System

Abstract

A system is provided.SOLUTION: A system comprising: means for uploading an image of a face selected by a user; means for a server to analyze the uploaded image to extract facial features; means comprising a generation AI to generate a specific makeup technique based on the generated facial features; and means for providing the generated makeup technique to 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] In modern society, while opportunities to wear masks have decreased and situations where people are showing their faces have increased, many people are in a situation where they lack confidence in their appearance in real life. While it is possible to make yourself look beautiful in photos using photo editing apps, it is difficult to master the makeup techniques required to achieve the same effect in real life. In particular, beginners to makeup have few opportunities to learn specific techniques to achieve their ideal look. The present invention aims to solve these problems. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. The system includes a means for uploading a facial image selected by the user, a means for a server to analyze the uploaded image and extract facial features, a means equipped with a generation AI that generates specific makeup techniques based on the generated facial features, and a means for providing the generated makeup techniques to the user. The means for extracting facial features also includes a means for executing a process to individually recognize the features of each body part, such as the eyebrows, eyes, nose bridge, and lips, and presenting instructions for the generated makeup techniques to the user in natural language. This makes it possible for anyone, from makeup beginners to advanced users, to easily achieve their ideal face in realistic scenes.

[0006] "User" refers to a person who uses the system.

[0007] "Facial Image" refers to a photograph of a face selected by a User and uploaded to the System.

[0008] "Means for uploading" refers to the method or interface by which a user submits an image of their face to the system.

[0009] The "server" refers to the central computing unit that analyzes images, extracts facial features, and generates makeup applications.

[0010] "Means for analyzing images" refers to the technology or algorithms used to process uploaded images and recognize facial features.

[0011] "Facial features" refers to information about the shape, color, and placement of major facial features, such as eyebrows, eyes, nose, and lips.

[0012] "Generative AI" refers to artificial intelligence models or systems that create specific makeup techniques based on facial features.

[0013] "Makeup techniques" refer to specific cosmetic methods and procedures used to recreate facial features.

[0014] "Natural language" refers to the language used by humans on a daily basis, and is used to explain the generated makeup instructions in an easy-to-understand manner.

[0015] "Presentation means" refers to a method or interface for visually displaying the generated makeup technique instructions to the user. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image. The following describes in detail the modes for implementing the present invention.

[0038] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0039] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0040] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[0041] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[0042] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use and extremely useful for many people, from beginners to advanced makeup artists.

[0043] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts the shape (arched) and color (medium darkness) of Celebrity B's eyebrows. Next, based on this information, it generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate beautiful eyebrows like Celebrity B's on their own.

[0044] In this way, this invention makes full use of image recognition technology and generative AI to help users appear confidently in real-life situations.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The user selects an ideal celebrity face image from their device and uploads the image by pressing the upload button. This operation is performed through a user interface that has a file dialog for selecting an image and an upload button.

[0048] Step 2:

[0049] The terminal transmits the image data selected by the user to the server as a POST request using the HTTP protocol.

[0050] Step 3:

[0051] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[0052] Step 4:

[0053] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[0054] Step 5:

[0055] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[0056] Step 6:

[0057] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, which describe in detail how the user should apply the makeup.

[0058] Step 7:

[0059] The makeup instructions generated by the generative AI are converted into an appropriate data format, such as JSON, and sent back to the user's device.

[0060] Step 8:

[0061] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[0062] Step 9:

[0063] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[0064] Step 10:

[0065] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[0066] Through the above steps, the present invention has explained in detail a system that integrates image recognition technology and generative AI to enable users to easily recreate their ideal celebrity makeup techniques.

[0067] Example 1

[0068] 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."

[0069] With conventional systems, it was difficult for users to independently find makeup techniques that would bring them closer to their ideal celebrity face. While image recognition technology and generative AI models were needed to provide specific and easy-to-understand makeup techniques, the general methods were too complicated for users to execute efficiently.

[0070] 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.

[0071] In this invention, the server includes means for uploading a facial image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to save the received image data, means for the server to analyze the saved image data and extract facial features, means for inputting the extracted feature data into a generative AI model and generating specific makeup techniques based on the generated facial features, and means for sending instructions for the generated makeup techniques in an appropriate format to the user's terminal and displaying them on the user's terminal. This enables the user to easily obtain and practice specific and easy-to-understand makeup techniques to get closer to the face of an ideal celebrity.

[0072] A "user" is an individual who uses the system to acquire the makeup techniques of an ideal celebrity's face.

[0073] A "terminal" is a device operated by a user, and is a device used to upload images and communicate data with a server.

[0074] "Server" refers to a computer system that stores, analyzes, and processes image data received from users.

[0075] "Image data" refers to digital data of images of celebrities' faces uploaded by users.

[0076] The "storage means" is a part that has the function of storing image data received by the server in a storage medium.

[0077] The "analysis means" is a part that has the function of extracting features of each part of the face from the received image data using an image recognition model.

[0078] "Facial features" refer to the specific shapes and colors of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0079] A "generative AI model" is an artificial intelligence model that generates specific makeup techniques based on extracted feature data.

[0080] "Makeup techniques" are makeup techniques that allow users to recreate the facial features of their ideal celebrity.

[0081] The "transmission means" is a part having a function for the terminal to transmit image data to the server, and for the server to transmit the generated makeup technique instructions to the user terminal.

[0082] The "display means" is a part that has the function of displaying makeup technique instructions generated on the user terminal in a visually easy-to-understand manner.

[0083] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generative AI model provides specific makeup techniques based on that image. The following describes in detail the implementation of this system.

[0084] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. On the device, the operation is performed using a web browser or an image upload form in a dedicated application. A file selection dialog is displayed, and the user selects an image file from local storage. This image data supports common image formats such as JPEG and PNG.

[0085] Next, the device sends the image data selected by the user to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. When sending, the binary information of the image data is multiplexed and sent, along with metadata (e.g., file name, file size, etc.). The server saves the received image data in local storage or cloud storage (e.g., Amazon S3). The save path and metadata are recorded in a database (e.g., MySQL, PostgreSQL).

[0086] The server analyzes the saved image data using an image recognition model. For example, OpenCV or Google's FaceNet is used as the image recognition model. This allows the features of each part of the face (eyebrows, eyes, nose, lips, etc.) to be extracted. Here, for example, the face area is detected from the image and the feature values ​​for each part are calculated.

[0087] Next, the server inputs the extracted feature data into a generative AI model (e.g., GPT-4). Specific feature data includes eyebrow shape, eye features, and lip color. For example, the prompt "Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used. The generative AI model generates specific makeup techniques based on this data. The generated results include instructions such as "Draw an arched eyebrow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[0088] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. Here, the makeup instructions are displayed on a web page, neatly laid out using HTML / CSS. Adding explanations with images and video links makes it easier for users to understand.

[0089] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts Celebrity B's eyebrow shape (arched) and color (medium dark). Next, based on this information, the generative AI model generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate Celebrity B's beautiful eyebrows on his or her own.

[0090] An example of a prompt for the generative AI model is as follows:

[0091] "Based on the following facial feature data, please generate a makeup look that the user can recreate. Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink. Please explain the specific makeup techniques in a step-by-step manner."

[0092] As described above, the present invention provides a system that utilizes image recognition technology and generative AI models to help users appear confidently in real-life situations.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1:

[0095] The user selects an image of the ideal celebrity's face from their device and uploads it to the system. The user uses an image upload form in a web browser or a dedicated application, and selects an image file from local storage using a file selection dialog. The input is the image data selected by the user, and the output is image data ready to be uploaded.

[0096] Step 2:

[0097] The device sends the selected image data to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. Metadata (file name, file size, etc.) is also sent. The input is image data ready to be uploaded, and the output is the image data and metadata sent to the server.

[0098] Step 3:

[0099] The server saves the received image data. The server stores the image data in local storage or cloud storage (e.g., Amazon S3) and records the path and metadata in a database (e.g., MySQL, PostgreSQL). The input is the image data and metadata sent to the server, and the output is the image data saved in storage and the path and metadata recorded in the database.

[0100] Step 4:

[0101] The server analyzes the stored image data using an image recognition model. Specifically, it uses OpenCV or Google's FaceNet to extract features of each part of the image, such as the eyebrows, eyes, nose, and lips. The input is the image data stored in storage, and the output is specific feature data for each part of the face (e.g., eyebrow shape, eye features, lip color, etc.).

[0102] Step 5:

[0103] The server inputs the extracted feature data into a generative AI model to generate specific makeup techniques. Feature data such as "eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used as a prompt. The generative AI model (e.g., GPT-4) generates makeup techniques based on this data. The input is facial feature data, and the output is specific instructions for makeup techniques.

[0104] Step 6:

[0105] The generated makeup instructions are returned to the user's device in an appropriate format (e.g., JSON). The server sends the data as an HTTP response, and the device receives it. The input is the generated makeup instructions, and the output is the makeup instruction data sent to the user's device.

[0106] Step 7:

[0107] The device analyzes the generated makeup instruction data and displays it on the user's screen in a visually easy-to-understand format. It uses HTML / CSS to display beautifully laid out makeup instructions on a web page, including explanations with images and video links. The input is the makeup instruction data sent from the server, and the output is the makeup instructions displayed on the user's device.

[0108] Step 8:

[0109] The user follows the displayed makeup technique steps to actually apply makeup. The user refers to the instructions and performs tasks such as how to use an eyebrow pencil, how to apply eyeshadow, and how to choose a lipstick. The input is the specific makeup technique instructions displayed on the device, and the output is the actual makeup applied.

[0110] (Application example 1)

[0111] 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."

[0112] Current makeup support systems require users to apply makeup themselves while referring to images of their ideal face, making it difficult for makeup novices. Furthermore, because real-time feedback is not available, it takes a lot of time and effort to achieve the desired celebrity look. Furthermore, the lack of specific makeup steps and information on the products to use makes it difficult for users to apply makeup properly. Therefore, there is a need for a system that allows users to easily apply their ideal makeup.

[0113] 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.

[0114] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI for generating specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means connected to a mirror located in a physical store for capturing the user's face in real time and virtually applying makeup, thereby enabling the user to apply their ideal makeup while receiving feedback in real time in the physical store.

[0115] A "user" is a person who uses this system to receive makeup technique suggestions.

[0116] "Facial image" is image data of a face that the user considers ideal or that they use as a reference.

[0117] "Uploading" is the act of sending image data from a user's device to a server.

[0118] The "server" is a computer system that analyzes and stores image data and generates makeup techniques.

[0119] "Facial features" refers to data that indicates the individual features and shapes of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0120] "Generative AI" is an artificial intelligence model that generates specific makeup techniques based on facial feature data.

[0121] "Makeup techniques" are specific makeup methods and procedures that allow users to recreate specific facial features.

[0122] "Providing" refers to the act of visually displaying and explaining the generated makeup steps and methods to the user.

[0123] A "brick and mortar store" is a physical location, such as a cosmetics store or beauty salon, that a user can visit.

[0124] A "mirror" is a device that has a reflective surface that allows the user to see their own face and is linked to a device such as a camera.

[0125] "Real-time capture" means instantly obtaining images or footage the moment the user stands in front of the mirror.

[0126] "Virtually applying makeup" refers to the virtual display of makeup effects on a user's face captured in real time through digital processing.

[0127] The present invention provides a system that allows users to experience ideal makeup techniques in real time at a physical store. Hereinafter, an embodiment of the present invention will be specifically described.

[0128] System Overview

[0129] 1. User Interface:

[0130] The user stands in front of a mirror in a brick-and-mortar store, which has a built-in high-definition camera and display.

[0131] Users use their smartphones to upload an image of their ideal celebrity's face.

[0132] 2. Image upload and analysis:

[0133] The image of the face selected by the user is sent from the smartphone to the server, where the image data is sent as a POST request using the HTTP protocol.

[0134] The server stores the received image data in storage and analyzes the facial features using an image recognition model, extracting the individual features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[0135] 3. Makeup Creation:

[0136] The extracted facial feature data is fed into a generative AI, which then generates specific makeup techniques, including detailed instructions such as how to shape the eyebrows and what eyeshadow to use.

[0137] 4. Real-time makeup application:

[0138] The server sends the generated makeup instructions to the mirror, whose camera captures the user's face in real time and virtually applies the makeup.

[0139] The generated makeup instructions are displayed on the mirror display in a visually easy-to-understand format.

[0140] Hardware and software used

[0141] Hardware:

[0142] Smart mirror (with camera)

[0143] Smartphone

[0144] Server (with high-performance CPU and memory)

[0145] software:

[0146] Flask (as a web server)

[0147] OpenCV (image processing library)

[0148] Pillow (image manipulation library)

[0149] AI model library (ImageRecognitionModel and MakeupGenerator)

[0150] Processing flow

[0151] The server receives and analyzes image data sent from the user's smartphone. Based on the analysis results, the generative AI generates specific makeup techniques. The generated makeup techniques are virtually applied to the user's face, captured in real time, and displayed in the mirror. This allows the user to receive instant feedback and apply the makeup they desire.

[0152] Specific examples

[0153] For example, if a user wants to copy the makeup of a certain actress, they upload an image of the actress's face from their smartphone to the system. The server analyzes the image and extracts the actress's facial features (arched eyebrows, cat-eye crease, etc.). Based on this information, the generative AI generates specific makeup steps and virtually applies them to the user's face in real time. Specific steps, such as "draw an arched brow with an eyebrow pencil and blend with powder," are displayed on the mirror, allowing the user to recreate the makeup themselves.

[0154] Prompt Sentence Examples

[0155] "Analyze the eyebrow shape in this image and generate the ideal makeup steps. Specifically, please provide detailed instructions on how to use an eyebrow pencil and blend the lines."

[0156] By inputting these prompts into a generative AI model, it is possible to provide the specific makeup techniques that the user desires.

[0157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0158] Step 1:

[0159] A user uses a smartphone to select an ideal celebrity's facial image and upload it to the system. This is done using an image selection interface displayed on the smartphone's application screen. Once the user selects an image, the image data is sent to the server as a POST request using the HTTP protocol. The input is the facial image selected by the user, and the output is the image data sent to the server.

[0160] Step 2:

[0161] The server stores the received image data in storage. The stored image data is used in subsequent analysis processing. The input is the image data sent to the server, and the output is the image data stored in the storage.

[0162] Step 3:

[0163] The server analyzes the facial images stored in storage using an image recognition model (ImageRecognitionModel). This analysis process extracts the features of each part of the face (eyebrows, eyes, nose, lips, etc.) individually. The input is the stored image data, and the output is the extracted feature data for each part of the face. Specifically, it uses an image processing library such as OpenCV to detect feature points and analyze the shape.

[0164] Step 4:

[0165] The server inputs the extracted facial feature data into a generative AI model (Makeup Generator). This generative AI model generates the user's ideal makeup technique based on the facial feature data. The input is the extracted facial feature data, and the output is a specific makeup technique. Specifically, the makeup technique is generated using a prompt to the generative AI model. A prompt such as "Analyze the eyebrow shape in this image and generate the ideal makeup procedure. Specifically, please explain in detail how to use an eyebrow pencil and how to blend it" is used.

[0166] Step 5:

[0167] The generated makeup instructions are sent from the server in an appropriate format (e.g., JSON) to a smart mirror in a physical store. The smart mirror captures the user's face in real time with its built-in camera and processes the data to apply virtual makeup. The input is the generated makeup instructions data, and the output is an image of the virtual makeup applied by the smart mirror. Specific operations include real-time image processing and graphic processing for applying makeup.

[0168] Step 6:

[0169] The results of the virtual makeup displayed on the smart mirror and the specific makeup steps are presented to the user in a visually easy-to-understand format. This allows the user to receive specific instructions for actually applying the makeup. The input is a real-time captured image of the user's face and the generated makeup technique, and the output is an image of the virtual makeup displayed on the mirror and the makeup steps. Specific instructions displayed include "Draw an arched shape with an eyebrow pencil and blend with powder" and "Create a cat eye with black liquid eyeliner."

