System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to take a photograph of his / her own face and upload the photograph; means for a server to receive the uploaded photograph of the face and analyze a face pattern using a face recognition technique; means for the server to compare the analyzed face pattern data with a makeup database and generate optimal makeup advice for the user; and means for the server to transmit the generated makeup advice to a terminal and display the makeup advice for the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The problem that the present invention aims to solve is to provide personalized makeup advice to each user. Conventional makeup methods and suggestions are general and do not take into account individual facial features or skin tone, making it difficult to find the best makeup. As a result, finding the makeup that suits you requires a lot of time and effort. The present invention proposes a system that uses face recognition technology and a makeup database to automatically provide the best makeup advice to each user. [Means for solving the problem]

[0005] The present invention solves the above problems by using the following means:

[0006] 1. A means for users to take a photo of themselves and upload that photo;

[0007] 2. A means for the server to receive the uploaded facial photograph and analyze the facial pattern using facial recognition technology;

[0008] 3. A means for comparing the analyzed face pattern data with a makeup database and generating optimal makeup advice for the user;

[0009] 4. Provide a system including a means for transmitting makeup advice generated by a server to a terminal and displaying it to a user.

[0010] This system allows users to easily obtain makeup advice that best suits their face. Furthermore, by including a means for the terminal to temporarily store a user's face photo and prepare it for transmission to the server, and a means for the server to score multiple makeup styles based on face pattern data and select the most suitable makeup style, the system can improve the accuracy and personalization of makeup advice provided to users.

[0011] "User" refers to an individual who has their face photograph taken and receives makeup advice.

[0012] "Facial photo" refers to image data of the user's face, and is input data for analysis by the system.

[0013] "Upload" refers to the operation of sending a facial photo from a terminal to a server.

[0014] "Server" refers to a computer system for receiving and analyzing facial photographs.

[0015] "Facial recognition technology" refers to technology that recognizes and analyzes facial features from image data.

[0016] "Facial pattern" refers to facial feature data extracted using facial recognition technology.

[0017] "Analysis" refers to the process of using facial recognition technology to extract facial features from photographs and digitize them.

[0018] A "makeup database" refers to a collection of information that stores data on various makeup styles.

[0019] "Matching" refers to the process of comparing analyzed facial pattern data with a makeup database to find the optimal makeup style.

[0020] "Makeup advice" refers to specific instructions regarding makeup procedures and items suggested to users.

[0021] "Generation" refers to the process of creating specific makeup advice based on the matching results.

[0022] "Terminal" refers to the device that a user uses to take photos and exchange information with the server. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] This invention relates to a system that provides personalized makeup advice to individual users. This system uses face recognition technology to analyze the user's facial patterns and generate optimal makeup advice based on the results. The system's basic configuration consists of a user's terminal, a central server, and a makeup database.

[0045] Specific Embodiments of the System

[0046] 1. Upload a photo of the user's face

[0047] The user opens the camera application on their smartphone, tablet, or other device and takes a photo of their face. After taking the photo, the user presses the "upload" button in the application to send the photo to the server.

[0048] 2. Facial recognition and analysis by the server

[0049] When the user presses the upload button, the terminal creates and sends a request to send the face photo to the server.

[0050] The server analyzes the received facial photo using a facial recognition algorithm. In this analysis process, facial feature points (eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[0051] 3. Matching face pattern data with makeup database

[0052] The server compares the generated facial pattern data with a makeup database. This database stores information on various makeup styles, and scores the data to select the most suitable style based on the facial pattern. Based on the scoring results, the makeup style that best suits the user is selected.

[0053] 4. Generating and sending makeup advice

[0054] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[0055] The server transmits the generated makeup advice to the terminal.

[0056] 5. Users receive makeup advice

[0057] The device analyzes the makeup advice data received from the server and displays it on the user interface, allowing the user to view and try out the suggested makeup.

[0058] Specific examples

[0059] For example, if a 25-year-old female user uses the application to find makeup that suits her, the system will provide the best makeup advice by taking and uploading a photo of her face through the following steps:

[0060] 1. Upload a photo

[0061] Take a photo of your face using your smartphone and press the "Upload" button.

[0062] 2. Facial Recognition and Analysis

[0063] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[0064] 3. Database Verification

[0065] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[0066] 4. Advice Generation and Delivery

[0067] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[0068] 5. User Visibility

[0069] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0070] By following the above steps, users of this system can easily receive makeup advice that best suits them and enjoy their daily makeup routine.By automating analysis and suggestions, this system provides personalized makeup advice to users.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user starts the application and takes a photo of their face using the camera function.

[0074] The device will temporarily store the captured facial photo within the app.

[0075] The user presses the "upload" button to indicate their intention to upload a photo of their face.

[0076] Step 2:

[0077] The device generates an upload request and prepares to send the stored facial photo to the server.

[0078] The terminal sends a facial photo along with the request to the server.

[0079] Step 3:

[0080] The server receives the facial photograph sent from the terminal.

[0081] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[0082] The server generates face pattern data as the analysis result.

[0083] Step 4:

[0084] The server compares the generated facial pattern data with a makeup database.

[0085] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern.

[0086] Step 5:

[0087] The server generates specific makeup advice based on the selected makeup style.

[0088] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[0089] Step 6:

[0090] The terminal receives the makeup advice data transmitted from the server.

[0091] The device analyzes the received data and displays makeup advice on the user interface.

[0092] Step 7:

[0093] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[0094] Examples:

[0095] For example, when a 25-year-old female user uses this system, the following process takes place:

[0096] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[0097] Step 2: The device sends a photo of the face to the server.

[0098] Step 3: The server receives the facial photo and analyzes it.

[0099] Step 4: The server uses the face pattern data to match the makeup database.

[0100] Step 5: The server generates optimal makeup advice and sends it to the device.

[0101] Step 6: The device receives and displays the advice.

[0102] Step 7: The user applies makeup based on the advice.

[0103] This allows users to easily find and apply makeup that suits them. The system uses facial recognition technology and database matching to automatically provide personalized makeup advice.

[0104] Example 1

[0105] 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."

[0106] Conventional makeup advice systems have faced the challenge of requiring a great deal of time and effort to provide personalized makeup advice to individual users. Another issue is that it is difficult to accurately recognize the user's facial features and suggest the optimal makeup style. Furthermore, there are cases where the accuracy and speed of the advice provided are lacking, leading to concerns about a poor user experience.

[0107] 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.

[0108] In this invention, the server includes: [means for a user to take an image of themselves and send that image;] [means for the server to receive the sent image and analyze the facial pattern using image recognition technology; and [means for the server to compare the analyzed facial pattern data with a database and generate advice that is best suited to the user.] This makes it possible to analyze the facial features of users quickly and with high accuracy, and to provide each individual user with makeup advice that is best suited to them in real time.

[0109] "User" refers to a person who uses the system to receive makeup advice.

[0110] "Images" refers to visual data such as photos and videos taken by users using their device's camera.

[0111] "Send" refers to the process of sending data from a terminal to a server.

[0112] "Receiving" refers to the process in which the server receives data sent from the terminal.

[0113] "Image recognition technology" refers to the technology of extracting features from images using computer vision and analyzing them.

[0114] A "face pattern" refers to a collection of facial feature points and shape data extracted using face recognition technology.

[0115] "Analysis" refers to the process of extracting facial features based on received image data and generating pattern data.

[0116] A "database" refers to a collection of accumulated data that stores multiple makeup styles and the corresponding information.

[0117] "Matching" refers to the process of comparing analyzed facial pattern data with data in a database to find a match.

[0118] "Advice" refers to makeup techniques and style suggestions generated by the server based on facial pattern data.

[0119] This clarifies the meaning of key words contained in the claims.

[0120] The present invention relates to a system for providing personalized makeup advice to users. The system is composed of a user terminal, a central server, and a makeup database.

[0121] First, the user takes a photo of their face using a device such as a smartphone or tablet. To take the photo, they can use a commonly available camera application. After taking the photo, the user presses the "upload" button in the application to send the photo to the server. This causes the device to send the photo to the server in the form of an HTTP request.

[0122] The server receives the sent facial photo. To receive the photo, server software (e.g., Apache or NGINX) is used to process HTTP requests. The received facial photo is analyzed using an image processing library (e.g., OpenCV or Dlib). In this analysis process, facial feature points (e.g., eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[0123] The server then compares the generated facial pattern data with an internal makeup database. This makeup database stores information on various makeup styles and their corresponding facial patterns. A database management system (e.g., MySQL or PostgreSQL) is used for the comparison, and scoring is performed to select the optimal style based on the facial pattern. A machine learning model (e.g., a generative AI model) can be used as the scoring algorithm.

[0124] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation application method. The generated makeup advice is sent to the device in JSON format.

[0125] The device receives the JSON data sent from the server, parses it, and displays it on the user interface, allowing the user to check the displayed makeup advice and use the suggested products and makeup techniques to apply their makeup.

[0126] Specific examples

[0127] For example, consider the case where a 25-year-old female user uses the application to find makeup that suits her. When the user takes and uploads a photo of her face, the system goes through the following process to provide the best makeup advice.

[0128] 1. Upload a photo

[0129] Users take a photo of their face with their smartphone and press the "upload" button.

[0130] 2. Facial Recognition and Analysis

[0131] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[0132] 3. Database Verification

[0133] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[0134] 4. Advice Generation and Delivery

[0135] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[0136] 5. User Visibility

[0137] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0138] Prompt Sentence Examples

[0139] "I'm a 25-year-old woman with olive skin tone. I'd like some advice on finding a makeup style that suits me. Please provide the best makeup advice based on the photo of my face I upload."

[0140] As a result, in the system of the present invention, the user can quickly and accurately receive personalized makeup advice that reflects the user's facial features.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] A user opens a camera application on their device (smartphone or tablet). The camera is activated and the user adjusts the camera so that their face is clearly captured. After taking a photo of their face, the user presses the "upload" button in the application. The input of this step is the user's face photo, and the output is an HTTP request sent to the server.

[0144] Step 2:

[0145] When the user clicks the "Upload" button, the device generates an HTTP request including the captured face photo and sends it to the server. Specifically, the request includes face photo data (JPEG or PNG format) and necessary metadata (user ID, timestamp, etc.). The input is the user's face photo and user information, and the output is an HTTP request to the server.

[0146] Step 3:

[0147] The server receives the HTTP request sent from the device. The server software (e.g., Apache or NGINX) analyzes the request and extracts the facial photo data. The input is the HTTP request received by the server, and the output is the facial photo data required for analysis.

[0148] Step 4:

[0149] The server analyzes the facial photo data using an image recognition library (e.g., OpenCV, Dlib) and extracts facial feature points. This allows for information such as eye position, nose shape, mouth position, and skin tone to be obtained. The input is facial photo data, and the output is facial pattern data.

[0150] Step 5:

[0151] The server compares the analyzed facial pattern data with a makeup database. The database stores various makeup styles and their corresponding facial pattern data. The server uses a scoring algorithm to evaluate and select the optimal makeup style. The input is facial pattern data, and the output is information about the optimal makeup style.

[0152] Step 6:

[0153] The server generates specific makeup advice based on the selected makeup style. The advice includes eyeshadow color, lip color, foundation application method, etc. The input is the optimal makeup style information, and the output is the generated makeup advice.

[0154] Step 7:

[0155] The server converts the generated make advice into JSON format and sends it to the terminal. This uses a communication protocol between the server and the terminal (e.g., HTTP / S). The input is the generated make advice, and the output is the transmission of JSON data to the terminal.

[0156] Step 8:

[0157] The device receives and parses the JSON data sent from the server. The parsed data is displayed in the user interface and visually presented to the user. The input is the JSON data from the server, and the output is the makeup advice that is displayed.

[0158] Step 9:

[0159] The user checks the makeup advice displayed on the device and applies their makeup based on it. Using the provided items and process as a reference, the user can try out the makeup that best suits their face. The input is the makeup advice displayed on the device, and the output is the user's makeup result.

[0160] The above are the specific steps of the program processing in this system and the specific operations performed at each step.

[0161] (Application example 1)

[0162] 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."

[0163] Providing optimal makeup advice to customers is extremely important in modern beauty and cosmetic shops. However, with conventional methods, it takes a great deal of time and effort for customers to find the makeup style that suits them best. To solve this problem, a method is needed to provide customers with fast, accurate, personalized makeup advice.

[0164] 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.

[0165] In this invention, the server includes: [means for a user to take a photo of their face and upload the photo;] [means for the server to receive the uploaded photo of their face and analyze their facial pattern using facial recognition technology;] [means for comparing the analyzed facial pattern data with a makeup database and generating makeup advice that is optimal for the user;] [means for providing personalized makeup advice to each user at a beauty / cosmetic shop using an automated system; and [means for using a smartphone or smart glasses on a terminal to display the generated makeup advice. This enables users to quickly and easily receive makeup advice that is optimal for them when they visit a beauty / cosmetic shop.

[0166] "User" refers to an individual who uses the system to receive makeup advice that is best suited to them.

[0167] "Face Photo" refers to an image of a User's face that the User uploads to the System.

[0168] "Uploading" refers to the act of a user sending a photo of their own face to a server.

[0169] "Server" refers to a central computer that analyzes facial photos received from users and generates optimal makeup advice.

[0170] "Facial recognition technology" refers to technology for analyzing facial features from photographs of faces.

[0171] "Facial pattern" refers to facial feature data of a user extracted from a photograph of the user's face using facial recognition technology.

[0172] A "makeup database" refers to a database that stores information on various makeup styles.

[0173] "Makeup advice" refers to specific instructions and suggestions regarding the makeup style that is best suited to the user.

[0174] "Beauty and cosmetic shop" refers to a physical store that offers cosmetics and beauty-related products and services.

[0175] "Terminal" refers to an electronic device operated by a user, such as a smartphone or smart glasses.

[0176] This invention relates to a system that provides personalized makeup advice to individual users. This system works by having users take a photo of their face using a device such as a smartphone or smart glasses at a beauty or cosmetic shop and then uploading the photo.

[0177] Specific Embodiments of the System

[0178] 1. Take and upload a photo of your face

[0179] Users open the camera app on their smartphone or smart glasses and take a photo of their face. Once the photo is taken, they press the "upload" button in the app to send the photo to the server.

[0180] 2. Facial Recognition and Analysis

[0181] The server receives the uploaded facial photo and analyzes it using facial recognition technology. Specifically, it uses libraries such as OpenCV and dlib to extract facial feature points (such as the positions of the eyes, nose, and mouth) and generate facial pattern data.

[0182] 3. Matching face pattern data with makeup database

[0183] The server compares the generated facial pattern data with a makeup database, which stores information on various makeup styles. The server then scores the facial pattern data and makeup style data using KNN (K Nearest Neighbor) to select the optimal makeup style.

[0184] 4. Generating and sending makeup advice

[0185] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[0186] 5. Display of makeup advice

[0187] The device analyzes the makeup advice data received from the server and displays it on a user interface, allowing the user to try out the suggested makeup.

[0188] Hardware and software used

[0189] Hardware:

[0190] Smartphones, smart glasses, servers

[0191] software:

[0192] OpenCV: An image processing library used to analyze facial photos.

[0193] dlib: A library used for facial landmark detection.

[0194] scikit-learn: Used to suggest makeup styles using KNN (K nearest neighbors).

[0195] Specific examples

[0196] For example, suppose a 35-year-old female user visits a beauty and cosmetics shop. She takes a photo of her face using a device in the store (such as a smartphone or smart glasses) and uploads it to the server. The server analyzes the photo and generates optimal makeup advice based on the user's facial features. The advice generated includes specific suggestions such as purple eyeshadow, nude lip color, and peachy pink blush.

[0197] Prompt Sentence Examples

[0198] "Based on the user's facial photo data, please analyze the user's skin tone, face shape, and features, and provide the best makeup advice."

[0199] Example format:

[0200] The dataset should be in the following format:

[0201] [Age, Skin Tone, Face Shape, Eye Shape, Nose Shape, Lip Shape] -> [Eyeshadow Color, Lip Color, Cheek Color]

[0202] example:

[0203] Input: [35, "Winter", "Round", "Average", "Average", "Thick"]

[0204] Output: [Purple, Nude, Peach Pink]

[0205] As described above, by implementing this system in beauty and cosmetic shops, users can quickly and easily receive makeup advice that is best suited to them.

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1:

[0208] The user takes a photo of their face using a smartphone or smart glasses, using the camera application on the device. Once the photo is taken, the user presses the "upload" button in the application to send the photo to the next step.

[0209] Input: User's face photo

[0210] Output: Face photo waiting to be sent to the server

[0211] Step 2:

[0212] When a user presses the upload button, the device sends the face photo to the server, a process that involves creating a data transfer request and sending it over the network.

[0213] Input: A photo of your face waiting to be sent

[0214] Output: Face photo uploaded to the server

[0215] Step 3:

[0216] The server receives the uploaded facial photo, then converts it to grayscale using OpenCV and detects facial feature points using dlib to analyze it.

[0217] Input: Uploaded face photo

[0218] Output: Facial feature point data

[0219] Step 4:

[0220] The server generates a face pattern based on the facial feature point data. Specifically, it records the coordinates of each point on the face as an array and constructs face pattern data.

[0221] Input: Facial feature point data

[0222] Output: Face pattern data

[0223] Step 5:

[0224] The server compares the generated facial pattern data with a makeup database, which contains information on various makeup styles. It then uses KNN (K Nearest Neighbor) to score the facial pattern data and makeup style data and selects the most suitable makeup style.

[0225] Input: Face pattern data

[0226] Output: Selected makeup style data

[0227] Step 6:

[0228] The server generates specific makeup advice based on the selected makeup style data, such as advice on eye shadow colors, lip colors, and foundation application methods that are tailored to the user.

[0229] Input: Selected makeup style data

[0230] Output: Specific makeup advice data

[0231] Step 7:

[0232] The server sends the generated makeup advice to the user's device using the notification function of the application and a data transfer protocol.

[0233] Input: Specific makeup advice data

[0234] Output: Makeup advice sent to the user's terminal

[0235] Step 8:

[0236] The device analyzes the makeup advice data received from the server and displays it on the user interface. The user can then view the displayed makeup advice and try out the makeup that suits them best.

[0237] Input: Make advice data received from the server

[0238] Output: Makeup advice displayed on the terminal

[0239] Through these steps, users can quickly and easily receive personalized makeup advice when they visit a beauty or cosmetics store.

[0240] 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.

