system

Abstract

We provide the system. [Solution] A means of acquiring images of the user using a video acquisition device, A means for identifying a face and extracting its appearance based on acquired image data, A means of presenting a style suitable for the user using an AI model via an information processing device, based on extracted appearance data, A means of virtually applying the presented style to an image in real time and providing it to the user using a display device, A means of presenting product information related to the selected style and providing a purchase interface, A means of providing an interface that enables users to make purchase decisions based on the presented virtual style, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is difficult for an individual to find the makeup and hairstyle that best suits them. As a result, there is a problem that they are afraid of failure when challenging a new style. In addition, there is a lack of an environment where they can easily try and error without going to beauty salons or stores, and it is difficult to find appropriate products because of a wide variety of options. It is necessary to overcome these problems and enable users to easily find the most suitable style for themselves.

Means for Solving the Problems

[0005] This invention provides a system that uses a camera to acquire video footage of a user and extracts facial features based on that footage. Using this feature data, it then employs an AI model to automatically suggest suitable makeup and hairstyles for the user. Furthermore, it virtually applies the suggested styles to the user's video footage in real time, visually presenting the results to the user and supporting them in trying out new styles. It also provides product information related to the suggested makeup and hairstyles, assisting with purchase decisions and facilitating quick and easy purchasing decisions. This allows users to safely and effectively explore and try out styles that suit them.

[0006] A "recording device" is a device used to acquire images of the user, and primarily refers to a camera.

[0007] A "data processing device" refers to a computer system that analyzes acquired video data and uses an AI model to suggest makeup and hairstyles.

[0008] An "AI model" refers to a program that uses machine learning algorithms to suggest the most suitable makeup and hairstyles based on the user's facial features.

[0009] A "display device" refers to a display or screen used to visually present to a user virtually applied makeup or hairstyle.

[0010] "Product information" refers to detailed data about products related to the suggested makeup and hairstyles, including prices and purchase links.

[0011] A "purchase interface" refers to the screen or system that users use when purchasing products based on product information. [Brief explanation of the drawing]

[0012] [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]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that enables individuals to easily find the makeup and hairstyle that best suits them. When a user stands in front of the system, a camera installed in the terminal captures the user's image. The terminal sends this image data to a server, which analyzes the data using facial recognition technology to identify the user's face. In this process, facial features such as skin tone, face shape, and eye position are extracted.

[0034] The server applies an AI model based on extracted feature data to suggest makeup and hairstyles that suit the user. This AI model takes into account past selection history and the latest trend data to provide optimal suggestions for each user.

[0035] The device, based on suggestions received from the server, virtually applies makeup and hairstyles to the user's video in real time and displays them visually to the user. This allows the user to instantly see styles that suit them. If the user likes the displayed style, they can select it and proceed to the next step.

[0036] Furthermore, the server presents product information for cosmetics related to the suggested style, and the terminal displays this to the user through a purchase interface. Users can quickly review product information and complete the purchase process, further enhancing convenience.

[0037] For example, if a user wants to try a new lip color that suits them using the system, the system will suggest several lip colors based on the user's facial data. Then, the color selected by the AI ​​model's analysis will be applied to the user's face in real time, and the result will be displayed on the device screen. The user can then choose their favorite color and decide to purchase it.

[0038] This invention provides a groundbreaking system that combines AI technology and real-time image processing to enable individuals to easily make choices related to their own beauty.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] When a user stands in front of the system, the device automatically activates its camera and captures an image of the user's face.

[0042] Step 2:

[0043] The terminal sends the acquired video data to the server and prepares it for processing.

[0044] Step 3:

[0045] The server runs a facial recognition algorithm on the received video data to identify the user's face. In this process, it accurately captures the contours of the face and the positions of its features.

[0046] Step 4:

[0047] The server extracts facial features, converting data such as skin tone, face shape, and the position of eyes and mouth into digital format.

[0048] Step 5:

[0049] The server uses extracted features to apply an AI model and generate suitable makeup and hairstyle options for the user. The AI ​​model makes optimal suggestions by analyzing past user data and current trends.

[0050] Step 6:

[0051] The server sends the generated style suggestions to the terminal.

[0052] Step 7:

[0053] The terminal virtually applies style suggestions received from the server to the user's video in real time and displays the result in a mirror.

[0054] Step 8:

[0055] Users can check the style reflected in the mirror and select their preferred style using voice or gestures.

[0056] Step 9:

[0057] The server processes information about products related to the selected cosmetics or hairstyles, based on the user's choices, and provides it to the terminal.

[0058] Step 10:

[0059] The terminal displays product information in a mirror and provides a purchase interface, allowing users to easily complete their shopping.

[0060] (Example 1)

[0061] 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."

[0062] Finding the perfect beauty style for oneself is generally a time-consuming and laborious process. The challenge lies in simplifying this process and providing more personalized suggestions to each individual. Furthermore, it's necessary to provide an environment where suggested styles can be instantly visualized, streamlining the user's selection and purchase process.

[0063] 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.

[0064] In this invention, the server includes means for acquiring images of the user using a video acquisition device, means for recognizing faces based on the acquired image data and extracting facial attributes, and means for providing beauty suggestions suitable for the user by utilizing a generated AI model via a data processing unit based on the extracted facial attribute data. This enables the user to quickly find a beauty style that suits them, try out styles in real time, and efficiently carry out a series of processes to purchase products if necessary.

[0065] A "video acquisition device" is a device used to photograph a user and acquire that video data.

[0066] "Facial recognition" is a technology that identifies a user's face from acquired video data and recognizes its characteristics.

[0067] "Facial attributes" refer to physical characteristics such as skin tone, face shape, and eye position that can be obtained in facial identification.

[0068] A "data processing unit" is an element or device within a system that analyzes acquired data and performs necessary calculations and data processing to provide information appropriately.

[0069] A "generative AI model" is a type of artificial intelligence technology used to provide users with appropriate beauty suggestions based on input data.

[0070] A "prompt statement" is an input statement used to give a generative AI model specific instructions that are tailored to the user's particular characteristics and desired results.

[0071] "Beauty suggestions" refer to recommendations regarding optimal makeup and hairstyle styles, based on the user's facial attribute data.

[0072] A "virtual space" is an environment that allows users to visually experiment with styles on a computer-generated, real-time interface.

[0073] A "visualization device" is a device used to visually display proposed styles and their trial results to the user.

[0074] A "sales interface" is an online commerce platform provided to users for purchasing products related to their chosen makeup or hairstyle.

[0075] This invention is a system designed to help individuals quickly find the beauty style that best suits them, visually confirm it, and then purchase the product. An embodiment of this system is shown below.

[0076] First, the user stands in front of the camera installed on the device. The device uses a high-resolution camera to capture the user's image. This image data is sent to a server for facial recognition.

[0077] The server uses facial recognition algorithms to identify the user's face from video data and extract facial attributes. These attributes include skin tone, face shape, and eye position. Facial recognition serves as the starting point for providing personalized suggestions to each user.

[0078] Based on this extracted facial attribute data, the server applies a generative AI model. The generative AI model takes into account past selection history and trend data to provide personalized beauty suggestions to the user. An example of a prompt message is, "Based on the user's facial data, please suggest the most suitable lip color, taking into account the latest trends."

[0079] Next, the terminal reflects the suggestions received from the server onto the user's video in real time. Using virtual space technology, the selected makeup and hairstyle are applied to the user's face, and the results are displayed on the screen.

[0080] Users can instantly review the presented styles and select any they like. Product information related to the selected style is provided by the server and displayed via the terminal's sales interface. Using this interface, users can quickly view product details and proceed with the purchase.

[0081] Through the above procedure, this system provides users with innovative and efficient beauty solutions. By utilizing generative AI models, personalized beauty recommendations and the convenience of product purchases are significantly improved.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The device detects when the user stands in front of the camera. It acquires the user's video data in real time through the high-resolution camera. The input for this step is the user's actual appearance, and the output is digital video data. This video data is then converted to an appropriate format for later analysis.

[0085] Step 2:

[0086] The terminal transmits the acquired video data to the server. The transmitted data is used by the server as input for a face recognition algorithm. The data processing here involves identifying the user's face data from the video data. The output is the user's facial attribute data (skin tone, face shape, eye position, etc.).

[0087] Step 3:

[0088] The server utilizes an AI model that generates data based on facial attribute data. This attribute data is input to the AI ​​model as elements of prompt statements. The prompt statements also consider past selection history and trend data. The output generates beauty suggestions best suited to the user. These suggestions include makeup and hairstyles that best match the user's features.

[0089] Step 4:

[0090] The terminal receives beauty suggestions sent from the server. Here, to display the suggestions in real time on the interface, video processing is performed, and the generated style is applied to the user's video. The output is a visual result, including the style virtually applied to the user's video. The user can view the results through the screen.

[0091] Step 5:

[0092] The user selects their preferred style from the presented options. The selected information is then used for the next step, which involves displaying detailed information about related products and entering it into the purchase interface.

[0093] Step 6:

[0094] The server processes product information related to the selected style and sends it to the terminal. The terminal displays the product information in detail and provides a sales interface that allows the user to compare and consider options. As an output, the user can quickly select a product and proceed with the purchase.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern times, individuals face the problem of having to spend time and money on trial and error to choose the makeup and hairstyle that best suits them. Furthermore, when choosing cosmetics in stores, the number of products available for try-on is often limited, narrowing the range of choices. There is a need for a system that solves these problems and supports efficient and satisfying style selection.

[0098] 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.

[0099] In this invention, the server includes means for acquiring an image of the user using an image acquisition device; means for identifying a face and extracting an appearance based on the acquired image data; means for presenting a style suitable for the user using an AI model via an information processing device based on the extracted appearance data; means for virtually applying the presented style to the image in real time and providing it to the user using a display device; means for presenting product information related to the selected style and providing a purchase interface; and means for providing an interface that enables the user to decide on a purchase based on the presented virtual style. This makes it possible for consumers to easily try out the style that best suits them and efficiently go through the process up to purchase.

[0100] A "video acquisition device" is a device used to acquire images and video data from users, and mainly refers to cameras and other imaging equipment.

[0101] "Identifying faces and extracting their appearance" refers to the process of identifying a specific face from acquired image data and extracting its facial features in detail.

[0102] An "information processing device" refers to a computer system used to analyze and process data and generate specific deliverables.

[0103] An "AI model" refers to a mathematical algorithm or computational model that uses artificial intelligence technology to perform data analysis.

[0104] "Presenting a style" refers to the act of recommending and displaying appropriate appearances and styles based on the user's characteristics.

[0105] "Applying something virtually in real time to an image" refers to a process that uses digital technology to instantly overlay an appearance onto an image that is not actually present.

[0106] A "display device" refers to a screen or display used to provide visual information to a user.

[0107] A "purchase interface" refers to a system or screen layout designed to assist consumers in the process of purchasing a product.

[0108] "Making a purchase decision based on a virtual style" refers to a user making a purchase choice based on a trialed digital appearance.

[0109] The system for carrying out the present invention is a multifunctional device that streamlines the process of users finding the most suitable makeup and hairstyle for themselves. This system mainly consists of a camera, a display, and an information processing device.

[0110] First, the device uses its camera to capture video of the user. The captured video data is then processed by a server for face recognition. Specifically, image processing software such as OpenCV is used to extract facial features.

[0111] Next, the information processing device analyzes the facial data acquired using a facial recognition library such as Dlib to clarify its features. The analyzed feature data is then passed to an AI model via the information processing device. This AI model considers past selection history and trend data to suggest the most suitable makeup and hairstyle for the user. For example, the MakeupRecommendationModel is used as the AI ​​model.

[0112] Subsequently, the server virtually applies the proposed style to the video in real time and presents it to the user through a display device. This real-time image processing is achieved using OpenCV and other image synthesis technologies.

[0113] Finally, users can review the virtual style presented through their device and decide whether to purchase. A product information display and purchase interface are provided, enabling smooth transactions.

[0114] As a concrete example, imagine a scenario where a user picks up their smartphone and takes a photo of their face in a store. The AI ​​model uses this image to suggest the most suitable cosmetic colors and displays the results on the screen. The user can then choose their favorite from the options.

[0115] An example of a prompt message is: "The user takes a picture of their face with their smartphone. The AI ​​model analyzes this image and suggests the best lipstick color. The user looks at the displayed color and decides whether to purchase it." This prompt allows the system to accurately capture the user's features and quickly suggest an attractive style.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The device uses its camera to capture video of the user's face. The captured video is processed as RAW data and temporarily stored for processing in the next step. The input is live video from the camera, and the output is RAW image data.

[0119] Step 2:

[0120] The server receives RAW image data provided by the terminal and performs face recognition. Using OpenCV, an image processing software, it identifies faces from the image data and extracts features such as position and shape. The input is RAW image data, and the output is facial feature data.

[0121] Step 3:

[0122] The server inputs facial feature data into an AI model and suggests appropriate makeup and hairstyles. The AI ​​model used is the MakeupRecommendationModel, which takes into account past selection history and trend data. The input is facial feature data, and the output is suggested style data.

