system

Abstract

We provide the system. [Solution] A means to detect a user's face and track facial feature points in real time, A means for analyzing user instructions in voice or text and generating appropriate makeup advice, A means of presenting the generated advice to the user via voice and virtual reality display, A means of monitoring user progress and providing timely feedback, A means of regularly updating and providing users with the latest trend information, A method for providing makeup suggestions while taking the user's schedule information into consideration, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to provide more intuitive and effective guidance to users in online makeup technique acquisition. Many consumers have the problem that they lack confidence in makeup techniques, and due to the lack of visual guidance and real-time feedback, it is difficult to provide individually adapted guidance. There is also a problem that it is difficult to keep up with rapidly changing makeup trends and new technologies.

Means for Solving the Problems

[0005] This invention enables individually customized makeup instruction by providing means for detecting a user's face and tracking facial landmarks in real time. Furthermore, by including means for analyzing user instructions in voice or text and generating appropriate makeup advice, users can receive effective and specific guidance. In addition, means for presenting the generated advice to the user with voice and augmented reality overlays promote more intuitive understanding. Means for monitoring the user's progress and providing timely feedback enable users to modify and improve their techniques in real time. Moreover, means for regularly updating and providing the latest trend information continuously supports the acquisition of new makeup techniques.

[0006] "Face detection" is a technology that analyzes camera footage to determine the location of the user's face.

[0007] "Real-time tracking of facial landmarks" is a process that continuously tracks specific characteristic points of a face (such as the position of the eyes, nose, and mouth) over time.

[0008] "User instructions via voice or text" refers to voice or text input provided by the user to the system.

[0009] "Generating makeup advice" means creating instructional content on appropriate makeup techniques and methods based on user requests.

[0010] Augmented reality overlay is a visual technology that overlays virtual effects, such as makeup, onto the user's real-world image.

[0011] "Real-time feedback" refers to advice and suggestions that users receive immediately as they apply their makeup.

[0012] "Regularly updating trend information" refers to the process of continuously collecting the latest makeup trends and technology information and reflecting it in the system database. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system of this invention mainly consists of a user terminal, a server, and a network that connects them. The user captures their face in real time using the camera on the terminal and transmits the video to the system. Face recognition software installed on the terminal detects the user's face and identifies landmarks. This information is used to indicate the precise location of makeup.

[0035] Next, voice or text instructions from the user are taken into the system. For example, if a user requests by voice, "Tell me how to apply blush," the device uses speech recognition technology to convert this into text and sends it to the server. The server uses natural language processing technology to analyze the intent of this text and generate appropriate makeup advice.

[0036] The generated advice is sent from the server to the terminal. The terminal provides instructions to the user using voice guidance and an augmented reality (AR) overlay. The AR overlay indicates where to apply virtual makeup to the user's face based on facial landmarks, and the voice guidance explains the steps. This allows the user to visually understand and practice the makeup process.

[0037] Furthermore, the device tracks the user's progress and provides real-time feedback. For example, if the user hasn't applied the blush as instructed, the device will immediately provide instructions such as, "Please apply it a little closer to the center of your cheeks."

[0038] Finally, the server updates its database with the latest trend information and makeup techniques, allowing users to continuously access new information and techniques. This continuous learning environment ensures that users are always learning techniques that keep up with the latest makeup trends.

[0039] Thus, the present invention provides users with personalized makeup instruction and helps them acquire makeup techniques with confidence.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user activates the device's camera and points their face at the system. The device processes the captured video in real time and uses facial recognition software to detect the user's face. It identifies facial landmarks (such as the position of the eyes, nose, and mouth) and prepares to track them.

[0043] Step 2:

[0044] The user gives a voice command saying, "Teach me how to apply lipstick." The device receives this voice input, converts it to text using speech recognition technology, and sends that data to the server.

[0045] Step 3:

[0046] The server analyzes the received text data using a natural language processing engine to interpret the user's intent. It understands the makeup advice the user is seeking (in this case, how to apply lipstick) and generates appropriate instructions.

[0047] Step 4:

[0048] The generated advice is sent from the server to the terminal. The terminal receives these instructions and conveys them to the user through voice guidance. Additionally, an AR rendering engine is used to display an overlay on the user's face indicating the lip application locations. This allows the user to understand the procedure both visually and audibly.

[0049] Step 5:

[0050] The user applies lipstick following instructions from the device. The device tracks the user's face throughout the process and monitors their progress in real time. If the user is not following the instructions, it immediately provides feedback such as, "You need to apply a little more to the inner part of your lips."

[0051] Step 6:

[0052] The server regularly updates its trend information and makeup database. Whenever the latest makeup trends are discovered, they are reflected in the system database and provided to users as new information. This ensures users always have access to the latest makeup techniques.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Traditional makeup instruction systems struggled to provide personalized instruction to users in real time and lacked the mechanisms to quickly adapt to the latest makeup trends and technological advancements. This resulted in ineffective learning experiences for users.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for detecting the user's face and tracking its features in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for presenting the generated guidance to the user with voice and virtual reality overlays. This allows the user to learn techniques adapted to the latest makeup trends while receiving personalized instruction.

[0058] A "user" refers to an individual who wants to learn makeup procedures and techniques using the system.

[0059] "Facial features" refer to a collection of points and lines that indicate the position and shape of each part of the face, providing the information necessary for applying makeup.

[0060] "Voice or written instructions" refers to voice commands or text inputs that users use to ask for specific advice on makeup procedures and methods.

[0061] "Makeup guide" refers to instructions on how to apply makeup and the techniques involved, generated based on the user's preferences.

[0062] A "virtual reality overlay" is virtual graphic information that is superimposed onto the user's real-world image and is used to indicate areas where makeup should be applied.

[0063] "Trend information" refers to information about the latest makeup techniques and trends, provided to enable users to always incorporate the latest styles.

[0064] The system of the present invention consists of a user terminal, a server, and a communication network connecting them. The user uses a camera mounted on the terminal to capture images of their face in real time and transmits the data to the terminal. This terminal is equipped with software for facial recognition, specifically using a computer vision library to identify facial features. This information is used to determine the precise locations where makeup should be applied.

[0065] The user inputs instructions, such as "Tell me how to apply eyeshadow," either by voice or text. The terminal has technology to convert voice input to text through its interface, and this is done using a speech recognition library. The converted text data is sent to a server, which analyzes this data using natural language processing technology. This analysis utilizes a natural language processing model to accurately understand the intent of the user's instructions and generate corresponding makeup instructions.

[0066] The server generates instructions which are sent to the terminal, which then presents them to the user through audio and a virtual reality overlay. The virtual reality overlay shows the areas on the user's face where makeup should be applied, and the audio guide explains the steps in detail. This allows the user to visually understand the steps and learn the actual makeup process. An example of a prompt would be, "Please show me the steps for natural makeup." This system allows users to receive timely makeup instruction tailored to their individual needs and acquire skills that keep up with the latest trends.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The user captures their face in real time using the device's camera. The captured video is input to the device and processed by facial recognition software. Through this processing, the device identifies the positions of facial features (eyes, nose, mouth, etc.) and outputs this information as data.

[0070] Step 2:

[0071] The user inputs voice or text instructions into the device, such as "Teach me how to apply blush." ​​In the case of voice instructions, the device uses a speech recognition library to convert the voice into text. The converted text is sent to the server, which receives it as input.

[0072] Step 3:

[0073] The server analyzes the text sent by the user using natural language processing technology. In this step, data calculations are performed using the analysis model to understand the user's intent. Based on the analysis results, the server generates cosmetic recommendations suitable for the user and outputs the results.

[0074] Step 4:

[0075] The generated makeup instructions are sent from the server to the terminal. Based on the received instructions, the terminal overlays a virtual reality overlay onto the user's video. This overlay indicates where makeup should be applied, and the terminal uses audio guidance to present the procedure to the user. This data is output from the terminal to the user.

[0076] Step 5:

[0077] While the user applies makeup according to the terminal's instructions, the terminal monitors the user's actions in real time. The terminal analyzes the progress and, if there are any deficiencies, provides feedback such as supplementary instructions like "Please apply the blush a little higher." This information is also output to the user through the terminal.

[0078] (Application Example 1)

[0079] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] While personalized makeup instruction is increasingly important in modern times, traditional systems struggle to provide appropriate makeup advice in real time based on a user's facial features, and also have difficulty offering suggestions that align with the user's schedule and current trends. As a result, users are unable to apply makeup that is appropriate for the time and situation, making it difficult to improve self-satisfaction and manage time efficiently.

[0081] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0082] In this invention, the server includes means for making makeup suggestions while considering the user's schedule information, means for detecting the user's face and tracking facial feature points in real time, and means for analyzing the user's instructions in voice or text and generating appropriate makeup advice. This enables optimal makeup suggestions and real-time guidance tailored to the user's individual characteristics and circumstances.

[0083] A "user" is an individual who uses this system to receive makeup instruction.

[0084] "Facial feature points" are landmarks that indicate specific locations or parts of a user's face.

[0085] "Voice or text instructions" are means of expressing requests or questions that a user makes to the system.

[0086] "Makeup advice" refers to advice aimed at guiding and suggesting how users apply makeup.

[0087] "Virtual reality display" refers to a virtual visual guide that can be overlaid on the user's real-world image.

[0088] "Latest trend information" refers to makeup-related data based on current trends and new technologies in the industry.

[0089] "Real-time" is a concept that describes a temporal situation in which processing or reactions take place at the very moment an event occurs.

[0090] "Schedule information" refers to data related to the user's plans and schedule.

[0091] "Feedback" refers to advice that includes appropriate responses and adjustments based on the user's actions and progress.

[0092] The system that realizes this invention is designed to allow users to receive makeup guidance best suited to their individual characteristics and circumstances. The user captures their face through a camera built into a consumer robot. The robot uses OpenCV to detect the user's facial feature points. Google® Cloud Speech-to-Text is used for speech recognition to convert the user's instructions into text. The server then uses this text data as input and analyzes it using TENSORFLOW®, which incorporates NLP technology. Based on this analysis, it generates makeup advice that takes into account the user's preferences and schedule.

[0093] The generated advice is visualized using ARKit for virtual reality display and presented to the user via the device. This allows the user to receive visual and interactive makeup instruction. Furthermore, a feedback system monitors the progress of the makeup in real time, providing corrections and additional instructions as needed. This feedback function is a crucial component for making appropriate corrections if the user makes a mistake in the makeup process.

[0094] As a concrete example, suppose a user instructs the robot, "I have a presentation this afternoon, so please teach me how to do formal makeup." The robot recognizes the instruction and sends the data to the server. The server then provides the user with makeup advice based on the analysis results. In this process, ARKit visually displays the placement of eyeshadow, blush, and other elements accurately, allowing the user to proceed with their makeup step by step.

[0095] The AI ​​model can be input with example prompts such as: "I'm going to a friend's wedding tomorrow. Please suggest a modern makeup look that will suit the bright lighting at 5 PM." This allows the user to elicit accurate and useful makeup advice from the system.

[0096] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0097] Step 1:

[0098] The device captures the user's face with a high-resolution camera. This image, used as input data, is processed using OpenCV to detect facial feature points. Once the facial feature points are identified, that information is generated as output for the next processing step.

[0099] Step 2:

[0100] The user enters instructions about makeup into the device using voice. This voice input is converted to text using Google Cloud Speech-to-Text. This converted text becomes input data for analyzing the user's intent and is sent to the next server.

[0101] Step 3:

[0102] The server uses TensorFlow, which incorporates NLP technology, to analyze the transcribed instructions. The output of this analysis is makeup advice tailored to the user's requests and schedule. This generated data is then prepared for visualization in the next stage.

[0103] Step 4:

[0104] The device uses ARKit to visualize makeup advice received from the server as a virtual reality display. Here, the makeup guide is overlaid in precise locations using the user's facial feature point information. This visualized information becomes the output presented to the user.

[0105] Step 5:

[0106] The user applies makeup according to the presented virtual reality display. The device then uses its camera to track the user's face and monitor the progress in real time. The results of this monitoring are used as input for the next feedback step.

[0107] Step 6:

[0108] The server uses tracking data to evaluate whether the user is applying makeup correctly. Based on this evaluation, it generates feedback as needed. The generated feedback is communicated to the user via the device, either verbally or visually.

