system

Abstract

We provide the system. [Solution] A means for acquiring a user's image and analyzing the features of the said image, A means for generating makeup suggestions based on the characteristics analyzed above, A means for visually simulating and displaying the proposed makeup, A means for providing purchase information of products related to the simulated makeup, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] Finding a makeup method suitable for individual users is a time-consuming and laborious task for many people, and there is a problem that it is difficult to identify the most suitable makeup for oneself. Also, among many products existing in the market, it is difficult to determine which product to choose, and furthermore, the means for effectively learning the makeup method are limited.

Means for Solving the Problems

[0005] This invention provides a system that acquires user image data, analyzes its characteristics, and generates personalized makeup suggestions based on that data. Furthermore, it allows users to easily select the optimal makeup method by visually simulating and displaying these suggestions and providing information on purchasing related products. It also provides a more personalized experience by customizing suggestions based on the user's preferences and past history, and enables effective makeup learning by providing video tutorials to help users learn makeup procedures.

[0006] "User images" refer to digital or analog visual data acquired for the purpose of visually recognizing and analyzing an individual's facial features.

[0007] "Analyzing features" is the process of extracting and interpreting important appearance data from a user's image, such as facial shape, skin tone, and the shape of the eyes and lips.

[0008] "Makeup suggestions" are recommendations that, based on analyzed characteristic data, show the selection and usage methods of cosmetics best suited to each individual user.

[0009] "Simulation display" is a technology that visually reproduces suggested makeup effects on the user's image, allowing the user to see in real time how they will look.

[0010] "Purchase information" refers to data used to provide users with product information, prices, and purchase locations for cosmetics related to the suggested makeup.

[0011] "Customizing" means personalizing services and suggestions based on individual user preferences and past usage history.

[0012] A "video tutorial" is a form of digital content that visually explains specific steps and techniques to make it easier for users to learn the suggested makeup methods. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

[0018] In the following embodiments, a 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, etc.

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention is an intelligent system that suggests the optimal makeup method for each individual user and facilitates the purchase of related products. This system facilitates collaboration between the user terminal, server, and user, realizing a seamless process from makeup suggestions to purchase.

[0035] First, the user's device allows the user to upload a photo of their face. The user uses the application to send a photo taken with the camera or selected from the gallery to the server. Once the photo reaches the server, the server uses a face recognition algorithm to analyze the image and extract facial features.

[0036] The server analyzes not only the user's facial shape and skin tone, but also the shape of their eyes and lips in detail. This yields diverse facial feature data. Next, the server uses this data to compare it with a previously accumulated makeup database and statistically selects the most suitable makeup method.

[0037] Furthermore, the server customizes the suggestions based on the user's preferences and past history. For example, suggestions are adjusted based on previously preferred lip colors or specific brands of cosmetics. These generated suggestions are then presented to the user as a visual simulation. The user's device displays this simulation in real time, allowing the user to see the effect of the suggested makeup.

[0038] After reviewing the suggestions, the server provides purchase information for products the user is interested in. Through detailed product information and links to the online store, users can proceed directly to purchase. To enhance user convenience during this process, video tutorials on makeup application are also provided. This allows users to effectively learn about makeup and reduces the difficulties they may encounter when actually applying it.

[0039] For example, if a user has a fair skin tone and large eyes, the server's suggestions might include a combination of bright base makeup and blue-toned eyeshadow. Furthermore, if data shows the user has previously favored pink lipsticks, this information will also be considered in the suggestions, and the user's device will display links to related products. In this way, the system highly personalizes makeup suggestions to individual needs, improving the user experience.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users use a dedicated app to take or select a photo of their face and upload it to the server via their device.

[0043] Step 2:

[0044] The server analyzes the received facial photograph and applies a facial recognition algorithm to extract features such as facial shape, skin tone, and the shape of the eyes and lips.

[0045] Step 3:

[0046] The server compares the extracted facial feature data with a database of previously accumulated data to generate makeup rules tailored to the user. This process utilizes machine learning algorithms to improve the accuracy of the suggestions.

[0047] Step 4:

[0048] The server prepares a visual simulation of the makeup based on the generated suggestions. It then performs image processing so that the user can see in real time how the suggestions will look.

[0049] Step 5:

[0050] The terminal displays a visual simulation received from the server to the user. The user can then review the suggested makeup on the interface and take further action.

[0051] Step 6:

[0052] If a user is interested in the suggested makeup, the server provides information on purchasing the relevant makeup products. Detailed information, including a link to the product page, is sent to the user's device.

[0053] Step 7:

[0054] Users can watch video tutorials on their devices to learn the suggested makeup steps. This allows them to understand and practice the actual makeup process.

[0055] (Example 1)

[0056] 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."

[0057] In today's world, finding the optimal makeup method for each individual user is difficult, time-consuming, and laborious. Furthermore, efficiently obtaining product information corresponding to that makeup method is also challenging. This hinders user satisfaction with makeup and the benefits of purchasing products, which poses a significant challenge.

[0058] 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.

[0059] In this invention, the server includes means for acquiring image data from a user's electronic device, data processing means for analyzing multiple features of the image data, and means for selecting the optimal makeup method based on the analyzed features. This makes it possible to provide optimal makeup suggestions to individual users and to quickly acquire relevant product information.

[0060] A "user" is an individual or group that uses the system to obtain the optimal makeup method.

[0061] "Electronic devices" is a general term for devices that allow users to acquire and transmit image data, and includes smartphones, personal computers, tablets, and other similar devices.

[0062] "Image data" refers to a digital image of a user's face and features, and is a data format used for analysis.

[0063] "Data processing means" refers to a series of processing techniques and algorithms used to extract and analyze features from image data within a server.

[0064] "Features" refer to elements that influence individual makeup suggestions, such as facial shape, skin tone, and the shape of the eyes and lips, which are extracted from image data.

[0065] "Makeup techniques" refer to a collection of specific makeup advice and techniques aimed at enhancing beauty and style, suggested based on characteristics analyzed from the user's image data.

[0066] "Virtual display" is a display method that uses digital technology to visually simulate the selected makeup method, allowing the user to check the results.

[0067] "Product information" refers to detailed information about a virtual display of cosmetic-related products and information that prompts users to proceed with the purchase.

[0068] This invention is a system in which a user takes a photo of their own face with an electronic device, obtains the optimal makeup method, and receives relevant product information.

[0069] 1. Regarding user terminals:

[0070] Users utilize electronic devices such as smartphones, tablets, and personal computers. These devices use their camera functions to capture photos of the user's face and send the image data to a server via an application or web browser. The devices are equipped with software that enables image uploading and the display of makeup simulations.

[0071] 2. About the server:

[0072] The server processes the received image data. Specifically, it extracts features from the images using a face recognition algorithm. This process utilizes image processing technologies such as OpenCV and TENSORFLOW®. The extracted features provide the information necessary to generate makeup application methods. The server leverages previously accumulated data and a generative AI model to select a makeup application method suitable for the user.

[0073] 3. Regarding proposals and labeling:

[0074] The selected makeup application is sent to the user's device and virtually displayed in real time. This allows the user to check the simulated makeup on the application and visually understand its effects. Furthermore, relevant product information and purchase links are also displayed on the device to assist with online purchases.

[0075] Specific example:

[0076] For example, if a user has a fair skin tone and large eyes, the makeup recommendations selected by the server may include a bright base makeup and blue-toned eyeshadow. Also, if the user has previously preferred pink lipstick, that history will be reflected in the makeup suggestions.

[0077] Example of a prompt:

[0078] The following prompt statements can be used in the generative AI model.

[0079] "Please suggest the best makeup techniques for users with fair skin tones and large eyes."

[0080] "Customize product suggestions based on the user's past makeup usage history."

[0081] This system allows users to instantly receive personalized makeup suggestions tailored to their individual characteristics, enabling them to efficiently select and purchase cosmetics.

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

[0083] Step 1:

[0084] The user either takes a photo of their face using the camera on their electronic device or selects a photo from an existing image library. The input is the user's face photo, which is processed within the application and prepared to be sent to the server. The action in this step is a touch operation by the user to take or select the photo.

[0085] Step 2:

[0086] The device sends a facial photograph selected by the user to the server. The input is the image data that the user has instructed to upload, which is sent to the server as a data packet. The output is a notification that the image data has been successfully transferred to the server. In operation, the device transmits data over the network.

[0087] Step 3:

[0088] The server analyzes the received image data using a face recognition algorithm. The input is a user's face photograph received from the terminal, and the output is numerical data obtained through feature extraction. This data will include facial shape, skin tone, and features of the eyes and lips. Specifically, the server uses software libraries such as OpenCV and TensorFlow.

[0089] Step 4:

[0090] The server processes the extracted feature data and uses a generative AI model to select the optimal makeup method. The input is numerical data obtained through feature extraction, and the output is a specific suggestion of a makeup method. This step involves matching with a database and AI optimization. The operation involves computational processing to utilize the AI ​​model.

[0091] Step 5:

[0092] The server sends the generated makeup suggestions to the user's terminal. The input is suggestion data generated by the AI ​​model, and the output is visualized information. This allows the user's terminal to display a virtualized makeup preview in real time. Operationally, the server converts the data into an appropriate format and transmits it over the network.

[0093] Step 6:

[0094] The user terminal displays received makeup suggestions as a visual simulation. The input is makeup suggestion data sent from the server, and the output is a virtual makeup image on the terminal screen. In operation, the terminal uses augmented reality technology to display the effects in real time.

[0095] Step 7:

[0096] The user reviews the suggested products and obtains detailed information about the products they are interested in. The input is the user's selection action, and the output is the display of product information and purchase links. The action includes the user selecting a product link using touch controls.

[0097] Step 8:

[0098] The server provides the user with product purchase information and assists with the purchase process as needed. The input is the user's purchase intention, and the output is detailed information and procedural guidance regarding the product purchase. Operationally, the server formats the purchase-related data and sends it to the terminal.

[0099] In this way, the series of steps allows users to enjoy a personalized makeup experience and smoothly proceed to the actual product purchase.

[0100] (Application Example 1)

[0101] 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."

[0102] Traditional makeup advice systems are limited to online suggestions and do not adequately integrate with the in-store shopping experience. Furthermore, there is a need for methods to quickly locate products and provide seamless suggestions to individual users, thereby enhancing the user shopping experience.

[0103] 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.

[0104] In this invention, the server includes means for acquiring image information and analyzing features based on the image information, means for generating suggestions for makeup methods based on the analyzed features, and means for visually simulating and displaying the suggested makeup methods. This makes it possible to quickly provide makeup suggestions tailored to the user's facial features and guide them to the location of related products, even in physical stores.

[0105] "Image information" refers to photographic and video data provided by users, and serves as the basic data for analyzing facial features.

[0106] "Analyzing features" is the process of extracting and analyzing detailed data such as facial shape and skin tone from acquired image information.

[0107] "Generating makeup method suggestions" means selecting and presenting the most suitable makeup style and products for each individual user based on analyzed feature data.

[0108] "Visually simulating" means virtually applying the proposed makeup method to the user's image, allowing them to see the effect on screen.

[0109] "Providing product information" means providing users with specific product details and purchase options related to cosmetic suggestions.

[0110] "Guiding customers to the location of products" refers to identifying the placement of related products within a physical store, and making it easy for customers to find them.

[0111] This system uses user terminals and servers to provide each user with the most suitable cosmetics and recommendations. First, the user takes a photo of their face using the terminal. This photo is then sent to the server via the internet.

[0112] The server executes advanced image processing algorithms for facial recognition and analysis. Specifically, it uses facial recognition technologies such as OpenCV and Amazon Rekognition to extract and digitize features such as facial shape and skin tone. This analyzed data is integrated with a large cosmetics database to generate personalized makeup suggestions for the user.

[0113] The generated suggestions are visually simulated on the user's device. Users can see the effects of the suggested makeup in real time, and if they like it, they are provided with related product information. This product information also includes the location of the products in physical stores, allowing users to quickly search for and purchase products within the store.

[0114] For example, if a user wants to try a cosmetic product they haven't used before, this system allows them to choose the best product for their face while checking how it looks when applied. Furthermore, when visiting a store, it makes it easier to locate specific product locations on the shelves, providing a more efficient shopping experience.