[0123] Step 4:

[0124] The server virtually applies the proposed style to the video in real time. Using OpenCV and image synthesis techniques, it overlays the proposed style onto the image. The input is the proposed style data and facial feature data, and the output is the visually transformed video data.

[0125] Step 5:

[0126] The terminal presents a virtual style to the user using a display device. Visually converted video data is displayed on the screen in real time for the user to confirm. The input is visually converted video data, and the output is a visual display for the user.

[0127] Step 6:

[0128] The user reviews the presented virtual style and decides whether to purchase it. The terminal displays product information via the purchase interface and provides options to complete the purchase process. The input is the user's visual confirmation result, and the output is the purchase decision data.

[0129] 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.

[0130] This invention is a system for users to find makeup and hairstyles that suit them, and aims to optimize suggestions based on an emotion engine that recognizes the user's emotions in real time. When a user stands in front of the system, a camera attached to the terminal captures an image of the user's face. This image data is sent to a server, where a face recognition algorithm identifies the user's face and extracts its features.

[0131] Once features are extracted, the server uses an AI model to suggest suitable makeup and hairstyles for the user. This AI model is configured to make more accurate suggestions by considering the user's past selection history and current trend data, as well as incorporating the user's emotional data read in real time by an emotion engine.

[0132] The device applies virtual makeup and hairstyles to the user's video based on suggestions received from the server, and displays the results on the screen. The user reacts to the presented style. At this time, the emotion engine analyzes the user's facial expressions and evaluates their emotions towards the suggestions. This is sent as feedback to the AI ​​model and used to dynamically adjust the suggestions.

[0133] For example, consider a scenario where a user is trying out a new hairstyle. The device sends video to a server, which uses an AI model to select an appropriate style based on the user's facial contours and features. Simultaneously, an emotion engine monitors the user's facial expressions and analyzes their emotions to determine whether they are excited or dissatisfied. Based on this analysis data, the server can adjust the next suggestion to better suit the user's preferences.

[0134] Furthermore, products related to the style selected by the user are displayed, and the purchase process can be easily completed through the device. This invention allows users to effectively explore and implement beauty styles based on their individual needs, thus providing a highly satisfying experience.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] When a user stands in front of the system, the terminal's camera automatically captures the user's face and acquires video data.

[0138] Step 2:

[0139] The terminal sends the acquired video data to the server and prepares to begin data processing.

[0140] Step 3:

[0141] The server uses a facial recognition algorithm to identify the user's face from the received video data, accurately extracting features such as skin tone, facial shape, and the position of the eyes and mouth.

[0142] Step 4:

[0143] Based on the extracted feature data, the server generates suggestions for the user's most suitable makeup and hairstyles via an AI model. Past selection history and trend data are also taken into consideration during this process.

[0144] Step 5:

[0145] The terminal receives suggestions from the server, virtually applies makeup and hairstyles to the user's video, and displays them to the user in real time.

[0146] Step 6:

[0147] The emotion engine analyzes the user's facial expressions and recognizes the user's emotions in response to the suggestion in step 5, such as joy or dissatisfaction.

[0148] Step 7:

[0149] The server receives feedback from the emotion engine, adjusts the suggestions in real time, and sends them back to the terminal to provide better suggestions.

[0150] Step 8:

[0151] Users can select a style they like and then view information about related products.

[0152] Step 9:

[0153] The terminal displays product information related to the selected style and provides a purchase interface.

[0154] Step 10:

[0155] Users can easily purchase products they are interested in through the purchase interface.

[0156] Step 11:

[0157] The server records the user's purchase history and selection data, which is then used to further refine future recommendations.

[0158] (Example 2)

[0159] 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".

[0160] Conventional beauty style suggestion systems have difficulty providing suggestions that perfectly match the user's characteristics and preferences, and they also struggle to adjust suggestions in real time to reflect the user's emotions. Furthermore, they often lack an adequate interface for users to easily purchase products related to the style they have selected.

[0161] 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.

[0162] In this invention, the server includes means for identifying faces and extracting features based on acquired video data, means for suggesting a suitable beauty style to the user using a generative model via an information processing device based on the extracted feature data, and means for analyzing the user's facial expressions to acquire emotional data and provide feedback to the suggestions. This enables more accurate beauty style suggestions that take into account the user's individual characteristics and real-time emotional state. It also enables the provision of an efficient interface that allows users to immediately purchase products related to the selected style.

[0163] "Shooting device" refers to hardware components used to acquire video footage of the user.

[0164] "Video data" refers to digital data containing visual information acquired by a camera or camera.

[0165] "Identifying faces and extracting features" refers to the process of analyzing acquired video data to identify the position and shape data of faces and quantify their features.

[0166] An "information processing device" refers to an electronic device used to process data and perform calculations.

[0167] A "generative model" refers to a machine learning algorithm that learns from data and generates the most optimal results for the user.

[0168] "Beauty style" refers to a combination of makeup and hairstyle, and is a design proposed to enhance the user's appearance.

[0169] "Applying virtually to video in real time" refers to the process of instantly visually demonstrating a proposed style by overlaying it onto real-world footage.

[0170] A "display device" refers to a hardware device used to present visual information to a user.

[0171] "Product information" refers to detailed data about products related to beauty styles.

[0172] A "purchase interface" refers to the user interface through which users select products and complete the purchase process.

[0173] "Analyzing facial expressions to obtain emotional data" refers to the process of analyzing a user's facial expressions and extracting their current emotional state as digital data.

[0174] "Providing feedback" refers to adjusting suggestions using analyzed sentiment data.

[0175] This invention is a system for users to find the optimal beauty style. This system includes a shooting device, an information processing device, a generative model, an emotion analysis means, and a display device.

[0176] The device incorporates a high-resolution camera to capture images of the user's face. The captured video data is sent to a server via a rapid data transfer protocol. The server uses facial recognition technology such as OpenCV to identify the face and extract feature data. These features include the contours of the user's face and the location information of important facial features.

[0177] The server inputs this extracted data into a generative model via an information processing device. The generative model uses Google's TENSORFLOW®, which combines the user's past selection history, the latest beauty trend data, and emotional data from the Emotion API to suggest a beauty style that suits the user.

[0178] The terminal uses augmented reality technologies such as Adobe Aero to apply suggested data received from the server onto the user's video in real time, and displays the results on a display device. The user reacts to the displayed beauty styles by indicating whether they like them or not. At that time, the terminal analyzes emotional data from the user's facial expressions and feeds it back to the server.

[0179] The server uses this feedback information to dynamically adjust the generative model, enabling suggestions that better fit the user's preferences. Furthermore, once the user has decided on a style, information on related products is presented, and purchases can be made through a simple purchase interface.

[0180] For example, if a user inputs "I want to try a trendy short hairstyle" into the device, the system will suggest trendy styles that suit the user's facial features and display images of them on the screen. The user indicates whether they like the style with their facial expression, and the system makes further suggestions based on that feedback.

[0181] An example of a prompt message might be: "Please suggest trendy short hairstyles for women in their 30s. The user has a round face shape, and the design should evoke excitement, taking into account recent trend data."

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] When a user stands in front of the device's camera, the device activates its high-resolution camera and captures the user's face in real time. The input data is the real-time face image, and the output is the video data. Because this video data is continuously acquired, it is possible to recognize the user's face even if they move naturally.

[0185] Step 2:

[0186] The terminal quickly and securely transmits the acquired video data to the server. The input data is the facial video acquired in step 1, and the output is the raw data received by the server. This ensures high-speed data processing.

[0187] Step 3:

[0188] The server uses facial recognition technology to identify facial features such as position, contour, eyes, and mouth from the received video data, and extracts feature vectors. The input data is the video data received from step 2, and the output is the identified facial feature vectors. By using machine learning techniques in this process, highly accurate feature extraction becomes possible.

[0189] Step 4:

[0190] The server inputs the identified feature vectors into the generative model. The generative AI model then proposes the most suitable beauty style for the user, taking into account previous selection history, trend data, sentiment analysis results, and other factors. The input data includes feature vectors and history data, and the output is the proposed beauty style.

[0191] Step 5:

[0192] The terminal uses augmented reality technology to overlay the generated suggestions onto the user's video in real time and present them to the user on a display device. The input data is the suggestions generated in step 4, and the output is a beauty style visually presented to the user. The user can check the results via the display.

[0193] Step 6:

[0194] If the user shows any emotion upon seeing the presented style, the device analyzes the facial expression and obtains emotion data. The input data is the user's facial expression, and the output is the analyzed emotion data. This emotion data is immediately sent to the server.

[0195] Step 7:

[0196] The server uses the acquired sentiment data to adjust the generative model and reflect it in new suggestions. The input data consists of sentiment data and feedback on the suggestions, and the output is the adjusted next suggestion. This results in a style that is more closely suited to the user's preferences.

[0197] (Application Example 2)

[0198] 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".

[0199] In the past, it was difficult to adequately consider the preferences and feelings of users when proposing beauty styles. Furthermore, there were few ways to virtually try out the suggested styles, and the process leading to purchase was cumbersome, making it difficult to provide a satisfying experience for users.

[0200] 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.

[0201] In this invention, the server includes means for acquiring the user's image using a camera and extracting facial features, means for suggesting a suitable beauty style to the user using an artificial intelligence model via an information processing device based on the extracted feature data, and means for analyzing the user's emotions in real time and adjusting the suggestions based on that data. This makes it possible to provide highly accurate beauty style suggestions based on the user's individual preferences, a real-time virtual try-on experience, and immediate purchase.

[0202] A "recording device" is a device used to acquire images of the user.

[0203] "Facial features" refer to information about the shape and structure of the user's face.

[0204] An "information processing device" is a computer device that analyzes acquired data and performs calculations and makes suggestions using artificial intelligence models.

[0205] An "artificial intelligence model" is an algorithm that performs analysis based on past data and real-time input, and outputs results that are suitable for the user.

[0206] "Analyzing user emotions in real time" refers to the process of instantly evaluating a user's emotional state based on video data and other information.

[0207] A "presentation device" refers to a display or projector used to visually present the proposed content.

[0208] This invention is a system that proposes and allows users to try out beauty styles that are suitable for them. The system functions as follows: A camera mounted on the terminal captures an image of the user's face. This image data is sent to a server in the cloud. The server uses a face recognition algorithm to extract facial features. For example, a general-purpose camera can be used as the camera, and the open-source library OpenCV can be used as the face recognition algorithm.

[0209] The server inputs facial feature data into a generating AI model via an information processing device to calculate the optimal beauty style for the user. This AI model is built using deep learning frameworks such as TensorFlow and PyTorch. In addition to the user's past selection history and trend information, real-time sentiment analysis data is also considered. Sentiment analysis uses Amazon Rekognition and Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0210] The terminal virtually applies beauty styles to the user's video based on suggestions received from the server and presents them through a display device. This allows the user to check styles that suit them in real time. Smart glasses or displays are used as the display device.

[0211] Furthermore, product information related to the user's preferred style is displayed, and the user can proceed with the purchase via their device. At this time, the user's emotional feedback is sent to the server and used to optimize the AI ​​model.

[0212] For example, if a user wearing smart glasses at a hair salon expresses slight dissatisfaction with the first hairstyle suggested, the next suggestion will immediately reflect that feedback and be adjusted to better suit the user's preferences. Furthermore, the user can order the suggested styling products on the spot.

[0213] An example of a prompt message for a generative AI model might be: "Based on the user's facial features and emotion data, please suggest the optimal makeup style. Consider the following data and output a list."

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server acquires the user's facial image through the camera installed in the terminal. The input here is the facial image obtained by the camera, and the output is the generated video data. The acquired video data is sent to the server, and processing begins.

[0217] Step 2:

[0218] The server uses a face recognition algorithm to identify the user's face from the received video data. The input is the video data acquired in step 1, and the output is data showing the contours and features of the face. This process uses a face recognition library such as OpenCV to extract the main landmarks of the face from the video data.

[0219] Step 3:

[0220] The server uses a generative AI model to suggest suitable beauty styles for the user based on facial feature data. Inputs include the facial feature data from step 2, as well as past selection history and trend information. Output is a list of suggested beauty styles. At this stage, an AI model using TensorFlow or PyTorch analyzes the feature data and generates suggestions.

[0221] Step 4:

[0222] The server uses an emotion analysis engine to analyze emotional data from the user's facial expressions in real time. The input is real-time video data, and the output is data indicating the user's emotional state. It uses Amazon Rekognition or Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0223] Step 5:

[0224] The device virtually applies a style to the user's image based on style suggestions sent from the server. The input is the suggested data from the server, and the output is the user's virtual styled image. The image is presented to the user through the device's display or smart glasses.

[0225] Step 6:

[0226] The user makes a selection from the presented styles, and the result is fed back to the server. The input is the user's selection, and the output is feedback data for updating the AI ​​model. This data is re-entered into the AI ​​model to improve the quality of future suggestions.