[0109] Step 7:

[0110] Based on the feedback received, the server can provide additional advice and correction instructions as needed until the user is satisfied with their makeup. This cycle is repeated until the user is happy with the final result.

[0111] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0112] This invention comprises a terminal capable of detecting a user's face and tracking facial landmarks in real time, and a system that provides comprehensive makeup guidance through cooperation between the terminal and a server. Furthermore, by incorporating an emotion engine that recognizes the user's emotions through facial expression analysis, it is possible to adjust the provided makeup advice and feedback to match the user's emotional state.

[0113] The device uses its camera to capture the user's face in real time and tracks landmarks using a facial recognition algorithm. Next, it captures voice commands from the user. For example, if the user requests, "Teach me how to apply eyeshadow," the device uses speech recognition technology to transcribe it into text and send it to the server.

[0114] The server analyzes the received text using a natural language processing engine and generates necessary makeup advice. Furthermore, an emotion engine analyzes the user's emotions from their face and adjusts the tone and content of the advice accordingly. For example, a user who is feeling anxious will receive more careful and slower guidance.

[0115] The advice is sent from the server to the device and communicated to the user using voice guidance and augmented reality (AR) overlays. The device uses AR technology to display examples of how makeup should be applied to the user's face, showing where and how the user should apply the makeup.

[0116] Furthermore, the device tracks user behavior, monitors progress in real time, and provides timely feedback. The server continuously updates trend information and makeup databases, ensuring access to the latest makeup techniques and styles.

[0117] By combining this with emotion analysis functionality, it becomes possible to provide more personalized makeup instruction and support users in improving their makeup skills with confidence. For example, it is possible to apply instruction tailored to a user's mood, such as suggesting vibrant colors for a user who is in a cheerful mood.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The user activates the device's camera and points their face at the system. The device captures the camera image and runs a facial recognition algorithm to detect and track facial landmarks. This allows the system to establish baseline data about the user's face.

[0121] Step 2:

[0122] The user gives a voice command saying, "I want to know how to highlight." The device receives this voice input and converts it into text using speech recognition technology. The converted text is sent to the server to understand the user's request.

[0123] Step 3:

[0124] The server analyzes the received text using a natural language processing engine and generates specific makeup advice based on the user's request. Additionally, an emotion engine analyzes the user's facial expressions using video data transmitted from the device to determine their emotional state. If anxiety or tension is detected, the instruction is adjusted accordingly.

[0125] Step 4:

[0126] The server sends the generated advice back to the device. The device receives this information and delivers the advice to the user as an audio guide. Simultaneously, using AR technology, a visual overlay is displayed on the user's face, including where highlights should be applied. This allows the user to receive guidance both visually and audibly.

[0127] Step 5:

[0128] The user attempts to highlight by following instructions from the device. The device tracks the user's actions and monitors in real time whether they are progressing correctly. If there are any inappropriate areas, it provides feedback such as, "Try to make the highlight follow the bridge of your nose a little more closely."

[0129] Step 6:

[0130] The server regularly updates with the latest makeup trends and technical data, providing it to users via their devices. This ensures users always have access to up-to-date makeup information and opportunities to learn new techniques. The entire system is customized according to the user's emotional state, providing a better user experience.

[0131] (Example 2)

[0132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0133] Modern beauty technology demands personalized makeup instruction tailored to each user's facial features and emotional state. However, conventional systems often lack sufficient real-time emotional analysis and emotionally-based adjustment of makeup instruction, making it difficult for users to learn optimal makeup techniques. Therefore, the challenge lies in providing more adaptive and intuitive makeup instruction that includes feedback responsive to the user's emotions.

[0134] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0135] In this invention, the server includes means for detecting the user's face and tracking facial feature points in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for performing emotion analysis and adjusting the generated guidance according to the user's emotions. This enables personalized makeup guidance that responds to the user's emotions, allowing the user to effectively improve their makeup skills.

[0136] A "user" is an individual who uses the system to receive makeup instruction on their own face.

[0137] "Facial feature points," also known as facial landmarks, are important points that indicate the positions of features such as the eyes, nose, and mouth.

[0138] "Makeup instruction" refers to the act of providing users with specific advice and guidelines on how to apply cosmetics.

[0139] Augmented reality display is a technology that overlays digital information onto real-world scenery, allowing users to virtually visualize the effects of cosmetics.

[0140] "Emotional analysis" is the process of analyzing a user's facial expressions to identify their emotional state, such as joy or sadness.

[0141] "Trend information" refers to information about the latest beauty techniques and styles, and is intended to provide users with the latest makeup trends.

[0142] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to interpret instructions from users.

[0143] This invention is a system that provides users with makeup guidance based on their individual facial features and emotions. Specific embodiments for carrying out the invention are described below.

[0144] The device uses a camera to capture the user's face in real time. It utilizes a facial recognition algorithm to track facial feature points. This technology typically employs software libraries such as Dlib and OpenCV.

[0145] Users can ask questions or make requests about makeup application methods using voice. The device uses speech recognition technology to convert the voice instructions into text and sends that text to the server. Speech recognition software such as the Google Speech API is commonly used.

[0146] The server analyzes the received text using natural language processing technology. In this process, it utilizes a generative AI model to provide users with appropriate makeup advice. For example, it generates specific advice such as, "Apply eyeshadow to the lash line to make your eyes appear larger, and choose a light color."

[0147] Furthermore, the device continuously captures video of the user's face, and the server performs emotion analysis based on this data. This emotion analysis identifies the user's emotional state, and adjusts the makeup instruction accordingly. This makes it possible to provide personalized instruction optimized for the user's emotions.

[0148] For example, for a user in a cheerful mood, we can suggest a pop color scheme. An example of a prompt to the generative AI model in this case would be, "Please provide advice on bright color makeup suitable for a user in a cheerful mood."

[0149] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0150] Step 1:

[0151] The user points the device's camera at their face. The device's camera captures a facial image in real time and collects the video. The input is the facial video obtained through the camera. The device processes the video using a facial recognition algorithm and extracts facial feature points. This process outputs positional information for the eyes, nose, and mouth, which is used in the next step.

[0152] Step 2:

[0153] The user asks questions about makeup using voice. For example, they might request, "How do I apply blush?" The device uses speech recognition technology to convert this voice input into text. The input is voice data, and the output is the corresponding string data. The device then sends the text instructions to the server.

[0154] Step 3:

[0155] The server analyzes the received text using a natural language processing engine. The generative AI model used here understands the user's instructions and generates optimal makeup guidance. The input is the user's text instructions, and the output is specific makeup advice. The server uses the AI ​​model to construct appropriate advice and provide detailed guidance tailored to the user's needs.

[0156] Step 4:

[0157] The server sends makeup advice generated via the terminal to the user. The advice is presented through audio output and augmented reality display. The input is the makeup advice from the server, and the output is the information the user receives visually and aurally. The terminal shows specific makeup techniques by overlaying virtual makeup onto the user's face.

[0158] Step 5:

[0159] The device continuously tracks the user's real-time reactions via a video feed. The server analyzes the user's facial expression data through an emotion analysis engine. The input is video data including the user's facial expressions, and the output is the analyzed emotion information. Based on this emotion information, the server adjusts the content and tone of the advice to suit the user.

[0160] Step 6:

[0161] The server regularly updates its database to reflect the latest trends during the makeup instruction process. The input is the latest beauty trend information collected from external sources, and the output is the updated makeup database. This ensures that users always receive information on the latest styles and techniques.

[0162] (Application Example 2)

[0163] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0164] In modern beauty instruction, providing personalized advice that takes into account a user's facial features and emotional state is difficult, and many users do not receive comprehensive and immediate guidance. It is also crucial to provide users with an environment where they can visualize the results before trying beauty treatments, allowing them to proceed with confidence. Furthermore, there is a lack of appropriate systems to quickly respond to ever-changing trends and technological information and deliver the latest beauty knowledge to users.

[0165] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0166] In this invention, the server includes means for detecting the user's face and tracking specific parts of the face in real time, means for analyzing the user's instructions in voice or text and generating appropriate beauty advice, and means for presenting the generated advice to the user in voice and artificial reality overlay. This makes it possible to provide beauty advice tailored to the user's individual needs. Furthermore, by analyzing the user's facial expressions, recognizing their emotional state, and adjusting the content of the advice, a high level of personalization tailored to the user can be achieved.

[0167] "Detecting a user's face" refers to the process of recognizing the face of a service user using sensors such as cameras.

[0168] "Methods for tracking specific parts of the face" refer to technologies that continuously monitor specific landmarks such as the eyes and mouth within the user's entire face and analyze their movements in real time.

[0169] "Means for analyzing user instructions in voice or text" refers to interpreting voice or text information entered by the user and developing appropriate instructional content based on that information.

[0170] "Methods for generating beauty advice" refers to the process of generating suggestions for optimal beauty methods and products based on analyzed user input.

[0171] "Artificial reality overlay" is a technology that overlays digital information onto real-world images to provide users with a virtual experience.

[0172] "Analyzing facial expressions and recognizing emotional states" refers to a technology that analyzes various facial expressions of a user and infers the user's feelings and emotional state from those expressions.

[0173] "Trend information" refers to the latest trends and technologies related to beauty, and is data that can constantly provide users with up-to-date information.

[0174] A "means of providing feedback" refers to a mechanism that can provide evaluations and advice in real time, tailored to the user's actions and circumstances.

[0175] To realize this invention, a server, terminal, and user cooperate to perform various functions. The server utilizes the OpenCV and Dlib libraries to detect the user's face and track specific body parts in real time. The user's face is captured by a camera, and the video data is analyzed to accurately recognize and track facial landmarks. This information is sent to the server and used for further data analysis.

[0176] The device uses the Google Cloud Speech-to-Text API to convert voice commands into text data, and then performs natural language processing using the spaCy library. This makes it possible to build beauty advice based on user instructions and questions. Furthermore, the device generates an artificial reality overlay using Unity and ARKit, allowing it to virtually try out beauty treatments on the user's face. This allows the user to visualize the results of the treatment before actually performing it.

[0177] The server uses TensorFlow to analyze the user's facial expressions and recognize their emotional state. This process adjusts the content and tone of the advice given to match the user's emotions. For example, if the user shows a positive expression, the server will provide beauty advice that matches the cheerful mood, improving the user experience.

[0178] As a concrete example of a prompt, the system instructs the generated AI model to "recognize from the user's facial expression that they are feeling joy, and suggest a glamorous makeup look themed around a special day." This prompt prompts the model to select appropriate advice and provide a tailored response. In this way, the user can receive personalized beauty advice based on their emotions and state of mind.

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] The device uses its camera to capture the user's face. Camera video data is taken as input, and facial landmark detection is performed using the Dlib library. This data is then sent to the server as coordinate information for identified facial features.

[0182] Step 2:

[0183] The user asks beauty-related questions or gives instructions via voice. The device uses the Google Cloud Speech-to-Text API to convert the received voice into text data. The converted text data is then sent to a server for natural language processing.

[0184] Step 3:

[0185] The server performs natural language processing using the spaCy library based on the received text data. It analyzes the text data as input and generates appropriate beauty advice. The generated advice is stored on the server side for use in the next step.

[0186] Step 4:

[0187] The server processes the received facial landmark data using TensorFlow to analyze the user's facial expressions. It analyzes the facial data as input to determine the user's emotional state. The output provides the type of emotion, which is then used to adjust the tone of the beauty advice.

[0188] Step 5:

[0189] The system adjusts the beauty advice generated by the server and sends it to the terminal. Specifically, it generates advice tailored to the user's emotional state and edits it into a format that is easy for the user to understand. It is also presented as a guideline to make it easier for the user to comprehend.

[0190] Step 6:

[0191] The device uses Unity and ARKit to generate an artificial reality overlay, superimposing virtual beauty results onto the user's face. This allows users to visually confirm the results without having to try the beauty treatments in the real world.

[0192] Step 7:

[0193] Users review an artificial reality overlay, and if satisfied, they undergo an actual cosmetic procedure based on that overlay. During this process, user experience is collected as feedback, which is then used to inform future advice.

[0194] The process involves specific actions taken at each step, allowing users to receive real-time beauty advice based on their emotions and individual needs.