[0241] The present invention is a system that provides personalized makeup advice to individual users by combining face recognition technology and an emotion engine. This system recognizes the user's emotions and generates appropriate makeup advice based on those emotions. Specific embodiments are described below.

[0242] Specific Embodiments of the System

[0243] 1. Upload a photo of the user's face

[0244] The user opens the camera application on their device and takes a photo of their face. After taking the photo, they press the "upload" button to send the photo to the server.

[0245] 2. Facial recognition and emotion analysis by the server

[0246] The terminal generates and transmits a request to transmit the face photo to the server.

[0247] The server analyzes the received facial photo using a facial recognition algorithm and extracts facial features.

[0248] The server uses an emotion engine based on the facial recognition results to analyze the user's emotions (e.g., joy, sadness, surprise, etc.).

[0249] 3. Matching face pattern data with makeup database

[0250] The server compares the generated facial pattern data and emotion data with a makeup database.

[0251] The server scores the makeup styles and selects the one that best suits the user's facial pattern and emotions.

[0252] 4. Generating and adjusting makeup advice

[0253] Based on the selected makeup style, the server generates specific makeup advice, including eyeshadow color, lip color, and foundation application.

[0254] The emotion engine adjusts makeup advice based on the user's emotions, for example, suggesting bright makeup if the user is sad.

[0255] 5. Sending and displaying makeup advice

[0256] The server transmits the generated makeup advice to the terminal.

[0257] The device analyzes the received makeup advice and displays it on the user interface, allowing the user to try out makeup according to the advice.

[0258] Specific examples

[0259] For example, when a 25-year-old female user uses this system, the following process takes place:

[0260] 1. Upload a photo

[0261] Take a photo of your face with your smartphone and press the "Upload" button.

[0262] 2. Facial Recognition and Emotion Analysis

[0263] The server receives the photo, analyzes facial features, and uses an emotion engine to recognize the emotion the user is currently feeling, for example, sensing that the user is sad.

[0264] 3. Database Verification

[0265] The server compares the facial pattern data and emotional data with a makeup database, and selects makeup styles that suit olive skin and makeup based on emotions through scoring. For example, it suggests bright makeup to alleviate sad moods.

[0266] 4. Advice Generation and Reconciliation

[0267] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a bright lip color that will make the user smile.

[0268] 5. User Visibility

[0269] The device receives the makeup advice and shows it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0270] This allows users to receive personalized makeup advice that takes into account their emotions at the time. By combining the user's facial expressions and emotions, the system provides more appropriate and satisfying makeup advice.

[0271] The processing flow will be explained below.

[0272] Step 1:

[0273] The user launches the application and uses the camera function to take a photo of their face.

[0274] The device will temporarily store the captured facial photo within the app.

[0275] The user presses the "Upload" button and uploads a photo of their face.

[0276] Step 2:

[0277] The device generates an upload request and prepares to send the stored facial photo to the server.

[0278] The device sends the request and the facial photo to the server.

[0279] Step 3:

[0280] The server receives the facial photograph sent from the terminal.

[0281] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[0282] The server generates face pattern data as the analysis result.

[0283] Step 4:

[0284] The server uses the facial pattern data to perform analysis using an emotion engine.

[0285] The server recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) and generates emotion data.

[0286] Step 5:

[0287] The server matches the facial pattern data and emotion data with a makeup database.

[0288] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern and emotion.

[0289] Step 6:

[0290] The server generates specific makeup advice based on the selected makeup style.

[0291] The server tailors its advice based on emotional data from the emotion engine, for example, suggesting bright makeup if the user is sad.

[0292] Step 7:

[0293] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[0294] Step 8:

[0295] The terminal receives the makeup advice data transmitted from the server.

[0296] The terminal analyzes the received data and displays makeup advice on the user interface.

[0297] Step 9:

[0298] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[0299] Examples:

[0300] For example, when a 25-year-old female user uses this system, the following process takes place:

[0301] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[0302] Step 2: The device sends a photo of the face to the server.

[0303] Step 3: The server receives the facial photo and analyzes it.

[0304] Step 4: The server uses the emotion engine to recognize the user's emotion. It detects that the user is sad.

[0305] Step 5: The server uses the face pattern data and emotion data to match the makeup database.

[0306] Step 6: The server generates optimal makeup advice and adjusts it based on emotion, e.g., suggesting a bright lip color.

[0307] Step 7: The server sends the advice data to the terminal.

[0308] Step 8: The device receives and displays the advice.

[0309] Step 9: The user applies makeup based on the advice.

[0310] This allows users to receive personalized makeup advice that takes into account their emotions at the time, allowing them to enjoy more appropriate and satisfying makeup. By combining facial recognition technology with an emotion engine, it becomes possible to provide makeup suggestions that match the user's emotions.

[0311] Example 2

[0312] 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."

[0313] In modern beauty, it is important for each user to receive appropriate makeup advice tailored to their facial features and current emotional state. However, conventional makeup advice systems have difficulty providing personalized advice that takes into account the user's facial expressions and emotions, resulting in a decrease in makeup satisfaction.

[0314] 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.

[0315] In this invention, the server includes: [means for receiving an uploaded facial photograph and analyzing facial patterns using facial recognition technology;] [means for analyzing the user's emotional state using emotion recognition technology based on the facial recognition results; and] [means for collating the analyzed facial pattern data and emotional state data and generating makeup advice optimal for the user.] This makes it possible to provide personalized, highly accurate makeup advice based on the user's facial features and emotional state.

[0316] "User" means a person who uses the system and operates a device or application to take a photograph of his or her face and upload it to the system.

[0317] A "face photo" is image data that captures the user's facial features and is the subject of analysis using face recognition technology and emotion recognition technology.

[0318] "Upload" refers to the operation by which a user transfers facial photo data from their own device to the server.

[0319] The "server" is a central processing unit that receives facial photo data, performs facial and emotion recognition, generates optimal makeup advice, and sends it to the terminal.

[0320] "Facial recognition technology" is a technology that analyzes facial photographs to identify facial features (eyes, nose, mouth, etc.) and generate facial pattern data.

[0321] "Emotion recognition technology" is a technology that analyzes a user's emotional state (happiness, sadness, surprise, etc.) based on facial feature point data obtained from a facial photograph.

[0322] "Facial pattern data" is facial feature point information obtained by facial recognition technology, and is basic data for generating makeup advice.

[0323] "Emotional state data" is emotional information about the user analyzed using emotion recognition technology, and is basic data for adjusting makeup advice.

[0324] A "makeup database" is a data storage that stores various makeup styles and their corresponding facial feature patterns and emotional state data.

[0325] "Makeup advice" is specific guidance on the most suitable makeup technique for the user, generated based on the face pattern data and emotional state data analyzed by the server.

[0326] A "terminal" is a device used by a user to take and upload a facial photo, and ultimately receive and display the generated makeup advice.

[0327] The present invention relates to a system that analyzes a user's facial photograph and provides personalized makeup advice based on their emotions. This system is composed of a user's terminal, a server, face recognition technology, emotion recognition technology, and a makeup database. Specific embodiments are described below.

[0328] System Overview

[0329] This system allows users to upload a photo of their face, and the server analyzes the photo to generate makeup advice tailored to the situation. Key software components include OpenCV (facial recognition technology) and Microsoft Azure Emotion API (emotion recognition technology).

[0330] Upload a user's photo

[0331] Users take a photo of their face using the camera application on their smartphone or computer, and then press the "upload" button to send the photo to the server.

[0332] The terminal generates and transmits a request to transmit this facial photograph data to the server.

[0333] Server-based face recognition and emotion analysis

[0334] The server temporarily stores the received facial photograph in a database.

[0335] The server uses a facial recognition algorithm (for example, OpenCV) to analyze facial feature points (eyes, nose, mouth, etc.) and generate facial pattern data.

[0336] The server then uses an emotion recognition algorithm (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state (e.g., happiness, sadness, surprise) from the facial pattern data.

[0337] Matching face patterns with makeup database

[0338] The server compares the analyzed facial pattern data and emotional state data with a makeup database, which contains a wide variety of makeup styles and their corresponding facial feature patterns and emotional states.

[0339] The server scores multiple candidate styles and selects the most suitable makeup style. For example, if the user is sad, it selects a bright-colored makeup style.

[0340] Generate and adjust makeup advice

[0341] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation type and application method.

[0342] The server uses an emotion recognition engine to tailor makeup advice based on the user's emotional state, such as suggesting bright makeup to alleviate sad emotions.

[0343] Providing makeup advice

[0344] The server transmits the generated makeup advice to the terminal.

[0345] The device analyzes the received makeup advice and displays it to the user, who can then try out different makeup looks based on the advice.

[0346] Specific examples

[0347] For example, if a 25-year-old female user uses this system, the following process will occur:

[0348] 1. Users upload photos of their faces

[0349] Take a photo of your face with your smartphone and press the "Upload" button.

[0350] 2. Facial Recognition and Emotion Analysis

[0351] The server receives the photo, analyzes facial features using OpenCV, and analyzes emotions using the Microsoft Azure Emotion API to determine whether the user is sad.

[0352] 3. Database Verification

[0353] The server compares the facial pattern data and emotion data with a makeup database to select the most suitable makeup style, including a bright lip color to alleviate sad emotions.

[0354] 4. Advice Generation and Adjustment

[0355] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a vibrant lip color to help users feel more energized.

[0356] 5. Display of makeup advice

[0357] The device receives the advice and presents it to the user, who can then try out the makeup look according to the advice.

[0358] Prompt Sentence Examples

[0359] Describe a system that analyzes a user's facial photo and provides optimal makeup advice based on their facial features and emotions. Specifically, what makeup would be suggested if the user is sad and has olive skin?

[0360] This allows users to receive personalized makeup advice that reflects their emotions at the time. The system analyzes and evaluates the user's facial expressions and emotions, and provides highly accurate makeup advice based on that.

[0361] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0362] Step 1:

[0363] Users take a photo of their face using the camera application on their smartphone or computer and press the "upload" button.

[0364] Input: The user operates the camera application and takes a photo of their face.

[0365] Output: The captured facial photo data.

[0366] Step 2:

[0367] The device generates a data packet containing the captured facial photo data and generates and sends a request to the server to send it.

[0368] Input: Facial photo data generated when the user presses the upload button.

[0369] Output: Face photo data packet sent to the server.

[0370] Step 3:

[0371] The server temporarily stores the received facial photo data and runs a facial recognition algorithm.

[0372] Input: Facial photo data sent from the device.

[0373] Output: Detected facial feature points data.

[0374] Specific operation: Using a facial recognition algorithm such as OpenCV, facial feature points (eyes, nose, mouth, etc.) are analyzed and facial pattern data is generated.

[0375] Step 4:

[0376] The server runs an emotion recognition algorithm based on the generated facial pattern data.

[0377] Input: Face pattern data.

[0378] Output: Emotional state data (e.g., happy, sad, surprised, etc.).

[0379] Specific operation: Analyzes the user's emotions from facial feature point data using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

[0380] Step 5:

[0381] The server compares the facial pattern data and emotional state data with a makeup database and selects the most suitable makeup style.

[0382] Input: Facial pattern data and emotional state data.

[0383] Output: Selection result of optimal makeup style.

[0384] Specific operation: Scores multiple makeup styles in the database and determines the makeup style that best suits the user's characteristics.

[0385] Step 6:

[0386] The server generates specific makeup advice based on the selected makeup style.

[0387] Input: Optimal makeup style and emotional state data.

[0388] Output: Detailed makeup advice.

[0389] Specific operation: Based on the selected makeup style, create makeup advice including eye shadow color, lip color type, foundation type and application method.

[0390] Step 7:

[0391] The server transmits the generated makeup advice to the user's terminal.

[0392] Input: Generated makeup advice.

[0393] Output: A data packet containing the make advice.

[0394] Specific operation: Generate and send a data packet to send makeup advice to the terminal.

[0395] Step 8:

[0396] The device analyzes the received makeup advice and displays it on the user interface.

[0397] Input: A data packet containing make advice sent by the server.

[0398] Output: Makeup advice displayed in the user interface.

[0399] Specific operation: Analyzes the data packet and displays the received makeup advice on the screen. The user can check it and try applying makeup.

[0400] (Application example 2)

[0401] 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."

[0402] Conventional makeup advice systems only provide uniform advice without considering the user's emotions, and have the problem of being unable to respond to needs that change depending on the user's condition. In addition, because users cannot actually try on the makeup items, it is difficult for them to select appropriate makeup items based on the advice.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0404] In this invention, the server includes: [means for a user to take an image of themselves and upload the image;] [means for the server to receive the uploaded image and analyze the pattern using feature extraction technology;] [means for the server to compare the analyzed pattern data with a database and generate optimal advice for the user;] [means for the server to send the generated advice to a client terminal and display it to the user;] [means for the server to analyze the user's emotions using an emotion engine and adjust the advice; and [means for enabling virtual try-on based on the advice. This makes it possible to provide personalized makeup advice that takes the user's emotions into consideration and to virtually try on how actual makeup items will look.

[0405] "User" means an end user of the Service or System.

[0406] An "image" is a photograph of a user's face or other visual data.

[0407] "Upload" is the process of sending data from a user's device to a server.

[0408] A "server" is a central computing resource that receives, analyzes, and transmits data.

[0409] "Feature extraction techniques" are algorithms and methods for extracting important information or features from images.

[0410] "Pattern data" is data related to the shape and features of a face obtained by image analysis.

[0411] A "database" is a data repository for storing makeup advice and other related information.

[0412] "Advice" refers to specific makeup techniques and product suggestions provided to users.

[0413] "Client Terminal" means a smartphone, tablet, or other computing device.

[0414] The "Emotion Engine" is a system that estimates and analyzes emotions from the user's facial expressions and other data.

[0415] "Virtual try-on" is a virtual experience that allows users to try on makeup items through video.

[0416] The present invention provides a system for providing personalized makeup advice to a user. The system analyzes the user's emotions and generates optimal makeup advice based on those emotions. Specific embodiments of the system are described below.

[0417] 1. Taking and uploading user images

[0418] The user takes a photo of their face using a client device such as a smartphone. After taking the photo, they press the "Upload" button in the application to send the image to the server. Once the server receives the image, it proceeds to the next step.

[0419] 2. Feature extraction and emotion analysis by the server

[0420] The server uses feature extraction technology to analyze the received image, extracting facial shapes and other important information to generate pattern data, and simultaneously analyzes the user's emotions (e.g., joy, sadness, surprise, etc.) using an emotion engine.

[0421] 3. Matching with the database and generating advice

[0422] The server compares the generated pattern data and emotion data with a database containing various makeup styles and corresponding advice. Based on this data, the server generates optimal makeup advice for the user.

[0423] 4. Virtual try-on

[0424] The generated advice includes a virtual try-on feature that allows users to virtually try on the suggested makeup items and see how they look on their own face.

[0425] 5. Providing advice and purchasing links

[0426] The client terminal receives the makeup advice sent from the server and displays it on the user interface. The displayed advice includes specific makeup techniques and product suggestions. Direct links to online shopping sites are also provided for the suggested makeup items, allowing the user to immediately purchase them.

[0427] Hardware and software used

[0428] This system uses the following hardware and software:

[0429] Smartphone or other client terminal: A device that takes an image of the user and sends it to the server.

[0430] Server: A central computing resource for image analysis, feature extraction, sentiment analysis, database matching, and advice generation.

[0431] Emotion engine: A system that estimates and analyzes emotions from the user's facial expressions.

[0432] Database: A data repository that stores makeup advice and related information.

[0433] Examples and prompts

[0434] For example, if a 28-year-old female user takes a photo of her face with her smartphone and emotional analysis shows that she is sad, the server will suggest a bright pink lip color and eyeshadow to accentuate the user's eyes based on the user's emotions.

[0435] Example prompt sentence:

[0436] "Design a virtual makeup advice system that suggests a bright pink lip color and eyeshadow to accentuate the eyes when a 28-year-old female user takes a photo of her face with a smartphone app and the emotion analysis indicates she is sad."

[0437] The system allows users to receive personalized makeup advice that takes into account their current emotions, and allows them to virtually try on the suggested makeup products, helping them make the right choice before actually purchasing them.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1: User takes and uploads an image

[0440] The user takes a photo of their face using a client device such as a smartphone. The input is the user's face photo, which is sent to the server by pressing the "upload" button in the application. The output is the face photo data sent to the server. Specifically, the face photo is taken using the smartphone's camera app and is transferred directly to the server.

[0441] Step 2: The server receives the image and performs feature extraction.

[0442] The server receives image data sent from the client device. The input is a photo of the user's face, which is analyzed to extract the shape and features of the face. Specifically, a facial recognition algorithm is used to identify facial feature points and generate pattern data. The output is the extracted facial feature information. Specifically, face recognition is performed using libraries such as OpenCV and Dlib.

[0443] Step 3: The server analyzes the emotion using the emotion engine

[0444] The server inputs the extracted facial feature data into the emotion engine. The input is facial feature data, and the emotion engine uses this to analyze the user's emotions. Specifically, it uses a neural network to perform emotion analysis and identify emotions such as joy, sadness, and surprise. The output is the analyzed emotion data. Specifically, it uses an AI model for emotion analysis to score emotions.

[0445] Step 4: The server checks the database and generates an advice

[0446] The server compares the facial feature data and emotion data with a makeup database. The input is facial feature data and emotion data, and based on this, it generates optimal makeup advice. Specifically, it searches the database for relevant makeup styles, scores them, and selects the most appropriate style. The output is makeup advice provided to the user. Specifically, it extracts and analyzes the most appropriate data from the database.

[0447] Step 5: The server provides the virtual try-on feature

[0448] The server provides a virtual try-on function based on the generated makeup advice. The input is the generated makeup advice, which is sent to the client terminal. Specifically, software is used to virtually apply makeup items to the user's face. The output is visual information of the virtual makeup displayed on the client terminal. Specifically, the virtual makeup is applied using AR technology.

[0449] Step 6: The server sends the advice to the client device and displays it.

[0450] The server sends the generated makeup advice to the client terminal. The input is advice data from the server, which the client terminal receives and displays on the user interface. The output is the situation in which the advice is displayed to the user. Specifically, the data is sent to the terminal using an HTTP request, and the received data is displayed within the application.

[0451] Step 7: Provide a link for users to purchase the makeup item

[0452] The makeup advice displayed on the client terminal includes a link to purchase the corresponding makeup item. The user can click on this link to access an online shopping site and purchase the makeup item. The input is the purchase link information sent from the server, and the user clicks on the link to access the purchasing site. The output is the situation in which the user actually purchases the makeup item. Specifically, the system processes the link click event and opens the corresponding web page.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] [Second embodiment]

[0457] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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."