[0227] Step 7:

[0228] The terminal displays product information related to the selected beauty style and assists with the purchase process. The input is product information related to the selected style, and the output is the purchase process interface. It supports users in purchasing their favorite products on the spot.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention is a system that enables individuals to easily find the makeup and hairstyle that best suits them. When a user stands in front of the system, a camera installed in the terminal captures the user's image. The terminal sends this image data to a server, which analyzes the data using facial recognition technology to identify the user's face. In this process, facial features such as skin tone, face shape, and eye position are extracted.

[0246] The server applies an AI model based on extracted feature data to suggest makeup and hairstyles that suit the user. This AI model takes into account past selection history and the latest trend data to provide optimal suggestions for each user.

[0247] The device, based on suggestions received from the server, virtually applies makeup and hairstyles to the user's video in real time and displays them visually to the user. This allows the user to instantly see styles that suit them. If the user likes the displayed style, they can select it and proceed to the next step.

[0248] Furthermore, the server presents product information for cosmetics related to the suggested style, and the terminal displays this to the user through a purchase interface. Users can quickly review product information and complete the purchase process, further enhancing convenience.

[0249] For example, if a user wants to try a new lip color that suits them using the system, the system will suggest several lip colors based on the user's facial data. Then, the color selected by the AI ​​model's analysis will be applied to the user's face in real time, and the result will be displayed on the device screen. The user can then choose their favorite color and decide to purchase it.

[0250] This invention provides a groundbreaking system that combines AI technology and real-time image processing to enable individuals to easily make choices related to their own beauty.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] When a user stands in front of the system, the device automatically activates its camera and captures an image of the user's face.

[0254] Step 2:

[0255] The terminal sends the acquired video data to the server and prepares it for processing.

[0256] Step 3:

[0257] The server runs a facial recognition algorithm on the received video data to identify the user's face. In this process, it accurately captures the contours of the face and the positions of its features.

[0258] Step 4:

[0259] The server extracts facial features, converting data such as skin tone, face shape, and the position of eyes and mouth into digital format.

[0260] Step 5:

[0261] The server uses extracted features to apply an AI model and generate suitable makeup and hairstyle options for the user. The AI ​​model makes optimal suggestions by analyzing past user data and current trends.

[0262] Step 6:

[0263] The server sends the generated style suggestions to the terminal.

[0264] Step 7:

[0265] The terminal virtually applies style suggestions received from the server to the user's video in real time and displays the result in a mirror.

[0266] Step 8:

[0267] Users can check the style reflected in the mirror and select their preferred style using voice or gestures.

[0268] Step 9:

[0269] The server processes information about products related to the selected cosmetics or hairstyles, based on the user's choices, and provides it to the terminal.

[0270] Step 10:

[0271] The terminal displays product information in a mirror and provides a purchase interface, allowing users to easily complete their shopping.

[0272] (Example 1)

[0273] 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."

[0274] Finding the perfect beauty style for oneself is generally a time-consuming and laborious process. The challenge lies in simplifying this process and providing more personalized suggestions to each individual. Furthermore, it's necessary to provide an environment where suggested styles can be instantly visualized, streamlining the user's selection and purchase process.

[0275] 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.

[0276] In this invention, the server includes means for acquiring images of the user using a video acquisition device, means for recognizing faces based on the acquired image data and extracting facial attributes, and means for providing beauty suggestions suitable for the user by utilizing a generated AI model via a data processing unit based on the extracted facial attribute data. This enables the user to quickly find a beauty style that suits them, try out styles in real time, and efficiently carry out a series of processes to purchase products if necessary.

[0277] A "video acquisition device" is a device used to photograph a user and acquire that video data.

[0278] "Facial recognition" is a technology that identifies a user's face from acquired video data and recognizes its characteristics.

[0279] "Facial attributes" refer to physical characteristics such as skin tone, face shape, and eye position obtained in face recognition.

[0280] "Data processing unit" refers to an element or device within a system that analyzes acquired data and performs necessary calculations and data processing to appropriately provide information.

[0281] "Generative AI model" is one of the artificial intelligence technologies utilized to provide appropriate beauty suggestions to users based on the input data.

[0282] "Prompt sentence" is an input sentence for giving specific instructions to the generative AI model according to the specific characteristics of the user and the required results.

[0283] "Beauty suggestion" refers to recommendations regarding optimal makeup and hairstyle styles proposed based on the user's facial attribute data.

[0284] "Virtual space" is an environment that enables users to visually try styles on a real-time interface generated by a computer.

[0285] "Visualization device" is a device used to visually display the proposed styles and their trial results to the user.

[0286] "Sales interface" is an online commercial transaction platform provided to users for purchasing products related to the selected makeup and hairstyle.

[0287] This invention is a system to help individuals find the most suitable beauty style for themselves in a short time, visually confirm it, and even proceed to purchase the product directly. The following shows the embodiments of this system.

[0288] First, the user stands in front of the camera installed on the device. The device uses a high-resolution camera to capture the user's image. This image data is sent to a server for facial recognition.

[0289] The server uses facial recognition algorithms to identify the user's face from video data and extract facial attributes. These attributes include skin tone, face shape, and eye position. Facial recognition serves as the starting point for providing personalized suggestions to each user.

[0290] Based on this extracted facial attribute data, the server applies a generative AI model. The generative AI model takes into account past selection history and trend data to provide personalized beauty suggestions to the user. An example of a prompt message is, "Based on the user's facial data, please suggest the most suitable lip color, taking into account the latest trends."

[0291] Next, the terminal reflects the suggestions received from the server onto the user's video in real time. Using virtual space technology, the selected makeup and hairstyle are applied to the user's face, and the results are displayed on the screen.

[0292] Users can instantly review the presented styles and select any they like. Product information related to the selected style is provided by the server and displayed via the terminal's sales interface. Using this interface, users can quickly view product details and proceed with the purchase.

[0293] Through the above procedure, this system provides users with innovative and efficient beauty solutions. By utilizing generative AI models, personalized beauty recommendations and the convenience of product purchases are significantly improved.

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] The device detects when the user stands in front of the camera. It acquires the user's video data in real time through the high-resolution camera. The input for this step is the user's actual appearance, and the output is digital video data. This video data is then converted to an appropriate format for later analysis.

[0297] Step 2:

[0298] The terminal transmits the acquired video data to the server. The transmitted data is used by the server as input for a face recognition algorithm. The data processing here involves identifying the user's face data from the video data. The output is the user's facial attribute data (skin tone, face shape, eye position, etc.).

[0299] Step 3:

[0300] The server utilizes an AI model that generates data based on facial attribute data. This attribute data is input to the AI ​​model as elements of prompt statements. The prompt statements also consider past selection history and trend data. The output generates beauty suggestions best suited to the user. These suggestions include makeup and hairstyles that best match the user's features.

[0301] Step 4:

[0302] The terminal receives beauty suggestions sent from the server. Here, to display the suggestions in real time on the interface, video processing is performed, and the generated style is applied to the user's video. The output is a visual result, including the style virtually applied to the user's video. The user can view the results through the screen.

[0303] Step 5:

[0304] The user selects a style from the presented styles. The selected information serves as the input for the next step, namely, the display of detailed information about related products and the purchase interface.

[0305] Step 6:

[0306] The server processes the product information related to the selected style and sends it to the terminal. On the terminal, the product information is displayed in detail, providing a sales interface for the user to compare and consider. As an output, the user can quickly select a product and proceed with the purchase.

[0307] (Application Example 1)

[0308] Next, Application Example 1 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".

[0309] In modern times, there is a problem that it takes time and cost for individuals to choose the most suitable makeup and hairstyle for themselves through trial and error. Also, when choosing cosmetics in a store, there are often limited products that can be tried on, resulting in a narrow selection range. There is a need for a system that solves such problems and supports efficient and satisfactory style selection.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0311] In this invention, the server includes means for acquiring an image of the user using an image acquisition device; means for identifying a face and extracting an appearance based on the acquired image data; means for presenting a style suitable for the user using an AI model via an information processing device based on the extracted appearance data; means for virtually applying the presented style to the image in real time and providing it to the user using a display device; means for presenting product information related to the selected style and providing a purchase interface; and means for providing an interface that enables the user to decide on a purchase based on the presented virtual style. This makes it possible for consumers to easily try out the style that best suits them and efficiently go through the process up to purchase.

[0312] A "video acquisition device" is a device used to acquire images and video data from users, and mainly refers to cameras and other imaging equipment.

[0313] "Identifying faces and extracting their appearance" refers to the process of identifying a specific face from acquired image data and extracting its facial features in detail.

[0314] An "information processing device" refers to a computer system used to analyze and process data and generate specific deliverables.

[0315] An "AI model" refers to a mathematical algorithm or computational model that uses artificial intelligence technology to perform data analysis.

[0316] "Presenting a style" refers to the act of recommending and displaying appropriate appearances and styles based on the user's characteristics.

[0317] "Applying something virtually in real time to an image" refers to a process that uses digital technology to instantly overlay an appearance onto an image that is not actually present.

[0318] A "display device" refers to a screen or display used to provide visual information to a user.

[0319] A "purchase interface" refers to a system or screen layout designed to assist consumers in the process of purchasing a product.

[0320] "Making a purchase decision based on a virtual style" refers to users making a purchase choice based on a trialed digital appearance.

[0321] The system for carrying out the present invention is a multifunctional device that streamlines the process of users finding the most suitable makeup and hairstyle for themselves. This system mainly consists of a camera, a display, and an information processing device.

[0322] First, the device uses its camera to capture video of the user. The captured video data is then processed by a server for face recognition. Specifically, image processing software such as OpenCV is used to extract facial features.

[0323] Next, the information processing device analyzes the facial data acquired using a facial recognition library such as Dlib to clarify its features. The analyzed feature data is then passed to an AI model via the information processing device. This AI model considers past selection history and trend data to suggest the most suitable makeup and hairstyle for the user. For example, the MakeupRecommendationModel is used as the AI ​​model.

[0324] Subsequently, the server virtually applies the proposed style to the video in real time and presents it to the user through a display device. This real-time image processing is achieved using OpenCV and other image synthesis technologies.

[0325] Finally, users can review the virtual style presented through their device and decide whether to purchase. A product information display and purchase interface are provided, enabling smooth transactions.

[0326] As a concrete example, imagine a scenario where a user picks up their smartphone and takes a photo of their face in a store. The AI ​​model uses this image to suggest the most suitable cosmetic colors and displays the results on the screen. The user can then choose their favorite from the options.

[0327] An example of a prompt message is: "The user takes a picture of their face with their smartphone. The AI ​​model analyzes this image and suggests the best lipstick color. The user looks at the displayed color and decides whether to purchase it." This prompt allows the system to accurately capture the user's features and quickly suggest an attractive style.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The device uses its camera to capture video of the user's face. The captured video is processed as RAW data and temporarily stored for processing in the next step. The input is live video from the camera, and the output is RAW image data.

[0331] Step 2:

[0332] The server receives RAW image data provided by the terminal and performs face recognition. Using OpenCV, an image processing software, it identifies faces from the image data and extracts features such as position and shape. The input is RAW image data, and the output is facial feature data.

[0333] Step 3:

[0334] The server inputs facial feature data into an AI model and suggests appropriate makeup and hairstyles. The AI ​​model used is the MakeupRecommendationModel, which takes into account past selection history and trend data. The input is facial feature data, and the output is suggested style data.

[0335] Step 4:

[0336] The server virtually applies the proposed style to the video in real time. Using OpenCV and image synthesis techniques, it overlays the proposed style onto the image. The input is the proposed style data and facial feature data, and the output is the visually transformed video data.

[0337] Step 5:

[0338] The terminal presents a virtual style to the user using a display device. Visually converted video data is displayed on the screen in real time for the user to confirm. The input is visually converted video data, and the output is a visual display for the user.

[0339] Step 6:

[0340] The user reviews the presented virtual style and decides whether to purchase it. The terminal displays product information via the purchase interface and provides options to complete the purchase process. The input is the user's visual confirmation result, and the output is the purchase decision data.

[0341] 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.

[0342] This invention is a system for users to find makeup and hairstyles that suit them, and aims to optimize suggestions based on an emotion engine that recognizes the user's emotions in real time. When a user stands in front of the system, a camera attached to the terminal captures an image of the user's face. This image data is sent to a server, where a face recognition algorithm identifies the user's face and extracts its features.

[0343] Once features are extracted, the server uses an AI model to suggest suitable makeup and hairstyles for the user. This AI model is configured to make more accurate suggestions by considering the user's past selection history and current trend data, as well as incorporating the user's emotional data read in real time by an emotion engine.

[0344] The device applies virtual makeup and hairstyles to the user's video based on suggestions received from the server, and displays the results on the screen. The user reacts to the presented style. At this time, the emotion engine analyzes the user's facial expressions and evaluates their emotions towards the suggestions. This is sent as feedback to the AI ​​model and used to dynamically adjust the suggestions.

[0345] For example, consider a scenario where a user is trying out a new hairstyle. The device sends video to a server, which uses an AI model to select an appropriate style based on the user's facial contours and features. Simultaneously, an emotion engine monitors the user's facial expressions and analyzes their emotions to determine whether they are excited or dissatisfied. Based on this analysis data, the server can then adjust the next suggestion to better suit the user's preferences.