[0195] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0196] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0197] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0201] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0202] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0203] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0205] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0206] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0207] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0208] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0209] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0210] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0211] The system of this invention mainly consists of a user terminal, a server, and a network that connects them. The user captures their face in real time using the camera on the terminal and transmits the video to the system. Face recognition software installed on the terminal detects the user's face and identifies landmarks. This information is used to indicate the precise location of makeup.

[0212] Next, voice or text instructions from the user are taken into the system. For example, if a user requests by voice, "Tell me how to apply blush," the device uses speech recognition technology to convert this into text and sends it to the server. The server uses natural language processing technology to analyze the intent of this text and generate appropriate makeup advice.

[0213] The generated advice is sent from the server to the terminal. The terminal provides instructions to the user using voice guidance and an augmented reality (AR) overlay. The AR overlay indicates where to apply virtual makeup to the user's face based on facial landmarks, and the voice guidance explains the steps. This allows the user to visually understand and practice the makeup process.

[0214] Furthermore, the device tracks the user's progress and provides real-time feedback. For example, if the user hasn't applied the blush as instructed, the device will immediately provide instructions such as, "Please apply it a little closer to the center of your cheeks."

[0215] Finally, the server updates its database with the latest trend information and makeup techniques, allowing users to continuously access new information and techniques. This continuous learning environment ensures that users are always learning techniques that keep up with the latest makeup trends.

[0216] Thus, the present invention provides users with personalized makeup instruction and helps them acquire makeup techniques with confidence.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The user activates the device's camera and points their face at the system. The device processes the captured video in real time and uses facial recognition software to detect the user's face. It identifies facial landmarks (such as the position of the eyes, nose, and mouth) and prepares to track them.

[0220] Step 2:

[0221] The user gives a voice command saying, "Teach me how to apply lipstick." The device receives this voice input, converts it to text using speech recognition technology, and sends that data to the server.

[0222] Step 3:

[0223] The server analyzes the received text data using a natural language processing engine to interpret the user's intent. It understands the makeup advice the user is seeking (in this case, how to apply lipstick) and generates appropriate instructions.

[0224] Step 4:

[0225] The generated advice is sent from the server to the terminal. The terminal receives these instructions and conveys them to the user through voice guidance. Additionally, an AR rendering engine is used to display an overlay on the user's face indicating the lip application locations. This allows the user to understand the procedure both visually and audibly.

[0226] Step 5:

[0227] The user applies lipstick following instructions from the device. The device tracks the user's face throughout the process and monitors their progress in real time. If the user is not following the instructions, it immediately provides feedback such as, "You need to apply a little more to the inner part of your lips."

[0228] Step 6:

[0229] The server regularly updates its trend information and makeup database. Whenever the latest makeup trends are discovered, they are reflected in the system database and provided to users as new information. This ensures users always have access to the latest makeup techniques.

[0230] (Example 1)

[0231] Next, we will describe Example 1. 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."

[0232] Traditional makeup instruction systems struggled to provide personalized instruction to users in real time and lacked the mechanisms to quickly adapt to the latest makeup trends and technological advancements. This resulted in ineffective learning experiences for users.

[0233] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0234] In this invention, the server includes means for detecting the user's face and tracking its features in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for presenting the generated guidance to the user with voice and virtual reality overlays. This allows the user to learn techniques adapted to the latest makeup trends while receiving personalized instruction.

[0235] A "user" refers to an individual who wants to learn makeup procedures and techniques using the system.

[0236] "Facial features" refer to a collection of points and lines that indicate the position and shape of each part of the face, providing the information necessary for applying makeup.

[0237] "Voice or written instructions" refers to voice commands or text inputs that users use to ask for specific advice on makeup procedures and methods.

[0238] "Makeup guide" refers to instructions on how to apply makeup and the techniques involved, generated based on the user's preferences.

[0239] A "virtual reality overlay" is virtual graphic information that is superimposed onto the user's real-world image and is used to indicate areas where makeup should be applied.

[0240] "Trend information" refers to information about the latest makeup techniques and trends, provided to enable users to always incorporate the latest styles.

[0241] The system of the present invention consists of a user terminal, a server, and a communication network connecting them. The user uses a camera mounted on the terminal to capture images of their face in real time and transmits the data to the terminal. This terminal is equipped with software for facial recognition, specifically using a computer vision library to identify facial features. This information is used to determine the precise locations where makeup should be applied.

[0242] The user inputs instructions, such as "Tell me how to apply eyeshadow," either by voice or text. The terminal has technology to convert voice input to text through its interface, and this is done using a speech recognition library. The converted text data is sent to a server, which analyzes this data using natural language processing technology. This analysis utilizes a natural language processing model to accurately understand the intent of the user's instructions and generate corresponding makeup instructions.

[0243] The server generates instructions which are sent to the terminal, which then presents them to the user through audio and a virtual reality overlay. The virtual reality overlay shows the areas on the user's face where makeup should be applied, and the audio guide explains the steps in detail. This allows the user to visually understand the steps and learn the actual makeup process. An example of a prompt would be, "Please show me the steps for natural makeup." This system allows users to receive timely makeup instruction tailored to their individual needs and acquire skills that keep up with the latest trends.

[0244] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0245] Step 1:

[0246] The user captures their face in real time using the device's camera. The captured video is input to the device and processed by facial recognition software. Through this processing, the device identifies the positions of facial features (eyes, nose, mouth, etc.) and outputs this information as data.

[0247] Step 2:

[0248] The user inputs voice or text instructions into the device, such as "Teach me how to apply blush." ​​In the case of voice instructions, the device uses a speech recognition library to convert the voice into text. The converted text is sent to the server, which receives it as input.

[0249] Step 3:

[0250] The server analyzes the text sent by the user using natural language processing technology. In this step, data calculations are performed using the analysis model to understand the user's intent. Based on the analysis results, the server generates cosmetic recommendations suitable for the user and outputs the results.

[0251] Step 4:

[0252] The generated makeup instructions are sent from the server to the terminal. Based on the received instructions, the terminal overlays a virtual reality overlay onto the user's video. This overlay indicates where makeup should be applied, and the terminal uses audio guidance to present the procedure to the user. This data is output from the terminal to the user.

[0253] Step 5:

[0254] While the user applies makeup according to the terminal's instructions, the terminal monitors the user's actions in real time. The terminal analyzes the progress and, if there are any deficiencies, provides feedback such as supplementary instructions like "Please apply the blush a little higher." This information is also output to the user through the terminal.

[0255] (Application Example 1)

[0256] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] While personalized makeup instruction is increasingly important in modern times, traditional systems struggle to provide appropriate makeup advice in real time based on a user's facial features, and also have difficulty offering suggestions that align with the user's schedule and current trends. As a result, users are unable to apply makeup that is appropriate for the time and situation, making it difficult to improve self-satisfaction and manage time efficiently.

[0258] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0259] In this invention, the server includes means for making makeup suggestions while considering the user's schedule information, means for detecting the user's face and tracking facial feature points in real time, and means for analyzing the user's instructions in voice or text and generating appropriate makeup advice. This enables optimal makeup suggestions and real-time guidance tailored to the user's individual characteristics and circumstances.

[0260] A "user" is an individual who uses this system to receive makeup instruction.

[0261] "Facial feature points" are landmarks that indicate specific locations or parts of a user's face.

[0262] "Voice or text instructions" are means of expressing requests or questions that a user makes to the system.

[0263] "Makeup advice" refers to advice aimed at guiding and suggesting how users apply makeup.

[0264] "Virtual reality display" refers to a virtual visual guide that can be overlaid on the user's real-world image.

[0265] "Latest trend information" refers to makeup-related data based on current trends and new technologies in the industry.

[0266] "Real-time" is a concept that describes a temporal situation in which processing or reactions take place at the very moment an event occurs.

[0267] "Schedule information" refers to data related to the user's plans and schedule.

[0268] "Feedback" refers to advice that includes appropriate responses and adjustments based on the user's actions and progress.

[0269] The system that realizes this invention is designed to allow users to receive makeup guidance tailored to their individual characteristics and circumstances. The user captures their face through a camera built into a consumer robot. The robot uses OpenCV to detect feature points of the user's face. Google Cloud Speech-to-Text is used for speech recognition to convert the user's instructions into text. The server then uses this text data as input and analyzes it using TensorFlow, which incorporates NLP technology. Based on this analysis, it generates makeup advice that takes into account the user's preferences and schedule.

[0270] The generated advice is visualized using ARKit for virtual reality display and presented to the user via the device. This allows the user to receive visual and interactive makeup instruction. Furthermore, a feedback system monitors the progress of the makeup in real time, providing corrections and additional instructions as needed. This feedback function is a crucial component for making appropriate corrections if the user makes a mistake in the makeup process.

[0271] As a concrete example, suppose a user instructs the robot, "I have a presentation this afternoon, so please teach me how to do formal makeup." The robot recognizes the instruction and sends the data to the server. The server then provides the user with makeup advice based on the analysis results. In this process, ARKit visually displays the placement of eyeshadow, blush, and other elements accurately, allowing the user to proceed with their makeup step by step.

[0272] The AI ​​model can be input with example prompts such as: "I'm going to a friend's wedding tomorrow. Please suggest a modern makeup look that will suit the bright lighting at 5 PM." This allows the user to elicit accurate and useful makeup advice from the system.

[0273] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0274] Step 1:

[0275] The device captures the user's face with a high-resolution camera. This image, used as input data, is processed using OpenCV to detect facial feature points. Once the facial feature points are identified, that information is generated as output for the next processing step.

[0276] Step 2:

[0277] The user inputs instructions regarding makeup into the terminal by voice. This voice input is converted into text using Google Cloud Speech-to-Text. This converted text serves as input data for analyzing the user's intention and is sent to the next server.

[0278] Step 3:

[0279] The server analyzes the texturized instructions using TensorFlow equipped with NLP technology. As the output of this analysis, makeup advice that conforms to the user's requirements and schedule is generated. This generated data is prepared for visualization in the next stage.

[0280] Step 4:

[0281] The terminal visualizes the makeup advice received from the server as a virtual reality display using ARKit. Here, using the feature point information of the user's face, the makeup guide is overlaid at the correct position. This visualized information becomes the output presented to the user.

[0282] Step 5:

[0283] The user performs actual makeup according to the presented virtual reality display. The terminal uses the camera again to track the user's face and monitors the progress in real time. The results of this monitoring serve as input for the next feedback step.

[0284] Step 6:

[0285] The server evaluates whether the user is applying makeup appropriately based on the tracking data. Based on this evaluation, feedback as needed is generated. The generated feedback is conveyed to the user audibly or visually through the terminal.

[0286] Step 7:

[0287] Based on the feedback received, the server can provide additional advice and correction instructions as needed until the user is satisfied with their makeup. This cycle is repeated until the user is happy with the final result.

[0288] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0289] This invention comprises a terminal capable of detecting a user's face and tracking facial landmarks in real time, and a system that provides comprehensive makeup guidance through cooperation between the terminal and a server. Furthermore, by incorporating an emotion engine that recognizes the user's emotions through facial expression analysis, it is possible to adjust the provided makeup advice and feedback to match the user's emotional state.

[0290] The device uses its camera to capture the user's face in real time and tracks landmarks using a facial recognition algorithm. Next, it captures voice commands from the user. For example, if the user requests, "Teach me how to apply eyeshadow," the device uses speech recognition technology to transcribe it into text and send it to the server.

[0291] The server analyzes the received text using a natural language processing engine and generates necessary makeup advice. Furthermore, an emotion engine analyzes the user's emotions from their face and adjusts the tone and content of the advice accordingly. For example, a user who is feeling anxious will receive more careful and slower guidance.

[0292] The advice is sent from the server to the device and communicated to the user using voice guidance and augmented reality (AR) overlays. The device uses AR technology to display examples of how makeup should be applied to the user's face, showing where and how the user should apply the makeup.

[0293] Furthermore, the device tracks user behavior, monitors progress in real time, and provides timely feedback. The server continuously updates trend information and makeup databases, ensuring access to the latest makeup techniques and styles.

