System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting lifestyle and eating habits of a user; means for capturing facial images of the user; means for predicting a future appearance of the user based on the collected lifestyle and eating habits; means for visualizing and displaying the predicted future appearance to the user; means for providing self-care method and countermeasures based on the prediction; and means for providing real-time feedback of changes due to the implementation of the self-care method and countermeasures.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, people's lifestyles and poor eating habits are causing an increasing number of beauty and health problems. In particular, the inability to predict what one's future appearance will be leads to a lack of motivation to improve lifestyle habits. Furthermore, many people are unable to implement effective health management and beauty measures because they do not know what kind of self-care or measures they should take. Furthermore, there is no way to understand in real time how their current lifestyle habits will affect their future, making it difficult to receive appropriate feedback. The present invention aims to solve these problems and provide a system that helps people achieve a healthier and more beautiful future. [Means for solving the problem]

[0005] The present invention provides a system including: means for collecting lifestyle and dietary data of a user; means for capturing a user's facial image; means for predicting the user's future appearance based on the collected lifestyle and dietary data; means for visualizing and displaying the predicted future appearance to the user; means for providing self-care methods and measures based on the prediction; and means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures. The lifestyle and dietary data are collected from a smart device, and the means for predicting future appearance utilizes a statistical model or machine learning model. The self-care methods and measures include suggestions for improving diet, recommended skin care products, and exercise plans. This system provides users with specific methods for maintaining a healthy lifestyle and achieving their ideal future appearance.

[0006] "User" refers to an individual who uses this system and provides their own lifestyle and dietary data.

[0007] "Lifestyle data" refers to information such as a user's daily activities, exercise, sleep patterns, and stress levels.

[0008] "Dietary data" refers to information such as the user's dietary habits, calorie intake, nutritional balance, and meal times.

[0009] "Facial image" refers to image data obtained by photographing and digitizing the user's face.

[0010] "Capturing" refers to obtaining an image or video as digital data using a photographic device such as a camera.

[0011] A "statistical model" is a model that uses mathematical techniques to predict future trends and relationships based on collected data.

[0012] A "machine learning model" refers to a model that uses artificial intelligence technology to learn from data and make future predictions and classifications.

[0013] "Self-care methods" refer to specific actions and means by which users manage and improve their own health and beauty.

[0014] "Measures" refer to means or methods for solving health or beauty problems.

[0015] "Visualization" refers to displaying data or information visually to make it easier to understand.

[0016] "Real-time feedback" refers to providing instant evaluations and advice based on the latest data entered by the user.

[0017] "Smart device" refers to any device (e.g., smartphone, smartwatch) that can connect to the Internet and has data collection and communication functions.

[0018] "Exercise plan" refers to a specific exercise program designed to improve a user's exercise habits.

[0019] "Future appearance" refers to the future appearance and body shape of the user if they maintain their current lifestyle.

[0020] "Predicting" refers to estimating future situations or conditions based on current data and trends. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] MODE FOR CARRYING OUT THE INVENTION

[0043] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, visualizes the prediction results, and presents them to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. The following describes in detail how this system is implemented.

[0044] System Configuration

[0045] The system consists of the following major components:

[0046] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[0047] 2. Server

[0048] 3. Display devices (e.g. smart mirrors)

[0049] Program processing

[0050] User Data Collection

[0051] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0052] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0053] The server stores the received data in a database and manages it with a unique ID for each user.

[0054] Capture your current appearance

[0055] The user activates the smart mirror and reflects their face in the mirror.

[0056] The device (smart mirror) captures the user's face with its built-in camera and sends the image to a server.

[0057] The server analyzes the received image using a facial recognition algorithm to extract facial features.

[0058] Predicting future appearance

[0059] The server analyzes the user's saved lifestyle and dietary data and predicts the user's future appearance using statistical and machine learning models.

[0060] The predictive model predicts future changes in skin condition, body shape, etc. based on the user's current data patterns.

[0061] The server generates an image to visualize the future appearance based on the prediction results.

[0062] Displaying prediction results

[0063] The server sends the generated future appearance image to the smart mirror.

[0064] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm the future changes.

[0065] Self-care and countermeasure suggestions

[0066] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0067] Suggestions include plant-based meal plans, specific skin care product recommendations, and exercise plans.

[0068] The device (smart mirror or smartphone app) notifies the user of the suggestions.

[0069] Real-time feedback

[0070] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0071] New data is sent from the device (smartwatch, smartphone app) to the server.

[0072] The server reassess the user's progress based on the new data and provides feedback on the effectiveness of improvements.

[0073] The device (smart mirror, smartphone app) notifies the user with updated feedback and provides continuous motivation.

[0074] Specific examples

[0075] For example, if a user regularly eats a high-fat diet and exercises infrequently, this data is collected through a smartwatch and smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction is then visualized and displayed on a smart mirror. Seeing this future appearance motivates the user to improve their lifestyle habits, leading them to adopt a plant-based diet and regular exercise. The smartwatch and smartphone app track these efforts, and the server provides feedback, allowing the user to see their progress in real time.

[0076] This system allows users to concretely understand the impact of their lifestyle habits and take concrete actions to achieve a healthy and beautiful future.

[0077] The processing flow will be explained below.

[0078] Program processing

[0079] Step 1: Collect user data

[0080] 1. Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0081] 2. The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0082] 3. The server stores the received data in a database and manages it with a unique ID for each user.

[0083] Step 2: Capture your current appearance

[0084] 1. The user activates the smart mirror and reflects their face in the mirror.

[0085] 2. The device (smart mirror) captures the user's face with its built-in camera. This capture process takes a few seconds to obtain a high-resolution image.

[0086] 3. The device (smart mirror) processes the captured facial image at runtime and sends it to a server via the Internet.

[0087] Step 3: Face recognition and feature extraction

[0088] 1. The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[0089] 2. The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[0090] Step 4: Predict your future appearance

[0091] 1. The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[0092] 2. The server predicts the user's future appearance based on statistical and machine learning models, including changes in skin condition and body shape.

[0093] 3. The server generates an image to visualize the prediction results. This image shows the future appearance of the user if they maintain their current lifestyle.

[0094] Step 5: View the prediction results

[0095] 1. The server sends the generated image of the future appearance to the smart mirror.

[0096] 2. The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[0097] Step 6: Self-care and strategy suggestions

[0098] 1. Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0099] 2. The device (smart mirror or smartphone app) notifies the user of the suggestions, either through visual pop-up messages or periodic reminders.

[0100] Step 7: Real-time feedback

[0101] 1. Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[0102] 2. The device (smartwatch, smartphone app) sends the new collected data to the server.

[0103] 3. The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[0104] 4. The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[0105] Through this series of processes, users can concretely understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] Currently, systems that collect data on a user's lifestyle and diet, predict future appearance based on that data, and provide self-care methods are not fully developed. In particular, they lack real-time feedback on the effectiveness of self-care and continuous motivation in response to changes in the user's lifestyle. This makes it difficult for users to effectively manage and maintain their health and beauty.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a user's facial image, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing and displaying the predicted future appearance to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for automatically transmitting the records of the lifestyle and dietary data to the server via the Internet, means for analyzing the collected images with a face recognition algorithm and extracting facial features, means for predicting the user's future appearance using a statistical model or a machine learning model, means for displaying the generated future appearance image to the user in real time, means for notifying the user of the suggested self-care methods and measures, and means for reevaluating the user's improvement based on the progress data and providing continuous feedback. This allows the user to specifically understand the impact of their lifestyle and to confirm and continuously implement specific actions to maintain a healthy and beautiful future appearance in real time.

[0111] "User's lifestyle data and dietary data" is a general term for data including the content and amount of food eaten, the type and amount of exercise, and the duration and quality of sleep in the user's daily life.

[0112] "Means for capturing a user's facial image" refers to a device or software for photographing a user's face and acquiring the image.

[0113] "Means for predicting a user's future appearance" refers to algorithms or models for predicting future changes in a user's face or body based on the user's current lifestyle and dietary data.

[0114] "Means for visualizing the predicted future appearance and displaying it to the user" refers to devices or software that display the predicted changes in appearance as images or graphs, etc., so that the user can visually confirm them.

[0115] "Means for providing self-care methods and measures" refers to devices and software that suggest specific methods for improvement such as diet, exercise, and skin care based on the user's prediction results.

[0116] "Means for providing real-time feedback on changes resulting from the implementation of self-care methods and measures" refers to devices and software that evaluate the results of a user's implementation of self-care methods and measures in real time and notify the user of the results.

[0117] "Means for automatically transmitting records of lifestyle and dietary data to a server via the Internet" refers to devices or software for automatically transmitting collected data to a server via a smart device or the like.

[0118] "Facial recognition algorithm" refers to a computer program that analyzes a user's facial image and extracts certain features.

[0119] "Statistical model or machine learning model" refers to a mathematical or algorithmic model that analyzes patterns in data and predicts future changes.

[0120] The "means for displaying a future appearance image to a user in real time" refers to a device or software for instantly displaying the generated future appearance image to a user.

[0121] "Means for notifying the user of self-care methods and suggested measures" refers to devices and software for notifying the user of specific self-care methods based on the analysis results.

[0122] "Means for reevaluating the user's improvement based on progress data and providing continuous feedback" refers to devices and software that evaluate the effectiveness of the self-care methods and measures implemented by the user based on data, and continuously notify the user of the results.

[0123] MODE FOR CARRYING OUT THE INVENTION

[0124] The present invention is a system that collects lifestyle and dietary data of a user and predicts future appearance based on this data. This system is composed of the following main components:

[0125] System Configuration

[0126] This system consists of a user data collection device (e.g., smart watch, smartphone app), a server, and a display device (e.g., smart mirror).

[0127] User Data Collection

[0128] Users use smartwatches or smartphone apps to record data such as meals, exercise, and sleep in their daily lives. The devices (smartwatches, smartphone apps) send the recorded data to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user. For example, detailed data such as what a user had for breakfast, how many kilometers they walked, and how many hours they slept can be collected.

[0129] Capture your current appearance

[0130] The user activates the smart mirror and reflects their face in the mirror. The device (smart mirror) captures the user's face with its built-in camera and sends the image to the server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. These features include skin condition, number of wrinkles, and facial contours.

[0131] Predicting future appearance

[0132] The server analyzes the user's saved lifestyle and facial feature data and predicts the user's future appearance using statistical and machine learning models (e.g., TensorFlow and PyTorch). The predictive model predicts future skin conditions and changes in body shape based on the user's current data patterns. For example, if an irregular lifestyle continues, sagging skin and weight gain are predicted.

[0133] Displaying prediction results

[0134] The server generates an image to visualize the user's future appearance based on the prediction results. This image is sent to the smart mirror, which then displays the image to the user, allowing the user to visually confirm future changes.

[0135] Self-care and countermeasure suggestions

[0136] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, recommendations for specific skin care products, and exercise plans. The device (smart mirror or smartphone app) notifies the user of the suggestions. For example, specific advice such as "do 30 minutes of aerobic exercise every day" and "consume foods rich in vitamin C" is displayed.

[0137] Real-time feedback

[0138] The user incorporates the suggested self-care methods into their daily life and records their progress using a smartwatch or smartphone app. New data is sent from the device (smartwatch, smartphone app) to the server. The server reevaluates the user's progress based on the new data and provides feedback on the effectiveness of the improvements. The device (smart mirror or smartphone app) notifies the user of the updated feedback, providing ongoing motivation. For example, feedback such as "skin moisture has improved" or "weight loss of 1 kg" may be provided.

[0139] Prompt Sentence Examples

[0140] "Please tell me how to build a system that collects data on a user's daily diet, exercise, sleep, etc., and uses this data to predict future appearance and suggest self-care methods."

[0141] This system allows users to specifically understand the impact of their lifestyle habits, and to confirm and continuously implement specific actions in real time to maintain a healthy and beautiful appearance in the future.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: Collect user data

[0144] 1. The user enters information about their daily diet, exercise, sleep, etc. into a smartwatch or smartphone app. For example, they record what they had for breakfast, the number of steps they took, and the amount of sleep they received.

[0145] 2. The device (smartwatch or smartphone app) sends the recorded data to a server via the Internet.

[0146] 3. The server stores the received data in a database, including information such as dietary habits, exercise, and sleep duration.

[0147] 4. Input: User-recorded food, exercise, and sleep data.

[0148] Output: User's lifestyle and dietary data stored on the server.

[0149] Step 2: Capture your current appearance

[0150] 1. The user activates the smart mirror and places their face in the mirror.

[0151] 2. The device (smart mirror) captures the user's facial image using its built-in camera.

[0152] 3. The captured image is sent to a server via the Internet.

[0153] 4. The server analyzes the received image using an AI-based facial recognition algorithm.

[0154] 5. Input: User's face image.

[0155] Output: Analyzed facial feature data (skin condition, number of wrinkles, facial contours, etc.).

[0156] Step 3: Predict your future appearance

[0157] 1. The server analyzes the user's stored lifestyle data and facial feature data.

[0158] 2. The server predicts future appearance using statistical or machine learning models (e.g., TensorFlow, PyTorch).

[0159] 3. The server generates an image to visualize the future appearance based on the prediction results.

[0160] 4. Input: User's lifestyle data, facial feature data.

[0161] Output: Predicted image of future appearance.

[0162] Step 4: View the prediction results

[0163] 1. The server sends the generated image of the future appearance to the smart mirror.

[0164] 2. The device (smart mirror) displays an image of the user's future appearance.

[0165] 3. Users can visually see what their future appearance will be.

[0166] 4. Input: Predicted image of future appearance.

[0167] Output: The predicted image to be displayed on the smart mirror.

[0168] Step 5: Self-care and strategy suggestions

[0169] 1. The server suggests self-care methods and measures to the user based on the analysis results.

[0170] 2. Suggestions include plant-based meal plans, recommendations for specific skin care products, and exercise plans.

[0171] 3. The device (smart mirror or smartphone app) notifies the user of the suggestions.

[0172] 4. Input: Analysis results.

[0173] Output: Self-care methods and measures notified to the user.

[0174] Step 6: Real-time feedback

[0175] 1. The user incorporates the suggested self-care methods into their daily lives and records their implementation status using a smartwatch or smartphone app.

[0176] 2. New data is sent from the device (smartwatch, smartphone app) to the server.

[0177] 3. The server reassess the user's progress based on the new data.

[0178] 4. The server provides feedback on the effectiveness of improvements and provides ongoing motivation.

[0179] 5. The device (smart mirror or smartphone app) notifies the user of the feedback.

[0180] 6. Input: Data on the implementation status of self-care methods.

[0181] Output: Feedback notification of the improvement effect.

[0182] (Application example 1)

[0183] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0184] Conventional appearance prediction systems only predict and display future appearance based on a user's lifestyle and dietary data, making it difficult for users to select specific self-care methods based on the results. They also lacked the functionality to accurately suggest what products and services users should use. Furthermore, the lack of real-time feedback made it difficult for users to maintain motivation to continue self-care.

[0185] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0186] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, and means for suggesting in-store care products and services to the user. This allows the user to easily select the self-care methods, products, and services that are best suited to them and to engage in self-care while continuously maintaining motivation.

[0187] "Lifestyle data" is information relating to the activities and behaviors of the user in their daily lives.

[0188] "Dietary data" is information about the types, amounts, and frequency of food and beverage intake by a user.

[0189] A "face image" is image data of the user's face.

[0190] "Visualization" is the visual display of data or information.

[0191] A "self-care method" is a method for health management or beauty care that the user himself or herself carries out.

[0192] "Countermeasures" are specific measures or methods taken to address a specific problem or issue.

[0193] "Real-time feedback" refers to instantly evaluating the results of the actions and measures taken by the user and returning this information to the user immediately.

[0194] A "server" is a computer system that manages and processes data.

