system

Abstract

Provide a system. An image acquisition means for a user to take a picture of their own face; a transmitting means for transmitting the acquired image data to a server; an image analysis means for analyzing the image data received in the server and generating facial bone structure information, facial feature position information, and skin information; A display means for generating and displaying skin care and makeup guides in real time based on the analyzed information; A system including:

Description

[Technical Field]

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

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

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

[0004] The present invention relates to a system that properly analyzes a user's skin type, lifestyle habits, and personal color, and provides optimal skin care and makeup advice based on the results. Many people have difficulty understanding their own skin type and personal color, and are therefore faced with the challenge of not knowing how to properly apply skin care and makeup. Furthermore, because it is difficult to receive instruction from a professional beautician, there are limited ways to easily learn the optimal beauty regimen for oneself. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, an image acquisition means is provided for a user to photograph their own face. Next, a transmission means is provided for transmitting the acquired image data to a server. The server uses image analysis means to analyze the received image data and generate facial bone structure information, feature position information, and skin information. A system is constructed that includes display means for generating and displaying skin care and makeup guides in real time based on the analyzed information. In addition, a prediction means is provided for predicting future facial conditions based on the analyzed current facial information, and a suggestion means is incorporated for displaying the future facial condition and providing improvement suggestions. This system allows users to have an experience similar to receiving a lecture from a professional hairdresser, allowing them to easily learn the beauty methods that are best suited to them.

[0006] "Image acquisition means" refers to a device or function that allows a user to take a picture of their own face, and includes camera devices such as smartphones and digital cameras.

[0007] The "transmission means" refers to a communication device or function for transmitting the acquired image data to a server via a network.

[0008] The "image analysis means" refers to an algorithm or program for analyzing facial bone structure information, feature position information, and skin information from received image data and generating this data.

[0009] The "means for generating guides in real time" refers to a device or function that displays skin care and makeup guides based on analyzed information so that they can be provided to the user immediately.

[0010] The "prediction means" is an algorithm or program for predicting the state of the face after a specified period of time based on the analyzed current face information.

[0011] The "suggestion means" is a device or function that provides the user with suggestions for skin care and makeup to improve the facial condition based on the predicted future facial condition.

[0012] "Display means" refers to a display device or function for visually presenting the acquired and analyzed information, as well as the generated guides and suggestions, to the user. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal skin care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvement.

[0035] Program processing and specific examples

[0036] Taking a picture of your face and sending image data

[0037] The user installs and launches a dedicated application on their smartphone. The application activates the camera function and takes a picture of the user's face. The image is taken according to guidance on appropriate lighting and angles. The image data thus obtained is sent from the device to a server.

[0038] Facial image analysis

[0039] The server analyzes the received image data by running an algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.). It also analyzes skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) to identify the user's personal color. This provides information on the facial structure, the position of facial features, skin type, and personal color.

[0040] Real-time guide generation and display

[0041] The device receives the analysis results sent from the server and displays guides for skin care massage and makeup lessons in real time. For example, in the case of a skin care massage, arrows and lines are displayed showing how to massage along the cheekbones. In the case of a makeup lesson, animations are displayed showing specific areas where eye shadow should be applied and how to apply lipstick.

[0042] Future facial prediction and improvement suggestions

[0043] Based on current facial information, the system predicts changes in the face after a specified period of time (for example, one month, three months, etc.). The server simulates the progression of wrinkles, changes in skin tone, etc., and predicts the future state of the face. Based on this prediction, it generates improvement suggestions. For example, it provides specific advice such as "using a specific beauty cream to prevent wrinkles" or "recommending a highly moisturizing lotion."

[0044] Specific examples

[0045] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates and sends back information about the user's facial bone structure, facial feature position, skin type, and personal color. Based on the received information, the device displays skin care and makeup guides in real time, including instructions on how to massage the face in circular motions around the cheekbones and how to apply eyeshadow to the eyes. Furthermore, the device predicts the user's facial condition three months from now based on the current facial information, simulating changes in wrinkle depth and skin tone, and suggesting appropriate skin care products. This allows the user to understand changes in their face and how to address them, and receive optimal beauty advice.

[0046] This system allows users to experience a level of beauty that is similar to that of a professional hairdresser, all from the comfort of their own home. It provides real-time guidance and predictions for future improvements, enabling more effective skincare and makeup application.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The user picks up their smartphone and launches the dedicated application, which activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing the user's face.

[0050] Step 2:

[0051] The user follows the instructions of the application to take a picture of their face with the camera. Once the picture is taken, the image data is saved in the application.

[0052] Step 3:

[0053] The device sends the stored image data to a server using encryption technology, via communication over the Internet.

[0054] Step 4:

[0055] The server analyzes the received image data and runs a facial recognition algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.) and extract facial bone structure information.

[0056] Step 5:

[0057] The server then uses image analysis technology to assess skin type (dry, oily, combination, etc.) and identify skin concerns (blemishes, wrinkles, redness, etc.), providing the basis for skin care advice.

[0058] Step 6:

[0059] The server analyzes the user's skin pigmentation and tone to determine their personal color, which serves as the basis for makeup suggestions.

[0060] Step 7:

[0061] The server compiles the analysis results and generates data including facial bone structure, facial feature position, skin type, and personal color information, which is then sent to the device.

[0062] Step 8:

[0063] Based on the analysis data received, the device generates real-time skincare massage and makeup guides, such as arrows to massage along the cheekbones and animations showing how to properly apply lipstick.

[0064] Step 9:

[0065] The server uses current facial information to perform simulations to predict future facial conditions, specifically predicting the progression of wrinkles and changes in skin color.

[0066] Step 10:

[0067] The server generates improvement suggestions based on the predicted future facial condition, including recommendations for specific skin care products and appropriate makeup application techniques.

[0068] Step 11:

[0069] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[0070] Step 12:

[0071] The device visually displays the received improvement suggestions to the user, showing them a simulated image of their future face along with the optimal skincare products and makeup techniques, allowing the user to immediately take appropriate action.

[0072] Through this series of steps, users can experience a level similar to that of a professional hairdresser and learn the best beauty treatment methods for their individual skin condition and facial features.

[0073] Example 1

[0074] 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."

[0075] In today's world, effective beauty care and makeup applications require advanced knowledge and skills. However, receiving instruction from a professional hairdresser or makeup artist is time-consuming and expensive. It is also difficult to automatically obtain appropriate advice tailored to each user's individual conditions, such as skin type, facial features, and personal color. Furthermore, there are limited means of predicting future facial conditions and receiving suggestions for continuous improvement. There is a need for a system that can solve these problems and enable users to enjoy professional beauty care at home.

[0076] 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.

[0077] In this invention, the server includes an image acquisition means for a user to photograph their own face, a transmission means for transmitting the acquired image data to an external server, an image analysis means for analyzing the image data received by the external server and generating facial feature information, location information, and skin condition information, a display means for generating and displaying beauty care and makeup guides in real time based on the analyzed information, a prediction means for predicting future facial conditions based on the analyzed current facial information, a suggestion means for displaying the predicted future facial conditions and providing improvement suggestions, and a means for using a dedicated generative AI model to detect facial feature points and perform skin condition analysis and individual color analysis. This allows users to receive appropriate beauty care and makeup advice tailored to their individual conditions from the comfort of their own home, and also predicts future facial conditions and receives continuous improvement suggestions.

[0078] "Image acquisition means" refers to a device or software that allows a user to accurately photograph their own face.

[0079] The "transmission means" refers to a device or software for transferring acquired image data from the terminal to an external server.

[0080] The "image analysis means" refers to algorithms or software that analyzes image data received by the server and generates facial feature information, position information, and skin condition information.

[0081] The "display means" refers to a device or software that visually presents the analyzed information to the user.

[0082] A "prediction method" is an algorithm or software that calculates and predicts future facial states based on current facial information.

[0083] The "suggestion means" is a device or software that provides the user with beauty care and improvement suggestions based on the predicted future facial condition.

[0084] A "generative AI model" is a trained artificial intelligence model that detects facial feature points and performs skin condition analysis and individual color analysis.

[0085] "Facial feature information" is data that includes position information of the eyes, nose, mouth, and other parts of the user's face.

[0086] "Skin condition information" is data related to the user's skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[0087] "Individual color analysis" is a process of analyzing the color tone of the user's face and identifying their personal color.

[0088] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal beauty care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvements.

[0089] Taking a picture of your face and sending image data

[0090] The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera function to capture a picture of the user's face. When taking a picture, the application displays guidance on appropriate lighting and angles, and the user follows the guidance to capture their face. Once the picture is taken, the image data is sent from the device to a server.

[0091] Facial image analysis

[0092] The server performs several processing steps to analyze the received image data. First, it uses an algorithm (e.g., Dlib, OpenCV, etc.) to detect facial feature points (the positions of the eyes, nose, mouth, etc.). Next, it uses a dedicated generative AI model (e.g., a model using TENSORFLOW® or PyTorch) to analyze skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) and identify the person's personal color. As a result of the analysis, facial bone structure information, feature position information, skin information, and personal color information are generated.

[0093] Real-time guide generation and display

[0094] The server sends the analysis results to the device, which then generates and displays guides for beauty care and makeup lessons in real time. For example, for beauty care, it displays arrows and lines showing how to massage along the cheekbones. For makeup lessons, it displays animations that specifically show how to apply eyeshadow to the eyes and how to use lipstick.

[0095] Future facial prediction and improvement suggestions

[0096] The server predicts the future state of the face based on the current facial information. For example, it simulates the progression of wrinkles and changes in skin tone. Based on this future prediction, it generates improvement suggestions and sends them to the device. Specific suggestions include "using a specific beauty cream to prevent wrinkles" and "recommending a highly moisturizing lotion." By referring to these suggestions, users can carry out more effective beauty care.

[0097] Specific examples

[0098] Here is an example prompt:

[0099] This system allows users to take a picture of their face, send it to a server, and then provide real-time beauty care and makeup advice based on the results of image analysis. It also predicts future facial conditions and makes appropriate suggestions for improvements. Please explain the specific steps and results. For example, how do you take a picture of your face and send the image data to the server? Also, please provide specific examples of the kind of advice you can receive based on the analysis results.

[0100] This invention allows users to experience a lesson similar to that of a professional hairdresser from the comfort of their own home, enabling more effective beauty care and makeup application through real-time guidance and improvement suggestions based on future predictions.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1:

[0103] The user installs and launches a dedicated application on their smartphone. The application activates the smartphone's camera function and takes a picture of the user's face. When taking a photo, the application displays guidance on appropriate lighting and angles. The user follows the guidance to take a picture of their face.

[0104] Input: A guided face image taken by the user.

[0105] Output: Image data of the captured face.

[0106] Step 2:

[0107] The device sends the captured facial image data to an external server, and after the capture is complete, the application automatically uploads the image data to the specified server.

[0108] Input: Image data of a captured face.

[0109] Output: Facial image data sent to the server.

[0110] Step 3:

[0111] The server analyzes the received image data and runs a facial feature detection algorithm, specifically using computer vision libraries such as Dlib or OpenCV to identify features such as the eyes, nose, and mouth.

[0112] Input: Facial image data sent to the server.

[0113] Output: Facial feature points such as eyes, nose, and mouth.

[0114] Step 4:

[0115] The server uses a dedicated generative AI model trained using TensorFlow and PyTorch to analyze skin type and personal color based on feature point information.

[0116] Input: Feature point information, facial image data.

[0117] Output: Skin type information, personal color information.

[0118] Step 5:

[0119] The server sends the analysis results (skin type information, personal color information, feature point information) to the device. The sent information will be used in the next step.

[0120] Input: Skin type information, personal color information, feature point information.

[0121] Output: Data containing analysis results (skin type information, personal color information, feature point information).

[0122] Step 6:

[0123] The device generates real-time beauty care and makeup guides based on the analysis results it receives, such as displaying arrows and lines to show how to massage along the cheekbones, or an animation showing how to apply eyeshadow to the eyes.

[0124] Input: Analysis results (skin type information, personal color information, feature point information).

[0125] Output: A visual guide to beauty care and makeup.

[0126] Step 7:

[0127] The server runs an algorithm that uses current facial information to predict future facial states, for example, simulating what the face will look like one month or three months from now.

[0128] Input: Current face information (skin type, personal color, and feature point information).

[0129] Output: Prediction data about future face states.

[0130] Step 8:

[0131] Based on the prediction results, the server generates improvement suggestions for the user, such as recommending the use of a specific beauty cream or a moisturizing lotion.

[0132] Input: Prediction data about future face states.

[0133] Output: Improvement proposal data.

[0134] Step 9:

[0135] The terminal displays the improvement suggestions received from the server to the user, allowing the user to obtain information for carrying out appropriate beauty care.

[0136] Input: Improvement proposal data.

[0137] Output: Displaying the suggestion information to the user.

[0138] (Application example 1)

[0139] 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."

[0140] At beauty salons and cosmetic stores, it is currently difficult for customers to receive optimal skin care and makeup advice based on their skin type and personal color. It is also difficult to predict future facial conditions and provide future beauty care and improvement suggestions in real time. Therefore, a system that provides an experience similar to that of a professional hairdresser is needed.

[0141] 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.

[0142] In this invention, the server includes an image acquisition means for allowing a user to photograph their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial bone structure information, facial feature position information, and skin information, a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information, a prediction means for predicting the user's future facial condition and generating suggestions for improving skin care and makeup, and a suggestion means for predicting the user's future skin condition based on the acquired current facial information and past data and suggesting appropriate skin care products and care methods. This allows users to receive professional beauty care and advice in real time when they visit the salon. Furthermore, by receiving appropriate skin care and makeup suggestions based on the predicted future skin condition, effective beauty care can be achieved.

[0143] "Image acquisition means" refers to a device or function that allows a user to take a picture of their own face.

[0144] "Transmission means" refers to the technology or method for transmitting the acquired image data to the server.

[0145] "Image analysis means" refers to a technology for analyzing image data received by the server and generating facial bone structure information, feature position information, and skin information.

[0146] "Display means" refers to a device or system that generates skin care and makeup guides in real time based on the analyzed information and visually presents them to the user.

[0147] "Predictive methods" refer to technologies and algorithms that predict the user's future facial condition and generate suggestions for future skincare and makeup improvements.

[0148] "Suggestion means" refers to a system or function that predicts future skin conditions based on acquired current facial information and past data, and suggests skin care products and care methods to the user based on that prediction.

[0149] MODE FOR CARRYING OUT THE INVENTION

[0150] System Overview

[0151] A system for implementing the present invention includes the following components:

[0152] 1. Image acquisition means: A device with a camera function that allows the user to take a picture of their own face (e.g., smartphone, tablet, smart mirror).

[0153] 2. Transmission method: The communication technology (e.g., Wi-Fi, mobile data communication) used to transmit the acquired image data to the server.

[0154] 3. Image analysis means: Technology for analyzing image data received by the server and generating facial skeletal information, facial feature position information, and skin information (e.g., image analysis algorithm using OpenCV).

[0155] 4. Display means: A device or application that generates skin care and makeup guides in real time based on the analyzed information and displays them to the user (e.g., a guide display app created with Unity).

[0156] 5. Prediction means: Algorithms and server-side processing for predicting the user's future facial condition.

[0157] 6. Proposal method: A system that predicts future skin condition based on current facial information and past data, and suggests appropriate skin care products and care methods.

[0158] Program processing and hardware and software used

[0159] 1. Image acquisition and transmission:

[0160] Users take a picture of their face using the camera on their smart mirror, tablet, or smartphone, and the captured image data is sent to a server via Wi-Fi or mobile data.

[0161] 2. Image analysis and data generation:

[0162] The server runs image analysis algorithms such as OpenCV to generate facial bone structure information, facial feature position information, and skin information, such as the position of the eyes, nose shape, and degree of skin dryness.

[0163] 3. Real-time display:

[0164] The analysis results are sent to the user's device in real time and displayed to the user as a visual skincare and makeup guide via an application created with Unity or similar software.

[0165] 4. Future state prediction and suggestions:

[0166] The server runs algorithms based on current and historical data to predict future facial conditions, such as wrinkle depth and changes in skin tone three months from now, and then suggests specific skin care products and methods.

[0167] Examples of concrete examples and prompts

[0168] As a concrete example, consider the case where a customer at a beauty salon uses an application to take a photo of their face. The system analyzes the customer's skin condition and determines that they have dry skin. The system immediately displays skin care products for dry skin and instructions on how to use them. It also simulates predicted changes in the customer's skin over the next three months and recommends the use of a specific moisturizing cream.

[0169] Example prompt sentence:

[0170] I am a customer of a beauty salon and would like to use this application to find out how to improve my skin care. I have dry skin and am concerned about dark spots. I would like to know the appropriate skin care products and how to use them, as well as a prediction of my future skin condition.

[0171] This system allows users to receive personalized beauty advice in real time, as well as specific suggestions for predicting future skin conditions.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1:

[0174] The user takes a photo of their face using the device's camera. Guides for proper lighting and angles are displayed to help the user capture an accurate image. The captured image is temporarily stored on the device.

[0175] Input: A face image of the user.

[0176] Output: The captured face image data.

[0177] Step 2:

[0178] The facial image data acquired by the device is sent to a server. Data communication is via Wi-Fi or mobile data communication technology. The sent image data is received by the server and prepared for analysis.

[0179] Input: Facial image data stored on the device.

[0180] Output: Facial image data sent to the server.

[0181] Step 3:

[0182] The server analyzes the received facial image data. This analysis uses image analysis algorithms such as OpenCV to extract facial bone structure information, feature position information, and skin information. Specifically, the positions of the eyes, nose, and mouth, the dryness of the skin, and the condition of blemishes and wrinkles are identified.

[0183] Input: Facial image data sent to the server.

[0184] Output: Facial skeleton information, part position information, skin information.

[0185] Step 4:

[0186] The server generates skin care and makeup guides in real time based on the analysis results. The analyzed data is converted into appropriate skin care and makeup procedures, creating visual guidelines to display to the user.

[0187] Input: Facial skeletal information, part position information, skin information.

[0188] Output: Visual guidelines for skincare and makeup.

[0189] Step 5:

[0190] Based on the analysis results received by the device from the server, the device visually displays skin care and makeup guides to the user, such as animations showing how to massage the area around the cheekbones or how to apply eyeshadow around the eyes.

[0191] Enter: visual guidelines for skincare and makeup.

[0192] Output: A visual guide that is displayed on the device.

[0193] Step 6:

[0194] The server runs an algorithm based on current and past facial information to predict future facial conditions. For example, it predicts changes in wrinkle depth and skin tone three months from now. Based on this prediction, it recommends appropriate skin care products and specific care methods to the user.