[0170] Through these steps, a system is realized that allows users to apply their ideal makeup while receiving real-time feedback in a physical store.

[0171] 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.

[0172] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the modes for implementing the present invention.

[0173] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0174] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0175] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[0176] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades of makeup.

[0177] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[0178] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use for anyone, from beginners to advanced makeup artists, and it is extremely useful for many people because it provides makeup techniques that take the user's emotions into consideration.

[0179] As a specific example, consider the case where user A uploads an image of celebrity B, who has beautiful eyebrows, to the system. When user A uploads the image, the server analyzes the image and extracts celebrity B's eyebrow shape (arched) and color (medium dark). Next, the emotion engine analyzes user A's facial expression and identifies, for example, that the user is depressed. Based on this information, the server suggests specific makeup instructions, such as "draw an arched shape with an eyebrow pencil and blend with powder," as well as a lighter shade to create a more energetic impression. By displaying this to user A, user A can recreate celebrity B's beautiful eyebrows while applying makeup that suits their own emotions.

[0180] As described above, this invention utilizes image recognition technology, generative AI, and emotion recognition technology to help users appear confidently in real-life situations.

[0181] The processing flow will be explained below.

[0182] Step 1:

[0183] Users select an ideal celebrity face image from their device and upload the image by pressing the upload button. This is done through a user interface that includes a file dialog for selecting an image and an upload button.

[0184] Step 2:

[0185] The terminal transmits the image data selected by the user to the server. The image data is transmitted to the server as a POST request using the HTTP protocol.

[0186] Step 3:

[0187] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[0188] Step 4:

[0189] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[0190] Step 5:

[0191] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[0192] Step 6:

[0193] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, including detailed instructions on how to draw eyebrows and which eyeshadow to use.

[0194] Step 7:

[0195] The server converts the makeup instructions generated by the generative AI into an appropriate data format, such as JSON.

[0196] Step 8:

[0197] The server invokes an emotion engine to recognize the user's emotions, and analyzes the user's facial expression data and voice data to identify the user's current emotion (e.g., joy, sadness, surprise, etc.).

[0198] Step 9:

[0199] The server then adjusts the generated makeup techniques based on the identified emotion data. For example, if the user is feeling down, the server may suggest lighter colors.

[0200] Step 10:

[0201] The server transmits the adjusted makeup technique instructions to the user's terminal.

[0202] Step 11:

[0203] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[0204] Step 12:

[0205] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[0206] Step 13:

[0207] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[0208] Through the above steps, the present invention has provided a detailed description of a system that integrates image recognition technology, generative AI, and an emotion engine to enable users to easily recreate their ideal celebrity makeup techniques.

[0209] Example 2

[0210] 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."

[0211] Many existing makeup instruction systems simply analyze images and provide basic makeup techniques. However, few systems can respond to the user's emotions and individual facial features, and they lack detailed instruction to maximize the makeup effect. This makes it difficult for users to learn makeup techniques that suit their emotions and specific facial features.

[0212] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading a face image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to analyze the uploaded image and extract facial features, means having a generative AI model that generates specific makeup techniques based on the extracted facial features, means for providing the generated makeup techniques to the user, and means having an emotion engine that analyzes the user's emotions and adjusts the makeup technique content based on the emotions. This allows the user to not only learn makeup techniques that match their facial features but also to apply makeup that suits their emotions at the time.

[0213] "User" refers to a person who uses the system to upload a facial image and receive makeup instruction.

[0214] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to access the System.

[0215] A "server" is a group of computers that serves as the core of a system, and is a device that receives requests from users and processes and analyzes data.

[0216] "Facial Image" means an image that clearly shows facial features and that is uploaded by a User to the System.

[0217] "Means for uploading" refers to the ability of a user to use their own device to send an image of the selected face to the system.

[0218] "Image data" refers to electronic data relating to a facial image uploaded by a user.

[0219] "Means of analysis" refers to the technology in which the server receives image data, recognizes facial features, and extracts information about each part of the face.

[0220] "Facial features" refers to the specific shape, color, and other characteristics of each part of the face (e.g., eyebrows, eyes, nose, lips).

[0221] A "generative AI model" refers to artificial intelligence (AI) that generates specific makeup techniques based on facial feature data.

[0222] "Means for providing" refers to the function for presenting the generated makeup techniques to the user.

[0223] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice data to identify their emotions.

[0224] "Adjustment means" refers to the function of modifying and optimizing the content of the generated makeup techniques based on the emotional data identified by the emotion engine.

[0225] The present invention is a system that allows users to upload an image of their ideal celebrity's face, and provides specific makeup techniques that are analyzed and generated by AI based on that image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0226] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. The device provides an interface for uploading images, and the user selects an image using a file browser and clicks the "Upload" button to send the image data to the system. This process sends the image data to the server as a POST request using the HTTP protocol.

[0227] The server temporarily stores the received image data in memory and then saves it to storage (e.g., Amazon S3 or Google Cloud Storage). The server then analyzes the stored image using an image recognition model (e.g., OpenCV or TensorFlow) to extract features for each part of the face, such as the eyebrows, eyes, nose, and lips. This analysis generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color may be extracted.

[0228] The server then uses a generative AI model (e.g., GPT) to input the extracted feature data as prompts and generate specific makeup tips for the user to replicate the features, including specific makeup techniques such as how to shape the eyebrows and which eyeshadow to use.

[0229] Examples of prompt sentences include the following:

[0230] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[0231] The system also includes an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the acquired emotion data with facial feature data to adjust the makeup content generated based on the user's emotions. For example, if the system determines that the user is depressed, it will suggest lighter shades of makeup.

[0232] Finally, the generated makeup instructions are sent back to the user's device in JSON format. The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed on the screen.

[0233] This system is easy to use for anyone, from makeup beginners to advanced users, and provides makeup techniques that take the user's emotions into consideration, making it extremely useful for many people.

[0234] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0235] Explain the program's processing flow in detail

[0236] Step 1:

[0237] The user selects an image

[0238] Description: The user selects an image of their ideal celebrity face from their device.

[0239] Input: A face image file stored in the user's local storage.

[0240] Output: The path of the selected face image file.

[0241] Specific operation: The user opens the file browser on the device and selects a face image. This operation is performed by the user clicking on the file to select it.

[0242] Step 2:

[0243] The user uploads an image

[0244] Description: User selects an image and clicks the "Upload" button to send it to the system.

[0245] Input: The path of the selected face image file.

[0246] Output: Image data sent to the server.

[0247] Specific operation: When the user clicks the "Upload" button, the device sends the image data to the server as an HTTP POST request.

[0248] Step 3:

[0249] The device sends the image data to the server.

[0250] Description: The terminal sends image data to the server using the HTTP protocol.

[0251] Input: Image data.

[0252] Output: Image data sent to the server.

[0253] Specific operation: The terminal embeds the selected image data in binary format in an HTTP POST request and sends it to the specified server URL.

[0254] Step 4:

[0255] The server receives the image data.

[0256] Description: The server temporarily stores the received image data in memory and then stores it in storage.

[0257] Input: Image data sent to the server.

[0258] Output: Image data saved in storage.

[0259] Specific operation: The server extracts the image data from the body of the POST request and saves the data in the specified storage path.

[0260] Step 5:

[0261] The server analyzes the image

[0262] Description: The server uses an image recognition model to analyze the stored image data and extract features for each part of the face.

[0263] Input: Image data stored in storage.

[0264] Output: Parsed facial feature data.

[0265] Specific operation: The server calls an image recognition model (e.g., OpenCV or TensorFlow) to detect and analyze each part of the face (eyebrows, eyes, nose, lips, etc.).

[0266] Step 6:

[0267] The server generates facial feature data

[0268] Description: From the analysis results, the server extracts feature data for each part of the body, such as eyebrows, eyes, nose, and lips, and structures it in JSON format.

[0269] Input: Analyzed facial features.

[0270] Output: Facial feature data in JSON format.

[0271] Specific operation: Based on the analysis results, the server compiles the characteristics of each part (e.g., eyebrow shape, eye style, lip color) as JSON.

[0272] Step 7:

[0273] The server inputs data into the generated AI.

[0274] Description: The server inputs the feature data as prompts into the generative AI model to generate specific makeup techniques.

[0275] Input: Facial feature data in JSON format.

[0276] Output: Generated makeup technique data.

[0277] Specific operation: The server calls a generative AI model (e.g., GPT) and inputs feature data as a prompt. The generative AI model generates a makeup technique based on the data.

[0278] Example prompt sentence:

[0279] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[0280] Step 8:

[0281] The server uses an emotion engine to analyze the user's emotions.

[0282] Description: The server analyzes the user's facial and voice data and uses an emotion engine to identify emotions.

[0283] Input: User's facial expression data and voice data.

[0284] Output: Identified emotion data.

[0285] Specific operation: The server calls an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) and analyzes the user's facial and voice data to identify emotions.

[0286] Step 9:

[0287] The server combines emotional data with makeup techniques

[0288] Description: The server adjusts the content of the generated makeup techniques based on the emotional data.

[0289] Input: Generated makeup technique data, identified emotion data.

[0290] Output: Emotion-specific makeup technique data.

[0291] Specific operation: The server refers to the emotional data and adjusts the makeup technique, such as recommending lighter colors if the person is not feeling well.

[0292] Step 10:

[0293] The server sends makeup instructions back to the device.

[0294] Description: The generated makeup instructions are sent back to the user's device in JSON format.

[0295] Input: Emotion-aware makeup technique data.

[0296] Output: Makeup technique data sent to the user's device.

[0297] Specific operation: The server converts the adjusted makeup technique data into JSON format and sends it to the user's device as an HTTP response.

[0298] Step 11:

[0299] The device displays the makeup technique steps

[0300] Description: The device displays the received data on the user's screen in a visually understandable format.

[0301] Input: Received makeup technique data.

[0302] Output: The makeup technique displayed on the user's screen.

[0303] Specific operation: The device parses the received JSON data and displays it on the screen as HTML and view components. For example, it displays a list such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[0304] (Application example 2)

[0305] 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."

[0306] In modern society, the pursuit of each individual's ideal beauty is an important factor that contributes to improving self-esteem and confidence. However, many people find it difficult to find the makeup method that best suits them, and makeup beginners in particular often lack proper guidance. In addition, makeup advice that responds to users' emotions is not provided, and users are unable to find a makeup method that suits their current condition.

[0307] 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.

[0308] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI that generates specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means having an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques according to the emotions. This makes it possible to provide makeup techniques that are optimized for each user and to provide immediate advice according to the user's emotions.

[0309] "User" refers to a person who uses the system to receive makeup technique suggestions.

[0310] "Face image" refers to a photo of a celebrity or the user's own face that they consider ideal.

[0311] "Means for uploading" refers to the functionality that allows users to send images of their faces to the system.

[0312] The "means for analyzing and extracting facial features" is a function that processes the facial image received by the server and identifies detailed features of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0313] "Generative AI" refers to artificial intelligence technology that automatically generates specific makeup techniques suited to the user based on extracted facial features.

[0314] The "means of providing" is a function that displays the generated makeup techniques to the user in a visual and easy-to-understand format.

[0315] An "emotion recognition engine" refers to technology that analyzes a user's facial expressions and voice data and recognizes their emotions.

[0316] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generation AI provides specific makeup techniques based on that image. It also combines an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques accordingly. Details for implementing this invention are described below.

[0317] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. A user interface is provided for uploading the image. Through this interface, the user sends the image from their device to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0318] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0319] The server then inputs these feature data into a generative AI, which generates specific makeup techniques for the user to recreate those features. The generated makeup techniques include detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques. This generative AI uses OpenAI's GPT-3 model and other models.

[0320] The system also features an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. Emotion recognition models such as the OpenCV library and EmotionNet are used for emotion recognition. The server combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades.

[0321] The generated makeup instructions are sent to the user's device in an appropriate format (e.g., JSON), which receives the data and displays it on the user's screen in a visually understandable format.

[0322] As a specific example, if a user uploads an image of their ideal celebrity, the server analyzes the image to extract the celebrity's eyebrow shape and color. The emotion engine analyzes the user's facial expressions and identifies that the user is depressed. Based on this information, the server suggests lighter shades to create a more cheerful impression. These suggestions are displayed to the user, who can then follow specific makeup techniques to achieve their ideal face.

[0323] An example of a prompt sentence to be input to a generative AI model is in the following format:

[0324] "Generate the following makeup techniques based on facial feature data.\nFeature data: {Eyebrow shape: arched, Eyes: cat eyeliner, Lip color: natural pink}\nOutput format: Draw an arched eyebrow with an eyebrow pencil and blend with powder. Create a cat eye with black liquid eyeliner. Use natural pink lipstick."

[0325] By inputting this prompt into the generation AI, appropriate makeup techniques for the user are automatically generated.

[0326] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0327] Step 1:

[0328] Users select an image of their ideal celebrity face from their device and upload it to the system.

[0329] Input: A face image selected by the user.

[0330] Processing: The device sends the selected image to the server through the image upload interface. The image data is sent as a POST request using the HTTP protocol.

[0331] Output: Facial image data sent to the server.

[0332] Step 2:

[0333] The server stores the received image data in storage.

[0334] Input: Uploaded image data.

[0335] Processing: The server receives the image data and saves it to the specified storage. This saving process is performed using a Python library, etc.

[0336] Output: Image data saved in storage.

[0337] Step 3:

[0338] The server analyzes the stored images and extracts facial features.

[0339] Input: Saved image data.

[0340] Processing: The server analyzes the image data using an image recognition model (e.g., ResNet50 using PyTorch). The model extracts features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[0341] Output: Parsed facial feature data.

[0342] Step 4:

[0343] Based on facial feature data, generative AI generates specific makeup techniques.

[0344] Input: Facial feature data.

[0345] Processing: The server inputs facial feature data into OpenAI's GPT-3 model as a prompt. Based on the prompt, the generation AI outputs specific makeup application steps.

[0346] Output: Generated specific makeup techniques.

[0347] Step 5:

[0348] The server recognizes the user's emotions and adjusts the makeup application procedure.

[0349] Input: User's real-time facial expression data and generated makeup instructions.

[0350] Processing: Using an emotion recognition engine (e.g., OpenCV library and EmotionNet), the server analyzes the user's facial expression data. Based on the recognized emotion data, the server adjusts the makeup application (e.g., if the user is depressed, it suggests lighter shades).

[0351] Output: Adjusted makeup application steps.

[0352] Step 6:

[0353] The generated makeup technique instructions are provided to the user's terminal.

[0354] Enter: a coordinated makeup routine.

[0355] Processing: The server sends the adjusted makeup instructions to the user's device in an appropriate format (e.g., JSON). The device displays the received data on the screen in a visually understandable format.

[0356] Output: Specific makeup application instructions displayed on the user's device.

[0357] The above are the processing steps of the system that realizes this application example. This process allows users to easily obtain the ideal makeup technique.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] [Second embodiment]

[0362] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0363] 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.

[0364] 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).

[0365] 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.

[0366] 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.

[0367] 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).

[0368] 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. 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.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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."

[0374] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image. The following describes in detail the modes for implementing the present invention.

[0375] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0376] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0377] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[0378] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[0379] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use and extremely useful for many people, from beginners to advanced makeup artists.

[0380] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts the shape (arched) and color (medium darkness) of Celebrity B's eyebrows. Next, based on this information, it generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate beautiful eyebrows like Celebrity B's on their own.

[0381] In this way, this invention makes full use of image recognition technology and generative AI to help users appear confidently in real-life situations.

[0382] The processing flow will be explained below.