[0195] A "smart device" is a digital device equipped with internet connectivity and sensors.

[0196] A "statistical model" is a mathematical model that numerically describes and predicts phenomena based on data.

[0197] A "machine learning model" is a model that uses algorithms to learn patterns and knowledge from data and make predictions and classifications.

[0198] "In-store care products and services" are products and services related to health management and beauty care provided in stores.

[0199] The system that realizes this application example consists of multiple hardware and software components. First, smart devices (e.g., smartwatches and smartphone apps) are used to collect the user's lifestyle and dietary data. These devices record the user's daily data, such as diet, exercise, and sleep, in real time and send it to a server via the Internet.

[0200] The server stores the received data in a database and manages it with a unique ID for each user, which makes it possible to accurately track the data of each individual user.

[0201] Next, the user stands in front of the smart mirror and reflects their face. The smart mirror uses its built-in camera to capture the user's face and sends the image to a server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. This step could use OpenCV, an open-source software for facial recognition.

[0202] The server then analyzes the user's stored lifestyle and dietary data using statistical or machine learning models to predict the user's future appearance. These models use generative AI models to predict future changes in skin condition and body shape based on the user's data patterns.

[0203] The future appearance image generated by the predictive model is visualized by the server and sent to the smart mirror, allowing the user to visually confirm their future appearance.

[0204] Furthermore, based on the analysis results, the server will suggest appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, specific skin care products, exercise plans, etc. The smart mirror and smartphone app will notify the user of these suggestions and encourage them to use in-store care products and services.

[0205] Finally, the user incorporates the suggested self-care methods and measures into their daily lives and records their progress using a smartphone app or smartwatch. The server then reevaluates their progress based on the new data and provides real-time feedback on the effectiveness. This allows users to constantly check their progress and maintain motivation while engaging in self-care.

[0206] As a concrete example, consider a scenario in which the system is used at a beauty salon. The user stands in front of the salon's smart mirror and projects their face onto the camera. The smart mirror sends the user's lifestyle data and current facial image to a server, which then uses this data to predict their future appearance. The smart mirror then displays the predicted future appearance and suggests specific beauty care products and services. This allows the user to select the best care method for themselves and make more effective use of the salon's services.

[0207] Examples of prompts to input to a generative AI model might include the following:

[0208] "Write a program to create an app that predicts future appearance. Include a function to send the user's face image and lifestyle data to a server, and retrieve and display the predicted future appearance."

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Processing Steps

[0211] Step 1:

[0212] Users use smartphone apps or smartwatches to record daily life data such as diet, exercise, and sleep.

[0213] Input: Lifestyle and dietary data entered by users into their smart devices.

[0214] Data processing: These data are temporarily stored in the smart device.

[0215] Output: Lifestyle and dietary data are ready to be sent to the server.

[0216] Step 2:

[0217] The smart device transmits the recorded data to a server via the Internet.

[0218] Input: Lifestyle and dietary data recorded on a smart device.

[0219] Data computation: Data is sent to a server via the Internet as an HTTP request.

[0220] Output: Each user's lifestyle and dietary data is sent to the server.

[0221] Step 3:

[0222] The server stores the received data in a database and manages it with a unique ID for each user.

[0223] Input: Lifestyle and dietary data sent to the server.

[0224] Data processing: Data is stored in a database and managed with a unique ID to identify and accumulate data for each user.

[0225] Output: Each user's lifestyle and dietary data is stored in a database.

[0226] Step 4:

[0227] The user stands in front of the smart mirror and reflects their face in the mirror, which then captures the user's face using its built-in camera.

[0228] Input: Current face image of the user.

[0229] Data processing: Facial image data captured by the smart mirror's built-in camera.

[0230] Output: A face image is captured by the smart mirror.

[0231] Step 5:

[0232] The smart mirror sends the captured facial image to a server.

[0233] Input: A face image captured by a smart mirror.

[0234] Data calculation: The smart mirror sends the facial image data to the server as an HTTP request.

[0235] Output: The facial image data arrives at the server.

[0236] Step 6:

[0237] The server analyzes the received facial image using a facial recognition algorithm to extract the user's facial features.

[0238] Input: Facial image data sent to the server.

[0239] Data processing: Analyze and extract facial features using a facial recognition algorithm (e.g., OpenCV).

[0240] Output: Extracted facial feature data.

[0241] Step 7:

[0242] The server uses a statistical model or machine learning model to predict the user's future appearance based on the stored lifestyle and dietary data.

[0243] Input: Lifestyle and dietary data in the database, extracted facial feature data.

[0244] Data computation: Predict future appearance using statistical and machine learning models (generative AI models).

[0245] Output: Image of future appearance.

[0246] Step 8:

[0247] The server sends the predicted future appearance image to the smart mirror.

[0248] Input: Server-generated image of future appearance.

[0249] Data calculation: Send the future appearance image to the smart mirror as an HTTP response.

[0250] Output: An image of your future appearance is sent to the smart mirror.

[0251] Step 9:

[0252] The smart mirror displays an image of the user's future appearance.

[0253] Input: Future appearance image sent from the server.

[0254] Data processing: Displaying an image of your future appearance on the smart mirror display.

[0255] Output: The user can visually confirm the future appearance image.

[0256] Step 10:

[0257] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0258] Input: Lifestyle data, dietary data, and future appearance prediction results.

[0259] Data calculation: Self-care methods and measures are generated using the proposed algorithm.

[0260] Output: Suggested self-care methods and measures.

[0261] Step 11:

[0262] Smart mirrors and smartphone apps will notify users of the suggestions.

[0263] Input: Self-care methods and measures suggested by the server.

[0264] Data processing: The proposed content is displayed on the screen of a smart mirror or smartphone app.

[0265] Output: The user can check the suggestions and put the self-care methods and measures into practice.

[0266] Step 12:

[0267] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0268] Input: Data on self-care methods and measures taken by the user.

[0269] Data processing: Smart devices record and temporarily store new data.

[0270] Output: New lifestyle and dietary data is generated.

[0271] Step 13:

[0272] The smart device sends new data to the server, which then re-evaluates it.

[0273] Input: New lifestyle and dietary data.

[0274] Data calculation: The server uses a re-evaluation algorithm to analyze your progress and generate feedback.

[0275] Output: Real-time feedback results.

[0276] Step 14:

[0277] Smart mirrors and smartphone apps provide updated feedback to users and provide ongoing motivation.

[0278] Input: The feedback result sent by the server.

[0279] Data processing: The feedback content is displayed on the screen of a smart mirror or smartphone app.

[0280] Output: The user receives real-time feedback and is motivated to continue the action.

[0281] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0282] MODE FOR CARRYING OUT THE INVENTION

[0283] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. It also has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[0284] System Configuration

[0285] The system consists of the following major components:

[0286] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[0287] 2. Server

[0288] 3. Display devices (e.g. smart mirrors)

[0289] 4. Emotion Engine

[0290] Program processing

[0291] User Data Collection

[0292] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0293] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0294] The server stores the received data in a database and manages it with a unique ID for each user.

[0295] Capture your current appearance

[0296] The user activates the smart mirror and reflects their face in the mirror.

[0297] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[0298] The device (smart mirror) processes the captured facial image at runtime and transmits it to a server via the Internet.

[0299] Face Recognition and Feature Extraction

[0300] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[0301] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[0302] Predicting future appearance

[0303] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[0304] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[0305] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[0306] Capturing and analyzing user emotions

[0307] Users express their emotions through facial expressions and voice in front of the smart mirror.

[0308] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[0309] The device processes the captured data in real time and transmits it to a server over the Internet.

[0310] The server uses an emotion engine to analyze the received data and determine the user's emotional state.

[0311] Displaying prediction results

[0312] The server sends the generated future appearance image to the smart mirror.

[0313] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[0314] Self-care and countermeasure suggestions

[0315] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0316] The server uses an emotion engine to tailor the optimal care regimen based on the user's emotional state.

[0317] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[0318] Real-time feedback

[0319] Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[0320] The device (smartwatch, smartphone app) sends the collected new data to the server.

[0321] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[0322] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[0323] Specific examples

[0324] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[0325] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[0326] The processing flow will be explained below.

[0327] Program processing

[0328] Step 1: Collect user data

[0329] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0330] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0331] The server stores the received data in a database and manages it with a unique ID for each user.

[0332] Step 2: Capture your current appearance

[0333] The user activates the smart mirror and reflects their face in the mirror.

[0334] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[0335] The device (smart mirror) processes the captured facial images in real time and transmits them to a server via the Internet.

[0336] Step 3: Face recognition and feature extraction

[0337] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[0338] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[0339] Step 4: Predict your future appearance

[0340] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[0341] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[0342] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[0343] Step 5: Capturing and analyzing user emotions

[0344] Users express their emotions through facial expressions and voice in front of the smart mirror.

[0345] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[0346] The device processes the captured data in real time and transmits it to a server over the Internet.

[0347] The server uses an emotion engine to analyze the received data and identify the user's emotional state. For example, it classifies emotions such as "happiness," "sadness," "anger," and "surprise" based on facial expressions captured by a camera, and also analyzes emotions from voice.

[0348] Step 6: View the prediction results

[0349] The server sends the generated future appearance image to the smart mirror.

[0350] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[0351] Step 7: Self-care and strategy suggestions

[0352] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0353] The server uses an emotion engine to tailor optimal care methods based on the user's emotional state. For example, if the user is feeling stressed, it will suggest relaxation and mindfulness techniques.

[0354] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[0355] Step 8: Real-time feedback

[0356] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0357] The device (smartwatch, smartphone app) sends the collected new data to the server.

[0358] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[0359] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[0360] Specific examples

[0361] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[0362] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[0363] Example 2

[0364] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0365] Conventional systems could easily predict a user's future appearance based on their lifestyle and dietary data and suggest self-care methods, but they were unable to provide optimal care methods based on the user's emotional state. Furthermore, they lacked real-time feedback on changes in the user's lifestyle, making it difficult to maintain the user's motivation. This resulted in the challenge of making it difficult to achieve long-term health improvements.

[0366] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0367] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for capturing and analyzing the user's emotional state, and means for suggesting an optimal self-care method based on the emotional state. This allows for more effective health improvement by suggesting an optimal care method according to the user's emotional state and providing continuous motivation.

[0368] "User" refers to a person who uses the system to record their own lifestyle and dietary data and receives self-care methods and feedback provided by the system.

[0369] "Lifestyle data" refers to information such as exercise, sleep, and activity level in the user's daily life.

[0370] "Dietary data" refers to information about the food and beverages a user regularly consumes.

[0371] "Facial image" refers to image data of a photograph of the user's face.

[0372] "Smart devices" refers to electronic devices used to collect users' lifestyle and dietary data, including smartwatches and smartphone apps.

[0373] "Means of capturing" refers to a method of acquiring an image or data of a user using a camera or sensor.

[0374] "Predictive methods" refer to methods that use statistical models or machine learning models to estimate future situations based on collected data.

[0375] "Means for visualization and display" refers to a method for displaying the calculated prediction results as images or graphs so that the user can visually confirm them.

[0376] "Self-care methods" refer to specific actions and measures that users take to maintain and improve their own health.

[0377] "Real-time feedback" refers to methods that provide immediate progress information and advice based on user behavior and data.

[0378] "Means for capturing and analyzing emotional state" refers to a method for capturing a user's facial expressions and voice and analyzing that data to identify emotions.

[0379] The "means for suggesting an optimal self-care method" refers to a method for providing the most effective self-care method for the user based on the analyzed emotional state.

[0380] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[0381] System Configuration

[0382] The system consists of the following major components:

[0383] 1. User data collection devices (smartwatches, smartphone apps)

[0384] 2. Server

[0385] 3. Display device (smart mirror)

[0386] 4. Emotion Engine

[0387] User Data Collection

[0388] Users use smartwatches or smartphone apps in their daily lives to record data such as diet, exercise, and sleep. The collected data is sent from the device (smartwatch, smartphone app) to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user.

[0389] Capture your current appearance

[0390] A user activates the smart mirror and looks at their face. The device (smart mirror) captures the user's face using its built-in camera. After capturing high-resolution images, these images are processed in runtime and sent over the internet to a server.

[0391] Face Recognition and Feature Extraction

[0392] The server analyzes the received facial images using a facial recognition algorithm (e.g., Dlib, OpenCV), identifying key facial features such as the user's eyes, nose, and mouth, and storing this data in a database.

[0393] Predicting future appearance

[0394] The server uses statistical and machine learning models (e.g., TensorFlow, PyTorch) to analyze the user's stored lifestyle and dietary data, predicting the user's future appearance and generating predicted results including changes in skin condition and body shape. Furthermore, it uses a generative adversarial network (GAN) to create a visualized image of the user's future appearance.

[0395] Capturing and analyzing user emotions

[0396] Users express their emotions through facial expressions and voice in front of the smart mirror. The device (smart mirror) captures the user's facial expressions and voice using a built-in camera and microphone, processes them in real time, and then sends them to a server via the Internet. The server then uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state.

[0397] Displaying prediction results

[0398] The server sends the generated image of the future appearance to the smart mirror, which then displays the image to the user, allowing the user to visually confirm the future changes.

[0399] Self-care and countermeasure suggestions

[0400] The server then suggests appropriate self-care methods and measures to the user based on the analysis results. It uses an emotion engine to tailor the advice based on the user's emotional state. The device (smart mirror or smartphone app) then provides the suggestions to the user as visual pop-up messages or periodic reminders.

[0401] Real-time feedback

[0402] The user incorporates the suggested self-care methods and measures into their daily life and records their progress using a smartphone app or smartwatch. The device (smartwatch, smartphone app) sends the collected new data to the server. The server analyzes the new data and reassess the user's progress. The latest feedback is sent to the user via the smart mirror or smartphone app, providing ongoing motivation.

[0403] Specific examples

[0404] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques and relaxation products to help them relax. This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. The emotion engine also provides optimal suggestions based on the user's emotional state.

[0405] Prompt Sentence Examples

[0406] For example, if a user stands in front of a smart mirror and says, "Show me what I'll look like in five years," the system will visualize and display their future appearance. Similarly, if a user says, "Tell me the best self-care method to suit my mood today," the emotion engine will analyze the user's current mood and suggest the best self-care method.

[0407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0408] Step 1: Collect user data

[0409] Users use smartwatches or smartphone apps in their daily lives to input data on diet, exercise, sleep, etc. This input data includes calorie intake, number of steps, exercise time, and sleep time.

[0410] The device (smartwatch, smartphone app) automatically records data using various sensors and transmits the collected data to a server via the Internet. For example, step count data is obtained from an acceleration sensor, and heart rate data is obtained from a heart rate sensor.

[0411] The server stores the received data in a database and manages it with a unique ID for each user. When saving the data, preprocessing is performed to eliminate duplicate data and check the consistency of the data.

[0412] Input: Data collected from smartwatches and smartphone apps

[0413] Output: Consistent lifestyle data for each user is saved in the database.

[0414] Step 2: Capture your current appearance

[0415] The user activates the smart mirror and reflects their face in the mirror, adjusting the position of their face accordingly.

[0416] The device (smart mirror) uses a built-in camera to capture the user's face in high resolution, adapting to lighting conditions and correcting for facial angles during the capture process.

[0417] The device (smart mirror) compresses the captured facial image in real time and transmits it to a server via the Internet.

[0418] Input: User's face image

[0419] Output: High-resolution face image sent to the server

[0420] Step 3: Face recognition and feature extraction

[0421] The server analyzes the received facial images using an open-source facial recognition library (e.g., Dlib, OpenCV).

[0422] The server uses a facial recognition algorithm to identify and extract key facial features such as the user's eyes, nose, and mouth, which includes identifying facial landmark points.

[0423] The server stores the extracted facial feature data in a database for subsequent analysis and prediction processing.