[0346] Furthermore, products related to the style selected by the user are displayed, and the purchase process can be easily completed through the device. This invention allows users to effectively explore and implement beauty styles based on their individual needs, thus providing a highly satisfying experience.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] When a user stands in front of the system, the terminal's camera automatically captures the user's face and acquires video data.

[0350] Step 2:

[0351] The terminal sends the acquired video data to the server and prepares to begin data processing.

[0352] Step 3:

[0353] The server uses a facial recognition algorithm to identify the user's face from the received video data, accurately extracting features such as skin tone, facial shape, and the position of the eyes and mouth.

[0354] Step 4:

[0355] Based on the extracted feature data, the server generates suggestions for the user's most suitable makeup and hairstyles via an AI model. Past selection history and trend data are also taken into consideration during this process.

[0356] Step 5:

[0357] The terminal receives suggestions from the server, virtually applies makeup and hairstyles to the user's video, and displays them to the user in real time.

[0358] Step 6:

[0359] The emotion engine analyzes the user's facial expressions and recognizes the user's emotions in response to the suggestion in step 5, such as joy or dissatisfaction.

[0360] Step 7:

[0361] The server receives feedback from the emotion engine, adjusts the suggestions in real time, and sends them back to the terminal to provide better suggestions.

[0362] Step 8:

[0363] Users can select a style they like and then view information about related products.

[0364] Step 9:

[0365] The terminal displays product information related to the selected style and provides a purchase interface.

[0366] Step 10:

[0367] Users can easily purchase products they are interested in through the purchase interface.

[0368] Step 11:

[0369] The server records the user's purchase history and selection data, which is then used to further refine future recommendations.

[0370] (Example 2)

[0371] 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 glasses 214 will be referred to as the "terminal".

[0372] Conventional beauty style suggestion systems have difficulty providing suggestions that perfectly match the user's characteristics and preferences, and they also struggle to adjust suggestions in real time to reflect the user's emotions. Furthermore, they often lack an adequate interface for users to easily purchase products related to the style they have selected.

[0373] 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.

[0374] In this invention, the server includes means for identifying faces and extracting features based on acquired video data, means for suggesting a suitable beauty style to the user using a generative model via an information processing device based on the extracted feature data, and means for analyzing the user's facial expressions to acquire emotional data and provide feedback to the suggestions. This enables more accurate beauty style suggestions that take into account the user's individual characteristics and real-time emotional state. It also enables the provision of an efficient interface that allows users to immediately purchase products related to the selected style.

[0375] "Shooting device" refers to hardware components used to acquire video footage of the user.

[0376] "Video data" refers to digital data containing visual information acquired by a camera or camera.

[0377] "Identifying faces and extracting features" refers to the process of analyzing acquired video data to identify the position and shape data of faces and quantify their features.

[0378] An "information processing device" refers to an electronic device used to process data and perform calculations.

[0379] A "generative model" refers to a machine learning algorithm that learns from data and generates the most optimal results for the user.

[0380] "Beauty style" refers to a combination of makeup and hairstyle, and is a design proposed to enhance the user's appearance.

[0381] "Applying virtually to video in real time" refers to the process of instantly visually demonstrating a proposed style by overlaying it onto real-world footage.

[0382] A "display device" refers to a hardware device used to present visual information to a user.

[0383] "Product information" refers to detailed data about products related to beauty styles.

[0384] A "purchase interface" refers to the user interface through which users select products and complete the purchase process.

[0385] "Analyzing facial expressions to obtain emotional data" refers to the process of analyzing a user's facial expressions and extracting their current emotional state as digital data.

[0386] "Providing feedback" refers to adjusting suggestions using analyzed sentiment data.

[0387] This invention is a system for users to find the optimal beauty style. This system includes a shooting device, an information processing device, a generative model, an emotion analysis means, and a display device.

[0388] The device incorporates a high-resolution camera to capture images of the user's face. The captured video data is sent to a server via a rapid data transfer protocol. The server uses facial recognition technology such as OpenCV to identify the face and extract feature data. These features include the contours of the user's face and the location information of important facial features.

[0389] The server inputs this extracted data into a generative model via an information processing device. The generative model uses Google's TensorFlow and combines the user's past selection history, the latest beauty trend data, and emotion data from the Emotion API to suggest a beauty style that suits the user.

[0390] The terminal uses augmented reality technologies such as Adobe Aero to apply suggested data received from the server onto the user's video in real time, and displays the results on a display device. The user reacts to the displayed beauty styles by indicating whether they like them or not. At that time, the terminal analyzes emotional data from the user's facial expressions and feeds it back to the server.

[0391] The server uses this feedback information to dynamically adjust the generative model, enabling suggestions that better fit the user's preferences. Furthermore, once the user has decided on a style, information on related products is presented, and purchases can be made through a simple purchase interface.

[0392] For example, if a user inputs "I want to try a trendy short hairstyle" into the device, the system will suggest trendy styles that suit the user's facial features and display images of them on the screen. The user indicates whether they like the style with their facial expression, and the system makes further suggestions based on that feedback.

[0393] An example of a prompt message might be: "Please suggest trendy short hairstyles for women in their 30s. The user has a round face shape, and the design should evoke excitement, taking into account recent trend data."

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] When a user stands in front of the device's camera, the device activates its high-resolution camera and captures the user's face in real time. The input data is the real-time face image, and the output is the video data. Because this video data is continuously acquired, it is possible to recognize the user's face even if they move naturally.

[0397] Step 2:

[0398] The terminal quickly and securely transmits the acquired video data to the server. The input data is the facial video acquired in step 1, and the output is the raw data received by the server. This ensures high-speed data processing.

[0399] Step 3:

[0400] The server uses facial recognition technology to identify facial features such as position, contour, eyes, and mouth from the received video data, and extracts feature vectors. The input data is the video data received from step 2, and the output is the identified facial feature vectors. By using machine learning techniques in this process, highly accurate feature extraction becomes possible.

[0401] Step 4:

[0402] The server inputs the identified feature vectors into the generative model. The generative AI model then proposes the most suitable beauty style for the user, taking into account previous selection history, trend data, sentiment analysis results, and other factors. The input data includes feature vectors and history data, and the output is the proposed beauty style.

[0403] Step 5:

[0404] The terminal uses augmented reality technology to overlay the generated suggestions onto the user's video in real time and present them to the user on a display device. The input data is the suggestions generated in step 4, and the output is a beauty style visually presented to the user. The user can check the results via the display.

[0405] Step 6:

[0406] If the user shows any emotion upon seeing the presented style, the device analyzes the facial expression and obtains emotion data. The input data is the user's facial expression, and the output is the analyzed emotion data. This emotion data is immediately sent to the server.

[0407] Step 7:

[0408] The server uses the acquired sentiment data to adjust the generative model and reflect it in new suggestions. The input data consists of sentiment data and feedback on the suggestions, and the output is the adjusted next suggestion. This results in a style that is more closely suited to the user's preferences.

[0409] (Application Example 2)

[0410] 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 as the "terminal".

[0411] In the past, it was difficult to adequately consider the preferences and feelings of users when proposing beauty styles. Furthermore, there were few ways to virtually try out the suggested styles, and the process leading to purchase was cumbersome, making it difficult to provide a satisfying experience for users.

[0412] 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.

[0413] In this invention, the server includes means for acquiring the user's image using a camera and extracting facial features, means for suggesting a suitable beauty style to the user using an artificial intelligence model via an information processing device based on the extracted feature data, and means for analyzing the user's emotions in real time and adjusting the suggestions based on that data. This makes it possible to provide highly accurate beauty style suggestions based on the user's individual preferences, a real-time virtual try-on experience, and immediate purchase.

[0414] A "recording device" is a device used to acquire images of the user.

[0415] "Facial features" refer to information about the shape and structure of the user's face that is extracted from it.

[0416] An "information processing device" is a computer device that analyzes acquired data and performs calculations and makes suggestions using artificial intelligence models.

[0417] An "artificial intelligence model" is an algorithm that performs analysis based on past data and real-time input, and outputs results that are suitable for the user.

[0418] "Analyzing user emotions in real time" refers to the process of instantly evaluating a user's emotional state based on video data and other information.

[0419] A "presentation device" refers to a display or projector used to visually present the proposed content.

[0420] This invention is a system that proposes and allows users to try out beauty styles that are suitable for them. The system functions as follows: A camera mounted on the terminal captures an image of the user's face. This image data is sent to a server in the cloud. The server uses a face recognition algorithm to extract facial features. For example, a general-purpose camera can be used as the camera, and the open-source library OpenCV can be used as the face recognition algorithm.

[0421] The server inputs facial feature data into a generating AI model via an information processing device to calculate the optimal beauty style for the user. This AI model is built using deep learning frameworks such as TensorFlow and PyTorch. In addition to the user's past selection history and trend information, real-time sentiment analysis data is also considered. Sentiment analysis uses Amazon Rekognition and Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0422] The terminal virtually applies beauty styles to the user's video based on suggestions received from the server and presents them through a display device. This allows the user to check styles that suit them in real time. Smart glasses or displays are used as the display device.

[0423] Furthermore, product information related to the user's preferred style is displayed, and the user can proceed with the purchase via their device. At this time, the user's emotional feedback is sent to the server and used to optimize the AI ​​model.

[0424] For example, if a user wearing smart glasses at a hair salon expresses slight dissatisfaction with the first hairstyle suggested, the next suggestion will immediately reflect that feedback and be adjusted to better suit the user's preferences. Furthermore, the user can order the suggested styling products on the spot.

[0425] An example of a prompt message for a generative AI model might be: "Based on the user's facial features and emotion data, please suggest the optimal makeup style. Consider the following data and output a list."

[0426] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0427] Step 1:

[0428] The server acquires the user's facial image through the camera installed in the terminal. The input here is the facial image obtained by the camera, and the output is the generated video data. The acquired video data is sent to the server, and processing begins.

[0429] Step 2:

[0430] The server uses a face recognition algorithm to identify the user's face from the received video data. The input is the video data acquired in step 1, and the output is data showing the contours and features of the face. This process uses a face recognition library such as OpenCV to extract the main landmarks of the face from the video data.

[0431] Step 3:

[0432] The server uses a generative AI model to suggest suitable beauty styles for the user based on facial feature data. Inputs include the facial feature data from step 2, as well as past selection history and trend information. Output is a list of suggested beauty styles. At this stage, an AI model using TensorFlow or PyTorch analyzes the feature data and generates suggestions.

[0433] Step 4:

[0434] The server uses an emotion analysis engine to analyze emotional data from the user's facial expressions in real time. The input is real-time video data, and the output is data indicating the user's emotional state. It uses Amazon Rekognition or Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0435] Step 5:

[0436] The device virtually applies a style to the user's image based on style suggestions sent from the server. The input is the suggested data from the server, and the output is the user's virtual styled image. The image is presented to the user through the device's display or smart glasses.

[0437] Step 6:

[0438] The user makes a selection from the presented styles, and the result is fed back to the server. The input is the user's selection, and the output is feedback data for updating the AI ​​model. This data is re-entered into the AI ​​model to improve the quality of future suggestions.

[0439] Step 7:

[0440] The terminal displays product information related to the selected beauty style and assists with the purchase process. The input is product information related to the selected style, and the output is the purchase process interface. It supports users in purchasing their favorite products on the spot.

[0441] 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.

[0442] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0443] 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.

[0444] [Third Embodiment]

[0445] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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".

[0457] This invention is a system that enables individuals to easily find the makeup and hairstyle that best suits them. When a user stands in front of the system, a camera installed in the terminal captures the user's image. The terminal sends this image data to a server, which analyzes the data using facial recognition technology to identify the user's face. In this process, facial features such as skin tone, face shape, and eye position are extracted.

[0458] The server applies an AI model based on extracted feature data to suggest makeup and hairstyles that suit the user. This AI model takes into account past selection history and the latest trend data to provide optimal suggestions for each user.

[0459] The device, based on suggestions received from the server, virtually applies makeup and hairstyles to the user's video in real time and displays them visually to the user. This allows the user to instantly see styles that suit them. If the user likes the displayed style, they can select it and proceed to the next step.

[0460] Furthermore, the server presents product information for cosmetics related to the suggested style, and the terminal displays this to the user through a purchase interface. Users can quickly review product information and complete the purchase process, further enhancing convenience.

[0461] For example, if a user wants to try a new lip color that suits them using the system, the system will suggest several lip colors based on the user's facial data. Then, the color selected by the AI ​​model's analysis will be applied to the user's face in real time, and the result will be displayed on the device screen. The user can then choose their favorite color and decide to purchase it.

[0462] This invention provides a groundbreaking system that combines AI technology and real-time image processing to enable individuals to easily make choices related to their own beauty.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] When a user stands in front of the system, the device automatically activates its camera and captures an image of the user's face.

[0466] Step 2:

[0467] The terminal sends the acquired video data to the server and prepares it for processing.