[0469] This invention relates to a system that provides personalized makeup advice to individual users. This system uses face recognition technology to analyze the user's facial patterns and generate optimal makeup advice based on the results. The system's basic configuration consists of a user's terminal, a central server, and a makeup database.

[0470] Specific Embodiments of the System

[0471] 1. Upload a photo of the user's face

[0472] The user opens the camera application on their smartphone, tablet, or other device and takes a photo of their face. After taking the photo, the user presses the "upload" button in the application to send the photo to the server.

[0473] 2. Facial recognition and analysis by the server

[0474] When the user presses the upload button, the terminal creates and sends a request to send the face photo to the server.

[0475] The server analyzes the received facial photo using a facial recognition algorithm. In this analysis process, facial feature points (eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[0476] 3. Matching face pattern data with makeup database

[0477] The server compares the generated facial pattern data with a makeup database. This database stores information on various makeup styles, and scores the data to select the most suitable style based on the facial pattern. Based on the scoring results, the makeup style that best suits the user is selected.

[0478] 4. Generating and sending makeup advice

[0479] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[0480] The server transmits the generated makeup advice to the terminal.

[0481] 5. Users receive makeup advice

[0482] The device analyzes the makeup advice data received from the server and displays it on the user interface, allowing the user to view and try out the suggested makeup.

[0483] Specific examples

[0484] For example, if a 25-year-old female user uses the application to find makeup that suits her, the system will provide the best makeup advice by taking and uploading a photo of her face through the following steps:

[0485] 1. Upload a photo

[0486] Take a photo of your face using your smartphone and press the "Upload" button.

[0487] 2. Facial Recognition and Analysis

[0488] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[0489] 3. Database Verification

[0490] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[0491] 4. Advice Generation and Delivery

[0492] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[0493] 5. User Visibility

[0494] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0495] By following the above steps, users of this system can easily receive makeup advice that best suits them and enjoy their daily makeup routine.By automating analysis and suggestions, this system provides personalized makeup advice to users.

[0496] The processing flow will be explained below.

[0497] Step 1:

[0498] The user starts the application and takes a photo of their face using the camera function.

[0499] The device will temporarily store the captured facial photo within the app.

[0500] The user presses the "upload" button to indicate their intention to upload a photo of their face.

[0501] Step 2:

[0502] The device generates an upload request and prepares to send the stored facial photo to the server.

[0503] The terminal sends a facial photo along with the request to the server.

[0504] Step 3:

[0505] The server receives the facial photograph sent from the terminal.

[0506] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[0507] The server generates face pattern data as the analysis result.

[0508] Step 4:

[0509] The server compares the generated facial pattern data with a makeup database.

[0510] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern.

[0511] Step 5:

[0512] The server generates specific makeup advice based on the selected makeup style.

[0513] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[0514] Step 6:

[0515] The terminal receives the makeup advice data transmitted from the server.

[0516] The device analyzes the received data and displays makeup advice on the user interface.

[0517] Step 7:

[0518] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[0519] Examples:

[0520] For example, when a 25-year-old female user uses this system, the following process takes place:

[0521] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[0522] Step 2: The device sends a photo of the face to the server.

[0523] Step 3: The server receives the facial photo and analyzes it.

[0524] Step 4: The server uses the face pattern data to match the makeup database.

[0525] Step 5: The server generates optimal makeup advice and sends it to the device.

[0526] Step 6: The device receives and displays the advice.

[0527] Step 7: The user applies makeup based on the advice.

[0528] This allows users to easily find and apply makeup that suits them. The system uses facial recognition technology and database matching to automatically provide personalized makeup advice.

[0529] Example 1

[0530] 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."

[0531] Conventional makeup advice systems have faced the challenge of requiring a great deal of time and effort to provide personalized makeup advice to individual users. Another issue is that it is difficult to accurately recognize the user's facial features and suggest the optimal makeup style. Furthermore, there are cases where the accuracy and speed of the advice provided are lacking, leading to concerns about a poor user experience.

[0532] 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.

[0533] In this invention, the server includes: [means for a user to take an image of themselves and send that image;] [means for the server to receive the sent image and analyze the facial pattern using image recognition technology; and [means for the server to compare the analyzed facial pattern data with a database and generate advice that is best suited to the user.] This makes it possible to analyze the facial features of users quickly and with high accuracy, and to provide each individual user with makeup advice that is best suited to them in real time.

[0534] "User" refers to a person who uses the system to receive makeup advice.

[0535] "Images" refers to visual data such as photos and videos taken by users using their device's camera.

[0536] "Send" refers to the process of sending data from a terminal to a server.

[0537] "Receiving" refers to the process in which the server receives data sent from the terminal.

[0538] "Image recognition technology" refers to the technology of extracting features from images using computer vision and analyzing them.

[0539] A "face pattern" refers to a collection of facial feature points and shape data extracted using face recognition technology.

[0540] "Analysis" refers to the process of extracting facial features based on received image data and generating pattern data.

[0541] A "database" refers to a collection of accumulated data that stores multiple makeup styles and the corresponding information.

[0542] "Matching" refers to the process of comparing analyzed facial pattern data with data in a database to find a match.

[0543] "Advice" refers to makeup techniques and style suggestions generated by the server based on facial pattern data.

[0544] This clarifies the meaning of key words contained in the claims.

[0545] The present invention relates to a system for providing personalized makeup advice to users. The system is composed of a user terminal, a central server, and a makeup database.

[0546] First, the user takes a photo of their face using a device such as a smartphone or tablet. To take the photo, they can use a commonly available camera application. After taking the photo, the user presses the "upload" button in the application to send the photo to the server. This causes the device to send the photo to the server in the form of an HTTP request.

[0547] The server receives the sent facial photo. To receive the photo, server software (e.g., Apache or NGINX) is used to process HTTP requests. The received facial photo is analyzed using an image processing library (e.g., OpenCV or Dlib). In this analysis process, facial feature points (e.g., eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[0548] The server then compares the generated facial pattern data with an internal makeup database. This makeup database stores information on various makeup styles and their corresponding facial patterns. A database management system (e.g., MySQL or PostgreSQL) is used for the comparison, and scoring is performed to select the optimal style based on the facial pattern. A machine learning model (e.g., a generative AI model) can be used as the scoring algorithm.

[0549] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation application method. The generated makeup advice is sent to the device in JSON format.

[0550] The device receives the JSON data sent from the server, parses it, and displays it on the user interface, allowing the user to check the displayed makeup advice and use the suggested products and makeup techniques to apply their makeup.

[0551] Specific examples

[0552] For example, consider the case where a 25-year-old female user uses the application to find makeup that suits her. When the user takes and uploads a photo of her face, the system goes through the following process to provide the best makeup advice.

[0553] 1. Upload a photo

[0554] Users take a photo of their face with their smartphone and press the "upload" button.

[0555] 2. Facial Recognition and Analysis

[0556] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[0557] 3. Database Verification

[0558] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[0559] 4. Advice Generation and Delivery

[0560] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[0561] 5. User Visibility

[0562] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0563] Prompt Sentence Examples

[0564] "I'm a 25-year-old woman with olive skin tone. I'd like some advice on finding a makeup style that suits me. Please provide the best makeup advice based on the photo of my face I upload."

[0565] As a result, in the system of the present invention, the user can quickly and accurately receive personalized makeup advice that reflects the user's facial features.

[0566] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0567] Step 1:

[0568] A user opens a camera application on their device (smartphone or tablet). The camera is activated and the user adjusts the camera so that their face is clearly captured. After taking a photo of their face, the user presses the "upload" button in the application. The input of this step is the user's face photo, and the output is an HTTP request sent to the server.

[0569] Step 2:

[0570] When the user clicks the "Upload" button, the device generates an HTTP request including the captured face photo and sends it to the server. Specifically, the request includes face photo data (JPEG or PNG format) and necessary metadata (user ID, timestamp, etc.). The input is the user's face photo and user information, and the output is an HTTP request to the server.

[0571] Step 3:

[0572] The server receives the HTTP request sent from the device. The server software (e.g., Apache or NGINX) analyzes the request and extracts the facial photo data. The input is the HTTP request received by the server, and the output is the facial photo data required for analysis.

[0573] Step 4:

[0574] The server analyzes the facial photo data using an image recognition library (e.g., OpenCV, Dlib) and extracts facial feature points. This allows for information such as eye position, nose shape, mouth position, and skin tone to be obtained. The input is facial photo data, and the output is facial pattern data.

[0575] Step 5:

[0576] The server compares the analyzed facial pattern data with a makeup database. The database stores various makeup styles and their corresponding facial pattern data. The server uses a scoring algorithm to evaluate and select the optimal makeup style. The input is facial pattern data, and the output is information about the optimal makeup style.

[0577] Step 6:

[0578] The server generates specific makeup advice based on the selected makeup style. The advice includes eyeshadow color, lip color, foundation application method, etc. The input is the optimal makeup style information, and the output is the generated makeup advice.

[0579] Step 7:

[0580] The server converts the generated make advice into JSON format and sends it to the terminal. This uses a communication protocol between the server and the terminal (e.g., HTTP / S). The input is the generated make advice, and the output is the transmission of JSON data to the terminal.

[0581] Step 8:

[0582] The device receives and parses the JSON data sent from the server. The parsed data is displayed in the user interface and visually presented to the user. The input is the JSON data from the server, and the output is the makeup advice that is displayed.

[0583] Step 9:

[0584] The user checks the makeup advice displayed on the device and applies their makeup based on it. Using the provided items and process as a reference, the user can try out the makeup that best suits their face. The input is the makeup advice displayed on the device, and the output is the user's makeup result.

[0585] The above are the specific steps of the program processing in this system and the specific operations performed at each step.

[0586] (Application example 1)

[0587] 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."

[0588] Providing optimal makeup advice to customers is extremely important in modern beauty and cosmetic shops. However, with conventional methods, it takes a great deal of time and effort for customers to find the makeup style that suits them best. To solve this problem, a method is needed to provide customers with fast, accurate, personalized makeup advice.

[0589] 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.

[0590] In this invention, the server includes: [means for a user to take a photo of their face and upload the photo;] [means for the server to receive the uploaded photo of their face and analyze their facial pattern using facial recognition technology;] [means for comparing the analyzed facial pattern data with a makeup database and generating makeup advice that is optimal for the user;] [means for providing personalized makeup advice to each user at a beauty / cosmetic shop using an automated system; and [means for using a smartphone or smart glasses on a terminal to display the generated makeup advice. This enables users to quickly and easily receive makeup advice that is optimal for them when they visit a beauty / cosmetic shop.

[0591] "User" refers to an individual who uses the system to receive makeup advice that is best suited to them.

[0592] "Face Photo" refers to an image of a User's face that the User uploads to the System.

[0593] "Uploading" refers to the act of a user sending a photo of their own face to a server.

[0594] "Server" refers to a central computer that analyzes facial photos received from users and generates optimal makeup advice.

[0595] "Facial recognition technology" refers to technology for analyzing facial features from photographs of faces.

[0596] "Facial pattern" refers to facial feature data of a user extracted from a photograph of the user's face using facial recognition technology.

[0597] A "makeup database" refers to a database that stores information on various makeup styles.

[0598] "Makeup advice" refers to specific instructions and suggestions regarding the makeup style that is best suited to the user.

[0599] "Beauty and cosmetic shop" refers to a physical store that offers cosmetics and beauty-related products and services.

[0600] "Terminal" refers to an electronic device operated by a user, such as a smartphone or smart glasses.

[0601] This invention relates to a system that provides personalized makeup advice to individual users. This system works by having users take a photo of their face using a device such as a smartphone or smart glasses at a beauty or cosmetic shop and then uploading the photo.

[0602] Specific Embodiments of the System

[0603] 1. Take and upload a photo of your face

[0604] Users open the camera app on their smartphone or smart glasses and take a photo of their face. Once the photo is taken, they press the "upload" button in the app to send the photo to the server.

[0605] 2. Facial Recognition and Analysis

[0606] The server receives the uploaded facial photo and analyzes it using facial recognition technology. Specifically, it uses libraries such as OpenCV and dlib to extract facial feature points (such as the positions of the eyes, nose, and mouth) and generate facial pattern data.

[0607] 3. Matching face pattern data with makeup database

[0608] The server compares the generated facial pattern data with a makeup database, which stores information on various makeup styles. The server then scores the facial pattern data and makeup style data using KNN (K Nearest Neighbor) to select the optimal makeup style.

[0609] 4. Generating and sending makeup advice

[0610] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[0611] 5. Display of makeup advice

[0612] The device analyzes the makeup advice data received from the server and displays it on a user interface, allowing the user to try out the suggested makeup.

[0613] Hardware and software used

[0614] Hardware:

[0615] Smartphones, smart glasses, servers

[0616] software:

[0617] OpenCV: An image processing library used to analyze facial photos.

[0618] dlib: A library used for facial landmark detection.

[0619] scikit-learn: Used to suggest makeup styles using KNN (K nearest neighbors).

[0620] Specific examples

[0621] For example, suppose a 35-year-old female user visits a beauty and cosmetics shop. She takes a photo of her face using a device in the store (such as a smartphone or smart glasses) and uploads it to the server. The server analyzes the photo and generates optimal makeup advice based on the user's facial features. The advice generated includes specific suggestions such as purple eyeshadow, nude lip color, and peachy pink blush.

[0622] Prompt Sentence Examples

[0623] "Based on the user's facial photo data, please analyze the user's skin tone, face shape, and features, and provide the best makeup advice."

[0624] Example format:

[0625] The dataset should be in the following format:

[0626] [Age, Skin Tone, Face Shape, Eye Shape, Nose Shape, Lip Shape] -> [Eyeshadow Color, Lip Color, Cheek Color]

[0627] example:

[0628] Input: [35, "Winter", "Round", "Average", "Average", "Thick"]

[0629] Output: [Purple, Nude, Peach Pink]

[0630] As described above, by implementing this system in beauty and cosmetic shops, users can quickly and easily receive makeup advice that is best suited to them.

[0631] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0632] Step 1:

[0633] The user takes a photo of their face using a smartphone or smart glasses, using the camera application on the device. Once the photo is taken, the user presses the "upload" button in the application to send the photo to the next step.

[0634] Input: User's face photo

[0635] Output: Face photo waiting to be sent to the server

[0636] Step 2:

[0637] When a user presses the upload button, the device sends the face photo to the server, a process that involves creating a data transfer request and sending it over the network.

[0638] Input: A photo of your face waiting to be sent

[0639] Output: Face photo uploaded to the server

[0640] Step 3:

[0641] The server receives the uploaded facial photo, then converts it to grayscale using OpenCV and detects facial feature points using dlib to analyze it.

[0642] Input: Uploaded face photo

[0643] Output: Facial feature point data

[0644] Step 4:

[0645] The server generates a face pattern based on the facial feature point data. Specifically, it records the coordinates of each point on the face as an array and constructs face pattern data.

[0646] Input: Facial feature point data

[0647] Output: Face pattern data

[0648] Step 5:

[0649] The server compares the generated facial pattern data with a makeup database, which contains information on various makeup styles. It then uses KNN (K Nearest Neighbor) to score the facial pattern data and makeup style data and selects the most suitable makeup style.

[0650] Input: Face pattern data

[0651] Output: Selected makeup style data

[0652] Step 6:

[0653] The server generates specific makeup advice based on the selected makeup style data, such as advice on eye shadow colors, lip colors, and foundation application methods that are tailored to the user.

[0654] Input: Selected makeup style data

[0655] Output: Specific makeup advice data

[0656] Step 7:

[0657] The server sends the generated makeup advice to the user's device using the notification function of the application and a data transfer protocol.

[0658] Input: Specific makeup advice data

[0659] Output: Makeup advice sent to the user's terminal

[0660] Step 8:

[0661] The device analyzes the makeup advice data received from the server and displays it on the user interface. The user can then view the displayed makeup advice and try out the makeup that suits them best.

[0662] Input: Make advice data received from the server

[0663] Output: Makeup advice displayed on the terminal

[0664] Through these steps, users can quickly and easily receive personalized makeup advice when they visit a beauty or cosmetics store.

[0665] 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.

[0666] The present invention is a system that provides personalized makeup advice to individual users by combining face recognition technology and an emotion engine. This system recognizes the user's emotions and generates appropriate makeup advice based on those emotions. Specific embodiments are described below.

[0667] Specific Embodiments of the System

[0668] 1. Upload a photo of the user's face

[0669] The user opens the camera application on their device and takes a photo of their face. After taking the photo, they press the "upload" button to send the photo to the server.

[0670] 2. Facial recognition and emotion analysis by the server

[0671] The terminal generates and transmits a request to transmit the face photo to the server.

[0672] The server analyzes the received facial photo using a facial recognition algorithm and extracts facial features.

[0673] The server uses an emotion engine based on the facial recognition results to analyze the user's emotions (e.g., joy, sadness, surprise, etc.).

[0674] 3. Matching face pattern data with makeup database

[0675] The server compares the generated facial pattern data and emotion data with a makeup database.

[0676] The server scores the makeup styles and selects the one that best suits the user's facial pattern and emotions.

[0677] 4. Generating and adjusting makeup advice

[0678] Based on the selected makeup style, the server generates specific makeup advice, including eyeshadow color, lip color, and foundation application.

[0679] The emotion engine adjusts makeup advice based on the user's emotions, for example, suggesting bright makeup if the user is sad.

[0680] 5. Sending and displaying makeup advice

[0681] The server transmits the generated makeup advice to the terminal.

[0682] The device analyzes the received makeup advice and displays it on the user interface, allowing the user to try out makeup according to the advice.

[0683] Specific examples

[0684] For example, when a 25-year-old female user uses this system, the following process takes place:

[0685] 1. Upload a photo

[0686] Take a photo of your face with your smartphone and press the "Upload" button.

[0687] 2. Facial Recognition and Emotion Analysis

[0688] The server receives the photo, analyzes facial features, and uses an emotion engine to recognize the emotion the user is currently feeling, for example, sensing that the user is sad.

[0689] 3. Database Verification

[0690] The server compares the facial pattern data and emotional data with a makeup database, and selects makeup styles that suit olive skin and makeup based on emotions through scoring. For example, it suggests bright makeup to alleviate sad moods.

[0691] 4. Advice Generation and Reconciliation

[0692] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a bright lip color that will make the user smile.

