that stores makeup styles and skin care procedures for each scene.

[1517] Input: Scene information

[1518] Output: Makeup style information suitable for the scene

[1519] Step 10:

[1520] The server combines the user's facial feature dataset with scene information to generate an ideal face.

[1521] Specific operation: Rendering an ideal facial image via an image generation algorithm.

[1522] Input: Facial feature dataset and scene-appropriate makeup style information

[1523] Output: Ideal face image

[1524] Step 11:

[1525] The generated ideal face image is sent to the terminal and displayed to the user.

[1526] Specific operation: The image data is downloaded to the device and displayed in the app's UI.

[1527] Input: Ideal face image

[1528] Output: What is displayed to the user

[1529] Step 12:

[1530] The server analyzes the gap between your current face and your ideal face.

[1531] Specific operation: Compare two images and calculate the difference.

[1532] Input: Current face image and ideal face image

[1533] Output: Gap data

[1534] Step 13:

[1535] The server will suggest specific makeup techniques and skin care routines to fill in the gaps.

[1536] Specific operation: A text generation algorithm generates suggestions to send to the user.

[1537] Input: Gap data

[1538] Output: Specific makeup and skin care procedures

[1539] Step 14:

[1540] The suggestions are sent to the terminal and displayed to the user.

[1541] What it does: Display the suggestion in a notification or popup format.

[1542] Input: Proposal

[1543] Output: What is displayed to the user

[1544] Step 15:

[1545] The server uses an emotion engine to identify the user's emotion.

[1546] Specific operation: Extracting emotional information from the user's facial expression data.

[1547] Input: Facial expression data

[1548] Output: Emotion data

[1549] Step 16:

[1550] Emotional data is reflected in makeup suggestions and skin care recommendations.

[1551] Specific behavior: Executes logic to dynamically change makeup style depending on emotions.

[1552] Input: Emotion data

[1553] Output: Emotion-based makeup and skincare recommendations

[1554] Step 17:

[1555] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup methods.

[1556] Specific operation: Camera images are displayed in real time on the app's UI, and operation guides are overlaid.

[1557] Input: Camera footage and proposal content

[1558] Output: Real-time mirror display

[1559] Step 18:

[1560] The user provides feedback after applying makeup or skin care.

[1561] Specific action: Fill out the feedback form within the app and press the submit button.

[1562] Input: User feedback

[1563] Output: Feedback data

[1564] Step 19:

[1565] The terminal transmits the feedback data to the server.

[1566] Specific operation: The feedback content is sent to the server via a POST request.

[1567] Input: Feedback data

[1568] Output: Feedback forwarding to the server

[1569] Step 20:

[1570] The server uses the feedback as training data for the AI ​​model to help improve the system.

[1571] Specific operation: The feedback data is stored in a database and used the next time the AI ​​model is trained.

[1572] Input: Feedback data

[1573] Output: An improved AI model

[1574] (Application example 2)

[1575] 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."

[1576] In remote meetings and online communications, it is important for users to feel confident and comfortable with their appearance. However, conventional technologies only provide simple advice on appearance, and it is difficult to provide real-time makeup application or emotional suggestions. Furthermore, support for passenger relaxation and appearance improvement in autonomous vehicles is insufficient. To solve these issues, a function that analyzes the user's facial feature data in detail and supports real-time makeup application is required.

[1577] 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.

[1578] In this invention, the server includes means for capturing a user's face, means for analyzing the captured facial image and generating facial feature data, means for generating an ideal face based on a scene selected by the user, means for analyzing the gap between the user's current face and the ideal face and suggesting specific makeup techniques and skin care, means for displaying the user's facial image in real time and providing an interface for trying out the suggested makeup, means for recognizing the user's emotions and suggesting makeup techniques and skin care according to the emotions, means for confirming and implementing the suggested makeup techniques and skin care to support the user's relaxation and appearance improvement within the self-driving vehicle, and means for collecting user feedback and using it to improve the system. This enables the user to improve their appearance while relaxing within the self-driving vehicle.

[1579] "User" refers to an individual who uses this system or their actions.

[1580] "Face capture" refers to the act of obtaining image data of a user's face using a device such as a camera.

