system

Abstract

We provide the system. [Solution] A means for analyzing facial and body shape characteristics based on image data obtained from a user, A means for generating a virtual three-dimensional model of the user based on the analyzed features, A means for enabling virtual try-on of cosmetics and clothing using the aforementioned virtual three-dimensional model, A means for presenting the results of the virtual trial to the user, 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with a plethora of various cosmetics and fashion items on the market, it is not easy for consumers to select the products that are most suitable for themselves. Also, in situations where physical trials or fittings are difficult, consumers often become disappointed with results different from their expectations after purchase. To solve such problems, there is a need for a system that enables consumers to virtually try products suitable for themselves in advance and supports more appropriate selections.

Means for Solving the Problems

[0005] This invention provides a system that analyzes facial and body features based on image data acquired from a user and generates a virtual three-dimensional model from it. Using this virtual model, users can virtually try on cosmetics and clothing and check the results in real time. Furthermore, it includes a function to collect and analyze user feedback and improve the suggested products based on that feedback. This enables users to efficiently select the products that are best suited to them.

[0006] A "user" refers to a consumer who uses the system and provides their own facial and body shape data.

[0007] "Image data" refers to photographic data of a user's face or entire body, and is digital information used for analysis by the system.

[0008] "Analysis of facial and body features" refers to the process of identifying unique shapes and dimensions from image data obtained from users.

[0009] A "virtual three-dimensional model" refers to a three-dimensional digital model of a user's face and body shape, generated within a computer based on image data.

[0010] "Virtual trial" refers to a digital process that allows users to try on cosmetics or clothing on a virtual 3D model to check their effects and appearance.

[0011] "Feedback information" refers to opinions and evaluations regarding user experience and suggestions provided by users after using the system.

[0012] "Improving the proposal" refers to the process of analyzing user feedback and using that feedback to create future proposals that better match user preferences. [Brief explanation of the drawing]

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

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0016] In the following embodiments, a tagged 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 tagged 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 tagged 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, a tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[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] In an embodiment for carrying out this invention, the system is configured as follows.

[0035] Users take photos of their face and full body using their smartphones or camera-equipped devices and upload them to the system. This image data is used as basic data to analyze the user's facial features (such as the position of eyes, nose, and mouth) and skin condition (such as oily skin, dry skin, blemishes, and wrinkles). In addition, users are given the option to input additional information such as their height, weight, lifestyle, and preferred fashion style.

[0036] The server analyzes the acquired image data to generate a 3D model of the user's face and body shape. This model generation utilizes facial recognition technology and algorithms that analyze body shape features. This 3D model is used for virtual try-on and virtual fitting.

[0037] The device receives 3D models generated by the server and can perform virtual try-on simulations using AR (augmented reality) technology to try on cosmetics and clothing selected by the user. Users can conduct virtual try-ons in real time, checking the effects and appearance before making a selection.

[0038] For example, if a user is trying to select a new lipstick and a dress, the system applies the lipstick color to the lips of a 3D model and virtually tries on the dress over the entire model. The user can see how it looks in real time through the camera, and if they like it, they can provide feedback to help the system learn their preferences.

[0039] The server stores this feedback information and uses it to improve future suggestions. This allows for more personalized product recommendations that take into account user preferences and trends.

[0040] In this way, users can easily try out various cosmetics and fashion items from the comfort of their homes, minimizing disappointment when making a purchase.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Users also input style information such as height and weight, as well as their preferred fashion style.

[0044] Step 2:

[0045] Server: Analyzes uploaded image data. Uses a face recognition algorithm to identify facial feature points and evaluate facial shape and skin condition. Simultaneously, extracts body shape features from full-body photographs.

[0046] Step 3:

[0047] Server: Generates a virtual 3D model of the user based on the analysis results. This 3D model is a three-dimensional reproduction of the user's face and body shape and is used for virtual try-on and fitting.

[0048] Step 4:

[0049] Server: Refers to a database of accumulated information to select the most suitable cosmetics and fashion items for the user. It generates personalized suggestions considering the user's age, lifestyle, and past preferences.

[0050] Step 5:

[0051] Terminal: Using AR technology, the system performs real-time simulations to virtually try on suggested cosmetics and fashion items on 3D models. Users visually confirm the trial results through these simulations.

[0052] Step 6:

[0053] User: Review trial results and provide feedback to the system. Input information about product preferences and trial experience to contribute to improving the accuracy of the system's recommendations.

[0054] Step 7:

[0055] Server: Analyzes user feedback and stores it in a database. This will enable the generation of more accurate, personalized suggestions based on user preferences in the future.

[0056] (Example 1)

[0057] 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."

[0058] When users purchase cosmetics or clothing online, they face the challenge of not being able to accurately determine whether the selected product is suitable for them because they cannot actually try it on or test it. Furthermore, it is difficult to provide a highly satisfying purchasing experience because it is not possible to individually suggest products based on the user's preferences and characteristics.

[0059] 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.

[0060] In this invention, the server includes means for analyzing image data acquired from the user to extract facial and body shape features, means for generating a three-dimensional model of the user based on the extracted features, and means for virtually trying on cosmetics and clothing using the generated three-dimensional model. This allows the user to virtually try on products through their own three-dimensional model, improving the accuracy and satisfaction of product selection.

[0061] A "user" refers to an individual who uses the system to upload their own photos and conduct a virtual trial.

[0062] "Image data" refers to data containing visual information that a user captures using their device and uploads to the system.

[0063] "Analysis" refers to the process of extracting information such as facial and body shape features from image data.

[0064] "Features" refer to information that can be used to identify an individual user, such as their face or body shape.

[0065] A "three-dimensional model" refers to a three-dimensional digital representation of a user generated based on extracted feature information.

[0066] "Virtual trial" refers to performing visual simulations of cosmetics and clothing on the generated three-dimensional models.

[0067] Augmented reality technology refers to technology that uses computers to add information to the real world and present it to the user.

[0068] "Evaluation information" refers to the feedback and opinions that users provide regarding the results of their virtual trial.

[0069] "Selection information" refers to data related to product selections made by users through the system in the past.

[0070] "Lifestyle information" refers to data about the user's lifestyle and is used to generate personalized suggestions.

[0071] This invention begins with a user taking photos of their face and full body using a smartphone or camera-equipped device and uploading them to a server. The user's device acts as the receiver for the captured image data, and also receives and transmits additional information such as height, weight, lifestyle, and preferred style as needed.

[0072] The server uses the "OpenCV" library as software to analyze image data. This allows the server to extract facial feature points and body shape information. Next, 3D modeling software such as "Blender" is used to generate a 3D model of the user based on the extracted features. This 3D model is used for virtual try-on and fitting.

[0073] The generated 3D model is sent from the server to the user's terminal. The terminal uses the received 3D model to virtually try on cosmetics and clothing selected by the user, utilizing augmented reality technologies such as "ARKit" and "Unity." This allows the user to see the results of the virtual try-on in real time, and to test the appearance and effects of the product before actually purchasing it.

[0074] As a concrete example, if a user wants to try a new lipstick and dress, the server applies the lipstick color to the lips of a 3D model and virtually tries on the dress on a full-body model. The user can check the appearance through their device and provide feedback to the system if they like it. This feedback is used to improve the system's personalized suggestions.

[0075] An example of a prompt message might be, "Create a 3D model of the user and virtually have them try on red lipstick and a blue dress." Based on this prompt message, the system aims to provide personalized product suggestions based on the user's preferences and tendencies.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Input includes the user's image data and optional additional information (height, weight, lifestyle, etc.), and output is this data sent to the server. Specifically, the system works by having the user review the images taken with the camera through the device's application and then sending them to the server in the appropriate file format.

[0079] Step 2:

[0080] The server analyzes the received image data. The input consists of image data of the user's face and body shape, and the output extracts facial feature points and body shape information. Specific data processing involves using the "OpenCV" library to analyze feature points and recognize facial contours and skin condition.

[0081] Step 3:

[0082] The server generates a 3D model of the user based on the extracted features. The input is the feature point information extracted in the previous step, and the output is the user's 3D model data. Specific operations include constructing a 3D model of the user's face and body shape using 3D modeling software such as "Blender."

[0083] Step 4:

[0084] The generated 3D model is sent from the server to the terminal. The input is 3D model data, and the output is this data received by the user's terminal. Specifically, the server efficiently compresses the model data and transmits it to the terminal via the communication line.

[0085] Step 5:

[0086] The terminal uses the received 3D model to virtually try on selected cosmetics and clothing. Input includes 3D model data and information about the user's selected products, while output generates visual data for the virtual try-on, which is displayed on the terminal screen. Specifically, it utilizes AR technologies such as "ARKit" and "Unity" to simulate the color and shape of the user's selected products in real time.

[0087] Step 6:

[0088] The user reviews the results of the virtual trial and provides feedback. Visual data of the virtual trial results is the input, and user feedback data is sent to the server as output. The specific operation involves the user evaluating the virtual trial, entering feedback within the application, and sending that feedback to the server.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern society, as consumers increasingly use online shopping, a problem arises: they cannot physically examine products before purchasing them. This is especially true for cosmetics and clothing, where color and fit are crucial, creating a need for an online environment that mimics a physical store where customers can try things out. Furthermore, there is a lack of systems that can provide personalized recommendations based on consumer preferences and body types, highlighting the need to improve efficiency and satisfaction in product selection.

[0092] 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.

[0093] In this invention, the server includes means for analyzing biological and external characteristics based on data acquired from the user; means for generating a virtual 3D model of the user based on the analyzed characteristics; means for enabling the virtual application of cosmetics and clothing using the virtual 3D model; means for displaying images of the virtual application in real time using augmented reality technology; and means for presenting the results of the virtual application to the user. This allows consumers to try out products in real time from the comfort of their homes, receive personalized product recommendations, and significantly improve the satisfaction and efficiency of online shopping.

[0094] "Data obtained from users" refers to digital information, including images and personal information, provided by users. This information is used to analyze biological and external characteristics.

[0095] "Means of analyzing biological and external characteristics" refers to a technical process that analyzes the details of a user's face and body shape and extracts those characteristics as digital data.

[0096] A "virtual 3D model" refers to a model with a 3D shape reproduced in digital space based on the user's biological and external characteristics. This model is used for virtual trials.

[0097] "Means of enabling virtual application" refers to technologies that digitally incorporate specific products or decorative items into virtual 3D models, allowing users to visually confirm their effects.

[0098] Augmented reality technology refers to technology that overlays digital information onto the real world, providing users with a visual experience that blends reality and virtuality.

[0099] "Real-time display methods" refer to technologies that process digital information instantly and provide immediate visual feedback to the user. This technology allows users to see the results of a virtual trial with virtually no delay.

[0100] "Means of presenting the results of virtual application to the user" refers to technologies that present users with information regarding the effectiveness and appearance of a virtual trial, and use that information to support their purchasing decisions.

[0101] This invention is implemented by a user using a smart device to capture images of their face and entire body, and then transmitting that data to a server. The server uses facial recognition technology and body feature analysis algorithms to analyze the user's biological and external characteristics and generate a virtual 3D model. Software such as OpenCV and body shape analysis APIs are used in this process. The generated 3D model is then transferred to the terminal.

[0102] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Specifically, it uses ARCore or ARKit to overlay digital information onto the user's field of view in real time, presenting a virtual try-on experience to the user.

[0103] Users can try on multiple products in real time using their virtual 3D model and make purchasing decisions. For example, consider a user who wants to try on lipstick and a dress. If the user is choosing a new lipstick and a summer dress, the system applies the lipstick color to the virtual model's lips and virtually tries on the dress over the entire model. The user can see the results in real time through the camera and, if they like it, provide feedback to add their preferences to the learning data. This information will be used to improve personalized product recommendations in the future.

[0104] An example of a prompt message is: "Generate a 3D model based on the user's photo, virtually try on the selected lipstick and dress, and provide real-time feedback to the user." This enables a new online shopping experience that supports consumer behavior from the comfort of the user's home.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The user uses the camera on their smart device to take photos of their face or full body and sends these digital images to the server. The input is the user's image data, and the output is the transfer of the image data to the server. The camera application takes photos based on the user's actions and automatically uploads them to the server.

[0108] Step 2:

[0109] The server receives image data from the user and analyzes the user's biological and external features using facial recognition technology and body feature analysis algorithms. This analysis extracts biometric information such as the positions of the eyes, nose, and mouth, as well as body shape data. The input is the user's image data, and the output is biometric information. Specifically, image analysis is performed using libraries such as OpenCV.