[0294] By combining this with emotion analysis functionality, it becomes possible to provide more personalized makeup instruction and support users in improving their makeup skills with confidence. For example, it is possible to apply instruction tailored to a user's mood, such as suggesting vibrant colors for a user who is in a cheerful mood.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] The user activates the device's camera and points their face at the system. The device captures the camera image and runs a facial recognition algorithm to detect and track facial landmarks. This allows the system to establish baseline data about the user's face.

[0298] Step 2:

[0299] The user gives a voice command saying, "I want to know how to highlight." The device receives this voice input and converts it into text using speech recognition technology. The converted text is sent to the server to understand the user's request.

[0300] Step 3:

[0301] The server analyzes the received text using a natural language processing engine and generates specific makeup advice based on the user's request. Additionally, an emotion engine analyzes the user's facial expressions using video data transmitted from the device to determine their emotional state. If anxiety or tension is detected, the instruction is adjusted accordingly.

[0302] Step 4:

[0303] The server sends back the advice generated to the terminal. The terminal receives this information and distributes the content of the advice as a voice guide to the user. At the same time, using AR technology, a visual overlay including the positions where highlights should be applied to the user's face is displayed. As a result, the user can receive guidance visually and auditorily.

[0304] Step 5:

[0305] The user attempts to apply highlights according to the instructions from the terminal. The terminal tracks the user's operation and monitors in real time whether the process is proceeding appropriately. If there are inappropriate parts, feedback such as "Please try applying the highlights a little more along the bridge of your nose" is provided.

[0306] Step 6:

[0307] The server periodically updates the latest makeup trend information and technical data and provides it to the user via the terminal. As a result, the user can always access the updated makeup information and has the opportunity to learn new techniques. By customizing the entire system according to the user's emotional state, a better user experience can be provided.

[0308] (Example 2)

[0309] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In modern beauty technology, there is a demand to provide personalized makeup guidance tailored to the facial features and emotional states of individual users. However, in conventional systems, there are problems such as insufficient real-time emotional analysis of users and adjustment of makeup guidance based on appropriate emotions, making it difficult for users to learn the optimal makeup techniques. Therefore, it is an issue to provide more adaptable and intuitive makeup guidance including feedback according to the user's emotions.

[0311] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0312] In this invention, the server includes means for detecting the user's face and tracking facial feature points in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for performing emotion analysis and adjusting the generated guidance according to the user's emotions. This enables personalized makeup guidance that responds to the user's emotions, allowing the user to effectively improve their makeup skills.

[0313] A "user" is an individual who uses the system to receive makeup instruction on their own face.

[0314] "Facial feature points," also known as facial landmarks, are important points that indicate the positions of features such as the eyes, nose, and mouth.

[0315] "Makeup instruction" refers to the act of providing users with specific advice and guidelines on how to apply cosmetics.

[0316] Augmented reality display is a technology that overlays digital information onto real-world scenery, allowing users to virtually visualize the effects of cosmetics.

[0317] "Emotional analysis" is the process of analyzing a user's facial expressions to identify their emotional state, such as joy or sadness.

[0318] "Trend information" refers to information about the latest beauty techniques and styles, and is intended to provide users with the latest makeup trends.

[0319] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to interpret instructions from users.

[0320] This invention is a system that provides users with makeup guidance based on their individual facial features and emotions. Specific embodiments for carrying out the invention are described below.

[0321] The device uses a camera to capture the user's face in real time. It utilizes a facial recognition algorithm to track facial feature points. This technology typically employs software libraries such as Dlib and OpenCV.

[0322] Users can ask questions or make requests about makeup application methods using voice. The device uses speech recognition technology to convert the voice instructions into text and sends that text to the server. Speech recognition software such as the Google Speech API is commonly used.

[0323] The server analyzes the received text using natural language processing technology. In this process, it utilizes a generative AI model to provide users with appropriate makeup advice. For example, it generates specific advice such as, "Apply eyeshadow to the lash line to make your eyes appear larger, and choose a light color."

[0324] Furthermore, the device continuously captures video of the user's face, and the server performs emotion analysis based on this data. This emotion analysis identifies the user's emotional state, and adjusts the makeup instruction accordingly. This makes it possible to provide personalized instruction optimized for the user's emotions.

[0325] For example, for a user in a cheerful mood, we can suggest a pop color scheme. An example of a prompt to the generative AI model in this case would be, "Please provide advice on bright color makeup suitable for a user in a cheerful mood."

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The user points the device's camera at their face. The device's camera captures a facial image in real time and collects the video. The input is the facial video obtained through the camera. The device processes the video using a facial recognition algorithm and extracts facial feature points. This process outputs positional information for the eyes, nose, and mouth, which is used in the next step.

[0329] Step 2:

[0330] The user asks questions about makeup using voice. For example, they might request, "How do I apply blush?" The device uses speech recognition technology to convert this voice input into text. The input is voice data, and the output is the corresponding string data. The device then sends the text instructions to the server.

[0331] Step 3:

[0332] The server analyzes the received text using a natural language processing engine. The generative AI model used here understands the user's instructions and generates optimal makeup guidance. The input is the user's text instructions, and the output is specific makeup advice. The server uses the AI ​​model to construct appropriate advice and provide detailed guidance tailored to the user's needs.

[0333] Step 4:

[0334] The server sends makeup advice generated via the terminal to the user. The advice is presented through audio output and augmented reality display. The input is the makeup advice from the server, and the output is the information the user receives visually and aurally. The terminal shows specific makeup techniques by overlaying virtual makeup onto the user's face.

[0335] Step 5:

[0336] The device continuously tracks the user's real-time reactions via a video feed. The server analyzes the user's facial expression data through an emotion analysis engine. The input is video data including the user's facial expressions, and the output is the analyzed emotion information. Based on this emotion information, the server adjusts the content and tone of the advice to suit the user.

[0337] Step 6:

[0338] The server regularly updates its database to reflect the latest trends during the makeup instruction process. The input is the latest beauty trend information collected from external sources, and the output is the updated makeup database. This ensures that users always receive information on the latest styles and techniques.

[0339] (Application Example 2)

[0340] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0341] In modern beauty instruction, providing personalized advice that takes into account a user's facial features and emotional state is difficult, and many users do not receive comprehensive and immediate guidance. It is also crucial to provide users with an environment where they can visualize the results before trying beauty treatments, allowing them to proceed with confidence. Furthermore, there is a lack of appropriate systems to quickly respond to ever-changing trends and technological information and deliver the latest beauty knowledge to users.

[0342] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0343] In this invention, the server includes means for detecting the user's face and tracking specific parts of the face in real time, means for analyzing the user's instructions in voice or text and generating appropriate beauty advice, and means for presenting the generated advice to the user in voice and artificial reality overlay. This makes it possible to provide beauty advice tailored to the user's individual needs. Furthermore, by analyzing the user's facial expressions, recognizing their emotional state, and adjusting the content of the advice, a high level of personalization tailored to the user can be achieved.

[0344] "Detecting a user's face" refers to the process of recognizing the face of a service user using sensors such as cameras.

[0345] "Methods for tracking specific parts of the face" refer to technologies that continuously monitor specific landmarks such as the eyes and mouth within the user's entire face and analyze their movements in real time.

[0346] "Means for analyzing user instructions in voice or text" refers to interpreting voice or text information entered by the user and developing appropriate instructional content based on that information.

[0347] "Methods for generating beauty advice" refers to the process of generating suggestions for optimal beauty methods and products based on analyzed user input.

[0348] "Artificial reality overlay" is a technology that overlays digital information onto real-world images to provide users with a virtual experience.

[0349] "Analyzing facial expressions and recognizing emotional states" refers to a technology that analyzes various facial expressions of a user and infers the user's feelings and emotional state from those expressions.

[0350] "Trend information" refers to the latest trends and technologies related to beauty, and is data that can constantly provide users with up-to-date information.

[0351] A "means of providing feedback" refers to a mechanism that can provide evaluations and advice in real time, tailored to the user's actions and circumstances.

[0352] To realize this invention, a server, terminal, and user cooperate to perform various functions. The server utilizes the OpenCV and Dlib libraries to detect the user's face and track specific body parts in real time. The user's face is captured by a camera, and the video data is analyzed to accurately recognize and track facial landmarks. This information is sent to the server and used for further data analysis.

[0353] The device uses the Google Cloud Speech-to-Text API to convert voice commands into text data, and then performs natural language processing using the spaCy library. This makes it possible to build beauty advice based on user instructions and questions. Furthermore, the device generates an artificial reality overlay using Unity and ARKit, allowing it to virtually try out beauty treatments on the user's face. This allows the user to visualize the results of the treatment before actually performing it.

[0354] The server uses TensorFlow to analyze the user's facial expressions and recognize their emotional state. This process adjusts the content and tone of the advice given to match the user's emotions. For example, if the user shows a positive expression, the server will provide beauty advice that matches the cheerful mood, improving the user experience.

[0355] As a concrete example of a prompt, the system instructs the generated AI model to "recognize from the user's facial expression that they are feeling joy, and suggest a glamorous makeup look themed around a special day." This prompt prompts the model to select appropriate advice and provide a tailored response. In this way, the user can receive personalized beauty advice based on their emotions and state of mind.

[0356] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0357] Step 1:

[0358] The device uses its camera to capture the user's face. Camera video data is taken as input, and facial landmark detection is performed using the Dlib library. This data is then sent to the server as coordinate information for identified facial features.

[0359] Step 2:

[0360] The user asks beauty-related questions or gives instructions via voice. The device uses the Google Cloud Speech-to-Text API to convert the received voice into text data. The converted text data is then sent to a server for natural language processing.

[0361] Step 3:

[0362] The server performs natural language processing using the spaCy library based on the received text data. It analyzes the text data as input and generates appropriate beauty advice. The generated advice is stored on the server side for use in the next step.

[0363] Step 4:

[0364] The server processes the received facial landmark data using TensorFlow to analyze the user's facial expressions. It analyzes the facial data as input to determine the user's emotional state. The output provides the type of emotion, which is then used to adjust the tone of the beauty advice.

[0365] Step 5:

[0366] The system adjusts the beauty advice generated by the server and sends it to the terminal. Specifically, it generates advice tailored to the user's emotional state and edits it into a format that is easy for the user to understand. It is also presented as a guideline to make it easier for the user to comprehend.

[0367] Step 6:

[0368] The device uses Unity and ARKit to generate an artificial reality overlay, superimposing virtual beauty results onto the user's face. This allows users to visually confirm the results without having to try the beauty treatments in the real world.

[0369] Step 7:

[0370] Users review an artificial reality overlay, and if satisfied, they undergo an actual cosmetic procedure based on that overlay. During this process, user experience is collected as feedback, which is then used to inform future advice.

[0371] The process involves specific actions taken at each step, allowing users to receive real-time beauty advice based on their emotions and individual needs.

[0372] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0373] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0374] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0378] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0379] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0380] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0381] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0382] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0383] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0384] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0385] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0386] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0387] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0388] The system of this invention mainly consists of a user terminal, a server, and a network that connects them. The user captures their face in real time using the camera on the terminal and transmits the video to the system. Face recognition software installed on the terminal detects the user's face and identifies landmarks. This information is used to indicate the precise location of makeup.

[0389] Next, voice or text instructions from the user are taken into the system. For example, if a user requests by voice, "Tell me how to apply blush," the device uses speech recognition technology to convert this into text and sends it to the server. The server uses natural language processing technology to analyze the intent of this text and generate appropriate makeup advice.

[0390] The generated advice is sent from the server to the terminal. The terminal provides instructions to the user using voice guidance and an augmented reality (AR) overlay. The AR overlay indicates where to apply virtual makeup to the user's face based on facial landmarks, and the voice guidance explains the steps. This allows the user to visually understand and practice the makeup process.

[0391] Furthermore, the device tracks the user's progress and provides real-time feedback. For example, if the user hasn't applied the blush as instructed, the device will immediately provide instructions such as, "Please apply it a little closer to the center of your cheeks."

[0392] Finally, the server updates its database with the latest trend information and makeup techniques, allowing users to continuously access new information and techniques. This continuous learning environment ensures that users are always learning techniques that keep up with the latest makeup trends.