[1581] "Facial image analysis" is the process of extracting and analyzing facial feature data using AI algorithms based on acquired facial image data.

[1582] "Facial feature data" is information that includes detailed data about each part of the user's face (e.g., eyes, nose, mouth, and contours).

[1583] A "scene" refers to an event or activity that a user is about to participate in, such as a business meeting or a casual date.

[1584] The "ideal face" refers to the facial condition that best suits the scene selected by the user, and is achieved through the proposed makeup and skin care methods.

[1585] "Gap analysis" is the process of identifying the differences between your current facial condition and your ideal face and suggesting specific steps to close those differences.

[1586] The "makeup method" refers to the makeup techniques and procedures used to make the user's face closer to their ideal face.

[1587] "Skin care" refers to a method of care used to keep a user's skin healthy and beautiful.

[1588] The "real-time mirror function" is an interface that uses a camera to display the user's face in real time and allows them to try out suggested makeup looks at the same time.

[1589] "Emotion recognition" is a technology that analyzes a user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise from those expressions.

[1590] "Feedback collection" is the process of collecting information provided by users about their experience with the product, its effectiveness, areas for improvement, and so on.

[1591] "Relaxation" refers to the user relaxing and refreshing inside the self-driving vehicle.

[1592] "Autonomous vehicle" refers to a vehicle that operates autonomously without driver intervention.

[1593] The present invention is a system that helps users improve their appearance while relaxing in an autonomous vehicle. This system has the following functions: First, when a user launches an application, the device's camera is activated and captures the user's face. This captured facial image is sent to a server. Next, the server analyzes the received image data and applies AI algorithms to identify facial features (eyes, nose, mouth, contours, etc.) and detect skin conditions (dryness, oiliness, acne, etc.). Based on the analysis results, the server generates a facial feature dataset.

[1594] The user then selects a desired scene (e.g., a business meeting) within the application. The selected scene information is sent from the device to the server, which then retrieves information from a database to generate an ideal face suited to the scene. The server then combines the user's facial feature dataset with information on makeup styles suited to the scene to generate an ideal face. This generated ideal face image is sent to the device and displayed to the user.

[1595] The server then analyzes the gap between the current face and the ideal face and suggests specific makeup and skin care procedures to close the gap. For example, this could include using eyeshadow to accentuate the eyes, recommending lip colors, or recommending moisturizing creams and lotions for dry skin. These suggestions are also sent to the device and displayed to the user.

[1596] Furthermore, the present invention also includes an emotion engine that allows the server to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and identifies emotions such as joy, sadness, anger, and surprise. This emotion data is reflected in makeup suggestions and skin care recommendations. For example, if the user has a happy expression, a bright and vibrant makeup style is suggested. If the user has a tired face, a skin care product that gives a refreshing feeling is recommended.

[1597] The device provides a real-time mirror function and an interface that allows users to check and try out suggested makeup applications. Users can check their own face in real time through the camera and apply makeup according to specific instructions from the server. For example, specific instructions such as "apply eyeshadow to the right eye" or "apply blush to the cheek" are displayed on the screen.

[1598] After applying makeup or skincare, users can provide feedback within the app. This feedback includes how the cosmetics they used felt, their effectiveness, and areas for improvement. The device sends this feedback data to the server, which uses the collected feedback as training data for the AI ​​model to help improve the system. This allows the app to provide more accurate advice that reflects user feedback.

[1599] The hardware used includes cameras, smartphones, head-mounted displays (HMDs), and displays inside autonomous vehicles. The software uses OpenCV for facial and emotion recognition, AI algorithms for analysis and proposal generation, and OpenCV for real-time video display.

[1600] Specific examples

[1601] For example, consider a passenger relaxing in the car while also getting ready for a business meeting. To do this, the user uses a smartphone or head-mounted display to capture their face with a camera. The captured facial image is sent to a server for analysis and suggestions. The passenger can then check and implement makeup and skin care procedures in real time.

[1602] Prompt Sentence Examples

[1603] "A relaxation and appearance improvement assistance system in a self-driving car. Describe an application that captures the passenger's face with a camera and provides makeup suggestions."