[0110] Step 3:

[0111] The server generates a virtual 3D model of the user based on the obtained biometric information. The generating AI model constructs a 3D model based on the biometric information. The input is biometric information, and the output is a virtual 3D model. The 3D model is rendered on the server using a modeling algorithm.

[0112] Step 4:

[0113] The server transfers the generated virtual 3D model to the terminal. The input is the virtual 3D model, and the output is the transfer of the model data to the terminal. The model is securely transmitted to the terminal via a data transfer protocol.

[0114] Step 5:

[0115] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Input is data for the virtual 3D model and the user-selected products, and output is a real-time display of the virtual try-on. ARCore or ARKit is used to overlay digital information onto real-world camera footage.

[0116] Step 6:

[0117] Users view the results of a virtual trial in real time via their device and evaluate its appearance. Input is visual data from the virtual trial, and output is user feedback information. Users evaluate their satisfaction and preferences on the operation screen and make their selections.

[0118] Step 7:

[0119] The server collects and stores user feedback information, which is then used to create personalized product recommendations for the future. The input is user feedback information, and the output is the storage of this information in the database. A data analysis program learns user preference patterns, which are then stored in the database.

[0120] 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.

[0121] In an embodiment of this invention, the system, in addition to its basic function of analyzing facial and body features and generating a virtual three-dimensional model, incorporates an emotion engine to recognize the user's emotional state and reflect the results in adjusting the suggestions.

[0122] Users take photos of their face and full body using their smartphones or camera-equipped devices and upload them to the system. Furthermore, it's possible to provide a video feed to track the user's facial expressions during the session. This allows the emotion engine to analyze the user's facial data in real time and recognize their emotional state (joy, surprise, sadness, etc.).

[0123] The server generates a virtual 3D model based on facial and body features, and uses data from the emotion engine to create a virtual trial environment that reflects the user's emotional state. For example, if the user feels surprised, the server can select colors and adjust styles based on that surprise.

[0124] The device uses AR technology to perform a virtual trial simulation based on information received from the server and presents the results to the user. During this process, the system considers the user's emotional state and makes suggestions that are a better match for the user.

[0125] For example, if a user tries on eyeshadow and then decides to add lip color, the system infers the user's preference from their facial expression and adjusts and presents a pre-set palette of lip colors. Based on this selection, the user can then decide which item is best suited to them.

[0126] Throughout this entire process, the server collects user feedback and emotional state data and stores it in a database. This allows for improved accuracy of future suggestions and increased user satisfaction.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] User: Uses a smartphone or camera-equipped device to provide the system with photos of their face and full body, as well as real-time video feeds. This allows the system to acquire data to simultaneously understand facial and body characteristics and emotional state.

[0130] Step 2:

[0131] Server: Analyzes received image data to identify the user's facial features and skin condition. Simultaneously, extracts body shape features from full-body photos and generates a virtual 3D model. This model serves as the basic structure used for virtual try-on and fitting.

[0132] Step 3:

[0133] Server: Analyzes the user's facial expressions from a real-time video feed and recognizes their emotional state using an emotion engine. Based on this information, it adjusts the virtual trial environment to suit the user's emotions.

[0134] Step 4:

[0135] Server: Based on a virtual 3D model and emotional state, the server initiates a process to select cosmetics and clothing suitable for the user. It also takes into account the user's age, past history, and lifestyle information to generate personalized suggestions.

[0136] Step 5:

[0137] Terminal: Utilizing information from the server, it performs real-time virtual try-on simulations using AR technology. Suggested cosmetics and clothing are applied to the user's virtual model, and their appearance is displayed. Colors and other aspects are adjusted according to the user's emotional state.

[0138] Step 6:

[0139] User: Review the virtual trial results and provide feedback to the system. Enter your favorite items and areas for improvement, providing data that the system will use to improve the accuracy of future suggestions.

[0140] Step 7:

[0141] Server: This server stores user feedback and emotional state data in a database and uses it to improve the suggestion algorithm. This makes it possible to provide more sophisticated personalized suggestions that reflect the user's preferences in subsequent visits.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] It is difficult for users to receive appropriate recommendations based on their individual characteristics and emotional state when selecting a product. Furthermore, recommendations that do not consider emotional state can decrease user satisfaction. This results in users spending a lot of time finding a product that suits them, and the trial experience is often uniform.

[0145] 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.

[0146] In this invention, the server includes means for analyzing a person's characteristics based on visual data acquired from the user, means for generating a virtual three-dimensional structure, and means for adjusting suggestions to suit the user's preferences based on their emotional state. This makes it possible to make suggestions that reflect the user's individual emotions and characteristics.

[0147] "Visual data" refers to digital image information such as photographs and videos used to acquire the facial and body characteristics of a user.

[0148] "Personal characteristics" refer to identifiable information about the user's face and body shape, and are elements used to generate a virtual three-dimensional structure.

[0149] A "virtual three-dimensional structure" is a three-dimensional model built based on the user's visual data and is used for virtual trials.

[0150] "Virtual product testing" refers to a process where users can check the appearance and style of a product using a visualized virtual three-dimensional structure.

[0151] "Emotional state" refers to the internal emotional state of a user, analyzed from their facial expressions and behavior, and can be used to adjust suggestions.

[0152] A "generative artificial intelligence model" refers to a computer program that learns from large amounts of data and generates suggestions and prompts based on the user's emotional state.

[0153] A "prompt statement" is an instruction given to a generative AI model, and refers to a statement that indicates the conditions for obtaining a specific output result.

[0154] To implement this invention, a system is used in which a user, a terminal, and a server work together. The user uses a smartphone or a camera-equipped terminal to acquire image data of their face and body shape and uploads it to the system. This data is used to extract the user's features and generate a virtual three-dimensional model.

[0155] The terminal, specifically a device such as a smartphone or tablet, uses AR technology to present results to the user. In this process, the user's face and body are first photographed with a camera, and that image data is sent to a server.

[0156] The server refers to a computer system with high-performance computing capabilities, to which an emotion engine and generative artificial intelligence model are connected to analyze the user's facial expression data. Specifically, the server uses image processing software to analyze features, recognizes the emotional state using the generative AI model, and generates prompts based on that. Using these prompts, it becomes possible to suggest appropriate virtual trials that match the user's emotions.

[0157] As a concrete example, when a user is selecting eyeshadow, the generative AI model is prompted with the message, "Suggest eyeshadow shades that match the user's emotion of joy." This prompts the AI ​​to suggest the optimal shades based on the user's facial expression.

[0158] This system allows users to make product choices that consider not only appearance but also emotional satisfaction.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users take pictures of their face and entire body using a smartphone or camera-equipped device. The captured image data is uploaded to the server as input. The server uses this input to perform image processing to extract facial and body features, and based on this, generates analysis data for use in the next step.

[0162] Step 2:

[0163] The server generates a virtual three-dimensional model using facial and body shape analysis data. This model generation process utilizes 3D modeling software to construct a three-dimensional model that reflects the user's characteristics. The output is a user-specific virtual three-dimensional model.

[0164] Step 3:

[0165] The user's device displays a virtual 3D model received from the server using AR technology. The input here is the data of the virtual 3D model, which the device visually synthesizes, overlaying the virtual model onto the real environment and presenting it to the user. The output of this process is visual feedback to the user.

[0166] Step 4:

[0167] The user views the presented virtual trial results and makes a selection that suits their preferences. The user's facial expressions are captured again by the camera and transmitted to the server in real time via the device. The input at this point is the user's latest facial expression data, which the server analyzes using an emotion engine. The output is the result of the judgment of the user's emotional state.

[0168] Step 5:

[0169] The server uses an AI model based on the emotion analysis results to generate prompts that match the user's emotional state. Specifically, it creates prompts such as "Fashion styles that match the feeling of surprise." By inputting these prompts into the AI ​​model, suggestions that match the user's emotions are output.

[0170] Step 6:

[0171] The user's device then displays suggestions again, adjusted based on their emotions. At this point, the input is suggestion data from a generative AI model based on emotion analysis, which is visualized and output for the user to review. The user can then consider these suggestions and make the best choice for themselves.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] Modern consumers tend to seek more personalized suggestions when purchasing everyday goods, expecting customized product selections based on their own characteristics and emotional states. However, traditional systems struggle to provide product suggestions that take into account the user's real-time emotions. Furthermore, effectively utilizing the user's past selection history and lifestyle information to create personalized suggestions is also challenging.

[0175] 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.

[0176] In this invention, the server includes means for analyzing facial and body shape features based on image data acquired from the user, means for analyzing the user's real-time emotional state and generating customized suggestions based on the emotional state, and means for generating personalized prompt sentences based on the user's past selection history and lifestyle information using a generation AI model and making suggestions based on these prompts. This makes it possible to provide users with more accurate and personalized suggestions.

[0177] "Image data acquired from users" refers to digital information of facial and body shape photos taken by users using smartphones or camera-equipped devices and uploaded to the system.

[0178] A "virtual three-dimensional model" is a three-dimensional digital model generated based on the user's facial and body characteristics, and is used for virtual try-on of everyday products.

[0179] "Emotional state" refers to the user's psychological state (e.g., joy, surprise, sadness, etc.) as recognized by the system through real-time analysis of the user's facial expression data.

[0180] "Customized suggestions" refer to a selection of lifestyle products tailored to each user, taking into account their real-time emotional state and past selection history.

[0181] A "generative AI model" is an artificial intelligence algorithm used to generate prompt messages and provide personalized suggestions based on a user's past selection history and lifestyle information.

[0182] A "prompt message" is a message generated based on the user's characteristics and interests, serving as a guideline for the system when suggesting everyday products.

[0183] The system that realizes this invention performs a series of processes, including generating a virtual three-dimensional model based on the user's face and body shape, real-time emotion analysis, and then suggesting customized lifestyle products based on this analysis. The specific operation is described below.

[0184] The server receives and analyzes image data acquired by the user using a smartphone or camera-equipped device, and extracts facial and body features. This is done using an image processing library (e.g., OpenCV). Based on this feature information, 3D model generation software (e.g., Unity or Unreal Engine) is used to create a virtual 3D model of the user.

[0185] The device analyzes the user's facial expressions based on a captured real-time video feed. This analysis uses an emotion recognition API (e.g., Microsoft® Face API) to identify emotional states such as joy, surprise, and sadness. The obtained emotional states are then sent to the server.

[0186] The server receives the sentiment analysis results and uses a generative AI model to generate personalized prompts, taking into account the user's past selection history and lifestyle information as input data. These prompts then derive a selection of lifestyle products to suggest to the user.

[0187] The device uses augmented reality (AR) technology (such as ARKit or ARCore) based on data received from the server to present the user with the results of a virtual trial. The user can then make a decision about the household goods, referring to the prompt message presented as the median.

[0188] For example, if a user first tries on a red lipstick and smiles, the system could, based on emotion analysis, suggest additional, more vibrant red options. By combining previously selected patterns with the user's current emotional state, it can suggest a more optimal lip color.

[0189] Examples of prompt statements are as follows:

[0190] "Select the optimal cosmetic color based on the user's real-time emotional data and past preferences."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] Users take images of their face and body shape using a smartphone or camera-equipped device and upload them to the system. This image data is then acquired as input.

[0194] Step 2:

[0195] The server analyzes facial and body features from the acquired image data. Here, it uses an image processing library to extract feature points and analyze facial contours and body shape information, outputting characteristic information about the face and body shape.

[0196] Step 3:

[0197] The server uses feature information obtained through image analysis to generate a virtual 3D model of the user using virtual 3D model generation software. It receives this feature information as input and outputs the user's virtual avatar data.

[0198] Step 4:

[0199] The device captures facial expression data in real time using its camera while the user is trying out lifestyle products. This data is acquired as input and sent to an emotion recognition API for analysis.

[0200] Step 5:

[0201] The server uses the captured facial expression data to analyze the user's emotional state. Using an emotion recognition API, it generates emotion labels such as joy and surprise, and outputs these as analysis results.

[0202] Step 6:

[0203] The server collects emotional state analysis results, as well as the user's past selection history and lifestyle information as input. Using a generative AI model, it generates personalized prompt messages based on this data and outputs these prompt messages.