[0115] Examples of prompts for a generative AI model:

[0116] "Design a system that analyzes a user's face and suggests the optimal makeup method and corresponding products. Based on facial feature data, it will provide dynamic product recommendations tailored to past makeup history and individual preferences."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user takes or selects a photo of their face using their device. The input is the user's facial image data, which is sent to the server via the internet. On the device, a function is executed to select and send the photo.

[0120] Step 2:

[0121] The server receives photo data and analyzes facial features using facial recognition technology. The input is a facial photograph sent by the user, and the output is facial feature data. Software such as OpenCV and Amazon Rekognition are used to extract facial shape, skin tone, and the positions of the eyes and mouth.

[0122] Step 3:

[0123] The server compares the obtained feature data with a cosmetics database and generates the optimal makeup method for the user. The input is facial feature data and an existing cosmetics database, and the output is the proposed makeup method. Statistical methods are used to select the optimal combination of cosmetics.

[0124] Step 4:

[0125] The server sends a suggested makeup method to the terminal, which then visually simulates it. The input is the suggested makeup method, and the output is a visually simulated image. The user can then see the effect of the makeup on the screen.

[0126] Step 5:

[0127] After the user reviews the suggestions, the server provides detailed information about the relevant products and guides them to the product's location within the store on their device. The input is the user's selected cosmetic product information, and the output is detailed product information and location data. This allows the user to quickly find the product within the physical store.

[0128] 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.

[0129] This invention integrates an emotion engine that recognizes the user's emotions with a system that acquires a user's image, analyzes its features, generates makeup suggestions, and visually simulates them. The aim is to dynamically adjust makeup suggestions according to the user's emotional state, thereby providing a more personalized experience.

[0130] The system operates by coordinating user terminals, servers, and an emotion engine.

[0131] First, the user's device takes or selects a photo of the user's face and sends that data to the server. The server uses an image recognition algorithm to analyze the facial features in detail and generate makeup suggestions. Information from the emotion engine is then incorporated. The emotion engine recognizes the user's real-time emotional state, and this information is used to adjust the makeup suggestions.

[0132] For example, if the emotion engine recognizes the user's emotion as "relaxed," the server will suggest a makeup style with calming colors. On the other hand, if the user is judged to be "energetic," it can suggest a more vibrant and bold makeup style. In this way, an emotion-recognition-based feedback loop is incorporated into the suggestion process, providing the user with an appropriate makeup experience.

[0133] The suggested makeup look is visually simulated in real time and displayed on the user's device. The user can review the suggested makeup look and access detailed information and purchase links for products that interest them.

[0134] Furthermore, this system can record the user's past emotional data and optimize suggestions based on this long-term data. As a result, users can receive personalized makeup advice tailored to their daily mood and preferences.

[0135] For example, if a user wants to try a bright lip color that's trending online, but is feeling "nervous" that day, the emotion engine can transmit this information to the server, which can then suggest a more subdued lip color. Through this functionality, the system considers the user's subjective emotional state while providing optimal makeup suggestions.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users use a dedicated app to take or select a photo of their face and send it from their device to the server.

[0139] Step 2:

[0140] The server uses an image recognition algorithm on the received facial photograph to analyze detailed facial features, including facial contours, skin tone, and features of the eyes and lips.

[0141] Step 3:

[0142] The server generates makeup suggestions based on the analysis results. At this stage, it compares them with past databases to identify the statistically most suitable makeup style.

[0143] Step 4:

[0144] The emotion engine analyzes the user's facial image or real-time video to recognize their emotional state. For example, it estimates emotions by analyzing facial expressions such as smiles, tension, and surprise.

[0145] Step 5:

[0146] The server incorporates the emotional state obtained from the emotion engine and adjusts makeup suggestions accordingly. For example, if the user's emotion is recognized as "stress," it will suggest makeup in calming colors.

[0147] Step 6:

[0148] The server generates a simulation to visualize how the adjusted makeup suggestion would actually look, and sends it to the terminal.

[0149] Step 7:

[0150] The device presents the user with a visual simulation, allowing them to see how the suggested makeup would look.

[0151] Step 8:

[0152] If a user reviews a simulation and becomes interested in a particular makeup product, the server will provide detailed information and a purchase link for that product, displaying it to the user via their device.

[0153] Step 9:

[0154] The server records emotional data over the long term and uses this to continuously optimize makeup suggestions for the user. The suggestions are updated in response to changes in the user's emotions.

[0155] (Example 2)

[0156] 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 will be referred to as the "terminal."

[0157] There is a problem in that users have difficulty obtaining optimal makeup suggestions based on their own emotional state, and they cannot receive customized suggestions that take into account their individual emotions and past history.

[0158] 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.

[0159] In this invention, the server includes means for acquiring a user's image and analyzing the features of the image; means for recognizing the user's emotional state and analyzing the emotional state; and means for generating makeup suggestions based on the analyzed features and emotional state. This makes it possible to visually and dynamically provide makeup suggestions that are tailored to the user's individual emotional state and features.

[0160] "User images" refer to still image data that has been taken or selected from the user's face or related parts.

[0161] "Methods for analyzing features" refers to the process of analyzing facial shape, color tone, skin texture, etc., from acquired user images to extract specific patterns and attributes.

[0162] "Means of recognizing emotional state" refers to the process of determining a user's current emotions using their facial expressions, voice, and other biometric information.

[0163] "Methods for generating suggestions" refers to the process of determining and recommending a makeup style suitable for the user based on the analyzed characteristics and perceived emotional state.

[0164] "Means of visually simulating and displaying" refers to a visualization process that shows how a proposed makeup style would actually look by virtually applying it to the user's image.

[0165] "Means of providing purchasing information" refers to the process of presenting users with product details and purchase links related to the simulated makeup look.

[0166] "Preferences and past history" refers to personal data such as styles previously selected by the user, products used, and records of emotions.

[0167] "Means of providing video instruction" refers to the process of providing users with visual learning materials to learn how to apply the proposed makeup style and techniques.

[0168] This invention relates to a system that provides personalized makeup suggestions based on a user's facial image and emotional state. The system primarily operates with a configuration including a terminal, a server, and an emotion recognition engine.

[0169] The terminal is a device equipped with a user interface, such as a smartphone or tablet. The user uses it to take a picture of their face or select an existing image. This image data is transmitted to a server via the internet. The user's emotional state is also captured in real time and sent to the server.

[0170] The server plays a crucial role in analyzing the received images and emotion data. It uses image processing libraries such as OpenCV to extract facial features and then performs analysis using deep learning models like TensorFlow.

[0171] Furthermore, an emotion engine is used for emotion recognition, incorporating common natural language processing tools to analyze the user's voice and biometric information. Based on this information, the user's emotional state is classified into categories such as "relaxed," "stressed," and "energetic."

[0172] Once the analysis is complete, the server generates suitable makeup styles based on facial features and emotional state. Using a generative AI model, it generates a variety of makeup styles as prompts and suggests them to the user. For example, a prompt such as "Generate makeup suggestions considering the user's image and emotional state. Please provide an example suggestion for when the emotion is 'relaxed'" can be used.

[0173] Ultimately, the device visually simulates and displays makeup suggestions received from the server to the user. This system allows users to try out makeup styles that match their emotional state, while simultaneously providing purchasing information, thus offering a seamless experience.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user either takes a picture of their face using the device's camera or selects an existing face image. In this step, the user's face image is obtained as input, and this data is temporarily stored on the device.

[0177] Step 2:

[0178] The device acquires voice and biometric information while the user is using the service and prepares it as data to evaluate their emotional state. Inputs include the user's voice and heart rate, which are then prepared to be sent to the emotion recognition engine.

[0179] Step 3:

[0180] The device transmits the facial image data acquired in Step 1 and the emotion evaluation data obtained in Step 2 to the server via the internet. The server then receives the necessary data to analyze the user's facial features and emotional state.

[0181] Step 4:

[0182] The server uses the OpenCV library to analyze image data and extract features such as facial shape and color tone. The input is the facial image data sent in step 3, and the output is a set of detailed facial features. This data processing provides facial information that can be used to generate subsequent makeup suggestions.

[0183] Step 5:

[0184] The server analyzes emotional data acquired by the emotion recognition engine. Natural language processing tools are used to classify the user's emotional state into categories such as "relaxed" or "stressed." Input is voice or other biometric information, and output is the classification result of the emotional state.

[0185] Step 6:

[0186] The server generates makeup suggestions based on the user's facial features and emotional state. This process uses a generative AI model to generate diverse makeup styles based on prompt text. The input is the output data from steps 4 and 5, and the output is a customized makeup suggestion.

[0187] Step 7:

[0188] The server simulates the generated makeup suggestions onto the user's face image to visualize them. This allows the user to see how the suggested style would look on them. The input is the makeup suggestions and face image data, and the output is a visual simulation image.

[0189] Step 8:

[0190] The terminal displays a visual simulation image received from the server to the user, and provides product information and purchase links related to that makeup style. The input is the visual simulation image and product information, and the output is a screen display for the user to review and interact with.

[0191] (Application Example 2)

[0192] 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 device 14 will be referred to as the "terminal."

[0193] Conventional makeup recommendation systems only analyze the user's facial features and fail to consider the user's emotional state. Therefore, providing more appropriate and personalized makeup recommendations for each user was a challenge. Furthermore, there was a need to visually simulate suitable recommendations and facilitate the purchase of actual makeup products.

[0194] 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.

[0195] In this invention, the server includes means for acquiring a user's image and analyzing its features, means for recognizing the user's emotions and adjusting makeup suggestions based on their emotional state, means for visually simulating and displaying the suggested makeup, and means for providing purchase information for products related to the simulated makeup. This makes it possible to provide personalized makeup suggestions that correspond to the user's emotional state, along with product information based on those suggestions.

[0196] "Means for acquiring user images" refers to functions for collecting user facial images using a camera or similar device.

[0197] "Methods for analyzing image features" refer to techniques that extract feature points from acquired facial images and analyze data that forms the basis for makeup suggestions.

[0198] "A means of generating makeup suggestions" refers to an algorithm that creates an appropriate makeup style based on analyzed facial features.

[0199] "Means of recognizing emotions" refers to technology that analyzes a user's facial expressions, voice, and other data to determine their emotions in real time.

[0200] "Methods for adjusting makeup suggestions based on emotional state" refers to the process of dynamically adapting makeup styles and colors according to perceived emotions.

[0201] "Means of visually simulating and displaying" refers to a technology that superimposes the proposed makeup onto the user's facial image and displays it on a monitor or screen.

[0202] "Means of providing product purchase information" refers to a function that guides users through detailed information and purchase procedures for the makeup items used in the simulation.

[0203] The system for implementing this invention consists of a user terminal, a server, and a program that links an emotion recognition engine. The user terminal has the function of capturing an image of the user's face using a camera and sending that data to the server. Based on this face image, the server extracts and analyzes facial features using image analysis software (e.g., OpenCV).

[0204] Next, the server uses an emotion recognition engine to determine the user's emotional state. This engine analyzes facial expressions and voice data to identify the user's current emotions. Based on this emotional data, the server dynamically adjusts the style and color of the makeup suggestions. For example, if emotion recognition determines the user's state to be "relaxed," the server will suggest makeup in calming colors.

[0205] The visual simulation superimposes makeup effects onto the user's facial image in real time and displays them on the device's screen. This simulation allows the user to virtually try out the suggested makeup look.

[0206] Furthermore, the server provides users with product information related to the generated makeup suggestions. This includes product details, pricing, and purchase links, allowing users to use this information to buy their selected makeup products.

[0207] For example, if the system detects that the user is feeling "energetic," it can suggest a bright and vibrant lip color. An example of a prompt message would be: "Determine if the user is having fun and suggest appropriate makeup. Refer to this data for a facial image."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The user terminal acquires an image of the user's face using its built-in camera. This image data is then prepared for transmission to the server. The input is a still image from the camera, and the output is digital facial image data ready for transmission to the server.

[0211] Step 2:

[0212] The server processes the received facial image data using image analysis software to extract facial features. Specifically, it performs calculations to identify the contours of the face and the positions of features such as the eyes, nose, and mouth from the image data. The input is digital facial image data, and the output is a dataset representing facial features.

[0213] Step 3:

[0214] The server uses a generative AI model to recognize the user's emotional state. It analyzes facial expressions from image and audio data to identify emotions such as "relaxed" or "energetic." The input is a dataset representing facial features, and the output is the identified emotional state.