[0383] Step 1:

[0384] The user selects an ideal celebrity face image from their device and uploads the image by pressing the upload button. This operation is performed through a user interface that has a file dialog for selecting an image and an upload button.

[0385] Step 2:

[0386] The terminal transmits the image data selected by the user to the server as a POST request using the HTTP protocol.

[0387] Step 3:

[0388] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[0389] Step 4:

[0390] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[0391] Step 5:

[0392] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[0393] Step 6:

[0394] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, which describe in detail how the user should apply the makeup.

[0395] Step 7:

[0396] The makeup instructions generated by the generative AI are converted into an appropriate data format, such as JSON, and sent back to the user's device.

[0397] Step 8:

[0398] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[0399] Step 9:

[0400] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[0401] Step 10:

[0402] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[0403] Through the above steps, the present invention has explained in detail a system that integrates image recognition technology and generative AI to enable users to easily recreate their ideal celebrity makeup techniques.

[0404] Example 1

[0405] 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."

[0406] With conventional systems, it was difficult for users to independently find makeup techniques that would bring them closer to their ideal celebrity face. While image recognition technology and generative AI models were needed to provide specific and easy-to-understand makeup techniques, the general methods were too complicated for users to execute efficiently.

[0407] 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.

[0408] In this invention, the server includes means for uploading a facial image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to save the received image data, means for the server to analyze the saved image data and extract facial features, means for inputting the extracted feature data into a generative AI model and generating specific makeup techniques based on the generated facial features, and means for sending instructions for the generated makeup techniques in an appropriate format to the user's terminal and displaying them on the user's terminal. This enables the user to easily obtain and practice specific and easy-to-understand makeup techniques to get closer to the face of an ideal celebrity.

[0409] A "user" is an individual who uses the system to acquire the makeup techniques of an ideal celebrity's face.

[0410] A "terminal" is a device operated by a user, and is a device used to upload images and communicate data with a server.

[0411] "Server" refers to a computer system that stores, analyzes, and processes image data received from users.

[0412] "Image data" refers to digital data of images of celebrities' faces uploaded by users.

[0413] The "storage means" is a part that has the function of storing image data received by the server in a storage medium.

[0414] The "analysis means" is a part that has the function of extracting features of each part of the face from the received image data using an image recognition model.

[0415] "Facial features" refer to the specific shapes and colors of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0416] A "generative AI model" is an artificial intelligence model that generates specific makeup techniques based on extracted feature data.

[0417] "Makeup techniques" are makeup techniques that allow users to recreate the facial features of their ideal celebrity.

[0418] The "transmission means" is a part having a function for the terminal to transmit image data to the server, and for the server to transmit the generated makeup technique instructions to the user terminal.

[0419] The "display means" is a part that has the function of displaying makeup technique instructions generated on the user terminal in a visually easy-to-understand manner.

[0420] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generative AI model provides specific makeup techniques based on that image. The following describes in detail the implementation of this system.

[0421] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. On the device, the operation is performed using a web browser or an image upload form in a dedicated application. A file selection dialog is displayed, and the user selects an image file from local storage. This image data supports common image formats such as JPEG and PNG.

[0422] Next, the device sends the image data selected by the user to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. When sending, the binary information of the image data is multiplexed and sent, along with metadata (e.g., file name, file size, etc.). The server saves the received image data in local storage or cloud storage (e.g., Amazon S3). The save path and metadata are recorded in a database (e.g., MySQL, PostgreSQL).

[0423] The server analyzes the saved image data using an image recognition model. For example, OpenCV or Google's FaceNet is used as the image recognition model. This allows the features of each part of the face (eyebrows, eyes, nose, lips, etc.) to be extracted. Here, for example, the face area is detected from the image and the feature values ​​for each part are calculated.

[0424] Next, the server inputs the extracted feature data into a generative AI model (e.g., GPT-4). Specific feature data includes eyebrow shape, eye features, and lip color. For example, the prompt "Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used. The generative AI model generates specific makeup techniques based on this data. The generated results include instructions such as "Draw an arched eyebrow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[0425] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. Here, the makeup instructions are displayed on a web page, neatly laid out using HTML / CSS. Adding explanations with images and video links makes it easier for users to understand.

[0426] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts Celebrity B's eyebrow shape (arched) and color (medium dark). Next, based on this information, the generative AI model generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate Celebrity B's beautiful eyebrows on his or her own.

[0427] An example of a prompt for the generative AI model is as follows:

[0428] "Based on the following facial feature data, please generate a makeup look that the user can recreate. Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink. Please explain the specific makeup techniques in a step-by-step manner."

[0429] As described above, the present invention provides a system that utilizes image recognition technology and generative AI models to help users appear confidently in real-life situations.

[0430] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0431] Step 1:

[0432] The user selects an image of the ideal celebrity's face from their device and uploads it to the system. The user uses an image upload form in a web browser or a dedicated application, and selects an image file from local storage using a file selection dialog. The input is the image data selected by the user, and the output is image data ready to be uploaded.

[0433] Step 2:

[0434] The device sends the selected image data to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. Metadata (file name, file size, etc.) is also sent. The input is image data ready to be uploaded, and the output is the image data and metadata sent to the server.

[0435] Step 3:

[0436] The server saves the received image data. The server stores the image data in local storage or cloud storage (e.g., Amazon S3) and records the path and metadata in a database (e.g., MySQL, PostgreSQL). The input is the image data and metadata sent to the server, and the output is the image data saved in storage and the path and metadata recorded in the database.

[0437] Step 4:

[0438] The server analyzes the stored image data using an image recognition model. Specifically, it uses OpenCV or Google's FaceNet to extract features of each part of the image, such as the eyebrows, eyes, nose, and lips. The input is the image data stored in storage, and the output is specific feature data for each part of the face (e.g., eyebrow shape, eye features, lip color, etc.).

[0439] Step 5:

[0440] The server inputs the extracted feature data into a generative AI model to generate specific makeup techniques. Feature data such as "eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used as a prompt. The generative AI model (e.g., GPT-4) generates makeup techniques based on this data. The input is facial feature data, and the output is specific instructions for makeup techniques.

[0441] Step 6:

[0442] The generated makeup instructions are returned to the user's device in an appropriate format (e.g., JSON). The server sends the data as an HTTP response, and the device receives it. The input is the generated makeup instructions, and the output is the makeup instruction data sent to the user's device.

[0443] Step 7:

[0444] The device analyzes the generated makeup instruction data and displays it on the user's screen in a visually easy-to-understand format. It uses HTML / CSS to display beautifully laid out makeup instructions on a web page, including explanations with images and video links. The input is the makeup instruction data sent from the server, and the output is the makeup instructions displayed on the user's device.

[0445] Step 8:

[0446] The user follows the displayed makeup technique steps to actually apply makeup. The user refers to the instructions and performs tasks such as how to use an eyebrow pencil, how to apply eyeshadow, and how to choose a lipstick. The input is the specific makeup technique instructions displayed on the device, and the output is the actual makeup applied.

[0447] (Application example 1)

[0448] 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."

[0449] Current makeup support systems require users to apply makeup themselves while referring to images of their ideal face, making it difficult for makeup novices. Furthermore, because real-time feedback is not available, it takes a lot of time and effort to achieve the desired celebrity look. Furthermore, the lack of specific makeup steps and information on the products to use makes it difficult for users to apply makeup properly. Therefore, there is a need for a system that allows users to easily apply their ideal makeup.

[0450] 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.

[0451] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI for generating specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means connected to a mirror located in a physical store for capturing the user's face in real time and virtually applying makeup, thereby enabling the user to apply their ideal makeup while receiving feedback in real time in the physical store.

[0452] A "user" is a person who uses this system to receive makeup technique suggestions.

[0453] "Facial image" is image data of a face that the user considers ideal or that they use as a reference.

[0454] "Uploading" is the act of sending image data from a user's device to a server.

[0455] The "server" is a computer system that analyzes and stores image data and generates makeup techniques.

[0456] "Facial features" refers to data that indicates the individual features and shapes of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0457] "Generative AI" is an artificial intelligence model that generates specific makeup techniques based on facial feature data.

[0458] "Makeup techniques" are specific makeup methods and procedures that allow users to recreate specific facial features.

[0459] "Providing" refers to the act of visually displaying and explaining the generated makeup steps and methods to the user.

[0460] A "brick and mortar store" is a physical location, such as a cosmetics store or beauty salon, that a user can visit.

[0461] A "mirror" is a device that has a reflective surface that allows the user to see their own face and is linked to a device such as a camera.

[0462] "Real-time capture" means instantly obtaining images or footage the moment the user stands in front of the mirror.

[0463] "Virtually applying makeup" refers to the virtual display of makeup effects on a user's face captured in real time through digital processing.

[0464] The present invention provides a system that allows users to experience ideal makeup techniques in real time at a physical store. Hereinafter, an embodiment of the present invention will be specifically described.

[0465] System Overview

[0466] 1. User Interface:

[0467] The user stands in front of a mirror in a brick-and-mortar store, which has a built-in high-definition camera and display.

[0468] Users use their smartphones to upload an image of their ideal celebrity's face.

[0469] 2. Image upload and analysis:

[0470] The image of the face selected by the user is sent from the smartphone to the server, where the image data is sent as a POST request using the HTTP protocol.

[0471] The server stores the received image data in storage and analyzes the facial features using an image recognition model, extracting the individual features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[0472] 3. Makeup Creation:

[0473] The extracted facial feature data is fed into a generative AI, which then generates specific makeup techniques, including detailed instructions such as how to shape the eyebrows and what eyeshadow to use.

[0474] 4. Real-time makeup application:

[0475] The server sends the generated makeup instructions to the mirror, whose camera captures the user's face in real time and virtually applies the makeup.

[0476] The generated makeup instructions are displayed on the mirror display in a visually easy-to-understand format.

[0477] Hardware and software used

[0478] Hardware:

[0479] Smart mirror (with camera)

[0480] Smartphone

[0481] Server (with high-performance CPU and memory)

[0482] software:

[0483] Flask (as a web server)

[0484] OpenCV (image processing library)

[0485] Pillow (image manipulation library)

[0486] AI model library (ImageRecognitionModel and MakeupGenerator)

[0487] Processing flow

[0488] The server receives and analyzes image data sent from the user's smartphone. Based on the analysis results, the generative AI generates specific makeup techniques. The generated makeup techniques are virtually applied to the user's face, captured in real time, and displayed in the mirror. This allows the user to receive instant feedback and apply the makeup they desire.

[0489] Specific examples

[0490] For example, if a user wants to copy the makeup of a certain actress, they upload an image of the actress's face from their smartphone to the system. The server analyzes the image and extracts the actress's facial features (arched eyebrows, cat-eye crease, etc.). Based on this information, the generative AI generates specific makeup steps and virtually applies them to the user's face in real time. Specific steps, such as "draw an arched brow with an eyebrow pencil and blend with powder," are displayed on the mirror, allowing the user to recreate the makeup themselves.

[0491] Prompt Sentence Examples

[0492] "Analyze the eyebrow shape in this image and generate the ideal makeup steps. Specifically, please provide detailed instructions on how to use an eyebrow pencil and blend the lines."

[0493] By inputting these prompts into a generative AI model, it is possible to provide the specific makeup techniques that the user desires.

[0494] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0495] Step 1:

[0496] A user uses a smartphone to select an ideal celebrity's facial image and upload it to the system. This is done using an image selection interface displayed on the smartphone's application screen. Once the user selects an image, the image data is sent to the server as a POST request using the HTTP protocol. The input is the facial image selected by the user, and the output is the image data sent to the server.

[0497] Step 2:

[0498] The server stores the received image data in storage. The stored image data is used in subsequent analysis processing. The input is the image data sent to the server, and the output is the image data stored in the storage.

[0499] Step 3:

[0500] The server analyzes the facial images stored in storage using an image recognition model (ImageRecognitionModel). This analysis process extracts the features of each part of the face (eyebrows, eyes, nose, lips, etc.) individually. The input is the stored image data, and the output is the extracted feature data for each part of the face. Specifically, it uses an image processing library such as OpenCV to detect feature points and analyze the shape.

[0501] Step 4:

[0502] The server inputs the extracted facial feature data into a generative AI model (Makeup Generator). This generative AI model generates the user's ideal makeup technique based on the facial feature data. The input is the extracted facial feature data, and the output is a specific makeup technique. Specifically, the makeup technique is generated using a prompt to the generative AI model. A prompt such as "Analyze the eyebrow shape in this image and generate the ideal makeup procedure. Specifically, please explain in detail how to use an eyebrow pencil and how to blend it" is used.

[0503] Step 5:

[0504] The generated makeup instructions are sent from the server in an appropriate format (e.g., JSON) to a smart mirror in a physical store. The smart mirror captures the user's face in real time with its built-in camera and processes the data to apply virtual makeup. The input is the generated makeup instructions data, and the output is an image of the virtual makeup applied by the smart mirror. Specific operations include real-time image processing and graphic processing for applying makeup.

[0505] Step 6:

[0506] The results of the virtual makeup displayed on the smart mirror and the specific makeup steps are presented to the user in a visually easy-to-understand format. This allows the user to receive specific instructions for actually applying the makeup. The input is a real-time captured image of the user's face and the generated makeup technique, and the output is an image of the virtual makeup displayed on the mirror and the makeup steps. Specific instructions displayed include "Draw an arched shape with an eyebrow pencil and blend with powder" and "Create a cat eye with black liquid eyeliner."

[0507] Through these steps, a system is realized that allows users to apply their ideal makeup while receiving real-time feedback in a physical store.

[0508] 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.

[0509] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the modes for implementing the present invention.

[0510] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0511] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0512] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[0513] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades of makeup.

[0514] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[0515] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use for anyone, from beginners to advanced makeup artists, and it is extremely useful for many people because it provides makeup techniques that take the user's emotions into consideration.

[0516] As a specific example, consider the case where user A uploads an image of celebrity B, who has beautiful eyebrows, to the system. When user A uploads the image, the server analyzes the image and extracts celebrity B's eyebrow shape (arched) and color (medium dark). Next, the emotion engine analyzes user A's facial expression and identifies, for example, that the user is depressed. Based on this information, the server suggests specific makeup instructions, such as "draw an arched shape with an eyebrow pencil and blend with powder," as well as a lighter shade to create a more energetic impression. By displaying this to user A, user A can recreate celebrity B's beautiful eyebrows while applying makeup that suits their own emotions.

[0517] As described above, this invention utilizes image recognition technology, generative AI, and emotion recognition technology to help users appear confidently in real-life situations.

[0518] The processing flow will be explained below.

[0519] Step 1:

[0520] Users select an ideal celebrity face image from their device and upload the image by pressing the upload button. This is done through a user interface that includes a file dialog for selecting an image and an upload button.

[0521] Step 2:

[0522] The terminal transmits the image data selected by the user to the server. The image data is transmitted to the server as a POST request using the HTTP protocol.

[0523] Step 3:

[0524] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[0525] Step 4:

[0526] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[0527] Step 5:

[0528] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[0529] Step 6:

[0530] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, including detailed instructions on how to draw eyebrows and which eyeshadow to use.

[0531] Step 7:

[0532] The server converts the makeup instructions generated by the generative AI into an appropriate data format, such as JSON.

[0533] Step 8:

[0534] The server invokes an emotion engine to recognize the user's emotions, and analyzes the user's facial expression data and voice data to identify the user's current emotion (e.g., joy, sadness, surprise, etc.).

[0535] Step 9:

[0536] The server then adjusts the generated makeup techniques based on the identified emotion data. For example, if the user is feeling down, the server may suggest lighter colors.

[0537] Step 10:

[0538] The server transmits the adjusted makeup technique instructions to the user's terminal.