[0424] Input: High-resolution face image

[0425] Output: Facial feature data is saved in a database

[0426] Step 4: Predict your future appearance

[0427] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models (e.g., TensorFlow, PyTorch), which includes data normalization and feature engineering.

[0428] The server uses machine learning models to predict the user's future appearance, based on data pattern and trend analysis.

[0429] The server uses a generative adversarial network (GAN) to visualize the prediction results and generate future appearance images. In the process, a digital image is generated based on the predicted features.

[0430] Input: User's lifestyle data, dietary data, facial feature data

[0431] Output: An image is generated that visualizes the future appearance.

[0432] Step 5: Capturing and analyzing user emotions

[0433] The user expresses facial expressions and sounds in front of the smart mirror. For example, the user speaks to the smart mirror or makes facial expressions.

[0434] The device (smart mirror) uses a built-in camera and microphone to capture facial expressions and voice, and pre-processes them on an edge device (e.g., NVIDIA Jetson).

[0435] The terminal transmits the processed data to a server via the Internet.

[0436] The server uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state using facial expression recognition and voice analysis technologies.

[0437] Input: User's facial expression data, voice data

[0438] Output: User emotional state data is identified

[0439] Step 6: View the prediction results

[0440] The server sends the generated future appearance image to the smart mirror.

[0441] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes.

[0442] The images displayed include changes in skin condition and body shape, providing a realistic depiction of what the user will look like in the future if they maintain their current lifestyle habits.

[0443] Input: Image of future appearance

[0444] Output: Image of future appearance displayed in a smart mirror

[0445] Step 7: Self-care and strategy suggestions

[0446] Based on the analysis results and emotional state data, the server will suggest appropriate self-care methods and measures to the user, including specific methods such as improving diet, recommending exercise, and managing stress.

[0447] Based on the analysis results of the emotion engine, the server adjusts the care method that is optimized for the user's emotional state.

[0448] The device (smart mirror or smartphone app) will notify the user of the suggestions, either as a visual pop-up message or periodic reminder.

[0449] Input: Analysis results and emotional state data

[0450] Output: Self-care suggestions displayed on a smart mirror or smartphone app

[0451] Step 8: Real-time feedback

[0452] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0453] The device (smartwatch, smartphone app) sends the collected new data to a server via the Internet, including new step count data, meal details, etc.

[0454] The server analyzes the new data and reassess the user's progress, for example, to see if the user is achieving the suggested exercise volume.

[0455] The device (smart mirror or smartphone app) provides ongoing motivation by providing users with up-to-date feedback, including progress and additional advice.

[0456] Input: New lifestyle data

[0457] Output: The latest feedback given to the user

[0458] (Application example 2)

[0459] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0460] In modern society, individual lifestyles and poor dietary habits have a significant impact on beauty and health, but it is difficult to specifically understand how one's lifestyle will affect the future. In particular, many people who lead busy lives often lack the time or knowledge to continuously manage their health and beauty status. As a result, lifestyle changes are delayed, leading to endless serious health and beauty concerns. Furthermore, current systems and tools on the market lack the ability to propose self-care and countermeasures that take into account the user's emotional state. There is a need for an effective and comprehensive system to solve these issues.

[0461] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for scanning the user's facial image using a smartphone, visualizing the user's future appearance and suggesting measures, and means for analyzing the user's emotional state using an emotion engine and suggesting optimal care methods and products based on the results. This allows the user to specifically understand how their lifestyle will affect their future and to receive optimal self-care and product recommendations tailored to their emotional state.

[0462] "User's lifestyle data" is data that reflects the user's daily life, and includes dietary details, exercise habits, sleep patterns, and the like.

[0463] "Dietary data" refers to information such as the types, amounts, and times of consumption of foods and beverages that a user consumes on a daily basis.

[0464] "Capture" is the act of using a digital device to obtain a user's facial image or other visual data.

[0465] "Prediction" is the act of estimating future conditions or events based on collected data.

[0466] "Visualization" is the act of converting digital data into a visual format such as a diagram or image and displaying it.

[0467] A "self-care method" is a health or beauty care method that a user carries out by himself or herself.

[0468] A "countermeasure" is an action or measure taken in response to a specific problem or risk.

[0469] "Real-time feedback" is a process of instantly evaluating the effectiveness of the self-care methods and measures implemented by the user and immediately returning the results to the user.

[0470] A "smartphone" is a portable information terminal that can connect to the Internet and has a camera and various applications.

[0471] An "emotion engine" is software that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[0472] "Product suggestion" is the act of recommending an appropriate product based on the user's condition and needs.

[0473] The system for implementing this invention collects lifestyle and dietary data of a user, predicts future appearance based on this data, visualizes and displays it to the user, and also provides self-care methods and measures, providing feedback on their effectiveness in real time.

[0474] Hardware and software used

[0475] Smartphones: Used to collect data from everyday life and capture facial images.

[0476] Cloud server: Data analysis and processing is performed using AWS Lambda, Google Cloud Functions, etc.

[0477] Facial Recognition Algorithm: Uses OpenCV and AWS Rekognition to identify the user's facial features.

[0478] Emotion Engine: Analyzes the user's emotional state using Azure Emotion API.

[0479] Database: Use Firebase Database or Amazon DynamoDB to manage collected data.

[0480] System Operation

[0481] 1. Collection of User Data:

[0482] A smartphone app collects data about the user's daily life (diet, exercise, sleep), and sends this data to a cloud server.

[0483] 2. Capture your current appearance:

[0484] A user scans a face image using the smartphone camera and sends this image data to a cloud server.

[0485] 3. Face Recognition and Feature Extraction:

[0486] The cloud server uses a facial recognition algorithm to identify the user's facial features and store them in a database.

[0487] 4. Predicting your future appearance:

[0488] The cloud server uses machine learning models to predict future appearance based on collected lifestyle and facial feature data.

[0489] 5. Emotion capture and analysis:

[0490] The smartphone's camera and microphone are used to capture the user's emotions from facial expressions and voice, and the emotions are then analyzed on a cloud server.

[0491] 6. Displaying prediction results and countermeasures:

[0492] The prediction results are visualized, and self-care methods and products tailored to the user's emotional state are displayed to the user on their smartphone.

[0493] 7. Real-time feedback:

[0494] It records the progress of the user's self-care, sends new data to a cloud server for analysis, and provides immediate feedback.

[0495] Specific examples

[0496] For example, if a user regularly eats high-fat meals and exercises insufficiently, a smartphone app records this data and sends it to a cloud server. The cloud server analyzes this data and predicts the likelihood that the user will be overweight and have poor skin condition in five years. When the user scans their face with their smartphone camera, a prediction of their future appearance is displayed, and additional suggestions are added, such as, "If you continue like this, you will easily gain weight. Here are some recommended diet plans and skin care products." If the emotion engine detects stress from the user's facial expression, it also makes additional suggestions, such as, "We also recommend a yoga program and aromatherapy products to help you relax."

[0497] Prompt Sentence Examples

[0498] "Build an app that uses a user's lifestyle data and current facial image to predict their appearance in five years' time, and uses an emotion engine to suggest the best skincare products."

[0499] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0500] Step 1:

[0501] Users use a smartphone app to input lifestyle and dietary data, including dietary habits, exercise habits, and sleep patterns. This data is sent from the smartphone to a cloud server. The smartphone application organizes the input data and stores it on the cloud server as a series of numerical and text data. This allows the user's daily lifestyle to be recorded in detail.

[0502] Step 2:

[0503] The user captures a current facial image using the smartphone camera. The captured image is saved in high resolution and sent to the cloud server. The smartphone application converts the image data into the required format and compresses it to optimize the transfer speed when transferring it to the cloud server.

[0504] Step 3:

[0505] The cloud server analyzes the received facial image using a facial recognition algorithm (for example, OpenCV or AWS Rekognition) to identify the user's main facial features, such as the eyes, nose, and mouth. The facial feature data obtained through the analysis is stored in a database. Specifically, the facial recognition algorithm reads the facial image, identifies the position of each part of the face, and generates its coordinate data.

[0506] Step 4:

[0507] The cloud server uses a machine learning model to predict future appearance based on the stored lifestyle data and facial feature data. This machine learning model uses statistical methods and predictive algorithms based on past data to estimate changes in skin condition and body shape. Data processing includes appropriately preprocessing the input data, converting it into a format applicable to the model, and then inputting it into the model.

[0508] Step 5:

[0509] The server generates an image showing the user's future appearance based on the prediction results. The generated image is intended to visually represent the user's future appearance if they maintain their current lifestyle. The server uses an image processing library to convert the prediction results into a visual format and provides it to the user in an easy-to-understand format.

[0510] Step 6:

[0511] The smartphone app captures the user's facial expressions and voice data and sends it to a cloud server. The cloud server then uses an emotion engine (for example, Azure Emotion API) to analyze the user's emotional state. Based on this, optimal self-care methods and products are suggested. For example, if the user is under stress, stretching techniques and relaxation products will be recommended.

[0512] Step 7:

[0513] The cloud server monitors changes resulting from the implementation of suggested self-care methods and measures in real time and provides feedback. The progress of the user's self-care is recorded and reanalyzed on the cloud server. For example, if a user exercises regularly, the effects are reflected in facial images and lifestyle data, and feedback is provided based on this.

[0514] Through these steps, users can concretely understand how their lifestyle habits will affect their future and receive optimal self-care and product recommendations based on their emotional state.

[0515] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0516] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0517] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0518] [Second embodiment]

[0519] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0520] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0521] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0522] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0523] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0524] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0525] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0526] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0527] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0528] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0529] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0530] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0531] MODE FOR CARRYING OUT THE INVENTION

[0532] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, visualizes the prediction results, and presents them to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. The following describes in detail how this system is implemented.

[0533] System Configuration

[0534] The system consists of the following major components:

[0535] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[0536] 2. Server

[0537] 3. Display devices (e.g. smart mirrors)

[0538] Program processing

[0539] User Data Collection

[0540] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0541] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0542] The server stores the received data in a database and manages it with a unique ID for each user.

[0543] Capture your current appearance

[0544] The user activates the smart mirror and reflects their face in the mirror.

[0545] The device (smart mirror) captures the user's face with its built-in camera and sends the image to a server.

[0546] The server analyzes the received image using a facial recognition algorithm to extract facial features.

[0547] Predicting future appearance

[0548] The server analyzes the user's saved lifestyle and dietary data and predicts the user's future appearance using statistical and machine learning models.

[0549] The predictive model predicts future changes in skin condition, body shape, etc. based on the user's current data patterns.

[0550] The server generates an image to visualize the future appearance based on the prediction results.

[0551] Displaying prediction results

[0552] The server sends the generated future appearance image to the smart mirror.

[0553] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm the future changes.

[0554] Self-care and countermeasure suggestions

[0555] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0556] Suggestions include plant-based meal plans, specific skin care product recommendations, and exercise plans.

[0557] The device (smart mirror or smartphone app) notifies the user of the suggestions.

[0558] Real-time feedback

[0559] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0560] New data is sent from the device (smartwatch, smartphone app) to the server.

[0561] The server reassess the user's progress based on the new data and provides feedback on the effectiveness of improvements.

[0562] The device (smart mirror, smartphone app) notifies the user with updated feedback and provides continuous motivation.

[0563] Specific examples

[0564] For example, if a user regularly eats a high-fat diet and exercises infrequently, this data is collected through a smartwatch and smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction is then visualized and displayed on a smart mirror. Seeing this future appearance motivates the user to improve their lifestyle habits, leading them to adopt a plant-based diet and regular exercise. The smartwatch and smartphone app track these efforts, and the server provides feedback, allowing the user to see their progress in real time.

[0565] This system allows users to concretely understand the impact of their lifestyle habits and take concrete actions to achieve a healthy and beautiful future.

[0566] The processing flow will be explained below.

[0567] Program processing

[0568] Step 1: Collect user data

[0569] 1. Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0570] 2. The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0571] 3. The server stores the received data in a database and manages it with a unique ID for each user.

[0572] Step 2: Capture your current appearance

[0573] 1. The user activates the smart mirror and reflects their face in the mirror.

[0574] 2. The device (smart mirror) captures the user's face with its built-in camera. This capture process takes a few seconds to obtain a high-resolution image.

[0575] 3. The device (smart mirror) processes the captured facial image at runtime and sends it to a server via the Internet.

[0576] Step 3: Face recognition and feature extraction

[0577] 1. The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[0578] 2. The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[0579] Step 4: Predict your future appearance

[0580] 1. The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[0581] 2. The server predicts the user's future appearance based on statistical and machine learning models, including changes in skin condition and body shape.

[0582] 3. The server generates an image to visualize the prediction results. This image shows the future appearance of the user if they maintain their current lifestyle.

[0583] Step 5: View the prediction results

[0584] 1. The server sends the generated image of the future appearance to the smart mirror.

[0585] 2. The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[0586] Step 6: Self-care and strategy suggestions

[0587] 1. Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0588] 2. The device (smart mirror or smartphone app) notifies the user of the suggestions, either through visual pop-up messages or periodic reminders.

[0589] Step 7: Real-time feedback

[0590] 1. Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[0591] 2. The device (smartwatch, smartphone app) sends the new collected data to the server.

[0592] 3. The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[0593] 4. The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[0594] Through this series of processes, users can concretely understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future.

[0595] Example 1

[0596] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0597] Currently, systems that collect data on a user's lifestyle and diet, predict future appearance based on that data, and provide self-care methods are not fully developed. In particular, they lack real-time feedback on the effectiveness of self-care and continuous motivation in response to changes in the user's lifestyle. This makes it difficult for users to effectively manage and maintain their health and beauty.

[0598] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0599] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a user's facial image, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing and displaying the predicted future appearance to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for automatically transmitting the records of the lifestyle and dietary data to the server via the Internet, means for analyzing the collected images with a face recognition algorithm and extracting facial features, means for predicting the user's future appearance using a statistical model or a machine learning model, means for displaying the generated future appearance image to the user in real time, means for notifying the user of the suggested self-care methods and measures, and means for reevaluating the user's improvement based on the progress data and providing continuous feedback. This allows the user to specifically understand the impact of their lifestyle and to confirm and continuously implement specific actions to maintain a healthy and beautiful future appearance in real time.

[0600] "User's lifestyle data and dietary data" is a general term for data including the content and amount of food eaten, the type and amount of exercise, and the duration and quality of sleep in the user's daily life.

[0601] "Means for capturing a user's facial image" refers to a device or software for photographing a user's face and acquiring the image.

[0602] "Means for predicting a user's future appearance" refers to algorithms or models for predicting future changes in a user's face or body based on the user's current lifestyle and dietary data.

[0603] "Means for visualizing the predicted future appearance and displaying it to the user" refers to devices or software that display the predicted changes in appearance as images or graphs, etc., so that the user can visually confirm them.

[0604] "Means for providing self-care methods and measures" refers to devices and software that suggest specific methods for improvement such as diet, exercise, and skin care based on the user's prediction results.

[0605] "Means for providing real-time feedback on changes resulting from the implementation of self-care methods and measures" refers to devices and software that evaluate the results of a user's implementation of self-care methods and measures in real time and notify the user of the results.

[0606] "Means for automatically transmitting records of lifestyle and dietary data to a server via the Internet" refers to devices or software for automatically transmitting collected data to a server via a smart device or the like.

[0607] "Facial recognition algorithm" refers to a computer program that analyzes a user's facial image and extracts certain features.

[0608] "Statistical model or machine learning model" refers to a mathematical or algorithmic model that analyzes patterns in data and predicts future changes.

[0609] The "means for displaying a future appearance image to a user in real time" refers to a device or software for instantly displaying the generated future appearance image to a user.