[0215] Step 4:

[0216] The server generates makeup suggestions based on the results of emotion recognition. These suggestions select colors and styles that harmonize with the extracted facial features. The input is facial features and emotional state, and the output is information on the adjusted makeup style.

[0217] Step 5:

[0218] The user terminal visually simulates the makeup style received from the server and displays it on the screen. Specifically, it overlays the makeup effect onto the user's facial image and displays it in real time. The input is makeup style information, and the output is the simulated makeup image.

[0219] Step 6:

[0220] The server provides the user's terminal with information related to makeup products. This includes processing product details and purchase links. The input is makeup style information, and the output is related product information.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention is an intelligent system that suggests the optimal makeup method for each individual user and facilitates the purchase of related products. This system facilitates collaboration between the user terminal, server, and user, realizing a seamless process from makeup suggestions to purchase.

[0238] First, the user's device allows the user to upload a photo of their face. The user uses the application to send a photo taken with the camera or selected from the gallery to the server. Once the photo reaches the server, the server uses a face recognition algorithm to analyze the image and extract facial features.

[0239] The server analyzes not only the user's facial shape and skin tone, but also the shape of their eyes and lips in detail. This yields diverse facial feature data. Next, the server uses this data to compare it with a previously accumulated makeup database and statistically selects the most suitable makeup method.

[0240] Furthermore, the server customizes the suggestions based on the user's preferences and past history. For example, suggestions are adjusted based on previously preferred lip colors or specific brands of cosmetics. These generated suggestions are then presented to the user as a visual simulation. The user's device displays this simulation in real time, allowing the user to see the effect of the suggested makeup.

[0241] After reviewing the suggestions, the server provides purchase information for products the user is interested in. Through detailed product information and links to the online store, users can proceed directly to purchase. To enhance user convenience during this process, video tutorials on makeup application are also provided. This allows users to effectively learn about makeup and reduces the difficulties they may encounter when actually applying it.

[0242] For example, if a user has a fair skin tone and large eyes, the server's suggestions might include a combination of bright base makeup and blue-toned eyeshadow. Furthermore, if data shows the user has previously favored pink lipsticks, this information will also be considered in the suggestions, and the user's device will display links to related products. In this way, the system highly personalizes makeup suggestions to individual needs, improving the user experience.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] Users use a dedicated app to take or select a photo of their face and upload it to the server via their device.

[0246] Step 2:

[0247] The server analyzes the received facial photograph and applies a facial recognition algorithm to extract features such as facial shape, skin tone, and the shape of the eyes and lips.

[0248] Step 3:

[0249] The server compares the extracted facial feature data with a database of previously accumulated data to generate makeup rules tailored to the user. This process utilizes machine learning algorithms to improve the accuracy of the suggestions.

[0250] Step 4:

[0251] The server prepares a visual simulation of the makeup based on the generated suggestions. It then performs image processing so that the user can see in real time how the suggestions will look.

[0252] Step 5:

[0253] The terminal displays a visual simulation received from the server to the user. The user can then review the suggested makeup on the interface and take further action.

[0254] Step 6:

[0255] If a user is interested in the suggested makeup, the server provides information on purchasing the relevant makeup products. Detailed information, including a link to the product page, is sent to the user's device.

[0256] Step 7:

[0257] Users can watch video tutorials on their devices to learn the suggested makeup steps. This allows them to understand and practice the actual makeup process.

[0258] (Example 1)

[0259] 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."

[0260] In today's world, finding the optimal makeup method for each individual user is difficult, time-consuming, and laborious. Furthermore, efficiently obtaining product information corresponding to that makeup method is also challenging. This hinders user satisfaction with makeup and the benefits of purchasing products, which poses a significant challenge.

[0261] 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.

[0262] In this invention, the server includes means for acquiring image data from a user's electronic device, data processing means for analyzing multiple features of the image data, and means for selecting the optimal makeup method based on the analyzed features. This makes it possible to provide optimal makeup suggestions to individual users and to quickly acquire relevant product information.

[0263] A "user" is an individual or group that uses the system to obtain the optimal makeup method.

[0264] "Electronic devices" is a general term for devices that allow users to acquire and transmit image data, and includes smartphones, personal computers, tablets, and other similar devices.

[0265] "Image data" refers to a digital image of a user's face and features, and is a data format used for analysis.

[0266] "Data processing means" refers to a series of processing techniques and algorithms used to extract and analyze features from image data within a server.

[0267] "Features" refer to elements that influence individual makeup suggestions, such as facial shape, skin tone, and the shape of the eyes and lips, which are extracted from image data.

[0268] "Makeup techniques" refer to a collection of specific makeup advice and techniques aimed at enhancing beauty and style, suggested based on characteristics analyzed from the user's image data.

[0269] "Virtual display" is a display method that uses digital technology to visually simulate the selected makeup method, allowing the user to check the results.

[0270] "Product information" refers to detailed information about a virtual display of cosmetic-related products and information that prompts users to proceed with the purchase.

[0271] This invention is a system in which a user takes a photo of their own face with an electronic device, obtains the optimal makeup method, and receives relevant product information.

[0272] 1. Regarding user terminals:

[0273] Users utilize electronic devices such as smartphones, tablets, and personal computers. These devices use their camera functions to capture photos of the user's face and send the image data to a server via an application or web browser. The devices are equipped with software that enables image uploading and the display of makeup simulations.

[0274] 2. About the server:

[0275] The server processes the received image data. Specifically, it extracts features from the images using a face recognition algorithm. This process utilizes image processing technologies such as OpenCV and TensorFlow. The extracted features provide the information necessary to generate makeup application methods. The server leverages previously accumulated data and generative AI models to select a makeup application method suitable for the user.

[0276] 3. Regarding proposals and labeling:

[0277] The selected makeup application is sent to the user's device and virtually displayed in real time. This allows the user to check the simulated makeup on the application and visually understand its effects. Furthermore, relevant product information and purchase links are also displayed on the device to assist with online purchases.

[0278] Specific example:

[0279] For example, if a user has a fair skin tone and large eyes, the makeup recommendations selected by the server may include a bright base makeup and blue-toned eyeshadow. Also, if the user has previously preferred pink lipstick, that history will be reflected in the makeup suggestions.

[0280] Example of a prompt:

[0281] The following prompt statements can be used in the generative AI model.

[0282] "Please suggest the best makeup techniques for users with fair skin tones and large eyes."

[0283] "Customize product suggestions based on the user's past makeup usage history."

[0284] With this system, users can immediately receive personalized makeup suggestions according to their individual characteristics, and can efficiently select and purchase cosmetics.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user takes a face photo using the camera of the electronic device or selects a photo from an existing image library. The input is the user's face photo, which is processed within the application and prepared to be sent to the server. The operation at this step is a touch operation of shooting or selecting by the user.

[0288] Step 2:

[0289] The terminal sends the face photo selected by the user to the server. The input is the image data that the user instructed to upload, and this is sent to the server as a data packet. The output is a notification of successful transfer of the image data to the server. As an operation, the terminal performs data transmission via the network.

[0290] Step 3:

[0291] The server analyzes the received image data using a face recognition algorithm. The input is the user's face photo received from the terminal, and the output is numerical data obtained by feature extraction. This data will include the shape of the face, skin tone, and features of the eyes and lips. As a specific operation, the server uses software libraries such as OpenCV and TensorFlow.

[0292] Step 4:

[0293] The server processes the extracted feature data and uses a generative AI model to select the optimal makeup method. The input is numerical data obtained through feature extraction, and the output is a specific suggestion of a makeup method. This step involves matching with a database and AI optimization. The operation involves computational processing to utilize the AI ​​model.

[0294] Step 5:

[0295] The server sends the generated makeup suggestions to the user's terminal. The input is suggestion data generated by the AI ​​model, and the output is visualized information. This allows the user's terminal to display a virtualized makeup preview in real time. Operationally, the server converts the data into an appropriate format and transmits it over the network.

[0296] Step 6:

[0297] The user terminal displays received makeup suggestions as a visual simulation. The input is makeup suggestion data sent from the server, and the output is a virtual makeup image on the terminal screen. In operation, the terminal uses augmented reality technology to display the effects in real time.

[0298] Step 7:

[0299] The user reviews the suggested products and obtains detailed information about the products they are interested in. The input is the user's selection action, and the output is the display of product information and purchase links. The action includes the user selecting a product link using touch controls.

[0300] Step 8:

[0301] The server provides the user with product purchase information and assists with the purchase process as needed. The input is the user's purchase intention, and the output is detailed information and procedural guidance regarding the product purchase. Operationally, the server formats the purchase-related data and sends it to the terminal.

[0302] In this way, through a series of steps, the user can enjoy a personalized makeup experience and smoothly proceed to actual product purchases.

[0303] (Application Example 1)

[0304] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as "terminals".

[0305] In a conventional makeup advice system, it only makes proposals online and has insufficient coordination with the purchasing experience in actual stores. Also, there is a need for a method to quickly locate products to enrich the buying experience for users and to make seamless proposals to individual users.

[0306] 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.

[0307] In this invention, the server includes means for acquiring image information, analyzing features based on the image information, generating a proposal for a makeup method based on the analyzed features, and visually simulating and displaying the proposed makeup method. Thereby, it becomes possible to quickly make makeup proposals according to the facial features of the user even in an actual store and to indicate the locations of related products.

[0308] "Image information" refers to photo or video data provided by the user and is the basic data for analyzing facial features.

[0309] "Analyzing features" is a process of extracting and analyzing detailed data such as facial shape and skin color from the acquired image information.

[0310] "Generating a proposal for a makeup method" means selecting and presenting the most suitable makeup style and products to be used for individual users based on the analyzed feature data.

[0311] "Visually simulating" means virtually applying the proposed makeup method to the user's image, allowing them to see the effect on screen.

[0312] "Providing product information" means providing users with specific product details and purchase options related to cosmetic suggestions.

[0313] "Guiding customers to the location of products" refers to identifying the placement of related products within a physical store, and making it easy for customers to find them.

[0314] This system uses user terminals and servers to provide each user with the most suitable cosmetics and recommendations. First, the user takes a photo of their face using the terminal. This photo is then sent to the server via the internet.

[0315] The server executes advanced image processing algorithms for facial recognition and analysis. Specifically, it uses facial recognition technologies such as OpenCV and Amazon Rekognition to extract and digitize features such as facial shape and skin tone. This analyzed data is integrated with a large cosmetics database to generate personalized makeup suggestions for the user.

[0316] The generated suggestions are visually simulated on the user's device. Users can see the effects of the suggested makeup in real time, and if they like it, they are provided with related product information. This product information also includes the location of the products in physical stores, allowing users to quickly search for and purchase products within the store.

[0317] For example, if a user wants to try a cosmetic product they haven't used before, this system allows them to choose the best product for their face while checking how it looks when applied. Furthermore, when visiting a store, it makes it easier to locate specific product locations on the shelves, providing a more efficient shopping experience.

[0318] Examples of prompts for a generative AI model:

[0319] "Design a system that analyzes a user's face and suggests the optimal makeup method and corresponding products. Based on facial feature data, it will provide dynamic product recommendations tailored to past makeup history and individual preferences."

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] The user takes or selects a photo of their face using their device. The input is the user's facial image data, which is sent to the server via the internet. On the device, a function is executed to select and send the photo.

[0323] Step 2:

[0324] The server receives photo data and analyzes facial features using facial recognition technology. The input is a facial photograph sent by the user, and the output is facial feature data. Software such as OpenCV and Amazon Rekognition are used to extract facial shape, skin tone, and the positions of the eyes and mouth.

[0325] Step 3:

[0326] The server compares the obtained feature data with a cosmetics database and generates the optimal makeup method for the user. The input is facial feature data and an existing cosmetics database, and the output is the proposed makeup method. Statistical methods are used to select the optimal combination of cosmetics.

[0327] Step 4:

[0328] The server sends a suggested makeup method to the terminal, which then visually simulates it. The input is the suggested makeup method, and the output is a visually simulated image. The user can then see the effect of the makeup on the screen.

[0329] Step 5:

[0330] After the user reviews the suggestions, the server provides detailed information about the relevant products and guides them to the product's location within the store on their device. The input is the user's selected cosmetic product information, and the output is detailed product information and location data. This allows the user to quickly find the product within the physical store.

[0331] 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.