[0195] Input: Current face information, past data.

[0196] Output: Prediction of future facial condition and recommendations for skin care products and methods based on that prediction.

[0197] Step 7:

[0198] The device receives from the server a visual display of the predicted future facial condition and suggestions for improvement, such as the recommendation to use a specific beauty cream to prevent wrinkles or to use a moisturizing lotion.

[0199] Input: Prediction results of future facial states, improvement suggestions.

[0200] Output: Suggestions for improvement and skin care products to use displayed on the device.

[0201] 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.

[0202] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[0203] Program processing and specific examples

[0204] Taking a picture of your face and sending image data

[0205] Users install and launch a dedicated application on their smartphone. The application activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing a photo of the user's face.

[0206] Facial image analysis

[0207] The user follows the instructions to take a photo of their face, and the image data is saved in the application. The device then sends the image data to the server. The server analyzes the received image data and determines facial features (the position of the eyes, nose, mouth, etc.), skin type, skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[0208] Emotion recognition

[0209] The server recognizes the user's emotions using an additional emotion engine, which analyzes facial expressions and complexions from the image data to determine the user's emotional state (e.g., joy, sadness, stress, etc.).

[0210] Real-time guide generation and display

[0211] The device receives the analysis results sent from the server and generates real-time skincare massage and makeup guides. For example, in the case of a skincare massage, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed. Furthermore, based on the emotional information recognized by the emotion engine, suggestions are made that match the user's mood.

[0212] Future facial prediction and improvement suggestions

[0213] Based on the current facial information, the server runs simulations to predict the future state of the face. Specifically, it predicts the progression of wrinkles and changes in skin color, and generates improvement suggestions based on the results. Improvement suggestions include recommendations for specific skin care products and appropriate makeup techniques. Additionally, depending on the emotional state determined by the emotion engine, it also suggests relaxation methods and beauty products for stress relief.

[0214] Specific examples

[0215] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[0216] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[0217] This system allows users to have an experience similar to that of a lecture by a professional hairdresser, and teaches them the best beauty techniques to suit their individual skin condition and emotional state.

[0218] The processing flow will be explained below.

[0219] Step 1:

[0220] The user picks up their smartphone and launches the dedicated application. The application activates the camera function and displays a screen for taking a picture of the user's face. The screen also displays guidance on appropriate lighting conditions and angles.

[0221] Step 2:

[0222] The user follows the instructions of the application to take a picture of their face with the camera, and once the picture is taken, the image data is saved locally.

[0223] Step 3:

[0224] The device encrypts the stored image data and sends it to a server over the internet connection, and a notification is displayed to the user to confirm the successful transmission.

[0225] Step 4:

[0226] The server analyzes the received image data and executes a facial recognition algorithm to detect facial features (eyes, nose, mouth), which then extracts facial skeletal information.

[0227] Step 5:

[0228] The server then runs skin analysis algorithms to determine skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[0229] Step 6:

[0230] The server uses a color analysis algorithm to analyze the user's skin pigments and tones to determine their personal color.

[0231] Step 7:

[0232] The server compiles the analysis results and generates facial bone structure, facial feature position, skin type, and personal color information, which is then encrypted and sent to the device.

[0233] Step 8:

[0234] The server runs an emotion recognition algorithm to recognize the user's emotions from the received image data, analyzing facial expressions and changes in facial color to determine their emotional state (e.g., joy, sadness, stress).

[0235] Step 9:

[0236] Based on the analysis data received from the server, the device generates real-time skincare massage and makeup guides, such as arrows for massaging along the cheekbones and animations showing how to apply lipstick.

[0237] Step 10:

[0238] The device then uses the emotional information received from the emotion engine to suggest skincare and makeup products that match the user's mood. For example, if the user is feeling stressed, it will suggest products with a relaxing effect or colors with a calming effect.

[0239] Step 11:

[0240] Based on the current facial information, the server runs simulations to predict the future state of the face, including the progression of wrinkles and changes in skin color.

[0241] Step 12:

[0242] The server generates improvement suggestions for the user based on the predicted future facial condition, such as recommendations for specific skin care products and appropriate makeup application techniques.

[0243] Step 13:

[0244] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[0245] Step 14:

[0246] The device visually displays the received improvement suggestions to the user, showing a simulated image of the future face along with the optimal skincare products and makeup techniques, allowing the user to take immediate action.

[0247] Through this series of steps, users can experience something similar to that of a professional hairdresser and learn the best beauty treatment methods to suit their individual skin condition and emotional state.

[0248] Example 2

[0249] 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."

[0250] Conventional skincare and makeup systems struggle to provide comprehensive guidance based on individual skin conditions and emotional states, and are unable to fully meet the specific needs of users. They also lack the technology to predict future facial conditions and provide appropriate improvement suggestions.

[0251] 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.

[0252] In this invention, the server includes an image acquisition means for allowing the user to capture a photograph of their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial skeletal information, facial feature position information, and skin information, an emotion analysis means for recognizing the real-time emotional state based on the subject being analyzed, and a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information and emotional state. This allows the user to receive appropriate skin care and makeup guides in real time based on their skin condition and emotional state. Furthermore, by predicting future facial condition based on current facial information and receiving appropriate improvement suggestions, long-term beauty care can also be achieved.

[0253] The "image acquisition means" is a function that allows a user to take a picture of their own face using a smartphone or other photographing device.

[0254] The "transmission means" is a communication function for transmitting the acquired image data to a server. This function transfers the data via an Internet connection.

[0255] The "image analysis means" is a function that analyzes the image data received by the server and generates facial bone structure information, facial feature position information, and skin information. This analysis uses deep learning models and image processing algorithms.

[0256] The "emotion analysis means" is a function for recognizing the user's emotions based on image data. It uses a facial expression recognition algorithm to determine emotional states such as joy, sadness, and stress.

[0257] The "display means" is a function that generates skin care and makeup guides in real time based on the analyzed information and emotional state, and visually displays them to the user. For example, it displays animations and arrows on the smartphone screen.

[0258] The "prediction method" is a function that predicts future facial conditions based on analyzed current facial information. It uses machine learning models to simulate the progression of wrinkles and changes in skin color.

[0259] The "suggestion means" is a function that generates appropriate improvement suggestions based on the predicted future facial condition and displays them to the user. These suggestions include skin care products, makeup techniques, relaxation methods, etc.

[0260] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[0261] The system includes the following main means:

[0262] 1. Image Acquisition Method: The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera to capture a picture of the user's face. The application displays guidance on appropriate lighting conditions and shooting angles, allowing the user to capture an accurate picture of their face.

[0263] 2. Transmission method: The image data captured by the user is saved in the application and then transmitted to the server by the device, using the SSL / TLS protocol to ensure data security.

[0264] 3. Image analysis: The server uses OpenCV or deep learning models (e.g., YOLO, ResNet) to analyze the received image data. It determines facial bone structure, facial feature position, skin type, and personal color, and the analysis results are stored in a database.

[0265] 4. Emotion analysis means: The server recognizes the user's emotions using an additional emotion engine, which uses a facial expression recognition algorithm (e.g., Face Emotion Recognition model) to analyze facial muscle movements from image data to determine the user's emotional state.

[0266] 5. Display: Based on the analysis results and emotional state sent from the server, the device displays skin care and makeup guides to the user in real time. The guides are displayed as visual guidelines, such as massage steps along the contours of the face or the proper way to apply lipstick.

[0267] 6. Prediction method: The server performs simulations to predict future facial conditions based on current facial information. Machine learning models are used to predict the progression of wrinkles and changes in skin color.

[0268] 7. Recommendation method: Based on the prediction results, the server recommends appropriate skin care products and makeup techniques. The recommendations are stored in a database and displayed to the user via their device. Relaxation methods and beauty products are also suggested according to the user's emotional state.

[0269] Specific Examples

[0270] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[0271] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[0272] Example prompts to input to the generative AI model

[0273] "Please explain in detail each processing step of the system that analyzes the user's facial photo and makes skincare and makeup recommendations."

[0274] "Please explain in detail how the system works to recognize a user's face and emotions and generate personalized skincare and makeup suggestions."

[0275] "Please tell me the functions and specific processing steps of the system that predicts the future state of the face and makes suggestions for improvement."

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1: Image acquisition

[0278] The user installs and launches a dedicated application on their smartphone. The user activates the camera function within the application and takes a picture of their face. The application displays on the screen a guide for appropriate lighting conditions and shooting angles, and the user follows these to take a picture of their own face.

[0279] Input: User's photo

[0280] Output: Image data of the captured face (e.g., JPEG format)

[0281] Step 2: Sending image data

[0282] The device sends the facial image data stored in the application to a server over the Internet, using the SSL / TLS protocol to ensure data security.

[0283] Input: Captured face image data

[0284] Output: Facial image data sent to the server

[0285] Step 3: Image analysis

[0286] The server analyzes the received image data. The analysis software used here is OpenCV or a deep learning model (e.g., YOLO, ResNet). The server first identifies facial feature points (the positions of the eyes, nose, mouth, etc.), and then determines skin type (dry skin, oily skin, etc.), skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[0287] Input: Facial image data sent to the server

[0288] Output: Analysis results such as facial features, skin type, skin concerns, and personal color

[0289] Step 4: Recognize emotions

[0290] The server recognizes the user's emotions using an emotion engine. This emotion engine uses a facial expression recognition algorithm (e.g., the Face Emotion Recognition model) to analyze the movements of facial muscles from image data and determine the user's emotional state, such as joy, sadness, or stress.

[0291] Input: Facial feature points obtained from image data

[0292] Output: Emotional state such as happiness, sadness, stress, etc.

[0293] Step 5: Generate real-time guides

[0294] Based on the analysis and emotion recognition results, the server generates skin care massage and makeup guides, such as massage steps along the contours of the face or the proper way to apply lipstick. These guides are generated in a way that best suits the user's current skin condition and emotional state.

[0295] Input: Face analysis results, emotional state

[0296] Output: Real-time skincare and makeup guide

[0297] Step 6: Viewing the guide

[0298] The device receives the analysis results and guides sent from the server and displays visual guides to the user in real time. Specifically, skin care and makeup techniques are displayed on the smartphone screen using animations and arrows. For example, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed.

[0299] Input: Guide information sent from the server

[0300] Output: Real-time guide on smartphone screen

[0301] Step 7: Predicting future faces

[0302] The server runs a simulation based on current facial information to predict future facial conditions, specifically using machine learning models to run algorithms that predict wrinkle progression and changes in skin tone.

[0303] Input: Current face analysis result

[0304] Output: Simulation result of future face (e.g. after 3 months)

[0305] Step 8: Generate improvement suggestions

[0306] Based on the predictions of the future face, the server will suggest appropriate skin care products and makeup methods, as well as relaxation methods and beauty products according to the user's emotional state.

[0307] Input: Future face simulation results, current emotional state

[0308] Output: Suggestions for improvement, such as specific skin care products, makeup techniques, relaxation techniques, etc.

[0309] (Application example 2)

[0310] 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."

[0311] Conventional skincare and makeup instruction systems do not take into account the user's individual emotional state, resulting in a lack of psychological satisfaction and effective suggestions. Furthermore, even if systems exist that can predict future facial conditions, the improvement suggestions based on these predictions are often merely theoretical and lack comprehensiveness. There is a need to solve these issues and provide personalized, high-quality skincare and makeup instruction to users.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image analysis means for analyzing the user's facial image, an emotion recognition means for analyzing the user's emotional state, a display means for generating and displaying a real-time guide, a prediction means for predicting the user's future facial state, and a suggestion means for providing suggestions based on the prediction results and the user's emotional state. This makes it possible to provide not only skin care and makeup lessons tailored to the user's facial state, but also personalized suggestions based on the user's emotional state. Furthermore, by predicting the user's future facial state and providing specific improvement suggestions based on the prediction, the user's psychological satisfaction can be increased.

[0313] "Facial skeletal information" is data that indicates the main structural features of the face, and includes position information of the eyes, nose, mouth, etc.

[0314] "Feature position information" is data that indicates the specific positions of individual facial features (eyes, nose, mouth, etc.).

[0315] "Skin information" is data relating to the condition of the skin, including information on blemishes, wrinkles, redness, etc.

[0316] "Image analysis means" is a technology for analyzing received image data and extracting information such as facial features and skin condition.

[0317] The "display means" is a technology that generates a guide in real time based on the analyzed information and provides the information visually to the user.

[0318] "Emotion recognition means" is a technology that analyzes the user's emotional state from their facial expressions and complexion.

[0319] The "suggestion means" is a technology that provides appropriate skin care and makeup suggestions to the user based on the analysis results and emotional state.

[0320] The "prediction means" is a technology that predicts the future state of a face based on current face information.

[0321] "Image acquisition means" refers to a device or technology that allows a user to take a picture of their own face.

[0322] "Transmission means" refers to a technique for sending the acquired image data to the server.

[0323] MODE FOR CARRYING OUT THE INVENTION

[0324] This system analyzes a user's facial information and emotional state, and provides skin care information and makeup lessons in real time based on the analysis. This system is intended to be installed on devices such as smart glasses and used in brick-and-mortar stores.

[0325] Hardware and Software Configuration

[0326] The system includes the following major hardware and software components:

[0327] Smart glasses (e.g., general-purpose smart glasses device)

[0328] Cloud server (e.g. general-purpose cloud service)

[0329] Facial image analysis software (e.g., general-purpose image analysis engine)

[0330] Emotion recognition software (e.g., general-purpose emotion recognition API)

[0331] Natural language processing explanation

[0332] Taking a picture of your face and sending image data

[0333] A user wearing smart glasses takes a picture of their face using the device's built-in camera. The captured image data is then sent from the smart glasses to a cloud server. This transmission process is carried out in real time, and the data is encrypted before being sent.

[0334] Facial image analysis

[0335] The cloud server analyzes the received images using facial image analysis software. Specifically, it uses AWS® Rekognition and Microsoft® Azure® Face API to detect facial bone structure, facial feature position, and skin condition. The analyzed data is organized by category and passed on to the next processing step.

[0336] Emotion recognition

[0337] At the same time, the server uses emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API) to determine the user's emotional state from the image data. The results of this analysis are used in real time to suggest skincare and makeup lessons.

[0338] Real-time guide generation and display

[0339] Based on the analysis, the server generates skin care and makeup guides and sends them to the smart glasses' display device, which can show, for example, massage instructions along the cheekbones or animations of specific makeup application techniques.

[0340] Future facial prediction and improvement suggestions

[0341] The server uses prediction methods to simulate future facial conditions based on current and past facial information. For example, it predicts the progression of wrinkles and changes in skin color, and suggests skin care products and beauty treatments based on the results. Taking emotion recognition results into account, it also suggests relaxation and stress reduction methods.

[0342] Specific example explanation

[0343] User Scenarios

[0344] 1. Facial imaging and analysis

[0345] A user enters a store and puts on smart glasses.

[0346] The smart glasses automatically take a picture of your face and send the data to a cloud server.

[0347] The server analyzes the face using AWS Rekognition and Microsoft Azure Face API to generate facial features and skin information.

[0348] 2. Real-time guide

[0349] The smart glasses display animated skin care and makeup instructions (for example, how to massage along the cheekbones).

[0350] Users follow the guide and perform their own skin care.

[0351] 3. Future face prediction and suggestions

[0352] The server predicts the future condition of the face based on the current facial information and suggests specific moisturizing creams and skin care methods.

[0353] Emotion-recognition software displays suggestions tailored to the user's emotional state, such as relaxing skin care products.

[0354] Input prompts for generative AI models

[0355] "Analyze the user's facial image and recognize facial features, skin type, and personal color. Furthermore, predict the future state of the user's face and make appropriate suggestions for improvement. Also, recognize emotions and make suggestions based on those emotions."

[0356] This format allows users to receive more personalized suggestions and, especially in physical stores, to receive effective skin care and makeup lessons.

[0357] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0358] Step 1: Taking a picture of your face and sending the image data

[0359] The user puts on the smart glasses and photographs their face. The built-in camera captures the entire face in high resolution. When photographing, the smart glasses display a guide to the optimal lighting conditions and shooting angle. The photographed image data is sent from the smart glasses to a cloud server in real time. The transmitted data is encrypted and reaches the cloud server securely.

[0360] Step 2: Facial image analysis

[0361] The cloud server analyzes the received facial image data using facial image analysis software. Specifically, it uses AWS Rekognition and Microsoft Azure Face API to extract facial bone structure information, facial feature position information, and skin information. The input data is the facial image, and the output data is specific information on analyzed facial feature points (e.g., position of the eyes, nose, and mouth) and skin condition (e.g., blemishes, wrinkles, redness, etc.).

[0362] Step 3: Recognize emotions

[0363] The server analyzes the emotional state based on the facial image data using emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API). The input data is a facial image, and the output data is the emotional state, such as joy, sadness, or stress. Emotion recognition information is generated based on changes in facial expressions and complexion.

[0364] Step 4: Generate and display real-time guides

[0365] The server generates real-time guides based on the analysis results. Specifically, visual guidelines showing skin care massage and makeup actions (e.g., how to massage along cheekbones or how to apply eyeshadow) are created. These guides are displayed on the smart glasses' display. The input data are the facial analysis results and emotional state, and the output data is a video or animation of the guideline.

[0366] Step 5: Predicting future facial features and suggesting improvements

[0367] The server predicts future facial conditions based on current facial information and past data. Specifically, it uses an AI model to perform simulations to predict the progression of wrinkles and changes in skin color. The input data is the facial analysis results and historical data, and the output data is a simulation of future facial conditions and suggestions for improvement. Suggested improvements include the use of specific skincare products and beauty techniques.

[0368] Step 6: Generating suggestions according to emotional state

[0369] The server makes suggestions for relaxation methods and stress relief based on the emotion recognition results. Specific examples include suggestions for skin care products and makeup colors that have a relaxing effect. The input data is the emotion recognition results, and the output data is suggestions for relaxation methods and skin care products.

[0370] Through this series of steps, users can receive highly personalized skincare and makeup lessons, which can improve customer satisfaction in physical stores.

[0371] 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.

[0372] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0373] 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.

[0374] [Second embodiment]

[0375] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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."

[0387] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal skin care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvement.

[0388] Program processing and specific examples

[0389] Taking a picture of your face and sending image data

[0390] The user installs and launches a dedicated application on their smartphone. The application activates the camera function and takes a picture of the user's face. The image is taken according to guidance on appropriate lighting and angles. The image data thus obtained is sent from the device to a server.