[0693] 5. User Visibility

[0694] The device receives the makeup advice and shows it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0695] This allows users to receive personalized makeup advice that takes into account their emotions at the time. By combining the user's facial expressions and emotions, the system provides more appropriate and satisfying makeup advice.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The user launches the application and uses the camera function to take a photo of their face.

[0699] The device will temporarily store the captured facial photo within the app.

[0700] The user presses the "Upload" button and uploads a photo of their face.

[0701] Step 2:

[0702] The device generates an upload request and prepares to send the stored facial photo to the server.

[0703] The device sends the request and the facial photo to the server.

[0704] Step 3:

[0705] The server receives the facial photograph sent from the terminal.

[0706] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[0707] The server generates face pattern data as the analysis result.

[0708] Step 4:

[0709] The server uses the facial pattern data to perform analysis using an emotion engine.

[0710] The server recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) and generates emotion data.

[0711] Step 5:

[0712] The server matches the facial pattern data and emotion data with a makeup database.

[0713] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern and emotion.

[0714] Step 6:

[0715] The server generates specific makeup advice based on the selected makeup style.

[0716] The server tailors its advice based on emotional data from the emotion engine, for example, suggesting bright makeup if the user is sad.

[0717] Step 7:

[0718] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[0719] Step 8:

[0720] The terminal receives the makeup advice data transmitted from the server.

[0721] The terminal analyzes the received data and displays makeup advice on the user interface.

[0722] Step 9:

[0723] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[0724] Examples:

[0725] For example, when a 25-year-old female user uses this system, the following process takes place:

[0726] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[0727] Step 2: The device sends a photo of the face to the server.

[0728] Step 3: The server receives the facial photo and analyzes it.

[0729] Step 4: The server uses the emotion engine to recognize the user's emotion. It detects that the user is sad.

[0730] Step 5: The server uses the face pattern data and emotion data to match the makeup database.

[0731] Step 6: The server generates optimal makeup advice and adjusts it based on emotion, e.g., suggesting a bright lip color.

[0732] Step 7: The server sends the advice data to the terminal.

[0733] Step 8: The device receives and displays the advice.

[0734] Step 9: The user applies makeup based on the advice.

[0735] This allows users to receive personalized makeup advice that takes into account their emotions at the time, allowing them to enjoy more appropriate and satisfying makeup. By combining facial recognition technology with an emotion engine, it becomes possible to provide makeup suggestions that match the user's emotions.

[0736] Example 2

[0737] 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."

[0738] In modern beauty, it is important for each user to receive appropriate makeup advice tailored to their facial features and current emotional state. However, conventional makeup advice systems have difficulty providing personalized advice that takes into account the user's facial expressions and emotions, resulting in a decrease in makeup satisfaction.

[0739] 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.

[0740] In this invention, the server includes: [means for receiving an uploaded facial photograph and analyzing facial patterns using facial recognition technology;] [means for analyzing the user's emotional state using emotion recognition technology based on the facial recognition results; and] [means for collating the analyzed facial pattern data and emotional state data and generating makeup advice optimal for the user.] This makes it possible to provide personalized, highly accurate makeup advice based on the user's facial features and emotional state.

[0741] "User" means a person who uses the system and operates a device or application to take a photograph of his or her face and upload it to the system.

[0742] A "face photo" is image data that captures the user's facial features and is the subject of analysis using face recognition technology and emotion recognition technology.

[0743] "Upload" refers to the operation by which a user transfers facial photo data from their own device to the server.

[0744] The "server" is a central processing unit that receives facial photo data, performs facial and emotion recognition, generates optimal makeup advice, and sends it to the terminal.

[0745] "Facial recognition technology" is a technology that analyzes facial photographs to identify facial features (eyes, nose, mouth, etc.) and generate facial pattern data.

[0746] "Emotion recognition technology" is a technology that analyzes a user's emotional state (happiness, sadness, surprise, etc.) based on facial feature point data obtained from a facial photograph.

[0747] "Facial pattern data" is facial feature point information obtained by facial recognition technology, and is basic data for generating makeup advice.

[0748] "Emotional state data" is emotional information about the user analyzed using emotion recognition technology, and is basic data for adjusting makeup advice.

[0749] A "makeup database" is a data storage that stores various makeup styles and their corresponding facial feature patterns and emotional state data.

[0750] "Makeup advice" is specific guidance on the most suitable makeup technique for the user, generated based on the face pattern data and emotional state data analyzed by the server.

[0751] A "terminal" is a device used by a user to take and upload a facial photo, and ultimately receive and display the generated makeup advice.

[0752] The present invention relates to a system that analyzes a user's facial photograph and provides personalized makeup advice based on their emotions. This system is composed of a user's terminal, a server, face recognition technology, emotion recognition technology, and a makeup database. Specific embodiments are described below.

[0753] System Overview

[0754] This system allows users to upload a photo of their face, and the server analyzes the photo to generate makeup advice tailored to the situation. Key software components include OpenCV (facial recognition technology) and Microsoft Azure Emotion API (emotion recognition technology).

[0755] Upload a user's photo

[0756] Users take a photo of their face using the camera application on their smartphone or computer, and then press the "upload" button to send the photo to the server.

[0757] The terminal generates and transmits a request to transmit this facial photograph data to the server.

[0758] Server-based face recognition and emotion analysis

[0759] The server temporarily stores the received facial photograph in a database.

[0760] The server uses a facial recognition algorithm (for example, OpenCV) to analyze facial feature points (eyes, nose, mouth, etc.) and generate facial pattern data.

[0761] The server then uses an emotion recognition algorithm (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state (e.g., happiness, sadness, surprise) from the facial pattern data.

[0762] Matching face patterns with makeup database

[0763] The server compares the analyzed facial pattern data and emotional state data with a makeup database, which contains a wide variety of makeup styles and their corresponding facial feature patterns and emotional states.

[0764] The server scores multiple candidate styles and selects the most suitable makeup style. For example, if the user is sad, it selects a bright-colored makeup style.

[0765] Generate and adjust makeup advice

[0766] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation type and application method.

[0767] The server uses an emotion recognition engine to tailor makeup advice based on the user's emotional state, such as suggesting bright makeup to alleviate sad emotions.

[0768] Providing makeup advice

[0769] The server transmits the generated makeup advice to the terminal.

[0770] The device analyzes the received makeup advice and displays it to the user, who can then try out different makeup looks based on the advice.

[0771] Specific examples

[0772] For example, if a 25-year-old female user uses this system, the following process will occur:

[0773] 1. Users upload photos of their faces

[0774] Take a photo of your face with your smartphone and press the "Upload" button.

[0775] 2. Facial Recognition and Emotion Analysis

[0776] The server receives the photo, analyzes facial features using OpenCV, and analyzes emotions using the Microsoft Azure Emotion API to determine whether the user is sad.

[0777] 3. Database Verification

[0778] The server compares the facial pattern data and emotion data with a makeup database to select the most suitable makeup style, including a bright lip color to alleviate sad emotions.

[0779] 4. Advice Generation and Adjustment

[0780] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a vibrant lip color to help users feel more energized.

[0781] 5. Display of makeup advice

[0782] The device receives the advice and presents it to the user, who can then try out the makeup look according to the advice.

[0783] Prompt Sentence Examples

[0784] Describe a system that analyzes a user's facial photo and provides optimal makeup advice based on their facial features and emotions. Specifically, what makeup would be suggested if the user is sad and has olive skin?

[0785] This allows users to receive personalized makeup advice that reflects their emotions at the time. The system analyzes and evaluates the user's facial expressions and emotions, and provides highly accurate makeup advice based on that.

[0786] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0787] Step 1:

[0788] Users take a photo of their face using the camera application on their smartphone or computer and press the "upload" button.

[0789] Input: The user operates the camera application and takes a photo of their face.

[0790] Output: The captured facial photo data.

[0791] Step 2:

[0792] The device generates a data packet containing the captured facial photo data and generates and sends a request to the server to send it.

[0793] Input: Facial photo data generated when the user presses the upload button.

[0794] Output: Face photo data packet sent to the server.

[0795] Step 3:

[0796] The server temporarily stores the received facial photo data and runs a facial recognition algorithm.

[0797] Input: Facial photo data sent from the device.

[0798] Output: Detected facial feature points data.

[0799] Specific operation: Using a facial recognition algorithm such as OpenCV, facial feature points (eyes, nose, mouth, etc.) are analyzed and facial pattern data is generated.

[0800] Step 4:

[0801] The server runs an emotion recognition algorithm based on the generated facial pattern data.

[0802] Input: Face pattern data.

[0803] Output: Emotional state data (e.g., happy, sad, surprised, etc.).

[0804] Specific operation: Analyzes the user's emotions from facial feature point data using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

[0805] Step 5:

[0806] The server compares the facial pattern data and emotional state data with a makeup database and selects the most suitable makeup style.

[0807] Input: Facial pattern data and emotional state data.

[0808] Output: Selection result of optimal makeup style.

[0809] Specific operation: Scores multiple makeup styles in the database and determines the makeup style that best suits the user's characteristics.

[0810] Step 6:

[0811] The server generates specific makeup advice based on the selected makeup style.

[0812] Input: Optimal makeup style and emotional state data.

[0813] Output: Detailed makeup advice.

[0814] Specific operation: Based on the selected makeup style, create makeup advice including eye shadow color, lip color type, foundation type and application method.

[0815] Step 7:

[0816] The server transmits the generated makeup advice to the user's terminal.

[0817] Input: Generated makeup advice.

[0818] Output: A data packet containing the make advice.

[0819] Specific operation: Generate and send a data packet to send makeup advice to the terminal.

[0820] Step 8:

[0821] The device analyzes the received makeup advice and displays it on the user interface.

[0822] Input: A data packet containing make advice sent by the server.

[0823] Output: Makeup advice displayed in the user interface.

[0824] Specific operation: Analyzes the data packet and displays the received makeup advice on the screen. The user can check it and try applying makeup.

[0825] (Application example 2)

[0826] 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."

[0827] Conventional makeup advice systems only provide uniform advice without considering the user's emotions, and have the problem of being unable to respond to needs that change depending on the user's condition. In addition, because users cannot actually try on the makeup items, it is difficult for them to select appropriate makeup items based on the advice.

[0828] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0829] In this invention, the server includes: [means for a user to take an image of themselves and upload the image;] [means for the server to receive the uploaded image and analyze the pattern using feature extraction technology;] [means for the server to compare the analyzed pattern data with a database and generate optimal advice for the user;] [means for the server to send the generated advice to a client terminal and display it to the user;] [means for the server to analyze the user's emotions using an emotion engine and adjust the advice; and [means for enabling virtual try-on based on the advice. This makes it possible to provide personalized makeup advice that takes the user's emotions into consideration and to virtually try on how actual makeup items will look.

[0830] "User" means an end user of the Service or System.

[0831] An "image" is a photograph of a user's face or other visual data.

[0832] "Upload" is the process of sending data from a user's device to a server.

[0833] A "server" is a central computing resource that receives, analyzes, and transmits data.

[0834] "Feature extraction techniques" are algorithms and methods for extracting important information or features from images.

[0835] "Pattern data" is data related to the shape and features of a face obtained by image analysis.

[0836] A "database" is a data repository for storing makeup advice and other related information.

[0837] "Advice" refers to specific makeup techniques and product suggestions provided to users.

[0838] "Client Terminal" means a smartphone, tablet, or other computing device.

[0839] The "Emotion Engine" is a system that estimates and analyzes emotions from the user's facial expressions and other data.

[0840] "Virtual try-on" is a virtual experience that allows users to try on makeup items through video.

[0841] The present invention provides a system for providing personalized makeup advice to a user. The system analyzes the user's emotions and generates optimal makeup advice based on those emotions. Specific embodiments of the system are described below.

[0842] 1. Taking and uploading user images

[0843] The user takes a photo of their face using a client device such as a smartphone. After taking the photo, they press the "Upload" button in the application to send the image to the server. Once the server receives the image, it proceeds to the next step.

[0844] 2. Feature extraction and emotion analysis by the server

[0845] The server uses feature extraction technology to analyze the received image, extracting facial shapes and other important information to generate pattern data, and simultaneously analyzes the user's emotions (e.g., joy, sadness, surprise, etc.) using an emotion engine.

[0846] 3. Matching with the database and generating advice

[0847] The server compares the generated pattern data and emotion data with a database containing various makeup styles and corresponding advice. Based on this data, the server generates optimal makeup advice for the user.

[0848] 4. Virtual try-on

[0849] The generated advice includes a virtual try-on feature that allows users to virtually try on the suggested makeup items and see how they look on their own face.

[0850] 5. Providing advice and purchasing links

[0851] The client terminal receives the makeup advice sent from the server and displays it on the user interface. The displayed advice includes specific makeup techniques and product suggestions. Direct links to online shopping sites are also provided for the suggested makeup items, allowing the user to immediately purchase them.

[0852] Hardware and software used

[0853] This system uses the following hardware and software:

[0854] Smartphone or other client terminal: A device that takes an image of the user and sends it to the server.

[0855] Server: A central computing resource for image analysis, feature extraction, sentiment analysis, database matching, and advice generation.

[0856] Emotion engine: A system that estimates and analyzes emotions from the user's facial expressions.

[0857] Database: A data repository that stores makeup advice and related information.

[0858] Examples and prompts

[0859] For example, if a 28-year-old female user takes a photo of her face with her smartphone and emotional analysis shows that she is sad, the server will suggest a bright pink lip color and eyeshadow to accentuate the user's eyes based on the user's emotions.

[0860] Example prompt sentence:

[0861] "Design a virtual makeup advice system that suggests a bright pink lip color and eyeshadow to accentuate the eyes when a 28-year-old female user takes a photo of her face with a smartphone app and the emotion analysis indicates she is sad."

[0862] The system allows users to receive personalized makeup advice that takes into account their current emotions, and allows them to virtually try on the suggested makeup products, helping them make the right choice before actually purchasing them.

[0863] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0864] Step 1: User takes and uploads an image

[0865] The user takes a photo of their face using a client device such as a smartphone. The input is the user's face photo, which is sent to the server by pressing the "upload" button in the application. The output is the face photo data sent to the server. Specifically, the face photo is taken using the smartphone's camera app and is transferred directly to the server.

[0866] Step 2: The server receives the image and performs feature extraction.

[0867] The server receives image data sent from the client device. The input is a photo of the user's face, which is analyzed to extract the shape and features of the face. Specifically, a facial recognition algorithm is used to identify facial feature points and generate pattern data. The output is the extracted facial feature information. Specifically, face recognition is performed using libraries such as OpenCV and Dlib.

[0868] Step 3: The server analyzes the emotion using the emotion engine

[0869] The server inputs the extracted facial feature data into the emotion engine. The input is facial feature data, and the emotion engine uses this to analyze the user's emotions. Specifically, it uses a neural network to perform emotion analysis and identify emotions such as joy, sadness, and surprise. The output is the analyzed emotion data. Specifically, it uses an AI model for emotion analysis to score emotions.

[0870] Step 4: The server checks the database and generates an advice

[0871] The server compares the facial feature data and emotion data with a makeup database. The input is facial feature data and emotion data, and based on this, it generates optimal makeup advice. Specifically, it searches the database for relevant makeup styles, scores them, and selects the most appropriate style. The output is makeup advice provided to the user. Specifically, it extracts and analyzes the most appropriate data from the database.

[0872] Step 5: The server provides the virtual try-on feature

[0873] The server provides a virtual try-on function based on the generated makeup advice. The input is the generated makeup advice, which is sent to the client terminal. Specifically, software is used to virtually apply makeup items to the user's face. The output is visual information of the virtual makeup displayed on the client terminal. Specifically, the virtual makeup is applied using AR technology.

[0874] Step 6: The server sends the advice to the client device and displays it.

[0875] The server sends the generated makeup advice to the client terminal. The input is advice data from the server, which the client terminal receives and displays on the user interface. The output is the situation in which the advice is displayed to the user. Specifically, the data is sent to the terminal using an HTTP request, and the received data is displayed within the application.

[0876] Step 7: Provide a link for users to purchase the makeup item

[0877] The makeup advice displayed on the client terminal includes a link to purchase the corresponding makeup item. The user can click on this link to access an online shopping site and purchase the makeup item. The input is the purchase link information sent from the server, and the user clicks on the link to access the purchasing site. The output is the situation in which the user actually purchases the makeup item. Specifically, the system processes the link click event and opens the corresponding web page.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 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.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] This invention relates to a system that provides personalized makeup advice to individual users. This system uses face recognition technology to analyze the user's facial patterns and generate optimal makeup advice based on the results. The system's basic configuration consists of a user's terminal, a central server, and a makeup database.

[0895] Specific Embodiments of the System

[0896] 1. Upload a photo of the user's face

[0897] The user opens the camera application on their smartphone, tablet, or other device and takes a photo of their face. After taking the photo, the user presses the "upload" button in the application to send the photo to the server.

[0898] 2. Facial recognition and analysis by the server

[0899] When the user presses the upload button, the terminal creates and sends a request to send the face photo to the server.

[0900] The server analyzes the received facial photo using a facial recognition algorithm. In this analysis process, facial feature points (eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[0901] 3. Matching face pattern data with makeup database

[0902] The server compares the generated facial pattern data with a makeup database. This database stores information on various makeup styles, and scores the data to select the most suitable style based on the facial pattern. Based on the scoring results, the makeup style that best suits the user is selected.

[0903] 4. Generating and sending makeup advice

[0904] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[0905] The server transmits the generated makeup advice to the terminal.

[0906] 5. Users receive makeup advice

[0907] The device analyzes the makeup advice data received from the server and displays it on the user interface, allowing the user to view and try out the suggested makeup.

[0908] Specific examples

[0909] For example, if a 25-year-old female user uses the application to find makeup that suits her, the system will provide the best makeup advice by taking and uploading a photo of her face through the following steps:

[0910] 1. Upload a photo

[0911] Take a photo of your face using your smartphone and press the "Upload" button.

[0912] 2. Facial Recognition and Analysis

[0913] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[0914] 3. Database Verification

[0915] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[0916] 4. Advice Generation and Delivery

[0917] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[0918] 5. User Visibility

[0919] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0920] By following the above steps, users of this system can easily receive makeup advice that best suits them and enjoy their daily makeup routine.By automating analysis and suggestions, this system provides personalized makeup advice to users.

[0921] The processing flow will be explained below.

[0922] Step 1:

[0923] The user starts the application and takes a photo of their face using the camera function.

[0924] The device will temporarily store the captured facial photo within the app.

[0925] The user presses the "upload" button to indicate their intention to upload a photo of their face.