[0610] "Means for notifying the user of self-care methods and suggested measures" refers to devices and software for notifying the user of specific self-care methods based on the analysis results.

[0611] "Means for reevaluating the user's improvement based on progress data and providing continuous feedback" refers to devices and software that evaluate the effectiveness of the self-care methods and measures implemented by the user based on data, and continuously notify the user of the results.

[0612] MODE FOR CARRYING OUT THE INVENTION

[0613] The present invention is a system that collects lifestyle and dietary data of a user and predicts future appearance based on this data. This system is composed of the following main components:

[0614] System Configuration

[0615] This system consists of a user data collection device (e.g., smart watch, smartphone app), a server, and a display device (e.g., smart mirror).

[0616] User Data Collection

[0617] Users use smartwatches or smartphone apps to record data such as meals, exercise, and sleep in their daily lives. The devices (smartwatches, smartphone apps) send the recorded data to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user. For example, detailed data such as what a user had for breakfast, how many kilometers they walked, and how many hours they slept can be collected.

[0618] Capture your current appearance

[0619] The user activates the smart mirror and reflects their face in the mirror. The device (smart mirror) captures the user's face with its built-in camera and sends the image to the server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. These features include skin condition, number of wrinkles, and facial contours.

[0620] Predicting future appearance

[0621] The server analyzes the user's saved lifestyle and facial feature data and predicts the user's future appearance using statistical and machine learning models (e.g., TensorFlow and PyTorch). The predictive model predicts future skin conditions and changes in body shape based on the user's current data patterns. For example, if an irregular lifestyle continues, sagging skin and weight gain are predicted.

[0622] Displaying prediction results

[0623] The server generates an image to visualize the user's future appearance based on the prediction results. This image is sent to the smart mirror, which then displays the image to the user, allowing the user to visually confirm future changes.

[0624] Self-care and countermeasure suggestions

[0625] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, recommendations for specific skin care products, and exercise plans. The device (smart mirror or smartphone app) notifies the user of the suggestions. For example, specific advice such as "do 30 minutes of aerobic exercise every day" and "consume foods rich in vitamin C" is displayed.

[0626] Real-time feedback

[0627] The user incorporates the suggested self-care methods into their daily life and records their progress using a smartwatch or smartphone app. New data is sent from the device (smartwatch, smartphone app) to the server. The server reevaluates the user's progress based on the new data and provides feedback on the effectiveness of the improvements. The device (smart mirror or smartphone app) notifies the user of the updated feedback, providing ongoing motivation. For example, feedback such as "skin moisture has improved" or "weight loss of 1 kg" may be provided.

[0628] Prompt Sentence Examples

[0629] "Please tell me how to build a system that collects data on a user's daily diet, exercise, sleep, etc., and uses this data to predict future appearance and suggest self-care methods."

[0630] This system allows users to specifically understand the impact of their lifestyle habits, and to confirm and continuously implement specific actions in real time to maintain a healthy and beautiful appearance in the future.

[0631] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0632] Step 1: Collect user data

[0633] 1. The user enters information about their daily diet, exercise, sleep, etc. into a smartwatch or smartphone app. For example, they record what they had for breakfast, the number of steps they took, and the amount of sleep they received.

[0634] 2. The device (smartwatch or smartphone app) sends the recorded data to a server via the Internet.

[0635] 3. The server stores the received data in a database, including information such as dietary habits, exercise, and sleep duration.

[0636] 4. Input: User-recorded food, exercise, and sleep data.

[0637] Output: User's lifestyle and dietary data stored on the server.

[0638] Step 2: Capture your current appearance

[0639] 1. The user activates the smart mirror and places their face in the mirror.

[0640] 2. The device (smart mirror) captures the user's facial image using its built-in camera.

[0641] 3. The captured image is sent to a server via the Internet.

[0642] 4. The server analyzes the received image using an AI-based facial recognition algorithm.

[0643] 5. Input: User's face image.

[0644] Output: Analyzed facial feature data (skin condition, number of wrinkles, facial contours, etc.).

[0645] Step 3: Predict your future appearance

[0646] 1. The server analyzes the user's stored lifestyle data and facial feature data.

[0647] 2. The server predicts future appearance using statistical or machine learning models (e.g., TensorFlow, PyTorch).

[0648] 3. The server generates an image to visualize the future appearance based on the prediction results.

[0649] 4. Input: User's lifestyle data, facial feature data.

[0650] Output: Predicted image of future appearance.

[0651] Step 4: View the prediction results

[0652] 1. The server sends the generated image of the future appearance to the smart mirror.

[0653] 2. The device (smart mirror) displays an image of the user's future appearance.

[0654] 3. Users can visually see what their future appearance will be.

[0655] 4. Input: Predicted image of future appearance.

[0656] Output: The predicted image to be displayed on the smart mirror.

[0657] Step 5: Self-care and strategy suggestions

[0658] 1. The server suggests self-care methods and measures to the user based on the analysis results.

[0659] 2. Suggestions include plant-based meal plans, recommendations for specific skin care products, and exercise plans.

[0660] 3. The device (smart mirror or smartphone app) notifies the user of the suggestions.

[0661] 4. Input: Analysis results.

[0662] Output: Self-care methods and measures notified to the user.

[0663] Step 6: Real-time feedback

[0664] 1. The user incorporates the suggested self-care methods into their daily lives and records their implementation status using a smartwatch or smartphone app.

[0665] 2. New data is sent from the device (smartwatch, smartphone app) to the server.

[0666] 3. The server reassess the user's progress based on the new data.

[0667] 4. The server provides feedback on the effectiveness of improvements and provides ongoing motivation.

[0668] 5. The device (smart mirror or smartphone app) notifies the user of the feedback.

[0669] 6. Input: Data on the implementation status of self-care methods.

[0670] Output: Feedback notification of the improvement effect.

[0671] (Application example 1)

[0672] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0673] Conventional appearance prediction systems only predict and display future appearance based on a user's lifestyle and dietary data, making it difficult for users to select specific self-care methods based on the results. They also lacked the functionality to accurately suggest what products and services users should use. Furthermore, the lack of real-time feedback made it difficult for users to maintain motivation to continue self-care.

[0674] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0675] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, and means for suggesting in-store care products and services to the user. This allows the user to easily select the self-care methods, products, and services that are best suited to them and to engage in self-care while continuously maintaining motivation.

[0676] "Lifestyle data" is information relating to the activities and behaviors of the user in their daily lives.

[0677] "Dietary data" is information about the types, amounts, and frequency of food and beverage intake by a user.

[0678] A "face image" is image data of the user's face.

[0679] "Visualization" is the visual display of data or information.

[0680] A "self-care method" is a method for health management or beauty care that the user himself or herself carries out.

[0681] "Countermeasures" are specific measures or methods taken to address a specific problem or issue.

[0682] "Real-time feedback" refers to instantly evaluating the results of the actions and measures taken by the user and returning this information to the user immediately.

[0683] A "server" is a computer system that manages and processes data.

[0684] A "smart device" is a digital device equipped with internet connectivity and sensors.

[0685] A "statistical model" is a mathematical model that numerically describes and predicts phenomena based on data.

[0686] A "machine learning model" is a model that uses algorithms to learn patterns and knowledge from data and make predictions and classifications.

[0687] "In-store care products and services" are products and services related to health management and beauty care provided in stores.

[0688] The system that realizes this application example consists of multiple hardware and software components. First, smart devices (e.g., smartwatches and smartphone apps) are used to collect the user's lifestyle and dietary data. These devices record the user's daily data, such as diet, exercise, and sleep, in real time and send it to a server via the Internet.

[0689] The server stores the received data in a database and manages it with a unique ID for each user, which makes it possible to accurately track the data of each individual user.

[0690] Next, the user stands in front of the smart mirror and reflects their face. The smart mirror uses its built-in camera to capture the user's face and sends the image to a server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. This step could use OpenCV, an open-source software for facial recognition.

[0691] The server then analyzes the user's stored lifestyle and dietary data using statistical or machine learning models to predict the user's future appearance. These models use generative AI models to predict future changes in skin condition and body shape based on the user's data patterns.

[0692] The future appearance image generated by the predictive model is visualized by the server and sent to the smart mirror, allowing the user to visually confirm their future appearance.

[0693] Furthermore, based on the analysis results, the server will suggest appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, specific skin care products, exercise plans, etc. The smart mirror and smartphone app will notify the user of these suggestions and encourage them to use in-store care products and services.

[0694] Finally, the user incorporates the suggested self-care methods and measures into their daily lives and records their progress using a smartphone app or smartwatch. The server then reevaluates their progress based on the new data and provides real-time feedback on the effectiveness. This allows users to constantly check their progress and maintain motivation while engaging in self-care.

[0695] As a concrete example, consider a scenario in which the system is used at a beauty salon. The user stands in front of the salon's smart mirror and projects their face onto the camera. The smart mirror sends the user's lifestyle data and current facial image to a server, which then uses this data to predict their future appearance. The smart mirror then displays the predicted future appearance and suggests specific beauty care products and services. This allows the user to select the best care method for themselves and make more effective use of the salon's services.

[0696] Examples of prompts to input to a generative AI model might include the following:

[0697] "Write a program to create an app that predicts future appearance. Include a function to send the user's face image and lifestyle data to a server, and retrieve and display the predicted future appearance."

[0698] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0699] Processing Steps

[0700] Step 1:

[0701] Users use smartphone apps or smartwatches to record daily life data such as diet, exercise, and sleep.

[0702] Input: Lifestyle and dietary data entered by users into their smart devices.

[0703] Data processing: These data are temporarily stored in the smart device.

[0704] Output: Lifestyle and dietary data are ready to be sent to the server.

[0705] Step 2:

[0706] The smart device transmits the recorded data to a server via the Internet.

[0707] Input: Lifestyle and dietary data recorded on a smart device.

[0708] Data computation: Data is sent to a server via the Internet as an HTTP request.

[0709] Output: Each user's lifestyle and dietary data is sent to the server.

[0710] Step 3:

[0711] The server stores the received data in a database and manages it with a unique ID for each user.

[0712] Input: Lifestyle and dietary data sent to the server.

[0713] Data processing: Data is stored in a database and managed with a unique ID to identify and accumulate data for each user.

[0714] Output: Each user's lifestyle and dietary data is stored in a database.

[0715] Step 4:

[0716] The user stands in front of the smart mirror and reflects their face in the mirror, which then captures the user's face using its built-in camera.

[0717] Input: Current face image of the user.

[0718] Data processing: Facial image data captured by the smart mirror's built-in camera.

[0719] Output: A face image is captured by the smart mirror.

[0720] Step 5:

[0721] The smart mirror sends the captured facial image to a server.

[0722] Input: A face image captured by a smart mirror.

[0723] Data calculation: The smart mirror sends the facial image data to the server as an HTTP request.

[0724] Output: The facial image data arrives at the server.

[0725] Step 6:

[0726] The server analyzes the received facial image using a facial recognition algorithm to extract the user's facial features.

[0727] Input: Facial image data sent to the server.

[0728] Data processing: Analyze and extract facial features using a facial recognition algorithm (e.g., OpenCV).

[0729] Output: Extracted facial feature data.

[0730] Step 7:

[0731] The server uses a statistical model or machine learning model to predict the user's future appearance based on the stored lifestyle and dietary data.

[0732] Input: Lifestyle and dietary data in the database, extracted facial feature data.

[0733] Data computation: Predict future appearance using statistical and machine learning models (generative AI models).

[0734] Output: Image of future appearance.

[0735] Step 8:

[0736] The server sends the predicted future appearance image to the smart mirror.

[0737] Input: Server-generated image of future appearance.

[0738] Data calculation: Send the future appearance image to the smart mirror as an HTTP response.

[0739] Output: An image of your future appearance is sent to the smart mirror.

[0740] Step 9:

[0741] The smart mirror displays an image of the user's future appearance.

[0742] Input: Future appearance image sent from the server.

[0743] Data processing: Displaying an image of your future appearance on the smart mirror display.

[0744] Output: The user can visually confirm the future appearance image.

[0745] Step 10:

[0746] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0747] Input: Lifestyle data, dietary data, and future appearance prediction results.

[0748] Data calculation: Self-care methods and measures are generated using the proposed algorithm.

[0749] Output: Suggested self-care methods and measures.

[0750] Step 11:

[0751] Smart mirrors and smartphone apps will notify users of the suggestions.

[0752] Input: Self-care methods and measures suggested by the server.

[0753] Data processing: The proposed content is displayed on the screen of a smart mirror or smartphone app.

[0754] Output: The user can check the suggestions and put the self-care methods and measures into practice.

[0755] Step 12:

[0756] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0757] Input: Data on self-care methods and measures taken by the user.

[0758] Data processing: Smart devices record and temporarily store new data.

[0759] Output: New lifestyle and dietary data is generated.

[0760] Step 13:

[0761] The smart device sends new data to the server, which then re-evaluates it.

[0762] Input: New lifestyle and dietary data.

[0763] Data calculation: The server uses a re-evaluation algorithm to analyze your progress and generate feedback.

[0764] Output: Real-time feedback results.

[0765] Step 14:

[0766] Smart mirrors and smartphone apps provide updated feedback to users and provide ongoing motivation.

[0767] Input: The feedback result sent by the server.

[0768] Data processing: The feedback content is displayed on the screen of a smart mirror or smartphone app.

[0769] Output: The user receives real-time feedback and is motivated to continue the action.

[0770] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0771] MODE FOR CARRYING OUT THE INVENTION

[0772] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. It also has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[0773] System Configuration

[0774] The system consists of the following major components:

[0775] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[0776] 2. Server

[0777] 3. Display devices (e.g. smart mirrors)

[0778] 4. Emotion Engine

[0779] Program processing

[0780] User Data Collection

[0781] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0782] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0783] The server stores the received data in a database and manages it with a unique ID for each user.

[0784] Capture your current appearance

[0785] The user activates the smart mirror and reflects their face in the mirror.

[0786] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[0787] The device (smart mirror) processes the captured facial image at runtime and transmits it to a server via the Internet.

[0788] Face Recognition and Feature Extraction

[0789] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[0790] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[0791] Predicting future appearance

[0792] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[0793] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[0794] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[0795] Capturing and analyzing user emotions

[0796] Users express their emotions through facial expressions and voice in front of the smart mirror.

[0797] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[0798] The device processes the captured data in real time and transmits it to a server over the Internet.

[0799] The server uses an emotion engine to analyze the received data and determine the user's emotional state.

[0800] Displaying prediction results

[0801] The server sends the generated future appearance image to the smart mirror.

[0802] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[0803] Self-care and countermeasure suggestions

[0804] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0805] The server uses an emotion engine to tailor the optimal care regimen based on the user's emotional state.

[0806] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[0807] Real-time feedback

[0808] Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[0809] The device (smartwatch, smartphone app) sends the collected new data to the server.

[0810] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[0811] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[0812] Specific examples

[0813] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[0814] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[0815] The processing flow will be explained below.

[0816] Program processing

[0817] Step 1: Collect user data

[0818] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[0819] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[0820] The server stores the received data in a database and manages it with a unique ID for each user.

[0821] Step 2: Capture your current appearance

[0822] The user activates the smart mirror and reflects their face in the mirror.

[0823] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[0824] The device (smart mirror) processes the captured facial images in real time and transmits them to a server via the Internet.

[0825] Step 3: Face recognition and feature extraction

[0826] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[0827] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[0828] Step 4: Predict your future appearance

[0829] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[0830] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[0831] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[0832] Step 5: Capturing and analyzing user emotions

[0833] Users express their emotions through facial expressions and voice in front of the smart mirror.

[0834] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[0835] The device processes the captured data in real time and transmits it to a server over the Internet.

[0836] The server uses an emotion engine to analyze the received data and identify the user's emotional state. For example, it classifies emotions such as "happiness," "sadness," "anger," and "surprise" based on facial expressions captured by a camera, and also analyzes emotions from voice.