[0391] Facial image analysis

[0392] The server analyzes the received image data by running an algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.). It also analyzes skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) to identify the user's personal color. This provides information on the facial structure, the position of facial features, skin type, and personal color.

[0393] Real-time guide generation and display

[0394] The device receives the analysis results sent from the server and displays guides for skin care massage and makeup lessons in real time. For example, in the case of a skin care massage, arrows and lines are displayed showing how to massage along the cheekbones. In the case of a makeup lesson, animations are displayed showing specific areas where eye shadow should be applied and how to apply lipstick.

[0395] Future facial prediction and improvement suggestions

[0396] Based on current facial information, the system predicts changes in the face after a specified period of time (for example, one month, three months, etc.). The server simulates the progression of wrinkles, changes in skin tone, etc., and predicts the future state of the face. Based on this prediction, it generates improvement suggestions. For example, it provides specific advice such as "using a specific beauty cream to prevent wrinkles" or "recommending a highly moisturizing lotion."

[0397] Specific examples

[0398] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates and sends back information about the user's facial bone structure, facial feature position, skin type, and personal color. Based on the received information, the device displays skin care and makeup guides in real time, including instructions on how to massage the face in circular motions around the cheekbones and how to apply eyeshadow to the eyes. Furthermore, the device predicts the user's facial condition three months from now based on the current facial information, simulating changes in wrinkle depth and skin tone, and suggesting appropriate skin care products. This allows the user to understand changes in their face and how to address them, and receive optimal beauty advice.

[0399] This system allows users to experience a level of beauty that is similar to that of a professional hairdresser, all from the comfort of their own home. It provides real-time guidance and predictions for future improvements, enabling more effective skincare and makeup application.

[0400] The processing flow will be explained below.

[0401] Step 1:

[0402] The user picks up their smartphone and launches the dedicated application, which activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing the user's face.

[0403] Step 2:

[0404] The user follows the instructions of the application to take a picture of their face with the camera. Once the picture is taken, the image data is saved in the application.

[0405] Step 3:

[0406] The device sends the stored image data to a server using encryption technology, via communication over the Internet.

[0407] Step 4:

[0408] The server analyzes the received image data and runs a facial recognition algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.) and extract facial bone structure information.

[0409] Step 5:

[0410] The server then uses image analysis technology to assess skin type (dry, oily, combination, etc.) and identify skin concerns (blemishes, wrinkles, redness, etc.), providing the basis for skin care advice.

[0411] Step 6:

[0412] The server analyzes the user's skin pigmentation and tone to determine their personal color, which serves as the basis for makeup suggestions.

[0413] Step 7:

[0414] The server compiles the analysis results and generates data including facial bone structure, facial feature position, skin type, and personal color information, which is then sent to the device.

[0415] Step 8:

[0416] Based on the analysis data received, the device generates real-time skincare massage and makeup guides, such as arrows to massage along the cheekbones and animations showing how to properly apply lipstick.

[0417] Step 9:

[0418] The server uses current facial information to perform simulations to predict future facial conditions, specifically predicting the progression of wrinkles and changes in skin color.

[0419] Step 10:

[0420] The server generates improvement suggestions based on the predicted future facial condition, including recommendations for specific skin care products and appropriate makeup application techniques.

[0421] Step 11:

[0422] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[0423] Step 12:

[0424] The device visually displays the received improvement suggestions to the user, showing them a simulated image of their future face along with the optimal skincare products and makeup techniques, allowing the user to immediately take appropriate action.

[0425] Through this series of steps, users can experience a level similar to that of a professional hairdresser and learn the best beauty treatment methods for their individual skin condition and facial features.

[0426] Example 1

[0427] 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."

[0428] In today's world, effective beauty care and makeup applications require advanced knowledge and skills. However, receiving instruction from a professional hairdresser or makeup artist is time-consuming and expensive. It is also difficult to automatically obtain appropriate advice tailored to each user's individual conditions, such as skin type, facial features, and personal color. Furthermore, there are limited means of predicting future facial conditions and receiving suggestions for continuous improvement. There is a need for a system that can solve these problems and enable users to enjoy professional beauty care at home.

[0429] 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.

[0430] In this invention, the server includes an image acquisition means for a user to photograph their own face, a transmission means for transmitting the acquired image data to an external server, an image analysis means for analyzing the image data received by the external server and generating facial feature information, location information, and skin condition information, a display means for generating and displaying beauty care and makeup guides in real time based on the analyzed information, a prediction means for predicting future facial conditions based on the analyzed current facial information, a suggestion means for displaying the predicted future facial conditions and providing improvement suggestions, and a means for using a dedicated generative AI model to detect facial feature points and perform skin condition analysis and individual color analysis. This allows users to receive appropriate beauty care and makeup advice tailored to their individual conditions from the comfort of their own home, and also predicts future facial conditions and receives continuous improvement suggestions.

[0431] "Image acquisition means" refers to a device or software that allows a user to accurately photograph their own face.

[0432] The "transmission means" refers to a device or software for transferring acquired image data from the terminal to an external server.

[0433] The "image analysis means" refers to algorithms or software that analyzes image data received by the server and generates facial feature information, position information, and skin condition information.

[0434] The "display means" refers to a device or software that visually presents the analyzed information to the user.

[0435] A "prediction method" is an algorithm or software that calculates and predicts future facial states based on current facial information.

[0436] The "suggestion means" is a device or software that provides the user with beauty care and improvement suggestions based on the predicted future facial condition.

[0437] A "generative AI model" is a trained artificial intelligence model that detects facial feature points and performs skin condition analysis and individual color analysis.

[0438] "Facial feature information" is data that includes position information of the eyes, nose, mouth, and other parts of the user's face.

[0439] "Skin condition information" is data related to the user's skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[0440] "Individual color analysis" is a process of analyzing the color tone of the user's face and identifying their personal color.

[0441] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal beauty care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvements.

[0442] Taking a picture of your face and sending image data

[0443] The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera function to capture a picture of the user's face. When taking a picture, the application displays guidance on appropriate lighting and angles, and the user follows the guidance to capture their face. Once the picture is taken, the image data is sent from the device to a server.

[0444] Facial image analysis

[0445] The server performs several processing steps to analyze the received image data. First, it uses an algorithm (e.g., Dlib, OpenCV, etc.) to detect facial feature points (the positions of the eyes, nose, mouth, etc.). Next, it uses a dedicated generative AI model (e.g., a model using TensorFlow or PyTorch) to analyze skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) and identify the person's personal color. As a result of the analysis, facial bone structure information, feature position information, skin information, and personal color information are generated.

[0446] Real-time guide generation and display

[0447] The server sends the analysis results to the device, which then generates and displays guides for beauty care and makeup lessons in real time. For example, for beauty care, it displays arrows and lines showing how to massage along the cheekbones. For makeup lessons, it displays animations that specifically show how to apply eyeshadow to the eyes and how to use lipstick.

[0448] Future facial prediction and improvement suggestions

[0449] The server predicts the future state of the face based on the current facial information. For example, it simulates the progression of wrinkles and changes in skin tone. Based on this future prediction, it generates improvement suggestions and sends them to the device. Specific suggestions include "using a specific beauty cream to prevent wrinkles" and "recommending a highly moisturizing lotion." By referring to these suggestions, users can carry out more effective beauty care.

[0450] Specific examples

[0451] Here is an example prompt:

[0452] This system allows users to take a picture of their face, send it to a server, and then provide real-time beauty care and makeup advice based on the results of image analysis. It also predicts future facial conditions and makes appropriate suggestions for improvements. Please explain the specific steps and results. For example, how do you take a picture of your face and send the image data to the server? Also, please provide specific examples of the kind of advice you can receive based on the analysis results.

[0453] This invention allows users to experience a lesson similar to that of a professional hairdresser from the comfort of their own home, enabling more effective beauty care and makeup application through real-time guidance and improvement suggestions based on future predictions.

[0454] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0455] Step 1:

[0456] The user installs and launches a dedicated application on their smartphone. The application activates the smartphone's camera function and takes a picture of the user's face. When taking a photo, the application displays guidance on appropriate lighting and angles. The user follows the guidance to take a picture of their face.

[0457] Input: A guided face image taken by the user.

[0458] Output: Image data of the captured face.

[0459] Step 2:

[0460] The device sends the captured facial image data to an external server, and after the capture is complete, the application automatically uploads the image data to the specified server.

[0461] Input: Image data of a captured face.

[0462] Output: Facial image data sent to the server.

[0463] Step 3:

[0464] The server analyzes the received image data and runs a facial feature detection algorithm, specifically using computer vision libraries such as Dlib or OpenCV to identify features such as the eyes, nose, and mouth.

[0465] Input: Facial image data sent to the server.

[0466] Output: Facial feature points such as eyes, nose, and mouth.

[0467] Step 4:

[0468] The server uses a dedicated generative AI model trained using TensorFlow and PyTorch to analyze skin type and personal color based on feature point information.

[0469] Input: Feature point information, facial image data.

[0470] Output: Skin type information, personal color information.

[0471] Step 5:

[0472] The server sends the analysis results (skin type information, personal color information, feature point information) to the device. The sent information will be used in the next step.

[0473] Input: Skin type information, personal color information, feature point information.

[0474] Output: Data containing analysis results (skin type information, personal color information, feature point information).

[0475] Step 6:

[0476] The device generates real-time beauty care and makeup guides based on the analysis results it receives, such as displaying arrows and lines to show how to massage along the cheekbones, or an animation showing how to apply eyeshadow to the eyes.

[0477] Input: Analysis results (skin type information, personal color information, feature point information).

[0478] Output: A visual guide to beauty care and makeup.

[0479] Step 7:

[0480] The server runs an algorithm that uses current facial information to predict future facial states, for example, simulating what the face will look like one month or three months from now.

[0481] Input: Current face information (skin type, personal color, and feature point information).

[0482] Output: Prediction data about future face states.

[0483] Step 8:

[0484] Based on the prediction results, the server generates improvement suggestions for the user, such as recommending the use of a specific beauty cream or a moisturizing lotion.

[0485] Input: Prediction data about future face states.

[0486] Output: Improvement proposal data.

[0487] Step 9:

[0488] The terminal displays the improvement suggestions received from the server to the user, allowing the user to obtain information for carrying out appropriate beauty care.

[0489] Input: Improvement proposal data.

[0490] Output: Displaying the suggestion information to the user.

[0491] (Application example 1)

[0492] 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."

[0493] At beauty salons and cosmetic stores, it is currently difficult for customers to receive optimal skin care and makeup advice based on their skin type and personal color. It is also difficult to predict future facial conditions and provide future beauty care and improvement suggestions in real time. Therefore, a system that provides an experience similar to that of a professional hairdresser is needed.

[0494] 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.

[0495] In this invention, the server includes an image acquisition means for allowing a user to photograph their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial bone structure information, facial feature position information, and skin information, a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information, a prediction means for predicting the user's future facial condition and generating suggestions for improving skin care and makeup, and a suggestion means for predicting the user's future skin condition based on the acquired current facial information and past data and suggesting appropriate skin care products and care methods. This allows users to receive professional beauty care and advice in real time when they visit the salon. Furthermore, by receiving appropriate skin care and makeup suggestions based on the predicted future skin condition, effective beauty care can be achieved.

[0496] "Image acquisition means" refers to a device or function that allows a user to take a picture of their own face.

[0497] "Transmission means" refers to the technology or method for transmitting the acquired image data to the server.

[0498] "Image analysis means" refers to a technology for analyzing image data received by the server and generating facial bone structure information, feature position information, and skin information.

[0499] "Display means" refers to a device or system that generates skin care and makeup guides in real time based on the analyzed information and visually presents them to the user.

[0500] "Predictive methods" refer to technologies and algorithms that predict the user's future facial condition and generate suggestions for future skincare and makeup improvements.

[0501] "Suggestion means" refers to a system or function that predicts future skin conditions based on acquired current facial information and past data, and suggests skin care products and care methods to the user based on that prediction.

[0502] MODE FOR CARRYING OUT THE INVENTION

[0503] System Overview

[0504] A system for implementing the present invention includes the following components:

[0505] 1. Image acquisition means: A device with a camera function that allows the user to take a picture of their own face (e.g., smartphone, tablet, smart mirror).

[0506] 2. Transmission method: The communication technology (e.g., Wi-Fi, mobile data communication) used to transmit the acquired image data to the server.

[0507] 3. Image analysis means: Technology for analyzing image data received by the server and generating facial skeletal information, facial feature position information, and skin information (e.g., image analysis algorithm using OpenCV).

[0508] 4. Display means: A device or application that generates skin care and makeup guides in real time based on the analyzed information and displays them to the user (e.g., a guide display app created with Unity).

[0509] 5. Prediction means: Algorithms and server-side processing for predicting the user's future facial condition.

[0510] 6. Proposal method: A system that predicts future skin condition based on current facial information and past data, and suggests appropriate skin care products and care methods.

[0511] Program processing and hardware and software used

[0512] 1. Image acquisition and transmission:

[0513] Users take a picture of their face using the camera on their smart mirror, tablet, or smartphone, and the captured image data is sent to a server via Wi-Fi or mobile data.

[0514] 2. Image analysis and data generation:

[0515] The server runs image analysis algorithms such as OpenCV to generate facial bone structure information, facial feature position information, and skin information, such as the position of the eyes, nose shape, and degree of skin dryness.

[0516] 3. Real-time display:

[0517] The analysis results are sent to the user's device in real time and displayed to the user as a visual skincare and makeup guide via an application created with Unity or similar software.

[0518] 4. Future state prediction and suggestions:

[0519] The server runs algorithms based on current and historical data to predict future facial conditions, such as wrinkle depth and changes in skin tone three months from now, and then suggests specific skin care products and methods.

[0520] Examples of concrete examples and prompts

[0521] As a concrete example, consider the case where a customer at a beauty salon uses an application to take a photo of their face. The system analyzes the customer's skin condition and determines that they have dry skin. The system immediately displays skin care products for dry skin and instructions on how to use them. It also simulates predicted changes in the customer's skin over the next three months and recommends the use of a specific moisturizing cream.

[0522] Example prompt sentence:

[0523] I am a customer of a beauty salon and would like to use this application to find out how to improve my skin care. I have dry skin and am concerned about dark spots. I would like to know the appropriate skin care products and how to use them, as well as a prediction of my future skin condition.

[0524] This system allows users to receive personalized beauty advice in real time, as well as specific suggestions for predicting future skin conditions.

[0525] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0526] Step 1:

[0527] The user takes a photo of their face using the device's camera. Guides for proper lighting and angles are displayed to help the user capture an accurate image. The captured image is temporarily stored on the device.

[0528] Input: A face image of the user.

[0529] Output: The captured face image data.

[0530] Step 2:

[0531] The facial image data acquired by the device is sent to a server. Data communication is via Wi-Fi or mobile data communication technology. The sent image data is received by the server and prepared for analysis.

[0532] Input: Facial image data stored on the device.

[0533] Output: Facial image data sent to the server.

[0534] Step 3:

[0535] The server analyzes the received facial image data. This analysis uses image analysis algorithms such as OpenCV to extract facial bone structure information, feature position information, and skin information. Specifically, the positions of the eyes, nose, and mouth, the dryness of the skin, and the condition of blemishes and wrinkles are identified.

[0536] Input: Facial image data sent to the server.

[0537] Output: Facial skeleton information, part position information, skin information.

[0538] Step 4:

[0539] The server generates skin care and makeup guides in real time based on the analysis results. The analyzed data is converted into appropriate skin care and makeup procedures, creating visual guidelines to display to the user.

[0540] Input: Facial skeletal information, part position information, skin information.

[0541] Output: Visual guidelines for skincare and makeup.

[0542] Step 5:

[0543] Based on the analysis results received by the device from the server, the device visually displays skin care and makeup guides to the user, such as animations showing how to massage the area around the cheekbones or how to apply eyeshadow around the eyes.

[0544] Enter: visual guidelines for skincare and makeup.

[0545] Output: A visual guide that is displayed on the device.

[0546] Step 6:

[0547] The server runs an algorithm based on current and past facial information to predict future facial conditions. For example, it predicts changes in wrinkle depth and skin tone three months from now. Based on this prediction, it recommends appropriate skin care products and specific care methods to the user.

[0548] Input: Current face information, past data.

[0549] Output: Prediction of future facial condition and recommendations for skin care products and methods based on that prediction.

[0550] Step 7:

[0551] The device receives from the server a visual display of the predicted future facial condition and suggestions for improvement, such as the recommendation to use a specific beauty cream to prevent wrinkles or to use a moisturizing lotion.

[0552] Input: Prediction results of future facial states, improvement suggestions.

[0553] Output: Suggestions for improvement and skin care products to use displayed on the device.

[0554] 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.

[0555] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[0556] Program processing and specific examples

[0557] Taking a picture of your face and sending image data

[0558] Users install and launch a dedicated application on their smartphone. The application activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing a photo of the user's face.

[0559] Facial image analysis

[0560] The user follows the instructions to take a photo of their face, and the image data is saved in the application. The device then sends the image data to the server. The server analyzes the received image data and determines facial features (the position of the eyes, nose, mouth, etc.), skin type, skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[0561] Emotion recognition

[0562] The server recognizes the user's emotions using an additional emotion engine, which analyzes facial expressions and complexions from the image data to determine the user's emotional state (e.g., joy, sadness, stress, etc.).

[0563] Real-time guide generation and display

[0564] The device receives the analysis results sent from the server and generates real-time skincare massage and makeup guides. For example, in the case of a skincare massage, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed. Furthermore, based on the emotional information recognized by the emotion engine, suggestions are made that match the user's mood.

[0565] Future facial prediction and improvement suggestions

[0566] Based on the current facial information, the server runs simulations to predict the future state of the face. Specifically, it predicts the progression of wrinkles and changes in skin color, and generates improvement suggestions based on the results. Improvement suggestions include recommendations for specific skin care products and appropriate makeup techniques. Additionally, depending on the emotional state determined by the emotion engine, it also suggests relaxation methods and beauty products for stress relief.

[0567] Specific examples

[0568] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[0569] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[0570] This system allows users to have an experience similar to that of a lecture by a professional hairdresser, and teaches them the best beauty techniques to suit their individual skin condition and emotional state.

[0571] The processing flow will be explained below.

[0572] Step 1:

[0573] The user picks up their smartphone and launches the dedicated application. The application activates the camera function and displays a screen for taking a picture of the user's face. The screen also displays guidance on appropriate lighting conditions and angles.

[0574] Step 2:

[0575] The user follows the instructions of the application to take a picture of their face with the camera, and once the picture is taken, the image data is saved locally.