[0926] Step 2:

[0927] The device generates an upload request and prepares to send the stored facial photo to the server.

[0928] The terminal sends a facial photo along with the request to the server.

[0929] Step 3:

[0930] The server receives the facial photograph sent from the terminal.

[0931] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[0932] The server generates face pattern data as the analysis result.

[0933] Step 4:

[0934] The server compares the generated facial pattern data with a makeup database.

[0935] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern.

[0936] Step 5:

[0937] The server generates specific makeup advice based on the selected makeup style.

[0938] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[0939] Step 6:

[0940] The terminal receives the makeup advice data transmitted from the server.

[0941] The device analyzes the received data and displays makeup advice on the user interface.

[0942] Step 7:

[0943] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[0944] Examples:

[0945] For example, when a 25-year-old female user uses this system, the following process takes place:

[0946] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[0947] Step 2: The device sends a photo of the face to the server.

[0948] Step 3: The server receives the facial photo and analyzes it.

[0949] Step 4: The server uses the face pattern data to match the makeup database.

[0950] Step 5: The server generates optimal makeup advice and sends it to the device.

[0951] Step 6: The device receives and displays the advice.

[0952] Step 7: The user applies makeup based on the advice.

[0953] This allows users to easily find and apply makeup that suits them. The system uses facial recognition technology and database matching to automatically provide personalized makeup advice.

[0954] Example 1

[0955] 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."

[0956] Conventional makeup advice systems have faced the challenge of requiring a great deal of time and effort to provide personalized makeup advice to individual users. Another issue is that it is difficult to accurately recognize the user's facial features and suggest the optimal makeup style. Furthermore, there are cases where the accuracy and speed of the advice provided are lacking, leading to concerns about a poor user experience.

[0957] 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.

[0958] In this invention, the server includes: [means for a user to take an image of themselves and send that image;] [means for the server to receive the sent image and analyze the facial pattern using image recognition technology; and [means for the server to compare the analyzed facial pattern data with a database and generate advice that is best suited to the user.] This makes it possible to analyze the facial features of users quickly and with high accuracy, and to provide each individual user with makeup advice that is best suited to them in real time.

[0959] "User" refers to a person who uses the system to receive makeup advice.

[0960] "Images" refers to visual data such as photos and videos taken by users using their device's camera.

[0961] "Send" refers to the process of sending data from a terminal to a server.

[0962] "Receiving" refers to the process in which the server receives data sent from the terminal.

[0963] "Image recognition technology" refers to the technology of extracting features from images using computer vision and analyzing them.

[0964] A "face pattern" refers to a collection of facial feature points and shape data extracted using face recognition technology.

[0965] "Analysis" refers to the process of extracting facial features based on received image data and generating pattern data.

[0966] A "database" refers to a collection of accumulated data that stores multiple makeup styles and the corresponding information.

[0967] "Matching" refers to the process of comparing analyzed facial pattern data with data in a database to find a match.

[0968] "Advice" refers to makeup techniques and style suggestions generated by the server based on facial pattern data.

[0969] This clarifies the meaning of key words contained in the claims.

[0970] The present invention relates to a system for providing personalized makeup advice to users. The system is composed of a user terminal, a central server, and a makeup database.

[0971] First, the user takes a photo of their face using a device such as a smartphone or tablet. To take the photo, they can use a commonly available camera application. After taking the photo, the user presses the "upload" button in the application to send the photo to the server. This causes the device to send the photo to the server in the form of an HTTP request.

[0972] The server receives the sent facial photo. To receive the photo, server software (e.g., Apache or NGINX) is used to process HTTP requests. The received facial photo is analyzed using an image processing library (e.g., OpenCV or Dlib). In this analysis process, facial feature points (e.g., eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[0973] The server then compares the generated facial pattern data with an internal makeup database. This makeup database stores information on various makeup styles and their corresponding facial patterns. A database management system (e.g., MySQL or PostgreSQL) is used for the comparison, and scoring is performed to select the optimal style based on the facial pattern. A machine learning model (e.g., a generative AI model) can be used as the scoring algorithm.

[0974] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation application method. The generated makeup advice is sent to the device in JSON format.

[0975] The device receives the JSON data sent from the server, parses it, and displays it on the user interface, allowing the user to check the displayed makeup advice and use the suggested products and makeup techniques to apply their makeup.

[0976] Specific examples

[0977] For example, consider the case where a 25-year-old female user uses the application to find makeup that suits her. When the user takes and uploads a photo of her face, the system goes through the following process to provide the best makeup advice.

[0978] 1. Upload a photo

[0979] Users take a photo of their face with their smartphone and press the "upload" button.

[0980] 2. Facial Recognition and Analysis

[0981] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[0982] 3. Database Verification

[0983] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[0984] 4. Advice Generation and Delivery

[0985] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[0986] 5. User Visibility

[0987] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[0988] Prompt Sentence Examples

[0989] "I'm a 25-year-old woman with olive skin tone. I'd like some advice on finding a makeup style that suits me. Please provide the best makeup advice based on the photo of my face I upload."

[0990] As a result, in the system of the present invention, the user can quickly and accurately receive personalized makeup advice that reflects the user's facial features.

[0991] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0992] Step 1:

[0993] A user opens a camera application on their device (smartphone or tablet). The camera is activated and the user adjusts the camera so that their face is clearly captured. After taking a photo of their face, the user presses the "upload" button in the application. The input of this step is the user's face photo, and the output is an HTTP request sent to the server.

[0994] Step 2:

[0995] When the user clicks the "Upload" button, the device generates an HTTP request including the captured face photo and sends it to the server. Specifically, the request includes face photo data (JPEG or PNG format) and necessary metadata (user ID, timestamp, etc.). The input is the user's face photo and user information, and the output is an HTTP request to the server.

[0996] Step 3:

[0997] The server receives the HTTP request sent from the device. The server software (e.g., Apache or NGINX) analyzes the request and extracts the facial photo data. The input is the HTTP request received by the server, and the output is the facial photo data required for analysis.

[0998] Step 4:

[0999] The server analyzes the facial photo data using an image recognition library (e.g., OpenCV, Dlib) and extracts facial feature points. This allows for information such as eye position, nose shape, mouth position, and skin tone to be obtained. The input is facial photo data, and the output is facial pattern data.

[1000] Step 5:

[1001] The server compares the analyzed facial pattern data with a makeup database. The database stores various makeup styles and their corresponding facial pattern data. The server uses a scoring algorithm to evaluate and select the optimal makeup style. The input is facial pattern data, and the output is information about the optimal makeup style.

[1002] Step 6:

[1003] The server generates specific makeup advice based on the selected makeup style. The advice includes eyeshadow color, lip color, foundation application method, etc. The input is the optimal makeup style information, and the output is the generated makeup advice.

[1004] Step 7:

[1005] The server converts the generated make advice into JSON format and sends it to the terminal. This uses a communication protocol between the server and the terminal (e.g., HTTP / S). The input is the generated make advice, and the output is the transmission of JSON data to the terminal.

[1006] Step 8:

[1007] The device receives and parses the JSON data sent from the server. The parsed data is displayed in the user interface and visually presented to the user. The input is the JSON data from the server, and the output is the makeup advice that is displayed.

[1008] Step 9:

[1009] The user checks the makeup advice displayed on the device and applies their makeup based on it. Using the provided items and process as a reference, the user can try out the makeup that best suits their face. The input is the makeup advice displayed on the device, and the output is the user's makeup result.

[1010] The above are the specific steps of the program processing in this system and the specific operations performed at each step.

[1011] (Application example 1)

[1012] 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."

[1013] Providing optimal makeup advice to customers is extremely important in modern beauty and cosmetic shops. However, with conventional methods, it takes a great deal of time and effort for customers to find the makeup style that suits them best. To solve this problem, a method is needed to provide customers with fast, accurate, personalized makeup advice.

[1014] 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.

[1015] In this invention, the server includes: [means for a user to take a photo of their face and upload the photo;] [means for the server to receive the uploaded photo of their face and analyze their facial pattern using facial recognition technology;] [means for comparing the analyzed facial pattern data with a makeup database and generating makeup advice that is optimal for the user;] [means for providing personalized makeup advice to each user at a beauty / cosmetic shop using an automated system; and [means for using a smartphone or smart glasses on a terminal to display the generated makeup advice. This enables users to quickly and easily receive makeup advice that is optimal for them when they visit a beauty / cosmetic shop.

[1016] "User" refers to an individual who uses the system to receive makeup advice that is best suited to them.

[1017] "Face Photo" refers to an image of a User's face that the User uploads to the System.

[1018] "Uploading" refers to the act of a user sending a photo of their own face to a server.

[1019] "Server" refers to a central computer that analyzes facial photos received from users and generates optimal makeup advice.

[1020] "Facial recognition technology" refers to technology for analyzing facial features from photographs of faces.

[1021] "Facial pattern" refers to facial feature data of a user extracted from a photograph of the user's face using facial recognition technology.

[1022] A "makeup database" refers to a database that stores information on various makeup styles.

[1023] "Makeup advice" refers to specific instructions and suggestions regarding the makeup style that is best suited to the user.

[1024] "Beauty and cosmetic shop" refers to a physical store that offers cosmetics and beauty-related products and services.

[1025] "Terminal" refers to an electronic device operated by a user, such as a smartphone or smart glasses.

[1026] This invention relates to a system that provides personalized makeup advice to individual users. This system works by having users take a photo of their face using a device such as a smartphone or smart glasses at a beauty or cosmetic shop and then uploading the photo.

[1027] Specific Embodiments of the System

[1028] 1. Take and upload a photo of your face

[1029] Users open the camera app on their smartphone or smart glasses and take a photo of their face. Once the photo is taken, they press the "upload" button in the app to send the photo to the server.

[1030] 2. Facial Recognition and Analysis

[1031] The server receives the uploaded facial photo and analyzes it using facial recognition technology. Specifically, it uses libraries such as OpenCV and dlib to extract facial feature points (such as the positions of the eyes, nose, and mouth) and generate facial pattern data.

[1032] 3. Matching face pattern data with makeup database

[1033] The server compares the generated facial pattern data with a makeup database, which stores information on various makeup styles. The server then scores the facial pattern data and makeup style data using KNN (K Nearest Neighbor) to select the optimal makeup style.

[1034] 4. Generating and sending makeup advice

[1035] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[1036] 5. Display of makeup advice

[1037] The device analyzes the makeup advice data received from the server and displays it on a user interface, allowing the user to try out the suggested makeup.

[1038] Hardware and software used

[1039] Hardware:

[1040] Smartphones, smart glasses, servers

[1041] software:

[1042] OpenCV: An image processing library used to analyze facial photos.

[1043] dlib: A library used for facial landmark detection.

[1044] scikit-learn: Used to suggest makeup styles using KNN (K nearest neighbors).

[1045] Specific examples

[1046] For example, suppose a 35-year-old female user visits a beauty and cosmetics shop. She takes a photo of her face using a device in the store (such as a smartphone or smart glasses) and uploads it to the server. The server analyzes the photo and generates optimal makeup advice based on the user's facial features. The advice generated includes specific suggestions such as purple eyeshadow, nude lip color, and peachy pink blush.

[1047] Prompt Sentence Examples

[1048] "Based on the user's facial photo data, please analyze the user's skin tone, face shape, and features, and provide the best makeup advice."

[1049] Example format:

[1050] The dataset should be in the following format:

[1051] [Age, Skin Tone, Face Shape, Eye Shape, Nose Shape, Lip Shape] -> [Eyeshadow Color, Lip Color, Cheek Color]

[1052] example:

[1053] Input: [35, "Winter", "Round", "Average", "Average", "Thick"]

[1054] Output: [Purple, Nude, Peach Pink]

[1055] As described above, by implementing this system in beauty and cosmetic shops, users can quickly and easily receive makeup advice that is best suited to them.

[1056] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1057] Step 1:

[1058] The user takes a photo of their face using a smartphone or smart glasses, using the camera application on the device. Once the photo is taken, the user presses the "upload" button in the application to send the photo to the next step.

[1059] Input: User's face photo

[1060] Output: Face photo waiting to be sent to the server

[1061] Step 2:

[1062] When a user presses the upload button, the device sends the face photo to the server, a process that involves creating a data transfer request and sending it over the network.

[1063] Input: A photo of your face waiting to be sent

[1064] Output: Face photo uploaded to the server

[1065] Step 3:

[1066] The server receives the uploaded facial photo, then converts it to grayscale using OpenCV and detects facial feature points using dlib to analyze it.

[1067] Input: Uploaded face photo

[1068] Output: Facial feature point data

[1069] Step 4:

[1070] The server generates a face pattern based on the facial feature point data. Specifically, it records the coordinates of each point on the face as an array and constructs face pattern data.

[1071] Input: Facial feature point data

[1072] Output: Face pattern data

[1073] Step 5:

[1074] The server compares the generated facial pattern data with a makeup database, which contains information on various makeup styles. It then uses KNN (K Nearest Neighbor) to score the facial pattern data and makeup style data and selects the most suitable makeup style.

[1075] Input: Face pattern data

[1076] Output: Selected makeup style data

[1077] Step 6:

[1078] The server generates specific makeup advice based on the selected makeup style data, such as advice on eye shadow colors, lip colors, and foundation application methods that are tailored to the user.

[1079] Input: Selected makeup style data

[1080] Output: Specific makeup advice data

[1081] Step 7:

[1082] The server sends the generated makeup advice to the user's device using the notification function of the application and a data transfer protocol.

[1083] Input: Specific makeup advice data

[1084] Output: Makeup advice sent to the user's terminal

[1085] Step 8:

[1086] The device analyzes the makeup advice data received from the server and displays it on the user interface. The user can then view the displayed makeup advice and try out the makeup that suits them best.

[1087] Input: Make advice data received from the server

[1088] Output: Makeup advice displayed on the terminal

[1089] Through these steps, users can quickly and easily receive personalized makeup advice when they visit a beauty or cosmetics store.

[1090] 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.

[1091] The present invention is a system that provides personalized makeup advice to individual users by combining face recognition technology and an emotion engine. This system recognizes the user's emotions and generates appropriate makeup advice based on those emotions. Specific embodiments are described below.

[1092] Specific Embodiments of the System

[1093] 1. Upload a photo of the user's face

[1094] The user opens the camera application on their device and takes a photo of their face. After taking the photo, they press the "upload" button to send the photo to the server.

[1095] 2. Facial recognition and emotion analysis by the server

[1096] The terminal generates and transmits a request to transmit the face photo to the server.

[1097] The server analyzes the received facial photo using a facial recognition algorithm and extracts facial features.

[1098] The server uses an emotion engine based on the facial recognition results to analyze the user's emotions (e.g., joy, sadness, surprise, etc.).

[1099] 3. Matching face pattern data with makeup database

[1100] The server compares the generated facial pattern data and emotion data with a makeup database.

[1101] The server scores the makeup styles and selects the one that best suits the user's facial pattern and emotions.

[1102] 4. Generating and adjusting makeup advice

[1103] Based on the selected makeup style, the server generates specific makeup advice, including eyeshadow color, lip color, and foundation application.

[1104] The emotion engine adjusts makeup advice based on the user's emotions, for example, suggesting bright makeup if the user is sad.

[1105] 5. Sending and displaying makeup advice

[1106] The server transmits the generated makeup advice to the terminal.

[1107] The device analyzes the received makeup advice and displays it on the user interface, allowing the user to try out makeup according to the advice.

[1108] Specific examples

[1109] For example, when a 25-year-old female user uses this system, the following process takes place:

[1110] 1. Upload a photo

[1111] Take a photo of your face with your smartphone and press the "Upload" button.

[1112] 2. Facial Recognition and Emotion Analysis

[1113] The server receives the photo, analyzes facial features, and uses an emotion engine to recognize the emotion the user is currently feeling, for example, sensing that the user is sad.

[1114] 3. Database Verification

[1115] The server compares the facial pattern data and emotional data with a makeup database, and selects makeup styles that suit olive skin and makeup based on emotions through scoring. For example, it suggests bright makeup to alleviate sad moods.

[1116] 4. Advice Generation and Reconciliation

[1117] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a bright lip color that will make the user smile.

[1118] 5. User Visibility

[1119] The device receives the makeup advice and shows it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[1120] This allows users to receive personalized makeup advice that takes into account their emotions at the time. By combining the user's facial expressions and emotions, the system provides more appropriate and satisfying makeup advice.

[1121] The processing flow will be explained below.

[1122] Step 1:

[1123] The user launches the application and uses the camera function to take a photo of their face.

[1124] The device will temporarily store the captured facial photo within the app.

[1125] The user presses the "Upload" button and uploads a photo of their face.

[1126] Step 2:

[1127] The device generates an upload request and prepares to send the stored facial photo to the server.

[1128] The device sends the request and the facial photo to the server.

[1129] Step 3:

[1130] The server receives the facial photograph sent from the terminal.

[1131] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[1132] The server generates face pattern data as the analysis result.

[1133] Step 4:

[1134] The server uses the facial pattern data to perform analysis using an emotion engine.

[1135] The server recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) and generates emotion data.

[1136] Step 5:

[1137] The server matches the facial pattern data and emotion data with a makeup database.

[1138] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern and emotion.

[1139] Step 6:

[1140] The server generates specific makeup advice based on the selected makeup style.

[1141] The server tailors its advice based on emotional data from the emotion engine, for example, suggesting bright makeup if the user is sad.

[1142] Step 7:

[1143] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[1144] Step 8:

[1145] The terminal receives the makeup advice data transmitted from the server.

[1146] The terminal analyzes the received data and displays makeup advice on the user interface.

[1147] Step 9:

[1148] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[1149] Examples:

[1150] For example, when a 25-year-old female user uses this system, the following process takes place:

[1151] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[1152] Step 2: The device sends a photo of the face to the server.

[1153] Step 3: The server receives the facial photo and analyzes it.

[1154] Step 4: The server uses the emotion engine to recognize the user's emotion. It detects that the user is sad.

[1155] Step 5: The server uses the face pattern data and emotion data to match the makeup database.

[1156] Step 6: The server generates optimal makeup advice and adjusts it based on emotion, e.g., suggesting a bright lip color.

[1157] Step 7: The server sends the advice data to the terminal.

[1158] Step 8: The device receives and displays the advice.

[1159] Step 9: The user applies makeup based on the advice.

[1160] This allows users to receive personalized makeup advice that takes into account their emotions at the time, allowing them to enjoy more appropriate and satisfying makeup. By combining facial recognition technology with an emotion engine, it becomes possible to provide makeup suggestions that match the user's emotions.