[0204] Step 7:

[0205] The terminal uses AR technology to present the user with virtual try-on results for lifestyle products, based on customized prompt messages and virtual 3D models received from the server. It outputs results that support product selection through visual feedback to the user.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] In an embodiment for carrying out this invention, the system is configured as follows.

[0223] Users take photos of their face and full body using their smartphones or camera-equipped devices and upload them to the system. This image data is used as basic data to analyze the user's facial features (such as the position of eyes, nose, and mouth) and skin condition (such as oily skin, dry skin, blemishes, and wrinkles). In addition, users are given the option to input additional information such as their height, weight, lifestyle, and preferred fashion style.

[0224] The server analyzes the acquired image data to generate a 3D model of the user's face and body shape. This model generation utilizes facial recognition technology and algorithms that analyze body shape features. This 3D model is used for virtual try-on and virtual fitting.

[0225] The device receives 3D models generated by the server and can perform virtual try-on simulations using AR (augmented reality) technology to try on cosmetics and clothing selected by the user. Users can conduct virtual try-ons in real time, checking the effects and appearance before making a selection.

[0226] For example, if a user is trying to select a new lipstick and a dress, the system applies the lipstick color to the lips of a 3D model and virtually tries on the dress over the entire model. The user can see how it looks in real time through the camera, and if they like it, they can provide feedback to help the system learn their preferences.

[0227] The server stores this feedback information and uses it to improve future suggestions. This allows for more personalized product recommendations that take into account user preferences and trends.

[0228] In this way, users can easily try out various cosmetics and fashion items from the comfort of their homes, minimizing disappointment when making a purchase.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Users also input style information such as height and weight, as well as their preferred fashion style.

[0232] Step 2:

[0233] Server: Analyzes uploaded image data. Uses a face recognition algorithm to identify facial feature points and evaluate facial shape and skin condition. Simultaneously, extracts body shape features from full-body photographs.

[0234] Step 3:

[0235] Server: Generates a virtual 3D model of the user based on the analysis results. This 3D model is a three-dimensional reproduction of the user's face and body shape and is used for virtual try-on and fitting.

[0236] Step 4:

[0237] Server: Refers to a database of accumulated information to select the most suitable cosmetics and fashion items for the user. It generates personalized suggestions considering the user's age, lifestyle, and past preferences.

[0238] Step 5:

[0239] Terminal: Using AR technology, the system performs real-time simulations to virtually try on suggested cosmetics and fashion items on 3D models. Users visually confirm the trial results through these simulations.

[0240] Step 6:

[0241] User: Review trial results and provide feedback to the system. Input information about product preferences and trial experience to contribute to improving the accuracy of the system's recommendations.

[0242] Step 7:

[0243] Server: Analyzes user feedback and stores it in a database. This will enable the generation of more accurate, personalized suggestions based on user preferences in the future.

[0244] (Example 1)

[0245] 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."

[0246] When users purchase cosmetics or clothing online, they face the challenge of not being able to accurately determine whether the selected product is suitable for them because they cannot actually try it on or test it. Furthermore, it is difficult to provide a highly satisfying purchasing experience because it is not possible to individually suggest products based on the user's preferences and characteristics.

[0247] 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.

[0248] In this invention, the server includes means for analyzing image data acquired from the user to extract facial and body shape features, means for generating a three-dimensional model of the user based on the extracted features, and means for virtually trying on cosmetics and clothing using the generated three-dimensional model. This allows the user to virtually try on products through their own three-dimensional model, improving the accuracy and satisfaction of product selection.

[0249] A "user" refers to an individual who uses the system to upload their own photos and conduct a virtual trial.

[0250] "Image data" refers to data containing visual information that a user captures using their device and uploads to the system.

[0251] "Analysis" refers to the process of extracting information such as facial and body shape features from image data.

[0252] "Features" refer to information that can be used to identify an individual user, such as their face or body shape.

[0253] A "three-dimensional model" refers to a three-dimensional digital representation of a user generated based on extracted feature information.

[0254] "Virtual trial" refers to performing visual simulations of cosmetics and clothing on the generated three-dimensional models.

[0255] Augmented reality technology refers to technology that uses computers to add information to the real world and present it to the user.

[0256] "Evaluation information" refers to the feedback and opinions that users provide regarding the results of their virtual trial.

[0257] "Selection information" refers to data related to product selections made by users through the system in the past.

[0258] "Lifestyle information" refers to data about the user's lifestyle and is used to generate personalized suggestions.

[0259] This invention begins with a user taking photos of their face and full body using a smartphone or camera-equipped device and uploading them to a server. The user's device acts as the receiver for the captured image data, and also receives and transmits additional information such as height, weight, lifestyle, and preferred style as needed.

[0260] The server uses the "OpenCV" library as software to analyze image data. This allows the server to extract facial feature points and body shape information. Next, 3D modeling software such as "Blender" is used to generate a 3D model of the user based on the extracted features. This 3D model is used for virtual try-on and fitting.

[0261] The generated 3D model is sent from the server to the user's terminal. The terminal uses the received 3D model to virtually try on cosmetics and clothing selected by the user, utilizing augmented reality technologies such as "ARKit" and "Unity." This allows the user to see the results of the virtual try-on in real time, and to test the appearance and effects of the product before actually purchasing it.

[0262] As a concrete example, if a user wants to try a new lipstick and dress, the server applies the lipstick color to the lips of a 3D model and virtually tries on the dress on a full-body model. The user can check the appearance through their device and provide feedback to the system if they like it. This feedback is used to improve the system's personalized suggestions.

[0263] An example of a prompt message might be, "Create a 3D model of the user and virtually have them try on red lipstick and a blue dress." Based on this prompt message, the system aims to provide personalized product suggestions based on the user's preferences and tendencies.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Input includes the user's image data and optional additional information (height, weight, lifestyle, etc.), and output is this data sent to the server. Specifically, the system works by having the user review the images taken with the camera through the device's application and then sending them to the server in the appropriate file format.

[0267] Step 2:

[0268] The server analyzes the received image data. The input consists of image data of the user's face and body shape, and the output extracts facial feature points and body shape information. Specific data processing involves using the "OpenCV" library to analyze feature points and recognize facial contours and skin condition.

[0269] Step 3:

[0270] The server generates a 3D model of the user based on the extracted features. The input is the feature point information extracted in the previous step, and the output is the user's 3D model data. Specific operations include constructing a 3D model of the user's face and body shape using 3D modeling software such as "Blender."

[0271] Step 4:

[0272] The generated 3D model is sent from the server to the terminal. The input is 3D model data, and the output is this data received by the user's terminal. Specifically, the server efficiently compresses the model data and transmits it to the terminal via the communication line.

[0273] Step 5:

[0274] The terminal uses the received 3D model to virtually try on selected cosmetics and clothing. Input includes 3D model data and information about the user's selected products, while output generates visual data for the virtual try-on, which is displayed on the terminal screen. Specifically, it utilizes AR technologies such as "ARKit" and "Unity" to simulate the color and shape of the user's selected products in real time.

[0275] Step 6:

[0276] The user reviews the results of the virtual trial and provides feedback. Visual data of the virtual trial results is the input, and user feedback data is sent to the server as output. The specific operation involves the user evaluating the virtual trial, entering feedback within the application, and sending that feedback to the server.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In modern society, as consumers increasingly use online shopping, a problem arises: they cannot physically examine products before purchasing them. This is especially true for cosmetics and clothing, where color and fit are crucial, creating a need for an online environment that mimics a physical store where customers can try things out. Furthermore, there is a lack of systems that can provide personalized recommendations based on consumer preferences and body types, highlighting the need to improve efficiency and satisfaction in product selection.

[0280] 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.

[0281] In this invention, the server includes means for analyzing biological and external characteristics based on data acquired from the user; means for generating a virtual 3D model of the user based on the analyzed characteristics; means for enabling the virtual application of cosmetics and clothing using the virtual 3D model; means for displaying images of the virtual application in real time using augmented reality technology; and means for presenting the results of the virtual application to the user. This allows consumers to try out products in real time from the comfort of their homes, receive personalized product recommendations, and significantly improve the satisfaction and efficiency of online shopping.

[0282] "Data obtained from the user" refers to digital information including images and personal information provided by the user. This information is used to analyze the characteristics of the living body and appearance.

[0283] "Means for analyzing the characteristics of the living body and appearance" refers to a technical process that analyzes the details of the user's face and body shape and extracts those characteristics as digital data.

[0284] "Virtual solid model" refers to a model with a 3D shape reproduced in the digital space based on the characteristics of the user's living body and appearance. This model is used for virtual try-on.

[0285] "Means for enabling virtual application" refers to a technology that digitally incorporates specific products or ornaments into the virtual solid model so that the user can visually confirm the effect.

[0286] "Augmented reality technology" refers to a technology that overlays digital information on the real world and provides the user with a visual experience that combines reality and virtuality.

[0287] "Means for displaying in real time" refers to a technology that instantaneously processes digital information and returns immediate visual feedback to the user. With this technology, the user can view the results of virtual try-on almost without delay.

[0288] "Means for presenting the results of virtual application to the user" refers to a technology that presents information regarding the effects and appearance of virtual try-on to the user and supports purchase decisions based on that information.

[0289] This invention is implemented by the user taking pictures of their face and whole body using a smart device and transmitting the data to a server. The server utilizes face recognition technology and body shape feature analysis algorithms to analyze the characteristics of the user's living body and appearance and generate a virtual solid model. At this time, software such as OpenCV and body shape analysis APIs is used. The generated 3D model is transferred to the terminal.

[0290] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Specifically, it uses ARCore or ARKit to overlay digital information onto the user's field of view in real time, presenting a virtual try-on experience to the user.

[0291] Users can try on multiple products in real time using their virtual 3D model and make purchasing decisions. For example, consider a user who wants to try on lipstick and a dress. If the user is choosing a new lipstick and a summer dress, the system applies the lipstick color to the virtual model's lips and virtually tries on the dress over the entire model. The user can see the results in real time through the camera and, if they like it, provide feedback to add their preferences to the learning data. This information will be used to improve personalized product recommendations in the future.

[0292] An example of a prompt message is: "Generate a 3D model based on the user's photo, virtually try on the selected lipstick and dress, and provide real-time feedback to the user." This enables a new online shopping experience that supports consumer behavior from the comfort of the user's home.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The user uses the camera on their smart device to take photos of their face or full body and sends these digital images to the server. The input is the user's image data, and the output is the transfer of the image data to the server. The camera application takes photos based on the user's actions and automatically uploads them to the server.

[0296] Step 2:

[0297] The server receives image data from the user and analyzes the user's biological and external features using facial recognition technology and body feature analysis algorithms. This analysis extracts biometric information such as the positions of the eyes, nose, and mouth, as well as body shape data. The input is the user's image data, and the output is biometric information. Specifically, image analysis is performed using libraries such as OpenCV.

[0298] Step 3:

[0299] The server generates a virtual 3D model of the user based on the obtained biometric information. The generating AI model constructs a 3D model based on the biometric information. The input is biometric information, and the output is a virtual 3D model. The 3D model is rendered on the server using a modeling algorithm.

[0300] Step 4:

[0301] The server transfers the generated virtual 3D model to the terminal. The input is the virtual 3D model, and the output is the transfer of the model data to the terminal. The model is securely transmitted to the terminal via a data transfer protocol.

[0302] Step 5:

[0303] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Input is data for the virtual 3D model and the user-selected products, and output is a real-time display of the virtual try-on. ARCore or ARKit is used to overlay digital information onto real-world camera footage.

[0304] Step 6:

[0305] Users view the results of a virtual trial in real time via their device and evaluate its appearance. Input is visual data from the virtual trial, and output is user feedback information. Users evaluate their satisfaction and preferences on the operation screen and make their selections.

[0306] Step 7:

[0307] The server collects the user's feedback information, accumulates it, and utilizes it for future individualized product proposals. The input is the user's feedback information, and the output is the accumulation of information in the database. Through a data analysis program, the user's preference pattern is learned and stored in the database.

[0308] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0309] As a form for implementing this invention, in addition to the basic function of analyzing facial and body shape features and generating a virtual three-dimensional model, by incorporating an emotion engine, the system can recognize the user's emotional state and reflect the result in the adjustment of the proposal.

[0310] The user uses a smartphone or a terminal with a camera to take pictures of their face and whole body and upload them to the system. Furthermore, it is also possible to provide a video feed for tracking the user's expression during the session. Thereby, the emotion engine can analyze the user's expression data in real time and recognize the emotional state (such as joy, surprise, sadness, etc.).