[0576] Step 3:

[0577] The device encrypts the stored image data and sends it to a server over the internet connection, and a notification is displayed to the user to confirm the successful transmission.

[0578] Step 4:

[0579] The server analyzes the received image data and executes a facial recognition algorithm to detect facial features (eyes, nose, mouth), which then extracts facial skeletal information.

[0580] Step 5:

[0581] The server then runs skin analysis algorithms to determine skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[0582] Step 6:

[0583] The server uses a color analysis algorithm to analyze the user's skin pigments and tones to determine their personal color.

[0584] Step 7:

[0585] The server compiles the analysis results and generates facial bone structure, facial feature position, skin type, and personal color information, which is then encrypted and sent to the device.

[0586] Step 8:

[0587] The server runs an emotion recognition algorithm to recognize the user's emotions from the received image data, analyzing facial expressions and changes in facial color to determine their emotional state (e.g., joy, sadness, stress).

[0588] Step 9:

[0589] Based on the analysis data received from the server, the device generates real-time skincare massage and makeup guides, such as arrows for massaging along the cheekbones and animations showing how to apply lipstick.

[0590] Step 10:

[0591] The device then uses the emotional information received from the emotion engine to suggest skincare and makeup products that match the user's mood. For example, if the user is feeling stressed, it will suggest products with a relaxing effect or colors with a calming effect.

[0592] Step 11:

[0593] Based on the current facial information, the server runs simulations to predict the future state of the face, including the progression of wrinkles and changes in skin color.

[0594] Step 12:

[0595] The server generates improvement suggestions for the user based on the predicted future facial condition, such as recommendations for specific skin care products and appropriate makeup application techniques.

[0596] Step 13:

[0597] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[0598] Step 14:

[0599] The device visually displays the received improvement suggestions to the user, showing a simulated image of the future face along with the optimal skincare products and makeup techniques, allowing the user to take immediate action.

[0600] Through this series of steps, users can experience something similar to that of a professional hairdresser and learn the best beauty treatment methods to suit their individual skin condition and emotional state.

[0601] Example 2

[0602] 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."

[0603] Conventional skincare and makeup systems struggle to provide comprehensive guidance based on individual skin conditions and emotional states, and are unable to fully meet the specific needs of users. They also lack the technology to predict future facial conditions and provide appropriate improvement suggestions.

[0604] 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.

[0605] In this invention, the server includes an image acquisition means for allowing the user to capture a photograph of their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial skeletal information, facial feature position information, and skin information, an emotion analysis means for recognizing the real-time emotional state based on the subject being analyzed, and a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information and emotional state. This allows the user to receive appropriate skin care and makeup guides in real time based on their skin condition and emotional state. Furthermore, by predicting future facial condition based on current facial information and receiving appropriate improvement suggestions, long-term beauty care can also be achieved.

[0606] The "image acquisition means" is a function that allows a user to take a picture of their own face using a smartphone or other photographing device.

[0607] The "transmission means" is a communication function for transmitting the acquired image data to a server. This function transfers the data via an Internet connection.

[0608] The "image analysis means" is a function that analyzes the image data received by the server and generates facial bone structure information, facial feature position information, and skin information. This analysis uses deep learning models and image processing algorithms.

[0609] The "emotion analysis means" is a function for recognizing the user's emotions based on image data. It uses a facial expression recognition algorithm to determine emotional states such as joy, sadness, and stress.

[0610] The "display means" is a function that generates skin care and makeup guides in real time based on the analyzed information and emotional state, and visually displays them to the user. For example, it displays animations and arrows on the smartphone screen.

[0611] The "prediction method" is a function that predicts future facial conditions based on analyzed current facial information. It uses machine learning models to simulate the progression of wrinkles and changes in skin color.

[0612] The "suggestion means" is a function that generates appropriate improvement suggestions based on the predicted future facial condition and displays them to the user. These suggestions include skin care products, makeup techniques, relaxation methods, etc.

[0613] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[0614] The system includes the following main means:

[0615] 1. Image Acquisition Method: The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera to capture a picture of the user's face. The application displays guidance on appropriate lighting conditions and shooting angles, allowing the user to capture an accurate picture of their face.

[0616] 2. Transmission method: The image data captured by the user is saved in the application and then transmitted to the server by the device, using the SSL / TLS protocol to ensure data security.

[0617] 3. Image analysis: The server uses OpenCV or deep learning models (e.g., YOLO, ResNet) to analyze the received image data. It determines facial bone structure, facial feature position, skin type, and personal color, and the analysis results are stored in a database.

[0618] 4. Emotion analysis means: The server recognizes the user's emotions using an additional emotion engine, which uses a facial expression recognition algorithm (e.g., Face Emotion Recognition model) to analyze facial muscle movements from image data to determine the user's emotional state.

[0619] 5. Display: Based on the analysis results and emotional state sent from the server, the device displays skin care and makeup guides to the user in real time. The guides are displayed as visual guidelines, such as massage steps along the contours of the face or the proper way to apply lipstick.

[0620] 6. Prediction method: The server performs simulations to predict future facial conditions based on current facial information. Machine learning models are used to predict the progression of wrinkles and changes in skin color.

[0621] 7. Recommendation method: Based on the prediction results, the server recommends appropriate skin care products and makeup techniques. The recommendations are stored in a database and displayed to the user via their device. Relaxation methods and beauty products are also suggested according to the user's emotional state.

[0622] Specific Examples

[0623] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[0624] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[0625] Example prompts to input to the generative AI model

[0626] "Please explain in detail each processing step of the system that analyzes the user's facial photo and makes skincare and makeup recommendations."

[0627] "Please explain in detail how the system works to recognize a user's face and emotions and generate personalized skincare and makeup suggestions."

[0628] "Please tell me the functions and specific processing steps of the system that predicts the future state of the face and makes suggestions for improvement."

[0629] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0630] Step 1: Image acquisition

[0631] The user installs and launches a dedicated application on their smartphone. The user activates the camera function within the application and takes a picture of their face. The application displays on the screen a guide for appropriate lighting conditions and shooting angles, and the user follows these to take a picture of their own face.

[0632] Input: User's photo

[0633] Output: Image data of the captured face (e.g., JPEG format)

[0634] Step 2: Sending image data

[0635] The device sends the facial image data stored in the application to a server over the Internet, using the SSL / TLS protocol to ensure data security.

[0636] Input: Captured face image data

[0637] Output: Facial image data sent to the server

[0638] Step 3: Image analysis

[0639] The server analyzes the received image data. The analysis software used here is OpenCV or a deep learning model (e.g., YOLO, ResNet). The server first identifies facial feature points (the positions of the eyes, nose, mouth, etc.), and then determines skin type (dry skin, oily skin, etc.), skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[0640] Input: Facial image data sent to the server

[0641] Output: Analysis results such as facial features, skin type, skin concerns, and personal color

[0642] Step 4: Recognize emotions

[0643] The server recognizes the user's emotions using an emotion engine. This emotion engine uses a facial expression recognition algorithm (e.g., the Face Emotion Recognition model) to analyze the movements of facial muscles from image data and determine the user's emotional state, such as joy, sadness, or stress.

[0644] Input: Facial feature points obtained from image data

[0645] Output: Emotional state such as happiness, sadness, stress, etc.

[0646] Step 5: Generate real-time guides

[0647] Based on the analysis and emotion recognition results, the server generates skin care massage and makeup guides, such as massage steps along the contours of the face or the proper way to apply lipstick. These guides are generated in a way that best suits the user's current skin condition and emotional state.

[0648] Input: Face analysis results, emotional state

[0649] Output: Real-time skincare and makeup guide

[0650] Step 6: Viewing the guide

[0651] The device receives the analysis results and guides sent from the server and displays visual guides to the user in real time. Specifically, skin care and makeup techniques are displayed on the smartphone screen using animations and arrows. For example, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed.

[0652] Input: Guide information sent from the server

[0653] Output: Real-time guide on smartphone screen

[0654] Step 7: Predicting future faces

[0655] The server runs a simulation based on current facial information to predict future facial conditions, specifically using machine learning models to run algorithms that predict wrinkle progression and changes in skin tone.

[0656] Input: Current face analysis result

[0657] Output: Simulation result of future face (e.g. after 3 months)

[0658] Step 8: Generate improvement suggestions

[0659] Based on the predictions of the future face, the server will suggest appropriate skin care products and makeup methods, as well as relaxation methods and beauty products according to the user's emotional state.

[0660] Input: Future face simulation results, current emotional state

[0661] Output: Suggestions for improvement, such as specific skin care products, makeup techniques, relaxation techniques, etc.

[0662] (Application example 2)

[0663] 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."

[0664] Conventional skincare and makeup instruction systems do not take into account the user's individual emotional state, resulting in a lack of psychological satisfaction and effective suggestions. Furthermore, even if systems exist that can predict future facial conditions, the improvement suggestions based on these predictions are often merely theoretical and lack comprehensiveness. There is a need to solve these issues and provide personalized, high-quality skincare and makeup instruction to users.

[0665] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image analysis means for analyzing the user's facial image, an emotion recognition means for analyzing the user's emotional state, a display means for generating and displaying a real-time guide, a prediction means for predicting the user's future facial state, and a suggestion means for providing suggestions based on the prediction results and the user's emotional state. This makes it possible to provide not only skin care and makeup lessons tailored to the user's facial state, but also personalized suggestions based on the user's emotional state. Furthermore, by predicting the user's future facial state and providing specific improvement suggestions based on the prediction, the user's psychological satisfaction can be increased.

[0666] "Facial skeletal information" is data that indicates the main structural features of the face, and includes position information of the eyes, nose, mouth, etc.

[0667] "Feature position information" is data that indicates the specific positions of individual facial features (eyes, nose, mouth, etc.).

[0668] "Skin information" is data relating to the condition of the skin, including information on blemishes, wrinkles, redness, etc.

[0669] "Image analysis means" is a technology for analyzing received image data and extracting information such as facial features and skin condition.

[0670] The "display means" is a technology that generates a guide in real time based on the analyzed information and provides the information visually to the user.

[0671] "Emotion recognition means" is a technology that analyzes the user's emotional state from their facial expressions and complexion.

[0672] The "suggestion means" is a technology that provides appropriate skin care and makeup suggestions to the user based on the analysis results and emotional state.

[0673] The "prediction means" is a technology that predicts the future state of a face based on current face information.

[0674] "Image acquisition means" refers to a device or technology that allows a user to take a picture of their own face.

[0675] "Transmission means" refers to a technique for sending the acquired image data to the server.

[0676] MODE FOR CARRYING OUT THE INVENTION

[0677] This system analyzes a user's facial information and emotional state, and provides skin care information and makeup lessons in real time based on the analysis. This system is intended to be installed on devices such as smart glasses and used in brick-and-mortar stores.

[0678] Hardware and Software Configuration

[0679] The system includes the following major hardware and software components:

[0680] Smart glasses (e.g., general-purpose smart glasses device)

[0681] Cloud server (e.g. general-purpose cloud service)

[0682] Facial image analysis software (e.g., general-purpose image analysis engine)

[0683] Emotion recognition software (e.g., general-purpose emotion recognition API)

[0684] Natural language processing explanation

[0685] Taking a picture of your face and sending image data

[0686] A user wearing smart glasses takes a picture of their face using the device's built-in camera. The captured image data is then sent from the smart glasses to a cloud server. This transmission process is carried out in real time, and the data is encrypted before being sent.

[0687] Facial image analysis

[0688] The cloud server analyzes the received images using facial image analysis software. Specifically, it uses AWS Rekognition and Microsoft Azure Face API to detect facial bone structure, facial feature position, and skin condition. The analyzed data is organized by category and passed on to the next processing step.

[0689] Emotion recognition

[0690] At the same time, the server uses emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API) to determine the user's emotional state from the image data. The results of this analysis are used in real time to suggest skincare and makeup lessons.

[0691] Real-time guide generation and display

[0692] Based on the analysis, the server generates skin care and makeup guides and sends them to the smart glasses' display device, which can show, for example, massage instructions along the cheekbones or animations of specific makeup application techniques.

[0693] Future facial prediction and improvement suggestions

[0694] The server uses prediction methods to simulate future facial conditions based on current and past facial information. For example, it predicts the progression of wrinkles and changes in skin color, and suggests skin care products and beauty treatments based on the results. Taking emotion recognition results into account, it also suggests relaxation and stress reduction methods.

[0695] Specific example explanation

[0696] User Scenarios

[0697] 1. Facial imaging and analysis

[0698] A user enters a store and puts on smart glasses.

[0699] The smart glasses automatically take a picture of your face and send the data to a cloud server.

[0700] The server analyzes the face using AWS Rekognition and Microsoft Azure Face API to generate facial features and skin information.

[0701] 2. Real-time guide

[0702] The smart glasses display animated skin care and makeup instructions (for example, how to massage along the cheekbones).

[0703] Users follow the guide and perform their own skin care.

[0704] 3. Future face prediction and suggestions

[0705] The server predicts the future condition of the face based on the current facial information and suggests specific moisturizing creams and skin care methods.

[0706] Emotion-recognition software displays suggestions tailored to the user's emotional state, such as relaxing skin care products.

[0707] Input prompts for generative AI models

[0708] "Analyze the user's facial image and recognize facial features, skin type, and personal color. Furthermore, predict the future state of the user's face and make appropriate suggestions for improvement. Also, recognize emotions and make suggestions based on those emotions."

[0709] This format allows users to receive more personalized suggestions and, especially in physical stores, to receive effective skin care and makeup lessons.

[0710] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0711] Step 1: Taking a picture of your face and sending the image data

[0712] The user puts on the smart glasses and photographs their face. The built-in camera captures the entire face in high resolution. When photographing, the smart glasses display a guide to the optimal lighting conditions and shooting angle. The photographed image data is sent from the smart glasses to a cloud server in real time. The transmitted data is encrypted and reaches the cloud server securely.

[0713] Step 2: Facial image analysis

[0714] The cloud server analyzes the received facial image data using facial image analysis software. Specifically, it uses AWS Rekognition and Microsoft Azure Face API to extract facial bone structure information, facial feature position information, and skin information. The input data is the facial image, and the output data is specific information on analyzed facial feature points (e.g., position of the eyes, nose, and mouth) and skin condition (e.g., blemishes, wrinkles, redness, etc.).

[0715] Step 3: Recognize emotions

[0716] The server analyzes the emotional state based on the facial image data using emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API). The input data is a facial image, and the output data is the emotional state, such as joy, sadness, or stress. Emotion recognition information is generated based on changes in facial expressions and complexion.

[0717] Step 4: Generate and display real-time guides

[0718] The server generates real-time guides based on the analysis results. Specifically, visual guidelines showing skin care massage and makeup actions (e.g., how to massage along cheekbones or how to apply eyeshadow) are created. These guides are displayed on the smart glasses' display. The input data are the facial analysis results and emotional state, and the output data is a video or animation of the guideline.

[0719] Step 5: Predicting future facial features and suggesting improvements

[0720] The server predicts future facial conditions based on current facial information and past data. Specifically, it uses an AI model to perform simulations to predict the progression of wrinkles and changes in skin color. The input data is the facial analysis results and historical data, and the output data is a simulation of future facial conditions and suggestions for improvement. Suggested improvements include the use of specific skincare products and beauty techniques.

[0721] Step 6: Generating suggestions according to emotional state

[0722] The server makes suggestions for relaxation methods and stress relief based on the emotion recognition results. Specific examples include suggestions for skin care products and makeup colors that have a relaxing effect. The input data is the emotion recognition results, and the output data is suggestions for relaxation methods and skin care products.

[0723] Through this series of steps, users can receive highly personalized skincare and makeup lessons, which can improve customer satisfaction in physical stores.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] [Third embodiment]

[0728] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0729] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0730] 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).

[0731] 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.

[0732] 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.

[0733] 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).

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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."

[0740] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal skin care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvement.

[0741] Program processing and specific examples

[0742] Taking a picture of your face and sending image data

[0743] The user installs and launches a dedicated application on their smartphone. The application activates the camera function and takes a picture of the user's face. The image is taken according to guidance on appropriate lighting and angles. The image data thus obtained is sent from the device to a server.

[0744] Facial image analysis

[0745] The server analyzes the received image data by running an algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.). It also analyzes skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) to identify the user's personal color. This provides information on the facial structure, the position of facial features, skin type, and personal color.

[0746] Real-time guide generation and display

[0747] The device receives the analysis results sent from the server and displays guides for skin care massage and makeup lessons in real time. For example, in the case of a skin care massage, arrows and lines are displayed showing how to massage along the cheekbones. In the case of a makeup lesson, animations are displayed showing specific areas where eye shadow should be applied and how to apply lipstick.

[0748] Future facial prediction and improvement suggestions

[0749] Based on current facial information, the system predicts changes in the face after a specified period of time (for example, one month, three months, etc.). The server simulates the progression of wrinkles, changes in skin tone, etc., and predicts the future state of the face. Based on this prediction, it generates improvement suggestions. For example, it provides specific advice such as "using a specific beauty cream to prevent wrinkles" or "recommending a highly moisturizing lotion."

[0750] Specific examples

[0751] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates and sends back information about the user's facial bone structure, facial feature position, skin type, and personal color. Based on the received information, the device displays skin care and makeup guides in real time, including instructions on how to massage the face in circular motions around the cheekbones and how to apply eyeshadow to the eyes. Furthermore, the device predicts the user's facial condition three months from now based on the current facial information, simulating changes in wrinkle depth and skin tone, and suggesting appropriate skin care products. This allows the user to understand changes in their face and how to address them, and receive optimal beauty advice.

[0752] This system allows users to experience a level of beauty that is similar to that of a professional hairdresser, all from the comfort of their own home. It provides real-time guidance and predictions for future improvements, enabling more effective skincare and makeup application.

[0753] The processing flow will be explained below.

[0754] Step 1:

[0755] The user picks up their smartphone and launches the dedicated application, which activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing the user's face.

[0756] Step 2:

[0757] The user follows the instructions of the application to take a picture of their face with the camera. Once the picture is taken, the image data is saved in the application.

[0758] Step 3:

[0759] The device sends the stored image data to a server using encryption technology, via communication over the Internet.

[0760] Step 4:

[0761] The server analyzes the received image data and runs a facial recognition algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.) and extract facial bone structure information.

[0762] Step 5:

[0763] The server then uses image analysis technology to assess skin type (dry, oily, combination, etc.) and identify skin concerns (blemishes, wrinkles, redness, etc.), providing the basis for skin care advice.