[0837] Step 6: View the prediction results

[0838] The server sends the generated future appearance image to the smart mirror.

[0839] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[0840] Step 7: Self-care and strategy suggestions

[0841] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[0842] The server uses an emotion engine to tailor optimal care methods based on the user's emotional state. For example, if the user is feeling stressed, it will suggest relaxation and mindfulness techniques.

[0843] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[0844] Step 8: Real-time feedback

[0845] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0846] The device (smartwatch, smartphone app) sends the collected new data to the server.

[0847] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[0848] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[0849] Specific examples

[0850] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[0851] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[0852] Example 2

[0853] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0854] Conventional systems could easily predict a user's future appearance based on their lifestyle and dietary data and suggest self-care methods, but they were unable to provide optimal care methods based on the user's emotional state. Furthermore, they lacked real-time feedback on changes in the user's lifestyle, making it difficult to maintain the user's motivation. This resulted in the challenge of making it difficult to achieve long-term health improvements.

[0855] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0856] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for capturing and analyzing the user's emotional state, and means for suggesting an optimal self-care method based on the emotional state. This allows for more effective health improvement by suggesting an optimal care method according to the user's emotional state and providing continuous motivation.

[0857] "User" refers to a person who uses the system to record their own lifestyle and dietary data and receives self-care methods and feedback provided by the system.

[0858] "Lifestyle data" refers to information such as exercise, sleep, and activity level in the user's daily life.

[0859] "Dietary data" refers to information about the food and beverages a user regularly consumes.

[0860] "Facial image" refers to image data of a photograph of the user's face.

[0861] "Smart devices" refers to electronic devices used to collect users' lifestyle and dietary data, including smartwatches and smartphone apps.

[0862] "Means of capturing" refers to a method of acquiring an image or data of a user using a camera or sensor.

[0863] "Predictive methods" refer to methods that use statistical models or machine learning models to estimate future situations based on collected data.

[0864] "Means for visualization and display" refers to a method for displaying the calculated prediction results as images or graphs so that the user can visually confirm them.

[0865] "Self-care methods" refer to specific actions and measures that users take to maintain and improve their own health.

[0866] "Real-time feedback" refers to methods that provide immediate progress information and advice based on user behavior and data.

[0867] "Means for capturing and analyzing emotional state" refers to a method for capturing a user's facial expressions and voice and analyzing that data to identify emotions.

[0868] The "means for suggesting an optimal self-care method" refers to a method for providing the most effective self-care method for the user based on the analyzed emotional state.

[0869] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[0870] System Configuration

[0871] The system consists of the following major components:

[0872] 1. User data collection devices (smartwatches, smartphone apps)

[0873] 2. Server

[0874] 3. Display device (smart mirror)

[0875] 4. Emotion Engine

[0876] User Data Collection

[0877] Users use smartwatches or smartphone apps in their daily lives to record data such as diet, exercise, and sleep. The collected data is sent from the device (smartwatch, smartphone app) to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user.

[0878] Capture your current appearance

[0879] A user activates the smart mirror and looks at their face. The device (smart mirror) captures the user's face using its built-in camera. After capturing high-resolution images, these images are processed in runtime and sent over the internet to a server.

[0880] Face Recognition and Feature Extraction

[0881] The server analyzes the received facial images using a facial recognition algorithm (e.g., Dlib, OpenCV), identifying key facial features such as the user's eyes, nose, and mouth, and storing this data in a database.

[0882] Predicting future appearance

[0883] The server uses statistical and machine learning models (e.g., TensorFlow, PyTorch) to analyze the user's stored lifestyle and dietary data, predicting the user's future appearance and generating predicted results including changes in skin condition and body shape. Furthermore, it uses a generative adversarial network (GAN) to create a visualized image of the user's future appearance.

[0884] Capturing and analyzing user emotions

[0885] Users express their emotions through facial expressions and voice in front of the smart mirror. The device (smart mirror) captures the user's facial expressions and voice using a built-in camera and microphone, processes them in real time, and then sends them to a server via the Internet. The server then uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state.

[0886] Displaying prediction results

[0887] The server sends the generated image of the future appearance to the smart mirror, which then displays the image to the user, allowing the user to visually confirm the future changes.

[0888] Self-care and countermeasure suggestions

[0889] The server then suggests appropriate self-care methods and measures to the user based on the analysis results. It uses an emotion engine to tailor the advice based on the user's emotional state. The device (smart mirror or smartphone app) then provides the suggestions to the user as visual pop-up messages or periodic reminders.

[0890] Real-time feedback

[0891] The user incorporates the suggested self-care methods and measures into their daily life and records their progress using a smartphone app or smartwatch. The device (smartwatch, smartphone app) sends the collected new data to the server. The server analyzes the new data and reassess the user's progress. The latest feedback is sent to the user via the smart mirror or smartphone app, providing ongoing motivation.

[0892] Specific examples

[0893] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques and relaxation products to help them relax. This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. The emotion engine also provides optimal suggestions based on the user's emotional state.

[0894] Prompt Sentence Examples

[0895] For example, if a user stands in front of a smart mirror and says, "Show me what I'll look like in five years," the system will visualize and display their future appearance. Similarly, if a user says, "Tell me the best self-care method to suit my mood today," the emotion engine will analyze the user's current mood and suggest the best self-care method.

[0896] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0897] Step 1: Collect user data

[0898] Users use smartwatches or smartphone apps in their daily lives to input data on diet, exercise, sleep, etc. This input data includes calorie intake, number of steps, exercise time, and sleep time.

[0899] The device (smartwatch, smartphone app) automatically records data using various sensors and transmits the collected data to a server via the Internet. For example, step count data is obtained from an acceleration sensor, and heart rate data is obtained from a heart rate sensor.

[0900] The server stores the received data in a database and manages it with a unique ID for each user. When saving the data, preprocessing is performed to eliminate duplicate data and check the consistency of the data.

[0901] Input: Data collected from smartwatches and smartphone apps

[0902] Output: Consistent lifestyle data for each user is saved in the database.

[0903] Step 2: Capture your current appearance

[0904] The user activates the smart mirror and reflects their face in the mirror, adjusting the position of their face accordingly.

[0905] The device (smart mirror) uses a built-in camera to capture the user's face in high resolution, adapting to lighting conditions and correcting for facial angles during the capture process.

[0906] The device (smart mirror) compresses the captured facial image in real time and transmits it to a server via the Internet.

[0907] Input: User's face image

[0908] Output: High-resolution face image sent to the server

[0909] Step 3: Face recognition and feature extraction

[0910] The server analyzes the received facial images using an open-source facial recognition library (e.g., Dlib, OpenCV).

[0911] The server uses a facial recognition algorithm to identify and extract key facial features such as the user's eyes, nose, and mouth, which includes identifying facial landmark points.

[0912] The server stores the extracted facial feature data in a database for subsequent analysis and prediction processing.

[0913] Input: High-resolution face image

[0914] Output: Facial feature data is saved in a database

[0915] Step 4: Predict your future appearance

[0916] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models (e.g., TensorFlow, PyTorch), which includes data normalization and feature engineering.

[0917] The server uses machine learning models to predict the user's future appearance, based on data pattern and trend analysis.

[0918] The server uses a generative adversarial network (GAN) to visualize the prediction results and generate future appearance images. In the process, a digital image is generated based on the predicted features.

[0919] Input: User's lifestyle data, dietary data, facial feature data

[0920] Output: An image is generated that visualizes the future appearance.

[0921] Step 5: Capturing and analyzing user emotions

[0922] The user expresses facial expressions and sounds in front of the smart mirror. For example, the user speaks to the smart mirror or makes facial expressions.

[0923] The device (smart mirror) uses a built-in camera and microphone to capture facial expressions and voice, and pre-processes them on an edge device (e.g., NVIDIA Jetson).

[0924] The terminal transmits the processed data to a server via the Internet.

[0925] The server uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state using facial expression recognition and voice analysis technologies.

[0926] Input: User's facial expression data, voice data

[0927] Output: User emotional state data is identified

[0928] Step 6: View the prediction results

[0929] The server sends the generated future appearance image to the smart mirror.

[0930] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes.

[0931] The images displayed include changes in skin condition and body shape, providing a realistic depiction of what the user will look like in the future if they maintain their current lifestyle habits.

[0932] Input: Image of future appearance

[0933] Output: Image of future appearance displayed in a smart mirror

[0934] Step 7: Self-care and strategy suggestions

[0935] Based on the analysis results and emotional state data, the server will suggest appropriate self-care methods and measures to the user, including specific methods such as improving diet, recommending exercise, and managing stress.

[0936] Based on the analysis results of the emotion engine, the server adjusts the care method that is optimized for the user's emotional state.

[0937] The device (smart mirror or smartphone app) will notify the user of the suggestions, either as a visual pop-up message or periodic reminder.

[0938] Input: Analysis results and emotional state data

[0939] Output: Self-care suggestions displayed on a smart mirror or smartphone app

[0940] Step 8: Real-time feedback

[0941] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[0942] The device (smartwatch, smartphone app) sends the collected new data to a server via the Internet, including new step count data, meal details, etc.

[0943] The server analyzes the new data and reassess the user's progress, for example, to see if the user is achieving the suggested exercise volume.

[0944] The device (smart mirror or smartphone app) provides ongoing motivation by providing users with up-to-date feedback, including progress and additional advice.

[0945] Input: New lifestyle data

[0946] Output: The latest feedback given to the user

[0947] (Application example 2)

[0948] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0949] In modern society, individual lifestyles and poor dietary habits have a significant impact on beauty and health, but it is difficult to specifically understand how one's lifestyle will affect the future. In particular, many people who lead busy lives often lack the time or knowledge to continuously manage their health and beauty status. As a result, lifestyle changes are delayed, leading to endless serious health and beauty concerns. Furthermore, current systems and tools on the market lack the ability to propose self-care and countermeasures that take into account the user's emotional state. There is a need for an effective and comprehensive system to solve these issues.

[0950] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for scanning the user's facial image using a smartphone, visualizing the user's future appearance and suggesting measures, and means for analyzing the user's emotional state using an emotion engine and suggesting optimal care methods and products based on the results. This allows the user to specifically understand how their lifestyle will affect their future and to receive optimal self-care and product recommendations tailored to their emotional state.

[0951] "User's lifestyle data" is data that reflects the user's daily life, and includes dietary details, exercise habits, sleep patterns, and the like.

[0952] "Dietary data" refers to information such as the types, amounts, and times of consumption of foods and beverages that a user consumes on a daily basis.

[0953] "Capture" is the act of using a digital device to obtain a user's facial image or other visual data.

[0954] "Prediction" is the act of estimating future conditions or events based on collected data.

[0955] "Visualization" is the act of converting digital data into a visual format such as a diagram or image and displaying it.

[0956] A "self-care method" is a health or beauty care method that a user carries out by himself or herself.

[0957] A "countermeasure" is an action or measure taken in response to a specific problem or risk.

[0958] "Real-time feedback" is a process of instantly evaluating the effectiveness of the self-care methods and measures implemented by the user and immediately returning the results to the user.

[0959] A "smartphone" is a portable information terminal that can connect to the Internet and has a camera and various applications.

[0960] An "emotion engine" is software that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[0961] "Product suggestion" is the act of recommending an appropriate product based on the user's condition and needs.

[0962] The system for implementing this invention collects lifestyle and dietary data of a user, predicts future appearance based on this data, visualizes and displays it to the user, and also provides self-care methods and measures, providing feedback on their effectiveness in real time.

[0963] Hardware and software used

[0964] Smartphones: Used to collect data from everyday life and capture facial images.

[0965] Cloud server: Data analysis and processing is performed using AWS Lambda, Google Cloud Functions, etc.

[0966] Facial Recognition Algorithm: Uses OpenCV and AWS Rekognition to identify the user's facial features.

[0967] Emotion Engine: Analyzes the user's emotional state using Azure Emotion API.

[0968] Database: Use Firebase Database or Amazon DynamoDB to manage collected data.

[0969] System Operation

[0970] 1. Collection of User Data:

[0971] A smartphone app collects data about the user's daily life (diet, exercise, sleep), and sends this data to a cloud server.

[0972] 2. Capture your current appearance:

[0973] A user scans a face image using the smartphone camera and sends this image data to a cloud server.

[0974] 3. Face Recognition and Feature Extraction:

[0975] The cloud server uses a facial recognition algorithm to identify the user's facial features and store them in a database.

[0976] 4. Predicting your future appearance:

[0977] The cloud server uses machine learning models to predict future appearance based on collected lifestyle and facial feature data.

[0978] 5. Emotion capture and analysis:

[0979] The smartphone's camera and microphone are used to capture the user's emotions from facial expressions and voice, and the emotions are then analyzed on a cloud server.

[0980] 6. Displaying prediction results and countermeasures:

[0981] The prediction results are visualized, and self-care methods and products tailored to the user's emotional state are displayed to the user on their smartphone.

[0982] 7. Real-time feedback:

[0983] It records the progress of the user's self-care, sends new data to a cloud server for analysis, and provides immediate feedback.

[0984] Specific examples

[0985] For example, if a user regularly eats high-fat meals and exercises insufficiently, a smartphone app records this data and sends it to a cloud server. The cloud server analyzes this data and predicts the likelihood that the user will be overweight and have poor skin condition in five years. When the user scans their face with their smartphone camera, a prediction of their future appearance is displayed, and additional suggestions are added, such as, "If you continue like this, you will easily gain weight. Here are some recommended diet plans and skin care products." If the emotion engine detects stress from the user's facial expression, it also makes additional suggestions, such as, "We also recommend a yoga program and aromatherapy products to help you relax."

[0986] Prompt Sentence Examples

[0987] "Build an app that uses a user's lifestyle data and current facial image to predict their appearance in five years' time, and uses an emotion engine to suggest the best skincare products."

[0988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0989] Step 1:

[0990] Users use a smartphone app to input lifestyle and dietary data, including dietary habits, exercise habits, and sleep patterns. This data is sent from the smartphone to a cloud server. The smartphone application organizes the input data and stores it on the cloud server as a series of numerical and text data. This allows the user's daily lifestyle to be recorded in detail.

[0991] Step 2:

[0992] The user captures a current facial image using the smartphone camera. The captured image is saved in high resolution and sent to the cloud server. The smartphone application converts the image data into the required format and compresses it to optimize the transfer speed when transferring it to the cloud server.

[0993] Step 3:

[0994] The cloud server analyzes the received facial image using a facial recognition algorithm (for example, OpenCV or AWS Rekognition) to identify the user's main facial features, such as the eyes, nose, and mouth. The facial feature data obtained through the analysis is stored in a database. Specifically, the facial recognition algorithm reads the facial image, identifies the position of each part of the face, and generates its coordinate data.

[0995] Step 4:

[0996] The cloud server uses a machine learning model to predict future appearance based on the stored lifestyle data and facial feature data. This machine learning model uses statistical methods and predictive algorithms based on past data to estimate changes in skin condition and body shape. Data processing includes appropriately preprocessing the input data, converting it into a format applicable to the model, and then inputting it into the model.

[0997] Step 5:

[0998] The server generates an image showing the user's future appearance based on the prediction results. The generated image is intended to visually represent the user's future appearance if they maintain their current lifestyle. The server uses an image processing library to convert the prediction results into a visual format and provides it to the user in an easy-to-understand format.

[0999] Step 6:

[1000] The smartphone app captures the user's facial expressions and voice data and sends it to a cloud server. The cloud server then uses an emotion engine (for example, Azure Emotion API) to analyze the user's emotional state. Based on this, optimal self-care methods and products are suggested. For example, if the user is under stress, stretching techniques and relaxation products will be recommended.