[0311] The server generates a virtual three-dimensional model based on facial and body shape features, and combines the data from the emotion engine to create a virtual trial environment that reflects the user's emotional state. For example, when the user feels surprised, color selection and style adjustment based on surprise can be performed.

[0312] The terminal executes a virtual trial simulation using AR technology based on the information received from the server and presents the result to the user. At this time, the system considers the user's emotional state and makes a proposal that better matches the user.

[0313] For example, if a user tries on eyeshadow and then decides to add lip color, the system infers the user's preference from their facial expression and adjusts and presents a pre-set palette of lip colors. Based on this selection, the user can then decide which item is best suited to them.

[0314] Throughout this entire process, the server collects user feedback and emotional state data and stores it in a database. This allows for improved accuracy of future suggestions and increased user satisfaction.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] User: Uses a smartphone or camera-equipped device to provide the system with photos of their face and full body, as well as real-time video feeds. This allows the system to acquire data to simultaneously understand facial and body characteristics and emotional state.

[0318] Step 2:

[0319] Server: Analyzes received image data to identify the user's facial features and skin condition. Simultaneously, extracts body shape features from full-body photos and generates a virtual 3D model. This model serves as the basic structure used for virtual try-on and fitting.

[0320] Step 3:

[0321] Server: Analyzes the user's facial expressions from a real-time video feed and recognizes their emotional state using an emotion engine. Based on this information, it adjusts the virtual trial environment to suit the user's emotions.

[0322] Step 4:

[0323] Server: Based on a virtual 3D model and emotional state, the server initiates a process to select cosmetics and clothing suitable for the user. It also takes into account the user's age, past history, and lifestyle information to generate personalized suggestions.

[0324] Step 5:

[0325] Terminal: Utilizing information from the server, it performs real-time virtual try-on simulations using AR technology. Suggested cosmetics and clothing are applied to the user's virtual model, and their appearance is displayed. Colors and other aspects are adjusted according to the user's emotional state.

[0326] Step 6:

[0327] User: Review the virtual trial results and provide feedback to the system. Enter your favorite items and areas for improvement, providing data that the system will use to improve the accuracy of future suggestions.

[0328] Step 7:

[0329] Server: This server stores user feedback and emotional state data in a database and uses it to improve the suggestion algorithm. This makes it possible to provide more sophisticated personalized suggestions that reflect the user's preferences in subsequent visits.

[0330] (Example 2)

[0331] 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".

[0332] It is difficult for users to receive appropriate recommendations based on their individual characteristics and emotional state when selecting a product. Furthermore, recommendations that do not consider emotional state can decrease user satisfaction. This results in users spending a lot of time finding a product that suits them, and the trial experience is often uniform.

[0333] 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.

[0334] In this invention, the server includes means for analyzing a person's characteristics based on visual data acquired from the user, means for generating a virtual three-dimensional structure, and means for adjusting suggestions to suit the user's preferences based on their emotional state. This makes it possible to make suggestions that reflect the user's individual emotions and characteristics.

[0335] "Visual data" refers to digital image information such as photographs and videos used to acquire the facial and body characteristics of a user.

[0336] "Personal characteristics" refer to identifiable information about the user's face and body shape, and are elements used to generate a virtual three-dimensional structure.

[0337] A "virtual three-dimensional structure" is a three-dimensional model built based on the user's visual data and is used for virtual trials.

[0338] "Virtual product testing" refers to a process where users can check the appearance and style of a product using a visualized virtual three-dimensional structure.

[0339] "Emotional state" refers to the internal emotional state of a user, analyzed from their facial expressions and behavior, and can be used to adjust suggestions.

[0340] A "generative artificial intelligence model" refers to a computer program that learns from large amounts of data and generates suggestions and prompts based on the user's emotional state.

[0341] A "prompt statement" is an instruction given to a generative AI model, and refers to a statement that indicates the conditions for obtaining a specific output result.

[0342] To implement this invention, a system is used in which a user, a terminal, and a server work together. The user uses a smartphone or a camera-equipped terminal to acquire image data of their face and body shape and uploads it to the system. This data is used to extract the user's features and generate a virtual three-dimensional model.

[0343] The terminal, specifically a device such as a smartphone or tablet, uses AR technology to present results to the user. In this process, the user's face and body are first photographed with a camera, and that image data is sent to a server.

[0344] The server refers to a computer system with high-performance computing capabilities, to which an emotion engine and generative artificial intelligence model are connected to analyze the user's facial expression data. Specifically, the server uses image processing software to analyze features, recognizes the emotional state using the generative AI model, and generates prompts based on that. Using these prompts, it becomes possible to suggest appropriate virtual trials that match the user's emotions.

[0345] As a concrete example, when a user is selecting eyeshadow, the generative AI model is prompted with the message, "Suggest eyeshadow shades that match the user's emotion of joy." This prompts the AI ​​to suggest the optimal shades based on the user's facial expression.

[0346] This system allows users to make product choices that consider not only appearance but also emotional satisfaction.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] Users take pictures of their face and entire body using a smartphone or camera-equipped device. The captured image data is uploaded to the server as input. The server uses this input to perform image processing to extract facial and body features, and based on this, generates analysis data for use in the next step.

[0350] Step 2:

[0351] The server generates a virtual three-dimensional model using facial and body shape analysis data. This model generation process utilizes 3D modeling software to construct a three-dimensional model that reflects the user's characteristics. The output is a user-specific virtual three-dimensional model.

[0352] Step 3:

[0353] The user's device displays a virtual 3D model received from the server using AR technology. The input here is the data of the virtual 3D model, which the device visually synthesizes, overlaying the virtual model onto the real environment and presenting it to the user. The output of this process is visual feedback to the user.

[0354] Step 4:

[0355] The user views the presented virtual trial results and makes a selection that suits their preferences. The user's facial expressions are captured again by the camera and transmitted to the server in real time via the device. The input at this point is the user's latest facial expression data, which the server analyzes using an emotion engine. The output is the result of the judgment of the user's emotional state.

[0356] Step 5:

[0357] The server uses an AI model based on the emotion analysis results to generate prompts that match the user's emotional state. Specifically, it creates prompts such as "Fashion styles that match the feeling of surprise." By inputting these prompts into the AI ​​model, suggestions that match the user's emotions are output.

[0358] Step 6:

[0359] The user's device then displays suggestions again, adjusted based on their emotions. At this point, the input is suggestion data from a generative AI model based on emotion analysis, which is visualized and output for the user to review. The user can then consider these suggestions and make the best choice for themselves.

[0360] (Application Example 2)

[0361] 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."

[0362] Modern consumers tend to seek more personalized suggestions when purchasing everyday goods, expecting customized product selections based on their own characteristics and emotional states. However, traditional systems struggle to provide product suggestions that take into account the user's real-time emotions. Furthermore, effectively utilizing the user's past selection history and lifestyle information to create personalized suggestions is also challenging.

[0363] 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.

[0364] In this invention, the server includes means for analyzing facial and body shape features based on image data acquired from the user, means for analyzing the user's real-time emotional state and generating customized suggestions based on the emotional state, and means for generating personalized prompt sentences based on the user's past selection history and lifestyle information using a generation AI model and making suggestions based on these prompts. This makes it possible to provide users with more accurate and personalized suggestions.

[0365] "Image data acquired from users" refers to digital information of facial and body shape photos taken by users using smartphones or camera-equipped devices and uploaded to the system.

[0366] A "virtual three-dimensional model" is a three-dimensional digital model generated based on the user's facial and body characteristics, and is used for virtual try-on of everyday products.

[0367] "Emotional state" refers to the user's psychological state (e.g., joy, surprise, sadness, etc.) as recognized by the system through real-time analysis of the user's facial expression data.

[0368] "Customized suggestions" refer to a selection of lifestyle products tailored to each user, taking into account their real-time emotional state and past selection history.

[0369] A "generative AI model" is an artificial intelligence algorithm used to generate prompt messages and provide personalized suggestions based on a user's past selection history and lifestyle information.

[0370] A "prompt message" is a message generated based on the user's characteristics and interests, serving as a guideline for the system when suggesting everyday products.

[0371] The system that realizes this invention performs a series of processes, including generating a virtual three-dimensional model based on the user's face and body shape, real-time emotion analysis, and then suggesting customized lifestyle products based on this analysis. The specific operation is described below.

[0372] The server receives and analyzes image data acquired by the user using a smartphone or camera-equipped device, and extracts facial and body features. This is done using an image processing library (e.g., OpenCV). Based on this feature information, 3D model generation software (e.g., Unity or Unreal Engine) is used to create a virtual 3D model of the user.

[0373] The device analyzes the user's facial expressions based on a captured real-time video feed. This analysis uses an emotion recognition API (e.g., Microsoft Face API) to identify emotional states such as joy, surprise, and sadness. The obtained emotional states are then sent to the server.

[0374] The server receives the sentiment analysis results and uses a generative AI model to generate personalized prompts, taking into account the user's past selection history and lifestyle information as input data. These prompts then derive a selection of lifestyle products to suggest to the user.

[0375] The device uses augmented reality (AR) technology (such as ARKit or ARCore) based on data received from the server to present the user with the results of a virtual trial. The user can then make a decision about the household goods, referring to the prompt message presented as the median.

[0376] For example, if a user first tries on a red lipstick and smiles, the system could, based on emotion analysis, suggest additional, more vibrant red options. By combining previously selected patterns with the user's current emotional state, it can suggest a more optimal lip color.

[0377] Examples of prompt statements are as follows:

[0378] "Select the optimal cosmetic color based on the user's real-time emotional data and past preferences."

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] Users take images of their face and body shape using a smartphone or camera-equipped device and upload them to the system. This image data is then acquired as input.

[0382] Step 2:

[0383] The server analyzes facial and body features from the acquired image data. Here, it uses an image processing library to extract feature points and analyze facial contours and body shape information, outputting characteristic information about the face and body shape.

[0384] Step 3:

[0385] The server uses feature information obtained through image analysis to generate a virtual 3D model of the user using virtual 3D model generation software. It receives this feature information as input and outputs the user's virtual avatar data.

[0386] Step 4:

[0387] The device captures facial expression data in real time using its camera while the user is trying out lifestyle products. This data is acquired as input and sent to an emotion recognition API for analysis.

[0388] Step 5:

[0389] The server uses the captured facial expression data to analyze the user's emotional state. Using an emotion recognition API, it generates emotion labels such as joy and surprise, and outputs these as analysis results.

[0390] Step 6:

[0391] The server collects emotional state analysis results, as well as the user's past selection history and lifestyle information as input. Using a generative AI model, it generates personalized prompt messages based on this data and outputs these prompt messages.

[0392] Step 7:

[0393] The terminal uses AR technology to present the user with virtual try-on results for lifestyle products, based on customized prompt messages and virtual 3D models received from the server. It outputs results that support product selection through visual feedback to the user.

[0394] 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.

[0395] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] 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.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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".

[0410] In an embodiment for carrying out this invention, the system is configured as follows.

[0411] Users take photos of their face and full body using their smartphones or camera-equipped devices and upload them to the system. This image data is used as basic data to analyze the user's facial features (such as the position of eyes, nose, and mouth) and skin condition (such as oily skin, dry skin, blemishes, and wrinkles). In addition, users are given the option to input additional information such as their height, weight, lifestyle, and preferred fashion style.

[0412] The server analyzes the acquired image data to generate a 3D model of the user's face and body shape. This model generation utilizes facial recognition technology and algorithms that analyze body shape features. This 3D model is used for virtual try-on and virtual fitting.

[0413] The device receives 3D models generated by the server and can perform virtual try-on simulations using AR (augmented reality) technology to try on cosmetics and clothing selected by the user. Users can conduct virtual try-ons in real time, checking the effects and appearance before making a selection.

[0414] For example, if a user is trying to select a new lipstick and a dress, the system applies the lipstick color to the lips of a 3D model and virtually tries on the dress over the entire model. The user can see how it looks in real time through the camera, and if they like it, they can provide feedback to help the system learn their preferences.

[0415] The server stores this feedback information and uses it to improve future suggestions. This allows for more personalized product recommendations that take into account user preferences and trends.