[1161] Example 2

[1162] 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."

[1163] In modern beauty, it is important for each user to receive appropriate makeup advice tailored to their facial features and current emotional state. However, conventional makeup advice systems have difficulty providing personalized advice that takes into account the user's facial expressions and emotions, resulting in a decrease in makeup satisfaction.

[1164] 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.

[1165] In this invention, the server includes: [means for receiving an uploaded facial photograph and analyzing facial patterns using facial recognition technology;] [means for analyzing the user's emotional state using emotion recognition technology based on the facial recognition results; and] [means for collating the analyzed facial pattern data and emotional state data and generating makeup advice optimal for the user.] This makes it possible to provide personalized, highly accurate makeup advice based on the user's facial features and emotional state.

[1166] "User" means a person who uses the system and operates a device or application to take a photograph of his or her face and upload it to the system.

[1167] A "face photo" is image data that captures the user's facial features and is the subject of analysis using face recognition technology and emotion recognition technology.

[1168] "Upload" refers to the operation by which a user transfers facial photo data from their own device to the server.

[1169] The "server" is a central processing unit that receives facial photo data, performs facial and emotion recognition, generates optimal makeup advice, and sends it to the terminal.

[1170] "Facial recognition technology" is a technology that analyzes facial photographs to identify facial features (eyes, nose, mouth, etc.) and generate facial pattern data.

[1171] "Emotion recognition technology" is a technology that analyzes a user's emotional state (happiness, sadness, surprise, etc.) based on facial feature point data obtained from a facial photograph.

[1172] "Facial pattern data" is facial feature point information obtained by facial recognition technology, and is basic data for generating makeup advice.

[1173] "Emotional state data" is emotional information about the user analyzed using emotion recognition technology, and is basic data for adjusting makeup advice.

[1174] A "makeup database" is a data storage that stores various makeup styles and their corresponding facial feature patterns and emotional state data.

[1175] "Makeup advice" is specific guidance on the most suitable makeup technique for the user, generated based on the face pattern data and emotional state data analyzed by the server.

[1176] A "terminal" is a device used by a user to take and upload a facial photo, and ultimately receive and display the generated makeup advice.

[1177] The present invention relates to a system that analyzes a user's facial photograph and provides personalized makeup advice based on their emotions. This system is composed of a user's terminal, a server, face recognition technology, emotion recognition technology, and a makeup database. Specific embodiments are described below.

[1178] System Overview

[1179] This system allows users to upload a photo of their face, and the server analyzes the photo to generate makeup advice tailored to the situation. Key software components include OpenCV (facial recognition technology) and Microsoft Azure Emotion API (emotion recognition technology).

[1180] Upload a user's photo

[1181] Users take a photo of their face using the camera application on their smartphone or computer, and then press the "upload" button to send the photo to the server.

[1182] The terminal generates and transmits a request to transmit this facial photograph data to the server.

[1183] Server-based face recognition and emotion analysis

[1184] The server temporarily stores the received facial photograph in a database.

[1185] The server uses a facial recognition algorithm (for example, OpenCV) to analyze facial feature points (eyes, nose, mouth, etc.) and generate facial pattern data.

[1186] The server then uses an emotion recognition algorithm (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state (e.g., happiness, sadness, surprise) from the facial pattern data.

[1187] Matching face patterns with makeup database

[1188] The server compares the analyzed facial pattern data and emotional state data with a makeup database, which contains a wide variety of makeup styles and their corresponding facial feature patterns and emotional states.

[1189] The server scores multiple candidate styles and selects the most suitable makeup style. For example, if the user is sad, it selects a bright-colored makeup style.

[1190] Generate and adjust makeup advice

[1191] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation type and application method.

[1192] The server uses an emotion recognition engine to tailor makeup advice based on the user's emotional state, such as suggesting bright makeup to alleviate sad emotions.

[1193] Providing makeup advice

[1194] The server transmits the generated makeup advice to the terminal.

[1195] The device analyzes the received makeup advice and displays it to the user, who can then try out different makeup looks based on the advice.

[1196] Specific examples

[1197] For example, if a 25-year-old female user uses this system, the following process will occur:

[1198] 1. Users upload photos of their faces

[1199] Take a photo of your face with your smartphone and press the "Upload" button.

[1200] 2. Facial Recognition and Emotion Analysis

[1201] The server receives the photo, analyzes facial features using OpenCV, and analyzes emotions using the Microsoft Azure Emotion API to determine whether the user is sad.

[1202] 3. Database Verification

[1203] The server compares the facial pattern data and emotion data with a makeup database to select the most suitable makeup style, including a bright lip color to alleviate sad emotions.

[1204] 4. Advice Generation and Adjustment

[1205] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a vibrant lip color to help users feel more energized.

[1206] 5. Display of makeup advice

[1207] The device receives the advice and presents it to the user, who can then try out the makeup look according to the advice.

[1208] Prompt Sentence Examples

[1209] Describe a system that analyzes a user's facial photo and provides optimal makeup advice based on their facial features and emotions. Specifically, what makeup would be suggested if the user is sad and has olive skin?

[1210] This allows users to receive personalized makeup advice that reflects their emotions at the time. The system analyzes and evaluates the user's facial expressions and emotions, and provides highly accurate makeup advice based on that.

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

[1212] Step 1:

[1213] Users take a photo of their face using the camera application on their smartphone or computer and press the "upload" button.

[1214] Input: The user operates the camera application and takes a photo of their face.

[1215] Output: The captured facial photo data.

[1216] Step 2:

[1217] The device generates a data packet containing the captured facial photo data and generates and sends a request to the server to send it.

[1218] Input: Facial photo data generated when the user presses the upload button.

[1219] Output: Face photo data packet sent to the server.

[1220] Step 3:

[1221] The server temporarily stores the received facial photo data and runs a facial recognition algorithm.

[1222] Input: Facial photo data sent from the device.

[1223] Output: Detected facial feature points data.

[1224] Specific operation: Using a facial recognition algorithm such as OpenCV, facial feature points (eyes, nose, mouth, etc.) are analyzed and facial pattern data is generated.

[1225] Step 4:

[1226] The server runs an emotion recognition algorithm based on the generated facial pattern data.

[1227] Input: Face pattern data.

[1228] Output: Emotional state data (e.g., happy, sad, surprised, etc.).

[1229] Specific operation: Analyzes the user's emotions from facial feature point data using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

[1230] Step 5:

[1231] The server compares the facial pattern data and emotional state data with a makeup database and selects the most suitable makeup style.

[1232] Input: Facial pattern data and emotional state data.

[1233] Output: Selection result of optimal makeup style.

[1234] Specific operation: Scores multiple makeup styles in the database and determines the makeup style that best suits the user's characteristics.

[1235] Step 6:

[1236] The server generates specific makeup advice based on the selected makeup style.

[1237] Input: Optimal makeup style and emotional state data.

[1238] Output: Detailed makeup advice.

[1239] Specific operation: Based on the selected makeup style, create makeup advice including eye shadow color, lip color type, foundation type and application method.

[1240] Step 7:

[1241] The server transmits the generated makeup advice to the user's terminal.

[1242] Input: Generated makeup advice.

[1243] Output: A data packet containing the make advice.

[1244] Specific operation: Generate and send a data packet to send makeup advice to the terminal.

[1245] Step 8:

[1246] The device analyzes the received makeup advice and displays it on the user interface.

[1247] Input: A data packet containing make advice sent by the server.

[1248] Output: Makeup advice displayed in the user interface.

[1249] Specific operation: Analyzes the data packet and displays the received makeup advice on the screen. The user can check it and try applying makeup.

[1250] (Application example 2)

[1251] 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."

[1252] Conventional makeup advice systems only provide uniform advice without considering the user's emotions, and have the problem of being unable to respond to needs that change depending on the user's condition. In addition, because users cannot actually try on the makeup items, it is difficult for them to select appropriate makeup items based on the advice.

[1253] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1254] In this invention, the server includes: [means for a user to take an image of themselves and upload the image;] [means for the server to receive the uploaded image and analyze the pattern using feature extraction technology;] [means for the server to compare the analyzed pattern data with a database and generate optimal advice for the user;] [means for the server to send the generated advice to a client terminal and display it to the user;] [means for the server to analyze the user's emotions using an emotion engine and adjust the advice; and [means for enabling virtual try-on based on the advice. This makes it possible to provide personalized makeup advice that takes the user's emotions into consideration and to virtually try on how actual makeup items will look.

[1255] "User" means an end user of the Service or System.

[1256] An "image" is a photograph of a user's face or other visual data.

[1257] "Upload" is the process of sending data from a user's device to a server.

[1258] A "server" is a central computing resource that receives, analyzes, and transmits data.

[1259] "Feature extraction techniques" are algorithms and methods for extracting important information or features from images.

[1260] "Pattern data" is data related to the shape and features of a face obtained by image analysis.

[1261] A "database" is a data repository for storing makeup advice and other related information.

[1262] "Advice" refers to specific makeup techniques and product suggestions provided to users.

[1263] "Client Terminal" means a smartphone, tablet, or other computing device.

[1264] The "Emotion Engine" is a system that estimates and analyzes emotions from the user's facial expressions and other data.

[1265] "Virtual try-on" is a virtual experience that allows users to try on makeup items through video.

[1266] The present invention provides a system for providing personalized makeup advice to a user. The system analyzes the user's emotions and generates optimal makeup advice based on those emotions. Specific embodiments of the system are described below.

[1267] 1. Taking and uploading user images

[1268] The user takes a photo of their face using a client device such as a smartphone. After taking the photo, they press the "Upload" button in the application to send the image to the server. Once the server receives the image, it proceeds to the next step.

[1269] 2. Feature extraction and emotion analysis by the server

[1270] The server uses feature extraction technology to analyze the received image, extracting facial shapes and other important information to generate pattern data, and simultaneously analyzes the user's emotions (e.g., joy, sadness, surprise, etc.) using an emotion engine.

[1271] 3. Matching with the database and generating advice

[1272] The server compares the generated pattern data and emotion data with a database containing various makeup styles and corresponding advice. Based on this data, the server generates optimal makeup advice for the user.

[1273] 4. Virtual try-on

[1274] The generated advice includes a virtual try-on feature that allows users to virtually try on the suggested makeup items and see how they look on their own face.

[1275] 5. Providing advice and purchasing links

[1276] The client terminal receives the makeup advice sent from the server and displays it on the user interface. The displayed advice includes specific makeup techniques and product suggestions. Direct links to online shopping sites are also provided for the suggested makeup items, allowing the user to immediately purchase them.

[1277] Hardware and software used

[1278] This system uses the following hardware and software:

[1279] Smartphone or other client terminal: A device that takes an image of the user and sends it to the server.

[1280] Server: A central computing resource for image analysis, feature extraction, sentiment analysis, database matching, and advice generation.

[1281] Emotion engine: A system that estimates and analyzes emotions from the user's facial expressions.

[1282] Database: A data repository that stores makeup advice and related information.

[1283] Examples and prompts

[1284] For example, if a 28-year-old female user takes a photo of her face with her smartphone and emotional analysis shows that she is sad, the server will suggest a bright pink lip color and eyeshadow to accentuate the user's eyes based on the user's emotions.

[1285] Example prompt sentence:

[1286] "Design a virtual makeup advice system that suggests a bright pink lip color and eyeshadow to accentuate the eyes when a 28-year-old female user takes a photo of her face with a smartphone app and the emotion analysis indicates she is sad."

[1287] The system allows users to receive personalized makeup advice that takes into account their current emotions, and allows them to virtually try on the suggested makeup products, helping them make the right choice before actually purchasing them.

[1288] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1289] Step 1: User takes and uploads an image

[1290] The user takes a photo of their face using a client device such as a smartphone. The input is the user's face photo, which is sent to the server by pressing the "upload" button in the application. The output is the face photo data sent to the server. Specifically, the face photo is taken using the smartphone's camera app and is transferred directly to the server.

[1291] Step 2: The server receives the image and performs feature extraction.

[1292] The server receives image data sent from the client device. The input is a photo of the user's face, which is analyzed to extract the shape and features of the face. Specifically, a facial recognition algorithm is used to identify facial feature points and generate pattern data. The output is the extracted facial feature information. Specifically, face recognition is performed using libraries such as OpenCV and Dlib.

[1293] Step 3: The server analyzes the emotion using the emotion engine

[1294] The server inputs the extracted facial feature data into the emotion engine. The input is facial feature data, and the emotion engine uses this to analyze the user's emotions. Specifically, it uses a neural network to perform emotion analysis and identify emotions such as joy, sadness, and surprise. The output is the analyzed emotion data. Specifically, it uses an AI model for emotion analysis to score emotions.

[1295] Step 4: The server checks the database and generates an advice

[1296] The server compares the facial feature data and emotion data with a makeup database. The input is facial feature data and emotion data, and based on this, it generates optimal makeup advice. Specifically, it searches the database for relevant makeup styles, scores them, and selects the most appropriate style. The output is makeup advice provided to the user. Specifically, it extracts and analyzes the most appropriate data from the database.

[1297] Step 5: The server provides the virtual try-on feature

[1298] The server provides a virtual try-on function based on the generated makeup advice. The input is the generated makeup advice, which is sent to the client terminal. Specifically, software is used to virtually apply makeup items to the user's face. The output is visual information of the virtual makeup displayed on the client terminal. Specifically, the virtual makeup is applied using AR technology.

[1299] Step 6: The server sends the advice to the client device and displays it.

[1300] The server sends the generated makeup advice to the client terminal. The input is advice data from the server, which the client terminal receives and displays on the user interface. The output is the situation in which the advice is displayed to the user. Specifically, the data is sent to the terminal using an HTTP request, and the received data is displayed within the application.

[1301] Step 7: Provide a link for users to purchase the makeup item

[1302] The makeup advice displayed on the client terminal includes a link to purchase the corresponding makeup item. The user can click on this link to access an online shopping site and purchase the makeup item. The input is the purchase link information sent from the server, and the user clicks on the link to access the purchasing site. The output is the situation in which the user actually purchases the makeup item. Specifically, the system processes the link click event and opens the corresponding web page.

[1303] 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.

[1304] 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.

[1305] 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.

[1306] [Fourth embodiment]

[1307] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1308] 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.

[1309] 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).

[1310] 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.

[1311] 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.

[1312] 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).

[1313] 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.

[1314] 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.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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."

[1320] This invention relates to a system that provides personalized makeup advice to individual users. This system uses face recognition technology to analyze the user's facial patterns and generate optimal makeup advice based on the results. The system's basic configuration consists of a user's terminal, a central server, and a makeup database.

[1321] Specific Embodiments of the System

[1322] 1. Upload a photo of the user's face

[1323] The user opens the camera application on their smartphone, tablet, or other device and takes a photo of their face. After taking the photo, the user presses the "upload" button in the application to send the photo to the server.

[1324] 2. Facial recognition and analysis by the server

[1325] When the user presses the upload button, the terminal creates and sends a request to send the face photo to the server.

[1326] The server analyzes the received facial photo using a facial recognition algorithm. In this analysis process, facial feature points (eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[1327] 3. Matching face pattern data with makeup database

[1328] The server compares the generated facial pattern data with a makeup database. This database stores information on various makeup styles, and scores the data to select the most suitable style based on the facial pattern. Based on the scoring results, the makeup style that best suits the user is selected.

[1329] 4. Generating and sending makeup advice

[1330] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[1331] The server transmits the generated makeup advice to the terminal.

[1332] 5. Users receive makeup advice

[1333] The device analyzes the makeup advice data received from the server and displays it on the user interface, allowing the user to view and try out the suggested makeup.

[1334] Specific examples

[1335] For example, if a 25-year-old female user uses the application to find makeup that suits her, the system will provide the best makeup advice by taking and uploading a photo of her face through the following steps:

[1336] 1. Upload a photo

[1337] Take a photo of your face using your smartphone and press the "Upload" button.

[1338] 2. Facial Recognition and Analysis

[1339] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[1340] 3. Database Verification

[1341] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[1342] 4. Advice Generation and Delivery

[1343] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[1344] 5. User Visibility

[1345] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[1346] By following the above steps, users of this system can easily receive makeup advice that best suits them and enjoy their daily makeup routine.By automating analysis and suggestions, this system provides personalized makeup advice to users.

[1347] The processing flow will be explained below.

[1348] Step 1:

[1349] The user starts the application and takes a photo of their face using the camera function.

[1350] The device will temporarily store the captured facial photo within the app.

[1351] The user presses the "upload" button to indicate their intention to upload a photo of their face.

[1352] Step 2:

[1353] The device generates an upload request and prepares to send the stored facial photo to the server.

[1354] The terminal sends a facial photo along with the request to the server.

[1355] Step 3:

[1356] The server receives the facial photograph sent from the terminal.

[1357] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[1358] The server generates face pattern data as the analysis result.

[1359] Step 4:

[1360] The server compares the generated facial pattern data with a makeup database.

[1361] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern.

[1362] Step 5:

[1363] The server generates specific makeup advice based on the selected makeup style.

[1364] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[1365] Step 6:

[1366] The terminal receives the makeup advice data transmitted from the server.

[1367] The device analyzes the received data and displays makeup advice on the user interface.

[1368] Step 7:

[1369] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[1370] Examples:

[1371] For example, when a 25-year-old female user uses this system, the following process takes place:

[1372] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[1373] Step 2: The device sends a photo of the face to the server.

[1374] Step 3: The server receives the facial photo and analyzes it.

[1375] Step 4: The server uses the face pattern data to match the makeup database.

[1376] Step 5: The server generates optimal makeup advice and sends it to the device.

[1377] Step 6: The device receives and displays the advice.

[1378] Step 7: The user applies makeup based on the advice.

[1379] This allows users to easily find and apply makeup that suits them. The system uses facial recognition technology and database matching to automatically provide personalized makeup advice.

[1380] Example 1

[1381] 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."

[1382] Conventional makeup advice systems have faced the challenge of requiring a great deal of time and effort to provide personalized makeup advice to individual users. Another issue is that it is difficult to accurately recognize the user's facial features and suggest the optimal makeup style. Furthermore, there are cases where the accuracy and speed of the advice provided are lacking, leading to concerns about a poor user experience.

[1383] 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.

[1384] In this invention, the server includes: [means for a user to take an image of themselves and send that image;] [means for the server to receive the sent image and analyze the facial pattern using image recognition technology; and [means for the server to compare the analyzed facial pattern data with a database and generate advice that is best suited to the user.] This makes it possible to analyze the facial features of users quickly and with high accuracy, and to provide each individual user with makeup advice that is best suited to them in real time.