[0468] Step 3:

[0469] The server runs a facial recognition algorithm on the received video data to identify the user's face. In this process, it accurately captures the contours of the face and the positions of its features.

[0470] Step 4:

[0471] The server extracts facial features, converting data such as skin tone, face shape, and the position of eyes and mouth into digital format.

[0472] Step 5:

[0473] The server uses extracted features to apply an AI model and generate suitable makeup and hairstyle options for the user. The AI ​​model makes optimal suggestions by analyzing past user data and current trends.

[0474] Step 6:

[0475] The server sends the generated style suggestions to the terminal.

[0476] Step 7:

[0477] The terminal virtually applies style suggestions received from the server to the user's video in real time and displays the result in a mirror.

[0478] Step 8:

[0479] Users can check the style reflected in the mirror and select their preferred style using voice or gestures.

[0480] Step 9:

[0481] The server processes information about products related to the selected cosmetics or hairstyles, based on the user's choices, and provides it to the terminal.

[0482] Step 10:

[0483] The terminal displays product information in a mirror and provides a purchase interface, allowing users to easily complete their shopping.

[0484] (Example 1)

[0485] 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."

[0486] Finding the perfect beauty style for oneself is generally a time-consuming and laborious process. The challenge lies in simplifying this process and providing more personalized suggestions to each individual. Furthermore, it's necessary to provide an environment where suggested styles can be instantly visualized, streamlining the user's selection and purchase process.

[0487] 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.

[0488] In this invention, the server includes means for acquiring images of the user using a video acquisition device, means for recognizing faces based on the acquired image data and extracting facial attributes, and means for providing beauty suggestions suitable for the user by utilizing a generated AI model via a data processing unit based on the extracted facial attribute data. This enables the user to quickly find a beauty style that suits them, try out styles in real time, and efficiently carry out a series of processes to purchase products if necessary.

[0489] A "video acquisition device" is a device used to photograph a user and acquire that video data.

[0490] "Facial recognition" is a technology that identifies a user's face from acquired video data and recognizes its characteristics.

[0491] "Facial attributes" refer to physical characteristics such as skin tone, face shape, and eye position that can be obtained in facial identification.

[0492] A "data processing unit" is an element or device within a system that analyzes acquired data and performs necessary calculations and data processing to provide information appropriately.

[0493] A "generative AI model" is a type of artificial intelligence technology used to provide users with appropriate beauty suggestions based on input data.

[0494] A "prompt statement" is an input statement used to give a generative AI model specific instructions that are tailored to the user's particular characteristics and desired results.

[0495] "Beauty suggestions" refer to recommendations regarding optimal makeup and hairstyle styles, based on the user's facial attribute data.

[0496] A "virtual space" is an environment that allows users to visually experiment with styles on a computer-generated, real-time interface.

[0497] A "visualization device" is a device used to visually display proposed styles and their trial results to the user.

[0498] A "sales interface" is an online commerce platform provided to users for purchasing products related to their chosen makeup or hairstyle.

[0499] This invention is a system designed to help individuals quickly find the beauty style that best suits them, visually confirm it, and then purchase the product. An embodiment of this system is shown below.

[0500] First, the user stands in front of the camera installed on the device. The device uses a high-resolution camera to capture the user's image. This image data is sent to a server for facial recognition.

[0501] The server uses facial recognition algorithms to identify the user's face from video data and extract facial attributes. These attributes include skin tone, face shape, and eye position. Facial recognition serves as the starting point for providing personalized suggestions to each user.

[0502] Based on this extracted facial attribute data, the server applies a generative AI model. The generative AI model takes into account past selection history and trend data to provide personalized beauty suggestions to the user. An example of a prompt message is, "Based on the user's facial data, please suggest the most suitable lip color, taking into account the latest trends."

[0503] Next, the terminal reflects the suggestions received from the server onto the user's video in real time. Using virtual space technology, the selected makeup and hairstyle are applied to the user's face, and the results are displayed on the screen.

[0504] Users can instantly review the presented styles and select any they like. Product information related to the selected style is provided by the server and displayed via the terminal's sales interface. Using this interface, users can quickly view product details and proceed with the purchase.

[0505] Through the above procedure, this system provides users with innovative and efficient beauty solutions. By utilizing generative AI models, personalized beauty recommendations and the convenience of product purchases are significantly improved.

[0506] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0507] Step 1:

[0508] The device detects when the user stands in front of the camera. It acquires the user's video data in real time through the high-resolution camera. The input for this step is the user's actual appearance, and the output is digital video data. This video data is then converted to an appropriate format for later analysis.

[0509] Step 2:

[0510] The terminal transmits the acquired video data to the server. The transmitted data is used by the server as input for a face recognition algorithm. The data processing here involves identifying the user's face data from the video data. The output is the user's facial attribute data (skin tone, face shape, eye position, etc.).

[0511] Step 3:

[0512] The server utilizes an AI model that generates data based on facial attribute data. This attribute data is input to the AI ​​model as elements of prompt statements. The prompt statements also consider past selection history and trend data. The output generates beauty suggestions best suited to the user. These suggestions include makeup and hairstyles that best match the user's features.

[0513] Step 4:

[0514] The terminal receives beauty suggestions sent from the server. Here, to display the suggestions in real time on the interface, video processing is performed, and the generated style is applied to the user's video. The output is a visual result, including the style virtually applied to the user's video. The user can view the results through the screen.

[0515] Step 5:

[0516] The user selects their preferred style from the presented options. The selected information is then used for the next step, which involves displaying detailed information about related products and entering it into the purchase interface.

[0517] Step 6:

[0518] The server processes product information related to the selected style and sends it to the terminal. The terminal displays the product information in detail and provides a sales interface that allows the user to compare and consider options. As an output, the user can quickly select a product and proceed with the purchase.

[0519] (Application Example 1)

[0520] 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."

[0521] In modern times, individuals face the problem of having to spend time and money on trial and error to choose the makeup and hairstyle that best suits them. Furthermore, when choosing cosmetics in stores, the number of products available for try-on is often limited, narrowing the range of choices. There is a need for a system that solves these problems and supports efficient and satisfying style selection.

[0522] 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.

[0523] In this invention, the server includes means for acquiring an image of the user using an image acquisition device; means for identifying a face and extracting an appearance based on the acquired image data; means for presenting a style suitable for the user using an AI model via an information processing device based on the extracted appearance data; means for virtually applying the presented style to the image in real time and providing it to the user using a display device; means for presenting product information related to the selected style and providing a purchase interface; and means for providing an interface that enables the user to decide on a purchase based on the presented virtual style. This makes it possible for consumers to easily try out the style that best suits them and efficiently go through the process up to purchase.

[0524] A "video acquisition device" is a device used to acquire images and video data from users, and mainly refers to cameras and other imaging equipment.

[0525] "Identifying faces and extracting their appearance" refers to the process of identifying a specific face from acquired image data and extracting its facial features in detail.

[0526] An "information processing device" refers to a computer system used to analyze and process data and generate specific deliverables.

[0527] An "AI model" refers to a mathematical algorithm or computational model that uses artificial intelligence technology to perform data analysis.

[0528] "Presenting a style" refers to the act of recommending and displaying appropriate appearances and styles based on the user's characteristics.

[0529] "Applying something virtually in real time to an image" refers to a process that uses digital technology to instantly overlay an appearance onto an image that is not actually present.

[0530] A "display device" refers to a screen or display used to provide visual information to a user.

[0531] A "purchase interface" refers to a system or screen layout designed to assist consumers in the process of purchasing a product.

[0532] "Making a purchase decision based on a virtual style" refers to a user making a purchase choice based on a trialed digital appearance.

[0533] The system for carrying out the present invention is a multifunctional device that streamlines the process of users finding the most suitable makeup and hairstyle for themselves. This system mainly consists of a camera, a display, and an information processing device.

[0534] First, the device uses its camera to capture video of the user. The captured video data is then processed by a server for face recognition. Specifically, image processing software such as OpenCV is used to extract facial features.

[0535] Next, the information processing device analyzes the facial data acquired using a facial recognition library such as Dlib to clarify its features. The analyzed feature data is then passed to an AI model via the information processing device. This AI model considers past selection history and trend data to suggest the most suitable makeup and hairstyle for the user. For example, the MakeupRecommendationModel is used as the AI ​​model.

[0536] Subsequently, the server virtually applies the proposed style to the video in real time and presents it to the user through a display device. This real-time image processing is achieved using OpenCV and other image synthesis technologies.

[0537] Finally, users can review the virtual style presented through their device and decide whether to purchase. A product information display and purchase interface are provided, enabling smooth transactions.

[0538] As a concrete example, imagine a scenario where a user picks up their smartphone and takes a photo of their face in a store. The AI ​​model uses this image to suggest the most suitable cosmetic colors and displays the results on the screen. The user can then choose their favorite from the options.

[0539] An example of a prompt message is: "The user takes a picture of their face with their smartphone. The AI ​​model analyzes this image and suggests the best lipstick color. The user looks at the displayed color and decides whether to purchase it." This prompt allows the system to accurately capture the user's features and quickly suggest an attractive style.

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] The device uses its camera to capture video of the user's face. The captured video is processed as RAW data and temporarily stored for processing in the next step. The input is live video from the camera, and the output is RAW image data.

[0543] Step 2:

[0544] The server receives RAW image data provided by the terminal and performs face recognition. Using OpenCV, an image processing software, it identifies faces from the image data and extracts features such as position and shape. The input is RAW image data, and the output is facial feature data.

[0545] Step 3:

[0546] The server inputs facial feature data into an AI model and suggests appropriate makeup and hairstyles. The AI ​​model used is the MakeupRecommendationModel, which takes into account past selection history and trend data. The input is facial feature data, and the output is suggested style data.

[0547] Step 4:

[0548] The server virtually applies the proposed style to the video in real time. Using OpenCV and image synthesis techniques, it overlays the proposed style onto the image. The input is the proposed style data and facial feature data, and the output is the visually transformed video data.

[0549] Step 5:

[0550] The terminal presents a virtual style to the user using a display device. Visually converted video data is displayed on the screen in real time for the user to confirm. The input is visually converted video data, and the output is a visual display for the user.

[0551] Step 6:

[0552] The user reviews the presented virtual style and decides whether to purchase it. The terminal displays product information via the purchase interface and provides options to complete the purchase process. The input is the user's visual confirmation result, and the output is the purchase decision data.

[0553] 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.

[0554] This invention is a system for users to find makeup and hairstyles that suit them, and aims to optimize suggestions based on an emotion engine that recognizes the user's emotions in real time. When a user stands in front of the system, a camera attached to the terminal captures an image of the user's face. This image data is sent to a server, where a face recognition algorithm identifies the user's face and extracts its features.

[0555] Once features are extracted, the server uses an AI model to suggest suitable makeup and hairstyles for the user. This AI model is configured to make more accurate suggestions by considering the user's past selection history and current trend data, as well as incorporating the user's emotional data read in real time by an emotion engine.

[0556] The device applies virtual makeup and hairstyles to the user's video based on suggestions received from the server, and displays the results on the screen. The user reacts to the presented style. At this time, the emotion engine analyzes the user's facial expressions and evaluates their emotions towards the suggestions. This is sent as feedback to the AI ​​model and used to dynamically adjust the suggestions.

[0557] For example, consider a scenario where a user is trying out a new hairstyle. The device sends video to a server, which uses an AI model to select an appropriate style based on the user's facial contours and features. Simultaneously, an emotion engine monitors the user's facial expressions and analyzes their emotions to determine whether they are excited or dissatisfied. Based on this analysis data, the server can adjust the next suggestion to better suit the user's preferences.

[0558] Furthermore, products related to the style selected by the user are displayed, and the purchase process can be easily completed through the device. This invention allows users to effectively explore and implement beauty styles based on their individual needs, thus providing a highly satisfying experience.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] When a user stands in front of the system, the terminal's camera automatically captures the user's face and acquires video data.

[0562] Step 2:

[0563] The terminal sends the acquired video data to the server and prepares to begin data processing.

[0564] Step 3:

[0565] The server uses a facial recognition algorithm to identify the user's face from the received video data, accurately extracting features such as skin tone, facial shape, and the position of the eyes and mouth.

[0566] Step 4:

[0567] Based on the extracted feature data, the server generates suggestions for the user's most suitable makeup and hairstyles via an AI model. Past selection history and trend data are also taken into consideration during this process.

[0568] Step 5:

[0569] The terminal receives suggestions from the server, virtually applies makeup and hairstyles to the user's video, and displays them to the user in real time.

[0570] Step 6:

[0571] The emotion engine analyzes the user's facial expressions and recognizes the user's emotions in response to the suggestion in step 5, such as joy or dissatisfaction.

[0572] Step 7:

[0573] The server receives feedback from the emotion engine, adjusts the suggestions in real time, and sends them back to the terminal to provide better suggestions.

[0574] Step 8:

[0575] Users can select a style they like and then view information about related products.

[0576] Step 9:

[0577] The terminal displays product information related to the selected style and provides a purchase interface.

[0578] Step 10:

[0579] Users can easily purchase products they are interested in through the purchase interface.