[0416] In this way, users can easily try out various cosmetics and fashion items from the comfort of their homes, minimizing disappointment when making a purchase.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Users also input style information such as height and weight, as well as their preferred fashion style.

[0420] Step 2:

[0421] Server: Analyzes uploaded image data. Uses a face recognition algorithm to identify facial feature points and evaluate facial shape and skin condition. Simultaneously, extracts body shape features from full-body photographs.

[0422] Step 3:

[0423] Server: Generates a virtual 3D model of the user based on the analysis results. This 3D model is a three-dimensional reproduction of the user's face and body shape and is used for virtual try-on and fitting.

[0424] Step 4:

[0425] Server: Refers to a database of accumulated information to select the most suitable cosmetics and fashion items for the user. It generates personalized suggestions considering the user's age, lifestyle, and past preferences.

[0426] Step 5:

[0427] Terminal: Using AR technology, the system performs real-time simulations to virtually try on suggested cosmetics and fashion items on 3D models. Users visually confirm the trial results through these simulations.

[0428] Step 6:

[0429] User: Review trial results and provide feedback to the system. Input information about product preferences and trial experience to contribute to improving the accuracy of the system's recommendations.

[0430] Step 7:

[0431] Server: Analyzes user feedback and stores it in a database. This will enable the generation of more accurate, personalized suggestions based on user preferences in the future.

[0432] (Example 1)

[0433] 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."

[0434] When users purchase cosmetics or clothing online, they face the challenge of not being able to accurately determine whether the selected product is suitable for them because they cannot actually try it on or test it. Furthermore, it is difficult to provide a highly satisfying purchasing experience because it is not possible to individually suggest products based on the user's preferences and characteristics.

[0435] 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.

[0436] In this invention, the server includes means for analyzing image data acquired from the user to extract facial and body shape features, means for generating a three-dimensional model of the user based on the extracted features, and means for virtually trying on cosmetics and clothing using the generated three-dimensional model. This allows the user to virtually try on products through their own three-dimensional model, improving the accuracy and satisfaction of product selection.

[0437] A "user" refers to an individual who uses the system to upload their own photos and conduct a virtual trial.

[0438] "Image data" refers to data containing visual information that a user captures using their device and uploads to the system.

[0439] "Analysis" refers to the process of extracting information such as facial and body shape features from image data.

[0440] "Features" refer to information that can be used to identify an individual user, such as their face or body shape.

[0441] A "three-dimensional model" refers to a three-dimensional digital representation of a user generated based on extracted feature information.

[0442] "Virtual trial" refers to performing visual simulations of cosmetics and clothing on the generated three-dimensional models.

[0443] Augmented reality technology refers to technology that uses computers to add information to the real world and present it to the user.

[0444] "Evaluation information" refers to the feedback and opinions that users provide regarding the results of their virtual trial.

[0445] "Selection information" refers to data related to product selections made by users through the system in the past.

[0446] "Lifestyle information" refers to data about the user's lifestyle and is used to generate personalized suggestions.

[0447] This invention begins with a user taking photos of their face and full body using a smartphone or camera-equipped device and uploading them to a server. The user's device acts as the receiver for the captured image data, and also receives and transmits additional information such as height, weight, lifestyle, and preferred style as needed.

[0448] The server uses the "OpenCV" library as software to analyze image data. This allows the server to extract facial feature points and body shape information. Next, 3D modeling software such as "Blender" is used to generate a 3D model of the user based on the extracted features. This 3D model is used for virtual try-on and fitting.

[0449] The generated 3D model is sent from the server to the user's terminal. The terminal uses the received 3D model to virtually try on cosmetics and clothing selected by the user, utilizing augmented reality technologies such as "ARKit" and "Unity." This allows the user to see the results of the virtual try-on in real time, and to test the appearance and effects of the product before actually purchasing it.

[0450] As a concrete example, if a user wants to try a new lipstick and dress, the server applies the lipstick color to the lips of a 3D model and virtually tries on the dress on a full-body model. The user can check the appearance through their device and provide feedback to the system if they like it. This feedback is used to improve the system's personalized suggestions.

[0451] An example of a prompt message might be, "Create a 3D model of the user and virtually have them try on red lipstick and a blue dress." Based on this prompt message, the system aims to provide personalized product suggestions based on the user's preferences and tendencies.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Input includes the user's image data and optional additional information (height, weight, lifestyle, etc.), and output is this data sent to the server. Specifically, the system works by having the user review the images taken with the camera through the device's application and then sending them to the server in the appropriate file format.

[0455] Step 2:

[0456] The server analyzes the received image data. The input consists of image data of the user's face and body shape, and the output extracts facial feature points and body shape information. Specific data processing involves using the "OpenCV" library to analyze feature points and recognize facial contours and skin condition.

[0457] Step 3:

[0458] The server generates a 3D model of the user based on the extracted features. The input is the feature point information extracted in the previous step, and the output is the user's 3D model data. Specific operations include constructing a 3D model of the user's face and body shape using 3D modeling software such as "Blender."

[0459] Step 4:

[0460] The generated 3D model is sent from the server to the terminal. The input is 3D model data, and the output is this data received by the user's terminal. Specifically, the server efficiently compresses the model data and transmits it to the terminal via the communication line.

[0461] Step 5:

[0462] The terminal uses the received 3D model to virtually try on selected cosmetics and clothing. Input includes 3D model data and information about the user's selected products, while output generates visual data for the virtual try-on, which is displayed on the terminal screen. Specifically, it utilizes AR technologies such as "ARKit" and "Unity" to simulate the color and shape of the user's selected products in real time.

[0463] Step 6:

[0464] The user reviews the results of the virtual trial and provides feedback. Visual data of the virtual trial results is the input, and user feedback data is sent to the server as output. The specific operation involves the user evaluating the virtual trial, entering feedback within the application, and sending that feedback to the server.

[0465] (Application Example 1)

[0466] 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."

[0467] In modern society, as consumers increasingly use online shopping, a problem arises: they cannot physically examine products before purchasing them. This is especially true for cosmetics and clothing, where color and fit are crucial, creating a need for an online environment that mimics a physical store where customers can try things out. Furthermore, there is a lack of systems that can provide personalized recommendations based on consumer preferences and body types, highlighting the need to improve efficiency and satisfaction in product selection.

[0468] 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.

[0469] In this invention, the server includes means for analyzing biological and external characteristics based on data acquired from the user; means for generating a virtual 3D model of the user based on the analyzed characteristics; means for enabling the virtual application of cosmetics and clothing using the virtual 3D model; means for displaying images of the virtual application in real time using augmented reality technology; and means for presenting the results of the virtual application to the user. This allows consumers to try out products in real time from the comfort of their homes, receive personalized product recommendations, and significantly improve the satisfaction and efficiency of online shopping.

[0470] "Data obtained from users" refers to digital information, including images and personal information, provided by users. This information is used to analyze biological and external characteristics.

[0471] "Means of analyzing biological and external characteristics" refers to a technical process that analyzes the details of a user's face and body shape and extracts those characteristics as digital data.

[0472] A "virtual 3D model" refers to a model with a 3D shape reproduced in digital space based on the user's biological and external characteristics. This model is used for virtual trials.

[0473] "Means of enabling virtual application" refers to technologies that digitally incorporate specific products or decorative items into virtual 3D models, allowing users to visually confirm their effects.

[0474] Augmented reality technology refers to technology that overlays digital information onto the real world, providing users with a visual experience that blends reality and virtuality.

[0475] "Real-time display methods" refer to technologies that process digital information instantly and provide immediate visual feedback to the user. This technology allows users to see the results of a virtual trial with virtually no delay.

[0476] "Means of presenting the results of virtual application to the user" refers to technologies that present users with information regarding the effectiveness and appearance of a virtual trial, and use that information to support their purchasing decisions.

[0477] This invention is implemented by a user using a smart device to capture images of their face and entire body, and then transmitting that data to a server. The server uses facial recognition technology and body feature analysis algorithms to analyze the user's biological and external characteristics and generate a virtual 3D model. Software such as OpenCV and body shape analysis APIs are used in this process. The generated 3D model is then transferred to the terminal.

[0478] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Specifically, it uses ARCore or ARKit to overlay digital information onto the user's field of view in real time, presenting a virtual try-on experience to the user.

[0479] Users can try on multiple products in real time using their virtual 3D model and make purchasing decisions. For example, consider a user who wants to try on lipstick and a dress. If the user is choosing a new lipstick and a summer dress, the system applies the lipstick color to the virtual model's lips and virtually tries on the dress over the entire model. The user can see the results in real time through the camera and, if they like it, provide feedback to add their preferences to the learning data. This information will be used to improve personalized product recommendations in the future.

[0480] An example of a prompt message is: "Generate a 3D model based on the user's photo, virtually try on the selected lipstick and dress, and provide real-time feedback to the user." This enables a new online shopping experience that supports consumer behavior from the comfort of the user's home.

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The user uses the camera on their smart device to take photos of their face or full body and sends these digital images to the server. The input is the user's image data, and the output is the transfer of the image data to the server. The camera application takes photos based on the user's actions and automatically uploads them to the server.

[0484] Step 2:

[0485] The server receives image data from the user and analyzes the user's biological and external features using facial recognition technology and body feature analysis algorithms. This analysis extracts biometric information such as the positions of the eyes, nose, and mouth, as well as body shape data. The input is the user's image data, and the output is biometric information. Specifically, image analysis is performed using libraries such as OpenCV.

[0486] Step 3:

[0487] The server generates a virtual 3D model of the user based on the obtained biometric information. The generating AI model constructs a 3D model based on the biometric information. The input is biometric information, and the output is a virtual 3D model. The 3D model is rendered on the server using a modeling algorithm.

[0488] Step 4:

[0489] The server transfers the generated virtual 3D model to the terminal. The input is the virtual 3D model, and the output is the transfer of the model data to the terminal. The model is securely transmitted to the terminal via a data transfer protocol.

[0490] Step 5:

[0491] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Input is data for the virtual 3D model and the user-selected products, and output is a real-time display of the virtual try-on. ARCore or ARKit is used to overlay digital information onto real-world camera footage.

[0492] Step 6:

[0493] Users view the results of a virtual trial in real time via their device and evaluate its appearance. Input is visual data from the virtual trial, and output is user feedback information. Users evaluate their satisfaction and preferences on the operation screen and make their selections.

[0494] Step 7:

[0495] The server collects and stores user feedback information, which is then used to create personalized product recommendations for the future. The input is user feedback information, and the output is the storage of this information in the database. A data analysis program learns user preference patterns, which are then stored in the database.

[0496] 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.

[0497] In an embodiment of this invention, the system, in addition to its basic function of analyzing facial and body features and generating a virtual three-dimensional model, incorporates an emotion engine to recognize the user's emotional state and reflect the results in adjusting the suggestions.

[0498] Users take photos of their face and full body using their smartphones or camera-equipped devices and upload them to the system. Furthermore, it's possible to provide a video feed to track the user's facial expressions during the session. This allows the emotion engine to analyze the user's facial data in real time and recognize their emotional state (joy, surprise, sadness, etc.).

[0499] The server generates a virtual 3D model based on facial and body features, and uses data from the emotion engine to create a virtual trial environment that reflects the user's emotional state. For example, if the user feels surprised, the server can select colors and adjust styles based on that surprise.

[0500] The device uses AR technology to perform a virtual trial simulation based on information received from the server and presents the results to the user. During this process, the system considers the user's emotional state and makes suggestions that are a better match for the user.

[0501] For example, if a user tries on eyeshadow and then decides to add lip color, the system infers the user's preference from their facial expression and adjusts and presents a pre-set palette of lip colors. Based on this selection, the user can then decide which item is best suited to them.

[0502] Throughout this entire process, the server collects user feedback and emotional state data and stores it in a database. This allows for improved accuracy of future suggestions and increased user satisfaction.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] User: Uses a smartphone or camera-equipped device to provide the system with photos of their face and full body, as well as real-time video feeds. This allows the system to acquire data to simultaneously understand facial and body characteristics and emotional state.

[0506] Step 2:

[0507] Server: Analyzes received image data to identify the user's facial features and skin condition. Simultaneously, extracts body shape features from full-body photos and generates a virtual 3D model. This model serves as the basic structure used for virtual try-on and fitting.

[0508] Step 3:

[0509] Server: Analyzes the user's facial expressions from a real-time video feed and recognizes their emotional state using an emotion engine. Based on this information, it adjusts the virtual trial environment to suit the user's emotions.