[1385] "User" refers to a person who uses the system to receive makeup advice.

[1386] "Images" refers to visual data such as photos and videos taken by users using their device's camera.

[1387] "Send" refers to the process of sending data from a terminal to a server.

[1388] "Receiving" refers to the process in which the server receives data sent from the terminal.

[1389] "Image recognition technology" refers to the technology of extracting features from images using computer vision and analyzing them.

[1390] A "face pattern" refers to a collection of facial feature points and shape data extracted using face recognition technology.

[1391] "Analysis" refers to the process of extracting facial features based on received image data and generating pattern data.

[1392] A "database" refers to a collection of accumulated data that stores multiple makeup styles and the corresponding information.

[1393] "Matching" refers to the process of comparing analyzed facial pattern data with data in a database to find a match.

[1394] "Advice" refers to makeup techniques and style suggestions generated by the server based on facial pattern data.

[1395] This clarifies the meaning of key words contained in the claims.

[1396] The present invention relates to a system for providing personalized makeup advice to users. The system is composed of a user terminal, a central server, and a makeup database.

[1397] First, the user takes a photo of their face using a device such as a smartphone or tablet. To take the photo, they can use a commonly available camera application. After taking the photo, the user presses the "upload" button in the application to send the photo to the server. This causes the device to send the photo to the server in the form of an HTTP request.

[1398] The server receives the sent facial photo. To receive the photo, server software (e.g., Apache or NGINX) is used to process HTTP requests. The received facial photo is analyzed using an image processing library (e.g., OpenCV or Dlib). In this analysis process, facial feature points (e.g., eye position, nose shape, mouth position, skin tone, etc.) are extracted and facial pattern data is generated.

[1399] The server then compares the generated facial pattern data with an internal makeup database. This makeup database stores information on various makeup styles and their corresponding facial patterns. A database management system (e.g., MySQL or PostgreSQL) is used for the comparison, and scoring is performed to select the optimal style based on the facial pattern. A machine learning model (e.g., a generative AI model) can be used as the scoring algorithm.

[1400] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation application method. The generated makeup advice is sent to the device in JSON format.

[1401] The device receives the JSON data sent from the server, parses it, and displays it on the user interface, allowing the user to check the displayed makeup advice and use the suggested products and makeup techniques to apply their makeup.

[1402] Specific examples

[1403] For example, consider the case where a 25-year-old female user uses the application to find makeup that suits her. When the user takes and uploads a photo of her face, the system goes through the following process to provide the best makeup advice.

[1404] 1. Upload a photo

[1405] Users take a photo of their face with their smartphone and press the "upload" button.

[1406] 2. Facial Recognition and Analysis

[1407] The server receives the photo, analyzes it, and determines that the user has olive skin, a small nose, and slightly thick lips.

[1408] 3. Database Verification

[1409] The server compares the facial pattern data with a makeup database and selects suggestions such as warm-colored eyeshadow and beige lip color that suit olive skin through scoring.

[1410] 4. Advice Generation and Delivery

[1411] The server generates specific makeup advice, detailing things like what eyeshadow and lip colors the user should use and how to shade to accentuate cheekbones.

[1412] 5. User Visibility

[1413] The device receives the makeup advice and displays it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[1414] Prompt Sentence Examples

[1415] "I'm a 25-year-old woman with olive skin tone. I'd like some advice on finding a makeup style that suits me. Please provide the best makeup advice based on the photo of my face I upload."

[1416] As a result, in the system of the present invention, the user can quickly and accurately receive personalized makeup advice that reflects the user's facial features.

[1417] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1418] Step 1:

[1419] A user opens a camera application on their device (smartphone or tablet). The camera is activated and the user adjusts the camera so that their face is clearly captured. After taking a photo of their face, the user presses the "upload" button in the application. The input of this step is the user's face photo, and the output is an HTTP request sent to the server.

[1420] Step 2:

[1421] When the user clicks the "Upload" button, the device generates an HTTP request including the captured face photo and sends it to the server. Specifically, the request includes face photo data (JPEG or PNG format) and necessary metadata (user ID, timestamp, etc.). The input is the user's face photo and user information, and the output is an HTTP request to the server.

[1422] Step 3:

[1423] The server receives the HTTP request sent from the device. The server software (e.g., Apache or NGINX) analyzes the request and extracts the facial photo data. The input is the HTTP request received by the server, and the output is the facial photo data required for analysis.

[1424] Step 4:

[1425] The server analyzes the facial photo data using an image recognition library (e.g., OpenCV, Dlib) and extracts facial feature points. This allows for information such as eye position, nose shape, mouth position, and skin tone to be obtained. The input is facial photo data, and the output is facial pattern data.

[1426] Step 5:

[1427] The server compares the analyzed facial pattern data with a makeup database. The database stores various makeup styles and their corresponding facial pattern data. The server uses a scoring algorithm to evaluate and select the optimal makeup style. The input is facial pattern data, and the output is information about the optimal makeup style.

[1428] Step 6:

[1429] The server generates specific makeup advice based on the selected makeup style. The advice includes eyeshadow color, lip color, foundation application method, etc. The input is the optimal makeup style information, and the output is the generated makeup advice.

[1430] Step 7:

[1431] The server converts the generated make advice into JSON format and sends it to the terminal. This uses a communication protocol between the server and the terminal (e.g., HTTP / S). The input is the generated make advice, and the output is the transmission of JSON data to the terminal.

[1432] Step 8:

[1433] The device receives and parses the JSON data sent from the server. The parsed data is displayed in the user interface and visually presented to the user. The input is the JSON data from the server, and the output is the makeup advice that is displayed.

[1434] Step 9:

[1435] The user checks the makeup advice displayed on the device and applies their makeup based on it. Using the provided items and process as a reference, the user can try out the makeup that best suits their face. The input is the makeup advice displayed on the device, and the output is the user's makeup result.

[1436] The above are the specific steps of the program processing in this system and the specific operations performed at each step.

[1437] (Application example 1)

[1438] 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."

[1439] Providing optimal makeup advice to customers is extremely important in modern beauty and cosmetic shops. However, with conventional methods, it takes a great deal of time and effort for customers to find the makeup style that suits them best. To solve this problem, a method is needed to provide customers with fast, accurate, personalized makeup advice.

[1440] 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.

[1441] In this invention, the server includes: [means for a user to take a photo of their face and upload the photo;] [means for the server to receive the uploaded photo of their face and analyze their facial pattern using facial recognition technology;] [means for comparing the analyzed facial pattern data with a makeup database and generating makeup advice that is optimal for the user;] [means for providing personalized makeup advice to each user at a beauty / cosmetic shop using an automated system; and [means for using a smartphone or smart glasses on a terminal to display the generated makeup advice. This enables users to quickly and easily receive makeup advice that is optimal for them when they visit a beauty / cosmetic shop.

[1442] "User" refers to an individual who uses the system to receive makeup advice that is best suited to them.

[1443] "Face Photo" refers to an image of a User's face that the User uploads to the System.

[1444] "Uploading" refers to the act of a user sending a photo of their own face to a server.

[1445] "Server" refers to a central computer that analyzes facial photos received from users and generates optimal makeup advice.

[1446] "Facial recognition technology" refers to technology for analyzing facial features from photographs of faces.

[1447] "Facial pattern" refers to facial feature data of a user extracted from a photograph of the user's face using facial recognition technology.

[1448] A "makeup database" refers to a database that stores information on various makeup styles.

[1449] "Makeup advice" refers to specific instructions and suggestions regarding the makeup style that is best suited to the user.

[1450] "Beauty and cosmetic shop" refers to a physical store that offers cosmetics and beauty-related products and services.

[1451] "Terminal" refers to an electronic device operated by a user, such as a smartphone or smart glasses.

[1452] This invention relates to a system that provides personalized makeup advice to individual users. This system works by having users take a photo of their face using a device such as a smartphone or smart glasses at a beauty or cosmetic shop and then uploading the photo.

[1453] Specific Embodiments of the System

[1454] 1. Take and upload a photo of your face

[1455] Users open the camera app on their smartphone or smart glasses and take a photo of their face. Once the photo is taken, they press the "upload" button in the app to send the photo to the server.

[1456] 2. Facial Recognition and Analysis

[1457] The server receives the uploaded facial photo and analyzes it using facial recognition technology. Specifically, it uses libraries such as OpenCV and dlib to extract facial feature points (such as the positions of the eyes, nose, and mouth) and generate facial pattern data.

[1458] 3. Matching face pattern data with makeup database

[1459] The server compares the generated facial pattern data with a makeup database, which stores information on various makeup styles. The server then scores the facial pattern data and makeup style data using KNN (K Nearest Neighbor) to select the optimal makeup style.

[1460] 4. Generating and sending makeup advice

[1461] The server generates specific makeup advice based on the selected makeup style, including detailed advice on eyeshadow colors, lip colors, and foundation application techniques.

[1462] 5. Display of makeup advice

[1463] The device analyzes the makeup advice data received from the server and displays it on a user interface, allowing the user to try out the suggested makeup.

[1464] Hardware and software used

[1465] Hardware:

[1466] Smartphones, smart glasses, servers

[1467] software:

[1468] OpenCV: An image processing library used to analyze facial photos.

[1469] dlib: A library used for facial landmark detection.

[1470] scikit-learn: Used to suggest makeup styles using KNN (K nearest neighbors).

[1471] Specific examples

[1472] For example, suppose a 35-year-old female user visits a beauty and cosmetics shop. She takes a photo of her face using a device in the store (such as a smartphone or smart glasses) and uploads it to the server. The server analyzes the photo and generates optimal makeup advice based on the user's facial features. The advice generated includes specific suggestions such as purple eyeshadow, nude lip color, and peachy pink blush.

[1473] Prompt Sentence Examples

[1474] "Based on the user's facial photo data, please analyze the user's skin tone, face shape, and features, and provide the best makeup advice."

[1475] Example format:

[1476] The dataset should be in the following format:

[1477] [Age, Skin Tone, Face Shape, Eye Shape, Nose Shape, Lip Shape] -> [Eyeshadow Color, Lip Color, Cheek Color]

[1478] example:

[1479] Input: [35, "Winter", "Round", "Average", "Average", "Thick"]

[1480] Output: [Purple, Nude, Peach Pink]

[1481] As described above, by implementing this system in beauty and cosmetic shops, users can quickly and easily receive makeup advice that is best suited to them.

[1482] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1483] Step 1:

[1484] The user takes a photo of their face using a smartphone or smart glasses, using the camera application on the device. Once the photo is taken, the user presses the "upload" button in the application to send the photo to the next step.

[1485] Input: User's face photo

[1486] Output: Face photo waiting to be sent to the server

[1487] Step 2:

[1488] When a user presses the upload button, the device sends the face photo to the server, a process that involves creating a data transfer request and sending it over the network.

[1489] Input: A photo of your face waiting to be sent

[1490] Output: Face photo uploaded to the server

[1491] Step 3:

[1492] The server receives the uploaded facial photo, then converts it to grayscale using OpenCV and detects facial feature points using dlib to analyze it.

[1493] Input: Uploaded face photo

[1494] Output: Facial feature point data

[1495] Step 4:

[1496] The server generates a face pattern based on the facial feature point data. Specifically, it records the coordinates of each point on the face as an array and constructs face pattern data.

[1497] Input: Facial feature point data

[1498] Output: Face pattern data

[1499] Step 5:

[1500] The server compares the generated facial pattern data with a makeup database, which contains information on various makeup styles. It then uses KNN (K Nearest Neighbor) to score the facial pattern data and makeup style data and selects the most suitable makeup style.

[1501] Input: Face pattern data

[1502] Output: Selected makeup style data

[1503] Step 6:

[1504] The server generates specific makeup advice based on the selected makeup style data, such as advice on eye shadow colors, lip colors, and foundation application methods that are tailored to the user.

[1505] Input: Selected makeup style data

[1506] Output: Specific makeup advice data

[1507] Step 7:

[1508] The server sends the generated makeup advice to the user's device using the notification function of the application and a data transfer protocol.

[1509] Input: Specific makeup advice data

[1510] Output: Makeup advice sent to the user's terminal

[1511] Step 8:

[1512] The device analyzes the makeup advice data received from the server and displays it on the user interface. The user can then view the displayed makeup advice and try out the makeup that suits them best.

[1513] Input: Make advice data received from the server

[1514] Output: Makeup advice displayed on the terminal

[1515] Through these steps, users can quickly and easily receive personalized makeup advice when they visit a beauty or cosmetics store.

[1516] 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.

[1517] The present invention is a system that provides personalized makeup advice to individual users by combining face recognition technology and an emotion engine. This system recognizes the user's emotions and generates appropriate makeup advice based on those emotions. Specific embodiments are described below.

[1518] Specific Embodiments of the System

[1519] 1. Upload a photo of the user's face

[1520] The user opens the camera application on their device and takes a photo of their face. After taking the photo, they press the "upload" button to send the photo to the server.

[1521] 2. Facial recognition and emotion analysis by the server

[1522] The terminal generates and transmits a request to transmit the face photo to the server.

[1523] The server analyzes the received facial photo using a facial recognition algorithm and extracts facial features.

[1524] The server uses an emotion engine based on the facial recognition results to analyze the user's emotions (e.g., joy, sadness, surprise, etc.).

[1525] 3. Matching face pattern data with makeup database

[1526] The server compares the generated facial pattern data and emotion data with a makeup database.

[1527] The server scores the makeup styles and selects the one that best suits the user's facial pattern and emotions.

[1528] 4. Generating and adjusting makeup advice

[1529] Based on the selected makeup style, the server generates specific makeup advice, including eyeshadow color, lip color, and foundation application.

[1530] The emotion engine adjusts makeup advice based on the user's emotions, for example, suggesting bright makeup if the user is sad.

[1531] 5. Sending and displaying makeup advice

[1532] The server transmits the generated makeup advice to the terminal.

[1533] The device analyzes the received makeup advice and displays it on the user interface, allowing the user to try out makeup according to the advice.

[1534] Specific examples

[1535] For example, when a 25-year-old female user uses this system, the following process takes place:

[1536] 1. Upload a photo

[1537] Take a photo of your face with your smartphone and press the "Upload" button.

[1538] 2. Facial Recognition and Emotion Analysis

[1539] The server receives the photo, analyzes facial features, and uses an emotion engine to recognize the emotion the user is currently feeling, for example, sensing that the user is sad.

[1540] 3. Database Verification

[1541] The server compares the facial pattern data and emotional data with a makeup database, and selects makeup styles that suit olive skin and makeup based on emotions through scoring. For example, it suggests bright makeup to alleviate sad moods.

[1542] 4. Advice Generation and Reconciliation

[1543] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a bright lip color that will make the user smile.

[1544] 5. User Visibility

[1545] The device receives the makeup advice and shows it to the user, who can then check the suggested products and makeup process to help them with their actual makeup application.

[1546] This allows users to receive personalized makeup advice that takes into account their emotions at the time. By combining the user's facial expressions and emotions, the system provides more appropriate and satisfying makeup advice.

[1547] The processing flow will be explained below.

[1548] Step 1:

[1549] The user launches the application and uses the camera function to take a photo of their face.

[1550] The device will temporarily store the captured facial photo within the app.

[1551] The user presses the "Upload" button and uploads a photo of their face.

[1552] Step 2:

[1553] The device generates an upload request and prepares to send the stored facial photo to the server.

[1554] The device sends the request and the facial photo to the server.

[1555] Step 3:

[1556] The server receives the facial photograph sent from the terminal.

[1557] The server uses a facial recognition algorithm to analyze facial features (eye position, nose shape, mouth position, skin tone, etc.) from the received photo.

[1558] The server generates face pattern data as the analysis result.

[1559] Step 4:

[1560] The server uses the facial pattern data to perform analysis using an emotion engine.

[1561] The server recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) and generates emotion data.

[1562] Step 5:

[1563] The server matches the facial pattern data and emotion data with a makeup database.

[1564] The server scores multiple makeup styles in the database and selects the makeup style that best matches the user's facial pattern and emotion.

[1565] Step 6:

[1566] The server generates specific makeup advice based on the selected makeup style.

[1567] The server tailors its advice based on emotional data from the emotion engine, for example, suggesting bright makeup if the user is sad.

[1568] Step 7:

[1569] The server converts the generated makeup advice into a data format (e.g., JSON) and sends it to the terminal.

[1570] Step 8:

[1571] The terminal receives the makeup advice data transmitted from the server.

[1572] The terminal analyzes the received data and displays makeup advice on the user interface.

[1573] Step 9:

[1574] The user checks the displayed makeup advice and tries out and practices makeup based on that advice.

[1575] Examples:

[1576] For example, when a 25-year-old female user uses this system, the following process takes place:

[1577] Step 1: The user takes a photo of their face with their smartphone and presses the "Upload" button.

[1578] Step 2: The device sends a photo of the face to the server.

[1579] Step 3: The server receives the facial photo and analyzes it.

[1580] Step 4: The server uses the emotion engine to recognize the user's emotion. It detects that the user is sad.

[1581] Step 5: The server uses the face pattern data and emotion data to match the makeup database.

[1582] Step 6: The server generates optimal makeup advice and adjusts it based on emotion, e.g., suggesting a bright lip color.

[1583] Step 7: The server sends the advice data to the terminal.

[1584] Step 8: The device receives and displays the advice.

[1585] Step 9: The user applies makeup based on the advice.

[1586] This allows users to receive personalized makeup advice that takes into account their emotions at the time, allowing them to enjoy more appropriate and satisfying makeup. By combining facial recognition technology with an emotion engine, it becomes possible to provide makeup suggestions that match the user's emotions.

[1587] Example 2

[1588] 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."

[1589] In modern beauty, it is important for each user to receive appropriate makeup advice tailored to their facial features and current emotional state. However, conventional makeup advice systems have difficulty providing personalized advice that takes into account the user's facial expressions and emotions, resulting in a decrease in makeup satisfaction.

[1590] 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.

[1591] In this invention, the server includes: [means for receiving an uploaded facial photograph and analyzing facial patterns using facial recognition technology;] [means for analyzing the user's emotional state using emotion recognition technology based on the facial recognition results; and] [means for collating the analyzed facial pattern data and emotional state data and generating makeup advice optimal for the user.] This makes it possible to provide personalized, highly accurate makeup advice based on the user's facial features and emotional state.

[1592] "User" means a person who uses the system and operates a device or application to take a photograph of his or her face and upload it to the system.