[0393] Thus, the present invention provides users with personalized makeup instruction and helps them acquire makeup techniques with confidence.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The user activates the device's camera and points their face at the system. The device processes the captured video in real time and uses facial recognition software to detect the user's face. It identifies facial landmarks (such as the position of the eyes, nose, and mouth) and prepares to track them.

[0397] Step 2:

[0398] The user gives a voice command saying, "Teach me how to apply lipstick." The device receives this voice input, converts it to text using speech recognition technology, and sends that data to the server.

[0399] Step 3:

[0400] The server analyzes the received text data using a natural language processing engine to interpret the user's intent. It understands the makeup advice the user is seeking (in this case, how to apply lipstick) and generates appropriate instructions.

[0401] Step 4:

[0402] The generated advice is sent from the server to the terminal. The terminal receives these instructions and conveys them to the user through voice guidance. Additionally, an AR rendering engine is used to display an overlay on the user's face indicating the lip application locations. This allows the user to understand the procedure both visually and audibly.

[0403] Step 5:

[0404] The user applies lipstick following instructions from the device. The device tracks the user's face throughout the process and monitors their progress in real time. If the user is not following the instructions, it immediately provides feedback such as, "You need to apply a little more to the inner part of your lips."

[0405] Step 6:

[0406] The server regularly updates its trend information and makeup database. Whenever the latest makeup trends are discovered, they are reflected in the system database and provided to users as new information. This ensures users always have access to the latest makeup techniques.

[0407] (Example 1)

[0408] Next, we will describe Example 1. 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."

[0409] Traditional makeup instruction systems struggled to provide personalized instruction to users in real time and lacked the mechanisms to quickly adapt to the latest makeup trends and technological advancements. This resulted in ineffective learning experiences for users.

[0410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0411] In this invention, the server includes means for detecting the user's face and tracking its features in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for presenting the generated guidance to the user with voice and virtual reality overlays. This allows the user to learn techniques adapted to the latest makeup trends while receiving personalized instruction.

[0412] A "user" refers to an individual who wants to learn makeup procedures and techniques using the system.

[0413] "Facial features" refer to a collection of points and lines that indicate the position and shape of each part of the face, providing the information necessary for applying makeup.

[0414] "Voice or written instructions" refers to voice commands or text inputs that users use to ask for specific advice on makeup procedures and methods.

[0415] "Makeup guide" refers to instructions on how to apply makeup and the techniques involved, generated based on the user's preferences.

[0416] A "virtual reality overlay" is virtual graphic information that is superimposed onto the user's real-world image and is used to indicate areas where makeup should be applied.

[0417] "Trend information" refers to information about the latest makeup techniques and trends, provided to enable users to always incorporate the latest styles.

[0418] The system of the present invention consists of a user terminal, a server, and a communication network connecting them. The user uses a camera mounted on the terminal to capture images of their face in real time and transmits the data to the terminal. This terminal is equipped with software for facial recognition, specifically using a computer vision library to identify facial features. This information is used to determine the precise locations where makeup should be applied.

[0419] The user inputs instructions, such as "Tell me how to apply eyeshadow," either by voice or text. The terminal has technology to convert voice input to text through its interface, and this is done using a speech recognition library. The converted text data is sent to a server, which analyzes this data using natural language processing technology. This analysis utilizes a natural language processing model to accurately understand the intent of the user's instructions and generate corresponding makeup instructions.

[0420] The server generates instructions which are sent to the terminal, which then presents them to the user through audio and a virtual reality overlay. The virtual reality overlay shows the areas on the user's face where makeup should be applied, and the audio guide explains the steps in detail. This allows the user to visually understand the steps and learn the actual makeup process. An example of a prompt would be, "Please show me the steps for natural makeup." This system allows users to receive timely makeup instruction tailored to their individual needs and acquire skills that keep up with the latest trends.

[0421] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0422] Step 1:

[0423] The user captures their face in real time using the device's camera. The captured video is input to the device and processed by facial recognition software. Through this processing, the device identifies the positions of facial features (eyes, nose, mouth, etc.) and outputs this information as data.

[0424] Step 2:

[0425] The user inputs voice or text instructions into the device, such as "Teach me how to apply blush." ​​In the case of voice instructions, the device uses a speech recognition library to convert the voice into text. The converted text is sent to the server, which receives it as input.

[0426] Step 3:

[0427] The server analyzes the text sent by the user using natural language processing technology. In this step, data calculations are performed using the analysis model to understand the user's intent. Based on the analysis results, the server generates cosmetic recommendations suitable for the user and outputs the results.

[0428] Step 4:

[0429] The generated makeup instructions are sent from the server to the terminal. Based on the received instructions, the terminal overlays a virtual reality overlay onto the user's video. This overlay indicates where makeup should be applied, and the terminal uses audio guidance to present the procedure to the user. This data is output from the terminal to the user.

[0430] Step 5:

[0431] While the user applies makeup according to the terminal's instructions, the terminal monitors the user's actions in real time. The terminal analyzes the progress and, if there are any deficiencies, provides feedback such as supplementary instructions like "Please apply the blush a little higher." This information is also output to the user through the terminal.

[0432] (Application Example 1)

[0433] Next, we will explain Application Example 1. In the following explanation, 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."

[0434] While personalized makeup instruction is increasingly important in modern times, traditional systems struggle to provide appropriate makeup advice in real time based on a user's facial features, and also have difficulty offering suggestions that align with the user's schedule and current trends. As a result, users are unable to apply makeup that is appropriate for the time and situation, making it difficult to improve self-satisfaction and manage time efficiently.

[0435] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0436] In this invention, the server includes means for making makeup suggestions while considering the user's schedule information, means for detecting the user's face and tracking facial feature points in real time, and means for analyzing the user's instructions in voice or text and generating appropriate makeup advice. This enables optimal makeup suggestions and real-time guidance tailored to the user's individual characteristics and circumstances.

[0437] A "user" is an individual who uses this system to receive makeup instruction.

[0438] "Facial feature points" are landmarks that indicate specific locations or parts of a user's face.

[0439] "Voice or text instructions" are means of expressing requests or questions that a user makes to the system.

[0440] "Makeup advice" refers to advice aimed at guiding and suggesting how users apply makeup.

[0441] "Virtual reality display" refers to a virtual visual guide that can be overlaid on the user's real-world image.

[0442] "Latest trend information" refers to makeup-related data based on current trends and new technologies in the industry.

[0443] "Real-time" is a concept that describes a temporal situation in which processing or reactions take place at the very moment an event occurs.

[0444] "Schedule information" refers to data related to the user's plans and schedule.

[0445] "Feedback" refers to advice that includes appropriate responses and adjustments based on the user's actions and progress.

[0446] The system that realizes this invention is designed to allow users to receive makeup guidance tailored to their individual characteristics and circumstances. The user captures their face through a camera built into a consumer robot. The robot uses OpenCV to detect feature points of the user's face. Google Cloud Speech-to-Text is used for speech recognition to convert the user's instructions into text. The server then uses this text data as input and analyzes it using TensorFlow, which incorporates NLP technology. Based on this analysis, it generates makeup advice that takes into account the user's preferences and schedule.

[0447] The generated advice is visualized using ARKit for virtual reality display and presented to the user via the device. This allows the user to receive visual and interactive makeup instruction. Furthermore, a feedback system monitors the progress of the makeup in real time, providing corrections and additional instructions as needed. This feedback function is a crucial component for making appropriate corrections if the user makes a mistake in the makeup process.

[0448] As a concrete example, suppose a user instructs the robot, "I have a presentation this afternoon, so please teach me how to do formal makeup." The robot recognizes the instruction and sends the data to the server. The server then provides the user with makeup advice based on the analysis results. In this process, ARKit visually displays the placement of eyeshadow, blush, and other elements accurately, allowing the user to proceed with their makeup step by step.

[0449] The AI ​​model can be input with example prompts such as: "I'm going to a friend's wedding tomorrow. Please suggest a modern makeup look that will suit the bright lighting at 5 PM." This allows the user to elicit accurate and useful makeup advice from the system.

[0450] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0451] Step 1:

[0452] The device captures the user's face with a high-resolution camera. This image, used as input data, is processed using OpenCV to detect facial feature points. Once the facial feature points are identified, that information is generated as output for the next processing step.

[0453] Step 2:

[0454] The user enters instructions about makeup into the device using voice. This voice input is converted to text using Google Cloud Speech-to-Text. This converted text becomes input data for analyzing the user's intent and is sent to the next server.

[0455] Step 3:

[0456] The server uses TensorFlow, which incorporates NLP technology, to analyze the transcribed instructions. The output of this analysis is makeup advice tailored to the user's requests and schedule. This generated data is then prepared for visualization in the next stage.

[0457] Step 4:

[0458] The device uses ARKit to visualize makeup advice received from the server as a virtual reality display. Here, the makeup guide is overlaid in precise locations using the user's facial feature point information. This visualized information becomes the output presented to the user.

[0459] Step 5:

[0460] The user applies makeup according to the presented virtual reality display. The device then uses its camera to track the user's face and monitor the progress in real time. The results of this monitoring are used as input for the next feedback step.

[0461] Step 6:

[0462] The server uses tracking data to evaluate whether the user is applying makeup correctly. Based on this evaluation, it generates feedback as needed. The generated feedback is communicated to the user via the device, either verbally or visually.

[0463] Step 7:

[0464] Based on the feedback received, the server can provide additional advice and correction instructions as needed until the user is satisfied with their makeup. This cycle is repeated until the user is happy with the final result.

[0465] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0466] This invention comprises a terminal capable of detecting a user's face and tracking facial landmarks in real time, and a system that provides comprehensive makeup guidance through cooperation between the terminal and a server. Furthermore, by incorporating an emotion engine that recognizes the user's emotions through facial expression analysis, it is possible to adjust the provided makeup advice and feedback to match the user's emotional state.

[0467] The device uses its camera to capture the user's face in real time and tracks landmarks using a facial recognition algorithm. Next, it captures voice commands from the user. For example, if the user requests, "Teach me how to apply eyeshadow," the device uses speech recognition technology to transcribe it into text and send it to the server.

[0468] The server analyzes the received text using a natural language processing engine and generates necessary makeup advice. Furthermore, an emotion engine analyzes the user's emotions from their face and adjusts the tone and content of the advice accordingly. For example, a user who is feeling anxious will receive more careful and slower guidance.

[0469] The advice is sent from the server to the device and communicated to the user using voice guidance and augmented reality (AR) overlays. The device uses AR technology to display examples of how makeup should be applied to the user's face, showing where and how the user should apply the makeup.

[0470] Furthermore, the device tracks user behavior, monitors progress in real time, and provides timely feedback. The server continuously updates trend information and makeup databases, ensuring access to the latest makeup techniques and styles.

[0471] By combining this with emotion analysis functionality, it becomes possible to provide more personalized makeup instruction and support users in improving their makeup skills with confidence. For example, it is possible to apply instruction tailored to a user's mood, such as suggesting vibrant colors for a user who is in a cheerful mood.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user activates the device's camera and points their face at the system. The device captures the camera image and runs a facial recognition algorithm to detect and track facial landmarks. This allows the system to establish baseline data about the user's face.

[0475] Step 2:

[0476] The user gives a voice command saying, "I want to know how to highlight." The device receives this voice input and converts it into text using speech recognition technology. The converted text is sent to the server to understand the user's request.

[0477] Step 3:

[0478] The server analyzes the received text using a natural language processing engine and generates specific makeup advice based on the user's request. Additionally, an emotion engine analyzes the user's facial expressions using video data transmitted from the device to determine their emotional state. If anxiety or tension is detected, the instruction is adjusted accordingly.

[0479] Step 4:

[0480] The server sends the generated advice back to the device. The device receives this information and delivers the advice to the user as an audio guide. Simultaneously, using AR technology, a visual overlay is displayed on the user's face, including where highlights should be applied. This allows the user to receive guidance both visually and audibly.

[0481] Step 5:

[0482] The user attempts to highlight by following instructions from the device. The device tracks the user's actions and monitors in real time whether they are progressing correctly. If there are any inappropriate areas, it provides feedback such as, "Try to make the highlight follow the bridge of your nose a little more closely."

[0483] Step 6:

[0484] The server regularly updates with the latest makeup trends and technical data, providing it to users via their devices. This ensures users always have access to up-to-date makeup information and opportunities to learn new techniques. The entire system is customized according to the user's emotional state, providing a better user experience.