[0539] Step 11:

[0540] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[0541] Step 12:

[0542] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[0543] Step 13:

[0544] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[0545] Through the above steps, the present invention has provided a detailed description of a system that integrates image recognition technology, generative AI, and an emotion engine to enable users to easily recreate their ideal celebrity makeup techniques.

[0546] Example 2

[0547] 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."

[0548] Many existing makeup instruction systems simply analyze images and provide basic makeup techniques. However, few systems can respond to the user's emotions and individual facial features, and they lack detailed instruction to maximize the makeup effect. This makes it difficult for users to learn makeup techniques that suit their emotions and specific facial features.

[0549] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading a face image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to analyze the uploaded image and extract facial features, means having a generative AI model that generates specific makeup techniques based on the extracted facial features, means for providing the generated makeup techniques to the user, and means having an emotion engine that analyzes the user's emotions and adjusts the makeup technique content based on the emotions. This allows the user to not only learn makeup techniques that match their facial features but also to apply makeup that suits their emotions at the time.

[0550] "User" refers to a person who uses the system to upload a facial image and receive makeup instruction.

[0551] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to access the System.

[0552] A "server" is a group of computers that serves as the core of a system, and is a device that receives requests from users and processes and analyzes data.

[0553] "Facial Image" means an image that clearly shows facial features and that is uploaded by a User to the System.

[0554] "Means for uploading" refers to the ability of a user to use their own device to send an image of the selected face to the system.

[0555] "Image data" refers to electronic data relating to a facial image uploaded by a user.

[0556] "Means of analysis" refers to the technology in which the server receives image data, recognizes facial features, and extracts information about each part of the face.

[0557] "Facial features" refers to the specific shape, color, and other characteristics of each part of the face (e.g., eyebrows, eyes, nose, lips).

[0558] A "generative AI model" refers to artificial intelligence (AI) that generates specific makeup techniques based on facial feature data.

[0559] "Means for providing" refers to the function for presenting the generated makeup techniques to the user.

[0560] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice data to identify their emotions.

[0561] "Adjustment means" refers to the function of modifying and optimizing the content of the generated makeup techniques based on the emotional data identified by the emotion engine.

[0562] The present invention is a system that allows users to upload an image of their ideal celebrity's face, and provides specific makeup techniques that are analyzed and generated by AI based on that image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0563] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. The device provides an interface for uploading images, and the user selects an image using a file browser and clicks the "Upload" button to send the image data to the system. This process sends the image data to the server as a POST request using the HTTP protocol.

[0564] The server temporarily stores the received image data in memory and then saves it to storage (e.g., Amazon S3 or Google Cloud Storage). The server then analyzes the stored image using an image recognition model (e.g., OpenCV or TensorFlow) to extract features for each part of the face, such as the eyebrows, eyes, nose, and lips. This analysis generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color may be extracted.

[0565] The server then uses a generative AI model (e.g., GPT) to input the extracted feature data as prompts and generate specific makeup tips for the user to replicate the features, including specific makeup techniques such as how to shape the eyebrows and which eyeshadow to use.

[0566] Examples of prompt sentences include the following:

[0567] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[0568] The system also includes an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the acquired emotion data with facial feature data to adjust the makeup content generated based on the user's emotions. For example, if the system determines that the user is depressed, it will suggest lighter shades of makeup.

[0569] Finally, the generated makeup instructions are sent back to the user's device in JSON format. The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed on the screen.

[0570] This system is easy to use for anyone, from makeup beginners to advanced users, and provides makeup techniques that take the user's emotions into consideration, making it extremely useful for many people.

[0571] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0572] Explain the program's processing flow in detail

[0573] Step 1:

[0574] The user selects an image

[0575] Description: The user selects an image of their ideal celebrity face from their device.

[0576] Input: A face image file stored in the user's local storage.

[0577] Output: The path of the selected face image file.

[0578] Specific operation: The user opens the file browser on the device and selects a face image. This operation is performed by the user clicking on the file to select it.

[0579] Step 2:

[0580] The user uploads an image

[0581] Description: User selects an image and clicks the "Upload" button to send it to the system.

[0582] Input: The path of the selected face image file.

[0583] Output: Image data sent to the server.

[0584] Specific operation: When the user clicks the "Upload" button, the device sends the image data to the server as an HTTP POST request.

[0585] Step 3:

[0586] The device sends the image data to the server.

[0587] Description: The terminal sends image data to the server using the HTTP protocol.

[0588] Input: Image data.

[0589] Output: Image data sent to the server.

[0590] Specific operation: The terminal embeds the selected image data in binary format in an HTTP POST request and sends it to the specified server URL.

[0591] Step 4:

[0592] The server receives the image data.

[0593] Description: The server temporarily stores the received image data in memory and then stores it in storage.

[0594] Input: Image data sent to the server.

[0595] Output: Image data saved in storage.

[0596] Specific operation: The server extracts the image data from the body of the POST request and saves the data in the specified storage path.

[0597] Step 5:

[0598] The server analyzes the image

[0599] Description: The server uses an image recognition model to analyze the stored image data and extract features for each part of the face.

[0600] Input: Image data stored in storage.

[0601] Output: Parsed facial feature data.

[0602] Specific operation: The server calls an image recognition model (e.g., OpenCV or TensorFlow) to detect and analyze each part of the face (eyebrows, eyes, nose, lips, etc.).

[0603] Step 6:

[0604] The server generates facial feature data

[0605] Description: From the analysis results, the server extracts feature data for each part of the body, such as eyebrows, eyes, nose, and lips, and structures it in JSON format.

[0606] Input: Analyzed facial features.

[0607] Output: Facial feature data in JSON format.

[0608] Specific operation: Based on the analysis results, the server compiles the characteristics of each part (e.g., eyebrow shape, eye style, lip color) as JSON.

[0609] Step 7:

[0610] The server inputs data into the generated AI.

[0611] Description: The server inputs the feature data as prompts into the generative AI model to generate specific makeup techniques.

[0612] Input: Facial feature data in JSON format.

[0613] Output: Generated makeup technique data.

[0614] Specific operation: The server calls a generative AI model (e.g., GPT) and inputs feature data as a prompt. The generative AI model generates a makeup technique based on the data.

[0615] Example prompt sentence:

[0616] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[0617] Step 8:

[0618] The server uses an emotion engine to analyze the user's emotions.

[0619] Description: The server analyzes the user's facial and voice data and uses an emotion engine to identify emotions.

[0620] Input: User's facial expression data and voice data.

[0621] Output: Identified emotion data.

[0622] Specific operation: The server calls an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) and analyzes the user's facial and voice data to identify emotions.

[0623] Step 9:

[0624] The server combines emotional data with makeup techniques

[0625] Description: The server adjusts the content of the generated makeup techniques based on the emotional data.

[0626] Input: Generated makeup technique data, identified emotion data.

[0627] Output: Emotion-specific makeup technique data.

[0628] Specific operation: The server refers to the emotional data and adjusts the makeup technique, such as recommending lighter colors if the person is not feeling well.

[0629] Step 10:

[0630] The server sends makeup instructions back to the device.

[0631] Description: The generated makeup instructions are sent back to the user's device in JSON format.

[0632] Input: Emotion-aware makeup technique data.

[0633] Output: Makeup technique data sent to the user's device.

[0634] Specific operation: The server converts the adjusted makeup technique data into JSON format and sends it to the user's device as an HTTP response.

[0635] Step 11:

[0636] The device displays the makeup technique steps

[0637] Description: The device displays the received data on the user's screen in a visually understandable format.

[0638] Input: Received makeup technique data.

[0639] Output: The makeup technique displayed on the user's screen.

[0640] Specific operation: The device parses the received JSON data and displays it on the screen as HTML and view components. For example, it displays a list such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[0641] (Application example 2)

[0642] 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."

[0643] In modern society, the pursuit of each individual's ideal beauty is an important factor that contributes to improving self-esteem and confidence. However, many people find it difficult to find the makeup method that best suits them, and makeup beginners in particular often lack proper guidance. In addition, makeup advice that responds to users' emotions is not provided, and users are unable to find a makeup method that suits their current condition.

[0644] 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.

[0645] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI that generates specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means having an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques according to the emotions. This makes it possible to provide makeup techniques that are optimized for each user and to provide immediate advice according to the user's emotions.

[0646] "User" refers to a person who uses the system to receive makeup technique suggestions.

[0647] "Face image" refers to a photo of a celebrity or the user's own face that they consider ideal.

[0648] "Means for uploading" refers to the functionality that allows users to send images of their faces to the system.

[0649] The "means for analyzing and extracting facial features" is a function that processes the facial image received by the server and identifies detailed features of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0650] "Generative AI" refers to artificial intelligence technology that automatically generates specific makeup techniques suited to the user based on extracted facial features.

[0651] The "means of providing" is a function that displays the generated makeup techniques to the user in a visual and easy-to-understand format.

[0652] An "emotion recognition engine" refers to technology that analyzes a user's facial expressions and voice data and recognizes their emotions.

[0653] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generation AI provides specific makeup techniques based on that image. It also combines an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques accordingly. Details for implementing this invention are described below.

[0654] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. A user interface is provided for uploading the image. Through this interface, the user sends the image from their device to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0655] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0656] The server then inputs these feature data into a generative AI, which generates specific makeup techniques for the user to recreate those features. The generated makeup techniques include detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques. This generative AI uses OpenAI's GPT-3 model and other models.

[0657] The system also features an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. Emotion recognition models such as the OpenCV library and EmotionNet are used for emotion recognition. The server combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades.

[0658] The generated makeup instructions are sent to the user's device in an appropriate format (e.g., JSON), which receives the data and displays it on the user's screen in a visually understandable format.

[0659] As a specific example, if a user uploads an image of their ideal celebrity, the server analyzes the image to extract the celebrity's eyebrow shape and color. The emotion engine analyzes the user's facial expressions and identifies that the user is depressed. Based on this information, the server suggests lighter shades to create a more cheerful impression. These suggestions are displayed to the user, who can then follow specific makeup techniques to achieve their ideal face.

[0660] An example of a prompt sentence to be input to a generative AI model is in the following format:

[0661] "Generate the following makeup techniques based on facial feature data.\nFeature data: {Eyebrow shape: arched, Eyes: cat eyeliner, Lip color: natural pink}\nOutput format: Draw an arched eyebrow with an eyebrow pencil and blend with powder. Create a cat eye with black liquid eyeliner. Use natural pink lipstick."

[0662] By inputting this prompt into the generation AI, appropriate makeup techniques for the user are automatically generated.

[0663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0664] Step 1:

[0665] Users select an image of their ideal celebrity face from their device and upload it to the system.

[0666] Input: A face image selected by the user.

[0667] Processing: The device sends the selected image to the server through the image upload interface. The image data is sent as a POST request using the HTTP protocol.

[0668] Output: Facial image data sent to the server.

[0669] Step 2:

[0670] The server stores the received image data in storage.

[0671] Input: Uploaded image data.

[0672] Processing: The server receives the image data and saves it to the specified storage. This saving process is performed using a Python library, etc.

[0673] Output: Image data saved in storage.

[0674] Step 3:

[0675] The server analyzes the stored images and extracts facial features.

[0676] Input: Saved image data.

[0677] Processing: The server analyzes the image data using an image recognition model (e.g., ResNet50 using PyTorch). The model extracts features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[0678] Output: Parsed facial feature data.

[0679] Step 4:

[0680] Based on facial feature data, generative AI generates specific makeup techniques.

[0681] Input: Facial feature data.

[0682] Processing: The server inputs facial feature data into OpenAI's GPT-3 model as a prompt. Based on the prompt, the generation AI outputs specific makeup application steps.

[0683] Output: Generated specific makeup techniques.

[0684] Step 5:

[0685] The server recognizes the user's emotions and adjusts the makeup application procedure.

[0686] Input: User's real-time facial expression data and generated makeup instructions.

[0687] Processing: Using an emotion recognition engine (e.g., OpenCV library and EmotionNet), the server analyzes the user's facial expression data. Based on the recognized emotion data, the server adjusts the makeup application (e.g., if the user is depressed, it suggests lighter shades).

[0688] Output: Adjusted makeup application steps.

[0689] Step 6:

[0690] The generated makeup technique instructions are provided to the user's terminal.

[0691] Enter: a coordinated makeup routine.

[0692] Processing: The server sends the adjusted makeup instructions to the user's device in an appropriate format (e.g., JSON). The device displays the received data on the screen in a visually understandable format.

[0693] Output: Specific makeup application instructions displayed on the user's device.

[0694] The above are the processing steps of the system that realizes this application example. This process allows users to easily obtain the ideal makeup technique.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] [Third embodiment]

[0699] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0700] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0701] 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).

[0702] 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.

[0703] 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.

[0704] 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).

[0705] 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. 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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."

[0711] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image. The following describes in detail the modes for implementing the present invention.

[0712] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0713] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0714] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[0715] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[0716] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use and extremely useful for many people, from beginners to advanced makeup artists.

[0717] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts the shape (arched) and color (medium darkness) of Celebrity B's eyebrows. Next, based on this information, it generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate beautiful eyebrows like Celebrity B's on their own.

[0718] In this way, this invention makes full use of image recognition technology and generative AI to help users appear confidently in real-life situations.

[0719] The processing flow will be explained below.

[0720] Step 1:

[0721] The user selects an ideal celebrity face image from their device and uploads the image by pressing the upload button. This operation is performed through a user interface that has a file dialog for selecting an image and an upload button.

[0722] Step 2:

[0723] The terminal transmits the image data selected by the user to the server as a POST request using the HTTP protocol.

[0724] Step 3:

[0725] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[0726] Step 4:

[0727] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[0728] Step 5:

[0729] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[0730] Step 6:

[0731] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, which describe in detail how the user should apply the makeup.

[0732] Step 7:

[0733] The makeup instructions generated by the generative AI are converted into an appropriate data format, such as JSON, and sent back to the user's device.

[0734] Step 8:

[0735] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[0736] Step 9:

[0737] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[0738] Step 10:

[0739] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[0740] Through the above steps, the present invention has explained in detail a system that integrates image recognition technology and generative AI to enable users to easily recreate their ideal celebrity makeup techniques.

[0741] Example 1

[0742] 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."

[0743] With conventional systems, it was difficult for users to independently find makeup techniques that would bring them closer to their ideal celebrity face. While image recognition technology and generative AI models were needed to provide specific and easy-to-understand makeup techniques, the general methods were too complicated for users to execute efficiently.

[0744] 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.

[0745] In this invention, the server includes means for uploading a facial image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to save the received image data, means for the server to analyze the saved image data and extract facial features, means for inputting the extracted feature data into a generative AI model and generating specific makeup techniques based on the generated facial features, and means for sending instructions for the generated makeup techniques in an appropriate format to the user's terminal and displaying them on the user's terminal. This enables the user to easily obtain and practice specific and easy-to-understand makeup techniques to get closer to the face of an ideal celebrity.

[0746] A "user" is an individual who uses the system to acquire the makeup techniques of an ideal celebrity's face.

[0747] A "terminal" is a device operated by a user, and is a device used to upload images and communicate data with a server.

[0748] "Server" refers to a computer system that stores, analyzes, and processes image data received from users.

[0749] "Image data" refers to digital data of images of celebrities' faces uploaded by users.

[0750] The "storage means" is a part that has the function of storing image data received by the server in a storage medium.

[0751] The "analysis means" is a part that has the function of extracting features of each part of the face from the received image data using an image recognition model.

[0752] "Facial features" refer to the specific shapes and colors of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0753] A "generative AI model" is an artificial intelligence model that generates specific makeup techniques based on extracted feature data.

[0754] "Makeup techniques" are makeup techniques that allow users to recreate the facial features of their ideal celebrity.

[0755] The "transmission means" is a part having a function for the terminal to transmit image data to the server, and for the server to transmit the generated makeup technique instructions to the user terminal.