[0580] Step 11:

[0581] The server records the user's purchase history and selection data, which is then used to further refine future recommendations.

[0582] (Example 2)

[0583] 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."

[0584] Conventional beauty style suggestion systems have difficulty providing suggestions that perfectly match the user's characteristics and preferences, and they also struggle to adjust suggestions in real time to reflect the user's emotions. Furthermore, they often lack an adequate interface for users to easily purchase products related to the style they have selected.

[0585] 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.

[0586] In this invention, the server includes means for identifying faces and extracting features based on acquired video data, means for suggesting a suitable beauty style to the user using a generative model via an information processing device based on the extracted feature data, and means for analyzing the user's facial expressions to acquire emotional data and provide feedback to the suggestions. This enables more accurate beauty style suggestions that take into account the user's individual characteristics and real-time emotional state. It also enables the provision of an efficient interface that allows users to immediately purchase products related to the selected style.

[0587] "Shooting device" refers to hardware components used to acquire video footage of the user.

[0588] "Video data" refers to digital data containing visual information acquired by a camera or camera.

[0589] "Identifying faces and extracting features" refers to the process of analyzing acquired video data to identify the position and shape data of faces and quantify their features.

[0590] An "information processing device" refers to an electronic device used to process data and perform calculations.

[0591] A "generative model" refers to a machine learning algorithm that learns from data and generates the most optimal results for the user.

[0592] "Beauty style" refers to a combination of makeup and hairstyle, and is a design proposed to enhance the user's appearance.

[0593] "Applying virtually to video in real time" refers to the process of instantly visually demonstrating a proposed style by overlaying it onto real-world footage.

[0594] A "display device" refers to a hardware device used to present visual information to a user.

[0595] "Product information" refers to detailed data about products related to beauty styles.

[0596] A "purchase interface" refers to the user interface through which users select products and complete the purchase process.

[0597] "Analyzing facial expressions to obtain emotional data" refers to the process of analyzing a user's facial expressions and extracting their current emotional state as digital data.

[0598] "Providing feedback" refers to adjusting suggestions using analyzed sentiment data.

[0599] This invention is a system for users to find the optimal beauty style. This system includes a shooting device, an information processing device, a generative model, an emotion analysis means, and a display device.

[0600] The device incorporates a high-resolution camera to capture images of the user's face. The captured video data is sent to a server via a rapid data transfer protocol. The server uses facial recognition technology such as OpenCV to identify the face and extract feature data. These features include the contours of the user's face and the location information of important facial features.

[0601] The server inputs this extracted data into a generative model via an information processing device. The generative model uses Google's TensorFlow and combines the user's past selection history, the latest beauty trend data, and emotion data from the Emotion API to suggest a beauty style that suits the user.

[0602] The terminal uses augmented reality technologies such as Adobe Aero to apply suggested data received from the server onto the user's video in real time, and displays the results on a display device. The user reacts to the displayed beauty styles by indicating whether they like them or not. At that time, the terminal analyzes emotional data from the user's facial expressions and feeds it back to the server.

[0603] The server uses this feedback information to dynamically adjust the generative model, enabling suggestions that better fit the user's preferences. Furthermore, once the user has decided on a style, information on related products is presented, and purchases can be made through a simple purchase interface.

[0604] For example, if a user inputs "I want to try a trendy short hairstyle" into the device, the system will suggest trendy styles that suit the user's facial features and display images of them on the screen. The user indicates whether they like the style with their facial expression, and the system makes further suggestions based on that feedback.

[0605] An example of a prompt might be: "Please suggest trendy short hairstyles for women in their 30s. The user has a round face shape, and the design should evoke excitement, taking into account recent trend data."

[0606] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0607] Step 1:

[0608] When a user stands in front of the device's camera, the device activates its high-resolution camera and captures the user's face in real time. The input data is the real-time face image, and the output is the video data. Because this video data is continuously acquired, it is possible to recognize the user's face even if they move naturally.

[0609] Step 2:

[0610] The terminal quickly and securely transmits the acquired video data to the server. The input data is the facial video acquired in step 1, and the output is the raw data received by the server. This ensures high-speed data processing.

[0611] Step 3:

[0612] The server uses facial recognition technology to identify facial features such as position, contour, eyes, and mouth from the received video data, and extracts feature vectors. The input data is the video data received from step 2, and the output is the identified facial feature vectors. By using machine learning techniques in this process, highly accurate feature extraction becomes possible.

[0613] Step 4:

[0614] The server inputs the identified feature vectors into the generative model. The generative AI model then proposes the most suitable beauty style for the user, taking into account previous selection history, trend data, sentiment analysis results, and other factors. The input data includes feature vectors and history data, and the output is the proposed beauty style.

[0615] Step 5:

[0616] The terminal uses augmented reality technology to overlay the generated suggestions onto the user's video in real time and present them to the user on a display device. The input data is the suggestions generated in step 4, and the output is a beauty style visually presented to the user. The user can check the results via the display.

[0617] Step 6:

[0618] If the user shows any emotion upon seeing the presented style, the device analyzes the facial expression and obtains emotion data. The input data is the user's facial expression, and the output is the analyzed emotion data. This emotion data is immediately sent to the server.

[0619] Step 7:

[0620] The server uses the acquired sentiment data to adjust the generative model and reflect it in new suggestions. The input data consists of sentiment data and feedback on the suggestions, and the output is the adjusted next suggestion. This results in a style that is more closely suited to the user's preferences.

[0621] (Application Example 2)

[0622] 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."

[0623] In the past, it was difficult to adequately consider the preferences and feelings of users when proposing beauty styles. Furthermore, there were few ways to virtually try out the suggested styles, and the process leading to purchase was cumbersome, making it difficult to provide a satisfying experience for users.

[0624] 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.

[0625] In this invention, the server includes means for acquiring the user's image using a camera and extracting facial features, means for suggesting a suitable beauty style to the user using an artificial intelligence model via an information processing device based on the extracted feature data, and means for analyzing the user's emotions in real time and adjusting the suggestions based on that data. This makes it possible to provide highly accurate beauty style suggestions based on the user's individual preferences, a real-time virtual try-on experience, and immediate purchase.

[0626] A "recording device" is a device used to acquire images of the user.

[0627] "Facial features" refer to information about the shape and structure of the user's face that is extracted from it.

[0628] An "information processing device" is a computer device that analyzes acquired data and performs calculations and makes suggestions using artificial intelligence models.

[0629] An "artificial intelligence model" is an algorithm that performs analysis based on past data and real-time input, and outputs results that are suitable for the user.

[0630] "Analyzing user emotions in real time" refers to the process of instantly evaluating a user's emotional state based on video data and other information.

[0631] A "presentation device" refers to a display or projector used to visually present the proposed content.

[0632] This invention is a system that proposes and allows users to try out beauty styles that are suitable for them. The system functions as follows: A camera mounted on the terminal captures an image of the user's face. This image data is sent to a server in the cloud. The server uses a face recognition algorithm to extract facial features. For example, a general-purpose camera can be used as the camera, and the open-source library OpenCV can be used as the face recognition algorithm.

[0633] The server inputs facial feature data into a generating AI model via an information processing device to calculate the optimal beauty style for the user. This AI model is built using deep learning frameworks such as TensorFlow and PyTorch. In addition to the user's past selection history and trend information, real-time sentiment analysis data is also considered. Sentiment analysis uses Amazon Rekognition and Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0634] The terminal virtually applies beauty styles to the user's video based on suggestions received from the server and presents them through a display device. This allows the user to check styles that suit them in real time. Smart glasses or displays are used as the display device.

[0635] Furthermore, product information related to the user's preferred style is displayed, and the user can proceed with the purchase via their device. At this time, the user's emotional feedback is sent to the server and used to optimize the AI ​​model.

[0636] For example, if a user wearing smart glasses at a hair salon expresses slight dissatisfaction with the first hairstyle suggested, the next suggestion will immediately reflect that feedback and be adjusted to better suit the user's preferences. Furthermore, the user can order the suggested styling products on the spot.

[0637] An example of a prompt message for a generative AI model might be: "Based on the user's facial features and emotion data, please suggest the optimal makeup style. Consider the following data and output a list."

[0638] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0639] Step 1:

[0640] The server acquires the user's facial image through the camera installed in the terminal. The input here is the facial image obtained by the camera, and the output is the generated video data. The acquired video data is sent to the server, and processing begins.

[0641] Step 2:

[0642] The server uses a face recognition algorithm to identify the user's face from the received video data. The input is the video data acquired in step 1, and the output is data showing the contours and features of the face. This process uses a face recognition library such as OpenCV to extract the main landmarks of the face from the video data.

[0643] Step 3:

[0644] The server uses a generative AI model to suggest suitable beauty styles for the user based on facial feature data. Inputs include the facial feature data from step 2, as well as past selection history and trend information. Output is a list of suggested beauty styles. At this stage, an AI model using TensorFlow or PyTorch analyzes the feature data and generates suggestions.

[0645] Step 4:

[0646] The server uses an emotion analysis engine to analyze emotional data from the user's facial expressions in real time. The input is real-time video data, and the output is data indicating the user's emotional state. It uses Amazon Rekognition or Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0647] Step 5:

[0648] The device virtually applies a style to the user's image based on style suggestions sent from the server. The input is the suggested data from the server, and the output is the user's virtual styled image. The image is presented to the user through the device's display or smart glasses.

[0649] Step 6:

[0650] The user makes a selection from the presented styles, and the result is fed back to the server. The input is the user's selection, and the output is feedback data for updating the AI ​​model. This data is re-entered into the AI ​​model to improve the quality of future suggestions.

[0651] Step 7:

[0652] The terminal displays product information related to the selected beauty style and assists with the purchase process. The input is product information related to the selected style, and the output is the purchase process interface. It supports users in purchasing their favorite products on the spot.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] [Fourth Embodiment]

[0657] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0658] 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.

[0659] 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).

[0660] 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.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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".

[0670] This invention is a system that enables individuals to easily find the makeup and hairstyle that best suits them. When a user stands in front of the system, a camera installed in the terminal captures the user's image. The terminal sends this image data to a server, which analyzes the data using facial recognition technology to identify the user's face. In this process, facial features such as skin tone, face shape, and eye position are extracted.

[0671] The server applies an AI model based on extracted feature data to suggest makeup and hairstyles that suit the user. This AI model takes into account past selection history and the latest trend data to provide optimal suggestions for each user.

[0672] The device, based on suggestions received from the server, virtually applies makeup and hairstyles to the user's video in real time and displays them visually to the user. This allows the user to instantly see styles that suit them. If the user likes the displayed style, they can select it and proceed to the next step.

[0673] Furthermore, the server presents product information for cosmetics related to the suggested style, and the terminal displays this to the user through a purchase interface. Users can quickly review product information and complete the purchase process, further enhancing convenience.

[0674] For example, if a user wants to try a new lip color that suits them using the system, the system will suggest several lip colors based on the user's facial data. Then, the color selected by the AI ​​model's analysis will be applied to the user's face in real time, and the result will be displayed on the device screen. The user can then choose their favorite color and decide to purchase it.

[0675] This invention provides a groundbreaking system that combines AI technology and real-time image processing to enable individuals to easily make choices related to their own beauty.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] When a user stands in front of the system, the device automatically activates its camera and captures an image of the user's face.

[0679] Step 2:

[0680] The terminal sends the acquired video data to the server and prepares it for processing.

[0681] Step 3:

[0682] The server runs a facial recognition algorithm on the received video data to identify the user's face. In this process, it accurately captures the contours of the face and the positions of its features.

[0683] Step 4:

[0684] The server extracts facial features, converting data such as skin tone, face shape, and the position of eyes and mouth into digital format.

[0685] Step 5:

[0686] The server uses extracted features to apply an AI model and generate suitable makeup and hairstyle options for the user. The AI ​​model makes optimal suggestions by analyzing past user data and current trends.

[0687] Step 6:

[0688] The server sends the generated style suggestions to the terminal.

[0689] Step 7:

[0690] The terminal virtually applies style suggestions received from the server to the user's video in real time and displays the result in a mirror.

[0691] Step 8:

[0692] Users can check the style reflected in the mirror and select their preferred style using voice or gestures.

[0693] Step 9:

[0694] The server processes information about products related to the selected cosmetics or hairstyles, based on the user's choices, and provides it to the terminal.

[0695] Step 10:

[0696] The terminal displays product information in a mirror and provides a purchase interface, allowing users to easily complete their shopping.

[0697] (Example 1)

[0698] 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".

[0699] Finding the perfect beauty style for oneself is generally a time-consuming and laborious process. The challenge lies in simplifying this process and providing more personalized suggestions to each individual. Furthermore, it's necessary to provide an environment where suggested styles can be instantly visualized, streamlining the user's selection and purchase process.

[0700] 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.

[0701] In this invention, the server includes means for acquiring images of the user using a video acquisition device, means for recognizing faces based on the acquired image data and extracting facial attributes, and means for providing beauty suggestions suitable for the user by utilizing a generated AI model via a data processing unit based on the extracted facial attribute data. This enables the user to quickly find a beauty style that suits them, try out styles in real time, and efficiently carry out a series of processes to purchase products if necessary.