[0332] This invention integrates an emotion engine that recognizes the user's emotions with a system that acquires a user's image, analyzes its features, generates makeup suggestions, and visually simulates them. The aim is to dynamically adjust makeup suggestions according to the user's emotional state, thereby providing a more personalized experience.

[0333] The system operates by coordinating user terminals, servers, and an emotion engine.

[0334] First, the user's device takes or selects a photo of the user's face and sends that data to the server. The server uses an image recognition algorithm to analyze the facial features in detail and generate makeup suggestions. Information from the emotion engine is then incorporated. The emotion engine recognizes the user's real-time emotional state, and this information is used to adjust the makeup suggestions.

[0335] For example, if the emotion engine recognizes the user's emotion as "relaxed," the server will suggest a makeup style with calming colors. On the other hand, if the user is judged to be "energetic," it can suggest a more vibrant and bold makeup style. In this way, an emotion-recognition-based feedback loop is incorporated into the suggestion process, providing the user with an appropriate makeup experience.

[0336] The suggested makeup look is visually simulated in real time and displayed on the user's device. The user can review the suggested makeup look and access detailed information and purchase links for products that interest them.

[0337] Furthermore, this system can record the user's past emotional data and optimize suggestions based on this long-term data. As a result, users can receive personalized makeup advice tailored to their daily mood and preferences.

[0338] For example, if a user wants to try a bright lip color that's trending online, but is feeling "nervous" that day, the emotion engine can transmit this information to the server, which can then suggest a more subdued lip color. Through this functionality, the system considers the user's subjective emotional state while providing optimal makeup suggestions.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] Users use a dedicated app to take or select a photo of their face and send it from their device to the server.

[0342] Step 2:

[0343] The server uses an image recognition algorithm on the received facial photograph to analyze detailed facial features, including facial contours, skin tone, and features of the eyes and lips.

[0344] Step 3:

[0345] The server generates makeup suggestions based on the analysis results. At this stage, it compares them with past databases to identify the statistically most suitable makeup style.

[0346] Step 4:

[0347] The emotion engine analyzes the user's facial image or real-time video to recognize their emotional state. For example, it estimates emotions by analyzing facial expressions such as smiles, tension, and surprise.

[0348] Step 5:

[0349] The server incorporates the emotional state obtained from the emotion engine and adjusts makeup suggestions accordingly. For example, if the user's emotion is recognized as "stress," it will suggest makeup in calming colors.

[0350] Step 6:

[0351] The server generates a simulation to visualize how the adjusted makeup suggestion would actually look, and sends it to the terminal.

[0352] Step 7:

[0353] The device presents the user with a visual simulation, allowing them to see how the suggested makeup would look.

[0354] Step 8:

[0355] If a user reviews a simulation and becomes interested in a particular makeup product, the server will provide detailed information and a purchase link for that product, displaying it to the user via their device.

[0356] Step 9:

[0357] The server records emotional data over the long term and uses this to continuously optimize makeup suggestions for the user. The suggestions are updated in response to changes in the user's emotions.

[0358] (Example 2)

[0359] 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".

[0360] There is a problem in that users have difficulty obtaining optimal makeup suggestions based on their own emotional state, and they cannot receive customized suggestions that take into account their individual emotions and past history.

[0361] 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.

[0362] In this invention, the server includes means for acquiring a user's image and analyzing the features of the image; means for recognizing the user's emotional state and analyzing the emotional state; and means for generating makeup suggestions based on the analyzed features and emotional state. This makes it possible to visually and dynamically provide makeup suggestions that are tailored to the user's individual emotional state and features.

[0363] "User images" refer to still image data that has been taken or selected from the user's face or related parts.

[0364] "Methods for analyzing features" refers to the process of analyzing facial shape, color tone, skin texture, etc., from acquired user images to extract specific patterns and attributes.

[0365] "Means of recognizing emotional state" refers to the process of determining a user's current emotions using their facial expressions, voice, and other biometric information.

[0366] "Methods for generating suggestions" refers to the process of determining and recommending a makeup style suitable for the user based on the analyzed characteristics and perceived emotional state.

[0367] "Means of visually simulating and displaying" refers to a visualization process that shows how a proposed makeup style would actually look by virtually applying it to the user's image.

[0368] "Means of providing purchasing information" refers to the process of presenting users with product details and purchase links related to the simulated makeup look.

[0369] "Preferences and past history" refers to personal data such as styles previously selected by the user, products used, and records of emotions.

[0370] "Means of providing video instruction" refers to the process of providing users with visual learning materials to learn how to apply the proposed makeup style and techniques.

[0371] This invention relates to a system that provides personalized makeup suggestions based on a user's facial image and emotional state. The system primarily operates with a configuration including a terminal, a server, and an emotion recognition engine.

[0372] The terminal is a device equipped with a user interface, such as a smartphone or tablet. The user uses it to take a picture of their face or select an existing image. This image data is transmitted to a server via the internet. The user's emotional state is also captured in real time and sent to the server.

[0373] The server plays a crucial role in analyzing the received images and emotion data. It uses image processing libraries such as OpenCV to extract facial features and then performs analysis using deep learning models like TensorFlow.

[0374] Furthermore, an emotion engine is used for emotion recognition, incorporating common natural language processing tools to analyze the user's voice and biometric information. Based on this information, the user's emotional state is classified into categories such as "relaxed," "stressed," and "energetic."

[0375] Once the analysis is complete, the server generates suitable makeup styles based on facial features and emotional state. Using a generative AI model, it generates a variety of makeup styles as prompts and suggests them to the user. For example, a prompt such as "Generate makeup suggestions considering the user's image and emotional state. Please provide an example suggestion for when the emotion is 'relaxed'" can be used.

[0376] Ultimately, the device visually simulates and displays makeup suggestions received from the server to the user. This system allows users to try out makeup styles that match their emotional state, while simultaneously providing purchasing information, thus offering a seamless experience.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The user either takes a picture of their face using the device's camera or selects an existing face image. In this step, the user's face image is obtained as input, and this data is temporarily stored on the device.

[0380] Step 2:

[0381] The device acquires voice and biometric information while the user is using the service and prepares it as data to evaluate their emotional state. Inputs include the user's voice and heart rate, which are then prepared to be sent to the emotion recognition engine.

[0382] Step 3:

[0383] The device transmits the facial image data acquired in Step 1 and the emotion evaluation data obtained in Step 2 to the server via the internet. The server then receives the necessary data to analyze the user's facial features and emotional state.

[0384] Step 4:

[0385] The server uses the OpenCV library to analyze image data and extract features such as facial shape and color tone. The input is the facial image data sent in step 3, and the output is a set of detailed facial features. This data processing provides facial information that can be used to generate subsequent makeup suggestions.

[0386] Step 5:

[0387] The server analyzes emotional data acquired by the emotion recognition engine. Natural language processing tools are used to classify the user's emotional state into categories such as "relaxed" or "stressed." Input is voice or other biometric information, and output is the classification result of the emotional state.

[0388] Step 6:

[0389] The server generates makeup suggestions based on the user's facial features and emotional state. This process uses a generative AI model to generate diverse makeup styles based on prompt text. The input is the output data from steps 4 and 5, and the output is a customized makeup suggestion.

[0390] Step 7:

[0391] The server simulates the generated makeup suggestions onto the user's face image to visualize them. This allows the user to see how the suggested style would look on them. The input is the makeup suggestions and face image data, and the output is a visual simulation image.

[0392] Step 8:

[0393] The terminal displays a visual simulation image received from the server to the user, and provides product information and purchase links related to that makeup style. The input is the visual simulation image and product information, and the output is a screen display for the user to review and interact with.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] Conventional makeup recommendation systems only analyze the user's facial features and fail to consider the user's emotional state. Therefore, providing more appropriate and personalized makeup recommendations for each user was a challenge. Furthermore, there was a need to visually simulate suitable recommendations and facilitate the purchase of actual makeup products.

[0397] 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.

[0398] In this invention, the server includes means for acquiring a user's image and analyzing its features, means for recognizing the user's emotions and adjusting makeup suggestions based on their emotional state, means for visually simulating and displaying the suggested makeup, and means for providing purchase information for products related to the simulated makeup. This makes it possible to provide personalized makeup suggestions that correspond to the user's emotional state, along with product information based on those suggestions.

[0399] "Means for acquiring user images" refers to functions for collecting user facial images using a camera or similar device.

[0400] "Methods for analyzing image features" refer to techniques that extract feature points from acquired facial images and analyze data that forms the basis for makeup suggestions.

[0401] "A means of generating makeup suggestions" refers to an algorithm that creates an appropriate makeup style based on analyzed facial features.

[0402] "Means of recognizing emotions" refers to technology that analyzes a user's facial expressions, voice, and other data to determine their emotions in real time.

[0403] "Methods for adjusting makeup suggestions based on emotional state" refers to the process of dynamically adapting makeup styles and colors according to perceived emotions.

[0404] "Means of visually simulating and displaying" refers to a technology that superimposes the proposed makeup onto the user's facial image and displays it on a monitor or screen.

[0405] "Means of providing product purchase information" refers to a function that guides users through detailed information and purchase procedures for the makeup items used in the simulation.

[0406] The system for implementing this invention consists of a user terminal, a server, and a program that links an emotion recognition engine. The user terminal has the function of capturing an image of the user's face using a camera and sending that data to the server. Based on this face image, the server extracts and analyzes facial features using image analysis software (e.g., OpenCV).

[0407] Next, the server uses an emotion recognition engine to determine the user's emotional state. This engine analyzes facial expressions and voice data to identify the user's current emotions. Based on this emotional data, the server dynamically adjusts the style and color of the makeup suggestions. For example, if emotion recognition determines the user's state to be "relaxed," the server will suggest makeup in calming colors.

[0408] The visual simulation superimposes makeup effects onto the user's facial image in real time and displays them on the device's screen. This simulation allows the user to virtually try out the suggested makeup look.

[0409] Furthermore, the server provides users with product information related to the generated makeup suggestions. This includes product details, pricing, and purchase links, allowing users to use this information to buy their selected makeup products.

[0410] For example, if the system detects that the user is feeling "energetic," it can suggest a bright and vibrant lip color. An example of a prompt message would be: "Determine if the user is having fun and suggest appropriate makeup. Refer to this data for a facial image."

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The user terminal acquires an image of the user's face using its built-in camera. This image data is then prepared for transmission to the server. The input is a still image from the camera, and the output is digital facial image data ready for transmission to the server.

[0414] Step 2:

[0415] The server processes the received facial image data using image analysis software to extract facial features. Specifically, it performs calculations to identify the contours of the face and the positions of features such as the eyes, nose, and mouth from the image data. The input is digital facial image data, and the output is a dataset representing facial features.

[0416] Step 3:

[0417] The server uses a generative AI model to recognize the user's emotional state. It analyzes facial expressions from image and audio data to identify emotions such as "relaxed" or "energetic." The input is a dataset representing facial features, and the output is the identified emotional state.

[0418] Step 4:

[0419] The server generates makeup suggestions based on the results of emotion recognition. These suggestions select colors and styles that harmonize with the extracted facial features. The input is facial features and emotional state, and the output is information on the adjusted makeup style.

[0420] Step 5:

[0421] The user terminal visually simulates the makeup style received from the server and displays it on the screen. Specifically, it overlays the makeup effect onto the user's facial image and displays it in real time. The input is makeup style information, and the output is the simulated makeup image.

[0422] Step 6:

[0423] The server provides the user's terminal with information related to makeup products. This includes processing product details and purchase links. The input is makeup style information, and the output is related product information.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] This invention is an intelligent system that suggests the optimal makeup method for each individual user and facilitates the purchase of related products. This system facilitates collaboration between the user terminal, server, and user, realizing a seamless process from makeup suggestions to purchase.

[0441] First, the user's device allows the user to upload a photo of their face. The user uses the application to send a photo taken with the camera or selected from the gallery to the server. Once the photo reaches the server, the server uses a face recognition algorithm to analyze the image and extract facial features.

[0442] The server analyzes not only the user's facial shape and skin tone, but also the shape of their eyes and lips in detail. This yields diverse facial feature data. Next, the server uses this data to compare it with a previously accumulated makeup database and statistically selects the most suitable makeup method.

[0443] Furthermore, the server customizes the suggestions based on the user's preferences and past history. For example, suggestions are adjusted based on previously preferred lip colors or specific brands of cosmetics. These generated suggestions are then presented to the user as a visual simulation. The user's device displays this simulation in real time, allowing the user to see the effect of the suggested makeup.