[0756] The "display means" is a part that has the function of displaying makeup technique instructions generated on the user terminal in a visually easy-to-understand manner.

[0757] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generative AI model provides specific makeup techniques based on that image. The following describes in detail the implementation of this system.

[0758] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. On the device, the operation is performed using a web browser or an image upload form in a dedicated application. A file selection dialog is displayed, and the user selects an image file from local storage. This image data supports common image formats such as JPEG and PNG.

[0759] Next, the device sends the image data selected by the user to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. When sending, the binary information of the image data is multiplexed and sent, along with metadata (e.g., file name, file size, etc.). The server saves the received image data in local storage or cloud storage (e.g., Amazon S3). The save path and metadata are recorded in a database (e.g., MySQL, PostgreSQL).

[0760] The server analyzes the saved image data using an image recognition model. For example, OpenCV or Google's FaceNet is used as the image recognition model. This allows the features of each part of the face (eyebrows, eyes, nose, lips, etc.) to be extracted. Here, for example, the face area is detected from the image and the feature values ​​for each part are calculated.

[0761] Next, the server inputs the extracted feature data into a generative AI model (e.g., GPT-4). Specific feature data includes eyebrow shape, eye features, and lip color. For example, the prompt "Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used. The generative AI model generates specific makeup techniques based on this data. The generated results include instructions such as "Draw an arched eyebrow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[0762] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. Here, the makeup instructions are displayed on a web page, neatly laid out using HTML / CSS. Adding explanations with images and video links makes it easier for users to understand.

[0763] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts Celebrity B's eyebrow shape (arched) and color (medium dark). Next, based on this information, the generative AI model generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate Celebrity B's beautiful eyebrows on his or her own.

[0764] An example of a prompt for the generative AI model is as follows:

[0765] "Based on the following facial feature data, please generate a makeup look that the user can recreate. Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink. Please explain the specific makeup techniques in a step-by-step manner."

[0766] As described above, the present invention provides a system that utilizes image recognition technology and generative AI models to help users appear confidently in real-life situations.

[0767] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0768] Step 1:

[0769] The user selects an image of the ideal celebrity's face from their device and uploads it to the system. The user uses an image upload form in a web browser or a dedicated application, and selects an image file from local storage using a file selection dialog. The input is the image data selected by the user, and the output is image data ready to be uploaded.

[0770] Step 2:

[0771] The device sends the selected image data to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. Metadata (file name, file size, etc.) is also sent. The input is image data ready to be uploaded, and the output is the image data and metadata sent to the server.

[0772] Step 3:

[0773] The server saves the received image data. The server stores the image data in local storage or cloud storage (e.g., Amazon S3) and records the path and metadata in a database (e.g., MySQL, PostgreSQL). The input is the image data and metadata sent to the server, and the output is the image data saved in storage and the path and metadata recorded in the database.

[0774] Step 4:

[0775] The server analyzes the stored image data using an image recognition model. Specifically, it uses OpenCV or Google's FaceNet to extract features of each part of the image, such as the eyebrows, eyes, nose, and lips. The input is the image data stored in storage, and the output is specific feature data for each part of the face (e.g., eyebrow shape, eye features, lip color, etc.).

[0776] Step 5:

[0777] The server inputs the extracted feature data into a generative AI model to generate specific makeup techniques. Feature data such as "eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used as a prompt. The generative AI model (e.g., GPT-4) generates makeup techniques based on this data. The input is facial feature data, and the output is specific instructions for makeup techniques.

[0778] Step 6:

[0779] The generated makeup instructions are returned to the user's device in an appropriate format (e.g., JSON). The server sends the data as an HTTP response, and the device receives it. The input is the generated makeup instructions, and the output is the makeup instruction data sent to the user's device.

[0780] Step 7:

[0781] The device analyzes the generated makeup instruction data and displays it on the user's screen in a visually easy-to-understand format. It uses HTML / CSS to display beautifully laid out makeup instructions on a web page, including explanations with images and video links. The input is the makeup instruction data sent from the server, and the output is the makeup instructions displayed on the user's device.

[0782] Step 8:

[0783] The user follows the displayed makeup technique steps to actually apply makeup. The user refers to the instructions and performs tasks such as how to use an eyebrow pencil, how to apply eyeshadow, and how to choose a lipstick. The input is the specific makeup technique instructions displayed on the device, and the output is the actual makeup applied.

[0784] (Application example 1)

[0785] 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."

[0786] Current makeup support systems require users to apply makeup themselves while referring to images of their ideal face, making it difficult for makeup novices. Furthermore, because real-time feedback is not available, it takes a lot of time and effort to achieve the desired celebrity look. Furthermore, the lack of specific makeup steps and information on the products to use makes it difficult for users to apply makeup properly. Therefore, there is a need for a system that allows users to easily apply their ideal makeup.

[0787] 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.

[0788] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI for generating specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means connected to a mirror located in a physical store for capturing the user's face in real time and virtually applying makeup, thereby enabling the user to apply their ideal makeup while receiving feedback in real time in the physical store.

[0789] A "user" is a person who uses this system to receive makeup technique suggestions.

[0790] "Facial image" is image data of a face that the user considers ideal or that they use as a reference.

[0791] "Uploading" is the act of sending image data from a user's device to a server.

[0792] The "server" is a computer system that analyzes and stores image data and generates makeup techniques.

[0793] "Facial features" refers to data that indicates the individual features and shapes of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0794] "Generative AI" is an artificial intelligence model that generates specific makeup techniques based on facial feature data.

[0795] "Makeup techniques" are specific makeup methods and procedures that allow users to recreate specific facial features.

[0796] "Providing" refers to the act of visually displaying and explaining the generated makeup steps and methods to the user.

[0797] A "brick and mortar store" is a physical location, such as a cosmetics store or beauty salon, that a user can visit.

[0798] A "mirror" is a device that has a reflective surface that allows the user to see their own face and is linked to a device such as a camera.

[0799] "Real-time capture" means instantly obtaining images or footage the moment the user stands in front of the mirror.

[0800] "Virtually applying makeup" refers to the virtual display of makeup effects on a user's face captured in real time through digital processing.

[0801] The present invention provides a system that allows users to experience ideal makeup techniques in real time at a physical store. Hereinafter, an embodiment of the present invention will be specifically described.

[0802] System Overview

[0803] 1. User Interface:

[0804] The user stands in front of a mirror in a brick-and-mortar store, which has a built-in high-definition camera and display.

[0805] Users use their smartphones to upload an image of their ideal celebrity's face.

[0806] 2. Image upload and analysis:

[0807] The image of the face selected by the user is sent from the smartphone to the server, where the image data is sent as a POST request using the HTTP protocol.

[0808] The server stores the received image data in storage and analyzes the facial features using an image recognition model, extracting the individual features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[0809] 3. Makeup Creation:

[0810] The extracted facial feature data is fed into a generative AI, which then generates specific makeup techniques, including detailed instructions such as how to shape the eyebrows and what eyeshadow to use.

[0811] 4. Real-time makeup application:

[0812] The server sends the generated makeup instructions to the mirror, whose camera captures the user's face in real time and virtually applies the makeup.

[0813] The generated makeup instructions are displayed on the mirror display in a visually easy-to-understand format.

[0814] Hardware and software used

[0815] Hardware:

[0816] Smart mirror (with camera)

[0817] Smartphone

[0818] Server (with high-performance CPU and memory)

[0819] software:

[0820] Flask (as a web server)

[0821] OpenCV (image processing library)

[0822] Pillow (image manipulation library)

[0823] AI model library (ImageRecognitionModel and MakeupGenerator)

[0824] Processing flow

[0825] The server receives and analyzes image data sent from the user's smartphone. Based on the analysis results, the generative AI generates specific makeup techniques. The generated makeup techniques are virtually applied to the user's face, captured in real time, and displayed in the mirror. This allows the user to receive instant feedback and apply the makeup they desire.

[0826] Specific examples

[0827] For example, if a user wants to copy the makeup of a certain actress, they upload an image of the actress's face from their smartphone to the system. The server analyzes the image and extracts the actress's facial features (arched eyebrows, cat-eye crease, etc.). Based on this information, the generative AI generates specific makeup steps and virtually applies them to the user's face in real time. Specific steps, such as "draw an arched brow with an eyebrow pencil and blend with powder," are displayed on the mirror, allowing the user to recreate the makeup themselves.

[0828] Prompt Sentence Examples

[0829] "Analyze the eyebrow shape in this image and generate the ideal makeup steps. Specifically, please provide detailed instructions on how to use an eyebrow pencil and blend the lines."

[0830] By inputting these prompts into a generative AI model, it is possible to provide the specific makeup techniques that the user desires.

[0831] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0832] Step 1:

[0833] A user uses a smartphone to select an ideal celebrity's facial image and upload it to the system. This is done using an image selection interface displayed on the smartphone's application screen. Once the user selects an image, the image data is sent to the server as a POST request using the HTTP protocol. The input is the facial image selected by the user, and the output is the image data sent to the server.

[0834] Step 2:

[0835] The server stores the received image data in storage. The stored image data is used in subsequent analysis processing. The input is the image data sent to the server, and the output is the image data stored in the storage.

[0836] Step 3:

[0837] The server analyzes the facial images stored in storage using an image recognition model (ImageRecognitionModel). This analysis process extracts the features of each part of the face (eyebrows, eyes, nose, lips, etc.) individually. The input is the stored image data, and the output is the extracted feature data for each part of the face. Specifically, it uses an image processing library such as OpenCV to detect feature points and analyze the shape.

[0838] Step 4:

[0839] The server inputs the extracted facial feature data into a generative AI model (Makeup Generator). This generative AI model generates the user's ideal makeup technique based on the facial feature data. The input is the extracted facial feature data, and the output is a specific makeup technique. Specifically, the makeup technique is generated using a prompt to the generative AI model. A prompt such as "Analyze the eyebrow shape in this image and generate the ideal makeup procedure. Specifically, please explain in detail how to use an eyebrow pencil and how to blend it" is used.

[0840] Step 5:

[0841] The generated makeup instructions are sent from the server in an appropriate format (e.g., JSON) to a smart mirror in a physical store. The smart mirror captures the user's face in real time with its built-in camera and processes the data to apply virtual makeup. The input is the generated makeup instructions data, and the output is an image of the virtual makeup applied by the smart mirror. Specific operations include real-time image processing and graphic processing for applying makeup.

[0842] Step 6:

[0843] The results of the virtual makeup displayed on the smart mirror and the specific makeup steps are presented to the user in a visually easy-to-understand format. This allows the user to receive specific instructions for actually applying the makeup. The input is a real-time captured image of the user's face and the generated makeup technique, and the output is an image of the virtual makeup displayed on the mirror and the makeup steps. Specific instructions displayed include "Draw an arched shape with an eyebrow pencil and blend with powder" and "Create a cat eye with black liquid eyeliner."

[0844] Through these steps, a system is realized that allows users to apply their ideal makeup while receiving real-time feedback in a physical store.

[0845] 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.

[0846] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the modes for implementing the present invention.

[0847] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0848] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0849] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[0850] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades of makeup.

[0851] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[0852] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use for anyone, from beginners to advanced makeup artists, and it is extremely useful for many people because it provides makeup techniques that take the user's emotions into consideration.

[0853] As a specific example, consider the case where user A uploads an image of celebrity B, who has beautiful eyebrows, to the system. When user A uploads the image, the server analyzes the image and extracts celebrity B's eyebrow shape (arched) and color (medium dark). Next, the emotion engine analyzes user A's facial expression and identifies, for example, that the user is depressed. Based on this information, the server suggests specific makeup instructions, such as "draw an arched shape with an eyebrow pencil and blend with powder," as well as a lighter shade to create a more energetic impression. By displaying this to user A, user A can recreate celebrity B's beautiful eyebrows while applying makeup that suits their own emotions.

[0854] As described above, this invention utilizes image recognition technology, generative AI, and emotion recognition technology to help users appear confidently in real-life situations.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] Users select an ideal celebrity face image from their device and upload the image by pressing the upload button. This is done through a user interface that includes a file dialog for selecting an image and an upload button.

[0858] Step 2:

[0859] The terminal transmits the image data selected by the user to the server. The image data is transmitted to the server as a POST request using the HTTP protocol.

[0860] Step 3:

[0861] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[0862] Step 4:

[0863] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[0864] Step 5:

[0865] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[0866] Step 6:

[0867] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, including detailed instructions on how to draw eyebrows and which eyeshadow to use.

[0868] Step 7:

[0869] The server converts the makeup instructions generated by the generative AI into an appropriate data format, such as JSON.

[0870] Step 8:

[0871] The server invokes an emotion engine to recognize the user's emotions, and analyzes the user's facial expression data and voice data to identify the user's current emotion (e.g., joy, sadness, surprise, etc.).

[0872] Step 9:

[0873] The server then adjusts the generated makeup techniques based on the identified emotion data. For example, if the user is feeling down, the server may suggest lighter colors.

[0874] Step 10:

[0875] The server transmits the adjusted makeup technique instructions to the user's terminal.

[0876] Step 11:

[0877] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[0878] Step 12:

[0879] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[0880] Step 13:

[0881] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[0882] Through the above steps, the present invention has provided a detailed description of a system that integrates image recognition technology, generative AI, and an emotion engine to enable users to easily recreate their ideal celebrity makeup techniques.

[0883] Example 2

[0884] 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."

[0885] Many existing makeup instruction systems simply analyze images and provide basic makeup techniques. However, few systems can respond to the user's emotions and individual facial features, and they lack detailed instruction to maximize the makeup effect. This makes it difficult for users to learn makeup techniques that suit their emotions and specific facial features.

[0886] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading a face image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to analyze the uploaded image and extract facial features, means having a generative AI model that generates specific makeup techniques based on the extracted facial features, means for providing the generated makeup techniques to the user, and means having an emotion engine that analyzes the user's emotions and adjusts the makeup technique content based on the emotions. This allows the user to not only learn makeup techniques that match their facial features but also to apply makeup that suits their emotions at the time.

[0887] "User" refers to a person who uses the system to upload a facial image and receive makeup instruction.

[0888] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to access the System.

[0889] A "server" is a group of computers that serves as the core of a system, and is a device that receives requests from users and processes and analyzes data.

[0890] "Facial Image" means an image that clearly shows facial features and that is uploaded by a User to the System.

[0891] "Means for uploading" refers to the ability of a user to use their own device to send an image of the selected face to the system.

[0892] "Image data" refers to electronic data relating to a facial image uploaded by a user.

[0893] "Means of analysis" refers to the technology in which the server receives image data, recognizes facial features, and extracts information about each part of the face.

[0894] "Facial features" refers to the specific shape, color, and other characteristics of each part of the face (e.g., eyebrows, eyes, nose, lips).

[0895] A "generative AI model" refers to artificial intelligence (AI) that generates specific makeup techniques based on facial feature data.

[0896] "Means for providing" refers to the function for presenting the generated makeup techniques to the user.

[0897] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice data to identify their emotions.

[0898] "Adjustment means" refers to the function of modifying and optimizing the content of the generated makeup techniques based on the emotional data identified by the emotion engine.

[0899] The present invention is a system that allows users to upload an image of their ideal celebrity's face, and provides specific makeup techniques that are analyzed and generated by AI based on that image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[0900] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. The device provides an interface for uploading images, and the user selects an image using a file browser and clicks the "Upload" button to send the image data to the system. This process sends the image data to the server as a POST request using the HTTP protocol.

[0901] The server temporarily stores the received image data in memory and then saves it to storage (e.g., Amazon S3 or Google Cloud Storage). The server then analyzes the stored image using an image recognition model (e.g., OpenCV or TensorFlow) to extract features for each part of the face, such as the eyebrows, eyes, nose, and lips. This analysis generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color may be extracted.