[0764] Step 6:

[0765] The server analyzes the user's skin pigmentation and tone to determine their personal color, which serves as the basis for makeup suggestions.

[0766] Step 7:

[0767] The server compiles the analysis results and generates data including facial bone structure, facial feature position, skin type, and personal color information, which is then sent to the device.

[0768] Step 8:

[0769] Based on the analysis data received, the device generates real-time skincare massage and makeup guides, such as arrows to massage along the cheekbones and animations showing how to properly apply lipstick.

[0770] Step 9:

[0771] The server uses current facial information to perform simulations to predict future facial conditions, specifically predicting the progression of wrinkles and changes in skin color.

[0772] Step 10:

[0773] The server generates improvement suggestions based on the predicted future facial condition, including recommendations for specific skin care products and appropriate makeup application techniques.

[0774] Step 11:

[0775] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[0776] Step 12:

[0777] The device visually displays the received improvement suggestions to the user, showing them a simulated image of their future face along with the optimal skincare products and makeup techniques, allowing the user to immediately take appropriate action.

[0778] Through this series of steps, users can experience a level similar to that of a professional hairdresser and learn the best beauty treatment methods for their individual skin condition and facial features.

[0779] Example 1

[0780] 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."

[0781] In today's world, effective beauty care and makeup applications require advanced knowledge and skills. However, receiving instruction from a professional hairdresser or makeup artist is time-consuming and expensive. It is also difficult to automatically obtain appropriate advice tailored to each user's individual conditions, such as skin type, facial features, and personal color. Furthermore, there are limited means of predicting future facial conditions and receiving suggestions for continuous improvement. There is a need for a system that can solve these problems and enable users to enjoy professional beauty care at home.

[0782] 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.

[0783] In this invention, the server includes an image acquisition means for a user to photograph their own face, a transmission means for transmitting the acquired image data to an external server, an image analysis means for analyzing the image data received by the external server and generating facial feature information, location information, and skin condition information, a display means for generating and displaying beauty care and makeup guides in real time based on the analyzed information, a prediction means for predicting future facial conditions based on the analyzed current facial information, a suggestion means for displaying the predicted future facial conditions and providing improvement suggestions, and a means for using a dedicated generative AI model to detect facial feature points and perform skin condition analysis and individual color analysis. This allows users to receive appropriate beauty care and makeup advice tailored to their individual conditions from the comfort of their own home, and also predicts future facial conditions and receives continuous improvement suggestions.

[0784] "Image acquisition means" refers to a device or software that allows a user to accurately photograph their own face.

[0785] The "transmission means" refers to a device or software for transferring acquired image data from the terminal to an external server.

[0786] The "image analysis means" refers to algorithms or software that analyzes image data received by the server and generates facial feature information, position information, and skin condition information.

[0787] The "display means" refers to a device or software that visually presents the analyzed information to the user.

[0788] A "prediction method" is an algorithm or software that calculates and predicts future facial states based on current facial information.

[0789] The "suggestion means" is a device or software that provides the user with beauty care and improvement suggestions based on the predicted future facial condition.

[0790] A "generative AI model" is a trained artificial intelligence model that detects facial feature points and performs skin condition analysis and individual color analysis.

[0791] "Facial feature information" is data that includes position information of the eyes, nose, mouth, and other parts of the user's face.

[0792] "Skin condition information" is data related to the user's skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[0793] "Individual color analysis" is a process of analyzing the color tone of the user's face and identifying their personal color.

[0794] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal beauty care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvements.

[0795] Taking a picture of your face and sending image data

[0796] The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera function to capture a picture of the user's face. When taking a picture, the application displays guidance on appropriate lighting and angles, and the user follows the guidance to capture their face. Once the picture is taken, the image data is sent from the device to a server.

[0797] Facial image analysis

[0798] The server performs several processing steps to analyze the received image data. First, it uses an algorithm (e.g., Dlib, OpenCV, etc.) to detect facial feature points (the positions of the eyes, nose, mouth, etc.). Next, it uses a dedicated generative AI model (e.g., a model using TensorFlow or PyTorch) to analyze skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) and identify the person's personal color. As a result of the analysis, facial bone structure information, feature position information, skin information, and personal color information are generated.

[0799] Real-time guide generation and display

[0800] The server sends the analysis results to the device, which then generates and displays guides for beauty care and makeup lessons in real time. For example, for beauty care, it displays arrows and lines showing how to massage along the cheekbones. For makeup lessons, it displays animations that specifically show how to apply eyeshadow to the eyes and how to use lipstick.

[0801] Future facial prediction and improvement suggestions

[0802] The server predicts the future state of the face based on the current facial information. For example, it simulates the progression of wrinkles and changes in skin tone. Based on this future prediction, it generates improvement suggestions and sends them to the device. Specific suggestions include "using a specific beauty cream to prevent wrinkles" and "recommending a highly moisturizing lotion." By referring to these suggestions, users can carry out more effective beauty care.

[0803] Specific examples

[0804] Here is an example prompt:

[0805] This system allows users to take a picture of their face, send it to a server, and then provide real-time beauty care and makeup advice based on the results of image analysis. It also predicts future facial conditions and makes appropriate suggestions for improvements. Please explain the specific steps and results. For example, how do you take a picture of your face and send the image data to the server? Also, please provide specific examples of the kind of advice you can receive based on the analysis results.

[0806] This invention allows users to experience a lesson similar to that of a professional hairdresser from the comfort of their own home, enabling more effective beauty care and makeup application through real-time guidance and improvement suggestions based on future predictions.

[0807] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0808] Step 1:

[0809] The user installs and launches a dedicated application on their smartphone. The application activates the smartphone's camera function and takes a picture of the user's face. When taking a photo, the application displays guidance on appropriate lighting and angles. The user follows the guidance to take a picture of their face.

[0810] Input: A guided face image taken by the user.

[0811] Output: Image data of the captured face.

[0812] Step 2:

[0813] The device sends the captured facial image data to an external server, and after the capture is complete, the application automatically uploads the image data to the specified server.

[0814] Input: Image data of a captured face.

[0815] Output: Facial image data sent to the server.

[0816] Step 3:

[0817] The server analyzes the received image data and runs a facial feature detection algorithm, specifically using computer vision libraries such as Dlib or OpenCV to identify features such as the eyes, nose, and mouth.

[0818] Input: Facial image data sent to the server.

[0819] Output: Facial feature points such as eyes, nose, and mouth.

[0820] Step 4:

[0821] The server uses a dedicated generative AI model trained using TensorFlow and PyTorch to analyze skin type and personal color based on feature point information.

[0822] Input: Feature point information, facial image data.

[0823] Output: Skin type information, personal color information.

[0824] Step 5:

[0825] The server sends the analysis results (skin type information, personal color information, feature point information) to the device. The sent information will be used in the next step.

[0826] Input: Skin type information, personal color information, feature point information.

[0827] Output: Data containing analysis results (skin type information, personal color information, feature point information).

[0828] Step 6:

[0829] The device generates real-time beauty care and makeup guides based on the analysis results it receives, such as displaying arrows and lines to show how to massage along the cheekbones, or an animation showing how to apply eyeshadow to the eyes.

[0830] Input: Analysis results (skin type information, personal color information, feature point information).

[0831] Output: A visual guide to beauty care and makeup.

[0832] Step 7:

[0833] The server runs an algorithm that uses current facial information to predict future facial states, for example, simulating what the face will look like one month or three months from now.

[0834] Input: Current face information (skin type, personal color, and feature point information).

[0835] Output: Prediction data about future face states.

[0836] Step 8:

[0837] Based on the prediction results, the server generates improvement suggestions for the user, such as recommending the use of a specific beauty cream or a moisturizing lotion.

[0838] Input: Prediction data about future face states.

[0839] Output: Improvement proposal data.

[0840] Step 9:

[0841] The terminal displays the improvement suggestions received from the server to the user, allowing the user to obtain information for carrying out appropriate beauty care.

[0842] Input: Improvement proposal data.

[0843] Output: Displaying the suggestion information to the user.

[0844] (Application example 1)

[0845] 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."

[0846] At beauty salons and cosmetic stores, it is currently difficult for customers to receive optimal skin care and makeup advice based on their skin type and personal color. It is also difficult to predict future facial conditions and provide future beauty care and improvement suggestions in real time. Therefore, a system that provides an experience similar to that of a professional hairdresser is needed.

[0847] 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.

[0848] In this invention, the server includes an image acquisition means for allowing a user to photograph their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial bone structure information, facial feature position information, and skin information, a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information, a prediction means for predicting the user's future facial condition and generating suggestions for improving skin care and makeup, and a suggestion means for predicting the user's future skin condition based on the acquired current facial information and past data and suggesting appropriate skin care products and care methods. This allows users to receive professional beauty care and advice in real time when they visit the salon. Furthermore, by receiving appropriate skin care and makeup suggestions based on the predicted future skin condition, effective beauty care can be achieved.

[0849] "Image acquisition means" refers to a device or function that allows a user to take a picture of their own face.

[0850] "Transmission means" refers to the technology or method for transmitting the acquired image data to the server.

[0851] "Image analysis means" refers to a technology for analyzing image data received by the server and generating facial bone structure information, feature position information, and skin information.

[0852] "Display means" refers to a device or system that generates skin care and makeup guides in real time based on the analyzed information and visually presents them to the user.

[0853] "Predictive methods" refer to technologies and algorithms that predict the user's future facial condition and generate suggestions for future skincare and makeup improvements.

[0854] "Suggestion means" refers to a system or function that predicts future skin conditions based on acquired current facial information and past data, and suggests skin care products and care methods to the user based on that prediction.

[0855] MODE FOR CARRYING OUT THE INVENTION

[0856] System Overview

[0857] A system for implementing the present invention includes the following components:

[0858] 1. Image acquisition means: A device with a camera function that allows the user to take a picture of their own face (e.g., smartphone, tablet, smart mirror).

[0859] 2. Transmission method: The communication technology (e.g., Wi-Fi, mobile data communication) used to transmit the acquired image data to the server.

[0860] 3. Image analysis means: Technology for analyzing image data received by the server and generating facial skeletal information, facial feature position information, and skin information (e.g., image analysis algorithm using OpenCV).

[0861] 4. Display means: A device or application that generates skin care and makeup guides in real time based on the analyzed information and displays them to the user (e.g., a guide display app created with Unity).

[0862] 5. Prediction means: Algorithms and server-side processing for predicting the user's future facial condition.

[0863] 6. Proposal method: A system that predicts future skin condition based on current facial information and past data, and suggests appropriate skin care products and care methods.

[0864] Program processing and hardware and software used

[0865] 1. Image acquisition and transmission:

[0866] Users take a picture of their face using the camera on their smart mirror, tablet, or smartphone, and the captured image data is sent to a server via Wi-Fi or mobile data.

[0867] 2. Image analysis and data generation:

[0868] The server runs image analysis algorithms such as OpenCV to generate facial bone structure information, facial feature position information, and skin information, such as the position of the eyes, nose shape, and degree of skin dryness.

[0869] 3. Real-time display:

[0870] The analysis results are sent to the user's device in real time and displayed to the user as a visual skincare and makeup guide via an application created with Unity or similar software.

[0871] 4. Future state prediction and suggestions:

[0872] The server runs algorithms based on current and historical data to predict future facial conditions, such as wrinkle depth and changes in skin tone three months from now, and then suggests specific skin care products and methods.

[0873] Examples of concrete examples and prompts

[0874] As a concrete example, consider the case where a customer at a beauty salon uses an application to take a photo of their face. The system analyzes the customer's skin condition and determines that they have dry skin. The system immediately displays skin care products for dry skin and instructions on how to use them. It also simulates predicted changes in the customer's skin over the next three months and recommends the use of a specific moisturizing cream.

[0875] Example prompt sentence:

[0876] I am a customer of a beauty salon and would like to use this application to find out how to improve my skin care. I have dry skin and am concerned about dark spots. I would like to know the appropriate skin care products and how to use them, as well as a prediction of my future skin condition.

[0877] This system allows users to receive personalized beauty advice in real time, as well as specific suggestions for predicting future skin conditions.

[0878] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0879] Step 1:

[0880] The user takes a photo of their face using the device's camera. Guides for proper lighting and angles are displayed to help the user capture an accurate image. The captured image is temporarily stored on the device.

[0881] Input: A face image of the user.

[0882] Output: The captured face image data.

[0883] Step 2:

[0884] The facial image data acquired by the device is sent to a server. Data communication is via Wi-Fi or mobile data communication technology. The sent image data is received by the server and prepared for analysis.

[0885] Input: Facial image data stored on the device.

[0886] Output: Facial image data sent to the server.

[0887] Step 3:

[0888] The server analyzes the received facial image data. This analysis uses image analysis algorithms such as OpenCV to extract facial bone structure information, feature position information, and skin information. Specifically, the positions of the eyes, nose, and mouth, the dryness of the skin, and the condition of blemishes and wrinkles are identified.

[0889] Input: Facial image data sent to the server.

[0890] Output: Facial skeleton information, part position information, skin information.

[0891] Step 4:

[0892] The server generates skin care and makeup guides in real time based on the analysis results. The analyzed data is converted into appropriate skin care and makeup procedures, creating visual guidelines to display to the user.

[0893] Input: Facial skeletal information, part position information, skin information.

[0894] Output: Visual guidelines for skincare and makeup.

[0895] Step 5:

[0896] Based on the analysis results received by the device from the server, the device visually displays skin care and makeup guides to the user, such as animations showing how to massage the area around the cheekbones or how to apply eyeshadow around the eyes.

[0897] Enter: visual guidelines for skincare and makeup.

[0898] Output: A visual guide that is displayed on the device.

[0899] Step 6:

[0900] The server runs an algorithm based on current and past facial information to predict future facial conditions. For example, it predicts changes in wrinkle depth and skin tone three months from now. Based on this prediction, it recommends appropriate skin care products and specific care methods to the user.

[0901] Input: Current face information, past data.

[0902] Output: Prediction of future facial condition and recommendations for skin care products and methods based on that prediction.

[0903] Step 7:

[0904] The device receives from the server a visual display of the predicted future facial condition and suggestions for improvement, such as the recommendation to use a specific beauty cream to prevent wrinkles or to use a moisturizing lotion.

[0905] Input: Prediction results of future facial states, improvement suggestions.

[0906] Output: Suggestions for improvement and skin care products to use displayed on the device.

[0907] 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.

[0908] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[0909] Program processing and specific examples

[0910] Taking a picture of your face and sending image data

[0911] Users install and launch a dedicated application on their smartphone. The application activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing a photo of the user's face.

[0912] Facial image analysis

[0913] The user follows the instructions to take a photo of their face, and the image data is saved in the application. The device then sends the image data to the server. The server analyzes the received image data and determines facial features (the position of the eyes, nose, mouth, etc.), skin type, skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[0914] Emotion recognition

[0915] The server recognizes the user's emotions using an additional emotion engine, which analyzes facial expressions and complexions from the image data to determine the user's emotional state (e.g., joy, sadness, stress, etc.).

[0916] Real-time guide generation and display

[0917] The device receives the analysis results sent from the server and generates real-time skincare massage and makeup guides. For example, in the case of a skincare massage, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed. Furthermore, based on the emotional information recognized by the emotion engine, suggestions are made that match the user's mood.

[0918] Future facial prediction and improvement suggestions

[0919] Based on the current facial information, the server runs simulations to predict the future state of the face. Specifically, it predicts the progression of wrinkles and changes in skin color, and generates improvement suggestions based on the results. Improvement suggestions include recommendations for specific skin care products and appropriate makeup techniques. Additionally, depending on the emotional state determined by the emotion engine, it also suggests relaxation methods and beauty products for stress relief.

[0920] Specific examples

[0921] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[0922] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[0923] This system allows users to have an experience similar to that of a lecture by a professional hairdresser, and teaches them the best beauty techniques to suit their individual skin condition and emotional state.

[0924] The processing flow will be explained below.

[0925] Step 1:

[0926] The user picks up their smartphone and launches the dedicated application. The application activates the camera function and displays a screen for taking a picture of the user's face. The screen also displays guidance on appropriate lighting conditions and angles.

[0927] Step 2:

[0928] The user follows the instructions of the application to take a picture of their face with the camera, and once the picture is taken, the image data is saved locally.

[0929] Step 3:

[0930] The device encrypts the stored image data and sends it to a server over the internet connection, and a notification is displayed to the user to confirm the successful transmission.

[0931] Step 4:

[0932] The server analyzes the received image data and executes a facial recognition algorithm to detect facial features (eyes, nose, mouth), which then extracts facial skeletal information.

[0933] Step 5:

[0934] The server then runs skin analysis algorithms to determine skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[0935] Step 6:

[0936] The server uses a color analysis algorithm to analyze the user's skin pigments and tones to determine their personal color.

[0937] Step 7:

[0938] The server compiles the analysis results and generates facial bone structure, facial feature position, skin type, and personal color information, which is then encrypted and sent to the device.

[0939] Step 8:

[0940] The server runs an emotion recognition algorithm to recognize the user's emotions from the received image data, analyzing facial expressions and changes in facial color to determine their emotional state (e.g., joy, sadness, stress).

[0941] Step 9:

[0942] Based on the analysis data received from the server, the device generates real-time skincare massage and makeup guides, such as arrows for massaging along the cheekbones and animations showing how to apply lipstick.

[0943] Step 10:

[0944] The device then uses the emotional information received from the emotion engine to suggest skincare and makeup products that match the user's mood. For example, if the user is feeling stressed, it will suggest products with a relaxing effect or colors with a calming effect.

[0945] Step 11:

[0946] Based on the current facial information, the server runs simulations to predict the future state of the face, including the progression of wrinkles and changes in skin color.

[0947] Step 12:

[0948] The server generates improvement suggestions for the user based on the predicted future facial condition, such as recommendations for specific skin care products and appropriate makeup application techniques.

[0949] Step 13:

[0950] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[0951] Step 14:

[0952] The device visually displays the received improvement suggestions to the user, showing a simulated image of the future face along with the optimal skincare products and makeup techniques, allowing the user to take immediate action.

[0953] Through this series of steps, users can experience something similar to that of a professional hairdresser and learn the best beauty treatment methods to suit their individual skin condition and emotional state.

[0954] Example 2

[0955] 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."

[0956] Conventional skincare and makeup systems struggle to provide comprehensive guidance based on individual skin conditions and emotional states, and are unable to fully meet the specific needs of users. They also lack the technology to predict future facial conditions and provide appropriate improvement suggestions.

[0957] 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.