[0444] After reviewing the suggestions, the server provides purchase information for products the user is interested in. Through detailed product information and links to the online store, users can proceed directly to purchase. To enhance user convenience during this process, video tutorials on makeup application are also provided. This allows users to effectively learn about makeup and reduces the difficulties they may encounter when actually applying it.

[0445] For example, if a user has a fair skin tone and large eyes, the server's suggestions might include a combination of bright base makeup and blue-toned eyeshadow. Furthermore, if data shows the user has previously favored pink lipsticks, this information will also be considered in the suggestions, and the user's device will display links to related products. In this way, the system highly personalizes makeup suggestions to individual needs, improving the user experience.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] Users use a dedicated app to take or select a photo of their face and upload it to the server via their device.

[0449] Step 2:

[0450] The server analyzes the received facial photograph and applies a facial recognition algorithm to extract features such as facial shape, skin tone, and the shape of the eyes and lips.

[0451] Step 3:

[0452] The server compares the extracted facial feature data with a database of previously accumulated data to generate makeup rules tailored to the user. This process utilizes machine learning algorithms to improve the accuracy of the suggestions.

[0453] Step 4:

[0454] The server prepares a visual simulation of the makeup based on the generated suggestions. It then performs image processing so that the user can see in real time how the suggestions will look.

[0455] Step 5:

[0456] The terminal displays a visual simulation received from the server to the user. The user can then review the suggested makeup on the interface and take further action.

[0457] Step 6:

[0458] If a user is interested in the suggested makeup, the server provides information on purchasing the relevant makeup products. Detailed information, including a link to the product page, is sent to the user's device.

[0459] Step 7:

[0460] Users can watch video tutorials on their devices to learn the suggested makeup steps. This allows them to understand and practice the actual makeup process.

[0461] (Example 1)

[0462] 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."

[0463] In today's world, finding the optimal makeup method for each individual user is difficult, time-consuming, and laborious. Furthermore, efficiently obtaining product information corresponding to that makeup method is also challenging. This hinders user satisfaction with makeup and the benefits of purchasing products, which poses a significant challenge.

[0464] 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.

[0465] In this invention, the server includes means for acquiring image data from a user's electronic device, data processing means for analyzing multiple features of the image data, and means for selecting the optimal makeup method based on the analyzed features. This makes it possible to provide optimal makeup suggestions to individual users and to quickly acquire relevant product information.

[0466] A "user" is an individual or group that uses the system to obtain the optimal makeup method.

[0467] "Electronic devices" is a general term for devices that allow users to acquire and transmit image data, and includes smartphones, personal computers, tablets, and other similar devices.

[0468] "Image data" refers to a digital image of a user's face and features, and is a data format used for analysis.

[0469] "Data processing means" refers to a series of processing techniques and algorithms used to extract and analyze features from image data within a server.

[0470] "Features" refer to elements that influence individual makeup suggestions, such as facial shape, skin tone, and the shape of the eyes and lips, which are extracted from image data.

[0471] "Makeup techniques" refer to a collection of specific makeup advice and techniques aimed at enhancing beauty and style, suggested based on characteristics analyzed from the user's image data.

[0472] "Virtual display" is a display method that uses digital technology to visually simulate the selected makeup method, allowing the user to check the results.

[0473] "Product information" refers to detailed information about a virtual display of cosmetic-related products and information that prompts users to proceed with the purchase.

[0474] This invention is a system in which a user takes a photo of their own face with an electronic device, obtains the optimal makeup method, and receives relevant product information.

[0475] 1. Regarding user terminals:

[0476] Users utilize electronic devices such as smartphones, tablets, and personal computers. These devices use their camera functions to capture photos of the user's face and send the image data to a server via an application or web browser. The devices are equipped with software that enables image uploading and the display of makeup simulations.

[0477] 2. About the server:

[0478] The server processes the received image data. Specifically, it extracts features from the images using a face recognition algorithm. This process utilizes image processing technologies such as OpenCV and TensorFlow. The extracted features provide the information necessary to generate makeup application methods. The server leverages previously accumulated data and generative AI models to select a makeup application method suitable for the user.

[0479] 3. Regarding proposals and labeling:

[0480] The selected makeup application is sent to the user's device and virtually displayed in real time. This allows the user to check the simulated makeup on the application and visually understand its effects. Furthermore, relevant product information and purchase links are also displayed on the device to assist with online purchases.

[0481] Specific example:

[0482] For example, if a user has a fair skin tone and large eyes, the makeup recommendations selected by the server may include a bright base makeup and blue-toned eyeshadow. Also, if the user has previously preferred pink lipstick, that history will be reflected in the makeup suggestions.

[0483] Example of a prompt:

[0484] The following prompt statements can be used in the generative AI model.

[0485] "Please suggest the best makeup techniques for users with fair skin tones and large eyes."

[0486] "Customize product suggestions based on the user's past makeup usage history."

[0487] This system allows users to instantly receive personalized makeup suggestions tailored to their individual characteristics, enabling them to efficiently select and purchase cosmetics.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The user either takes a photo of their face using the camera on their electronic device or selects a photo from an existing image library. The input is the user's face photo, which is processed within the application and prepared to be sent to the server. The action in this step is a touch operation by the user to take or select the photo.

[0491] Step 2:

[0492] The device sends a facial photograph selected by the user to the server. The input is the image data that the user has instructed to upload, which is sent to the server as a data packet. The output is a notification that the image data has been successfully transferred to the server. In operation, the device transmits data over the network.

[0493] Step 3:

[0494] The server analyzes the received image data using a face recognition algorithm. The input is a user's face photograph received from the terminal, and the output is numerical data obtained through feature extraction. This data will include facial shape, skin tone, and features of the eyes and lips. Specifically, the server uses software libraries such as OpenCV and TensorFlow.

[0495] Step 4:

[0496] The server processes the extracted feature data and uses a generative AI model to select the optimal makeup method. The input is numerical data obtained through feature extraction, and the output is a specific suggestion of a makeup method. This step involves matching with a database and AI optimization. The operation involves computational processing to utilize the AI ​​model.

[0497] Step 5:

[0498] The server sends the generated makeup suggestions to the user's terminal. The input is suggestion data generated by the AI ​​model, and the output is visualized information. This allows the user's terminal to display a virtualized makeup preview in real time. Operationally, the server converts the data into an appropriate format and transmits it over the network.

[0499] Step 6:

[0500] The user terminal displays received makeup suggestions as a visual simulation. The input is makeup suggestion data sent from the server, and the output is a virtual makeup image on the terminal screen. In operation, the terminal uses augmented reality technology to display the effects in real time.

[0501] Step 7:

[0502] The user reviews the suggested products and obtains detailed information about the products they are interested in. The input is the user's selection action, and the output is the display of product information and purchase links. The action includes the user selecting a product link using touch controls.

[0503] Step 8:

[0504] The server provides the user with product purchase information and assists with the purchase process as needed. The input is the user's purchase intention, and the output is detailed information and procedural guidance regarding the product purchase. Operationally, the server formats the purchase-related data and sends it to the terminal.

[0505] In this way, the series of steps allows users to enjoy a personalized makeup experience and smoothly proceed to the actual product purchase.

[0506] (Application Example 1)

[0507] 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."

[0508] Traditional makeup advice systems are limited to online suggestions and do not adequately integrate with the in-store shopping experience. Furthermore, there is a need for methods to quickly locate products and provide seamless suggestions to individual users, thereby enhancing the user shopping experience.

[0509] 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.

[0510] In this invention, the server includes means for acquiring image information and analyzing features based on the image information, means for generating suggestions for makeup methods based on the analyzed features, and means for visually simulating and displaying the suggested makeup methods. This makes it possible to quickly provide makeup suggestions tailored to the user's facial features and guide them to the location of related products, even in physical stores.

[0511] "Image information" refers to photographic and video data provided by users, and serves as the basic data for analyzing facial features.

[0512] "Analyzing features" is the process of extracting and analyzing detailed data such as facial shape and skin tone from acquired image information.

[0513] "Generating makeup method suggestions" means selecting and presenting the most suitable makeup style and products for each individual user based on analyzed feature data.

[0514] "Visually simulating" means virtually applying the proposed makeup method to the user's image, allowing them to see the effect on screen.

[0515] "Providing product information" means providing users with specific product details and purchase options related to cosmetic suggestions.

[0516] "Guiding customers to the location of products" refers to identifying the placement of related products within a physical store, and making it easy for customers to find them.

[0517] This system uses user terminals and servers to provide each user with the most suitable cosmetics and recommendations. First, the user takes a photo of their face using the terminal. This photo is then sent to the server via the internet.

[0518] The server executes advanced image processing algorithms for facial recognition and analysis. Specifically, it uses facial recognition technologies such as OpenCV and Amazon Rekognition to extract and digitize features such as facial shape and skin tone. This analyzed data is integrated with a large cosmetics database to generate personalized makeup suggestions for the user.

[0519] The generated suggestions are visually simulated on the user's device. Users can see the effects of the suggested makeup in real time, and if they like it, they are provided with related product information. This product information also includes the location of the products in physical stores, allowing users to quickly search for and purchase products within the store.

[0520] For example, if a user wants to try a cosmetic product they haven't used before, this system allows them to choose the best product for their face while checking how it looks when applied. Furthermore, when visiting a store, it makes it easier to locate specific product locations on the shelves, providing a more efficient shopping experience.

[0521] Examples of prompts for a generative AI model:

[0522] "Design a system that analyzes a user's face and suggests the optimal makeup method and corresponding products. Based on facial feature data, it will provide dynamic product recommendations tailored to past makeup history and individual preferences."

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The user takes or selects a photo of their face using their device. The input is the user's facial image data, which is sent to the server via the internet. On the device, a function is executed to select and send the photo.

[0526] Step 2:

[0527] The server receives photo data and analyzes facial features using facial recognition technology. The input is a facial photograph sent by the user, and the output is facial feature data. Software such as OpenCV and Amazon Rekognition are used to extract facial shape, skin tone, and the positions of the eyes and mouth.

[0528] Step 3:

[0529] The server compares the obtained feature data with a cosmetics database and generates the optimal makeup method for the user. The input is facial feature data and an existing cosmetics database, and the output is the proposed makeup method. Statistical methods are used to select the optimal combination of cosmetics.

[0530] Step 4:

[0531] The server sends a suggested makeup method to the terminal, which then visually simulates it. The input is the suggested makeup method, and the output is a visually simulated image. The user can then see the effect of the makeup on the screen.

[0532] Step 5:

[0533] After the user reviews the suggestions, the server provides detailed information about the relevant products and guides them to the product's location within the store on their device. The input is the user's selected cosmetic product information, and the output is detailed product information and location data. This allows the user to quickly find the product within the physical store.

[0534] 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.

[0535] This invention integrates an emotion engine that recognizes the user's emotions with a system that acquires a user's image, analyzes its features, generates makeup suggestions, and visually simulates them. The aim is to dynamically adjust makeup suggestions according to the user's emotional state, thereby providing a more personalized experience.

[0536] The system operates by coordinating user terminals, servers, and an emotion engine.

[0537] First, the user's device takes or selects a photo of the user's face and sends that data to the server. The server uses an image recognition algorithm to analyze the facial features in detail and generate makeup suggestions. Information from the emotion engine is then incorporated. The emotion engine recognizes the user's real-time emotional state, and this information is used to adjust the makeup suggestions.

[0538] For example, if the emotion engine recognizes the user's emotion as "relaxed," the server will suggest a makeup style with calming colors. On the other hand, if the user is judged to be "energetic," it can suggest a more vibrant and bold makeup style. In this way, an emotion-recognition-based feedback loop is incorporated into the suggestion process, providing the user with an appropriate makeup experience.

[0539] The suggested makeup look is visually simulated in real time and displayed on the user's device. The user can review the suggested makeup look and access detailed information and purchase links for products that interest them.

[0540] Furthermore, this system can record the user's past emotional data and optimize suggestions based on this long-term data. As a result, users can receive personalized makeup advice tailored to their daily mood and preferences.

[0541] For example, if a user wants to try a bright lip color that's trending online, but is feeling "nervous" that day, the emotion engine can transmit this information to the server, which can then suggest a more subdued lip color. Through this functionality, the system considers the user's subjective emotional state while providing optimal makeup suggestions.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] Users use a dedicated app to take or select a photo of their face and send it from their device to the server.