[0902] The server then uses a generative AI model (e.g., GPT) to input the extracted feature data as prompts and generate specific makeup tips for the user to replicate the features, including specific makeup techniques such as how to shape the eyebrows and which eyeshadow to use.

[0903] Examples of prompt sentences include the following:

[0904] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[0905] The system also includes an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the acquired emotion data with facial feature data to adjust the makeup content generated based on the user's emotions. For example, if the system determines that the user is depressed, it will suggest lighter shades of makeup.

[0906] Finally, the generated makeup instructions are sent back to the user's device in JSON format. The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed on the screen.

[0907] This system is easy to use for anyone, from makeup beginners to advanced users, and provides makeup techniques that take the user's emotions into consideration, making it extremely useful for many people.

[0908] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0909] Explain the program's processing flow in detail

[0910] Step 1:

[0911] The user selects an image

[0912] Description: The user selects an image of their ideal celebrity face from their device.

[0913] Input: A face image file stored in the user's local storage.

[0914] Output: The path of the selected face image file.

[0915] Specific operation: The user opens the file browser on the device and selects a face image. This operation is performed by the user clicking on the file to select it.

[0916] Step 2:

[0917] The user uploads an image

[0918] Description: User selects an image and clicks the "Upload" button to send it to the system.

[0919] Input: The path of the selected face image file.

[0920] Output: Image data sent to the server.

[0921] Specific operation: When the user clicks the "Upload" button, the device sends the image data to the server as an HTTP POST request.

[0922] Step 3:

[0923] The device sends the image data to the server.

[0924] Description: The terminal sends image data to the server using the HTTP protocol.

[0925] Input: Image data.

[0926] Output: Image data sent to the server.

[0927] Specific operation: The terminal embeds the selected image data in binary format in an HTTP POST request and sends it to the specified server URL.

[0928] Step 4:

[0929] The server receives the image data.

[0930] Description: The server temporarily stores the received image data in memory and then stores it in storage.

[0931] Input: Image data sent to the server.

[0932] Output: Image data saved in storage.

[0933] Specific operation: The server extracts the image data from the body of the POST request and saves the data in the specified storage path.

[0934] Step 5:

[0935] The server analyzes the image

[0936] Description: The server uses an image recognition model to analyze the stored image data and extract features for each part of the face.

[0937] Input: Image data stored in storage.

[0938] Output: Parsed facial feature data.

[0939] Specific operation: The server calls an image recognition model (e.g., OpenCV or TensorFlow) to detect and analyze each part of the face (eyebrows, eyes, nose, lips, etc.).

[0940] Step 6:

[0941] The server generates facial feature data

[0942] Description: From the analysis results, the server extracts feature data for each part of the body, such as eyebrows, eyes, nose, and lips, and structures it in JSON format.

[0943] Input: Analyzed facial features.

[0944] Output: Facial feature data in JSON format.

[0945] Specific operation: Based on the analysis results, the server compiles the characteristics of each part (e.g., eyebrow shape, eye style, lip color) as JSON.

[0946] Step 7:

[0947] The server inputs data into the generated AI.

[0948] Description: The server inputs the feature data as prompts into the generative AI model to generate specific makeup techniques.

[0949] Input: Facial feature data in JSON format.

[0950] Output: Generated makeup technique data.

[0951] Specific operation: The server calls a generative AI model (e.g., GPT) and inputs feature data as a prompt. The generative AI model generates a makeup technique based on the data.

[0952] Example prompt sentence:

[0953] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[0954] Step 8:

[0955] The server uses an emotion engine to analyze the user's emotions.

[0956] Description: The server analyzes the user's facial and voice data and uses an emotion engine to identify emotions.

[0957] Input: User's facial expression data and voice data.

[0958] Output: Identified emotion data.

[0959] Specific operation: The server calls an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) and analyzes the user's facial and voice data to identify emotions.

[0960] Step 9:

[0961] The server combines emotional data with makeup techniques

[0962] Description: The server adjusts the content of the generated makeup techniques based on the emotional data.

[0963] Input: Generated makeup technique data, identified emotion data.

[0964] Output: Emotion-specific makeup technique data.

[0965] Specific operation: The server refers to the emotional data and adjusts the makeup technique, such as recommending lighter colors if the person is not feeling well.

[0966] Step 10:

[0967] The server sends makeup instructions back to the device.

[0968] Description: The generated makeup instructions are sent back to the user's device in JSON format.

[0969] Input: Emotion-aware makeup technique data.

[0970] Output: Makeup technique data sent to the user's device.

[0971] Specific operation: The server converts the adjusted makeup technique data into JSON format and sends it to the user's device as an HTTP response.

[0972] Step 11:

[0973] The device displays the makeup technique steps

[0974] Description: The device displays the received data on the user's screen in a visually understandable format.

[0975] Input: Received makeup technique data.

[0976] Output: The makeup technique displayed on the user's screen.

[0977] Specific operation: The device parses the received JSON data and displays it on the screen as HTML and view components. For example, it displays a list such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[0978] (Application example 2)

[0979] 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."

[0980] In modern society, the pursuit of each individual's ideal beauty is an important factor that contributes to improving self-esteem and confidence. However, many people find it difficult to find the makeup method that best suits them, and makeup beginners in particular often lack proper guidance. In addition, makeup advice that responds to users' emotions is not provided, and users are unable to find a makeup method that suits their current condition.

[0981] 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.

[0982] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI that generates specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means having an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques according to the emotions. This makes it possible to provide makeup techniques that are optimized for each user and to provide immediate advice according to the user's emotions.

[0983] "User" refers to a person who uses the system to receive makeup technique suggestions.

[0984] "Face image" refers to a photo of a celebrity or the user's own face that they consider ideal.

[0985] "Means for uploading" refers to the functionality that allows users to send images of their faces to the system.

[0986] The "means for analyzing and extracting facial features" is a function that processes the facial image received by the server and identifies detailed features of each part of the face, such as the eyebrows, eyes, nose, and lips.

[0987] "Generative AI" refers to artificial intelligence technology that automatically generates specific makeup techniques suited to the user based on extracted facial features.

[0988] The "means of providing" is a function that displays the generated makeup techniques to the user in a visual and easy-to-understand format.

[0989] An "emotion recognition engine" refers to technology that analyzes a user's facial expressions and voice data and recognizes their emotions.

[0990] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generation AI provides specific makeup techniques based on that image. It also combines an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques accordingly. Details for implementing this invention are described below.

[0991] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. A user interface is provided for uploading the image. Through this interface, the user sends the image from their device to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[0992] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[0993] The server then inputs these feature data into a generative AI, which generates specific makeup techniques for the user to recreate those features. The generated makeup techniques include detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques. This generative AI uses OpenAI's GPT-3 model and other models.

[0994] The system also features an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. Emotion recognition models such as the OpenCV library and EmotionNet are used for emotion recognition. The server combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades.

[0995] The generated makeup instructions are sent to the user's device in an appropriate format (e.g., JSON), which receives the data and displays it on the user's screen in a visually understandable format.

[0996] As a specific example, if a user uploads an image of their ideal celebrity, the server analyzes the image to extract the celebrity's eyebrow shape and color. The emotion engine analyzes the user's facial expressions and identifies that the user is depressed. Based on this information, the server suggests lighter shades to create a more cheerful impression. These suggestions are displayed to the user, who can then follow specific makeup techniques to achieve their ideal face.

[0997] An example of a prompt sentence to be input to a generative AI model is in the following format:

[0998] "Generate the following makeup techniques based on facial feature data.\nFeature data: {Eyebrow shape: arched, Eyes: cat eyeliner, Lip color: natural pink}\nOutput format: Draw an arched eyebrow with an eyebrow pencil and blend with powder. Create a cat eye with black liquid eyeliner. Use natural pink lipstick."

[0999] By inputting this prompt into the generation AI, appropriate makeup techniques for the user are automatically generated.

[1000] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1001] Step 1:

[1002] Users select an image of their ideal celebrity face from their device and upload it to the system.

[1003] Input: A face image selected by the user.

[1004] Processing: The device sends the selected image to the server through the image upload interface. The image data is sent as a POST request using the HTTP protocol.

[1005] Output: Facial image data sent to the server.

[1006] Step 2:

[1007] The server stores the received image data in storage.

[1008] Input: Uploaded image data.

[1009] Processing: The server receives the image data and saves it to the specified storage. This saving process is performed using a Python library, etc.

[1010] Output: Image data saved in storage.

[1011] Step 3:

[1012] The server analyzes the stored images and extracts facial features.

[1013] Input: Saved image data.

[1014] Processing: The server analyzes the image data using an image recognition model (e.g., ResNet50 using PyTorch). The model extracts features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[1015] Output: Parsed facial feature data.

[1016] Step 4:

[1017] Based on facial feature data, generative AI generates specific makeup techniques.

[1018] Input: Facial feature data.

[1019] Processing: The server inputs facial feature data into OpenAI's GPT-3 model as a prompt. Based on the prompt, the generation AI outputs specific makeup application steps.

[1020] Output: Generated specific makeup techniques.

[1021] Step 5:

[1022] The server recognizes the user's emotions and adjusts the makeup application procedure.

[1023] Input: User's real-time facial expression data and generated makeup instructions.

[1024] Processing: Using an emotion recognition engine (e.g., OpenCV library and EmotionNet), the server analyzes the user's facial expression data. Based on the recognized emotion data, the server adjusts the makeup application (e.g., if the user is depressed, it suggests lighter shades).

[1025] Output: Adjusted makeup application steps.

[1026] Step 6:

[1027] The generated makeup technique instructions are provided to the user's terminal.

[1028] Enter: a coordinated makeup routine.

[1029] Processing: The server sends the adjusted makeup instructions to the user's device in an appropriate format (e.g., JSON). The device displays the received data on the screen in a visually understandable format.

[1030] Output: Specific makeup application instructions displayed on the user's device.

[1031] The above are the processing steps of the system that realizes this application example. This process allows users to easily obtain the ideal makeup technique.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] [Fourth embodiment]

[1036] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1037] 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.

[1038] 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).

[1039] 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.

[1040] 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.

[1041] 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).

[1042] 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. 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.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] 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."

[1049] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image. The following describes in detail the modes for implementing the present invention.

[1050] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[1051] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[1052] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[1053] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[1054] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use and extremely useful for many people, from beginners to advanced makeup artists.

[1055] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts the shape (arched) and color (medium darkness) of Celebrity B's eyebrows. Next, based on this information, it generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate beautiful eyebrows like Celebrity B's on their own.

[1056] In this way, this invention makes full use of image recognition technology and generative AI to help users appear confidently in real-life situations.

[1057] The processing flow will be explained below.

[1058] Step 1:

[1059] The user selects an ideal celebrity face image from their device and uploads the image by pressing the upload button. This operation is performed through a user interface that has a file dialog for selecting an image and an upload button.

[1060] Step 2:

[1061] The terminal transmits the image data selected by the user to the server as a POST request using the HTTP protocol.

[1062] Step 3:

[1063] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[1064] Step 4:

[1065] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[1066] Step 5:

[1067] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[1068] Step 6:

[1069] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, which describe in detail how the user should apply the makeup.

[1070] Step 7:

[1071] The makeup instructions generated by the generative AI are converted into an appropriate data format, such as JSON, and sent back to the user's device.

[1072] Step 8:

[1073] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[1074] Step 9:

[1075] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[1076] Step 10:

[1077] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[1078] Through the above steps, the present invention has explained in detail a system that integrates image recognition technology and generative AI to enable users to easily recreate their ideal celebrity makeup techniques.

[1079] Example 1

[1080] 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."

[1081] With conventional systems, it was difficult for users to independently find makeup techniques that would bring them closer to their ideal celebrity face. While image recognition technology and generative AI models were needed to provide specific and easy-to-understand makeup techniques, the general methods were too complicated for users to execute efficiently.

[1082] 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.

[1083] In this invention, the server includes means for uploading a facial image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to save the received image data, means for the server to analyze the saved image data and extract facial features, means for inputting the extracted feature data into a generative AI model and generating specific makeup techniques based on the generated facial features, and means for sending instructions for the generated makeup techniques in an appropriate format to the user's terminal and displaying them on the user's terminal. This enables the user to easily obtain and practice specific and easy-to-understand makeup techniques to get closer to the face of an ideal celebrity.

[1084] A "user" is an individual who uses the system to acquire the makeup techniques of an ideal celebrity's face.

[1085] A "terminal" is a device operated by a user, and is a device used to upload images and communicate data with a server.

[1086] "Server" refers to a computer system that stores, analyzes, and processes image data received from users.

[1087] "Image data" refers to digital data of images of celebrities' faces uploaded by users.

[1088] The "storage means" is a part that has the function of storing image data received by the server in a storage medium.

[1089] The "analysis means" is a part that has the function of extracting features of each part of the face from the received image data using an image recognition model.

[1090] "Facial features" refer to the specific shapes and colors of each part of the face, such as the eyebrows, eyes, nose, and lips.

[1091] A "generative AI model" is an artificial intelligence model that generates specific makeup techniques based on extracted feature data.

[1092] "Makeup techniques" are makeup techniques that allow users to recreate the facial features of their ideal celebrity.

[1093] The "transmission means" is a part having a function for the terminal to transmit image data to the server, and for the server to transmit the generated makeup technique instructions to the user terminal.

[1094] The "display means" is a part that has the function of displaying makeup technique instructions generated on the user terminal in a visually easy-to-understand manner.

[1095] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generative AI model provides specific makeup techniques based on that image. The following describes in detail the implementation of this system.

[1096] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. On the device, the operation is performed using a web browser or an image upload form in a dedicated application. A file selection dialog is displayed, and the user selects an image file from local storage. This image data supports common image formats such as JPEG and PNG.

[1097] Next, the device sends the image data selected by the user to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. When sending, the binary information of the image data is multiplexed and sent, along with metadata (e.g., file name, file size, etc.). The server saves the received image data in local storage or cloud storage (e.g., Amazon S3). The save path and metadata are recorded in a database (e.g., MySQL, PostgreSQL).

[1098] The server analyzes the saved image data using an image recognition model. For example, OpenCV or Google's FaceNet is used as the image recognition model. This allows the features of each part of the face (eyebrows, eyes, nose, lips, etc.) to be extracted. Here, for example, the face area is detected from the image and the feature values ​​for each part are calculated.

[1099] Next, the server inputs the extracted feature data into a generative AI model (e.g., GPT-4). Specific feature data includes eyebrow shape, eye features, and lip color. For example, the prompt "Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used. The generative AI model generates specific makeup techniques based on this data. The generated results include instructions such as "Draw an arched eyebrow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[1100] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. Here, the makeup instructions are displayed on a web page, neatly laid out using HTML / CSS. Adding explanations with images and video links makes it easier for users to understand.

[1101] As a concrete example, consider the case where User A uploads an image of Celebrity B, who has beautiful eyebrows, to the system. When User A uploads the image, the server analyzes the image and extracts Celebrity B's eyebrow shape (arched) and color (medium dark). Next, based on this information, the generative AI model generates specific makeup instructions, such as "Draw an arched shape with an eyebrow pencil and blend with powder." By displaying these instructions to User A, User A can recreate Celebrity B's beautiful eyebrows on his or her own.

[1102] An example of a prompt for the generative AI model is as follows:

[1103] "Based on the following facial feature data, please generate a makeup look that the user can recreate. Eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink. Please explain the specific makeup techniques in a step-by-step manner."

[1104] As described above, the present invention provides a system that utilizes image recognition technology and generative AI models to help users appear confidently in real-life situations.

[1105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1106] Step 1:

[1107] The user selects an image of the ideal celebrity's face from their device and uploads it to the system. The user uses an image upload form in a web browser or a dedicated application, and selects an image file from local storage using a file selection dialog. The input is the image data selected by the user, and the output is image data ready to be uploaded.