[0485] (Example 2)

[0486] Next, we will describe Example 2. 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."

[0487] Modern beauty technology demands personalized makeup instruction tailored to each user's facial features and emotional state. However, conventional systems often lack sufficient real-time emotional analysis and emotionally-based adjustment of makeup instruction, making it difficult for users to learn optimal makeup techniques. Therefore, the challenge lies in providing more adaptive and intuitive makeup instruction that includes feedback responsive to the user's emotions.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0489] In this invention, the server includes means for detecting the user's face and tracking facial feature points in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for performing emotion analysis and adjusting the generated guidance according to the user's emotions. This enables personalized makeup guidance that responds to the user's emotions, allowing the user to effectively improve their makeup skills.

[0490] A "user" is an individual who uses the system to receive makeup instruction on their own face.

[0491] "Facial feature points," also known as facial landmarks, are important points that indicate the positions of features such as the eyes, nose, and mouth.

[0492] "Makeup instruction" refers to the act of providing users with specific advice and guidelines on how to apply cosmetics.

[0493] Augmented reality display is a technology that overlays digital information onto real-world scenery, allowing users to virtually visualize the effects of cosmetics.

[0494] "Emotional analysis" is the process of analyzing a user's facial expressions to identify their emotional state, such as joy or sadness.

[0495] "Trend information" refers to information about the latest beauty techniques and styles, and is intended to provide users with the latest makeup trends.

[0496] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to interpret instructions from users.

[0497] This invention is a system that provides users with makeup guidance based on their individual facial features and emotions. Specific embodiments for carrying out the invention are described below.

[0498] The device uses a camera to capture the user's face in real time. It utilizes a facial recognition algorithm to track facial feature points. This technology typically employs software libraries such as Dlib and OpenCV.

[0499] Users can ask questions or make requests about makeup application methods using voice. The device uses speech recognition technology to convert the voice instructions into text and sends that text to the server. Speech recognition software such as the Google Speech API is commonly used.

[0500] The server analyzes the received text using natural language processing technology. In this process, it utilizes a generative AI model to provide users with appropriate makeup advice. For example, it generates specific advice such as, "Apply eyeshadow to the lash line to make your eyes appear larger, and choose a light color."

[0501] Furthermore, the device continuously captures video of the user's face, and the server performs emotion analysis based on this data. This emotion analysis identifies the user's emotional state, and adjusts the makeup instruction accordingly. This makes it possible to provide personalized instruction optimized for the user's emotions.

[0502] For example, for a user in a cheerful mood, we can suggest a pop color scheme. An example of a prompt to the generative AI model in this case would be, "Please provide advice on bright color makeup suitable for a user in a cheerful mood."

[0503] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0504] Step 1:

[0505] The user points the device's camera at their face. The device's camera captures a facial image in real time and collects the video. The input is the facial video obtained through the camera. The device processes the video using a facial recognition algorithm and extracts facial feature points. This process outputs positional information for the eyes, nose, and mouth, which is used in the next step.

[0506] Step 2:

[0507] The user asks questions about makeup using voice. For example, they might request, "How do I apply blush?" The device uses speech recognition technology to convert this voice input into text. The input is voice data, and the output is the corresponding string data. The device then sends the text instructions to the server.

[0508] Step 3:

[0509] The server analyzes the received text using a natural language processing engine. The generative AI model used here understands the user's instructions and generates optimal makeup guidance. The input is the user's text instructions, and the output is specific makeup advice. The server uses the AI ​​model to construct appropriate advice and provide detailed guidance tailored to the user's needs.

[0510] Step 4:

[0511] The server sends makeup advice generated via the terminal to the user. The advice is presented through audio output and augmented reality display. The input is the makeup advice from the server, and the output is the information the user receives visually and aurally. The terminal shows specific makeup techniques by overlaying virtual makeup onto the user's face.

[0512] Step 5:

[0513] The device continuously tracks the user's real-time reactions via a video feed. The server analyzes the user's facial expression data through an emotion analysis engine. The input is video data including the user's facial expressions, and the output is the analyzed emotion information. Based on this emotion information, the server adjusts the content and tone of the advice to suit the user.

[0514] Step 6:

[0515] The server regularly updates its database to reflect the latest trends during the makeup instruction process. The input is the latest beauty trend information collected from external sources, and the output is the updated makeup database. This ensures that users always receive information on the latest styles and techniques.

[0516] (Application Example 2)

[0517] Next, we will explain application example 2. In the following explanation, 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."

[0518] In modern beauty instruction, providing personalized advice that takes into account a user's facial features and emotional state is difficult, and many users do not receive comprehensive and immediate guidance. It is also crucial to provide users with an environment where they can visualize the results before trying beauty treatments, allowing them to proceed with confidence. Furthermore, there is a lack of appropriate systems to quickly respond to ever-changing trends and technological information and deliver the latest beauty knowledge to users.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0520] In this invention, the server includes means for detecting the user's face and tracking specific parts of the face in real time, means for analyzing the user's instructions in voice or text and generating appropriate beauty advice, and means for presenting the generated advice to the user in voice and artificial reality overlay. This makes it possible to provide beauty advice tailored to the user's individual needs. Furthermore, by analyzing the user's facial expressions, recognizing their emotional state, and adjusting the content of the advice, a high level of personalization tailored to the user can be achieved.

[0521] "Detecting a user's face" refers to the process of recognizing the face of a service user using sensors such as cameras.

[0522] "Methods for tracking specific parts of the face" refer to technologies that continuously monitor specific landmarks such as the eyes and mouth within the user's entire face and analyze their movements in real time.

[0523] "Means for analyzing user instructions in voice or text" refers to interpreting voice or text information entered by the user and developing appropriate instructional content based on that information.

[0524] "Methods for generating beauty advice" refers to the process of generating suggestions for optimal beauty methods and products based on analyzed user input.

[0525] "Artificial reality overlay" is a technology that overlays digital information onto real-world images to provide users with a virtual experience.

[0526] "Analyzing facial expressions and recognizing emotional states" refers to a technology that analyzes various facial expressions of a user and infers the user's feelings and emotional state from those expressions.

[0527] "Trend information" refers to the latest trends and technologies related to beauty, and is data that can constantly provide users with up-to-date information.

[0528] A "means of providing feedback" refers to a mechanism that can provide evaluations and advice in real time, tailored to the user's actions and circumstances.

[0529] To realize this invention, a server, terminal, and user cooperate to perform various functions. The server utilizes the OpenCV and Dlib libraries to detect the user's face and track specific body parts in real time. The user's face is captured by a camera, and the video data is analyzed to accurately recognize and track facial landmarks. This information is sent to the server and used for further data analysis.

[0530] The device uses the Google Cloud Speech-to-Text API to convert voice commands into text data, and then performs natural language processing using the spaCy library. This makes it possible to build beauty advice based on user instructions and questions. Furthermore, the device generates an artificial reality overlay using Unity and ARKit, allowing it to virtually try out beauty treatments on the user's face. This allows the user to visualize the results of the treatment before actually performing it.

[0531] The server uses TensorFlow to analyze the user's facial expressions and recognize their emotional state. This process adjusts the content and tone of the advice given to match the user's emotions. For example, if the user shows a positive expression, the server will provide beauty advice that matches the cheerful mood, improving the user experience.

[0532] As a concrete example of a prompt, the system instructs the generated AI model to "recognize from the user's facial expression that they are feeling joy, and suggest a glamorous makeup look themed around a special day." This prompt prompts the model to select appropriate advice and provide a tailored response. In this way, the user can receive personalized beauty advice based on their emotions and state of mind.

[0533] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0534] Step 1:

[0535] The device uses its camera to capture the user's face. Camera video data is taken as input, and facial landmark detection is performed using the Dlib library. This data is then sent to the server as coordinate information for identified facial features.

[0536] Step 2:

[0537] The user asks beauty-related questions or gives instructions via voice. The device uses the Google Cloud Speech-to-Text API to convert the received voice into text data. The converted text data is then sent to a server for natural language processing.

[0538] Step 3:

[0539] The server performs natural language processing using the spaCy library based on the received text data. It analyzes the text data as input and generates appropriate beauty advice. The generated advice is stored on the server side for use in the next step.

[0540] Step 4:

[0541] The server processes the received facial landmark data using TensorFlow to analyze the user's facial expressions. It analyzes the facial data as input to determine the user's emotional state. The output provides the type of emotion, which is then used to adjust the tone of the beauty advice.

[0542] Step 5:

[0543] The system adjusts the beauty advice generated by the server and sends it to the terminal. Specifically, it generates advice tailored to the user's emotional state and edits it into a format that is easy for the user to understand. It is also presented as a guideline to make it easier for the user to comprehend.

[0544] Step 6:

[0545] The device uses Unity and ARKit to generate an artificial reality overlay, superimposing virtual beauty results onto the user's face. This allows users to visually confirm the results without having to try the beauty treatments in the real world.

[0546] Step 7:

[0547] Users review an artificial reality overlay, and if satisfied, they undergo an actual cosmetic procedure based on that overlay. During this process, user experience is collected as feedback, which is then used to inform future advice.

[0548] The process involves specific actions taken at each step, allowing users to receive real-time beauty advice based on their emotions and individual needs.

[0549] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0550] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0552] [Fourth Embodiment]

[0553] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0554] As shown in Figure 7, the 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.

[0555] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0556] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0557] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0559] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0560] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0561] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0562] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0563] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0564] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0565] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0566] The system of this invention mainly consists of a user terminal, a server, and a network that connects them. The user captures their face in real time using the camera on the terminal and transmits the video to the system. Face recognition software installed on the terminal detects the user's face and identifies landmarks. This information is used to indicate the precise location of makeup.

[0567] Next, voice or text instructions from the user are taken into the system. For example, if a user requests by voice, "Tell me how to apply blush," the device uses speech recognition technology to convert this into text and sends it to the server. The server uses natural language processing technology to analyze the intent of this text and generate appropriate makeup advice.

[0568] The generated advice is sent from the server to the terminal. The terminal provides instructions to the user using voice guidance and an augmented reality (AR) overlay. The AR overlay indicates where to apply virtual makeup to the user's face based on facial landmarks, and the voice guidance explains the steps. This allows the user to visually understand and practice the makeup process.

[0569] Furthermore, the device tracks the user's progress and provides real-time feedback. For example, if the user hasn't applied the blush as instructed, the device will immediately provide instructions such as, "Please apply it a little closer to the center of your cheeks."

[0570] Finally, the server updates its database with the latest trend information and makeup techniques, allowing users to continuously access new information and techniques. This continuous learning environment ensures that users are always learning techniques that keep up with the latest makeup trends.

[0571] Thus, the present invention provides users with personalized makeup instruction and helps them acquire makeup techniques with confidence.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The user activates the device's camera and points their face at the system. The device processes the captured video in real time and uses facial recognition software to detect the user's face. It identifies facial landmarks (such as the position of the eyes, nose, and mouth) and prepares to track them.

[0575] Step 2:

[0576] The user gives a voice command saying, "Teach me how to apply lipstick." The device receives this voice input, converts it to text using speech recognition technology, and sends that data to the server.

[0577] Step 3:

[0578] The server analyzes the received text data using a natural language processing engine to interpret the user's intent. It understands the makeup advice the user is seeking (in this case, how to apply lipstick) and generates appropriate instructions.

[0579] Step 4:

[0580] The generated advice is sent from the server to the terminal. The terminal receives these instructions and conveys them to the user through voice guidance. Additionally, an AR rendering engine is used to display an overlay on the user's face indicating the lip application locations. This allows the user to understand the procedure both visually and audibly.

[0581] Step 5:

[0582] The user applies lipstick following instructions from the device. The device tracks the user's face throughout the process and monitors their progress in real time. If the user is not following the instructions, it immediately provides feedback such as, "You need to apply a little more to the inner part of your lips."

[0583] Step 6:

[0584] The server regularly updates its trend information and makeup database. Whenever the latest makeup trends are discovered, they are reflected in the system database and provided to users as new information. This ensures users always have access to the latest makeup techniques.