[0545] Step 2:

[0546] The server uses an image recognition algorithm on the received facial photograph to analyze detailed facial features, including facial contours, skin tone, and features of the eyes and lips.

[0547] Step 3:

[0548] The server generates makeup suggestions based on the analysis results. At this stage, it compares them with past databases to identify the statistically most suitable makeup style.

[0549] Step 4:

[0550] The emotion engine analyzes the user's facial image or real-time video to recognize their emotional state. For example, it estimates emotions by analyzing facial expressions such as smiles, tension, and surprise.

[0551] Step 5:

[0552] The server incorporates the emotional state obtained from the emotion engine and adjusts makeup suggestions accordingly. For example, if the user's emotion is recognized as "stress," it will suggest makeup in calming colors.

[0553] Step 6:

[0554] The server generates a simulation to visualize how the adjusted makeup suggestion would actually look, and sends it to the terminal.

[0555] Step 7:

[0556] The device presents the user with a visual simulation, allowing them to see how the suggested makeup would look.

[0557] Step 8:

[0558] If a user reviews a simulation and becomes interested in a particular makeup product, the server will provide detailed information and a purchase link for that product, displaying it to the user via their device.

[0559] Step 9:

[0560] The server records emotional data over the long term and uses this to continuously optimize makeup suggestions for the user. The suggestions are updated in response to changes in the user's emotions.

[0561] (Example 2)

[0562] 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."

[0563] There is a problem in that users have difficulty obtaining optimal makeup suggestions based on their own emotional state, and they cannot receive customized suggestions that take into account their individual emotions and past history.

[0564] 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.

[0565] In this invention, the server includes means for acquiring a user's image and analyzing the features of the image; means for recognizing the user's emotional state and analyzing the emotional state; and means for generating makeup suggestions based on the analyzed features and emotional state. This makes it possible to visually and dynamically provide makeup suggestions that are tailored to the user's individual emotional state and features.

[0566] "User images" refer to still image data that has been taken or selected from the user's face or related parts.

[0567] "Methods for analyzing features" refers to the process of analyzing facial shape, color tone, skin texture, etc., from acquired user images to extract specific patterns and attributes.

[0568] "Means of recognizing emotional state" refers to the process of determining a user's current emotions using their facial expressions, voice, and other biometric information.

[0569] "Methods for generating suggestions" refers to the process of determining and recommending a makeup style suitable for the user based on the analyzed characteristics and perceived emotional state.

[0570] "Means of visually simulating and displaying" refers to a visualization process that shows how a proposed makeup style would actually look by virtually applying it to the user's image.

[0571] "Means of providing purchasing information" refers to the process of presenting users with product details and purchase links related to the simulated makeup look.

[0572] "Preferences and past history" refers to personal data such as styles previously selected by the user, products used, and records of emotions.

[0573] "Means of providing video instruction" refers to the process of providing users with visual learning materials to learn how to apply the proposed makeup style and techniques.

[0574] This invention relates to a system that provides personalized makeup suggestions based on a user's facial image and emotional state. The system primarily operates with a configuration including a terminal, a server, and an emotion recognition engine.

[0575] The terminal is a device equipped with a user interface, such as a smartphone or tablet. The user uses it to take a picture of their face or select an existing image. This image data is transmitted to a server via the internet. The user's emotional state is also captured in real time and sent to the server.

[0576] The server plays a crucial role in analyzing the received images and emotion data. It uses image processing libraries such as OpenCV to extract facial features and then performs analysis using deep learning models like TensorFlow.

[0577] Furthermore, an emotion engine is used for emotion recognition, incorporating common natural language processing tools to analyze the user's voice and biometric information. Based on this information, the user's emotional state is classified into categories such as "relaxed," "stressed," and "energetic."

[0578] Once the analysis is complete, the server generates suitable makeup styles based on facial features and emotional state. Using a generative AI model, it generates a variety of makeup styles as prompts and suggests them to the user. For example, a prompt such as "Generate makeup suggestions considering the user's image and emotional state. Please provide an example suggestion for when the emotion is 'relaxed'" can be used.

[0579] Ultimately, the device visually simulates and displays makeup suggestions received from the server to the user. This system allows users to try out makeup styles that match their emotional state, while simultaneously providing purchasing information, thus offering a seamless experience.

[0580] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0581] Step 1:

[0582] The user either takes a picture of their face using the device's camera or selects an existing face image. In this step, the user's face image is obtained as input, and this data is temporarily stored on the device.

[0583] Step 2:

[0584] The device acquires voice and biometric information while the user is using the service and prepares it as data to evaluate their emotional state. Inputs include the user's voice and heart rate, which are then prepared to be sent to the emotion recognition engine.

[0585] Step 3:

[0586] The device transmits the facial image data acquired in Step 1 and the emotion evaluation data obtained in Step 2 to the server via the internet. The server then receives the necessary data to analyze the user's facial features and emotional state.

[0587] Step 4:

[0588] The server uses the OpenCV library to analyze image data and extract features such as facial shape and color tone. The input is the facial image data sent in step 3, and the output is a set of detailed facial features. This data processing provides facial information that can be used to generate subsequent makeup suggestions.

[0589] Step 5:

[0590] The server analyzes emotional data acquired by the emotion recognition engine. Natural language processing tools are used to classify the user's emotional state into categories such as "relaxed" or "stressed." Input is voice or other biometric information, and output is the classification result of the emotional state.

[0591] Step 6:

[0592] The server generates makeup suggestions based on the user's facial features and emotional state. This process uses a generative AI model to generate diverse makeup styles based on prompt text. The input is the output data from steps 4 and 5, and the output is a customized makeup suggestion.

[0593] Step 7:

[0594] The server simulates the generated makeup suggestions onto the user's face image to visualize them. This allows the user to see how the suggested style would look on them. The input is the makeup suggestions and face image data, and the output is a visual simulation image.

[0595] Step 8:

[0596] The terminal displays a visual simulation image received from the server to the user, and provides product information and purchase links related to that makeup style. The input is the visual simulation image and product information, and the output is a screen display for the user to review and interact with.

[0597] (Application Example 2)

[0598] 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."

[0599] Conventional makeup recommendation systems only analyze the user's facial features and fail to consider the user's emotional state. Therefore, providing more appropriate and personalized makeup recommendations for each user was a challenge. Furthermore, there was a need to visually simulate suitable recommendations and facilitate the purchase of actual makeup products.

[0600] 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.

[0601] In this invention, the server includes means for acquiring a user's image and analyzing its features, means for recognizing the user's emotions and adjusting makeup suggestions based on their emotional state, means for visually simulating and displaying the suggested makeup, and means for providing purchase information for products related to the simulated makeup. This makes it possible to provide personalized makeup suggestions that correspond to the user's emotional state, along with product information based on those suggestions.

[0602] "Means for acquiring user images" refers to functions for collecting user facial images using a camera or similar device.

[0603] "Methods for analyzing image features" refer to techniques that extract feature points from acquired facial images and analyze data that forms the basis for makeup suggestions.

[0604] "A means of generating makeup suggestions" refers to an algorithm that creates an appropriate makeup style based on analyzed facial features.

[0605] "Means of recognizing emotions" refers to technology that analyzes a user's facial expressions, voice, and other data to determine their emotions in real time.

[0606] "Methods for adjusting makeup suggestions based on emotional state" refers to the process of dynamically adapting makeup styles and colors according to perceived emotions.

[0607] "Means of visually simulating and displaying" refers to a technology that superimposes the proposed makeup onto the user's facial image and displays it on a monitor or screen.

[0608] "Means of providing product purchase information" refers to a function that guides users through detailed information and purchase procedures for the makeup items used in the simulation.

[0609] The system for implementing this invention consists of a user terminal, a server, and a program that links an emotion recognition engine. The user terminal has the function of capturing an image of the user's face using a camera and sending that data to the server. Based on this face image, the server extracts and analyzes facial features using image analysis software (e.g., OpenCV).

[0610] Next, the server uses an emotion recognition engine to determine the user's emotional state. This engine analyzes facial expressions and voice data to identify the user's current emotions. Based on this emotional data, the server dynamically adjusts the style and color of the makeup suggestions. For example, if emotion recognition determines the user's state to be "relaxed," the server will suggest makeup in calming colors.

[0611] The visual simulation superimposes makeup effects onto the user's facial image in real time and displays them on the device's screen. This simulation allows the user to virtually try out the suggested makeup look.

[0612] Furthermore, the server provides users with product information related to the generated makeup suggestions. This includes product details, pricing, and purchase links, allowing users to use this information to buy their selected makeup products.

[0613] For example, if the system detects that the user is feeling "energetic," it can suggest a bright and vibrant lip color. An example of a prompt message would be: "Determine if the user is having fun and suggest appropriate makeup. Refer to this data for a facial image."

[0614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0615] Step 1:

[0616] The user terminal acquires an image of the user's face using its built-in camera. This image data is then prepared for transmission to the server. The input is a still image from the camera, and the output is digital facial image data ready for transmission to the server.

[0617] Step 2:

[0618] The server processes the received facial image data using image analysis software to extract facial features. Specifically, it performs calculations to identify the contours of the face and the positions of features such as the eyes, nose, and mouth from the image data. The input is digital facial image data, and the output is a dataset representing facial features.

[0619] Step 3:

[0620] The server uses a generative AI model to recognize the user's emotional state. It analyzes facial expressions from image and audio data to identify emotions such as "relaxed" or "energetic." The input is a dataset representing facial features, and the output is the identified emotional state.

[0621] Step 4:

[0622] The server generates makeup suggestions based on the results of emotion recognition. These suggestions select colors and styles that harmonize with the extracted facial features. The input is facial features and emotional state, and the output is information on the adjusted makeup style.

[0623] Step 5:

[0624] The user terminal visually simulates the makeup style received from the server and displays it on the screen. Specifically, it overlays the makeup effect onto the user's facial image and displays it in real time. The input is makeup style information, and the output is the simulated makeup image.

[0625] Step 6:

[0626] The server provides the user's terminal with information related to makeup products. This includes processing product details and purchase links. The input is makeup style information, and the output is related product information.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] This invention is an intelligent system that suggests the optimal makeup method for each individual user and facilitates the purchase of related products. This system facilitates collaboration between the user terminal, server, and user, realizing a seamless process from makeup suggestions to purchase.

[0645] First, the user's device allows the user to upload a photo of their face. The user uses the application to send a photo taken with the camera or selected from the gallery to the server. Once the photo reaches the server, the server uses a face recognition algorithm to analyze the image and extract facial features.

[0646] The server analyzes not only the user's facial shape and skin tone, but also the shape of their eyes and lips in detail. This yields diverse facial feature data. Next, the server uses this data to compare it with a previously accumulated makeup database and statistically selects the most suitable makeup method.

[0647] Furthermore, the server customizes the suggestions based on the user's preferences and past history. For example, suggestions are adjusted based on previously preferred lip colors or specific brands of cosmetics. These generated suggestions are then presented to the user as a visual simulation. The user's device displays this simulation in real time, allowing the user to see the effect of the suggested makeup.

[0648] After reviewing the suggestions, the server provides purchase information for products the user is interested in. Through detailed product information and links to the online store, users can proceed directly to purchase. To enhance user convenience during this process, video tutorials on makeup application are also provided. This allows users to effectively learn about makeup and reduces the difficulties they may encounter when actually applying it.

[0649] For example, if a user has a fair skin tone and large eyes, the server's suggestions might include a combination of bright base makeup and blue-toned eyeshadow. Furthermore, if data shows the user has previously favored pink lipsticks, this information will also be considered in the suggestions, and the user's device will display links to related products. In this way, the system highly personalizes makeup suggestions to individual needs, improving the user experience.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] Users use a dedicated app to take or select a photo of their face and upload it to the server via their device.

[0653] Step 2:

[0654] The server analyzes the received facial photograph and applies a facial recognition algorithm to extract features such as facial shape, skin tone, and the shape of the eyes and lips.

[0655] Step 3:

[0656] The server compares the extracted facial feature data with a database of previously accumulated data to generate makeup rules tailored to the user. This process utilizes machine learning algorithms to improve the accuracy of the suggestions.

[0657] Step 4:

[0658] The server prepares a visual simulation of the makeup based on the generated suggestions. It then performs image processing so that the user can see in real time how the suggestions will look.