[0702] A "video acquisition device" is a device used to photograph a user and acquire that video data.

[0703] "Facial recognition" is a technology that identifies a user's face from acquired video data and recognizes its characteristics.

[0704] "Facial attributes" refer to physical characteristics such as skin tone, face shape, and eye position that can be obtained in facial identification.

[0705] A "data processing unit" is an element or device within a system that analyzes acquired data and performs necessary calculations and data processing to provide information appropriately.

[0706] A "generative AI model" is a type of artificial intelligence technology used to provide users with appropriate beauty suggestions based on input data.

[0707] A "prompt statement" is an input statement used to give a generative AI model specific instructions that are tailored to the user's particular characteristics and desired results.

[0708] "Beauty suggestions" refer to recommendations regarding optimal makeup and hairstyle styles, based on the user's facial attribute data.

[0709] A "virtual space" is an environment that allows users to visually experiment with styles on a computer-generated, real-time interface.

[0710] A "visualization device" is a device used to visually display proposed styles and their trial results to the user.

[0711] A "sales interface" is an online commerce platform provided to users for purchasing products related to their chosen makeup or hairstyle.

[0712] This invention is a system designed to help individuals quickly find the beauty style that best suits them, visually confirm it, and then purchase the product. An embodiment of this system is shown below.

[0713] First, the user stands in front of the camera installed on the device. The device uses a high-resolution camera to capture the user's image. This image data is sent to a server for facial recognition.

[0714] The server uses facial recognition algorithms to identify the user's face from video data and extract facial attributes. These attributes include skin tone, face shape, and eye position. Facial recognition serves as the starting point for providing personalized suggestions to each user.

[0715] Based on this extracted facial attribute data, the server applies a generative AI model. The generative AI model takes into account past selection history and trend data to provide personalized beauty suggestions to the user. An example of a prompt message is, "Based on the user's facial data, please suggest the most suitable lip color, taking into account the latest trends."

[0716] Next, the terminal reflects the suggestions received from the server onto the user's video in real time. Using virtual space technology, the selected makeup and hairstyle are applied to the user's face, and the results are displayed on the screen.

[0717] Users can instantly review the presented styles and select any they like. Product information related to the selected style is provided by the server and displayed via the terminal's sales interface. Using this interface, users can quickly view product details and proceed with the purchase.

[0718] Through the above procedure, this system provides users with innovative and efficient beauty solutions. By utilizing generative AI models, personalized beauty recommendations and the convenience of product purchases are significantly improved.

[0719] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0720] Step 1:

[0721] The device detects when the user stands in front of the camera. It acquires the user's video data in real time through the high-resolution camera. The input for this step is the user's actual appearance, and the output is digital video data. This video data is then converted to an appropriate format for later analysis.

[0722] Step 2:

[0723] The terminal transmits the acquired video data to the server. The transmitted data is used by the server as input for a face recognition algorithm. The data processing here involves identifying the user's face data from the video data. The output is the user's facial attribute data (skin tone, face shape, eye position, etc.).

[0724] Step 3:

[0725] The server utilizes an AI model that generates data based on facial attribute data. This attribute data is input to the AI ​​model as elements of prompt statements. The prompt statements also consider past selection history and trend data. The output generates beauty suggestions best suited to the user. These suggestions include makeup and hairstyles that best match the user's features.

[0726] Step 4:

[0727] The terminal receives beauty suggestions sent from the server. Here, to display the suggestions in real time on the interface, video processing is performed, and the generated style is applied to the user's video. The output is a visual result, including the style virtually applied to the user's video. The user can view the results through the screen.

[0728] Step 5:

[0729] The user selects their preferred style from the presented options. The selected information is then used for the next step, which involves displaying detailed information about related products and entering it into the purchase interface.

[0730] Step 6:

[0731] The server processes product information related to the selected style and sends it to the terminal. The terminal displays the product information in detail and provides a sales interface that allows the user to compare and consider options. As an output, the user can quickly select a product and proceed with the purchase.

[0732] (Application Example 1)

[0733] 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".

[0734] In modern times, individuals face the problem of having to spend time and money on trial and error to choose the makeup and hairstyle that best suits them. Furthermore, when choosing cosmetics in stores, the number of products available for try-on is often limited, narrowing the range of choices. There is a need for a system that solves these problems and supports efficient and satisfying style selection.

[0735] 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.

[0736] In this invention, the server includes means for acquiring an image of the user using an image acquisition device; means for identifying a face and extracting an appearance based on the acquired image data; means for presenting a style suitable for the user using an AI model via an information processing device based on the extracted appearance data; means for virtually applying the presented style to the image in real time and providing it to the user using a display device; means for presenting product information related to the selected style and providing a purchase interface; and means for providing an interface that enables the user to decide on a purchase based on the presented virtual style. This makes it possible for consumers to easily try out the style that best suits them and efficiently go through the process up to purchase.

[0737] A "video acquisition device" is a device used to acquire images and video data from users, and mainly refers to cameras and other imaging equipment.

[0738] "Identifying faces and extracting their appearance" refers to the process of identifying a specific face from acquired image data and extracting its facial features in detail.

[0739] An "information processing device" refers to a computer system used to analyze and process data and generate specific deliverables.

[0740] An "AI model" refers to a mathematical algorithm or computational model that uses artificial intelligence technology to perform data analysis.

[0741] "Presenting a style" refers to the act of recommending and displaying appropriate appearances and styles based on the user's characteristics.

[0742] "Applying something virtually in real time to an image" refers to a process that uses digital technology to instantly overlay an appearance onto an image that is not actually present.

[0743] A "display device" refers to a screen or display used to provide visual information to a user.

[0744] A "purchase interface" refers to a system or screen layout designed to assist consumers in the process of purchasing a product.

[0745] "Making a purchase decision based on a virtual style" refers to a user making a purchase choice based on a trialed digital appearance.

[0746] The system for carrying out the present invention is a multifunctional device that streamlines the process of users finding the most suitable makeup and hairstyle for themselves. This system mainly consists of a camera, a display, and an information processing device.

[0747] First, the device uses its camera to capture video of the user. The captured video data is then processed by a server for face recognition. Specifically, image processing software such as OpenCV is used to extract facial features.

[0748] Next, the information processing device analyzes the facial data acquired using a facial recognition library such as Dlib to clarify its features. The analyzed feature data is then passed to an AI model via the information processing device. This AI model considers past selection history and trend data to suggest the most suitable makeup and hairstyle for the user. For example, the MakeupRecommendationModel is used as the AI ​​model.

[0749] Subsequently, the server virtually applies the proposed style to the video in real time and presents it to the user through a display device. This real-time image processing is achieved using OpenCV and other image synthesis technologies.

[0750] Finally, users can review the virtual style presented through their device and decide whether to purchase. A product information display and purchase interface are provided, enabling smooth transactions.

[0751] As a concrete example, imagine a scenario where a user picks up their smartphone and takes a photo of their face in a store. The AI ​​model uses this image to suggest the most suitable cosmetic colors and displays the results on the screen. The user can then choose their favorite from the options.

[0752] An example of a prompt message is: "The user takes a picture of their face with their smartphone. The AI ​​model analyzes this image and suggests the best lipstick color. The user looks at the displayed color and decides whether to purchase it." This prompt allows the system to accurately capture the user's features and quickly suggest an attractive style.

[0753] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0754] Step 1:

[0755] The device uses its camera to capture video of the user's face. The captured video is processed as RAW data and temporarily stored for processing in the next step. The input is live video from the camera, and the output is RAW image data.

[0756] Step 2:

[0757] The server receives RAW image data provided by the terminal and performs face recognition. Using OpenCV, an image processing software, it identifies faces from the image data and extracts features such as position and shape. The input is RAW image data, and the output is facial feature data.

[0758] Step 3:

[0759] The server inputs facial feature data into an AI model and suggests appropriate makeup and hairstyles. The AI ​​model used is the MakeupRecommendationModel, which takes into account past selection history and trend data. The input is facial feature data, and the output is suggested style data.

[0760] Step 4:

[0761] The server virtually applies the proposed style to the video in real time. Using OpenCV and image synthesis techniques, it overlays the proposed style onto the image. The input is the proposed style data and facial feature data, and the output is the visually transformed video data.

[0762] Step 5:

[0763] The terminal presents a virtual style to the user using a display device. Visually converted video data is displayed on the screen in real time for the user to confirm. The input is visually converted video data, and the output is a visual display for the user.

[0764] Step 6:

[0765] The user reviews the presented virtual style and decides whether to purchase it. The terminal displays product information via the purchase interface and provides options to complete the purchase process. The input is the user's visual confirmation result, and the output is the purchase decision data.

[0766] 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.

[0767] This invention is a system for users to find makeup and hairstyles that suit them, and aims to optimize suggestions based on an emotion engine that recognizes the user's emotions in real time. When a user stands in front of the system, a camera attached to the terminal captures an image of the user's face. This image data is sent to a server, where a face recognition algorithm identifies the user's face and extracts its features.

[0768] Once features are extracted, the server uses an AI model to suggest suitable makeup and hairstyles for the user. This AI model is configured to make more accurate suggestions by considering the user's past selection history and current trend data, as well as incorporating the user's emotional data read in real time by an emotion engine.

[0769] The device applies virtual makeup and hairstyles to the user's video based on suggestions received from the server, and displays the results on the screen. The user reacts to the presented style. At this time, the emotion engine analyzes the user's facial expressions and evaluates their emotions towards the suggestions. This is sent as feedback to the AI ​​model and used to dynamically adjust the suggestions.

[0770] For example, consider a scenario where a user is trying out a new hairstyle. The device sends video to a server, which uses an AI model to select an appropriate style based on the user's facial contours and features. Simultaneously, an emotion engine monitors the user's facial expressions and analyzes their emotions to determine whether they are excited or dissatisfied. Based on this analysis data, the server can adjust the next suggestion to better suit the user's preferences.

[0771] Furthermore, products related to the style selected by the user are displayed, and the purchase process can be easily completed through the device. This invention allows users to effectively explore and implement beauty styles based on their individual needs, thus providing a highly satisfying experience.

[0772] The following describes the processing flow.

[0773] Step 1:

[0774] When a user stands in front of the system, the terminal's camera automatically captures the user's face and acquires video data.

[0775] Step 2:

[0776] The terminal sends the acquired video data to the server and prepares to begin data processing.

[0777] Step 3:

[0778] The server uses a facial recognition algorithm to identify the user's face from the received video data, accurately extracting features such as skin tone, facial shape, and the position of the eyes and mouth.

[0779] Step 4:

[0780] Based on the extracted feature data, the server generates suggestions for the user's most suitable makeup and hairstyles via an AI model. Past selection history and trend data are also taken into consideration during this process.

[0781] Step 5:

[0782] The terminal receives suggestions from the server, virtually applies makeup and hairstyles to the user's video, and displays them to the user in real time.

[0783] Step 6:

[0784] The emotion engine analyzes the user's facial expressions and recognizes the user's emotions in response to the suggestion in step 5, such as joy or dissatisfaction.

[0785] Step 7:

[0786] The server receives feedback from the emotion engine, adjusts the suggestions in real time, and sends them back to the terminal to provide better suggestions.

[0787] Step 8:

[0788] Users can select a style they like and then view information about related products.

[0789] Step 9:

[0790] The terminal displays product information related to the selected style and provides a purchase interface.

[0791] Step 10:

[0792] Users can easily purchase products they are interested in through the purchase interface.

[0793] Step 11:

[0794] The server records the user's purchase history and selection data, which is then used to further refine future recommendations.

[0795] (Example 2)

[0796] 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".

[0797] Conventional beauty style suggestion systems have difficulty providing suggestions that perfectly match the user's characteristics and preferences, and they also struggle to adjust suggestions in real time to reflect the user's emotions. Furthermore, they often lack an adequate interface for users to easily purchase products related to the style they have selected.

[0798] 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.

[0799] In this invention, the server includes means for identifying faces and extracting features based on acquired video data, means for suggesting a suitable beauty style to the user using a generative model via an information processing device based on the extracted feature data, and means for analyzing the user's facial expressions to acquire emotional data and provide feedback to the suggestions. This enables more accurate beauty style suggestions that take into account the user's individual characteristics and real-time emotional state. It also enables the provision of an efficient interface that allows users to immediately purchase products related to the selected style.

[0800] "Shooting device" refers to hardware components used to acquire video footage of the user.

[0801] "Video data" refers to digital data containing visual information acquired by a camera or camera.

[0802] "Identifying faces and extracting features" refers to the process of analyzing acquired video data to identify the position and shape data of faces and quantify their features.

[0803] An "information processing device" refers to an electronic device used to process data and perform calculations.

[0804] A "generative model" refers to a machine learning algorithm that learns from data and generates the most optimal results for the user.

[0805] "Beauty style" refers to a combination of makeup and hairstyle, and is a design proposed to enhance the user's appearance.