[0510] Step 4:

[0511] Server: Based on a virtual 3D model and emotional state, the server initiates a process to select cosmetics and clothing suitable for the user. It also takes into account the user's age, past history, and lifestyle information to generate personalized suggestions.

[0512] Step 5:

[0513] Terminal: Utilizing information from the server, it performs real-time virtual try-on simulations using AR technology. Suggested cosmetics and clothing are applied to the user's virtual model, and their appearance is displayed. Colors and other aspects are adjusted according to the user's emotional state.

[0514] Step 6:

[0515] User: Review the virtual trial results and provide feedback to the system. Enter your favorite items and areas for improvement, providing data that the system will use to improve the accuracy of future suggestions.

[0516] Step 7:

[0517] Server: This server stores user feedback and emotional state data in a database and uses it to improve the suggestion algorithm. This makes it possible to provide more sophisticated personalized suggestions that reflect the user's preferences in subsequent visits.

[0518] (Example 2)

[0519] 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."

[0520] It is difficult for users to receive appropriate recommendations based on their individual characteristics and emotional state when selecting a product. Furthermore, recommendations that do not consider emotional state can decrease user satisfaction. This results in users spending a lot of time finding a product that suits them, and the trial experience is often uniform.

[0521] 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.

[0522] In this invention, the server includes means for analyzing a person's characteristics based on visual data acquired from the user, means for generating a virtual three-dimensional structure, and means for adjusting suggestions to suit the user's preferences based on their emotional state. This makes it possible to make suggestions that reflect the user's individual emotions and characteristics.

[0523] "Visual data" refers to digital image information such as photographs and videos used to acquire the facial and body characteristics of a user.

[0524] "Personal characteristics" refer to identifiable information about the user's face and body shape, and are elements used to generate a virtual three-dimensional structure.

[0525] A "virtual three-dimensional structure" is a three-dimensional model built based on the user's visual data and is used for virtual trials.

[0526] "Virtual product testing" refers to a process where users can check the appearance and style of a product using a visualized virtual three-dimensional structure.

[0527] "Emotional state" refers to the internal emotional state of a user, analyzed from their facial expressions and behavior, and can be used to adjust suggestions.

[0528] A "generative artificial intelligence model" refers to a computer program that learns from large amounts of data and generates suggestions and prompts based on the user's emotional state.

[0529] A "prompt statement" is an instruction given to a generative AI model, and refers to a statement that indicates the conditions for obtaining a specific output result.

[0530] To implement this invention, a system is used in which a user, a terminal, and a server work together. The user uses a smartphone or a camera-equipped terminal to acquire image data of their face and body shape and uploads it to the system. This data is used to extract the user's features and generate a virtual three-dimensional model.

[0531] The terminal, specifically a device such as a smartphone or tablet, uses AR technology to present results to the user. In this process, the user's face and body are first photographed with a camera, and that image data is sent to a server.

[0532] The server refers to a computer system with high-performance computing capabilities, to which an emotion engine and generative artificial intelligence model are connected to analyze the user's facial expression data. Specifically, the server uses image processing software to analyze features, recognizes the emotional state using the generative AI model, and generates prompts based on that. Using these prompts, it becomes possible to suggest appropriate virtual trials that match the user's emotions.

[0533] As a concrete example, when a user is selecting eyeshadow, the generative AI model is prompted with the message, "Suggest eyeshadow shades that match the user's emotion of joy." This prompts the AI ​​to suggest the optimal shades based on the user's facial expression.

[0534] This system allows users to make product choices that consider not only appearance but also emotional satisfaction.

[0535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0536] Step 1:

[0537] Users take pictures of their face and entire body using a smartphone or camera-equipped device. The captured image data is uploaded to the server as input. The server uses this input to perform image processing to extract facial and body features, and based on this, generates analysis data for use in the next step.

[0538] Step 2:

[0539] The server generates a virtual three-dimensional model using facial and body shape analysis data. This model generation process utilizes 3D modeling software to construct a three-dimensional model that reflects the user's characteristics. The output is a user-specific virtual three-dimensional model.

[0540] Step 3:

[0541] The user's device displays a virtual 3D model received from the server using AR technology. The input here is the data of the virtual 3D model, which the device visually synthesizes, overlaying the virtual model onto the real environment and presenting it to the user. The output of this process is visual feedback to the user.

[0542] Step 4:

[0543] The user views the presented virtual trial results and makes a selection that suits their preferences. The user's facial expressions are captured again by the camera and transmitted to the server in real time via the device. The input at this point is the user's latest facial expression data, which the server analyzes using an emotion engine. The output is the result of the judgment of the user's emotional state.

[0544] Step 5:

[0545] The server uses an AI model based on the emotion analysis results to generate prompts that match the user's emotional state. Specifically, it creates prompts such as "Fashion styles that match the feeling of surprise." By inputting these prompts into the AI ​​model, suggestions that match the user's emotions are output.

[0546] Step 6:

[0547] The user's device then displays suggestions again, adjusted based on their emotions. At this point, the input is suggestion data from a generative AI model based on emotion analysis, which is visualized and output for the user to review. The user can then consider these suggestions and make the best choice for themselves.

[0548] (Application Example 2)

[0549] 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."

[0550] Modern consumers tend to seek more personalized suggestions when purchasing everyday goods, expecting customized product selections based on their own characteristics and emotional states. However, traditional systems struggle to provide product suggestions that take into account the user's real-time emotions. Furthermore, effectively utilizing the user's past selection history and lifestyle information to create personalized suggestions is also challenging.

[0551] 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.

[0552] In this invention, the server includes means for analyzing facial and body shape features based on image data acquired from the user, means for analyzing the user's real-time emotional state and generating customized suggestions based on the emotional state, and means for generating personalized prompt sentences based on the user's past selection history and lifestyle information using a generation AI model and making suggestions based on these prompts. This makes it possible to provide users with more accurate and personalized suggestions.

[0553] "Image data acquired from users" refers to digital information of facial and body shape photos taken by users using smartphones or camera-equipped devices and uploaded to the system.

[0554] A "virtual three-dimensional model" is a three-dimensional digital model generated based on the user's facial and body characteristics, and is used for virtual try-on of everyday products.

[0555] "Emotional state" refers to the user's psychological state (e.g., joy, surprise, sadness, etc.) as recognized by the system through real-time analysis of the user's facial expression data.

[0556] "Customized suggestions" refer to a selection of lifestyle products tailored to each user, taking into account their real-time emotional state and past selection history.

[0557] A "generative AI model" is an artificial intelligence algorithm used to generate prompt messages and provide personalized suggestions based on a user's past selection history and lifestyle information.

[0558] A "prompt message" is a message generated based on the user's characteristics and interests, serving as a guideline for the system when suggesting everyday products.

[0559] The system that realizes this invention performs a series of processes, including generating a virtual three-dimensional model based on the user's face and body shape, real-time emotion analysis, and then suggesting customized lifestyle products based on this analysis. The specific operation is described below.

[0560] The server receives and analyzes image data acquired by the user using a smartphone or camera-equipped device, and extracts facial and body features. This is done using an image processing library (e.g., OpenCV). Based on this feature information, 3D model generation software (e.g., Unity or Unreal Engine) is used to create a virtual 3D model of the user.

[0561] The device analyzes the user's facial expressions based on a captured real-time video feed. This analysis uses an emotion recognition API (e.g., Microsoft Face API) to identify emotional states such as joy, surprise, and sadness. The obtained emotional states are then sent to the server.

[0562] The server receives the sentiment analysis results and uses a generative AI model to generate personalized prompts, taking into account the user's past selection history and lifestyle information as input data. These prompts then derive a selection of lifestyle products to suggest to the user.

[0563] The device uses augmented reality (AR) technology (such as ARKit or ARCore) based on data received from the server to present the user with the results of a virtual trial. The user can then make a decision about the household goods, referring to the prompt message presented as the median.

[0564] For example, if a user first tries on a red lipstick and smiles, the system could, based on emotion analysis, suggest additional, more vibrant red options. By combining previously selected patterns with the user's current emotional state, it can suggest a more optimal lip color.

[0565] Examples of prompt statements are as follows:

[0566] "Select the optimal cosmetic color based on the user's real-time emotional data and past preferences."

[0567] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0568] Step 1:

[0569] Users take images of their face and body shape using a smartphone or camera-equipped device and upload them to the system. This image data is then acquired as input.

[0570] Step 2:

[0571] The server analyzes facial and body features from the acquired image data. Here, it uses an image processing library to extract feature points and analyze facial contours and body shape information, outputting characteristic information about the face and body shape.

[0572] Step 3:

[0573] The server uses feature information obtained through image analysis to generate a virtual 3D model of the user using virtual 3D model generation software. It receives this feature information as input and outputs the user's virtual avatar data.

[0574] Step 4:

[0575] The device captures facial expression data in real time using its camera while the user is trying out lifestyle products. This data is acquired as input and sent to an emotion recognition API for analysis.

[0576] Step 5:

[0577] The server uses the captured facial expression data to analyze the user's emotional state. Using an emotion recognition API, it generates emotion labels such as joy and surprise, and outputs these as analysis results.

[0578] Step 6:

[0579] The server collects emotional state analysis results, as well as the user's past selection history and lifestyle information as input. Using a generative AI model, it generates personalized prompt messages based on this data and outputs these prompt messages.

[0580] Step 7:

[0581] The terminal uses AR technology to present the user with virtual try-on results for lifestyle products, based on customized prompt messages and virtual 3D models received from the server. It outputs results that support product selection through visual feedback to the user.

[0582] 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.

[0583] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0584] 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.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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".

[0599] In an embodiment for carrying out this invention, the system is configured as follows.

[0600] Users take photos of their face and full body using their smartphones or camera-equipped devices and upload them to the system. This image data is used as basic data to analyze the user's facial features (such as the position of eyes, nose, and mouth) and skin condition (such as oily skin, dry skin, blemishes, and wrinkles). In addition, users are given the option to input additional information such as their height, weight, lifestyle, and preferred fashion style.

[0601] The server analyzes the acquired image data to generate a 3D model of the user's face and body shape. This model generation utilizes facial recognition technology and algorithms that analyze body shape features. This 3D model is used for virtual try-on and virtual fitting.

[0602] The device receives 3D models generated by the server and can perform virtual try-on simulations using AR (augmented reality) technology to try on cosmetics and clothing selected by the user. Users can conduct virtual try-ons in real time, checking the effects and appearance before making a selection.

[0603] For example, if a user is trying to select a new lipstick and a dress, the system applies the lipstick color to the lips of a 3D model and virtually tries on the dress over the entire model. The user can see how it looks in real time through the camera, and if they like it, they can provide feedback to help the system learn their preferences.

[0604] The server stores this feedback information and uses it to improve future suggestions. This allows for more personalized product recommendations that take into account user preferences and trends.

[0605] In this way, users can easily try out various cosmetics and fashion items from the comfort of their homes, minimizing disappointment when making a purchase.

[0606] The following describes the processing flow.

[0607] Step 1:

[0608] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Users also input style information such as height and weight, as well as their preferred fashion style.

[0609] Step 2:

[0610] Server: Analyzes uploaded image data. Uses a face recognition algorithm to identify facial feature points and evaluate facial shape and skin condition. Simultaneously, extracts body shape features from full-body photographs.

[0611] Step 3:

[0612] Server: Generates a virtual 3D model of the user based on the analysis results. This 3D model is a three-dimensional reproduction of the user's face and body shape and is used for virtual try-on and fitting.

[0613] Step 4:

[0614] Server: Refers to a database of accumulated information to select the most suitable cosmetics and fashion items for the user. It generates personalized suggestions considering the user's age, lifestyle, and past preferences.

[0615] Step 5:

[0616] Terminal: Using AR technology, the system performs real-time simulations to virtually try on suggested cosmetics and fashion items on 3D models. Users visually confirm the trial results through these simulations.

[0617] Step 6:

[0618] User: Review trial results and provide feedback to the system. Input information about product preferences and trial experience to contribute to improving the accuracy of the system's recommendations.

[0619] Step 7:

[0620] Server: Analyzes user feedback and stores it in a database. This will enable the generation of more accurate, personalized suggestions based on user preferences in the future.

[0621] (Example 1)

[0622] 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".