[1604] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1605] Processing steps of the system that realizes the application example

[1606] Step 1:

[1607] An application is launched on the device. When a user launches the application, the device's camera is activated and captures the user's face. The input at this point is the user's face image. The output is the captured face image data.

[1608] Step 2:

[1609] Send face image data to the server. The terminal sends the captured face image data to the server. The input is the face image data, and the output is the data sent to the server. The server receives this image data.

[1610] Step 3:

[1611] The facial image is analyzed and facial feature data is generated. The server analyzes the received facial image data, identifies each part of the face such as eyes, nose, mouth, and face contours, and applies AI algorithms to detect skin conditions (dryness, oiliness, acne, etc.). The input is facial image data, and the output is facial feature data.

[1612] Step 4:

[1613] The user selects a desired scene within the application. The user operates the application and inputs a scene selection, such as a business meeting. The output is the selected scene information.

[1614] Step 5:

[1615] Send scene information to the server. The terminal sends selected scene information to the server. The input is the scene information and the output is the data sent to the server. The server receives this information.

[1616] Step 6:

[1617] The information for generating an ideal face is obtained from a database. The server obtains information on makeup styles appropriate for the scene from the database and integrates it with the user's facial feature dataset. The input is the facial feature dataset and scene information, and the output is an ideal face image.

[1618] Step 7:

[1619] The server analyzes the gap between the current face and the ideal face and generates specific makeup and skin care procedures to fill the gap. The input is the current face data and the ideal face data, and the output is the makeup and skin care procedures.

[1620] Step 8:

[1621] Recognizes emotions and generates suggestions based on those emotions. The server uses an emotion engine to analyze the user's facial expressions and identify emotions such as joy, sadness, anger, and surprise. This emotion data is used to adjust makeup suggestions and skin care recommendations. The input is facial feature data and current expression data, and the output is makeup suggestions and skin care procedures based on the emotion.

[1622] Step 9:

[1623] It provides a real-time mirror function. The device uses a camera to display the user's face in real time, and simultaneously overlays makeup suggestions from the server on the screen. The input is a real-time facial image and suggested makeup instructions, and the output is the image presented to the user.

[1624] Step 10:

[1625] Feedback is collected and sent to the server. After applying makeup or skin care, the user provides feedback within the application. This feedback data is sent from the device to the server. The input is the user's feedback, and the output is the data sent to the server. The server uses this feedback as training data for the AI ​​model to help improve the system.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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).

[1633] 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.

[1634] 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."

[1635] 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.

[1636] 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).

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] The following is further disclosed regarding the above embodiment.

[1648] (Claim 1)

[1649] means for capturing a face of a user;

[1650] means for analyzing the captured facial image and generating facial feature data;

[1651] means for generating an ideal face based on a user-selected scene;

[1652] A method to analyze the gap between the user's current face and their ideal face, and suggest specific makeup and skin care methods.

[1653] It provides a real-time mirror function, allowing users to try out suggested makeup, and

[1654] A system that includes a means of collecting user feedback and using it to improve the system.

[1655] (Claim 2)

[1656] 2. The system according to claim 1, wherein the means for generating an ideal face acquires information on a makeup style suitable for a scene from a database and integrates it with the user's facial feature dataset.

[1657] (Claim 3)

[1658] 2. The system according to claim 1, wherein the real-time mirror function provides an interface that allows a user to apply makeup while checking their own face through a camera.

[1659] "Example 1"

[1660] (Claim 1)

[1661] means for capturing a face of a user;

[1662] means for analyzing the captured facial image and generating facial feature data;

[1663] means for generating an ideal face based on a user-selected scene;

[1664] A method to analyze the gap between the user's current face and their ideal face, and suggest specific makeup and skin care methods.

[1665] It provides a real-time mirror function, allowing users to try out suggested makeup, and

[1666] A system that includes a means of collecting user feedback and using it to improve the system.

[1667] (Claim 2)

[1668] 2. The system according to claim 1, wherein the means for generating an ideal face acquires information on a makeup style suitable for a scene from a database and integrates it with the user's facial feature dataset.