[0659] Step 5:

[0660] The terminal displays a visual simulation received from the server to the user. The user can then review the suggested makeup on the interface and take further action.

[0661] Step 6:

[0662] If a user is interested in the suggested makeup, the server provides information on purchasing the relevant makeup products. Detailed information, including a link to the product page, is sent to the user's device.

[0663] Step 7:

[0664] Users can watch video tutorials on their devices to learn the suggested makeup steps. This allows them to understand and practice the actual makeup process.

[0665] (Example 1)

[0666] 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".

[0667] In today's world, finding the optimal makeup method for each individual user is difficult, time-consuming, and laborious. Furthermore, efficiently obtaining product information corresponding to that makeup method is also challenging. This hinders user satisfaction with makeup and the benefits of purchasing products, which poses a significant challenge.

[0668] 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.

[0669] In this invention, the server includes means for acquiring image data from a user's electronic device, data processing means for analyzing multiple features of the image data, and means for selecting the optimal makeup method based on the analyzed features. This makes it possible to provide optimal makeup suggestions to individual users and to quickly acquire relevant product information.

[0670] A "user" is an individual or group that uses the system to obtain the optimal makeup method.

[0671] "Electronic devices" is a general term for devices that allow users to acquire and transmit image data, and includes smartphones, personal computers, tablets, and other similar devices.

[0672] "Image data" refers to a digital image of a user's face and features, and is a data format used for analysis.

[0673] "Data processing means" refers to a series of processing techniques and algorithms used to extract and analyze features from image data within a server.

[0674] "Features" refer to elements that influence individual makeup suggestions, such as facial shape, skin tone, and the shape of the eyes and lips, which are extracted from image data.

[0675] "Makeup techniques" refer to a collection of specific makeup advice and techniques aimed at enhancing beauty and style, suggested based on characteristics analyzed from the user's image data.

[0676] "Virtual display" is a display method that uses digital technology to visually simulate the selected makeup method, allowing the user to check the results.

[0677] "Product information" refers to detailed information about a virtual display of cosmetic-related products and information that prompts users to proceed with the purchase.

[0678] This invention is a system in which a user takes a photo of their own face with an electronic device, obtains the optimal makeup method, and receives relevant product information.

[0679] 1. Regarding user terminals:

[0680] Users utilize electronic devices such as smartphones, tablets, and personal computers. These devices use their camera functions to capture photos of the user's face and send the image data to a server via an application or web browser. The devices are equipped with software that enables image uploading and the display of makeup simulations.

[0681] 2. About the server:

[0682] The server processes the received image data. Specifically, it extracts features from the images using a face recognition algorithm. This process utilizes image processing technologies such as OpenCV and TensorFlow. The extracted features provide the information necessary to generate makeup application methods. The server leverages previously accumulated data and generative AI models to select a makeup application method suitable for the user.

[0683] 3. Regarding proposals and labeling:

[0684] The selected makeup application is sent to the user's device and virtually displayed in real time. This allows the user to check the simulated makeup on the application and visually understand its effects. Furthermore, relevant product information and purchase links are also displayed on the device to assist with online purchases.

[0685] Specific example:

[0686] For example, if a user has a fair skin tone and large eyes, the makeup recommendations selected by the server may include a bright base makeup and blue-toned eyeshadow. Also, if the user has previously preferred pink lipstick, that history will be reflected in the makeup suggestions.

[0687] Example of a prompt:

[0688] The following prompt statements can be used in the generative AI model.

[0689] "Please suggest the best makeup techniques for users with fair skin tones and large eyes."

[0690] "Customize product suggestions based on the user's past makeup usage history."

[0691] This system allows users to instantly receive personalized makeup suggestions tailored to their individual characteristics, enabling them to efficiently select and purchase cosmetics.

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The user either takes a photo of their face using the camera on their electronic device or selects a photo from an existing image library. The input is the user's face photo, which is processed within the application and prepared to be sent to the server. The action in this step is a touch operation by the user to take or select the photo.

[0695] Step 2:

[0696] The device sends a facial photograph selected by the user to the server. The input is the image data that the user has instructed to upload, which is sent to the server as a data packet. The output is a notification that the image data has been successfully transferred to the server. In operation, the device transmits data over the network.

[0697] Step 3:

[0698] The server analyzes the received image data using a face recognition algorithm. The input is a user's face photograph received from the terminal, and the output is numerical data obtained through feature extraction. This data will include facial shape, skin tone, and features of the eyes and lips. Specifically, the server uses software libraries such as OpenCV and TensorFlow.

[0699] Step 4:

[0700] The server processes the extracted feature data and uses a generative AI model to select the optimal makeup method. The input is numerical data obtained through feature extraction, and the output is a specific suggestion of a makeup method. This step involves matching with a database and AI optimization. The operation involves computational processing to utilize the AI ​​model.

[0701] Step 5:

[0702] The server sends the generated makeup suggestions to the user's terminal. The input is suggestion data generated by the AI ​​model, and the output is visualized information. This allows the user's terminal to display a virtualized makeup preview in real time. Operationally, the server converts the data into an appropriate format and transmits it over the network.

[0703] Step 6:

[0704] The user terminal displays received makeup suggestions as a visual simulation. The input is makeup suggestion data sent from the server, and the output is a virtual makeup image on the terminal screen. In operation, the terminal uses augmented reality technology to display the effects in real time.

[0705] Step 7:

[0706] The user reviews the suggested products and obtains detailed information about the products they are interested in. The input is the user's selection action, and the output is the display of product information and purchase links. The action includes the user selecting a product link using touch controls.

[0707] Step 8:

[0708] The server provides the user with product purchase information and assists with the purchase process as needed. The input is the user's purchase intention, and the output is detailed information and procedural guidance regarding the product purchase. Operationally, the server formats the purchase-related data and sends it to the terminal.

[0709] In this way, the series of steps allows users to enjoy a personalized makeup experience and smoothly proceed to the actual product purchase.

[0710] (Application Example 1)

[0711] 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".

[0712] Traditional makeup advice systems are limited to online suggestions and do not adequately integrate with the in-store shopping experience. Furthermore, there is a need for methods to quickly locate products and provide seamless suggestions to individual users, thereby enhancing the user shopping experience.

[0713] 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.

[0714] In this invention, the server includes means for acquiring image information and analyzing features based on the image information, means for generating suggestions for makeup methods based on the analyzed features, and means for visually simulating and displaying the suggested makeup methods. This makes it possible to quickly provide makeup suggestions tailored to the user's facial features and guide them to the location of related products, even in physical stores.

[0715] "Image information" refers to photographic and video data provided by users, and serves as the basic data for analyzing facial features.

[0716] "Analyzing features" is the process of extracting and analyzing detailed data such as facial shape and skin tone from acquired image information.

[0717] "Generating makeup method suggestions" means selecting and presenting the most suitable makeup style and products for each individual user based on analyzed feature data.

[0718] "Visually simulating" means virtually applying the proposed makeup method to the user's image, allowing them to see the effect on screen.

[0719] "Providing product information" means providing users with specific product details and purchase options related to cosmetic suggestions.

[0720] "Guiding customers to the location of products" refers to identifying the placement of related products within a physical store, and making it easy for customers to find them.

[0721] This system uses user terminals and servers to provide each user with the most suitable cosmetics and recommendations. First, the user takes a photo of their face using the terminal. This photo is then sent to the server via the internet.

[0722] The server executes advanced image processing algorithms for facial recognition and analysis. Specifically, it uses facial recognition technologies such as OpenCV and Amazon Rekognition to extract and digitize features such as facial shape and skin tone. This analyzed data is integrated with a large cosmetics database to generate personalized makeup suggestions for the user.

[0723] The generated suggestions are visually simulated on the user's device. Users can see the effects of the suggested makeup in real time, and if they like it, they are provided with related product information. This product information also includes the location of the products in physical stores, allowing users to quickly search for and purchase products within the store.

[0724] For example, if a user wants to try a cosmetic product they haven't used before, this system allows them to choose the best product for their face while checking how it looks when applied. Furthermore, when visiting a store, it makes it easier to locate specific product locations on the shelves, providing a more efficient shopping experience.

[0725] Examples of prompts for a generative AI model:

[0726] "Design a system that analyzes a user's face and suggests the optimal makeup method and corresponding products. Based on facial feature data, it will provide dynamic product recommendations tailored to past makeup history and individual preferences."

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] The user takes or selects a photo of their face using their device. The input is the user's facial image data, which is sent to the server via the internet. On the device, a function is executed to select and send the photo.

[0730] Step 2:

[0731] The server receives photo data and analyzes facial features using facial recognition technology. The input is a facial photograph sent by the user, and the output is facial feature data. Software such as OpenCV and Amazon Rekognition are used to extract facial shape, skin tone, and the positions of the eyes and mouth.

[0732] Step 3:

[0733] The server compares the obtained feature data with a cosmetics database and generates the optimal makeup method for the user. The input is facial feature data and an existing cosmetics database, and the output is the proposed makeup method. Statistical methods are used to select the optimal combination of cosmetics.

[0734] Step 4:

[0735] The server sends a suggested makeup method to the terminal, which then visually simulates it. The input is the suggested makeup method, and the output is a visually simulated image. The user can then see the effect of the makeup on the screen.

[0736] Step 5:

[0737] After the user reviews the suggestions, the server provides detailed information about the relevant products and guides them to the product's location within the store on their device. The input is the user's selected cosmetic product information, and the output is detailed product information and location data. This allows the user to quickly find the product within the physical store.

[0738] 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.

[0739] This invention integrates an emotion engine that recognizes the user's emotions with a system that acquires a user's image, analyzes its features, generates makeup suggestions, and visually simulates them. The aim is to dynamically adjust makeup suggestions according to the user's emotional state, thereby providing a more personalized experience.

[0740] The system operates by coordinating user terminals, servers, and an emotion engine.

[0741] First, the user's device takes or selects a photo of the user's face and sends that data to the server. The server uses an image recognition algorithm to analyze the facial features in detail and generate makeup suggestions. Information from the emotion engine is then incorporated. The emotion engine recognizes the user's real-time emotional state, and this information is used to adjust the makeup suggestions.

[0742] For example, if the emotion engine recognizes the user's emotion as "relaxed," the server will suggest a makeup style with calming colors. On the other hand, if the user is judged to be "energetic," it can suggest a more vibrant and bold makeup style. In this way, an emotion-recognition-based feedback loop is incorporated into the suggestion process, providing the user with an appropriate makeup experience.

[0743] The suggested makeup look is visually simulated in real time and displayed on the user's device. The user can review the suggested makeup look and access detailed information and purchase links for products that interest them.

[0744] Furthermore, this system can record the user's past emotional data and optimize suggestions based on this long-term data. As a result, users can receive personalized makeup advice tailored to their daily mood and preferences.

[0745] For example, if a user wants to try a bright lip color that's trending online, but is feeling "nervous" that day, the emotion engine can transmit this information to the server, which can then suggest a more subdued lip color. Through this functionality, the system considers the user's subjective emotional state while providing optimal makeup suggestions.

[0746] The following describes the processing flow.

[0747] Step 1:

[0748] Users use a dedicated app to take or select a photo of their face and send it from their device to the server.

[0749] Step 2:

[0750] The server uses an image recognition algorithm on the received facial photograph to analyze detailed facial features, including facial contours, skin tone, and features of the eyes and lips.

[0751] Step 3:

[0752] The server generates makeup suggestions based on the analysis results. At this stage, it compares them with past databases to identify the statistically most suitable makeup style.

[0753] Step 4:

[0754] The emotion engine analyzes the user's facial image or real-time video to recognize their emotional state. For example, it estimates emotions by analyzing facial expressions such as smiles, tension, and surprise.

[0755] Step 5:

[0756] The server incorporates the emotional state obtained from the emotion engine and adjusts makeup suggestions accordingly. For example, if the user's emotion is recognized as "stress," it will suggest makeup in calming colors.

[0757] Step 6:

[0758] The server generates a simulation to visualize how the adjusted makeup suggestion would actually look, and sends it to the terminal.

[0759] Step 7:

[0760] The device presents the user with a visual simulation, allowing them to see how the suggested makeup would look.

[0761] Step 8:

[0762] If a user reviews a simulation and becomes interested in a particular makeup product, the server will provide detailed information and a purchase link for that product, displaying it to the user via their device.