[0623] When users purchase cosmetics or clothing online, they face the challenge of not being able to accurately determine whether the selected product is suitable for them because they cannot actually try it on or test it. Furthermore, it is difficult to provide a highly satisfying purchasing experience because it is not possible to individually suggest products based on the user's preferences and characteristics.

[0624] 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.

[0625] In this invention, the server includes means for analyzing image data acquired from the user to extract facial and body shape features, means for generating a three-dimensional model of the user based on the extracted features, and means for virtually trying on cosmetics and clothing using the generated three-dimensional model. This allows the user to virtually try on products through their own three-dimensional model, improving the accuracy and satisfaction of product selection.

[0626] A "user" refers to an individual who uses the system to upload their own photos and conduct a virtual trial.

[0627] "Image data" refers to data containing visual information that a user captures using their device and uploads to the system.

[0628] "Analysis" refers to the process of extracting information such as facial and body shape features from image data.

[0629] "Features" refer to information that can be used to identify an individual user, such as their face or body shape.

[0630] A "three-dimensional model" refers to a three-dimensional digital representation of a user generated based on extracted feature information.

[0631] "Virtual trial" refers to performing visual simulations of cosmetics and clothing on the generated three-dimensional models.

[0632] Augmented reality technology refers to technology that uses computers to add information to the real world and present it to the user.

[0633] "Evaluation information" refers to the feedback and opinions that users provide regarding the results of their virtual trial.

[0634] "Selection information" refers to data related to product selections made by users through the system in the past.

[0635] "Lifestyle information" refers to data about the user's lifestyle and is used to generate personalized suggestions.

[0636] This invention begins with a user taking photos of their face and full body using a smartphone or camera-equipped device and uploading them to a server. The user's device acts as the receiver for the captured image data, and also receives and transmits additional information such as height, weight, lifestyle, and preferred style as needed.

[0637] The server uses the "OpenCV" library as software to analyze image data. This allows the server to extract facial feature points and body shape information. Next, 3D modeling software such as "Blender" is used to generate a 3D model of the user based on the extracted features. This 3D model is used for virtual try-on and fitting.

[0638] The generated 3D model is sent from the server to the user's terminal. The terminal uses the received 3D model to virtually try on cosmetics and clothing selected by the user, utilizing augmented reality technologies such as "ARKit" and "Unity." This allows the user to see the results of the virtual try-on in real time, and to test the appearance and effects of the product before actually purchasing it.

[0639] As a concrete example, if a user wants to try a new lipstick and dress, the server applies the lipstick color to the lips of a 3D model and virtually tries on the dress on a full-body model. The user can check the appearance through their device and provide feedback to the system if they like it. This feedback is used to improve the system's personalized suggestions.

[0640] An example of a prompt message might be, "Create a 3D model of the user and virtually have them try on red lipstick and a blue dress." Based on this prompt message, the system aims to provide personalized product suggestions based on the user's preferences and tendencies.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] Users take photos of their face and full body using a smartphone or camera-equipped device and upload them to the system. Input includes the user's image data and optional additional information (height, weight, lifestyle, etc.), and output is this data sent to the server. Specifically, the system works by having the user review the images taken with the camera through the device's application and then sending them to the server in the appropriate file format.

[0644] Step 2:

[0645] The server analyzes the received image data. The input consists of image data of the user's face and body shape, and the output extracts facial feature points and body shape information. Specific data processing involves using the "OpenCV" library to analyze feature points and recognize facial contours and skin condition.

[0646] Step 3:

[0647] The server generates a 3D model of the user based on the extracted features. The input is the feature point information extracted in the previous step, and the output is the user's 3D model data. Specific operations include constructing a 3D model of the user's face and body shape using 3D modeling software such as "Blender."

[0648] Step 4:

[0649] The generated 3D model is sent from the server to the terminal. The input is 3D model data, and the output is this data received by the user's terminal. Specifically, the server efficiently compresses the model data and transmits it to the terminal via the communication line.

[0650] Step 5:

[0651] The terminal uses the received 3D model to virtually try on selected cosmetics and clothing. Input includes 3D model data and information about the user's selected products, while output generates visual data for the virtual try-on, which is displayed on the terminal screen. Specifically, it utilizes AR technologies such as "ARKit" and "Unity" to simulate the color and shape of the user's selected products in real time.

[0652] Step 6:

[0653] The user reviews the results of the virtual trial and provides feedback. Visual data of the virtual trial results is the input, and user feedback data is sent to the server as output. The specific operation involves the user evaluating the virtual trial, entering feedback within the application, and sending that feedback to the server.

[0654] (Application Example 1)

[0655] 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".

[0656] In modern society, as consumers increasingly use online shopping, a problem arises: they cannot physically examine products before purchasing them. This is especially true for cosmetics and clothing, where color and fit are crucial, creating a need for an online environment that mimics a physical store where customers can try things out. Furthermore, there is a lack of systems that can provide personalized recommendations based on consumer preferences and body types, highlighting the need to improve efficiency and satisfaction in product selection.

[0657] 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.

[0658] In this invention, the server includes means for analyzing biological and external characteristics based on data acquired from the user; means for generating a virtual 3D model of the user based on the analyzed characteristics; means for enabling the virtual application of cosmetics and clothing using the virtual 3D model; means for displaying images of the virtual application in real time using augmented reality technology; and means for presenting the results of the virtual application to the user. This allows consumers to try out products in real time from the comfort of their homes, receive personalized product recommendations, and significantly improve the satisfaction and efficiency of online shopping.

[0659] "Data obtained from users" refers to digital information, including images and personal information, provided by users. This information is used to analyze biological and external characteristics.

[0660] "Means of analyzing biological and external characteristics" refers to a technical process that analyzes the details of a user's face and body shape and extracts those characteristics as digital data.

[0661] A "virtual 3D model" refers to a model with a 3D shape reproduced in digital space based on the user's biological and external characteristics. This model is used for virtual trials.

[0662] "Means of enabling virtual application" refers to technologies that digitally incorporate specific products or decorative items into virtual 3D models, allowing users to visually confirm their effects.

[0663] Augmented reality technology refers to technology that overlays digital information onto the real world, providing users with a visual experience that blends reality and virtuality.

[0664] "Real-time display methods" refer to technologies that process digital information instantly and provide immediate visual feedback to the user. This technology allows users to see the results of a virtual trial with virtually no delay.

[0665] "Means of presenting the results of virtual application to the user" refers to technologies that present users with information regarding the effectiveness and appearance of a virtual trial, and use that information to support their purchasing decisions.

[0666] This invention is implemented by a user using a smart device to capture images of their face and entire body, and then transmitting that data to a server. The server uses facial recognition technology and body feature analysis algorithms to analyze the user's biological and external characteristics and generate a virtual 3D model. Software such as OpenCV and body shape analysis APIs are used in this process. The generated 3D model is then transferred to the terminal.

[0667] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Specifically, it uses ARCore or ARKit to overlay digital information onto the user's field of view in real time, presenting a virtual try-on experience to the user.

[0668] Users can try on multiple products in real time using their virtual 3D model and make purchasing decisions. For example, consider a user who wants to try on lipstick and a dress. If the user is choosing a new lipstick and a summer dress, the system applies the lipstick color to the virtual model's lips and virtually tries on the dress over the entire model. The user can see the results in real time through the camera and, if they like it, provide feedback to add their preferences to the learning data. This information will be used to improve personalized product recommendations in the future.

[0669] An example of a prompt message is: "Generate a 3D model based on the user's photo, virtually try on the selected lipstick and dress, and provide real-time feedback to the user." This enables a new online shopping experience that supports consumer behavior from the comfort of the user's home.

[0670] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0671] Step 1:

[0672] The user uses the camera on their smart device to take photos of their face or full body and sends these digital images to the server. The input is the user's image data, and the output is the transfer of the image data to the server. The camera application takes photos based on the user's actions and automatically uploads them to the server.

[0673] Step 2:

[0674] The server receives image data from the user and analyzes the user's biological and external features using facial recognition technology and body feature analysis algorithms. This analysis extracts biometric information such as the positions of the eyes, nose, and mouth, as well as body shape data. The input is the user's image data, and the output is biometric information. Specifically, image analysis is performed using libraries such as OpenCV.

[0675] Step 3:

[0676] The server generates a virtual 3D model of the user based on the obtained biometric information. The generating AI model constructs a 3D model based on the biometric information. The input is biometric information, and the output is a virtual 3D model. The 3D model is rendered on the server using a modeling algorithm.

[0677] Step 4:

[0678] The server transfers the generated virtual 3D model to the terminal. The input is the virtual 3D model, and the output is the transfer of the model data to the terminal. The model is securely transmitted to the terminal via a data transfer protocol.

[0679] Step 5:

[0680] The device uses AR technology to apply user-selected cosmetics and clothing to a virtual 3D model. Input is data for the virtual 3D model and the user-selected products, and output is a real-time display of the virtual try-on. ARCore or ARKit is used to overlay digital information onto real-world camera footage.

[0681] Step 6:

[0682] Users view the results of a virtual trial in real time via their device and evaluate its appearance. Input is visual data from the virtual trial, and output is user feedback information. Users evaluate their satisfaction and preferences on the operation screen and make their selections.

[0683] Step 7:

[0684] The server collects and stores user feedback information, which is then used to create personalized product recommendations for the future. The input is user feedback information, and the output is the storage of this information in the database. A data analysis program learns user preference patterns, which are then stored in the database.

[0685] 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.

[0686] In an embodiment of this invention, the system, in addition to its basic function of analyzing facial and body features and generating a virtual three-dimensional model, incorporates an emotion engine to recognize the user's emotional state and reflect the results in adjusting the suggestions.

[0687] Users take photos of their face and full body using their smartphones or camera-equipped devices and upload them to the system. Furthermore, it's possible to provide a video feed to track the user's facial expressions during the session. This allows the emotion engine to analyze the user's facial data in real time and recognize their emotional state (joy, surprise, sadness, etc.).

[0688] The server generates a virtual 3D model based on facial and body features, and uses data from the emotion engine to create a virtual trial environment that reflects the user's emotional state. For example, if the user feels surprised, the server can select colors and adjust styles based on that surprise.

[0689] The device uses AR technology to perform a virtual trial simulation based on information received from the server and presents the results to the user. During this process, the system considers the user's emotional state and makes suggestions that are a better match for the user.

[0690] For example, if a user tries on eyeshadow and then decides to add lip color, the system infers the user's preference from their facial expression and adjusts and presents a pre-set palette of lip colors. Based on this selection, the user can then decide which item is best suited to them.

[0691] Throughout this entire process, the server collects user feedback and emotional state data and stores it in a database. This allows for improved accuracy of future suggestions and increased user satisfaction.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] User: Uses a smartphone or camera-equipped device to provide the system with photos of their face and full body, as well as real-time video feeds. This allows the system to acquire data to simultaneously understand facial and body characteristics and emotional state.

[0695] Step 2:

[0696] Server: Analyzes received image data to identify the user's facial features and skin condition. Simultaneously, extracts body shape features from full-body photos and generates a virtual 3D model. This model serves as the basic structure used for virtual try-on and fitting.

[0697] Step 3:

[0698] Server: Analyzes the user's facial expressions from a real-time video feed and recognizes their emotional state using an emotion engine. Based on this information, it adjusts the virtual trial environment to suit the user's emotions.

[0699] Step 4:

[0700] Server: Based on a virtual 3D model and emotional state, the server initiates a process to select cosmetics and clothing suitable for the user. It also takes into account the user's age, past history, and lifestyle information to generate personalized suggestions.

[0701] Step 5:

[0702] Terminal: Utilizing information from the server, it performs real-time virtual try-on simulations using AR technology. Suggested cosmetics and clothing are applied to the user's virtual model, and their appearance is displayed. Colors and other aspects are adjusted according to the user's emotional state.

[0703] Step 6:

[0704] User: Review the virtual trial results and provide feedback to the system. Enter your favorite items and areas for improvement, providing data that the system will use to improve the accuracy of future suggestions.

[0705] Step 7:

[0706] Server: This server stores user feedback and emotional state data in a database and uses it to improve the suggestion algorithm. This makes it possible to provide more sophisticated personalized suggestions that reflect the user's preferences in subsequent visits.

[0707] (Example 2)

[0708] 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".

[0709] It is difficult for users to receive appropriate recommendations based on their individual characteristics and emotional state when selecting a product. Furthermore, recommendations that do not consider emotional state can decrease user satisfaction. This results in users spending a lot of time finding a product that suits them, and the trial experience is often uniform.