[0585] (Example 1)

[0586] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0587] Traditional makeup instruction systems struggled to provide personalized instruction to users in real time and lacked the mechanisms to quickly adapt to the latest makeup trends and technological advancements. This resulted in ineffective learning experiences for users.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0589] In this invention, the server includes means for detecting the user's face and tracking its features in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for presenting the generated guidance to the user with voice and virtual reality overlays. This allows the user to learn techniques adapted to the latest makeup trends while receiving personalized instruction.

[0590] A "user" refers to an individual who wants to learn makeup procedures and techniques using the system.

[0591] "Facial features" refer to a collection of points and lines that indicate the position and shape of each part of the face, providing the information necessary for applying makeup.

[0592] "Voice or written instructions" refers to voice commands or text inputs that users use to ask for specific advice on makeup procedures and methods.

[0593] "Makeup guide" refers to instructions on how to apply makeup and the techniques involved, generated based on the user's preferences.

[0594] A "virtual reality overlay" is virtual graphic information that is superimposed onto the user's real-world image and is used to indicate areas where makeup should be applied.

[0595] "Trend information" refers to information about the latest makeup techniques and trends, provided to enable users to always incorporate the latest styles.

[0596] The system of the present invention consists of a user terminal, a server, and a communication network connecting them. The user uses a camera mounted on the terminal to capture images of their face in real time and transmits the data to the terminal. This terminal is equipped with software for facial recognition, specifically using a computer vision library to identify facial features. This information is used to determine the precise locations where makeup should be applied.

[0597] The user inputs instructions, such as "Tell me how to apply eyeshadow," either by voice or text. The terminal has technology to convert voice input to text through its interface, and this is done using a speech recognition library. The converted text data is sent to a server, which analyzes this data using natural language processing technology. This analysis utilizes a natural language processing model to accurately understand the intent of the user's instructions and generate corresponding makeup instructions.

[0598] The server generates instructions which are sent to the terminal, which then presents them to the user through audio and a virtual reality overlay. The virtual reality overlay shows the areas on the user's face where makeup should be applied, and the audio guide explains the steps in detail. This allows the user to visually understand the steps and learn the actual makeup process. An example of a prompt would be, "Please show me the steps for natural makeup." This system allows users to receive timely makeup instruction tailored to their individual needs and acquire skills that keep up with the latest trends.

[0599] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0600] Step 1:

[0601] The user captures their face in real time using the device's camera. The captured video is input to the device and processed by facial recognition software. Through this processing, the device identifies the positions of facial features (eyes, nose, mouth, etc.) and outputs this information as data.

[0602] Step 2:

[0603] The user inputs voice or text instructions into the device, such as "Teach me how to apply blush." ​​In the case of voice instructions, the device uses a speech recognition library to convert the voice into text. The converted text is sent to the server, which receives it as input.

[0604] Step 3:

[0605] The server analyzes the text sent by the user using natural language processing technology. In this step, data calculations are performed using the analysis model to understand the user's intent. Based on the analysis results, the server generates cosmetic recommendations suitable for the user and outputs the results.

[0606] Step 4:

[0607] The generated makeup instructions are sent from the server to the terminal. Based on the received instructions, the terminal overlays a virtual reality overlay onto the user's video. This overlay indicates where makeup should be applied, and the terminal uses audio guidance to present the procedure to the user. This data is output from the terminal to the user.

[0608] Step 5:

[0609] While the user applies makeup according to the terminal's instructions, the terminal monitors the user's actions in real time. The terminal analyzes the progress and, if there are any deficiencies, provides feedback such as supplementary instructions like "Please apply the blush a little higher." This information is also output to the user through the terminal.

[0610] (Application Example 1)

[0611] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] While personalized makeup instruction is increasingly important in modern times, traditional systems struggle to provide appropriate makeup advice in real time based on a user's facial features, and also have difficulty offering suggestions that align with the user's schedule and current trends. As a result, users are unable to apply makeup that is appropriate for the time and situation, making it difficult to improve self-satisfaction and manage time efficiently.

[0613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0614] In this invention, the server includes means for making makeup suggestions while considering the user's schedule information, means for detecting the user's face and tracking facial feature points in real time, and means for analyzing the user's instructions in voice or text and generating appropriate makeup advice. This enables optimal makeup suggestions and real-time guidance tailored to the user's individual characteristics and circumstances.

[0615] A "user" is an individual who uses this system to receive makeup instruction.

[0616] "Facial feature points" are landmarks that indicate specific locations or parts of a user's face.

[0617] "Voice or text instructions" are means of expressing requests or questions that a user makes to the system.

[0618] "Makeup advice" refers to advice aimed at guiding and suggesting how users apply makeup.

[0619] "Virtual reality display" refers to a virtual visual guide that can be overlaid on the user's real-world image.

[0620] "Latest trend information" refers to makeup-related data based on current trends and new technologies in the industry.

[0621] "Real-time" is a concept that describes a temporal situation in which processing or reactions take place at the very moment an event occurs.

[0622] "Schedule information" refers to data related to the user's plans and schedule.

[0623] "Feedback" refers to advice that includes appropriate responses and adjustments based on the user's actions and progress.

[0624] The system that realizes this invention is designed to allow users to receive makeup guidance tailored to their individual characteristics and circumstances. The user captures their face through a camera built into a consumer robot. The robot uses OpenCV to detect feature points of the user's face. Google Cloud Speech-to-Text is used for speech recognition to convert the user's instructions into text. The server then uses this text data as input and analyzes it using TensorFlow, which incorporates NLP technology. Based on this analysis, it generates makeup advice that takes into account the user's preferences and schedule.

[0625] The generated advice is visualized using ARKit for virtual reality display and presented to the user via the device. This allows the user to receive visual and interactive makeup instruction. Furthermore, a feedback system monitors the progress of the makeup in real time, providing corrections and additional instructions as needed. This feedback function is a crucial component for making appropriate corrections if the user makes a mistake in the makeup process.

[0626] As a concrete example, suppose a user instructs the robot, "I have a presentation this afternoon, so please teach me how to do formal makeup." The robot recognizes the instruction and sends the data to the server. The server then provides the user with makeup advice based on the analysis results. In this process, ARKit visually displays the placement of eyeshadow, blush, and other elements accurately, allowing the user to proceed with their makeup step by step.

[0627] The AI ​​model can be input with example prompts such as: "I'm going to a friend's wedding tomorrow. Please suggest a modern makeup look that will suit the bright lighting at 5 PM." This allows the user to elicit accurate and useful makeup advice from the system.

[0628] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0629] Step 1:

[0630] The device captures the user's face with a high-resolution camera. This image, used as input data, is processed using OpenCV to detect facial feature points. Once the facial feature points are identified, that information is generated as output for the next processing step.

[0631] Step 2:

[0632] The user enters instructions about makeup into the device using voice. This voice input is converted to text using Google Cloud Speech-to-Text. This converted text becomes input data for analyzing the user's intent and is sent to the next server.

[0633] Step 3:

[0634] The server uses TensorFlow, which incorporates NLP technology, to analyze the transcribed instructions. The output of this analysis is makeup advice tailored to the user's requests and schedule. This generated data is then prepared for visualization in the next stage.

[0635] Step 4:

[0636] The device uses ARKit to visualize makeup advice received from the server as a virtual reality display. Here, the makeup guide is overlaid in precise locations using the user's facial feature point information. This visualized information becomes the output presented to the user.

[0637] Step 5:

[0638] The user applies makeup according to the presented virtual reality display. The device then uses its camera to track the user's face and monitor the progress in real time. The results of this monitoring are used as input for the next feedback step.

[0639] Step 6:

[0640] The server uses tracking data to evaluate whether the user is applying makeup correctly. Based on this evaluation, it generates feedback as needed. The generated feedback is communicated to the user via the device, either verbally or visually.

[0641] Step 7:

[0642] Based on the feedback received, the server can provide additional advice and correction instructions as needed until the user is satisfied with their makeup. This cycle is repeated until the user is happy with the final result.

[0643] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0644] This invention comprises a terminal capable of detecting a user's face and tracking facial landmarks in real time, and a system that provides comprehensive makeup guidance through cooperation between the terminal and a server. Furthermore, by incorporating an emotion engine that recognizes the user's emotions through facial expression analysis, it is possible to adjust the provided makeup advice and feedback to match the user's emotional state.

[0645] The device uses its camera to capture the user's face in real time and tracks landmarks using a facial recognition algorithm. Next, it captures voice commands from the user. For example, if the user requests, "Teach me how to apply eyeshadow," the device uses speech recognition technology to transcribe it into text and send it to the server.

[0646] The server analyzes the received text using a natural language processing engine and generates necessary makeup advice. Furthermore, an emotion engine analyzes the user's emotions from their face and adjusts the tone and content of the advice accordingly. For example, a user who is feeling anxious will receive more careful and slower guidance.

[0647] The advice is sent from the server to the device and communicated to the user using voice guidance and augmented reality (AR) overlays. The device uses AR technology to display examples of how makeup should be applied to the user's face, showing where and how the user should apply the makeup.

[0648] Furthermore, the device tracks user behavior, monitors progress in real time, and provides timely feedback. The server continuously updates trend information and makeup databases, ensuring access to the latest makeup techniques and styles.

[0649] By combining this with emotion analysis functionality, it becomes possible to provide more personalized makeup instruction and support users in improving their makeup skills with confidence. For example, it is possible to apply instruction tailored to a user's mood, such as suggesting vibrant colors for a user who is in a cheerful mood.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The user activates the device's camera and points their face at the system. The device captures the camera image and runs a facial recognition algorithm to detect and track facial landmarks. This allows the system to establish baseline data about the user's face.

[0653] Step 2:

[0654] The user gives a voice command saying, "I want to know how to highlight." The device receives this voice input and converts it into text using speech recognition technology. The converted text is sent to the server to understand the user's request.

[0655] Step 3:

[0656] The server analyzes the received text using a natural language processing engine and generates specific makeup advice based on the user's request. Additionally, an emotion engine analyzes the user's facial expressions using video data transmitted from the device to determine their emotional state. If anxiety or tension is detected, the instruction is adjusted accordingly.

[0657] Step 4:

[0658] The server sends the generated advice back to the device. The device receives this information and delivers the advice to the user as an audio guide. Simultaneously, using AR technology, a visual overlay is displayed on the user's face, including where highlights should be applied. This allows the user to receive guidance both visually and audibly.

[0659] Step 5:

[0660] The user attempts to highlight by following instructions from the device. The device tracks the user's actions and monitors in real time whether they are progressing correctly. If there are any inappropriate areas, it provides feedback such as, "Try to make the highlight follow the bridge of your nose a little more closely."

[0661] Step 6:

[0662] The server regularly updates with the latest makeup trends and technical data, providing it to users via their devices. This ensures users always have access to up-to-date makeup information and opportunities to learn new techniques. The entire system is customized according to the user's emotional state, providing a better user experience.

[0663] (Example 2)

[0664] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0665] Modern beauty technology demands personalized makeup instruction tailored to each user's facial features and emotional state. However, conventional systems often lack sufficient real-time emotional analysis and emotionally-based adjustment of makeup instruction, making it difficult for users to learn optimal makeup techniques. Therefore, the challenge lies in providing more adaptive and intuitive makeup instruction that includes feedback responsive to the user's emotions.

[0666] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0667] In this invention, the server includes means for detecting the user's face and tracking facial feature points in real time, means for analyzing the user's voice or text instructions and generating appropriate makeup guidance, and means for performing emotion analysis and adjusting the generated guidance according to the user's emotions. This enables personalized makeup guidance that responds to the user's emotions, allowing the user to effectively improve their makeup skills.

[0668] A "user" is an individual who uses the system to receive makeup instruction on their own face.

[0669] "Facial feature points," also known as facial landmarks, are important points that indicate the positions of features such as the eyes, nose, and mouth.

[0670] "Makeup instruction" refers to the act of providing users with specific advice and guidelines on how to apply cosmetics.

[0671] Augmented reality display is a technology that overlays digital information onto real-world scenery, allowing users to virtually visualize the effects of cosmetics.

[0672] "Emotional analysis" is the process of analyzing a user's facial expressions to identify their emotional state, such as joy or sadness.

[0673] "Trend information" refers to information about the latest beauty techniques and styles, and is intended to provide users with the latest makeup trends.