[1001] Step 7:

[1002] The cloud server monitors changes resulting from the implementation of suggested self-care methods and measures in real time and provides feedback. The progress of the user's self-care is recorded and reanalyzed on the cloud server. For example, if a user exercises regularly, the effects are reflected in facial images and lifestyle data, and feedback is provided based on this.

[1003] Through these steps, users can concretely understand how their lifestyle habits will affect their future and receive optimal self-care and product recommendations based on their emotional state.

[1004] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1005] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1006] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1007] [Third embodiment]

[1008] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1009] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1010] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1011] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1012] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1013] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1014] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1015] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1016] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1017] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1018] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1019] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1020] MODE FOR CARRYING OUT THE INVENTION

[1021] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, visualizes the prediction results, and presents them to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. The following describes in detail how this system is implemented.

[1022] System Configuration

[1023] The system consists of the following major components:

[1024] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[1025] 2. Server

[1026] 3. Display devices (e.g. smart mirrors)

[1027] Program processing

[1028] User Data Collection

[1029] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1030] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1031] The server stores the received data in a database and manages it with a unique ID for each user.

[1032] Capture your current appearance

[1033] The user activates the smart mirror and reflects their face in the mirror.

[1034] The device (smart mirror) captures the user's face with its built-in camera and sends the image to a server.

[1035] The server analyzes the received image using a facial recognition algorithm to extract facial features.

[1036] Predicting future appearance

[1037] The server analyzes the user's saved lifestyle and dietary data and predicts the user's future appearance using statistical and machine learning models.

[1038] The predictive model predicts future changes in skin condition, body shape, etc. based on the user's current data patterns.

[1039] The server generates an image to visualize the future appearance based on the prediction results.

[1040] Displaying prediction results

[1041] The server sends the generated future appearance image to the smart mirror.

[1042] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm the future changes.

[1043] Self-care and countermeasure suggestions

[1044] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1045] Suggestions include plant-based meal plans, specific skin care product recommendations, and exercise plans.

[1046] The device (smart mirror or smartphone app) notifies the user of the suggestions.

[1047] Real-time feedback

[1048] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1049] New data is sent from the device (smartwatch, smartphone app) to the server.

[1050] The server reassess the user's progress based on the new data and provides feedback on the effectiveness of improvements.

[1051] The device (smart mirror, smartphone app) notifies the user with updated feedback and provides continuous motivation.

[1052] Specific examples

[1053] For example, if a user regularly eats a high-fat diet and exercises infrequently, this data is collected through a smartwatch and smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction is then visualized and displayed on a smart mirror. Seeing this future appearance motivates the user to improve their lifestyle habits, leading them to adopt a plant-based diet and regular exercise. The smartwatch and smartphone app track these efforts, and the server provides feedback, allowing the user to see their progress in real time.

[1054] This system allows users to concretely understand the impact of their lifestyle habits and take concrete actions to achieve a healthy and beautiful future.

[1055] The processing flow will be explained below.

[1056] Program processing

[1057] Step 1: Collect user data

[1058] 1. Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1059] 2. The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1060] 3. The server stores the received data in a database and manages it with a unique ID for each user.

[1061] Step 2: Capture your current appearance

[1062] 1. The user activates the smart mirror and reflects their face in the mirror.

[1063] 2. The device (smart mirror) captures the user's face with its built-in camera. This capture process takes a few seconds to obtain a high-resolution image.

[1064] 3. The device (smart mirror) processes the captured facial image at runtime and sends it to a server via the Internet.

[1065] Step 3: Face recognition and feature extraction

[1066] 1. The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[1067] 2. The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[1068] Step 4: Predict your future appearance

[1069] 1. The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[1070] 2. The server predicts the user's future appearance based on statistical and machine learning models, including changes in skin condition and body shape.

[1071] 3. The server generates an image to visualize the prediction results. This image shows the future appearance of the user if they maintain their current lifestyle.

[1072] Step 5: View the prediction results

[1073] 1. The server sends the generated image of the future appearance to the smart mirror.

[1074] 2. The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[1075] Step 6: Self-care and strategy suggestions

[1076] 1. Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1077] 2. The device (smart mirror or smartphone app) notifies the user of the suggestions, either through visual pop-up messages or periodic reminders.

[1078] Step 7: Real-time feedback

[1079] 1. Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[1080] 2. The device (smartwatch, smartphone app) sends the new collected data to the server.

[1081] 3. The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[1082] 4. The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[1083] Through this series of processes, users can concretely understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future.

[1084] Example 1

[1085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1086] Currently, systems that collect data on a user's lifestyle and diet, predict future appearance based on that data, and provide self-care methods are not fully developed. In particular, they lack real-time feedback on the effectiveness of self-care and continuous motivation in response to changes in the user's lifestyle. This makes it difficult for users to effectively manage and maintain their health and beauty.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1088] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a user's facial image, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing and displaying the predicted future appearance to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for automatically transmitting the records of the lifestyle and dietary data to the server via the Internet, means for analyzing the collected images with a face recognition algorithm and extracting facial features, means for predicting the user's future appearance using a statistical model or a machine learning model, means for displaying the generated future appearance image to the user in real time, means for notifying the user of the suggested self-care methods and measures, and means for reevaluating the user's improvement based on the progress data and providing continuous feedback. This allows the user to specifically understand the impact of their lifestyle and to confirm and continuously implement specific actions to maintain a healthy and beautiful future appearance in real time.

[1089] "User's lifestyle data and dietary data" is a general term for data including the content and amount of food eaten, the type and amount of exercise, and the duration and quality of sleep in the user's daily life.

[1090] "Means for capturing a user's facial image" refers to a device or software for photographing a user's face and acquiring the image.

[1091] "Means for predicting a user's future appearance" refers to algorithms or models for predicting future changes in a user's face or body based on the user's current lifestyle and dietary data.

[1092] "Means for visualizing the predicted future appearance and displaying it to the user" refers to devices or software that display the predicted changes in appearance as images or graphs, etc., so that the user can visually confirm them.

[1093] "Means for providing self-care methods and measures" refers to devices and software that suggest specific methods for improvement such as diet, exercise, and skin care based on the user's prediction results.

[1094] "Means for providing real-time feedback on changes resulting from the implementation of self-care methods and measures" refers to devices and software that evaluate the results of a user's implementation of self-care methods and measures in real time and notify the user of the results.

[1095] "Means for automatically transmitting records of lifestyle and dietary data to a server via the Internet" refers to devices or software for automatically transmitting collected data to a server via a smart device or the like.

[1096] "Facial recognition algorithm" refers to a computer program that analyzes a user's facial image and extracts certain features.

[1097] "Statistical model or machine learning model" refers to a mathematical or algorithmic model that analyzes patterns in data and predicts future changes.

[1098] The "means for displaying a future appearance image to a user in real time" refers to a device or software for instantly displaying the generated future appearance image to a user.

[1099] "Means for notifying the user of self-care methods and suggested measures" refers to devices and software for notifying the user of specific self-care methods based on the analysis results.

[1100] "Means for reevaluating the user's improvement based on progress data and providing continuous feedback" refers to devices and software that evaluate the effectiveness of the self-care methods and measures implemented by the user based on data, and continuously notify the user of the results.

[1101] MODE FOR CARRYING OUT THE INVENTION

[1102] The present invention is a system that collects lifestyle and dietary data of a user and predicts future appearance based on this data. This system is composed of the following main components:

[1103] System Configuration

[1104] This system consists of a user data collection device (e.g., smart watch, smartphone app), a server, and a display device (e.g., smart mirror).

[1105] User Data Collection

[1106] Users use smartwatches or smartphone apps to record data such as meals, exercise, and sleep in their daily lives. The devices (smartwatches, smartphone apps) send the recorded data to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user. For example, detailed data such as what a user had for breakfast, how many kilometers they walked, and how many hours they slept can be collected.

[1107] Capture your current appearance

[1108] The user activates the smart mirror and reflects their face in the mirror. The device (smart mirror) captures the user's face with its built-in camera and sends the image to the server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. These features include skin condition, number of wrinkles, and facial contours.

[1109] Predicting future appearance

[1110] The server analyzes the user's saved lifestyle and facial feature data and predicts the user's future appearance using statistical and machine learning models (e.g., TensorFlow and PyTorch). The predictive model predicts future skin conditions and changes in body shape based on the user's current data patterns. For example, if an irregular lifestyle continues, sagging skin and weight gain are predicted.

[1111] Displaying prediction results

[1112] The server generates an image to visualize the user's future appearance based on the prediction results. This image is sent to the smart mirror, which then displays the image to the user, allowing the user to visually confirm future changes.

[1113] Self-care and countermeasure suggestions

[1114] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, recommendations for specific skin care products, and exercise plans. The device (smart mirror or smartphone app) notifies the user of the suggestions. For example, specific advice such as "do 30 minutes of aerobic exercise every day" and "consume foods rich in vitamin C" is displayed.

[1115] Real-time feedback

[1116] The user incorporates the suggested self-care methods into their daily life and records their progress using a smartwatch or smartphone app. New data is sent from the device (smartwatch, smartphone app) to the server. The server reevaluates the user's progress based on the new data and provides feedback on the effectiveness of the improvements. The device (smart mirror or smartphone app) notifies the user of the updated feedback, providing ongoing motivation. For example, feedback such as "skin moisture has improved" or "weight loss of 1 kg" may be provided.

[1117] Prompt Sentence Examples

[1118] "Please tell me how to build a system that collects data on a user's daily diet, exercise, sleep, etc., and uses this data to predict future appearance and suggest self-care methods."

[1119] This system allows users to specifically understand the impact of their lifestyle habits, and to confirm and continuously implement specific actions in real time to maintain a healthy and beautiful appearance in the future.

[1120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1121] Step 1: Collect user data

[1122] 1. The user enters information about their daily diet, exercise, sleep, etc. into a smartwatch or smartphone app. For example, they record what they had for breakfast, the number of steps they took, and the amount of sleep they received.

[1123] 2. The device (smartwatch or smartphone app) sends the recorded data to a server via the Internet.

[1124] 3. The server stores the received data in a database, including information such as dietary habits, exercise, and sleep duration.

[1125] 4. Input: User-recorded food, exercise, and sleep data.

[1126] Output: User's lifestyle and dietary data stored on the server.

[1127] Step 2: Capture your current appearance

[1128] 1. The user activates the smart mirror and places their face in the mirror.

[1129] 2. The device (smart mirror) captures the user's facial image using its built-in camera.

[1130] 3. The captured image is sent to a server via the Internet.

[1131] 4. The server analyzes the received image using an AI-based facial recognition algorithm.

[1132] 5. Input: User's face image.

[1133] Output: Analyzed facial feature data (skin condition, number of wrinkles, facial contours, etc.).

[1134] Step 3: Predict your future appearance

[1135] 1. The server analyzes the user's stored lifestyle data and facial feature data.

[1136] 2. The server predicts future appearance using statistical or machine learning models (e.g., TensorFlow, PyTorch).

[1137] 3. The server generates an image to visualize the future appearance based on the prediction results.

[1138] 4. Input: User's lifestyle data, facial feature data.

[1139] Output: Predicted image of future appearance.

[1140] Step 4: View the prediction results

[1141] 1. The server sends the generated image of the future appearance to the smart mirror.

[1142] 2. The device (smart mirror) displays an image of the user's future appearance.

[1143] 3. Users can visually see what their future appearance will be.

[1144] 4. Input: Predicted image of future appearance.

[1145] Output: The predicted image to be displayed on the smart mirror.

[1146] Step 5: Self-care and strategy suggestions

[1147] 1. The server suggests self-care methods and measures to the user based on the analysis results.

[1148] 2. Suggestions include plant-based meal plans, recommendations for specific skin care products, and exercise plans.

[1149] 3. The device (smart mirror or smartphone app) notifies the user of the suggestions.

[1150] 4. Input: Analysis results.

[1151] Output: Self-care methods and measures notified to the user.

[1152] Step 6: Real-time feedback

[1153] 1. The user incorporates the suggested self-care methods into their daily lives and records their implementation status using a smartwatch or smartphone app.

[1154] 2. New data is sent from the device (smartwatch, smartphone app) to the server.

[1155] 3. The server reassess the user's progress based on the new data.

[1156] 4. The server provides feedback on the effectiveness of improvements and provides ongoing motivation.

[1157] 5. The device (smart mirror or smartphone app) notifies the user of the feedback.

[1158] 6. Input: Data on the implementation status of self-care methods.

[1159] Output: Feedback notification of the improvement effect.

[1160] (Application example 1)

[1161] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1162] Conventional appearance prediction systems only predict and display future appearance based on a user's lifestyle and dietary data, making it difficult for users to select specific self-care methods based on the results. They also lacked the functionality to accurately suggest what products and services users should use. Furthermore, the lack of real-time feedback made it difficult for users to maintain motivation to continue self-care.

[1163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1164] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, and means for suggesting in-store care products and services to the user. This allows the user to easily select the self-care methods, products, and services that are best suited to them and to engage in self-care while continuously maintaining motivation.

[1165] "Lifestyle data" is information relating to the activities and behaviors of the user in their daily lives.

[1166] "Dietary data" is information about the types, amounts, and frequency of food and beverage intake by a user.

[1167] A "face image" is image data of the user's face.

[1168] "Visualization" is the visual display of data or information.

[1169] A "self-care method" is a method for health management or beauty care that the user himself or herself carries out.

[1170] "Countermeasures" are specific measures or methods taken to address a specific problem or issue.

[1171] "Real-time feedback" refers to instantly evaluating the results of the actions and measures taken by the user and returning this information to the user immediately.

[1172] A "server" is a computer system that manages and processes data.

[1173] A "smart device" is a digital device equipped with internet connectivity and sensors.

[1174] A "statistical model" is a mathematical model that numerically describes and predicts phenomena based on data.

[1175] A "machine learning model" is a model that uses algorithms to learn patterns and knowledge from data and make predictions and classifications.

[1176] "In-store care products and services" are products and services related to health management and beauty care provided in stores.

[1177] The system that realizes this application example consists of multiple hardware and software components. First, smart devices (e.g., smartwatches and smartphone apps) are used to collect the user's lifestyle and dietary data. These devices record the user's daily data, such as diet, exercise, and sleep, in real time and send it to a server via the Internet.

[1178] The server stores the received data in a database and manages it with a unique ID for each user, which makes it possible to accurately track the data of each individual user.

[1179] Next, the user stands in front of the smart mirror and reflects their face. The smart mirror uses its built-in camera to capture the user's face and sends the image to a server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. This step could use OpenCV, an open-source software for facial recognition.

[1180] The server then analyzes the user's stored lifestyle and dietary data using statistical or machine learning models to predict the user's future appearance. These models use generative AI models to predict future changes in skin condition and body shape based on the user's data patterns.

[1181] The future appearance image generated by the predictive model is visualized by the server and sent to the smart mirror, allowing the user to visually confirm their future appearance.

[1182] Furthermore, based on the analysis results, the server will suggest appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, specific skin care products, exercise plans, etc. The smart mirror and smartphone app will notify the user of these suggestions and encourage them to use in-store care products and services.

[1183] Finally, the user incorporates the suggested self-care methods and measures into their daily lives and records their progress using a smartphone app or smartwatch. The server then reevaluates their progress based on the new data and provides real-time feedback on the effectiveness. This allows users to constantly check their progress and maintain motivation while engaging in self-care.

[1184] As a concrete example, consider a scenario in which the system is used at a beauty salon. The user stands in front of the salon's smart mirror and projects their face onto the camera. The smart mirror sends the user's lifestyle data and current facial image to a server, which then uses this data to predict their future appearance. The smart mirror then displays the predicted future appearance and suggests specific beauty care products and services. This allows the user to select the best care method for themselves and make more effective use of the salon's services.