[1108] Step 2:

[1109] The device sends the selected image data to the server. The device uses the HTTP protocol to send the image data to the server using the POST method. Metadata (file name, file size, etc.) is also sent. The input is image data ready to be uploaded, and the output is the image data and metadata sent to the server.

[1110] Step 3:

[1111] The server saves the received image data. The server stores the image data in local storage or cloud storage (e.g., Amazon S3) and records the path and metadata in a database (e.g., MySQL, PostgreSQL). The input is the image data and metadata sent to the server, and the output is the image data saved in storage and the path and metadata recorded in the database.

[1112] Step 4:

[1113] The server analyzes the stored image data using an image recognition model. Specifically, it uses OpenCV or Google's FaceNet to extract features of each part of the image, such as the eyebrows, eyes, nose, and lips. The input is the image data stored in storage, and the output is specific feature data for each part of the face (e.g., eyebrow shape, eye features, lip color, etc.).

[1114] Step 5:

[1115] The server inputs the extracted feature data into a generative AI model to generate specific makeup techniques. Feature data such as "eyebrow shape: arched, eyeliner: cat eye, lip color: natural pink" is used as a prompt. The generative AI model (e.g., GPT-4) generates makeup techniques based on this data. The input is facial feature data, and the output is specific instructions for makeup techniques.

[1116] Step 6:

[1117] The generated makeup instructions are returned to the user's device in an appropriate format (e.g., JSON). The server sends the data as an HTTP response, and the device receives it. The input is the generated makeup instructions, and the output is the makeup instruction data sent to the user's device.

[1118] Step 7:

[1119] The device analyzes the generated makeup instruction data and displays it on the user's screen in a visually easy-to-understand format. It uses HTML / CSS to display beautifully laid out makeup instructions on a web page, including explanations with images and video links. The input is the makeup instruction data sent from the server, and the output is the makeup instructions displayed on the user's device.

[1120] Step 8:

[1121] The user follows the displayed makeup technique steps to actually apply makeup. The user refers to the instructions and performs tasks such as how to use an eyebrow pencil, how to apply eyeshadow, and how to choose a lipstick. The input is the specific makeup technique instructions displayed on the device, and the output is the actual makeup applied.

[1122] (Application example 1)

[1123] 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."

[1124] Current makeup support systems require users to apply makeup themselves while referring to images of their ideal face, making it difficult for makeup novices. Furthermore, because real-time feedback is not available, it takes a lot of time and effort to achieve the desired celebrity look. Furthermore, the lack of specific makeup steps and information on the products to use makes it difficult for users to apply makeup properly. Therefore, there is a need for a system that allows users to easily apply their ideal makeup.

[1125] 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.

[1126] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI for generating specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means connected to a mirror located in a physical store for capturing the user's face in real time and virtually applying makeup, thereby enabling the user to apply their ideal makeup while receiving feedback in real time in the physical store.

[1127] A "user" is a person who uses this system to receive makeup technique suggestions.

[1128] "Facial image" is image data of a face that the user considers ideal or that they use as a reference.

[1129] "Uploading" is the act of sending image data from a user's device to a server.

[1130] The "server" is a computer system that analyzes and stores image data and generates makeup techniques.

[1131] "Facial features" refers to data that indicates the individual features and shapes of each part of the face, such as the eyebrows, eyes, nose, and lips.

[1132] "Generative AI" is an artificial intelligence model that generates specific makeup techniques based on facial feature data.

[1133] "Makeup techniques" are specific makeup methods and procedures that allow users to recreate specific facial features.

[1134] "Providing" refers to the act of visually displaying and explaining the generated makeup steps and methods to the user.

[1135] A "brick and mortar store" is a physical location, such as a cosmetics store or beauty salon, that a user can visit.

[1136] A "mirror" is a device that has a reflective surface that allows the user to see their own face and is linked to a device such as a camera.

[1137] "Real-time capture" means instantly obtaining images or footage the moment the user stands in front of the mirror.

[1138] "Virtually applying makeup" refers to the virtual display of makeup effects on a user's face captured in real time through digital processing.

[1139] The present invention provides a system that allows users to experience ideal makeup techniques in real time at a physical store. Hereinafter, an embodiment of the present invention will be specifically described.

[1140] System Overview

[1141] 1. User Interface:

[1142] The user stands in front of a mirror in a brick-and-mortar store, which has a built-in high-definition camera and display.

[1143] Users use their smartphones to upload an image of their ideal celebrity's face.

[1144] 2. Image upload and analysis:

[1145] The image of the face selected by the user is sent from the smartphone to the server, where the image data is sent as a POST request using the HTTP protocol.

[1146] The server stores the received image data in storage and analyzes the facial features using an image recognition model, extracting the individual features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[1147] 3. Makeup Creation:

[1148] The extracted facial feature data is fed into a generative AI, which then generates specific makeup techniques, including detailed instructions such as how to shape the eyebrows and what eyeshadow to use.

[1149] 4. Real-time makeup application:

[1150] The server sends the generated makeup instructions to the mirror, whose camera captures the user's face in real time and virtually applies the makeup.

[1151] The generated makeup instructions are displayed on the mirror display in a visually easy-to-understand format.

[1152] Hardware and software used

[1153] Hardware:

[1154] Smart mirror (with camera)

[1155] Smartphone

[1156] Server (with high-performance CPU and memory)

[1157] software:

[1158] Flask (as a web server)

[1159] OpenCV (image processing library)

[1160] Pillow (image manipulation library)

[1161] AI model library (ImageRecognitionModel and MakeupGenerator)

[1162] Processing flow

[1163] The server receives and analyzes image data sent from the user's smartphone. Based on the analysis results, the generative AI generates specific makeup techniques. The generated makeup techniques are virtually applied to the user's face, captured in real time, and displayed in the mirror. This allows the user to receive instant feedback and apply the makeup they desire.

[1164] Specific examples

[1165] For example, if a user wants to copy the makeup of a certain actress, they upload an image of the actress's face from their smartphone to the system. The server analyzes the image and extracts the actress's facial features (arched eyebrows, cat-eye crease, etc.). Based on this information, the generative AI generates specific makeup steps and virtually applies them to the user's face in real time. Specific steps, such as "draw an arched brow with an eyebrow pencil and blend with powder," are displayed on the mirror, allowing the user to recreate the makeup themselves.

[1166] Prompt Sentence Examples

[1167] "Analyze the eyebrow shape in this image and generate the ideal makeup steps. Specifically, please provide detailed instructions on how to use an eyebrow pencil and blend the lines."

[1168] By inputting these prompts into a generative AI model, it is possible to provide the specific makeup techniques that the user desires.

[1169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1170] Step 1:

[1171] A user uses a smartphone to select an ideal celebrity's facial image and upload it to the system. This is done using an image selection interface displayed on the smartphone's application screen. Once the user selects an image, the image data is sent to the server as a POST request using the HTTP protocol. The input is the facial image selected by the user, and the output is the image data sent to the server.

[1172] Step 2:

[1173] The server stores the received image data in storage. The stored image data is used in subsequent analysis processing. The input is the image data sent to the server, and the output is the image data stored in the storage.

[1174] Step 3:

[1175] The server analyzes the facial images stored in storage using an image recognition model (ImageRecognitionModel). This analysis process extracts the features of each part of the face (eyebrows, eyes, nose, lips, etc.) individually. The input is the stored image data, and the output is the extracted feature data for each part of the face. Specifically, it uses an image processing library such as OpenCV to detect feature points and analyze the shape.

[1176] Step 4:

[1177] The server inputs the extracted facial feature data into a generative AI model (Makeup Generator). This generative AI model generates the user's ideal makeup technique based on the facial feature data. The input is the extracted facial feature data, and the output is a specific makeup technique. Specifically, the makeup technique is generated using a prompt to the generative AI model. A prompt such as "Analyze the eyebrow shape in this image and generate the ideal makeup procedure. Specifically, please explain in detail how to use an eyebrow pencil and how to blend it" is used.

[1178] Step 5:

[1179] The generated makeup instructions are sent from the server in an appropriate format (e.g., JSON) to a smart mirror in a physical store. The smart mirror captures the user's face in real time with its built-in camera and processes the data to apply virtual makeup. The input is the generated makeup instructions data, and the output is an image of the virtual makeup applied by the smart mirror. Specific operations include real-time image processing and graphic processing for applying makeup.

[1180] Step 6:

[1181] The results of the virtual makeup displayed on the smart mirror and the specific makeup steps are presented to the user in a visually easy-to-understand format. This allows the user to receive specific instructions for actually applying the makeup. The input is a real-time captured image of the user's face and the generated makeup technique, and the output is an image of the virtual makeup displayed on the mirror and the makeup steps. Specific instructions displayed include "Draw an arched shape with an eyebrow pencil and blend with powder" and "Create a cat eye with black liquid eyeliner."

[1182] Through these steps, a system is realized that allows users to apply their ideal makeup while receiving real-time feedback in a physical store.

[1183] 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.

[1184] The present invention is a system that allows users to upload an image of their ideal celebrity face, and provides specific makeup techniques that are analyzed and generated by AI based on the image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the modes for implementing the present invention.

[1185] First, the user selects an image of the face of the ideal celebrity from their device and uploads it to the system. This process is done through an interface for uploading images. Next, the device sends the image data selected by the user to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[1186] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. This generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[1187] The server then inputs these feature data into a generative AI that generates specific makeup techniques for the user to recreate those features, including detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques.

[1188] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades of makeup.

[1189] The generated makeup instructions are sent back to the user's device in an appropriate format (e.g., JSON). The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched brow with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed.

[1190] By following the displayed makeup instructions, users can achieve the look of their ideal celebrity. This system is easy to use for anyone, from beginners to advanced makeup artists, and it is extremely useful for many people because it provides makeup techniques that take the user's emotions into consideration.

[1191] As a specific example, consider the case where user A uploads an image of celebrity B, who has beautiful eyebrows, to the system. When user A uploads the image, the server analyzes the image and extracts celebrity B's eyebrow shape (arched) and color (medium dark). Next, the emotion engine analyzes user A's facial expression and identifies, for example, that the user is depressed. Based on this information, the server suggests specific makeup instructions, such as "draw an arched shape with an eyebrow pencil and blend with powder," as well as a lighter shade to create a more energetic impression. By displaying this to user A, user A can recreate celebrity B's beautiful eyebrows while applying makeup that suits their own emotions.

[1192] As described above, this invention utilizes image recognition technology, generative AI, and emotion recognition technology to help users appear confidently in real-life situations.

[1193] The processing flow will be explained below.

[1194] Step 1:

[1195] Users select an ideal celebrity face image from their device and upload the image by pressing the upload button. This is done through a user interface that includes a file dialog for selecting an image and an upload button.

[1196] Step 2:

[1197] The terminal transmits the image data selected by the user to the server. The image data is transmitted to the server as a POST request using the HTTP protocol.

[1198] Step 3:

[1199] The server receives the transmitted image data and temporarily stores it in storage, making the images available for subsequent analysis.

[1200] Step 4:

[1201] The server then inputs the stored image into an image recognition model, which uses machine learning algorithms to extract facial features such as eyebrows, eyes, nose, and lips.

[1202] Step 5:

[1203] The feature data extracted using the image recognition model is collected by the server. For example, specific features such as arched eyebrows or cat-eye creases are identified at this stage.

[1204] Step 6:

[1205] The server inputs the extracted facial feature data into a generative AI to generate specific makeup techniques, including detailed instructions on how to draw eyebrows and which eyeshadow to use.

[1206] Step 7:

[1207] The server converts the makeup instructions generated by the generative AI into an appropriate data format, such as JSON.

[1208] Step 8:

[1209] The server invokes an emotion engine to recognize the user's emotions, and analyzes the user's facial expression data and voice data to identify the user's current emotion (e.g., joy, sadness, surprise, etc.).

[1210] Step 9:

[1211] The server then adjusts the generated makeup techniques based on the identified emotion data. For example, if the user is feeling down, the server may suggest lighter colors.

[1212] Step 10:

[1213] The server transmits the adjusted makeup technique instructions to the user's terminal.

[1214] Step 11:

[1215] The device receives makeup instruction data sent from the server, which is then parsed so that it can be displayed on the screen in a format that is easy for the user to understand.

[1216] Step 12:

[1217] The user applies makeup by following the instructions displayed on the device, which allows them to achieve the look of their ideal celebrity.

[1218] Step 13:

[1219] After completing the makeup application, the user checks the finished product and evaluates whether the makeup techniques provided by the system were satisfactory.

[1220] Through the above steps, the present invention has provided a detailed description of a system that integrates image recognition technology, generative AI, and an emotion engine to enable users to easily recreate their ideal celebrity makeup techniques.

[1221] Example 2

[1222] 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."

[1223] Many existing makeup instruction systems simply analyze images and provide basic makeup techniques. However, few systems can respond to the user's emotions and individual facial features, and they lack detailed instruction to maximize the makeup effect. This makes it difficult for users to learn makeup techniques that suit their emotions and specific facial features.

[1224] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading a face image selected by the user, means for the terminal to send the uploaded image data to the server, means for the server to analyze the uploaded image and extract facial features, means having a generative AI model that generates specific makeup techniques based on the extracted facial features, means for providing the generated makeup techniques to the user, and means having an emotion engine that analyzes the user's emotions and adjusts the makeup technique content based on the emotions. This allows the user to not only learn makeup techniques that match their facial features but also to apply makeup that suits their emotions at the time.

[1225] "User" refers to a person who uses the system to upload a facial image and receive makeup instruction.

[1226] "Terminal" refers to the device (e.g., smartphone, tablet, computer) used by a User to access the System.

[1227] A "server" is a group of computers that serves as the core of a system, and is a device that receives requests from users and processes and analyzes data.

[1228] "Facial Image" means an image that clearly shows facial features and that is uploaded by a User to the System.

[1229] "Means for uploading" refers to the ability of a user to use their own device to send an image of the selected face to the system.

[1230] "Image data" refers to electronic data relating to a facial image uploaded by a user.

[1231] "Means of analysis" refers to the technology in which the server receives image data, recognizes facial features, and extracts information about each part of the face.

[1232] "Facial features" refers to the specific shape, color, and other characteristics of each part of the face (e.g., eyebrows, eyes, nose, lips).

[1233] A "generative AI model" refers to artificial intelligence (AI) that generates specific makeup techniques based on facial feature data.

[1234] "Means for providing" refers to the function for presenting the generated makeup techniques to the user.

[1235] An "emotion engine" refers to technology that analyzes a user's facial expressions and voice data to identify their emotions.

[1236] "Adjustment means" refers to the function of modifying and optimizing the content of the generated makeup techniques based on the emotional data identified by the emotion engine.

[1237] The present invention is a system that allows users to upload an image of their ideal celebrity's face, and provides specific makeup techniques that are analyzed and generated by AI based on that image, and further combines it with an emotion engine that recognizes the user's emotions. The following describes in detail the embodiments of the present invention.

[1238] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. The device provides an interface for uploading images, and the user selects an image using a file browser and clicks the "Upload" button to send the image data to the system. This process sends the image data to the server as a POST request using the HTTP protocol.

[1239] The server temporarily stores the received image data in memory and then saves it to storage (e.g., Amazon S3 or Google Cloud Storage). The server then analyzes the stored image using an image recognition model (e.g., OpenCV or TensorFlow) to extract features for each part of the face, such as the eyebrows, eyes, nose, and lips. This analysis generates facial feature data. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color may be extracted.

[1240] The server then uses a generative AI model (e.g., GPT) to input the extracted feature data as prompts and generate specific makeup tips for the user to replicate the features, including specific makeup techniques such as how to shape the eyebrows and which eyeshadow to use.

[1241] Examples of prompt sentences include the following:

[1242] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[1243] The system also includes an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. The server then combines the acquired emotion data with facial feature data to adjust the makeup content generated based on the user's emotions. For example, if the system determines that the user is depressed, it will suggest lighter shades of makeup.