[0674] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to interpret instructions from users.

[0675] This invention is a system that provides users with makeup guidance based on their individual facial features and emotions. Specific embodiments for carrying out the invention are described below.

[0676] The device uses a camera to capture the user's face in real time. It utilizes a facial recognition algorithm to track facial feature points. This technology typically employs software libraries such as Dlib and OpenCV.

[0677] Users can ask questions or make requests about makeup application methods using voice. The device uses speech recognition technology to convert the voice instructions into text and sends that text to the server. Speech recognition software such as the Google Speech API is commonly used.

[0678] The server analyzes the received text using natural language processing technology. In this process, it utilizes a generative AI model to provide users with appropriate makeup advice. For example, it generates specific advice such as, "Apply eyeshadow to the lash line to make your eyes appear larger, and choose a light color."

[0679] Furthermore, the device continuously captures video of the user's face, and the server performs emotion analysis based on this data. This emotion analysis identifies the user's emotional state, and adjusts the makeup instruction accordingly. This makes it possible to provide personalized instruction optimized for the user's emotions.

[0680] For example, for a user in a cheerful mood, we can suggest a pop color scheme. An example of a prompt to the generative AI model in this case would be, "Please provide advice on bright color makeup suitable for a user in a cheerful mood."

[0681] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0682] Step 1:

[0683] The user points the device's camera at their face. The device's camera captures a facial image in real time and collects the video. The input is the facial video obtained through the camera. The device processes the video using a facial recognition algorithm and extracts facial feature points. This process outputs positional information for the eyes, nose, and mouth, which is used in the next step.

[0684] Step 2:

[0685] The user asks questions about makeup using voice. For example, they might request, "How do I apply blush?" The device uses speech recognition technology to convert this voice input into text. The input is voice data, and the output is the corresponding string data. The device then sends the text instructions to the server.

[0686] Step 3:

[0687] The server analyzes the received text using a natural language processing engine. The generative AI model used here understands the user's instructions and generates optimal makeup guidance. The input is the user's text instructions, and the output is specific makeup advice. The server uses the AI ​​model to construct appropriate advice and provide detailed guidance tailored to the user's needs.

[0688] Step 4:

[0689] The server sends makeup advice generated via the terminal to the user. The advice is presented through audio output and augmented reality display. The input is the makeup advice from the server, and the output is the information the user receives visually and aurally. The terminal shows specific makeup techniques by overlaying virtual makeup onto the user's face.

[0690] Step 5:

[0691] The device continuously tracks the user's real-time reactions via a video feed. The server analyzes the user's facial expression data through an emotion analysis engine. The input is video data including the user's facial expressions, and the output is the analyzed emotion information. Based on this emotion information, the server adjusts the content and tone of the advice to suit the user.

[0692] Step 6:

[0693] The server regularly updates its database to reflect the latest trends during the makeup instruction process. The input is the latest beauty trend information collected from external sources, and the output is the updated makeup database. This ensures that users always receive information on the latest styles and techniques.

[0694] (Application Example 2)

[0695] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] In modern beauty instruction, providing personalized advice that takes into account a user's facial features and emotional state is difficult, and many users do not receive comprehensive and immediate guidance. It is also crucial to provide users with an environment where they can visualize the results before trying beauty treatments, allowing them to proceed with confidence. Furthermore, there is a lack of appropriate systems to quickly respond to ever-changing trends and technological information and deliver the latest beauty knowledge to users.

[0697] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0698] In this invention, the server includes means for detecting the user's face and tracking specific parts of the face in real time, means for analyzing the user's instructions in voice or text and generating appropriate beauty advice, and means for presenting the generated advice to the user in voice and artificial reality overlay. This makes it possible to provide beauty advice tailored to the user's individual needs. Furthermore, by analyzing the user's facial expressions, recognizing their emotional state, and adjusting the content of the advice, a high level of personalization tailored to the user can be achieved.

[0699] "Detecting a user's face" refers to the process of recognizing the face of a service user using sensors such as cameras.

[0700] "Methods for tracking specific parts of the face" refer to technologies that continuously monitor specific landmarks such as the eyes and mouth within the user's entire face and analyze their movements in real time.

[0701] "Means for analyzing user instructions in voice or text" refers to interpreting voice or text information entered by the user and developing appropriate instructional content based on that information.

[0702] "Methods for generating beauty advice" refers to the process of generating suggestions for optimal beauty methods and products based on analyzed user input.

[0703] "Artificial reality overlay" is a technology that overlays digital information onto real-world images to provide users with a virtual experience.

[0704] "Analyzing facial expressions and recognizing emotional states" refers to a technology that analyzes various facial expressions of a user and infers the user's feelings and emotional state from those expressions.

[0705] "Trend information" refers to the latest trends and technologies related to beauty, and is data that can constantly provide users with up-to-date information.

[0706] A "means of providing feedback" refers to a mechanism that can provide evaluations and advice in real time, tailored to the user's actions and circumstances.

[0707] To realize this invention, a server, terminal, and user cooperate to perform various functions. The server utilizes the OpenCV and Dlib libraries to detect the user's face and track specific body parts in real time. The user's face is captured by a camera, and the video data is analyzed to accurately recognize and track facial landmarks. This information is sent to the server and used for further data analysis.

[0708] The device uses the Google Cloud Speech-to-Text API to convert voice commands into text data, and then performs natural language processing using the spaCy library. This makes it possible to build beauty advice based on user instructions and questions. Furthermore, the device generates an artificial reality overlay using Unity and ARKit, allowing it to virtually try out beauty treatments on the user's face. This allows the user to visualize the results of the treatment before actually performing it.

[0709] The server uses TensorFlow to analyze the user's facial expressions and recognize their emotional state. This process adjusts the content and tone of the advice given to match the user's emotions. For example, if the user shows a positive expression, the server will provide beauty advice that matches the cheerful mood, improving the user experience.

[0710] As a concrete example of a prompt, the system instructs the generated AI model to "recognize from the user's facial expression that they are feeling joy, and suggest a glamorous makeup look themed around a special day." This prompt prompts the model to select appropriate advice and provide a tailored response. In this way, the user can receive personalized beauty advice based on their emotions and state of mind.

[0711] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0712] Step 1:

[0713] The device uses its camera to capture the user's face. Camera video data is taken as input, and facial landmark detection is performed using the Dlib library. This data is then sent to the server as coordinate information for identified facial features.

[0714] Step 2:

[0715] The user asks beauty-related questions or gives instructions via voice. The device uses the Google Cloud Speech-to-Text API to convert the received voice into text data. The converted text data is then sent to a server for natural language processing.

[0716] Step 3:

[0717] The server performs natural language processing using the spaCy library based on the received text data. It analyzes the text data as input and generates appropriate beauty advice. The generated advice is stored on the server side for use in the next step.

[0718] Step 4:

[0719] The server processes the received facial landmark data using TensorFlow to analyze the user's facial expressions. It analyzes the facial data as input to determine the user's emotional state. The output provides the type of emotion, which is then used to adjust the tone of the beauty advice.

[0720] Step 5:

[0721] The system adjusts the beauty advice generated by the server and sends it to the terminal. Specifically, it generates advice tailored to the user's emotional state and edits it into a format that is easy for the user to understand. It is also presented as a guideline to make it easier for the user to comprehend.

[0722] Step 6:

[0723] The device uses Unity and ARKit to generate an artificial reality overlay, superimposing virtual beauty results onto the user's face. This allows users to visually confirm the results without having to try the beauty treatments in the real world.

[0724] Step 7:

[0725] Users review an artificial reality overlay, and if satisfied, they undergo an actual cosmetic procedure based on that overlay. During this process, user experience is collected as feedback, which is then used to inform future advice.

[0726] The process involves specific actions taken at each step, allowing users to receive real-time beauty advice based on their emotions and individual needs.

[0727] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0728] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0729] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0730] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0731] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0732] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0733] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0734] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0735] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0736] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0737] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0738] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0739] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0740] 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.

[0741] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0742] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0743] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0744] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0745] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0746] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0747] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0748] The following is further disclosed regarding the embodiments described above.

[0749] (Claim 1)

[0750] A means to detect the user's face and track facial landmarks in real time,

[0751] A means for analyzing user instructions in voice or text and generating appropriate makeup advice,

[0752] A means of presenting the generated advice to the user via voice and augmented reality overlay,

[0753] A means of monitoring user progress and providing timely feedback,

[0754] A means of regularly updating and providing users with the latest trend information,

[0755] A system that includes this.

[0756] (Claim 2)

[0757] The system according to claim 1, which generates an augmented reality overlay for virtually applying makeup to a user's face.

[0758] (Claim 3)

[0759] The system according to claim 1, comprising means for converting a user's voice input into text using speech recognition technology and analyzing the text using natural language processing technology.

[0760] "Example 1"

[0761] (Claim 1)

[0762] A device that detects the user's face and tracks facial features in real time,

[0763] A device that analyzes user voice or text instructions and generates appropriate makeup guidance,

[0764] A device that presents the generated guidance to the user using audio and a virtual reality overlay,

[0765] A device that monitors the user's implementation status and provides timely evaluations,

[0766] A device that regularly updates and provides users with the latest trend information,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, which generates a virtual reality overlay for virtually applying makeup to a user's face.

[0770] (Claim 3)

[0771] The system according to claim 1, comprising a device that converts a user's voice input into text using speech recognition technology and analyzes the text using natural language processing technology.

[0772] "Application Example 1"

[0773] (Claim 1)

[0774] A means to detect a user's face and track facial feature points in real time,

[0775] A means for analyzing user instructions in voice or text and generating appropriate makeup advice,

[0776] A means of presenting the generated advice to the user via voice and virtual reality display,

[0777] A means of monitoring user progress and providing timely feedback,

[0778] A means of regularly updating and providing users with the latest trend information,

[0779] A method for providing makeup suggestions while taking the user's schedule information into consideration,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, which generates a virtual reality display for virtually applying makeup to a user's face.

[0783] (Claim 3)

[0784] The system according to claim 1, comprising means for converting a user's voice input into text using speech recognition technology and analyzing the text using natural language processing technology.

[0785] "Example 2 of combining an emotion engine"

[0786] (Claim 1)

[0787] A means to detect a user's face and track facial feature points in real time,

[0788] A means for analyzing a user's voice or text instructions and generating appropriate makeup guidance,

[0789] A means of presenting the generated instruction to the user using sound and augmented reality display,

[0790] A means of monitoring user activity and providing timely advice,

[0791] A means of performing emotion analysis and adjusting the generated guidance according to the user's emotions,

[0792] A means of regularly updating and making users aware of the latest trends,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, which generates an augmented reality display for virtually applying makeup to a user's face.

[0796] (Claim 3)

[0797] The system according to claim 1, comprising means for converting a user's voice input into text using acoustic recognition technology and analyzing the text using natural language processing technology.

[0798] "Application example 2 when combining with an emotional engine"

[0799] (Claim 1)

[0800] A means for detecting a user's face and tracking specific parts of the face in real time,

[0801] A means for analyzing user instructions in voice or text and generating appropriate beauty advice,

[0802] A means of presenting the generated advice to the user via voice and artificial reality overlay,

[0803] A means of monitoring user progress and providing timely feedback,

[0804] A means of regularly updating and providing users with the latest trend information,

[0805] A means of analyzing the user's facial expressions, recognizing their emotional state, and adjusting the content of the advice accordingly.

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which generates an artificial reality overlay for virtually applying beauty treatments to a user's face.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising means for converting the user's voice input into text using speech recognition technology, analyzing the text using natural language processing technology, and further adjusting the advice content based on the user's emotional state. [Explanation of symbols]

[0811] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to detect a user's face and track facial feature points in real time, A means for analyzing user instructions in voice or text and generating appropriate makeup advice, A means of presenting the generated advice to the user via voice and virtual reality display, A means of monitoring user progress and providing timely feedback, A means of regularly updating and providing users with the latest trend information, A method for providing makeup suggestions while taking the user's schedule information into consideration, A system that includes this.

2. The system according to claim 1, which generates a virtual reality display for virtually applying makeup to a user's face.

3. The system according to claim 1, comprising means for converting a user's voice input into text using speech recognition technology and analyzing the text using natural language processing technology.

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