[1185] Examples of prompts to input to a generative AI model might include the following:

[1186] "Write a program to create an app that predicts future appearance. Include a function to send the user's face image and lifestyle data to a server, and retrieve and display the predicted future appearance."

[1187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1188] Processing Steps

[1189] Step 1:

[1190] Users use smartphone apps or smartwatches to record daily life data such as diet, exercise, and sleep.

[1191] Input: Lifestyle and dietary data entered by users into their smart devices.

[1192] Data processing: These data are temporarily stored in the smart device.

[1193] Output: Lifestyle and dietary data are ready to be sent to the server.

[1194] Step 2:

[1195] The smart device transmits the recorded data to a server via the Internet.

[1196] Input: Lifestyle and dietary data recorded on a smart device.

[1197] Data computation: Data is sent to a server via the Internet as an HTTP request.

[1198] Output: Each user's lifestyle and dietary data is sent to the server.

[1199] Step 3:

[1200] The server stores the received data in a database and manages it with a unique ID for each user.

[1201] Input: Lifestyle and dietary data sent to the server.

[1202] Data processing: Data is stored in a database and managed with a unique ID to identify and accumulate data for each user.

[1203] Output: Each user's lifestyle and dietary data is stored in a database.

[1204] Step 4:

[1205] The user stands in front of the smart mirror and reflects their face in the mirror, which then captures the user's face using its built-in camera.

[1206] Input: Current face image of the user.

[1207] Data processing: Facial image data captured by the smart mirror's built-in camera.

[1208] Output: A face image is captured by the smart mirror.

[1209] Step 5:

[1210] The smart mirror sends the captured facial image to a server.

[1211] Input: A face image captured by a smart mirror.

[1212] Data calculation: The smart mirror sends the facial image data to the server as an HTTP request.

[1213] Output: The facial image data arrives at the server.

[1214] Step 6:

[1215] The server analyzes the received facial image using a facial recognition algorithm to extract the user's facial features.

[1216] Input: Facial image data sent to the server.

[1217] Data processing: Analyze and extract facial features using a facial recognition algorithm (e.g., OpenCV).

[1218] Output: Extracted facial feature data.

[1219] Step 7:

[1220] The server uses a statistical model or machine learning model to predict the user's future appearance based on the stored lifestyle and dietary data.

[1221] Input: Lifestyle and dietary data in the database, extracted facial feature data.

[1222] Data computation: Predict future appearance using statistical and machine learning models (generative AI models).

[1223] Output: Image of future appearance.

[1224] Step 8:

[1225] The server sends the predicted future appearance image to the smart mirror.

[1226] Input: Server-generated image of future appearance.

[1227] Data calculation: Send the future appearance image to the smart mirror as an HTTP response.

[1228] Output: An image of your future appearance is sent to the smart mirror.

[1229] Step 9:

[1230] The smart mirror displays an image of the user's future appearance.

[1231] Input: Future appearance image sent from the server.

[1232] Data processing: Displaying an image of your future appearance on the smart mirror display.

[1233] Output: The user can visually confirm the future appearance image.

[1234] Step 10:

[1235] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1236] Input: Lifestyle data, dietary data, and future appearance prediction results.

[1237] Data calculation: Self-care methods and measures are generated using the proposed algorithm.

[1238] Output: Suggested self-care methods and measures.

[1239] Step 11:

[1240] Smart mirrors and smartphone apps will notify users of the suggestions.

[1241] Input: Self-care methods and measures suggested by the server.

[1242] Data processing: The proposed content is displayed on the screen of a smart mirror or smartphone app.

[1243] Output: The user can check the suggestions and put the self-care methods and measures into practice.

[1244] Step 12:

[1245] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1246] Input: Data on self-care methods and measures taken by the user.

[1247] Data processing: Smart devices record and temporarily store new data.

[1248] Output: New lifestyle and dietary data is generated.

[1249] Step 13:

[1250] The smart device sends new data to the server, which then re-evaluates it.

[1251] Input: New lifestyle and dietary data.

[1252] Data calculation: The server uses a re-evaluation algorithm to analyze your progress and generate feedback.

[1253] Output: Real-time feedback results.

[1254] Step 14:

[1255] Smart mirrors and smartphone apps provide updated feedback to users and provide ongoing motivation.

[1256] Input: The feedback result sent by the server.

[1257] Data processing: The feedback content is displayed on the screen of a smart mirror or smartphone app.

[1258] Output: The user receives real-time feedback and is motivated to continue the action.

[1259] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1260] MODE FOR CARRYING OUT THE INVENTION

[1261] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. It also has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[1262] System Configuration

[1263] The system consists of the following major components:

[1264] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[1265] 2. Server

[1266] 3. Display devices (e.g. smart mirrors)

[1267] 4. Emotion Engine

[1268] Program processing

[1269] User Data Collection

[1270] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1271] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1272] The server stores the received data in a database and manages it with a unique ID for each user.

[1273] Capture your current appearance

[1274] The user activates the smart mirror and reflects their face in the mirror.

[1275] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[1276] The device (smart mirror) processes the captured facial image at runtime and transmits it to a server via the Internet.

[1277] Face Recognition and Feature Extraction

[1278] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[1279] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[1280] Predicting future appearance

[1281] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[1282] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[1283] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[1284] Capturing and analyzing user emotions

[1285] Users express their emotions through facial expressions and voice in front of the smart mirror.

[1286] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[1287] The device processes the captured data in real time and transmits it to a server over the Internet.

[1288] The server uses an emotion engine to analyze the received data and determine the user's emotional state.

[1289] Displaying prediction results

[1290] The server sends the generated future appearance image to the smart mirror.

[1291] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[1292] Self-care and countermeasure suggestions

[1293] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1294] The server uses an emotion engine to tailor the optimal care regimen based on the user's emotional state.

[1295] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[1296] Real-time feedback

[1297] Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[1298] The device (smartwatch, smartphone app) sends the collected new data to the server.

[1299] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[1300] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[1301] Specific examples

[1302] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[1303] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[1304] The processing flow will be explained below.

[1305] Program processing

[1306] Step 1: Collect user data

[1307] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1308] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1309] The server stores the received data in a database and manages it with a unique ID for each user.

[1310] Step 2: Capture your current appearance

[1311] The user activates the smart mirror and reflects their face in the mirror.

[1312] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[1313] The device (smart mirror) processes the captured facial images in real time and transmits them to a server via the Internet.

[1314] Step 3: Face recognition and feature extraction

[1315] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[1316] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[1317] Step 4: Predict your future appearance

[1318] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[1319] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[1320] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[1321] Step 5: Capturing and analyzing user emotions

[1322] Users express their emotions through facial expressions and voice in front of the smart mirror.

[1323] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[1324] The device processes the captured data in real time and transmits it to a server over the Internet.

[1325] The server uses an emotion engine to analyze the received data and identify the user's emotional state. For example, it classifies emotions such as "happiness," "sadness," "anger," and "surprise" based on facial expressions captured by a camera, and also analyzes emotions from voice.

[1326] Step 6: View the prediction results

[1327] The server sends the generated future appearance image to the smart mirror.

[1328] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[1329] Step 7: Self-care and strategy suggestions

[1330] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1331] The server uses an emotion engine to tailor optimal care methods based on the user's emotional state. For example, if the user is feeling stressed, it will suggest relaxation and mindfulness techniques.

[1332] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[1333] Step 8: Real-time feedback

[1334] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1335] The device (smartwatch, smartphone app) sends the collected new data to the server.

[1336] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[1337] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[1338] Specific examples

[1339] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[1340] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[1341] Example 2

[1342] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1343] Conventional systems could easily predict a user's future appearance based on their lifestyle and dietary data and suggest self-care methods, but they were unable to provide optimal care methods based on the user's emotional state. Furthermore, they lacked real-time feedback on changes in the user's lifestyle, making it difficult to maintain the user's motivation. This resulted in the challenge of making it difficult to achieve long-term health improvements.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1345] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for capturing and analyzing the user's emotional state, and means for suggesting an optimal self-care method based on the emotional state. This allows for more effective health improvement by suggesting an optimal care method according to the user's emotional state and providing continuous motivation.

[1346] "User" refers to a person who uses the system to record their own lifestyle and dietary data and receives self-care methods and feedback provided by the system.

[1347] "Lifestyle data" refers to information such as exercise, sleep, and activity level in the user's daily life.

[1348] "Dietary data" refers to information about the food and beverages a user regularly consumes.

[1349] "Facial image" refers to image data of a photograph of the user's face.

[1350] "Smart devices" refers to electronic devices used to collect users' lifestyle and dietary data, including smartwatches and smartphone apps.

[1351] "Means of capturing" refers to a method of acquiring an image or data of a user using a camera or sensor.

[1352] "Predictive methods" refer to methods that use statistical models or machine learning models to estimate future situations based on collected data.

[1353] "Means for visualization and display" refers to a method for displaying the calculated prediction results as images or graphs so that the user can visually confirm them.

[1354] "Self-care methods" refer to specific actions and measures that users take to maintain and improve their own health.

[1355] "Real-time feedback" refers to methods that provide immediate progress information and advice based on user behavior and data.

[1356] "Means for capturing and analyzing emotional state" refers to a method for capturing a user's facial expressions and voice and analyzing that data to identify emotions.

[1357] The "means for suggesting an optimal self-care method" refers to a method for providing the most effective self-care method for the user based on the analyzed emotional state.

[1358] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[1359] System Configuration

[1360] The system consists of the following major components:

[1361] 1. User data collection devices (smartwatches, smartphone apps)

[1362] 2. Server

[1363] 3. Display device (smart mirror)

[1364] 4. Emotion Engine

[1365] User Data Collection

[1366] Users use smartwatches or smartphone apps in their daily lives to record data such as diet, exercise, and sleep. The collected data is sent from the device (smartwatch, smartphone app) to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user.

[1367] Capture your current appearance

[1368] A user activates the smart mirror and looks at their face. The device (smart mirror) captures the user's face using its built-in camera. After capturing high-resolution images, these images are processed in runtime and sent over the internet to a server.

[1369] Face Recognition and Feature Extraction

[1370] The server analyzes the received facial images using a facial recognition algorithm (e.g., Dlib, OpenCV), identifying key facial features such as the user's eyes, nose, and mouth, and storing this data in a database.

[1371] Predicting future appearance

[1372] The server uses statistical and machine learning models (e.g., TensorFlow, PyTorch) to analyze the user's stored lifestyle and dietary data, predicting the user's future appearance and generating predicted results including changes in skin condition and body shape. Furthermore, it uses a generative adversarial network (GAN) to create a visualized image of the user's future appearance.

[1373] Capturing and analyzing user emotions

[1374] Users express their emotions through facial expressions and voice in front of the smart mirror. The device (smart mirror) captures the user's facial expressions and voice using a built-in camera and microphone, processes them in real time, and then sends them to a server via the Internet. The server then uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state.

[1375] Displaying prediction results

[1376] The server sends the generated image of the future appearance to the smart mirror, which then displays the image to the user, allowing the user to visually confirm the future changes.

[1377] Self-care and countermeasure suggestions

[1378] The server then suggests appropriate self-care methods and measures to the user based on the analysis results. It uses an emotion engine to tailor the advice based on the user's emotional state. The device (smart mirror or smartphone app) then provides the suggestions to the user as visual pop-up messages or periodic reminders.

[1379] Real-time feedback

[1380] The user incorporates the suggested self-care methods and measures into their daily life and records their progress using a smartphone app or smartwatch. The device (smartwatch, smartphone app) sends the collected new data to the server. The server analyzes the new data and reassess the user's progress. The latest feedback is sent to the user via the smart mirror or smartphone app, providing ongoing motivation.

[1381] Specific examples

[1382] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques and relaxation products to help them relax. This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. The emotion engine also provides optimal suggestions based on the user's emotional state.

[1383] Prompt Sentence Examples

[1384] For example, if a user stands in front of a smart mirror and says, "Show me what I'll look like in five years," the system will visualize and display their future appearance. Similarly, if a user says, "Tell me the best self-care method to suit my mood today," the emotion engine will analyze the user's current mood and suggest the best self-care method.

[1385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1386] Step 1: Collect user data

[1387] Users use smartwatches or smartphone apps in their daily lives to input data on diet, exercise, sleep, etc. This input data includes calorie intake, number of steps, exercise time, and sleep time.

[1388] The device (smartwatch, smartphone app) automatically records data using various sensors and transmits the collected data to a server via the Internet. For example, step count data is obtained from an acceleration sensor, and heart rate data is obtained from a heart rate sensor.

[1389] The server stores the received data in a database and manages it with a unique ID for each user. When saving the data, preprocessing is performed to eliminate duplicate data and check the consistency of the data.

[1390] Input: Data collected from smartwatches and smartphone apps

[1391] Output: Consistent lifestyle data for each user is saved in the database.

[1392] Step 2: Capture your current appearance

[1393] The user activates the smart mirror and reflects their face in the mirror, adjusting the position of their face accordingly.

[1394] The device (smart mirror) uses a built-in camera to capture the user's face in high resolution, adapting to lighting conditions and correcting for facial angles during the capture process.

[1395] The device (smart mirror) compresses the captured facial image in real time and transmits it to a server via the Internet.

[1396] Input: User's face image

[1397] Output: High-resolution face image sent to the server

[1398] Step 3: Face recognition and feature extraction

[1399] The server analyzes the received facial images using an open-source facial recognition library (e.g., Dlib, OpenCV).

[1400] The server uses a facial recognition algorithm to identify and extract key facial features such as the user's eyes, nose, and mouth, which includes identifying facial landmark points.

[1401] The server stores the extracted facial feature data in a database for subsequent analysis and prediction processing.

[1402] Input: High-resolution face image

[1403] Output: Facial feature data is saved in a database

[1404] Step 4: Predict your future appearance

[1405] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models (e.g., TensorFlow, PyTorch), which includes data normalization and feature engineering.

[1406] The server uses machine learning models to predict the user's future appearance, based on data pattern and trend analysis.

[1407] The server uses a generative adversarial network (GAN) to visualize the prediction results and generate future appearance images. In the process, a digital image is generated based on the predicted features.

[1408] Input: User's lifestyle data, dietary data, facial feature data

[1409] Output: An image is generated that visualizes the future appearance.

[1410] Step 5: Capturing and analyzing user emotions

[1411] The user expresses facial expressions and sounds in front of the smart mirror. For example, the user speaks to the smart mirror or makes facial expressions.

[1412] The device (smart mirror) uses a built-in camera and microphone to capture facial expressions and voice, and pre-processes them on an edge device (e.g., NVIDIA Jetson).

[1413] The terminal transmits the processed data to a server via the Internet.

[1414] The server uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state using facial expression recognition and voice analysis technologies.

[1415] Input: User's facial expression data, voice data

[1416] Output: User emotional state data is identified

[1417] Step 6: View the prediction results

[1418] The server sends the generated future appearance image to the smart mirror.

[1419] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes.

[1420] The images displayed include changes in skin condition and body shape, providing a realistic depiction of what the user will look like in the future if they maintain their current lifestyle habits.

[1421] Input: Image of future appearance

[1422] Output: Image of future appearance displayed in a smart mirror

[1423] Step 7: Self-care and strategy suggestions

[1424] Based on the analysis results and emotional state data, the server will suggest appropriate self-care methods and measures to the user, including specific methods such as improving diet, recommending exercise, and managing stress.

[1425] Based on the analysis results of the emotion engine, the server adjusts the care method that is optimized for the user's emotional state.

[1426] The device (smart mirror or smartphone app) will notify the user of the suggestions, either as a visual pop-up message or periodic reminder.

[1427] Input: Analysis results and emotional state data

[1428] Output: Self-care suggestions displayed on a smart mirror or smartphone app

[1429] Step 8: Real-time feedback

[1430] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1431] The device (smartwatch, smartphone app) sends the collected new data to a server via the Internet, including new step count data, meal details, etc.