[0958] In this invention, the server includes an image acquisition means for allowing the user to capture a photograph of their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial skeletal information, facial feature position information, and skin information, an emotion analysis means for recognizing the real-time emotional state based on the subject being analyzed, and a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information and emotional state. This allows the user to receive appropriate skin care and makeup guides in real time based on their skin condition and emotional state. Furthermore, by predicting future facial condition based on current facial information and receiving appropriate improvement suggestions, long-term beauty care can also be achieved.

[0959] The "image acquisition means" is a function that allows a user to take a picture of their own face using a smartphone or other photographing device.

[0960] The "transmission means" is a communication function for transmitting the acquired image data to a server. This function transfers the data via an Internet connection.

[0961] The "image analysis means" is a function that analyzes the image data received by the server and generates facial bone structure information, facial feature position information, and skin information. This analysis uses deep learning models and image processing algorithms.

[0962] The "emotion analysis means" is a function for recognizing the user's emotions based on image data. It uses a facial expression recognition algorithm to determine emotional states such as joy, sadness, and stress.

[0963] The "display means" is a function that generates skin care and makeup guides in real time based on the analyzed information and emotional state, and visually displays them to the user. For example, it displays animations and arrows on the smartphone screen.

[0964] The "prediction method" is a function that predicts future facial conditions based on analyzed current facial information. It uses machine learning models to simulate the progression of wrinkles and changes in skin color.

[0965] The "suggestion means" is a function that generates appropriate improvement suggestions based on the predicted future facial condition and displays them to the user. These suggestions include skin care products, makeup techniques, relaxation methods, etc.

[0966] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[0967] The system includes the following main means:

[0968] 1. Image Acquisition Method: The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera to capture a picture of the user's face. The application displays guidance on appropriate lighting conditions and shooting angles, allowing the user to capture an accurate picture of their face.

[0969] 2. Transmission method: The image data captured by the user is saved in the application and then transmitted to the server by the device, using the SSL / TLS protocol to ensure data security.

[0970] 3. Image analysis: The server uses OpenCV or deep learning models (e.g., YOLO, ResNet) to analyze the received image data. It determines facial bone structure, facial feature position, skin type, and personal color, and the analysis results are stored in a database.

[0971] 4. Emotion analysis means: The server recognizes the user's emotions using an additional emotion engine, which uses a facial expression recognition algorithm (e.g., Face Emotion Recognition model) to analyze facial muscle movements from image data to determine the user's emotional state.

[0972] 5. Display: Based on the analysis results and emotional state sent from the server, the device displays skin care and makeup guides to the user in real time. The guides are displayed as visual guidelines, such as massage steps along the contours of the face or the proper way to apply lipstick.

[0973] 6. Prediction method: The server performs simulations to predict future facial conditions based on current facial information. Machine learning models are used to predict the progression of wrinkles and changes in skin color.

[0974] 7. Recommendation method: Based on the prediction results, the server recommends appropriate skin care products and makeup techniques. The recommendations are stored in a database and displayed to the user via their device. Relaxation methods and beauty products are also suggested according to the user's emotional state.

[0975] Specific Examples

[0976] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[0977] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[0978] Example prompts to input to the generative AI model

[0979] "Please explain in detail each processing step of the system that analyzes the user's facial photo and makes skincare and makeup recommendations."

[0980] "Please explain in detail how the system works to recognize a user's face and emotions and generate personalized skincare and makeup suggestions."

[0981] "Please tell me the functions and specific processing steps of the system that predicts the future state of the face and makes suggestions for improvement."

[0982] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0983] Step 1: Image acquisition

[0984] The user installs and launches a dedicated application on their smartphone. The user activates the camera function within the application and takes a picture of their face. The application displays on the screen a guide for appropriate lighting conditions and shooting angles, and the user follows these to take a picture of their own face.

[0985] Input: User's photo

[0986] Output: Image data of the captured face (e.g., JPEG format)

[0987] Step 2: Sending image data

[0988] The device sends the facial image data stored in the application to a server over the Internet, using the SSL / TLS protocol to ensure data security.

[0989] Input: Captured face image data

[0990] Output: Facial image data sent to the server

[0991] Step 3: Image analysis

[0992] The server analyzes the received image data. The analysis software used here is OpenCV or a deep learning model (e.g., YOLO, ResNet). The server first identifies facial feature points (the positions of the eyes, nose, mouth, etc.), and then determines skin type (dry skin, oily skin, etc.), skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[0993] Input: Facial image data sent to the server

[0994] Output: Analysis results such as facial features, skin type, skin concerns, and personal color

[0995] Step 4: Recognize emotions

[0996] The server recognizes the user's emotions using an emotion engine. This emotion engine uses a facial expression recognition algorithm (e.g., the Face Emotion Recognition model) to analyze the movements of facial muscles from image data and determine the user's emotional state, such as joy, sadness, or stress.

[0997] Input: Facial feature points obtained from image data

[0998] Output: Emotional state such as happiness, sadness, stress, etc.

[0999] Step 5: Generate real-time guides

[1000] Based on the analysis and emotion recognition results, the server generates skin care massage and makeup guides, such as massage steps along the contours of the face or the proper way to apply lipstick. These guides are generated in a way that best suits the user's current skin condition and emotional state.

[1001] Input: Face analysis results, emotional state

[1002] Output: Real-time skincare and makeup guide

[1003] Step 6: Viewing the guide

[1004] The device receives the analysis results and guides sent from the server and displays visual guides to the user in real time. Specifically, skin care and makeup techniques are displayed on the smartphone screen using animations and arrows. For example, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed.

[1005] Input: Guide information sent from the server

[1006] Output: Real-time guide on smartphone screen

[1007] Step 7: Predicting future faces

[1008] The server runs a simulation based on current facial information to predict future facial conditions, specifically using machine learning models to run algorithms that predict wrinkle progression and changes in skin tone.

[1009] Input: Current face analysis result

[1010] Output: Simulation result of future face (e.g. after 3 months)

[1011] Step 8: Generate improvement suggestions

[1012] Based on the predictions of the future face, the server will suggest appropriate skin care products and makeup methods, as well as relaxation methods and beauty products according to the user's emotional state.

[1013] Input: Future face simulation results, current emotional state

[1014] Output: Suggestions for improvement, such as specific skin care products, makeup techniques, relaxation techniques, etc.

[1015] (Application example 2)

[1016] 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."

[1017] Conventional skincare and makeup instruction systems do not take into account the user's individual emotional state, resulting in a lack of psychological satisfaction and effective suggestions. Furthermore, even if systems exist that can predict future facial conditions, the improvement suggestions based on these predictions are often merely theoretical and lack comprehensiveness. There is a need to solve these issues and provide personalized, high-quality skincare and makeup instruction to users.

[1018] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image analysis means for analyzing the user's facial image, an emotion recognition means for analyzing the user's emotional state, a display means for generating and displaying a real-time guide, a prediction means for predicting the user's future facial state, and a suggestion means for providing suggestions based on the prediction results and the user's emotional state. This makes it possible to provide not only skin care and makeup lessons tailored to the user's facial state, but also personalized suggestions based on the user's emotional state. Furthermore, by predicting the user's future facial state and providing specific improvement suggestions based on the prediction, the user's psychological satisfaction can be increased.

[1019] "Facial skeletal information" is data that indicates the main structural features of the face, and includes position information of the eyes, nose, mouth, etc.

[1020] "Feature position information" is data that indicates the specific positions of individual facial features (eyes, nose, mouth, etc.).

[1021] "Skin information" is data relating to the condition of the skin, including information on blemishes, wrinkles, redness, etc.

[1022] "Image analysis means" is a technology for analyzing received image data and extracting information such as facial features and skin condition.

[1023] The "display means" is a technology that generates a guide in real time based on the analyzed information and provides the information visually to the user.

[1024] "Emotion recognition means" is a technology that analyzes the user's emotional state from their facial expressions and complexion.

[1025] The "suggestion means" is a technology that provides appropriate skin care and makeup suggestions to the user based on the analysis results and emotional state.

[1026] The "prediction means" is a technology that predicts the future state of a face based on current face information.

[1027] "Image acquisition means" refers to a device or technology that allows a user to take a picture of their own face.

[1028] "Transmission means" refers to a technique for sending the acquired image data to the server.

[1029] MODE FOR CARRYING OUT THE INVENTION

[1030] This system analyzes a user's facial information and emotional state, and provides skin care information and makeup lessons in real time based on the analysis. This system is intended to be installed on devices such as smart glasses and used in brick-and-mortar stores.

[1031] Hardware and Software Configuration

[1032] The system includes the following major hardware and software components:

[1033] Smart glasses (e.g., general-purpose smart glasses device)

[1034] Cloud server (e.g. general-purpose cloud service)

[1035] Facial image analysis software (e.g., general-purpose image analysis engine)

[1036] Emotion recognition software (e.g., general-purpose emotion recognition API)

[1037] Natural language processing explanation

[1038] Taking a picture of your face and sending image data

[1039] A user wearing smart glasses takes a picture of their face using the device's built-in camera. The captured image data is then sent from the smart glasses to a cloud server. This transmission process is carried out in real time, and the data is encrypted before being sent.

[1040] Facial image analysis

[1041] The cloud server analyzes the received images using facial image analysis software. Specifically, it uses AWS Rekognition and Microsoft Azure Face API to detect facial bone structure, facial feature position, and skin condition. The analyzed data is organized by category and passed on to the next processing step.

[1042] Emotion recognition

[1043] At the same time, the server uses emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API) to determine the user's emotional state from the image data. The results of this analysis are used in real time to suggest skincare and makeup lessons.

[1044] Real-time guide generation and display

[1045] Based on the analysis, the server generates skin care and makeup guides and sends them to the smart glasses' display device, which can show, for example, massage instructions along the cheekbones or animations of specific makeup application techniques.

[1046] Future facial prediction and improvement suggestions

[1047] The server uses prediction methods to simulate future facial conditions based on current and past facial information. For example, it predicts the progression of wrinkles and changes in skin color, and suggests skin care products and beauty treatments based on the results. Taking emotion recognition results into account, it also suggests relaxation and stress reduction methods.

[1048] Specific example explanation

[1049] User Scenarios

[1050] 1. Facial imaging and analysis

[1051] A user enters a store and puts on smart glasses.

[1052] The smart glasses automatically take a picture of your face and send the data to a cloud server.

[1053] The server analyzes the face using AWS Rekognition and Microsoft Azure Face API to generate facial features and skin information.

[1054] 2. Real-time guide

[1055] The smart glasses display animated skin care and makeup instructions (for example, how to massage along the cheekbones).

[1056] Users follow the guide and perform their own skin care.

[1057] 3. Future face prediction and suggestions

[1058] The server predicts the future condition of the face based on the current facial information and suggests specific moisturizing creams and skin care methods.

[1059] Emotion-recognition software displays suggestions tailored to the user's emotional state, such as relaxing skin care products.

[1060] Input prompts for generative AI models

[1061] "Analyze the user's facial image and recognize facial features, skin type, and personal color. Furthermore, predict the future state of the user's face and make appropriate suggestions for improvement. Also, recognize emotions and make suggestions based on those emotions."

[1062] This format allows users to receive more personalized suggestions and, especially in physical stores, to receive effective skin care and makeup lessons.

[1063] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1064] Step 1: Taking a picture of your face and sending the image data

[1065] The user puts on the smart glasses and photographs their face. The built-in camera captures the entire face in high resolution. When photographing, the smart glasses display a guide to the optimal lighting conditions and shooting angle. The photographed image data is sent from the smart glasses to a cloud server in real time. The transmitted data is encrypted and reaches the cloud server securely.

[1066] Step 2: Facial image analysis

[1067] The cloud server analyzes the received facial image data using facial image analysis software. Specifically, it uses AWS Rekognition and Microsoft Azure Face API to extract facial bone structure information, facial feature position information, and skin information. The input data is the facial image, and the output data is specific information on analyzed facial feature points (e.g., position of the eyes, nose, and mouth) and skin condition (e.g., blemishes, wrinkles, redness, etc.).

[1068] Step 3: Recognize emotions

[1069] The server analyzes the emotional state based on the facial image data using emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API). The input data is a facial image, and the output data is the emotional state, such as joy, sadness, or stress. Emotion recognition information is generated based on changes in facial expressions and complexion.

[1070] Step 4: Generate and display real-time guides

[1071] The server generates real-time guides based on the analysis results. Specifically, visual guidelines showing skin care massage and makeup actions (e.g., how to massage along cheekbones or how to apply eyeshadow) are created. These guides are displayed on the smart glasses' display. The input data are the facial analysis results and emotional state, and the output data is a video or animation of the guideline.

[1072] Step 5: Predicting future facial features and suggesting improvements

[1073] The server predicts future facial conditions based on current facial information and past data. Specifically, it uses an AI model to perform simulations to predict the progression of wrinkles and changes in skin color. The input data is the facial analysis results and historical data, and the output data is a simulation of future facial conditions and suggestions for improvement. Suggested improvements include the use of specific skincare products and beauty techniques.

[1074] Step 6: Generating suggestions according to emotional state

[1075] The server makes suggestions for relaxation methods and stress relief based on the emotion recognition results. Specific examples include suggestions for skin care products and makeup colors that have a relaxing effect. The input data is the emotion recognition results, and the output data is suggestions for relaxation methods and skin care products.

[1076] Through this series of steps, users can receive highly personalized skincare and makeup lessons, which can improve customer satisfaction in physical stores.

[1077] 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.

[1078] 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.

[1079] 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.

[1080] [Fourth embodiment]

[1081] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1082] 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.

[1083] 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).

[1084] 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.

[1085] 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.

[1086] 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).

[1087] 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.

[1088] 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.

[1089] 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.

[1090] 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.

[1091] 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.

[1092] 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.

[1093] 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."

[1094] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal skin care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvement.

[1095] Program processing and specific examples

[1096] Taking a picture of your face and sending image data

[1097] The user installs and launches a dedicated application on their smartphone. The application activates the camera function and takes a picture of the user's face. The image is taken according to guidance on appropriate lighting and angles. The image data thus obtained is sent from the device to a server.

[1098] Facial image analysis

[1099] The server analyzes the received image data by running an algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.). It also analyzes skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) to identify the user's personal color. This provides information on the facial structure, the position of facial features, skin type, and personal color.

[1100] Real-time guide generation and display

[1101] The device receives the analysis results sent from the server and displays guides for skin care massage and makeup lessons in real time. For example, in the case of a skin care massage, arrows and lines are displayed showing how to massage along the cheekbones. In the case of a makeup lesson, animations are displayed showing specific areas where eye shadow should be applied and how to apply lipstick.

[1102] Future facial prediction and improvement suggestions

[1103] Based on current facial information, the system predicts changes in the face after a specified period of time (for example, one month, three months, etc.). The server simulates the progression of wrinkles, changes in skin tone, etc., and predicts the future state of the face. Based on this prediction, it generates improvement suggestions. For example, it provides specific advice such as "using a specific beauty cream to prevent wrinkles" or "recommending a highly moisturizing lotion."

[1104] Specific examples

[1105] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates and sends back information about the user's facial bone structure, facial feature position, skin type, and personal color. Based on the received information, the device displays skin care and makeup guides in real time, including instructions on how to massage the face in circular motions around the cheekbones and how to apply eyeshadow to the eyes. Furthermore, the device predicts the user's facial condition three months from now based on the current facial information, simulating changes in wrinkle depth and skin tone, and suggesting appropriate skin care products. This allows the user to understand changes in their face and how to address them, and receive optimal beauty advice.

[1106] This system allows users to experience a level of beauty that is similar to that of a professional hairdresser, all from the comfort of their own home. It provides real-time guidance and predictions for future improvements, enabling more effective skincare and makeup application.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] The user picks up their smartphone and launches the dedicated application, which activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing the user's face.

[1110] Step 2:

[1111] The user follows the instructions of the application to take a picture of their face with the camera. Once the picture is taken, the image data is saved in the application.

[1112] Step 3:

[1113] The device sends the stored image data to a server using encryption technology, via communication over the Internet.

[1114] Step 4:

[1115] The server analyzes the received image data and runs a facial recognition algorithm to detect facial features (the positions of the eyes, nose, mouth, etc.) and extract facial bone structure information.

[1116] Step 5:

[1117] The server then uses image analysis technology to assess skin type (dry, oily, combination, etc.) and identify skin concerns (blemishes, wrinkles, redness, etc.), providing the basis for skin care advice.

[1118] Step 6:

[1119] The server analyzes the user's skin pigmentation and tone to determine their personal color, which serves as the basis for makeup suggestions.

[1120] Step 7:

[1121] The server compiles the analysis results and generates data including facial bone structure, facial feature position, skin type, and personal color information, which is then sent to the device.

[1122] Step 8:

[1123] Based on the analysis data received, the device generates real-time skincare massage and makeup guides, such as arrows to massage along the cheekbones and animations showing how to properly apply lipstick.

[1124] Step 9:

[1125] The server uses current facial information to perform simulations to predict future facial conditions, specifically predicting the progression of wrinkles and changes in skin color.

[1126] Step 10:

[1127] The server generates improvement suggestions based on the predicted future facial condition, including recommendations for specific skin care products and appropriate makeup application techniques.

[1128] Step 11:

[1129] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[1130] Step 12:

[1131] The device visually displays the received improvement suggestions to the user, showing them a simulated image of their future face along with the optimal skincare products and makeup techniques, allowing the user to immediately take appropriate action.

[1132] Through this series of steps, users can experience a level similar to that of a professional hairdresser and learn the best beauty treatment methods for their individual skin condition and facial features.

[1133] Example 1

[1134] 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."

[1135] In today's world, effective beauty care and makeup applications require advanced knowledge and skills. However, receiving instruction from a professional hairdresser or makeup artist is time-consuming and expensive. It is also difficult to automatically obtain appropriate advice tailored to each user's individual conditions, such as skin type, facial features, and personal color. Furthermore, there are limited means of predicting future facial conditions and receiving suggestions for continuous improvement. There is a need for a system that can solve these problems and enable users to enjoy professional beauty care at home.

[1136] 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.

[1137] In this invention, the server includes an image acquisition means for a user to photograph their own face, a transmission means for transmitting the acquired image data to an external server, an image analysis means for analyzing the image data received by the external server and generating facial feature information, location information, and skin condition information, a display means for generating and displaying beauty care and makeup guides in real time based on the analyzed information, a prediction means for predicting future facial conditions based on the analyzed current facial information, a suggestion means for displaying the predicted future facial conditions and providing improvement suggestions, and a means for using a dedicated generative AI model to detect facial feature points and perform skin condition analysis and individual color analysis. This allows users to receive appropriate beauty care and makeup advice tailored to their individual conditions from the comfort of their own home, and also predicts future facial conditions and receives continuous improvement suggestions.