[1244] Finally, the generated makeup instructions are sent back to the user's device in JSON format. The device receives this data and displays it on the user's screen in a visually easy-to-understand format. For example, specific makeup instructions such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick" are displayed on the screen.

[1245] This system is easy to use for anyone, from makeup beginners to advanced users, and provides makeup techniques that take the user's emotions into consideration, making it extremely useful for many people.

[1246] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1247] Explain the program's processing flow in detail

[1248] Step 1:

[1249] The user selects an image

[1250] Description: The user selects an image of their ideal celebrity face from their device.

[1251] Input: A face image file stored in the user's local storage.

[1252] Output: The path of the selected face image file.

[1253] Specific operation: The user opens the file browser on the device and selects a face image. This operation is performed by the user clicking on the file to select it.

[1254] Step 2:

[1255] The user uploads an image

[1256] Description: User selects an image and clicks the "Upload" button to send it to the system.

[1257] Input: The path of the selected face image file.

[1258] Output: Image data sent to the server.

[1259] Specific operation: When the user clicks the "Upload" button, the device sends the image data to the server as an HTTP POST request.

[1260] Step 3:

[1261] The device sends the image data to the server.

[1262] Description: The terminal sends image data to the server using the HTTP protocol.

[1263] Input: Image data.

[1264] Output: Image data sent to the server.

[1265] Specific operation: The terminal embeds the selected image data in binary format in an HTTP POST request and sends it to the specified server URL.

[1266] Step 4:

[1267] The server receives the image data.

[1268] Description: The server temporarily stores the received image data in memory and then stores it in storage.

[1269] Input: Image data sent to the server.

[1270] Output: Image data saved in storage.

[1271] Specific operation: The server extracts the image data from the body of the POST request and saves the data in the specified storage path.

[1272] Step 5:

[1273] The server analyzes the image

[1274] Description: The server uses an image recognition model to analyze the stored image data and extract features for each part of the face.

[1275] Input: Image data stored in storage.

[1276] Output: Parsed facial feature data.

[1277] Specific operation: The server calls an image recognition model (e.g., OpenCV or TensorFlow) to detect and analyze each part of the face (eyebrows, eyes, nose, lips, etc.).

[1278] Step 6:

[1279] The server generates facial feature data

[1280] Description: From the analysis results, the server extracts feature data for each part of the body, such as eyebrows, eyes, nose, and lips, and structures it in JSON format.

[1281] Input: Analyzed facial features.

[1282] Output: Facial feature data in JSON format.

[1283] Specific operation: Based on the analysis results, the server compiles the characteristics of each part (e.g., eyebrow shape, eye style, lip color) as JSON.

[1284] Step 7:

[1285] The server inputs data into the generated AI.

[1286] Description: The server inputs the feature data as prompts into the generative AI model to generate specific makeup techniques.

[1287] Input: Facial feature data in JSON format.

[1288] Output: Generated makeup technique data.

[1289] Specific operation: The server calls a generative AI model (e.g., GPT) and inputs feature data as a prompt. The generative AI model generates a makeup technique based on the data.

[1290] Example prompt sentence:

[1291] "Generate makeup instructions based on the following image, adjusting the shades and techniques according to the user's emotions. Image URL: [IMAGE_URL]"

[1292] Step 8:

[1293] The server uses an emotion engine to analyze the user's emotions.

[1294] Description: The server analyzes the user's facial and voice data and uses an emotion engine to identify emotions.

[1295] Input: User's facial expression data and voice data.

[1296] Output: Identified emotion data.

[1297] Specific operation: The server calls an emotion engine (e.g., Affectiva or Microsoft Azure Emotion API) and analyzes the user's facial and voice data to identify emotions.

[1298] Step 9:

[1299] The server combines emotional data with makeup techniques

[1300] Description: The server adjusts the content of the generated makeup techniques based on the emotional data.

[1301] Input: Generated makeup technique data, identified emotion data.

[1302] Output: Emotion-specific makeup technique data.

[1303] Specific operation: The server refers to the emotional data and adjusts the makeup technique, such as recommending lighter colors if the person is not feeling well.

[1304] Step 10:

[1305] The server sends makeup instructions back to the device.

[1306] Description: The generated makeup instructions are sent back to the user's device in JSON format.

[1307] Input: Emotion-aware makeup technique data.

[1308] Output: Makeup technique data sent to the user's device.

[1309] Specific operation: The server converts the adjusted makeup technique data into JSON format and sends it to the user's device as an HTTP response.

[1310] Step 11:

[1311] The device displays the makeup technique steps

[1312] Description: The device displays the received data on the user's screen in a visually understandable format.

[1313] Input: Received makeup technique data.

[1314] Output: The makeup technique displayed on the user's screen.

[1315] Specific operation: The device parses the received JSON data and displays it on the screen as HTML and view components. For example, it displays a list such as "Draw an arched shape with an eyebrow pencil and blend with powder," "Create a cat eye with black liquid eyeliner," and "Use natural pink lipstick."

[1316] (Application example 2)

[1317] 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."

[1318] In modern society, the pursuit of each individual's ideal beauty is an important factor that contributes to improving self-esteem and confidence. However, many people find it difficult to find the makeup method that best suits them, and makeup beginners in particular often lack proper guidance. In addition, makeup advice that responds to users' emotions is not provided, and users are unable to find a makeup method that suits their current condition.

[1319] 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.

[1320] In this invention, the server includes means for uploading a facial image selected by the user, means for the server to analyze the uploaded image and extract facial features, means having a generation AI that generates specific makeup techniques based on the generated facial features, means for providing the generated makeup techniques to the user, and means having an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques according to the emotions. This makes it possible to provide makeup techniques that are optimized for each user and to provide immediate advice according to the user's emotions.

[1321] "User" refers to a person who uses the system to receive makeup technique suggestions.

[1322] "Face image" refers to a photo of a celebrity or the user's own face that they consider ideal.

[1323] "Means for uploading" refers to the functionality that allows users to send images of their faces to the system.

[1324] The "means for analyzing and extracting facial features" is a function that processes the facial image received by the server and identifies detailed features of each part of the face, such as the eyebrows, eyes, nose, and lips.

[1325] "Generative AI" refers to artificial intelligence technology that automatically generates specific makeup techniques suited to the user based on extracted facial features.

[1326] The "means of providing" is a function that displays the generated makeup techniques to the user in a visual and easy-to-understand format.

[1327] An "emotion recognition engine" refers to technology that analyzes a user's facial expressions and voice data and recognizes their emotions.

[1328] The present invention is a system in which a user uploads an image of their ideal celebrity face, and a generation AI provides specific makeup techniques based on that image. It also combines an emotion recognition engine that recognizes the user's emotions and adjusts the makeup techniques accordingly. Details for implementing this invention are described below.

[1329] First, the user selects an image of the ideal celebrity's face from their device and uploads it to the system. A user interface is provided for uploading the image. Through this interface, the user sends the image from their device to the server. The image data is sent to the server as a POST request using the HTTP protocol.

[1330] The server stores the received image data in storage and then analyzes it using an image recognition model. Through this analysis, the characteristics of each part of the face, such as the eyebrows, eyes, nose, and lips, are extracted. For example, specific features such as arched eyebrows, cat-eye shaped eyes, and natural pink lip color are extracted.

[1331] The server then inputs these feature data into a generative AI, which generates specific makeup techniques for the user to recreate those features. The generated makeup techniques include detailed instructions on how to draw the eyebrows, which eyeshadow to use, and other specific makeup techniques. This generative AI uses OpenAI's GPT-3 model and other models.

[1332] The system also features an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice data to identify their emotions. Emotion recognition models such as the OpenCV library and EmotionNet are used for emotion recognition. The server combines the identified emotion data with the extracted facial feature data and adjusts the makeup content generated based on the user's emotions. For example, if the user is feeling down, the system may suggest lighter shades.

[1333] The generated makeup instructions are sent to the user's device in an appropriate format (e.g., JSON), which receives the data and displays it on the user's screen in a visually understandable format.

[1334] As a specific example, if a user uploads an image of their ideal celebrity, the server analyzes the image to extract the celebrity's eyebrow shape and color. The emotion engine analyzes the user's facial expressions and identifies that the user is depressed. Based on this information, the server suggests lighter shades to create a more cheerful impression. These suggestions are displayed to the user, who can then follow specific makeup techniques to achieve their ideal face.

[1335] An example of a prompt sentence to be input to a generative AI model is in the following format:

[1336] "Generate the following makeup techniques based on facial feature data.\nFeature data: {Eyebrow shape: arched, Eyes: cat eyeliner, Lip color: natural pink}\nOutput format: Draw an arched eyebrow with an eyebrow pencil and blend with powder. Create a cat eye with black liquid eyeliner. Use natural pink lipstick."

[1337] By inputting this prompt into the generation AI, appropriate makeup techniques for the user are automatically generated.

[1338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1339] Step 1:

[1340] Users select an image of their ideal celebrity face from their device and upload it to the system.

[1341] Input: A face image selected by the user.

[1342] Processing: The device sends the selected image to the server through the image upload interface. The image data is sent as a POST request using the HTTP protocol.

[1343] Output: Facial image data sent to the server.

[1344] Step 2:

[1345] The server stores the received image data in storage.

[1346] Input: Uploaded image data.

[1347] Processing: The server receives the image data and saves it to the specified storage. This saving process is performed using a Python library, etc.

[1348] Output: Image data saved in storage.

[1349] Step 3:

[1350] The server analyzes the stored images and extracts facial features.

[1351] Input: Saved image data.

[1352] Processing: The server analyzes the image data using an image recognition model (e.g., ResNet50 using PyTorch). The model extracts features of each part of the face (eyebrows, eyes, nose, lips, etc.).

[1353] Output: Parsed facial feature data.

[1354] Step 4:

[1355] Based on facial feature data, generative AI generates specific makeup techniques.

[1356] Input: Facial feature data.

[1357] Processing: The server inputs facial feature data into OpenAI's GPT-3 model as a prompt. Based on the prompt, the generation AI outputs specific makeup application steps.

[1358] Output: Generated specific makeup techniques.

[1359] Step 5:

[1360] The server recognizes the user's emotions and adjusts the makeup application procedure.

[1361] Input: User's real-time facial expression data and generated makeup instructions.

[1362] Processing: Using an emotion recognition engine (e.g., OpenCV library and EmotionNet), the server analyzes the user's facial expression data. Based on the recognized emotion data, the server adjusts the makeup application (e.g., if the user is depressed, it suggests lighter shades).

[1363] Output: Adjusted makeup application steps.

[1364] Step 6:

[1365] The generated makeup technique instructions are provided to the user's terminal.

[1366] Enter: a coordinated makeup routine.

[1367] Processing: The server sends the adjusted makeup instructions to the user's device in an appropriate format (e.g., JSON). The device displays the received data on the screen in a visually understandable format.

[1368] Output: Specific makeup application instructions displayed on the user's device.

[1369] The above are the processing steps of the system that realizes this application example. This process allows users to easily obtain the ideal makeup technique.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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).

[1377] 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.

[1378] 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."

[1379] 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.

[1380] 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).

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] The following is further disclosed regarding the above embodiment.

[1392] (Claim 1)

[1393] a means for the user to upload an image of their chosen face;

[1394] A means for the server to analyze the uploaded image and extract facial features;

[1395] A means for generating a specific makeup technique based on the generated facial features, comprising a generation AI;

[1396] A means for providing the generated makeup techniques to users;

[1397] A system including:

[1398] (Claim 2)

[1399] 2. The system of claim 1, wherein the means for extracting facial features performs processing to individually recognize features of each part of the face, such as eyebrows, eyes, nose, and lips.

[1400] (Claim 3)

[1401] 10. The system of claim 1, further comprising: means for presenting the generated makeup application instructions to the user in natural language.

[1402] "Example 1"

[1403] (Claim 1)

[1404] a means for the user to upload an image of their chosen face;

[1405] A means for transmitting the uploaded image data from the terminal to a server;

[1406] A means for storing the image data received by the server;

[1407] A means for the server to analyze the stored image data and extract facial features;

[1408] A means for inputting the extracted feature data into a generative AI model and generating specific makeup techniques based on the generated facial features;

[1409] means for transmitting the generated makeup technique instructions in an appropriate format to a user's terminal and displaying the instructions on the user's terminal;

[1410] A system including:

[1411] (Claim 2)

[1412] 2. The system of claim 1, wherein the means for extracting facial features performs processing to individually recognize features of each part of the face, such as eyebrows, eyes, nose, and lips.

[1413] (Claim 3)

[1414] 10. The system of claim 1, further comprising: means for presenting the generated makeup application instructions to the user in natural language.

[1415] "Application Example 1"

[1416] Rewritten claims

[1417] (Claim 1)

[1418] a means for the user to upload an image of their chosen face;

[1419] A means for the server to analyze the uploaded image and extract facial features;

[1420] A means for generating a specific makeup technique based on the generated facial features, comprising a generation AI;

[1421] A means for providing the generated makeup techniques to users;

[1422] A means of connecting to mirrors placed in physical stores to capture the user's face in real time and virtually apply makeup;

[1423] A system including:

[1424] (Claim 2)

[1425] 2. The system of claim 1, wherein the means for extracting facial features performs processing to individually recognize features of each part of the face, such as eyebrows, eyes, nose, and lips.

[1426] (Claim 3)

[1427] The system of claim 1, further comprising means for presenting the generated makeup technique instructions to the user in natural language and displaying them on a mirror located in the physical store.

[1428] "Example 2: Combining Emotion Engines"

[1429] (Claim 1)

[1430] a means for the user to upload an image of their chosen face;

[1431] A means for transmitting the uploaded image data from the terminal to a server;

[1432] A means for the server to analyze the uploaded image and extract facial features;

[1433] A means for providing a generative AI model that generates specific makeup techniques based on the extracted facial features;

[1434] A means for providing the generated makeup techniques to users;

[1435] A means for analyzing the emotions of a user and providing an emotion engine for adjusting the makeup technique content based on the emotions;

[1436] A system including:

[1437] (Claim 2)

[1438] 2. The system of claim 1, wherein the means for extracting facial features performs processing to individually recognize features of each part of the face, such as eyebrows, eyes, nose, and lips.

[1439] (Claim 3)

[1440] 10. The system of claim 1, further comprising: means for presenting the generated makeup application instructions to the user in natural language.

[1441] "Application example 2 when combining emotion engines"

[1442] (Claim 1)

[1443] a means for the user to upload an image of their chosen face;

[1444] A means for the server to analyze the uploaded image and extract facial features;

[1445] A means for generating a specific makeup technique based on the generated facial features, comprising a generation AI;

[1446] A means for providing the generated makeup techniques to users;

[1447] a means for providing an emotion recognition engine that recognizes the emotion of a user and adjusts the makeup technique according to the emotion;

[1448] A system including:

[1449] (Claim 2)

[1450] 2. The system according to claim 1, wherein the means for extracting facial features executes a process for individually recognizing features of each part of the face, such as eyebrows, eyes, nose, and lips.

[1451] (Claim 3)

[1452] 10. The system of claim 1, further comprising means for presenting the generated makeup application instructions to the user in natural language. [Explanation of symbols]

[1453] 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 the user to upload an image of their chosen face; A means for the server to analyze the uploaded image and extract facial features; A means for generating a specific makeup technique based on the generated facial features, comprising a generation AI; A means for providing the generated makeup techniques to users; A system including:

2. 2. The system according to claim 1, wherein the means for extracting facial features executes processing for individually recognizing features of each part of the face, such as eyebrows, eyes, nose, and lips.

3. 10. The system of claim 1, further comprising: means for presenting the generated makeup application instructions to the user in natural language.

Images