[0763] Step 9:

[0764] The server records emotional data over the long term and uses this to continuously optimize makeup suggestions for the user. The suggestions are updated in response to changes in the user's emotions.

[0765] (Example 2)

[0766] 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".

[0767] There is a problem in that users have difficulty obtaining optimal makeup suggestions based on their own emotional state, and they cannot receive customized suggestions that take into account their individual emotions and past history.

[0768] 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.

[0769] In this invention, the server includes means for acquiring a user's image and analyzing the features of the image; means for recognizing the user's emotional state and analyzing the emotional state; and means for generating makeup suggestions based on the analyzed features and emotional state. This makes it possible to visually and dynamically provide makeup suggestions that are tailored to the user's individual emotional state and features.

[0770] "User images" refer to still image data that has been taken or selected from the user's face or related parts.

[0771] "Methods for analyzing features" refers to the process of analyzing facial shape, color tone, skin texture, etc., from acquired user images to extract specific patterns and attributes.

[0772] "Means of recognizing emotional state" refers to the process of determining a user's current emotions using their facial expressions, voice, and other biometric information.

[0773] "Methods for generating suggestions" refers to the process of determining and recommending a makeup style suitable for the user based on the analyzed characteristics and perceived emotional state.

[0774] "Means of visually simulating and displaying" refers to a visualization process that shows how a proposed makeup style would actually look by virtually applying it to the user's image.

[0775] "Means of providing purchasing information" refers to the process of presenting users with product details and purchase links related to the simulated makeup look.

[0776] "Preferences and past history" refers to personal data such as styles previously selected by the user, products used, and records of emotions.

[0777] "Means of providing video instruction" refers to the process of providing users with visual learning materials to learn how to apply the proposed makeup style and techniques.

[0778] This invention relates to a system that provides personalized makeup suggestions based on a user's facial image and emotional state. The system primarily operates with a configuration including a terminal, a server, and an emotion recognition engine.

[0779] The terminal is a device equipped with a user interface, such as a smartphone or tablet. The user uses it to take a picture of their face or select an existing image. This image data is transmitted to a server via the internet. The user's emotional state is also captured in real time and sent to the server.

[0780] The server plays a crucial role in analyzing the received images and emotion data. It uses image processing libraries such as OpenCV to extract facial features and then performs analysis using deep learning models like TensorFlow.

[0781] Furthermore, an emotion engine is used for emotion recognition, incorporating common natural language processing tools to analyze the user's voice and biometric information. Based on this information, the user's emotional state is classified into categories such as "relaxed," "stressed," and "energetic."

[0782] Once the analysis is complete, the server generates suitable makeup styles based on facial features and emotional state. Using a generative AI model, it generates a variety of makeup styles as prompts and suggests them to the user. For example, a prompt such as "Generate makeup suggestions considering the user's image and emotional state. Please provide an example suggestion for when the emotion is 'relaxed'" can be used.

[0783] Ultimately, the device visually simulates and displays makeup suggestions received from the server to the user. This system allows users to try out makeup styles that match their emotional state, while simultaneously providing purchasing information, thus offering a seamless experience.

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] The user either takes a picture of their face using the device's camera or selects an existing face image. In this step, the user's face image is obtained as input, and this data is temporarily stored on the device.

[0787] Step 2:

[0788] The device acquires voice and biometric information while the user is using the service and prepares it as data to evaluate their emotional state. Inputs include the user's voice and heart rate, which are then prepared to be sent to the emotion recognition engine.

[0789] Step 3:

[0790] The device transmits the facial image data acquired in Step 1 and the emotion evaluation data obtained in Step 2 to the server via the internet. The server then receives the necessary data to analyze the user's facial features and emotional state.

[0791] Step 4:

[0792] The server uses the OpenCV library to analyze image data and extract features such as facial shape and color tone. The input is the facial image data sent in step 3, and the output is a set of detailed facial features. This data processing provides facial information that can be used to generate subsequent makeup suggestions.

[0793] Step 5:

[0794] The server analyzes emotional data acquired by the emotion recognition engine. Natural language processing tools are used to classify the user's emotional state into categories such as "relaxed" or "stressed." Input is voice or other biometric information, and output is the classification result of the emotional state.

[0795] Step 6:

[0796] The server generates makeup suggestions based on the user's facial features and emotional state. This process uses a generative AI model to generate diverse makeup styles based on prompt text. The input is the output data from steps 4 and 5, and the output is a customized makeup suggestion.

[0797] Step 7:

[0798] The server simulates the generated makeup suggestions onto the user's face image to visualize them. This allows the user to see how the suggested style would look on them. The input is the makeup suggestions and face image data, and the output is a visual simulation image.

[0799] Step 8:

[0800] The terminal displays a visual simulation image received from the server to the user, and provides product information and purchase links related to that makeup style. The input is the visual simulation image and product information, and the output is a screen display for the user to review and interact with.

[0801] (Application Example 2)

[0802] 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".

[0803] Conventional makeup recommendation systems only analyze the user's facial features and fail to consider the user's emotional state. Therefore, providing more appropriate and personalized makeup recommendations for each user was a challenge. Furthermore, there was a need to visually simulate suitable recommendations and facilitate the purchase of actual makeup products.

[0804] 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.

[0805] In this invention, the server includes means for acquiring a user's image and analyzing its features, means for recognizing the user's emotions and adjusting makeup suggestions based on their emotional state, means for visually simulating and displaying the suggested makeup, and means for providing purchase information for products related to the simulated makeup. This makes it possible to provide personalized makeup suggestions that correspond to the user's emotional state, along with product information based on those suggestions.

[0806] "Means for acquiring user images" refers to functions for collecting user facial images using a camera or similar device.

[0807] "Methods for analyzing image features" refer to techniques that extract feature points from acquired facial images and analyze data that forms the basis for makeup suggestions.

[0808] "A means of generating makeup suggestions" refers to an algorithm that creates an appropriate makeup style based on analyzed facial features.

[0809] "Means of recognizing emotions" refers to technology that analyzes a user's facial expressions, voice, and other data to determine their emotions in real time.

[0810] "Methods for adjusting makeup suggestions based on emotional state" refers to the process of dynamically adapting makeup styles and colors according to perceived emotions.

[0811] "Means of visually simulating and displaying" refers to a technology that superimposes the proposed makeup onto the user's facial image and displays it on a monitor or screen.

[0812] "Means of providing product purchase information" refers to a function that guides users through detailed information and purchase procedures for the makeup items used in the simulation.

[0813] The system for implementing this invention consists of a user terminal, a server, and a program that links an emotion recognition engine. The user terminal has the function of capturing an image of the user's face using a camera and sending that data to the server. Based on this face image, the server extracts and analyzes facial features using image analysis software (e.g., OpenCV).

[0814] Next, the server uses an emotion recognition engine to determine the user's emotional state. This engine analyzes facial expressions and voice data to identify the user's current emotions. Based on this emotional data, the server dynamically adjusts the style and color of the makeup suggestions. For example, if emotion recognition determines the user's state to be "relaxed," the server will suggest makeup in calming colors.

[0815] The visual simulation superimposes makeup effects onto the user's facial image in real time and displays them on the device's screen. This simulation allows the user to virtually try out the suggested makeup look.

[0816] Furthermore, the server provides users with product information related to the generated makeup suggestions. This includes product details, pricing, and purchase links, allowing users to use this information to buy their selected makeup products.

[0817] For example, if the system detects that the user is feeling "energetic," it can suggest a bright and vibrant lip color. An example of a prompt message would be: "Determine if the user is having fun and suggest appropriate makeup. Refer to this data for a facial image."

[0818] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0819] Step 1:

[0820] The user terminal acquires an image of the user's face using its built-in camera. This image data is then prepared for transmission to the server. The input is a still image from the camera, and the output is digital facial image data ready for transmission to the server.

[0821] Step 2:

[0822] The server processes the received facial image data using image analysis software to extract facial features. Specifically, it performs calculations to identify the contours of the face and the positions of features such as the eyes, nose, and mouth from the image data. The input is digital facial image data, and the output is a dataset representing facial features.

[0823] Step 3:

[0824] The server uses a generative AI model to recognize the user's emotional state. It analyzes facial expressions from image and audio data to identify emotions such as "relaxed" or "energetic." The input is a dataset representing facial features, and the output is the identified emotional state.

[0825] Step 4:

[0826] The server generates makeup suggestions based on the results of emotion recognition. These suggestions select colors and styles that harmonize with the extracted facial features. The input is facial features and emotional state, and the output is information on the adjusted makeup style.

[0827] Step 5:

[0828] The user terminal visually simulates the makeup style received from the server and displays it on the screen. Specifically, it overlays the makeup effect onto the user's facial image and displays it in real time. The input is makeup style information, and the output is the simulated makeup image.

[0829] Step 6:

[0830] The server provides the user's terminal with information related to makeup products. This includes processing product details and purchase links. The input is makeup style information, and the output is related product information.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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."

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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 to be incorporated by reference.

[0852] The following is further disclosed regarding the embodiments described above.

[0853] (Claim 1)

[0854] A means for acquiring a user's image and analyzing the features of the said image,

[0855] A means for generating makeup suggestions based on the characteristics analyzed above,

[0856] A means for visually simulating and displaying the proposed makeup,

[0857] A means for providing purchase information of products related to the simulated makeup,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising means for customizing makeup suggestions based on the user's preferences and past history.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising means for providing a video tutorial for learning the proposed makeup procedure.

[0863] "Example 1"

[0864] (Claim 1)

[0865] A means of obtaining image data from a user's electronic device,

[0866] A data processing means for analyzing multiple features of the aforementioned image data,

[0867] A method for selecting the optimal makeup method based on the analyzed characteristics,

[0868] A means for virtually displaying the selected makeup method,

[0869] Means for providing product information related to the virtually displayed cosmetics,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, further comprising means for personalizing makeup suggestions based on user preferences and past history information.

[0873] (Claim 3)

[0874] The system according to claim 1, further comprising means for providing a video as teaching material for performing the virtually displayed makeup method.

[0875] "Application Example 1"

[0876] (Claim 1)

[0877] A means for acquiring image information and analyzing features based on said image information,

[0878] A means for generating a proposed makeup method based on the analyzed features,

[0879] A means of visually simulating and displaying the proposed makeup method,

[0880] Means for providing product information related to the simulated makeup method,

[0881] A means of guiding customers to the location of products within a physical store based on the product information provided,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, further comprising means for adjusting the suggested content based on the user's preferences and past history.

[0885] (Claim 3)

[0886] The system according to claim 1, further comprising means for providing video instruction for learning the steps of a proposed makeup method.

[0887] "Example 2 of combining an emotion engine"

[0888] (Claim 1)

[0889] A means for acquiring a user's image and analyzing the features of the said image,

[0890] A means for recognizing the user's emotional state and analyzing the said emotional state,

[0891] A means for generating makeup suggestions based on the analyzed characteristics and emotional state,

[0892] A means for visually simulating and displaying the proposed makeup,

[0893] A means for providing purchase information of products related to the simulated makeup,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising means for customizing makeup suggestions based on the user's preferences and past history.

[0897] (Claim 3)

[0898] The system according to claim 1, further comprising means for providing video instruction for learning the proposed makeup procedure.

[0899] "Application example 2 when combining with an emotional engine"

[0900] (Claim 1)

[0901] A means for acquiring a user's image and analyzing the features of the said image,

[0902] A means for generating makeup suggestions based on the characteristics analyzed above,

[0903] A means for recognizing the user's emotions and adjusting the makeup suggestion based on the emotional state,

[0904] A means for visually simulating and displaying the proposed makeup,

[0905] A means for providing purchase information of products related to the simulated makeup,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, further comprising means for customizing makeup suggestions based on the user's preferences and past history.

[0909] (Claim 3)

[0910] The system according to claim 1, further comprising means for providing educational information for learning the proposed makeup procedure. [Explanation of Symbols]

[0911] 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 for acquiring a user's image and analyzing the features of the said image, A means for generating makeup suggestions based on the characteristics analyzed above, A means for visually simulating and displaying the proposed makeup, A means for providing purchase information of products related to the simulated makeup, A system that includes this.

2. The system according to claim 1, further comprising means for customizing makeup suggestions based on the user's preferences and past history.

3. The system according to claim 1, further comprising means for providing a video tutorial for learning the proposed makeup procedure.

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