[0710] 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.

[0711] In this invention, the server includes means for analyzing a person's characteristics based on visual data acquired from the user, means for generating a virtual three-dimensional structure, and means for adjusting suggestions to suit the user's preferences based on their emotional state. This makes it possible to make suggestions that reflect the user's individual emotions and characteristics.

[0712] "Visual data" refers to digital image information such as photographs and videos used to acquire the facial and body characteristics of a user.

[0713] "Personal characteristics" refer to identifiable information about the user's face and body shape, and are elements used to generate a virtual three-dimensional structure.

[0714] A "virtual three-dimensional structure" is a three-dimensional model built based on the user's visual data and is used for virtual trials.

[0715] "Virtual product testing" refers to a process where users can check the appearance and style of a product using a visualized virtual three-dimensional structure.

[0716] "Emotional state" refers to the internal emotional state of a user, analyzed from their facial expressions and behavior, and can be used to adjust suggestions.

[0717] A "generative artificial intelligence model" refers to a computer program that learns from large amounts of data and generates suggestions and prompts based on the user's emotional state.

[0718] A "prompt statement" is an instruction given to a generative AI model, and refers to a statement that indicates the conditions for obtaining a specific output result.

[0719] To implement this invention, a system is used in which a user, a terminal, and a server work together. The user uses a smartphone or a camera-equipped terminal to acquire image data of their face and body shape and uploads it to the system. This data is used to extract the user's features and generate a virtual three-dimensional model.

[0720] The terminal, specifically a device such as a smartphone or tablet, uses AR technology to present results to the user. In this process, the user's face and body are first photographed with a camera, and that image data is sent to a server.

[0721] The server refers to a computer system with high-performance computing capabilities, to which an emotion engine and generative artificial intelligence model are connected to analyze the user's facial expression data. Specifically, the server uses image processing software to analyze features, recognizes the emotional state using the generative AI model, and generates prompts based on that. Using these prompts, it becomes possible to suggest appropriate virtual trials that match the user's emotions.

[0722] As a concrete example, when a user is selecting eyeshadow, the generative AI model is prompted with the message, "Suggest eyeshadow shades that match the user's emotion of joy." This prompts the AI ​​to suggest the optimal shades based on the user's facial expression.

[0723] This system allows users to make product choices that consider not only appearance but also emotional satisfaction.

[0724] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0725] Step 1:

[0726] Users take pictures of their face and entire body using a smartphone or camera-equipped device. The captured image data is uploaded to the server as input. The server uses this input to perform image processing to extract facial and body features, and based on this, generates analysis data for use in the next step.

[0727] Step 2:

[0728] The server generates a virtual three-dimensional model using facial and body shape analysis data. This model generation process utilizes 3D modeling software to construct a three-dimensional model that reflects the user's characteristics. The output is a user-specific virtual three-dimensional model.

[0729] Step 3:

[0730] The user's device displays a virtual 3D model received from the server using AR technology. The input here is the data of the virtual 3D model, which the device visually synthesizes, overlaying the virtual model onto the real environment and presenting it to the user. The output of this process is visual feedback to the user.

[0731] Step 4:

[0732] The user views the presented virtual trial results and makes a selection that suits their preferences. The user's facial expressions are captured again by the camera and transmitted to the server in real time via the device. The input at this point is the user's latest facial expression data, which the server analyzes using an emotion engine. The output is the result of the judgment of the user's emotional state.

[0733] Step 5:

[0734] The server uses an AI model based on the emotion analysis results to generate prompts that match the user's emotional state. Specifically, it creates prompts such as "Fashion styles that match the feeling of surprise." By inputting these prompts into the AI ​​model, suggestions that match the user's emotions are output.

[0735] Step 6:

[0736] The user's device then displays suggestions again, adjusted based on their emotions. At this point, the input is suggestion data from a generative AI model based on emotion analysis, which is visualized and output for the user to review. The user can then consider these suggestions and make the best choice for themselves.

[0737] (Application Example 2)

[0738] 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".

[0739] Modern consumers tend to seek more personalized suggestions when purchasing everyday goods, expecting customized product selections based on their own characteristics and emotional states. However, traditional systems struggle to provide product suggestions that take into account the user's real-time emotions. Furthermore, effectively utilizing the user's past selection history and lifestyle information to create personalized suggestions is also challenging.

[0740] 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.

[0741] In this invention, the server includes means for analyzing facial and body shape features based on image data acquired from the user, means for analyzing the user's real-time emotional state and generating customized suggestions based on the emotional state, and means for generating personalized prompt sentences based on the user's past selection history and lifestyle information using a generation AI model and making suggestions based on these prompts. This makes it possible to provide users with more accurate and personalized suggestions.

[0742] "Image data acquired from users" refers to digital information of facial and body shape photos taken by users using smartphones or camera-equipped devices and uploaded to the system.

[0743] A "virtual three-dimensional model" is a three-dimensional digital model generated based on the user's facial and body characteristics, and is used for virtual try-on of everyday products.

[0744] "Emotional state" refers to the user's psychological state (e.g., joy, surprise, sadness, etc.) as recognized by the system through real-time analysis of the user's facial expression data.

[0745] "Customized suggestions" refer to a selection of lifestyle products tailored to each user, taking into account their real-time emotional state and past selection history.

[0746] A "generative AI model" is an artificial intelligence algorithm used to generate prompt messages and provide personalized suggestions based on a user's past selection history and lifestyle information.

[0747] A "prompt message" is a message generated based on the user's characteristics and interests, serving as a guideline for the system when suggesting everyday products.

[0748] The system that realizes this invention performs a series of processes, including generating a virtual three-dimensional model based on the user's face and body shape, real-time emotion analysis, and then suggesting customized lifestyle products based on this analysis. The specific operation is described below.

[0749] The server receives and analyzes image data acquired by the user using a smartphone or camera-equipped device, and extracts facial and body features. This is done using an image processing library (e.g., OpenCV). Based on this feature information, 3D model generation software (e.g., Unity or Unreal Engine) is used to create a virtual 3D model of the user.

[0750] The device analyzes the user's facial expressions based on a captured real-time video feed. This analysis uses an emotion recognition API (e.g., Microsoft Face API) to identify emotional states such as joy, surprise, and sadness. The obtained emotional states are then sent to the server.

[0751] The server receives the sentiment analysis results and uses a generative AI model to generate personalized prompts, taking into account the user's past selection history and lifestyle information as input data. These prompts then derive a selection of lifestyle products to suggest to the user.

[0752] The device uses augmented reality (AR) technology (such as ARKit or ARCore) based on data received from the server to present the user with the results of a virtual trial. The user can then make a decision about the household goods, referring to the prompt message presented as the median.

[0753] For example, if a user first tries on a red lipstick and smiles, the system could, based on emotion analysis, suggest additional, more vibrant red options. By combining previously selected patterns with the user's current emotional state, it can suggest a more optimal lip color.

[0754] Examples of prompt statements are as follows:

[0755] "Select the optimal cosmetic color based on the user's real-time emotional data and past preferences."

[0756] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0757] Step 1:

[0758] Users take images of their face and body shape using a smartphone or camera-equipped device and upload them to the system. This image data is then acquired as input.

[0759] Step 2:

[0760] The server analyzes facial and body features from the acquired image data. Here, it uses an image processing library to extract feature points and analyze facial contours and body shape information, outputting characteristic information about the face and body shape.

[0761] Step 3:

[0762] The server uses feature information obtained through image analysis to generate a virtual 3D model of the user using virtual 3D model generation software. It receives this feature information as input and outputs the user's virtual avatar data.

[0763] Step 4:

[0764] The device captures facial expression data in real time using its camera while the user is trying out lifestyle products. This data is acquired as input and sent to an emotion recognition API for analysis.

[0765] Step 5:

[0766] The server uses the captured facial expression data to analyze the user's emotional state. Using an emotion recognition API, it generates emotion labels such as joy and surprise, and outputs these as analysis results.

[0767] Step 6:

[0768] The server collects emotional state analysis results, as well as the user's past selection history and lifestyle information as input. Using a generative AI model, it generates personalized prompt messages based on this data and outputs these prompt messages.

[0769] Step 7:

[0770] The terminal uses AR technology to present the user with virtual try-on results for lifestyle products, based on customized prompt messages and virtual 3D models received from the server. It outputs results that support product selection through visual feedback to the user.

[0771] 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.

[0772] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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."

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A means for analyzing facial and body shape characteristics based on image data obtained from a user,

[0795] A means for generating a virtual three-dimensional model of the user based on the analyzed features,

[0796] A means for enabling virtual try-on of cosmetics and clothing using the aforementioned virtual three-dimensional model,

[0797] A means for presenting the results of the virtual trial to the user,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising means for collecting user feedback information and improving the proposed cosmetics and clothing based on the feedback information.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising means for collecting the user's past selection history and lifestyle information and generating personalized suggestions based thereon.

[0803] "Example 1"

[0804] (Claim 1)

[0805] A method for analyzing image data obtained from users to extract facial and body shape features,

[0806] A means for generating a three-dimensional model of the user based on the extracted features,

[0807] A means of virtually trying on cosmetics and clothing using the generated three-dimensional model,

[0808] A means of presenting information to the user visually using augmented reality technology, based on the results of a virtual trial.

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, further comprising means for collecting user evaluation information and optimizing the provision of cosmetics and clothing based on the evaluation information.

[0812] (Claim 3)

[0813] The system according to claim 1, further comprising means for creating personalized suggestions based on the user's past choices and lifestyle information.

[0814] "Application Example 1"

[0815] (Claim 1)

[0816] A means for analyzing biological and external characteristics based on data obtained from users,

[0817] A means for generating a virtual 3D model of the user based on the analyzed features,

[0818] A means to enable the virtual application of cosmetics and clothing using the aforementioned virtual 3D model,

[0819] A means for displaying the image of the virtual application in real time using augmented reality technology,

[0820] A means for presenting the results of the virtual application to the user,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for collecting user response information and improving suggestions for cosmetics and clothing based on the response information.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for collecting the user's past choices and lifestyle information and generating personalized suggestions based thereon.

[0826] "Example 2 of combining an emotion engine"

[0827] (Claim 1)

[0828] A means of analyzing a person's characteristics based on visual data obtained from the user,

[0829] A means for generating a user's virtual three-dimensional structure based on the analyzed characteristics,

[0830] A means to enable virtual trial of the product using the aforementioned virtual three-dimensional structure,

[0831] A means for presenting the results of the virtual trial to the user,

[0832] A means of analyzing the user's facial expressions in real time and recognizing their emotional state,

[0833] A means of adjusting suggestions to suit the user's preferences based on recognized emotional states,

[0834] A means for generating prompt sentences based on emotional states using a generative artificial intelligence model,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising means for collecting user feedback information and emotional states, and for improving the product proposal based on the information.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising means for collecting information on the user's past selection history and behavior patterns, and generating personalized suggestions based thereon.

[0840] "Application example 2 when combining with an emotional engine"

[0841] (Claim 1)

[0842] A means for analyzing facial and body shape characteristics based on image data obtained from a user,

[0843] A means for generating a virtual three-dimensional model of the user based on the analyzed features,

[0844] A means for analyzing the user's real-time emotional state and generating customized suggestions based on the said emotional state,

[0845] A means to enable virtual testing of household goods using the aforementioned virtual three-dimensional model,

[0846] A means for presenting the results of the virtual trial to the user,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, further comprising means for collecting user feedback information and improving the suggested lifestyle products based on the feedback information and the results of sentiment analysis.

[0850] (Claim 3)

[0851] The system according to claim 1, further comprising means for generating personalized prompt sentences based on the user's past selection history and lifestyle information using a generative AI model, and making suggestions based on these. [Explanation of symbols]

[0852] 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 analyzing facial and body shape characteristics based on image data obtained from a user, A means for generating a virtual three-dimensional model of the user based on the analyzed features, A means for enabling virtual try-on of cosmetics and clothing using the aforementioned virtual three-dimensional model, A means for presenting the results of the virtual trial to the user, A system that includes this.

2. The system according to claim 1, further comprising means for collecting user feedback information and improving the proposed cosmetics and clothing based on the feedback information.

3. The system according to claim 1, further comprising means for collecting the user's past selection history and lifestyle information and generating personalized suggestions based thereon.

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