[1593] A "face photo" is image data that captures the user's facial features and is the subject of analysis using face recognition technology and emotion recognition technology.

[1594] "Upload" refers to the operation by which a user transfers facial photo data from their own device to the server.

[1595] The "server" is a central processing unit that receives facial photo data, performs facial and emotion recognition, generates optimal makeup advice, and sends it to the terminal.

[1596] "Facial recognition technology" is a technology that analyzes facial photographs to identify facial features (eyes, nose, mouth, etc.) and generate facial pattern data.

[1597] "Emotion recognition technology" is a technology that analyzes a user's emotional state (happiness, sadness, surprise, etc.) based on facial feature point data obtained from a facial photograph.

[1598] "Facial pattern data" is facial feature point information obtained by facial recognition technology, and is basic data for generating makeup advice.

[1599] "Emotional state data" is emotional information about the user analyzed using emotion recognition technology, and is basic data for adjusting makeup advice.

[1600] A "makeup database" is a data storage that stores various makeup styles and their corresponding facial feature patterns and emotional state data.

[1601] "Makeup advice" is specific guidance on the most suitable makeup technique for the user, generated based on the face pattern data and emotional state data analyzed by the server.

[1602] A "terminal" is a device used by a user to take and upload a facial photo, and ultimately receive and display the generated makeup advice.

[1603] The present invention relates to a system that analyzes a user's facial photograph and provides personalized makeup advice based on their emotions. This system is composed of a user's terminal, a server, face recognition technology, emotion recognition technology, and a makeup database. Specific embodiments are described below.

[1604] System Overview

[1605] This system allows users to upload a photo of their face, and the server analyzes the photo to generate makeup advice tailored to the situation. Key software components include OpenCV (facial recognition technology) and Microsoft Azure Emotion API (emotion recognition technology).

[1606] Upload a user's photo

[1607] Users take a photo of their face using the camera application on their smartphone or computer, and then press the "upload" button to send the photo to the server.

[1608] The terminal generates and transmits a request to transmit this facial photograph data to the server.

[1609] Server-based face recognition and emotion analysis

[1610] The server temporarily stores the received facial photograph in a database.

[1611] The server uses a facial recognition algorithm (for example, OpenCV) to analyze facial feature points (eyes, nose, mouth, etc.) and generate facial pattern data.

[1612] The server then uses an emotion recognition algorithm (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state (e.g., happiness, sadness, surprise) from the facial pattern data.

[1613] Matching face patterns with makeup database

[1614] The server compares the analyzed facial pattern data and emotional state data with a makeup database, which contains a wide variety of makeup styles and their corresponding facial feature patterns and emotional states.

[1615] The server scores multiple candidate styles and selects the most suitable makeup style. For example, if the user is sad, it selects a bright-colored makeup style.

[1616] Generate and adjust makeup advice

[1617] The server generates specific makeup advice based on the selected makeup style, including eyeshadow color, lip color, and foundation type and application method.

[1618] The server uses an emotion recognition engine to tailor makeup advice based on the user's emotional state, such as suggesting bright makeup to alleviate sad emotions.

[1619] Providing makeup advice

[1620] The server transmits the generated makeup advice to the terminal.

[1621] The device analyzes the received makeup advice and displays it to the user, who can then try out different makeup looks based on the advice.

[1622] Specific examples

[1623] For example, if a 25-year-old female user uses this system, the following process will occur:

[1624] 1. Users upload photos of their faces

[1625] Take a photo of your face with your smartphone and press the "Upload" button.

[1626] 2. Facial Recognition and Emotion Analysis

[1627] The server receives the photo, analyzes facial features using OpenCV, and analyzes emotions using the Microsoft Azure Emotion API to determine whether the user is sad.

[1628] 3. Database Verification

[1629] The server compares the facial pattern data and emotion data with a makeup database to select the most suitable makeup style, including a bright lip color to alleviate sad emotions.

[1630] 4. Advice Generation and Adjustment

[1631] The server generates specific makeup advice and tailors it based on emotions, for example, suggesting a vibrant lip color to help users feel more energized.

[1632] 5. Display of makeup advice

[1633] The device receives the advice and presents it to the user, who can then try out the makeup look according to the advice.

[1634] Prompt Sentence Examples

[1635] Describe a system that analyzes a user's facial photo and provides optimal makeup advice based on their facial features and emotions. Specifically, what makeup would be suggested if the user is sad and has olive skin?

[1636] This allows users to receive personalized makeup advice that reflects their emotions at the time. The system analyzes and evaluates the user's facial expressions and emotions, and provides highly accurate makeup advice based on that.

[1637] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1638] Step 1:

[1639] Users take a photo of their face using the camera application on their smartphone or computer and press the "upload" button.

[1640] Input: The user operates the camera application and takes a photo of their face.

[1641] Output: The captured facial photo data.

[1642] Step 2:

[1643] The device generates a data packet containing the captured facial photo data and generates and sends a request to the server to send it.

[1644] Input: Facial photo data generated when the user presses the upload button.

[1645] Output: Face photo data packet sent to the server.

[1646] Step 3:

[1647] The server temporarily stores the received facial photo data and runs a facial recognition algorithm.

[1648] Input: Facial photo data sent from the device.

[1649] Output: Detected facial feature points data.

[1650] Specific operation: Using a facial recognition algorithm such as OpenCV, facial feature points (eyes, nose, mouth, etc.) are analyzed and facial pattern data is generated.

[1651] Step 4:

[1652] The server runs an emotion recognition algorithm based on the generated facial pattern data.

[1653] Input: Face pattern data.

[1654] Output: Emotional state data (e.g., happy, sad, surprised, etc.).

[1655] Specific operation: Analyzes the user's emotions from facial feature point data using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

[1656] Step 5:

[1657] The server compares the facial pattern data and emotional state data with a makeup database and selects the most suitable makeup style.

[1658] Input: Facial pattern data and emotional state data.

[1659] Output: Selection result of optimal makeup style.

[1660] Specific operation: Scores multiple makeup styles in the database and determines the makeup style that best suits the user's characteristics.

[1661] Step 6:

[1662] The server generates specific makeup advice based on the selected makeup style.

[1663] Input: Optimal makeup style and emotional state data.

[1664] Output: Detailed makeup advice.

[1665] Specific operation: Based on the selected makeup style, create makeup advice including eye shadow color, lip color type, foundation type and application method.

[1666] Step 7:

[1667] The server transmits the generated makeup advice to the user's terminal.

[1668] Input: Generated makeup advice.

[1669] Output: A data packet containing the make advice.

[1670] Specific operation: Generate and send a data packet to send makeup advice to the terminal.

[1671] Step 8:

[1672] The device analyzes the received makeup advice and displays it on the user interface.

[1673] Input: A data packet containing make advice sent by the server.

[1674] Output: Makeup advice displayed in the user interface.

[1675] Specific operation: Analyzes the data packet and displays the received makeup advice on the screen. The user can check it and try applying makeup.

[1676] (Application example 2)

[1677] 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."

[1678] Conventional makeup advice systems only provide uniform advice without considering the user's emotions, and have the problem of being unable to respond to needs that change depending on the user's condition. In addition, because users cannot actually try on the makeup items, it is difficult for them to select appropriate makeup items based on the advice.

[1679] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1680] In this invention, the server includes: [means for a user to take an image of themselves and upload the image;] [means for the server to receive the uploaded image and analyze the pattern using feature extraction technology;] [means for the server to compare the analyzed pattern data with a database and generate optimal advice for the user;] [means for the server to send the generated advice to a client terminal and display it to the user;] [means for the server to analyze the user's emotions using an emotion engine and adjust the advice; and [means for enabling virtual try-on based on the advice. This makes it possible to provide personalized makeup advice that takes the user's emotions into consideration and to virtually try on how actual makeup items will look.

[1681] "User" means an end user of the Service or System.

[1682] An "image" is a photograph of a user's face or other visual data.

[1683] "Upload" is the process of sending data from a user's device to a server.

[1684] A "server" is a central computing resource that receives, analyzes, and transmits data.

[1685] "Feature extraction techniques" are algorithms and methods for extracting important information or features from images.

[1686] "Pattern data" is data related to the shape and features of a face obtained by image analysis.

[1687] A "database" is a data repository for storing makeup advice and other related information.

[1688] "Advice" refers to specific makeup techniques and product suggestions provided to users.

[1689] "Client Terminal" means a smartphone, tablet, or other computing device.

[1690] The "Emotion Engine" is a system that estimates and analyzes emotions from the user's facial expressions and other data.

[1691] "Virtual try-on" is a virtual experience that allows users to try on makeup items through video.

[1692] The present invention provides a system for providing personalized makeup advice to a user. The system analyzes the user's emotions and generates optimal makeup advice based on those emotions. Specific embodiments of the system are described below.

[1693] 1. Taking and uploading user images

[1694] The user takes a photo of their face using a client device such as a smartphone. After taking the photo, they press the "Upload" button in the application to send the image to the server. Once the server receives the image, it proceeds to the next step.

[1695] 2. Feature extraction and emotion analysis by the server

[1696] The server uses feature extraction technology to analyze the received image, extracting facial shapes and other important information to generate pattern data, and simultaneously analyzes the user's emotions (e.g., joy, sadness, surprise, etc.) using an emotion engine.

[1697] 3. Matching with the database and generating advice

[1698] The server compares the generated pattern data and emotion data with a database containing various makeup styles and corresponding advice. Based on this data, the server generates optimal makeup advice for the user.

[1699] 4. Virtual try-on

[1700] The generated advice includes a virtual try-on feature that allows users to virtually try on the suggested makeup items and see how they look on their own face.

[1701] 5. Providing advice and purchasing links

[1702] The client terminal receives the makeup advice sent from the server and displays it on the user interface. The displayed advice includes specific makeup techniques and product suggestions. Direct links to online shopping sites are also provided for the suggested makeup items, allowing the user to immediately purchase them.

[1703] Hardware and software used

[1704] This system uses the following hardware and software:

[1705] Smartphone or other client terminal: A device that takes an image of the user and sends it to the server.

[1706] Server: A central computing resource for image analysis, feature extraction, sentiment analysis, database matching, and advice generation.

[1707] Emotion engine: A system that estimates and analyzes emotions from the user's facial expressions.

[1708] Database: A data repository that stores makeup advice and related information.

[1709] Examples and prompts

[1710] For example, if a 28-year-old female user takes a photo of her face with her smartphone and emotional analysis shows that she is sad, the server will suggest a bright pink lip color and eyeshadow to accentuate the user's eyes based on the user's emotions.

[1711] Example prompt sentence:

[1712] "Design a virtual makeup advice system that suggests a bright pink lip color and eyeshadow to accentuate the eyes when a 28-year-old female user takes a photo of her face with a smartphone app and the emotion analysis indicates she is sad."

[1713] The system allows users to receive personalized makeup advice that takes into account their current emotions, and allows them to virtually try on the suggested makeup products, helping them make the right choice before actually purchasing them.

[1714] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1715] Step 1: User takes and uploads an image

[1716] The user takes a photo of their face using a client device such as a smartphone. The input is the user's face photo, which is sent to the server by pressing the "upload" button in the application. The output is the face photo data sent to the server. Specifically, the face photo is taken using the smartphone's camera app and is transferred directly to the server.

[1717] Step 2: The server receives the image and performs feature extraction.

[1718] The server receives image data sent from the client device. The input is a photo of the user's face, which is analyzed to extract the shape and features of the face. Specifically, a facial recognition algorithm is used to identify facial feature points and generate pattern data. The output is the extracted facial feature information. Specifically, face recognition is performed using libraries such as OpenCV and Dlib.

[1719] Step 3: The server analyzes the emotion using the emotion engine

[1720] The server inputs the extracted facial feature data into the emotion engine. The input is facial feature data, and the emotion engine uses this to analyze the user's emotions. Specifically, it uses a neural network to perform emotion analysis and identify emotions such as joy, sadness, and surprise. The output is the analyzed emotion data. Specifically, it uses an AI model for emotion analysis to score emotions.

[1721] Step 4: The server checks the database and generates an advice

[1722] The server compares the facial feature data and emotion data with a makeup database. The input is facial feature data and emotion data, and based on this, it generates optimal makeup advice. Specifically, it searches the database for relevant makeup styles, scores them, and selects the most appropriate style. The output is makeup advice provided to the user. Specifically, it extracts and analyzes the most appropriate data from the database.

[1723] Step 5: The server provides the virtual try-on feature

[1724] The server provides a virtual try-on function based on the generated makeup advice. The input is the generated makeup advice, which is sent to the client terminal. Specifically, software is used to virtually apply makeup items to the user's face. The output is visual information of the virtual makeup displayed on the client terminal. Specifically, the virtual makeup is applied using AR technology.

[1725] Step 6: The server sends the advice to the client device and displays it.

[1726] The server sends the generated makeup advice to the client terminal. The input is advice data from the server, which the client terminal receives and displays on the user interface. The output is the situation in which the advice is displayed to the user. Specifically, the data is sent to the terminal using an HTTP request, and the received data is displayed within the application.

[1727] Step 7: Provide a link for users to purchase the makeup item

[1728] The makeup advice displayed on the client terminal includes a link to purchase the corresponding makeup item. The user can click on this link to access an online shopping site and purchase the makeup item. The input is the purchase link information sent from the server, and the user clicks on the link to access the purchasing site. The output is the situation in which the user actually purchases the makeup item. Specifically, the system processes the link click event and opens the corresponding web page.

[1729] 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.

[1730] 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.

[1731] 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.

[1732] 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.

[1733] 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.

[1734] 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.

[1735] 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).

[1736] 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.

[1737] 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."

[1738] 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.

[1739] 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).

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] 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.

[1750] The following is further disclosed regarding the above embodiment.

[1751] (Claim 1)

[1752] [A means for users to take a photo of their face and upload that photo;

[1753] [Means for the server to receive the uploaded facial photograph and analyze the facial pattern using facial recognition technology;

[1754] [Means for comparing the face pattern data analyzed by the server with a makeup database and generating makeup advice that is optimal for the user;

[1755] [Means for transmitting makeup advice generated by the server to the terminal and displaying it to the user;

[1756] A system including:

[1757] (Claim 2)

[1758] The system according to claim 1, further comprising means for the terminal to temporarily store a facial photograph of the user and prepare to send it to the server.

[1759] (Claim 3)

[1760] The system according to claim 1, further comprising means for the server to score a plurality of makeup styles based on the face pattern data and select an optimum makeup style.

[1761] "Example 1"

[1762] (Claim 1)

[1763] [Means for users to take and transmit their own images;

[1764] [Means for the server to receive the transmitted image and analyze the face pattern using image recognition technology;

[1765] [Means for comparing the face pattern data analyzed by the server with a database and generating optimal advice for the user;

[1766] [Means for transmitting the advice generated by the server to the terminal and displaying it to the user;

[1767] A system including:

[1768] (Claim 2)

[1769] [The system according to claim 1, characterized in that the terminal comprises means for temporarily storing the user's image and preparing it for transmission to the server.]

[1770] (Claim 3)

[1771] The system according to claim 1, characterized in that the server comprises means for scoring a plurality of styles based on face pattern data and selecting the most suitable style.

[1772] That's it. Based on this format, we added new parts and wrote the claims.

[1773] "Application Example 1"

[1774] (Claim 1)

[1775] [A means for users to take a photo of their face and upload that photo;

[1776] [Means for the server to receive the uploaded facial photograph and analyze the facial pattern using facial recognition technology;

[1777] [Means for comparing the face pattern data analyzed by the server with a makeup database and generating makeup advice that is optimal for the user;

[1778] [Means for providing personalized makeup advice to individual users at beauty and cosmetic shops using an automated system;

[1779] [Means for using a smartphone or smart glasses to display the makeup advice generated by the device;

[1780] A system including:

[1781] (Claim 2)

[1782] [The system according to claim 1, wherein the terminal temporarily stores a photograph of the user's face and prepares to send it to the server.

[1783] (Claim 3)

[1784] [The system according to claim 1, wherein the server scores a plurality of makeup styles based on the facial pattern data and selects the most suitable makeup style.]

[1785] "Example 2: Combining Emotion Engines"

[1786] (Claim 1)

[1787] [A means for users to take a photo of their face and upload that photo;

[1788] [Means for the server to receive the uploaded facial photograph and analyze the facial pattern using facial recognition technology;

[1789] [Means for the server to use emotion recognition technology based on the face recognition result to analyze the user's emotional state;

[1790] [Means for collating the face pattern data and emotional state data analyzed by the server and generating optimal makeup advice for the user;

[1791] [Means for transmitting makeup advice generated by the server to the terminal and displaying it to the user;

[1792] ...

[1793] A system including:

[1794] (Claim 2)

[1795] The system according to claim 1, further comprising means for the terminal to temporarily store a facial photograph of the user and prepare to send it to the server.

[1796] (Claim 3)

[1797] The system according to claim 1, further comprising means for the server to score a plurality of makeup styles based on the facial pattern data and the emotional state data and select an optimal makeup style.

[1798] "Application example 2 when combining emotion engines"

[1799] (Claim 1)

[1800] [Means for users to take and upload images of themselves;

[1801] [Means for the server to receive the uploaded image and analyze the pattern using feature extraction techniques;

[1802] [Means for comparing the pattern data analyzed by the server with a database and generating optimal advice for the user;

[1803] [Means for transmitting advice generated by the server to a client terminal and displaying it to the user;

[1804] [Means for the server to analyze the user's emotions using an emotion engine and adjust the advice;

[1805] [Based on the advice, measures to enable virtual try-on and

[1806] A system including:

[1807] (Claim 2)

[1808] The system according to claim 1, further comprising means for the client terminal to temporarily store the user's image and prepare it for transmission to the server.

[1809] (Claim 3)

[1810] The system of claim 1, further comprising means for the server to score a plurality of styles based on the pattern data and select an optimal style. [Explanation of symbols]

[1811] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to take a photo of their face and upload that photo; a means for a server to receive the uploaded facial photograph and analyze the facial pattern using facial recognition technology; A means for comparing the analyzed face pattern data with a makeup database and generating optimal makeup advice for the user; A means for transmitting the makeup advice generated by the server to the terminal and displaying it to the user; A system including:

2. 2. The system according to claim 1, further comprising means for the terminal to temporarily store a facial photograph of the user and prepare it for transmission to the server.

3. 2. The system according to claim 1, further comprising means for the server to score a plurality of makeup styles based on the face pattern data and select an optimum makeup style.

Images