[1138] "Image acquisition means" refers to a device or software that allows a user to accurately photograph their own face.

[1139] The "transmission means" refers to a device or software for transferring acquired image data from the terminal to an external server.

[1140] The "image analysis means" refers to algorithms or software that analyzes image data received by the server and generates facial feature information, position information, and skin condition information.

[1141] The "display means" refers to a device or software that visually presents the analyzed information to the user.

[1142] A "prediction method" is an algorithm or software that calculates and predicts future facial states based on current facial information.

[1143] The "suggestion means" is a device or software that provides the user with beauty care and improvement suggestions based on the predicted future facial condition.

[1144] A "generative AI model" is a trained artificial intelligence model that detects facial feature points and performs skin condition analysis and individual color analysis.

[1145] "Facial feature information" is data that includes position information of the eyes, nose, mouth, and other parts of the user's face.

[1146] "Skin condition information" is data related to the user's skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[1147] "Individual color analysis" is a process of analyzing the color tone of the user's face and identifying their personal color.

[1148] This invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color, and then provides optimal beauty care information and makeup lessons based on the results. This system takes a picture of the user's face using a smartphone or other device's camera, and uses image analysis technology to display personalized beauty advice in real time. It can also predict future facial conditions and make suggestions for improvements.

[1149] Taking a picture of your face and sending image data

[1150] The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera function to capture a picture of the user's face. When taking a picture, the application displays guidance on appropriate lighting and angles, and the user follows the guidance to capture their face. Once the picture is taken, the image data is sent from the device to a server.

[1151] Facial image analysis

[1152] The server performs several processing steps to analyze the received image data. First, it uses an algorithm (e.g., Dlib, OpenCV, etc.) to detect facial feature points (the positions of the eyes, nose, mouth, etc.). Next, it uses a dedicated generative AI model (e.g., a model using TensorFlow or PyTorch) to analyze skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.) and identify the person's personal color. As a result of the analysis, facial bone structure information, feature position information, skin information, and personal color information are generated.

[1153] Real-time guide generation and display

[1154] The server sends the analysis results to the device, which then generates and displays guides for beauty care and makeup lessons in real time. For example, for beauty care, it displays arrows and lines showing how to massage along the cheekbones. For makeup lessons, it displays animations that specifically show how to apply eyeshadow to the eyes and how to use lipstick.

[1155] Future facial prediction and improvement suggestions

[1156] The server predicts the future state of the face based on the current facial information. For example, it simulates the progression of wrinkles and changes in skin tone. Based on this future prediction, it generates improvement suggestions and sends them to the device. Specific suggestions include "using a specific beauty cream to prevent wrinkles" and "recommending a highly moisturizing lotion." By referring to these suggestions, users can carry out more effective beauty care.

[1157] Specific examples

[1158] Here is an example prompt:

[1159] This system allows users to take a picture of their face, send it to a server, and then provide real-time beauty care and makeup advice based on the results of image analysis. It also predicts future facial conditions and makes appropriate suggestions for improvements. Please explain the specific steps and results. For example, how do you take a picture of your face and send the image data to the server? Also, please provide specific examples of the kind of advice you can receive based on the analysis results.

[1160] This invention allows users to experience a lesson similar to that of a professional hairdresser from the comfort of their own home, enabling more effective beauty care and makeup application through real-time guidance and improvement suggestions based on future predictions.

[1161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1162] Step 1:

[1163] The user installs and launches a dedicated application on their smartphone. The application activates the smartphone's camera function and takes a picture of the user's face. When taking a photo, the application displays guidance on appropriate lighting and angles. The user follows the guidance to take a picture of their face.

[1164] Input: A guided face image taken by the user.

[1165] Output: Image data of the captured face.

[1166] Step 2:

[1167] The device sends the captured facial image data to an external server, and after the capture is complete, the application automatically uploads the image data to the specified server.

[1168] Input: Image data of a captured face.

[1169] Output: Facial image data sent to the server.

[1170] Step 3:

[1171] The server analyzes the received image data and runs a facial feature detection algorithm, specifically using computer vision libraries such as Dlib or OpenCV to identify features such as the eyes, nose, and mouth.

[1172] Input: Facial image data sent to the server.

[1173] Output: Facial feature points such as eyes, nose, and mouth.

[1174] Step 4:

[1175] The server uses a dedicated generative AI model trained using TensorFlow and PyTorch to analyze skin type and personal color based on feature point information.

[1176] Input: Feature point information, facial image data.

[1177] Output: Skin type information, personal color information.

[1178] Step 5:

[1179] The server sends the analysis results (skin type information, personal color information, feature point information) to the device. The sent information will be used in the next step.

[1180] Input: Skin type information, personal color information, feature point information.

[1181] Output: Data containing analysis results (skin type information, personal color information, feature point information).

[1182] Step 6:

[1183] The device generates real-time beauty care and makeup guides based on the analysis results it receives, such as displaying arrows and lines to show how to massage along the cheekbones, or an animation showing how to apply eyeshadow to the eyes.

[1184] Input: Analysis results (skin type information, personal color information, feature point information).

[1185] Output: A visual guide to beauty care and makeup.

[1186] Step 7:

[1187] The server runs an algorithm that uses current facial information to predict future facial states, for example, simulating what the face will look like one month or three months from now.

[1188] Input: Current face information (skin type, personal color, and feature point information).

[1189] Output: Prediction data about future face states.

[1190] Step 8:

[1191] Based on the prediction results, the server generates improvement suggestions for the user, such as recommending the use of a specific beauty cream or a moisturizing lotion.

[1192] Input: Prediction data about future face states.

[1193] Output: Improvement proposal data.

[1194] Step 9:

[1195] The terminal displays the improvement suggestions received from the server to the user, allowing the user to obtain information for carrying out appropriate beauty care.

[1196] Input: Improvement proposal data.

[1197] Output: Displaying the suggestion information to the user.

[1198] (Application example 1)

[1199] 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."

[1200] At beauty salons and cosmetic stores, it is currently difficult for customers to receive optimal skin care and makeup advice based on their skin type and personal color. It is also difficult to predict future facial conditions and provide future beauty care and improvement suggestions in real time. Therefore, a system that provides an experience similar to that of a professional hairdresser is needed.

[1201] 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.

[1202] In this invention, the server includes an image acquisition means for allowing a user to photograph their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial bone structure information, facial feature position information, and skin information, a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information, a prediction means for predicting the user's future facial condition and generating suggestions for improving skin care and makeup, and a suggestion means for predicting the user's future skin condition based on the acquired current facial information and past data and suggesting appropriate skin care products and care methods. This allows users to receive professional beauty care and advice in real time when they visit the salon. Furthermore, by receiving appropriate skin care and makeup suggestions based on the predicted future skin condition, effective beauty care can be achieved.

[1203] "Image acquisition means" refers to a device or function that allows a user to take a picture of their own face.

[1204] "Transmission means" refers to the technology or method for transmitting the acquired image data to the server.

[1205] "Image analysis means" refers to a technology for analyzing image data received by the server and generating facial bone structure information, feature position information, and skin information.

[1206] "Display means" refers to a device or system that generates skin care and makeup guides in real time based on the analyzed information and visually presents them to the user.

[1207] "Predictive methods" refer to technologies and algorithms that predict the user's future facial condition and generate suggestions for future skincare and makeup improvements.

[1208] "Suggestion means" refers to a system or function that predicts future skin conditions based on acquired current facial information and past data, and suggests skin care products and care methods to the user based on that prediction.

[1209] MODE FOR CARRYING OUT THE INVENTION

[1210] System Overview

[1211] A system for implementing the present invention includes the following components:

[1212] 1. Image acquisition means: A device with a camera function that allows the user to take a picture of their own face (e.g., smartphone, tablet, smart mirror).

[1213] 2. Transmission method: The communication technology (e.g., Wi-Fi, mobile data communication) used to transmit the acquired image data to the server.

[1214] 3. Image analysis means: Technology for analyzing image data received by the server and generating facial skeletal information, facial feature position information, and skin information (e.g., image analysis algorithm using OpenCV).

[1215] 4. Display means: A device or application that generates skin care and makeup guides in real time based on the analyzed information and displays them to the user (e.g., a guide display app created with Unity).

[1216] 5. Prediction means: Algorithms and server-side processing for predicting the user's future facial condition.

[1217] 6. Proposal method: A system that predicts future skin condition based on current facial information and past data, and suggests appropriate skin care products and care methods.

[1218] Program processing and hardware and software used

[1219] 1. Image acquisition and transmission:

[1220] Users take a picture of their face using the camera on their smart mirror, tablet, or smartphone, and the captured image data is sent to a server via Wi-Fi or mobile data.

[1221] 2. Image analysis and data generation:

[1222] The server runs image analysis algorithms such as OpenCV to generate facial bone structure information, facial feature position information, and skin information, such as the position of the eyes, nose shape, and degree of skin dryness.

[1223] 3. Real-time display:

[1224] The analysis results are sent to the user's device in real time and displayed to the user as a visual skincare and makeup guide via an application created with Unity or similar software.

[1225] 4. Future state prediction and suggestions:

[1226] The server runs algorithms based on current and historical data to predict future facial conditions, such as wrinkle depth and changes in skin tone three months from now, and then suggests specific skin care products and methods.

[1227] Examples of concrete examples and prompts

[1228] As a concrete example, consider the case where a customer at a beauty salon uses an application to take a photo of their face. The system analyzes the customer's skin condition and determines that they have dry skin. The system immediately displays skin care products for dry skin and instructions on how to use them. It also simulates predicted changes in the customer's skin over the next three months and recommends the use of a specific moisturizing cream.

[1229] Example prompt sentence:

[1230] I am a customer of a beauty salon and would like to use this application to find out how to improve my skin care. I have dry skin and am concerned about dark spots. I would like to know the appropriate skin care products and how to use them, as well as a prediction of my future skin condition.

[1231] This system allows users to receive personalized beauty advice in real time, as well as specific suggestions for predicting future skin conditions.

[1232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1233] Step 1:

[1234] The user takes a photo of their face using the device's camera. Guides for proper lighting and angles are displayed to help the user capture an accurate image. The captured image is temporarily stored on the device.

[1235] Input: A face image of the user.

[1236] Output: The captured face image data.

[1237] Step 2:

[1238] The facial image data acquired by the device is sent to a server. Data communication is via Wi-Fi or mobile data communication technology. The sent image data is received by the server and prepared for analysis.

[1239] Input: Facial image data stored on the device.

[1240] Output: Facial image data sent to the server.

[1241] Step 3:

[1242] The server analyzes the received facial image data. This analysis uses image analysis algorithms such as OpenCV to extract facial bone structure information, feature position information, and skin information. Specifically, the positions of the eyes, nose, and mouth, the dryness of the skin, and the condition of blemishes and wrinkles are identified.

[1243] Input: Facial image data sent to the server.

[1244] Output: Facial skeleton information, part position information, skin information.

[1245] Step 4:

[1246] The server generates skin care and makeup guides in real time based on the analysis results. The analyzed data is converted into appropriate skin care and makeup procedures, creating visual guidelines to display to the user.

[1247] Input: Facial skeletal information, part position information, skin information.

[1248] Output: Visual guidelines for skincare and makeup.

[1249] Step 5:

[1250] Based on the analysis results received by the device from the server, the device visually displays skin care and makeup guides to the user, such as animations showing how to massage the area around the cheekbones or how to apply eyeshadow around the eyes.

[1251] Enter: visual guidelines for skincare and makeup.

[1252] Output: A visual guide that is displayed on the device.

[1253] Step 6:

[1254] The server runs an algorithm based on current and past facial information to predict future facial conditions. For example, it predicts changes in wrinkle depth and skin tone three months from now. Based on this prediction, it recommends appropriate skin care products and specific care methods to the user.

[1255] Input: Current face information, past data.

[1256] Output: Prediction of future facial condition and recommendations for skin care products and methods based on that prediction.

[1257] Step 7:

[1258] The device receives from the server a visual display of the predicted future facial condition and suggestions for improvement, such as the recommendation to use a specific beauty cream to prevent wrinkles or to use a moisturizing lotion.

[1259] Input: Prediction results of future facial states, improvement suggestions.

[1260] Output: Suggestions for improvement and skin care products to use displayed on the device.

[1261] 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.

[1262] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[1263] Program processing and specific examples

[1264] Taking a picture of your face and sending image data

[1265] Users install and launch a dedicated application on their smartphone. The application activates the camera function and displays guidance on the appropriate lighting conditions and shooting angles for capturing a photo of the user's face.

[1266] Facial image analysis

[1267] The user follows the instructions to take a photo of their face, and the image data is saved in the application. The device then sends the image data to the server. The server analyzes the received image data and determines facial features (the position of the eyes, nose, mouth, etc.), skin type, skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[1268] Emotion recognition

[1269] The server recognizes the user's emotions using an additional emotion engine, which analyzes facial expressions and complexions from the image data to determine the user's emotional state (e.g., joy, sadness, stress, etc.).

[1270] Real-time guide generation and display

[1271] The device receives the analysis results sent from the server and generates real-time skincare massage and makeup guides. For example, in the case of a skincare massage, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed. Furthermore, based on the emotional information recognized by the emotion engine, suggestions are made that match the user's mood.

[1272] Future facial prediction and improvement suggestions

[1273] Based on the current facial information, the server runs simulations to predict the future state of the face. Specifically, it predicts the progression of wrinkles and changes in skin color, and generates improvement suggestions based on the results. Improvement suggestions include recommendations for specific skin care products and appropriate makeup techniques. Additionally, depending on the emotional state determined by the emotion engine, it also suggests relaxation methods and beauty products for stress relief.

[1274] Specific examples

[1275] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[1276] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[1277] This system allows users to have an experience similar to that of a lecture by a professional hairdresser, and teaches them the best beauty techniques to suit their individual skin condition and emotional state.

[1278] The processing flow will be explained below.

[1279] Step 1:

[1280] The user picks up their smartphone and launches the dedicated application. The application activates the camera function and displays a screen for taking a picture of the user's face. The screen also displays guidance on appropriate lighting conditions and angles.

[1281] Step 2:

[1282] The user follows the instructions of the application to take a picture of their face with the camera, and once the picture is taken, the image data is saved locally.

[1283] Step 3:

[1284] The device encrypts the stored image data and sends it to a server over the internet connection, and a notification is displayed to the user to confirm the successful transmission.

[1285] Step 4:

[1286] The server analyzes the received image data and executes a facial recognition algorithm to detect facial features (eyes, nose, mouth), which then extracts facial skeletal information.

[1287] Step 5:

[1288] The server then runs skin analysis algorithms to determine skin type (dry, oily, combination, etc.) and skin concerns (blemishes, wrinkles, redness, etc.).

[1289] Step 6:

[1290] The server uses a color analysis algorithm to analyze the user's skin pigments and tones to determine their personal color.

[1291] Step 7:

[1292] The server compiles the analysis results and generates facial bone structure, facial feature position, skin type, and personal color information, which is then encrypted and sent to the device.

[1293] Step 8:

[1294] The server runs an emotion recognition algorithm to recognize the user's emotions from the received image data, analyzing facial expressions and changes in facial color to determine their emotional state (e.g., joy, sadness, stress).

[1295] Step 9:

[1296] Based on the analysis data received from the server, the device generates real-time skincare massage and makeup guides, such as arrows for massaging along the cheekbones and animations showing how to apply lipstick.

[1297] Step 10:

[1298] The device then uses the emotional information received from the emotion engine to suggest skincare and makeup products that match the user's mood. For example, if the user is feeling stressed, it will suggest products with a relaxing effect or colors with a calming effect.

[1299] Step 11:

[1300] Based on the current facial information, the server runs simulations to predict the future state of the face, including the progression of wrinkles and changes in skin color.

[1301] Step 12:

[1302] The server generates improvement suggestions for the user based on the predicted future facial condition, such as recommendations for specific skin care products and appropriate makeup application techniques.

[1303] Step 13:

[1304] The server then sends the generated improvement suggestions to the device, including a list of specific skin care products, how to use them, and makeup tips.

[1305] Step 14:

[1306] The device visually displays the received improvement suggestions to the user, showing a simulated image of the future face along with the optimal skincare products and makeup techniques, allowing the user to take immediate action.

[1307] Through this series of steps, users can experience something similar to that of a professional hairdresser and learn the best beauty treatment methods to suit their individual skin condition and emotional state.

[1308] Example 2

[1309] 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."

[1310] Conventional skincare and makeup systems struggle to provide comprehensive guidance based on individual skin conditions and emotional states, and are unable to fully meet the specific needs of users. They also lack the technology to predict future facial conditions and provide appropriate improvement suggestions.

[1311] 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.

[1312] In this invention, the server includes an image acquisition means for allowing the user to capture a photograph of their own face, a transmission means for transmitting the acquired image data to the server, an image analysis means for analyzing the image data received by the server and generating facial skeletal information, facial feature position information, and skin information, an emotion analysis means for recognizing the real-time emotional state based on the subject being analyzed, and a display means for generating and displaying skin care and makeup guides in real time based on the analyzed information and emotional state. This allows the user to receive appropriate skin care and makeup guides in real time based on their skin condition and emotional state. Furthermore, by predicting future facial condition based on current facial information and receiving appropriate improvement suggestions, long-term beauty care can also be achieved.

[1313] The "image acquisition means" is a function that allows a user to take a picture of their own face using a smartphone or other photographing device.

[1314] The "transmission means" is a communication function for transmitting the acquired image data to a server. This function transfers the data via an Internet connection.

[1315] The "image analysis means" is a function that analyzes the image data received by the server and generates facial bone structure information, facial feature position information, and skin information. This analysis uses deep learning models and image processing algorithms.

[1316] The "emotion analysis means" is a function for recognizing the user's emotions based on image data. It uses a facial expression recognition algorithm to determine emotional states such as joy, sadness, and stress.

[1317] The "display means" is a function that generates skin care and makeup guides in real time based on the analyzed information and emotional state, and visually displays them to the user. For example, it displays animations and arrows on the smartphone screen.

[1318] The "prediction method" is a function that predicts future facial conditions based on analyzed current facial information. It uses machine learning models to simulate the progression of wrinkles and changes in skin color.

[1319] The "suggestion means" is a function that generates appropriate improvement suggestions based on the predicted future facial condition and displays them to the user. These suggestions include skin care products, makeup techniques, relaxation methods, etc.