[0806] "Applying virtually to video in real time" refers to the process of instantly visually demonstrating a proposed style by overlaying it onto real-world footage.

[0807] A "display device" refers to a hardware device used to present visual information to a user.

[0808] "Product information" refers to detailed data about products related to beauty styles.

[0809] A "purchase interface" refers to the user interface through which users select products and complete the purchase process.

[0810] "Analyzing facial expressions to obtain emotional data" refers to the process of analyzing a user's facial expressions and extracting their current emotional state as digital data.

[0811] "Providing feedback" refers to adjusting suggestions using analyzed sentiment data.

[0812] This invention is a system for users to find the optimal beauty style. This system includes a shooting device, an information processing device, a generative model, an emotion analysis means, and a display device.

[0813] The device incorporates a high-resolution camera to capture images of the user's face. The captured video data is sent to a server via a rapid data transfer protocol. The server uses facial recognition technology such as OpenCV to identify the face and extract feature data. These features include the contours of the user's face and the location information of important facial features.

[0814] The server inputs this extracted data into a generative model via an information processing device. The generative model uses Google's TensorFlow and combines the user's past selection history, the latest beauty trend data, and emotion data from the Emotion API to suggest a beauty style that suits the user.

[0815] The terminal uses augmented reality technologies such as Adobe Aero to apply suggested data received from the server onto the user's video in real time, and displays the results on a display device. The user reacts to the displayed beauty styles by indicating whether they like them or not. At that time, the terminal analyzes emotional data from the user's facial expressions and feeds it back to the server.

[0816] The server uses this feedback information to dynamically adjust the generative model, enabling suggestions that better fit the user's preferences. Furthermore, once the user has decided on a style, information on related products is presented, and purchases can be made through a simple purchase interface.

[0817] For example, if a user inputs "I want to try a trendy short hairstyle" into the device, the system will suggest trendy styles that suit the user's facial features and display images of them on the screen. The user indicates whether they like the style with their facial expression, and the system makes further suggestions based on that feedback.

[0818] An example of a prompt might be: "Please suggest trendy short hairstyles for women in their 30s. The user has a round face shape, and the design should evoke excitement, taking into account recent trend data."

[0819] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0820] Step 1:

[0821] When a user stands in front of the device's camera, the device activates its high-resolution camera and captures the user's face in real time. The input data is the real-time face image, and the output is the video data. Because this video data is continuously acquired, it is possible to recognize the user's face even if they move naturally.

[0822] Step 2:

[0823] The terminal quickly and securely transmits the acquired video data to the server. The input data is the facial video acquired in step 1, and the output is the raw data received by the server. This ensures high-speed data processing.

[0824] Step 3:

[0825] The server uses facial recognition technology to identify facial features such as position, contour, eyes, and mouth from the received video data, and extracts feature vectors. The input data is the video data received from step 2, and the output is the identified facial feature vectors. By using machine learning techniques in this process, highly accurate feature extraction becomes possible.

[0826] Step 4:

[0827] The server inputs the identified feature vectors into the generative model. The generative AI model then proposes the most suitable beauty style for the user, taking into account previous selection history, trend data, sentiment analysis results, and other factors. The input data includes feature vectors and history data, and the output is the proposed beauty style.

[0828] Step 5:

[0829] The terminal uses augmented reality technology to overlay the generated suggestions onto the user's video in real time and present them to the user on a display device. The input data is the suggestions generated in step 4, and the output is a beauty style visually presented to the user. The user can check the results via the display.

[0830] Step 6:

[0831] If the user shows any emotion upon seeing the presented style, the device analyzes the facial expression and obtains emotion data. The input data is the user's facial expression, and the output is the analyzed emotion data. This emotion data is immediately sent to the server.

[0832] Step 7:

[0833] The server uses the acquired sentiment data to adjust the generative model and reflect it in new suggestions. The input data consists of sentiment data and feedback on the suggestions, and the output is the adjusted next suggestion. This results in a style that is more closely suited to the user's preferences.

[0834] (Application Example 2)

[0835] 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".

[0836] In the past, it was difficult to adequately consider the preferences and feelings of users when proposing beauty styles. Furthermore, there were few ways to virtually try out the suggested styles, and the process leading to purchase was cumbersome, making it difficult to provide a satisfying experience for users.

[0837] 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.

[0838] In this invention, the server includes means for acquiring the user's image using a camera and extracting facial features, means for suggesting a suitable beauty style to the user using an artificial intelligence model via an information processing device based on the extracted feature data, and means for analyzing the user's emotions in real time and adjusting the suggestions based on that data. This makes it possible to provide highly accurate beauty style suggestions based on the user's individual preferences, a real-time virtual try-on experience, and immediate purchase.

[0839] A "recording device" is a device used to acquire images of the user.

[0840] "Facial features" refer to information about the shape and structure of the user's face that is extracted from it.

[0841] An "information processing device" is a computer device that analyzes acquired data and performs calculations and makes suggestions using artificial intelligence models.

[0842] An "artificial intelligence model" is an algorithm that performs analysis based on past data and real-time input, and outputs results that are suitable for the user.

[0843] "Analyzing user emotions in real time" refers to the process of instantly evaluating a user's emotional state based on video data and other information.

[0844] A "presentation device" refers to a display or projector used to visually present the proposed content.

[0845] This invention is a system that proposes and allows users to try out beauty styles that are suitable for them. The system functions as follows: A camera mounted on the terminal captures an image of the user's face. This image data is sent to a server in the cloud. The server uses a face recognition algorithm to extract facial features. For example, a general-purpose camera can be used as the camera, and the open-source library OpenCV can be used as the face recognition algorithm.

[0846] The server inputs facial feature data into a generating AI model via an information processing device to calculate the optimal beauty style for the user. This AI model is built using deep learning frameworks such as TensorFlow and PyTorch. In addition to the user's past selection history and trend information, real-time sentiment analysis data is also considered. Sentiment analysis uses Amazon Rekognition and Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0847] The terminal virtually applies beauty styles to the user's video based on suggestions received from the server and presents them through a display device. This allows the user to check styles that suit them in real time. Smart glasses or displays are used as the display device.

[0848] Furthermore, product information related to the user's preferred style is displayed, and the user can proceed with the purchase via their device. At this time, the user's emotional feedback is sent to the server and used to optimize the AI ​​model.

[0849] For example, if a user wearing smart glasses at a hair salon expresses slight dissatisfaction with the first hairstyle suggested, the next suggestion will immediately reflect that feedback and be adjusted to better suit the user's preferences. Furthermore, the user can order the suggested styling products on the spot.

[0850] An example of a prompt message for a generative AI model might be: "Based on the user's facial features and emotion data, please suggest the optimal makeup style. Consider the following data and output a list."

[0851] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0852] Step 1:

[0853] The server acquires the user's facial image through the camera installed in the terminal. The input here is the facial image obtained by the camera, and the output is the generated video data. The acquired video data is sent to the server, and processing begins.

[0854] Step 2:

[0855] The server uses a face recognition algorithm to identify the user's face from the received video data. The input is the video data acquired in step 1, and the output is data showing the contours and features of the face. This process uses a face recognition library such as OpenCV to extract the main landmarks of the face from the video data.

[0856] Step 3:

[0857] The server uses a generative AI model to suggest suitable beauty styles for the user based on facial feature data. Inputs include the facial feature data from step 2, as well as past selection history and trend information. Output is a list of suggested beauty styles. At this stage, an AI model using TensorFlow or PyTorch analyzes the feature data and generates suggestions.

[0858] Step 4:

[0859] The server uses an emotion analysis engine to analyze emotional data from the user's facial expressions in real time. The input is real-time video data, and the output is data indicating the user's emotional state. It uses Amazon Rekognition or Microsoft Azure's Face API to evaluate emotions such as joy and dissatisfaction from the user's facial expressions.

[0860] Step 5:

[0861] The device virtually applies a style to the user's image based on style suggestions sent from the server. The input is the suggested data from the server, and the output is the user's virtual styled image. The image is presented to the user through the device's display or smart glasses.

[0862] Step 6:

[0863] The user makes a selection from the presented styles, and the result is fed back to the server. The input is the user's selection, and the output is feedback data for updating the AI ​​model. This data is re-entered into the AI ​​model to improve the quality of future suggestions.

[0864] Step 7:

[0865] The terminal displays product information related to the selected beauty style and assists with the purchase process. The input is product information related to the selected style, and the output is the purchase process interface. It supports users in purchasing their favorite products on the spot.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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."

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] The following is further disclosed regarding the embodiments described above.

[0888] (Claim 1)

[0889] A means of acquiring images of the user using a camera,

[0890] A means for identifying faces and extracting features based on acquired video data,

[0891] A means of suggesting suitable makeup and hairstyles for a user using an AI model via a data processing device, based on extracted feature data,

[0892] A means of virtually applying proposed makeup and hairstyles to a video in real time and presenting them to the user via a display device,

[0893] A means of presenting product information related to selected makeup and hairstyles and providing a purchase interface,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which applies an AI model that makes suggestions considering past selection history and trend data.

[0897] (Claim 3)

[0898] The system according to claim 1, which optimizes an AI model using feedback collected by a data processing device based on the trial results of proposed makeup or hairstyles.

[0899] "Example 1"

[0900] (Claim 1)

[0901] A means of acquiring images of the user using a video acquisition device,

[0902] A means for recognizing faces based on acquired image data and extracting facial attributes,

[0903] A means of providing beauty suggestions tailored to the user by utilizing a generated AI model via a data processing unit, based on extracted facial attribute data,

[0904] A means of concretizing beauty suggestions based on generated prompt sentences and determining individual makeup and hairstyles by applying a generational AI model,

[0905] A means of reflecting the decided makeup and hairstyle in real time in a virtual space as an image and presenting it to the user using a visualization device,

[0906] A means of displaying product information related to selected makeup and hairstyles and providing a sales interface,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which uses a generative AI model that references past selection records and trend information to make suggestions that meet demand.

[0910] (Claim 3)

[0911] The system according to claim 1, which improves a generative AI model using reactions collected in data processing units based on the results of a proposed beauty trial.

[0912] "Application Example 1"

[0913] (Claim 1)

[0914] A means of acquiring images of the user using a video acquisition device,

[0915] A means for identifying a face and extracting its appearance based on acquired image data,

[0916] A means of presenting a style suitable for the user using an AI model via an information processing device, based on extracted appearance data,

[0917] A means of virtually applying the presented style to an image in real time and providing it to the user using a display device,

[0918] A means of presenting product information related to the selected style and providing a purchase interface,

[0919] A means of providing an interface that enables users to make purchase decisions based on the presented virtual style,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, which applies an AI model that suggests styles considering past selection history and trend data.

[0923] (Claim 3)

[0924] The system according to claim 1, which optimizes an AI model using feedback collected by an information processing device based on the results of trial runs of the presented style.

[0925] "Example 2 of combining an emotion engine"

[0926] (Claim 1)

[0927] A means of acquiring images of the user using a camera,

[0928] A means for identifying faces and extracting features based on acquired video data,

[0929] A means of proposing a beauty style suitable for the user using a generative model via an information processing device, based on extracted feature data,

[0930] A means of virtually applying a proposed beauty style to a video in real time and presenting it to the user via a display device,

[0931] A means of presenting product information related to the selected beauty style and providing a purchase interface,

[0932] A method for analyzing users' facial expressions to acquire emotional data and providing feedback to suggestions,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, which applies a generative model that makes suggestions considering past selection history, trend data, and sentiment data.

[0936] (Claim 3)

[0937] The system according to claim 1, which optimizes a generative model using feedback collected by an information processing device based on the trial results of a proposed beauty style.

[0938] "Application example 2 when combining with an emotional engine"

[0939] (Claim 1)

[0940] A means of acquiring images of the user using a camera and extracting facial features,

[0941] A means of proposing a beauty style suitable for the user using an artificial intelligence model via an information processing device, based on extracted feature data,

[0942] A means of analyzing users' emotions in real time and adjusting suggestions based on that data,

[0943] A means of virtually applying the proposed beauty style onto a video and presenting it to the user using a display device,

[0944] A means of presenting product information related to the selected beauty style and providing a user interface for the purchase process,

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, which applies an artificial intelligence model that takes into account past selection history, trend information, and user sentiment data.

[0948] (Claim 3)

[0949] The system according to claim 1, which optimizes an artificial intelligence model using emotional feedback collected by an information processing device based on the trial results of a proposed beauty style. [Explanation of Symbols]

[0950] 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 of acquiring images of the user using a video acquisition device, A means for identifying a face and extracting its appearance based on acquired image data, A means of presenting a style suitable for the user using an AI model via an information processing device, based on extracted appearance data, A means of virtually applying the presented style to an image in real time and providing it to the user using a display device, A means of presenting product information related to the selected style and providing a purchase interface, A means of providing an interface that enables users to make purchase decisions based on the presented virtual style, A system that includes this.

2. The system according to claim 1, which applies an AI model that suggests styles considering past selection history and trend data.

3. The system according to claim 1, which optimizes the AI ​​model using feedback collected by an information processing device based on the trial results of the presented style.

Images