[1669] (Claim 3)

[1670] 2. The system according to claim 1, wherein the real-time mirror function provides an interface that allows a user to apply makeup while checking their own face through a camera.

[1671] "Application Example 1"

[1672] (Claim 1)

[1673] means for capturing a face of a user;

[1674] means for analyzing the captured facial image and generating facial feature data;

[1675] means for generating an ideal face based on a user-selected scene;

[1676] A method to analyze the gap between the user's current face and their ideal face, and suggest specific makeup and skin care methods.

[1677] It provides a real-time mirror function, allowing users to try out suggested makeup, and

[1678] A means of collecting user feedback and using it to improve the system;

[1679] A means to provide professional makeup and skincare advice in-store using smart devices installed in physical stores,

[1680] Using a database and AI models, we can integrate individual facial features and product information to recommend appropriate beauty products.

[1681] It provides real-time information about products sold in stores and provides video and text explanations on how to use them.

[1682] A system including:

[1683] (Claim 2)

[1684] 2. The system according to claim 1, wherein the means for generating an ideal face acquires information on a makeup style suitable for a scene from a database and integrates it with the user's facial feature dataset.

[1685] (Claim 3)

[1686] 2. The system according to claim 1, wherein the real-time mirror function provides an interface that allows a user to apply makeup while checking their own face through a camera.

[1687] "Example 2: Combining Emotion Engines"

[1688] (Claim 1)

[1689] means for capturing a face of a user;

[1690] means for analyzing the captured facial image and generating facial feature data;

[1691] means for generating an ideal face based on a user-selected scene;

[1692] A method to analyze the gap between the user's current face and their ideal face, and suggest specific makeup and skin care methods.

[1693] A means for identifying a user's emotion and suggesting a makeup method and skin care according to the emotion;

[1694] It provides a real-time mirror function, allowing users to try out suggested makeup, and

[1695] A system that includes a means of collecting user feedback and using it to improve the system.

[1696] (Claim 2)

[1697] 2. The system according to claim 1, wherein the means for generating an ideal face acquires information on a makeup style suitable for a scene from a database and integrates it with the user's facial feature dataset.

[1698] (Claim 3)

[1699] 2. The system according to claim 1, wherein the real-time mirror function provides an interface that allows a user to apply makeup while checking their own face through a camera.

[1700] "Application example 2 when combining emotion engines"

[1701] (Claim 1)

[1702] means for capturing a face of a user;

[1703] means for analyzing the captured facial image and generating facial feature data;

[1704] means for generating an ideal face based on a user-selected scene;

[1705] A method to analyze the gap between the user's current face and their ideal face, and suggest specific makeup and skin care methods.

[1706] A means for displaying a real-time image of the user's face and providing an interface that allows the user to try on suggested makeup;

[1707] A means for recognizing a user's emotions and suggesting makeup and skin care methods according to the emotions;

[1708] A means for users to check and implement suggested makeup and skin care methods to help them relax and improve their appearance while in an autonomous vehicle;

[1709] A system that includes a means of collecting user feedback and using it to improve the system.

[1710] (Claim 2)

[1711] 2. The system according to claim 1, wherein the means for generating an ideal face acquires information on a makeup style suitable for a scene from a database and integrates it with the user's facial feature dataset.

[1712] (Claim 3)

[1713] 2. The system according to claim 1, which displays a user's face image in real time and provides an interface that allows the user to apply makeup while checking their own face through a camera. [Explanation of symbols]

[1714] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for capturing a face of a user; means for analyzing the captured facial image and generating facial feature data; means for generating an ideal face based on a user-selected scene; A method to analyze the gap between the user's current face and their ideal face, and suggest specific makeup and skin care methods. It provides a real-time mirror function, allowing users to try out suggested makeup, and A system that includes a means of collecting user feedback and using it to improve the system.

2. 2. The system according to claim 1, wherein the means for generating an ideal face acquires information on a makeup style suited to a scene from a database and integrates it with the user's facial feature data set.

3. 2. The system according to claim 1, wherein the real-time mirror function provides an interface that enables a user to apply makeup while checking their own face through a camera.

Images