[1320] The present invention relates to a system that analyzes a user's skin type, lifestyle habits, and personal color and provides skin care information and makeup lessons based on the results. This system takes a picture of the user's face using the smartphone's camera function and displays a guide in real time based on the analysis results. It can also predict future facial conditions and make appropriate improvement suggestions. Furthermore, the present invention is equipped with an emotion engine that recognizes the user's emotions and makes suggestions based on those emotions.

[1321] The system includes the following main means:

[1322] 1. Image Acquisition Method: The user installs and launches a dedicated application on their smartphone. The application uses the smartphone's camera to capture a picture of the user's face. The application displays guidance on appropriate lighting conditions and shooting angles, allowing the user to capture an accurate picture of their face.

[1323] 2. Transmission method: The image data captured by the user is saved in the application and then transmitted to the server by the device, using the SSL / TLS protocol to ensure data security.

[1324] 3. Image analysis: The server uses OpenCV or deep learning models (e.g., YOLO, ResNet) to analyze the received image data. It determines facial bone structure, facial feature position, skin type, and personal color, and the analysis results are stored in a database.

[1325] 4. Emotion analysis means: The server recognizes the user's emotions using an additional emotion engine, which uses a facial expression recognition algorithm (e.g., Face Emotion Recognition model) to analyze facial muscle movements from image data to determine the user's emotional state.

[1326] 5. Display: Based on the analysis results and emotional state sent from the server, the device displays skin care and makeup guides to the user in real time. The guides are displayed as visual guidelines, such as massage steps along the contours of the face or the proper way to apply lipstick.

[1327] 6. Prediction method: The server performs simulations to predict future facial conditions based on current facial information. Machine learning models are used to predict the progression of wrinkles and changes in skin color.

[1328] 7. Recommendation method: Based on the prediction results, the server recommends appropriate skin care products and makeup techniques. The recommendations are stored in a database and displayed to the user via their device. Relaxation methods and beauty products are also suggested according to the user's emotional state.

[1329] Specific Examples

[1330] The user launches the application and takes a photo of their face. The device then sends the captured image data to the server. The server analyzes the image and generates information on facial bone structure, feature position, skin type, and personal color. Based on the analysis results, the device displays skin care and makeup guides in real time. For example, it shows specific instructions on how to massage the skin in a circular motion, focusing on the cheekbones, or how to apply eyeshadow around the eyes.

[1331] Furthermore, based on current facial information, the system predicts the state of the face three months from now, simulating changes in wrinkle depth and skin tone, and suggests appropriate skin care products. Additionally, an emotion engine recognizes the user's emotions from image data and suggests makeup colors and styles and relaxation methods that match that emotional state. For example, if the user is feeling stressed, the system suggests skin care products with a relaxing effect and makeup colors with a calming effect.

[1332] Example prompts to input to the generative AI model

[1333] "Please explain in detail each processing step of the system that analyzes the user's facial photo and makes skincare and makeup recommendations."

[1334] "Please explain in detail how the system works to recognize a user's face and emotions and generate personalized skincare and makeup suggestions."

[1335] "Please tell me the functions and specific processing steps of the system that predicts the future state of the face and makes suggestions for improvement."

[1336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1337] Step 1: Image acquisition

[1338] The user installs and launches a dedicated application on their smartphone. The user activates the camera function within the application and takes a picture of their face. The application displays on the screen a guide for appropriate lighting conditions and shooting angles, and the user follows these to take a picture of their own face.

[1339] Input: User's photo

[1340] Output: Image data of the captured face (e.g., JPEG format)

[1341] Step 2: Sending image data

[1342] The device sends the facial image data stored in the application to a server over the Internet, using the SSL / TLS protocol to ensure data security.

[1343] Input: Captured face image data

[1344] Output: Facial image data sent to the server

[1345] Step 3: Image analysis

[1346] The server analyzes the received image data. The analysis software used here is OpenCV or a deep learning model (e.g., YOLO, ResNet). The server first identifies facial feature points (the positions of the eyes, nose, mouth, etc.), and then determines skin type (dry skin, oily skin, etc.), skin concerns (blemishes, wrinkles, redness, etc.), and personal color.

[1347] Input: Facial image data sent to the server

[1348] Output: Analysis results such as facial features, skin type, skin concerns, and personal color

[1349] Step 4: Recognize emotions

[1350] The server recognizes the user's emotions using an emotion engine. This emotion engine uses a facial expression recognition algorithm (e.g., the Face Emotion Recognition model) to analyze the movements of facial muscles from image data and determine the user's emotional state, such as joy, sadness, or stress.

[1351] Input: Facial feature points obtained from image data

[1352] Output: Emotional state such as happiness, sadness, stress, etc.

[1353] Step 5: Generate real-time guides

[1354] Based on the analysis and emotion recognition results, the server generates skin care massage and makeup guides, such as massage steps along the contours of the face or the proper way to apply lipstick. These guides are generated in a way that best suits the user's current skin condition and emotional state.

[1355] Input: Face analysis results, emotional state

[1356] Output: Real-time skincare and makeup guide

[1357] Step 6: Viewing the guide

[1358] The device receives the analysis results and guides sent from the server and displays visual guides to the user in real time. Specifically, skin care and makeup techniques are displayed on the smartphone screen using animations and arrows. For example, arrows for massaging along the cheekbones and animations showing the proper way to apply lipstick are displayed.

[1359] Input: Guide information sent from the server

[1360] Output: Real-time guide on smartphone screen

[1361] Step 7: Predicting future faces

[1362] The server runs a simulation based on current facial information to predict future facial conditions, specifically using machine learning models to run algorithms that predict wrinkle progression and changes in skin tone.

[1363] Input: Current face analysis result

[1364] Output: Simulation result of future face (e.g. after 3 months)

[1365] Step 8: Generate improvement suggestions

[1366] Based on the predictions of the future face, the server will suggest appropriate skin care products and makeup methods, as well as relaxation methods and beauty products according to the user's emotional state.

[1367] Input: Future face simulation results, current emotional state

[1368] Output: Suggestions for improvement, such as specific skin care products, makeup techniques, relaxation techniques, etc.

[1369] (Application example 2)

[1370] 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."

[1371] Conventional skincare and makeup instruction systems do not take into account the user's individual emotional state, resulting in a lack of psychological satisfaction and effective suggestions. Furthermore, even if systems exist that can predict future facial conditions, the improvement suggestions based on these predictions are often merely theoretical and lack comprehensiveness. There is a need to solve these issues and provide personalized, high-quality skincare and makeup instruction to users.

[1372] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image analysis means for analyzing the user's facial image, an emotion recognition means for analyzing the user's emotional state, a display means for generating and displaying a real-time guide, a prediction means for predicting the user's future facial state, and a suggestion means for providing suggestions based on the prediction results and the user's emotional state. This makes it possible to provide not only skin care and makeup lessons tailored to the user's facial state, but also personalized suggestions based on the user's emotional state. Furthermore, by predicting the user's future facial state and providing specific improvement suggestions based on the prediction, the user's psychological satisfaction can be increased.

[1373] "Facial skeletal information" is data that indicates the main structural features of the face, and includes position information of the eyes, nose, mouth, etc.

[1374] "Feature position information" is data that indicates the specific positions of individual facial features (eyes, nose, mouth, etc.).

[1375] "Skin information" is data relating to the condition of the skin, including information on blemishes, wrinkles, redness, etc.

[1376] "Image analysis means" is a technology for analyzing received image data and extracting information such as facial features and skin condition.

[1377] The "display means" is a technology that generates a guide in real time based on the analyzed information and provides the information visually to the user.

[1378] "Emotion recognition means" is a technology that analyzes the user's emotional state from their facial expressions and complexion.

[1379] The "suggestion means" is a technology that provides appropriate skin care and makeup suggestions to the user based on the analysis results and emotional state.

[1380] The "prediction means" is a technology that predicts the future state of a face based on current face information.

[1381] "Image acquisition means" refers to a device or technology that allows a user to take a picture of their own face.

[1382] "Transmission means" refers to a technique for sending the acquired image data to the server.

[1383] MODE FOR CARRYING OUT THE INVENTION

[1384] This system analyzes a user's facial information and emotional state, and provides skin care information and makeup lessons in real time based on the analysis. This system is intended to be installed on devices such as smart glasses and used in brick-and-mortar stores.

[1385] Hardware and Software Configuration

[1386] The system includes the following major hardware and software components:

[1387] Smart glasses (e.g., general-purpose smart glasses device)

[1388] Cloud server (e.g. general-purpose cloud service)

[1389] Facial image analysis software (e.g., general-purpose image analysis engine)

[1390] Emotion recognition software (e.g., general-purpose emotion recognition API)

[1391] Natural language processing explanation

[1392] Taking a picture of your face and sending image data

[1393] A user wearing smart glasses takes a picture of their face using the device's built-in camera. The captured image data is then sent from the smart glasses to a cloud server. This transmission process is carried out in real time, and the data is encrypted before being sent.

[1394] Facial image analysis

[1395] The cloud server analyzes the received images using facial image analysis software. Specifically, it uses AWS Rekognition and Microsoft Azure Face API to detect facial bone structure, facial feature position, and skin condition. The analyzed data is organized by category and passed on to the next processing step.

[1396] Emotion recognition

[1397] At the same time, the server uses emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API) to determine the user's emotional state from the image data. The results of this analysis are used in real time to suggest skincare and makeup lessons.

[1398] Real-time guide generation and display

[1399] Based on the analysis, the server generates skin care and makeup guides and sends them to the smart glasses' display device, which can show, for example, massage instructions along the cheekbones or animations of specific makeup application techniques.

[1400] Future facial prediction and improvement suggestions

[1401] The server uses prediction methods to simulate future facial conditions based on current and past facial information. For example, it predicts the progression of wrinkles and changes in skin color, and suggests skin care products and beauty treatments based on the results. Taking emotion recognition results into account, it also suggests relaxation and stress reduction methods.

[1402] Specific example explanation

[1403] User Scenarios

[1404] 1. Facial imaging and analysis

[1405] A user enters a store and puts on smart glasses.

[1406] The smart glasses automatically take a picture of your face and send the data to a cloud server.

[1407] The server analyzes the face using AWS Rekognition and Microsoft Azure Face API to generate facial features and skin information.

[1408] 2. Real-time guide

[1409] The smart glasses display animated skin care and makeup instructions (for example, how to massage along the cheekbones).

[1410] Users follow the guide and perform their own skin care.

[1411] 3. Future face prediction and suggestions

[1412] The server predicts the future condition of the face based on the current facial information and suggests specific moisturizing creams and skin care methods.

[1413] Emotion-recognition software displays suggestions tailored to the user's emotional state, such as relaxing skin care products.

[1414] Input prompts for generative AI models

[1415] "Analyze the user's facial image and recognize facial features, skin type, and personal color. Furthermore, predict the future state of the user's face and make appropriate suggestions for improvement. Also, recognize emotions and make suggestions based on those emotions."

[1416] This format allows users to receive more personalized suggestions and, especially in physical stores, to receive effective skin care and makeup lessons.

[1417] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1418] Step 1: Taking a picture of your face and sending the image data

[1419] The user puts on the smart glasses and photographs their face. The built-in camera captures the entire face in high resolution. When photographing, the smart glasses display a guide to the optimal lighting conditions and shooting angle. The photographed image data is sent from the smart glasses to a cloud server in real time. The transmitted data is encrypted and reaches the cloud server securely.

[1420] Step 2: Facial image analysis

[1421] The cloud server analyzes the received facial image data using facial image analysis software. Specifically, it uses AWS Rekognition and Microsoft Azure Face API to extract facial bone structure information, facial feature position information, and skin information. The input data is the facial image, and the output data is specific information on analyzed facial feature points (e.g., position of the eyes, nose, and mouth) and skin condition (e.g., blemishes, wrinkles, redness, etc.).

[1422] Step 3: Recognize emotions

[1423] The server analyzes the emotional state based on the facial image data using emotion recognition software (e.g., Affectiva or Microsoft Azure Emotion API). The input data is a facial image, and the output data is the emotional state, such as joy, sadness, or stress. Emotion recognition information is generated based on changes in facial expressions and complexion.

[1424] Step 4: Generate and display real-time guides

[1425] The server generates real-time guides based on the analysis results. Specifically, visual guidelines showing skin care massage and makeup actions (e.g., how to massage along cheekbones or how to apply eyeshadow) are created. These guides are displayed on the smart glasses' display. The input data are the facial analysis results and emotional state, and the output data is a video or animation of the guideline.

[1426] Step 5: Predicting future facial features and suggesting improvements

[1427] The server predicts future facial conditions based on current facial information and past data. Specifically, it uses an AI model to perform simulations to predict the progression of wrinkles and changes in skin color. The input data is the facial analysis results and historical data, and the output data is a simulation of future facial conditions and suggestions for improvement. Suggested improvements include the use of specific skincare products and beauty techniques.

[1428] Step 6: Generating suggestions according to emotional state

[1429] The server makes suggestions for relaxation methods and stress relief based on the emotion recognition results. Specific examples include suggestions for skin care products and makeup colors that have a relaxing effect. The input data is the emotion recognition results, and the output data is suggestions for relaxation methods and skin care products.

[1430] Through this series of steps, users can receive highly personalized skincare and makeup lessons, which can improve customer satisfaction in physical stores.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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).

[1438] 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.

[1439] 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."

[1440] 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.

[1441] 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).

[1442] 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.

[1443] 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.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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.

[1449] 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.

[1450] 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.

[1451] 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.

[1452] The following is further disclosed regarding the above embodiment.

[1453] (Claim 1)

[1454] An image acquisition means for a user to photograph his or her own face;

[1455] a transmitting means for transmitting the acquired image data to a server;

[1456] an image analysis means for analyzing the image data received in the server and generating facial bone structure information, facial feature position information, and skin information;

[1457] A display means for generating and displaying skin care and makeup guides in real time based on the analyzed information;

[1458] A system including:

[1459] (Claim 2)

[1460] The system of claim 1, wherein the system displays visual guidelines indicating skin care massage and makeup actions in a real-time guide display.

[1461] (Claim 3)

[1462] A prediction means for predicting a future face state based on the analyzed current face information;

[1463] 10. The system of claim 1, further comprising suggestion means for displaying the predicted future facial state and providing improvement suggestions.

[1464] "Example 1"

[1465] (Claim 1)

[1466] An image acquisition means for a user to photograph his or her own face;

[1467] a transmitting means for transmitting the acquired image data to an external server;

[1468] an image analysis means for analyzing the received image data in the external server and generating facial feature information, position information, and skin condition information;

[1469] A display means for generating and displaying beauty care and makeup guides in real time based on the analyzed information;

[1470] a prediction means for predicting a future face state based on the analyzed current face information;

[1471] suggestion means for displaying the predicted future facial state and providing improvement suggestions;

[1472] A system including:

[1473] (Claim 2)

[1474] 10. The system of claim 1, wherein the real-time guide display displays visual guidelines showing beauty care and makeup instructions.

[1475] (Claim 3)

[1476] 10. The system of claim 1, wherein the system uses a dedicated generative AI model for facial feature detection, skin condition analysis, and personalized color analysis.

[1477] "Application Example 1"

[1478] (Claim 1)

[1479] An image acquisition means for a user to photograph his or her own face;

[1480] a transmitting means for transmitting the acquired image data to a server;

[1481] an image analysis means for analyzing the image data received in the server and generating facial bone structure information, facial feature position information, and skin information;

[1482] A display means for generating and displaying skin care and makeup guides in real time based on the analyzed information;

[1483] A prediction means for predicting the user's future facial condition and generating suggestions for improving skin care and makeup;

[1484] A suggestion means for predicting future skin conditions based on the acquired current facial information and past data, and suggesting appropriate skin care products and care methods;

[1485] A system including:

[1486] (Claim 2)

[1487] The system according to claim 1 analyzes the skin condition, such as dry skin and blemishes, based on the captured facial image data, and displays skin care products and methods as visual guidelines based on that information.

[1488] (Claim 3)

[1489] The system according to claim 1, further comprising a suggestion means for predicting a future facial condition based on the analyzed current facial information and providing specific advice on skin care and makeup based on the result of the prediction.

[1490] "Example 2: Combining Emotion Engines"

[1491] (Claim 1)

[1492] An image acquisition means for a user to photograph his or her own face;

[1493] a transmitting means for transmitting the acquired image data to a server;

[1494] an image analysis means for analyzing the image data received in the server and generating facial bone structure information, facial feature position information, and skin information;

[1495] emotion analysis means for recognizing a real-time emotional state based on an object to be analyzed;

[1496] A display means for generating and displaying skin care and makeup guides in real time based on the analyzed information and emotional state;

[1497] A system including:

[1498] (Claim 2)

[1499] The system of claim 1, wherein the system displays visual guidelines indicating skin care massage and makeup actions in a real-time guide display.

[1500] (Claim 3)

[1501] A prediction means for predicting a future face state based on the analyzed current face information;

[1502] 10. The system of claim 1, further comprising suggestion means for displaying the predicted future facial state and providing improvement suggestions.

[1503] "Application example 2 when combining emotion engines"

[1504] (Claim 1)

[1505] An image acquisition means for a user to photograph his or her own face;

[1506] a transmitting means for transmitting the acquired image data to a server;

[1507] an image analysis means for analyzing the image data received in the server and generating facial bone structure information, facial feature position information, and skin information;

[1508] A display means for generating and displaying skin care and makeup guides in real time based on the analyzed information;

[1509] an emotion recognition means for analyzing an emotional state;

[1510] suggestion means for providing suggestions based on the analyzed emotional state;

[1511] A system including:

[1512] (Claim 2)

[1513] 10. The system of claim 1, wherein the real-time guidance display displays visual guidelines indicating skin care massage and makeup actions and makes suggestions based on emotional state.

[1514] (Claim 3)

[1515] A prediction means for predicting a future face state based on the analyzed current face information;

[1516] 10. The system of claim 1, further comprising suggestion means for displaying the predicted future facial state and providing improvement suggestions, and suggesting skin care and relaxation methods based on the emotional state. [Explanation of symbols]

[1517] 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. An image acquisition means for a user to photograph his or her own face; a transmitting means for transmitting the acquired image data to a server; an image analysis means for analyzing the image data received in the server and generating facial bone structure information, facial feature position information, and skin information; A display means for generating and displaying skin care and makeup guides in real time based on the analyzed information; A system including:

2. The system of claim 1 , wherein the real-time guide display displays visual guidelines indicating skin care massage and makeup actions.

3. A prediction means for predicting a future face state based on the analyzed current face information; The system of claim 1 , further comprising suggestion means for displaying the predicted future facial state and providing improvement suggestions.

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