[1432] The server analyzes the new data and reassess the user's progress, for example, to see if the user is achieving the suggested exercise volume.

[1433] The device (smart mirror or smartphone app) provides ongoing motivation by providing users with up-to-date feedback, including progress and additional advice.

[1434] Input: New lifestyle data

[1435] Output: The latest feedback given to the user

[1436] (Application example 2)

[1437] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1438] In modern society, individual lifestyles and poor dietary habits have a significant impact on beauty and health, but it is difficult to specifically understand how one's lifestyle will affect the future. In particular, many people who lead busy lives often lack the time or knowledge to continuously manage their health and beauty status. As a result, lifestyle changes are delayed, leading to endless serious health and beauty concerns. Furthermore, current systems and tools on the market lack the ability to propose self-care and countermeasures that take into account the user's emotional state. There is a need for an effective and comprehensive system to solve these issues.

[1439] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for scanning the user's facial image using a smartphone, visualizing the user's future appearance and suggesting measures, and means for analyzing the user's emotional state using an emotion engine and suggesting optimal care methods and products based on the results. This allows the user to specifically understand how their lifestyle will affect their future and to receive optimal self-care and product recommendations tailored to their emotional state.

[1440] "User's lifestyle data" is data that reflects the user's daily life, and includes dietary details, exercise habits, sleep patterns, and the like.

[1441] "Dietary data" refers to information such as the types, amounts, and times of consumption of foods and beverages that a user consumes on a daily basis.

[1442] "Capture" is the act of using a digital device to obtain a user's facial image or other visual data.

[1443] "Prediction" is the act of estimating future conditions or events based on collected data.

[1444] "Visualization" is the act of converting digital data into a visual format such as a diagram or image and displaying it.

[1445] A "self-care method" is a health or beauty care method that a user carries out by himself or herself.

[1446] A "countermeasure" is an action or measure taken in response to a specific problem or risk.

[1447] "Real-time feedback" is a process of instantly evaluating the effectiveness of the self-care methods and measures implemented by the user and immediately returning the results to the user.

[1448] A "smartphone" is a portable information terminal that can connect to the Internet and has a camera and various applications.

[1449] An "emotion engine" is software that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[1450] "Product suggestion" is the act of recommending an appropriate product based on the user's condition and needs.

[1451] The system for implementing this invention collects lifestyle and dietary data of a user, predicts future appearance based on this data, visualizes and displays it to the user, and also provides self-care methods and measures, providing feedback on their effectiveness in real time.

[1452] Hardware and software used

[1453] Smartphones: Used to collect data from everyday life and capture facial images.

[1454] Cloud server: Data analysis and processing is performed using AWS Lambda, Google Cloud Functions, etc.

[1455] Facial Recognition Algorithm: Uses OpenCV and AWS Rekognition to identify the user's facial features.

[1456] Emotion Engine: Analyzes the user's emotional state using Azure Emotion API.

[1457] Database: Use Firebase Database or Amazon DynamoDB to manage collected data.

[1458] System Operation

[1459] 1. Collection of User Data:

[1460] A smartphone app collects data about the user's daily life (diet, exercise, sleep), and sends this data to a cloud server.

[1461] 2. Capture your current appearance:

[1462] A user scans a face image using the smartphone camera and sends this image data to a cloud server.

[1463] 3. Face Recognition and Feature Extraction:

[1464] The cloud server uses a facial recognition algorithm to identify the user's facial features and store them in a database.

[1465] 4. Predicting your future appearance:

[1466] The cloud server uses machine learning models to predict future appearance based on collected lifestyle and facial feature data.

[1467] 5. Emotion capture and analysis:

[1468] The smartphone's camera and microphone are used to capture the user's emotions from facial expressions and voice, and the emotions are then analyzed on a cloud server.

[1469] 6. Displaying prediction results and countermeasures:

[1470] The prediction results are visualized, and self-care methods and products tailored to the user's emotional state are displayed to the user on their smartphone.

[1471] 7. Real-time feedback:

[1472] It records the progress of the user's self-care, sends new data to a cloud server for analysis, and provides immediate feedback.

[1473] Specific examples

[1474] For example, if a user regularly eats high-fat meals and exercises insufficiently, a smartphone app records this data and sends it to a cloud server. The cloud server analyzes this data and predicts the likelihood that the user will be overweight and have poor skin condition in five years. When the user scans their face with their smartphone camera, a prediction of their future appearance is displayed, and additional suggestions are added, such as, "If you continue like this, you will easily gain weight. Here are some recommended diet plans and skin care products." If the emotion engine detects stress from the user's facial expression, it also makes additional suggestions, such as, "We also recommend a yoga program and aromatherapy products to help you relax."

[1475] Prompt Sentence Examples

[1476] "Build an app that uses a user's lifestyle data and current facial image to predict their appearance in five years' time, and uses an emotion engine to suggest the best skincare products."

[1477] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1478] Step 1:

[1479] Users use a smartphone app to input lifestyle and dietary data, including dietary habits, exercise habits, and sleep patterns. This data is sent from the smartphone to a cloud server. The smartphone application organizes the input data and stores it on the cloud server as a series of numerical and text data. This allows the user's daily lifestyle to be recorded in detail.

[1480] Step 2:

[1481] The user captures a current facial image using the smartphone camera. The captured image is saved in high resolution and sent to the cloud server. The smartphone application converts the image data into the required format and compresses it to optimize the transfer speed when transferring it to the cloud server.

[1482] Step 3:

[1483] The cloud server analyzes the received facial image using a facial recognition algorithm (for example, OpenCV or AWS Rekognition) to identify the user's main facial features, such as the eyes, nose, and mouth. The facial feature data obtained through the analysis is stored in a database. Specifically, the facial recognition algorithm reads the facial image, identifies the position of each part of the face, and generates its coordinate data.

[1484] Step 4:

[1485] The cloud server uses a machine learning model to predict future appearance based on the stored lifestyle data and facial feature data. This machine learning model uses statistical methods and predictive algorithms based on past data to estimate changes in skin condition and body shape. Data processing includes appropriately preprocessing the input data, converting it into a format applicable to the model, and then inputting it into the model.

[1486] Step 5:

[1487] The server generates an image showing the user's future appearance based on the prediction results. The generated image is intended to visually represent the user's future appearance if they maintain their current lifestyle. The server uses an image processing library to convert the prediction results into a visual format and provides it to the user in an easy-to-understand format.

[1488] Step 6:

[1489] The smartphone app captures the user's facial expressions and voice data and sends it to a cloud server. The cloud server then uses an emotion engine (for example, Azure Emotion API) to analyze the user's emotional state. Based on this, optimal self-care methods and products are suggested. For example, if the user is under stress, stretching techniques and relaxation products will be recommended.

[1490] Step 7:

[1491] The cloud server monitors changes resulting from the implementation of suggested self-care methods and measures in real time and provides feedback. The progress of the user's self-care is recorded and reanalyzed on the cloud server. For example, if a user exercises regularly, the effects are reflected in facial images and lifestyle data, and feedback is provided based on this.

[1492] Through these steps, users can concretely understand how their lifestyle habits will affect their future and receive optimal self-care and product recommendations based on their emotional state.

[1493] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1494] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1495] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1496] [Fourth embodiment]

[1497] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1498] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1499] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1500] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1501] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1502] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1503] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1504] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1505] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1506] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1507] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1508] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1509] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1510] MODE FOR CARRYING OUT THE INVENTION

[1511] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, visualizes the prediction results, and presents them to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. The following describes in detail how this system is implemented.

[1512] System Configuration

[1513] The system consists of the following major components:

[1514] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[1515] 2. Server

[1516] 3. Display devices (e.g. smart mirrors)

[1517] Program processing

[1518] User Data Collection

[1519] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1520] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1521] The server stores the received data in a database and manages it with a unique ID for each user.

[1522] Capture your current appearance

[1523] The user activates the smart mirror and reflects their face in the mirror.

[1524] The device (smart mirror) captures the user's face with its built-in camera and sends the image to a server.

[1525] The server analyzes the received image using a facial recognition algorithm to extract facial features.

[1526] Predicting future appearance

[1527] The server analyzes the user's saved lifestyle and dietary data and predicts the user's future appearance using statistical and machine learning models.

[1528] The predictive model predicts future changes in skin condition, body shape, etc. based on the user's current data patterns.

[1529] The server generates an image to visualize the future appearance based on the prediction results.

[1530] Displaying prediction results

[1531] The server sends the generated future appearance image to the smart mirror.

[1532] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm the future changes.

[1533] Self-care and countermeasure suggestions

[1534] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1535] Suggestions include plant-based meal plans, specific skin care product recommendations, and exercise plans.

[1536] The device (smart mirror or smartphone app) notifies the user of the suggestions.

[1537] Real-time feedback

[1538] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1539] New data is sent from the device (smartwatch, smartphone app) to the server.

[1540] The server reassess the user's progress based on the new data and provides feedback on the effectiveness of improvements.

[1541] The device (smart mirror, smartphone app) notifies the user with updated feedback and provides continuous motivation.

[1542] Specific examples

[1543] For example, if a user regularly eats a high-fat diet and exercises infrequently, this data is collected through a smartwatch and smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction is then visualized and displayed on a smart mirror. Seeing this future appearance motivates the user to improve their lifestyle habits, leading them to adopt a plant-based diet and regular exercise. The smartwatch and smartphone app track these efforts, and the server provides feedback, allowing the user to see their progress in real time.

[1544] This system allows users to concretely understand the impact of their lifestyle habits and take concrete actions to achieve a healthy and beautiful future.

[1545] The processing flow will be explained below.

[1546] Program processing

[1547] Step 1: Collect user data

[1548] 1. Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1549] 2. The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1550] 3. The server stores the received data in a database and manages it with a unique ID for each user.

[1551] Step 2: Capture your current appearance

[1552] 1. The user activates the smart mirror and reflects their face in the mirror.

[1553] 2. The device (smart mirror) captures the user's face with its built-in camera. This capture process takes a few seconds to obtain a high-resolution image.

[1554] 3. The device (smart mirror) processes the captured facial image at runtime and sends it to a server via the Internet.

[1555] Step 3: Face recognition and feature extraction

[1556] 1. The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[1557] 2. The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[1558] Step 4: Predict your future appearance

[1559] 1. The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[1560] 2. The server predicts the user's future appearance based on statistical and machine learning models, including changes in skin condition and body shape.

[1561] 3. The server generates an image to visualize the prediction results. This image shows the future appearance of the user if they maintain their current lifestyle.

[1562] Step 5: View the prediction results

[1563] 1. The server sends the generated image of the future appearance to the smart mirror.

[1564] 2. The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[1565] Step 6: Self-care and strategy suggestions

[1566] 1. Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1567] 2. The device (smart mirror or smartphone app) notifies the user of the suggestions, either through visual pop-up messages or periodic reminders.

[1568] Step 7: Real-time feedback

[1569] 1. Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[1570] 2. The device (smartwatch, smartphone app) sends the new collected data to the server.

[1571] 3. The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[1572] 4. The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[1573] Through this series of processes, users can concretely understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future.

[1574] Example 1

[1575] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1576] Currently, systems that collect data on a user's lifestyle and diet, predict future appearance based on that data, and provide self-care methods are not fully developed. In particular, they lack real-time feedback on the effectiveness of self-care and continuous motivation in response to changes in the user's lifestyle. This makes it difficult for users to effectively manage and maintain their health and beauty.

[1577] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1578] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a user's facial image, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing and displaying the predicted future appearance to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for automatically transmitting the records of the lifestyle and dietary data to the server via the Internet, means for analyzing the collected images with a face recognition algorithm and extracting facial features, means for predicting the user's future appearance using a statistical model or a machine learning model, means for displaying the generated future appearance image to the user in real time, means for notifying the user of the suggested self-care methods and measures, and means for reevaluating the user's improvement based on the progress data and providing continuous feedback. This allows the user to specifically understand the impact of their lifestyle and to confirm and continuously implement specific actions to maintain a healthy and beautiful future appearance in real time.

[1579] "User's lifestyle data and dietary data" is a general term for data including the content and amount of food eaten, the type and amount of exercise, and the duration and quality of sleep in the user's daily life.

[1580] "Means for capturing a user's facial image" refers to a device or software for photographing a user's face and acquiring the image.

[1581] "Means for predicting a user's future appearance" refers to algorithms or models for predicting future changes in a user's face or body based on the user's current lifestyle and dietary data.

[1582] "Means for visualizing the predicted future appearance and displaying it to the user" refers to devices or software that display the predicted changes in appearance as images or graphs, etc., so that the user can visually confirm them.

[1583] "Means for providing self-care methods and measures" refers to devices and software that suggest specific methods for improvement such as diet, exercise, and skin care based on the user's prediction results.

[1584] "Means for providing real-time feedback on changes resulting from the implementation of self-care methods and measures" refers to devices and software that evaluate the results of a user's implementation of self-care methods and measures in real time and notify the user of the results.

[1585] "Means for automatically transmitting records of lifestyle and dietary data to a server via the Internet" refers to devices or software for automatically transmitting collected data to a server via a smart device or the like.

[1586] "Facial recognition algorithm" refers to a computer program that analyzes a user's facial image and extracts certain features.

[1587] "Statistical model or machine learning model" refers to a mathematical or algorithmic model that analyzes patterns in data and predicts future changes.

[1588] The "means for displaying a future appearance image to a user in real time" refers to a device or software for instantly displaying the generated future appearance image to a user.

[1589] "Means for notifying the user of self-care methods and suggested measures" refers to devices and software for notifying the user of specific self-care methods based on the analysis results.

[1590] "Means for reevaluating the user's improvement based on progress data and providing continuous feedback" refers to devices and software that evaluate the effectiveness of the self-care methods and measures implemented by the user based on data, and continuously notify the user of the results.

[1591] MODE FOR CARRYING OUT THE INVENTION

[1592] The present invention is a system that collects lifestyle and dietary data of a user and predicts future appearance based on this data. This system is composed of the following main components:

[1593] System Configuration

[1594] This system consists of a user data collection device (e.g., smart watch, smartphone app), a server, and a display device (e.g., smart mirror).

[1595] User Data Collection

[1596] Users use smartwatches or smartphone apps to record data such as meals, exercise, and sleep in their daily lives. The devices (smartwatches, smartphone apps) send the recorded data to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user. For example, detailed data such as what a user had for breakfast, how many kilometers they walked, and how many hours they slept can be collected.

[1597] Capture your current appearance

[1598] The user activates the smart mirror and reflects their face in the mirror. The device (smart mirror) captures the user's face with its built-in camera and sends the image to the server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. These features include skin condition, number of wrinkles, and facial contours.

[1599] Predicting future appearance

[1600] The server analyzes the user's saved lifestyle and facial feature data and predicts the user's future appearance using statistical and machine learning models (e.g., TensorFlow and PyTorch). The predictive model predicts future skin conditions and changes in body shape based on the user's current data patterns. For example, if an irregular lifestyle continues, sagging skin and weight gain are predicted.

[1601] Displaying prediction results

[1602] The server generates an image to visualize the user's future appearance based on the prediction results. This image is sent to the smart mirror, which then displays the image to the user, allowing the user to visually confirm future changes.

[1603] Self-care and countermeasure suggestions

[1604] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, recommendations for specific skin care products, and exercise plans. The device (smart mirror or smartphone app) notifies the user of the suggestions. For example, specific advice such as "do 30 minutes of aerobic exercise every day" and "consume foods rich in vitamin C" is displayed.

[1605] Real-time feedback

[1606] The user incorporates the suggested self-care methods into their daily life and records their progress using a smartwatch or smartphone app. New data is sent from the device (smartwatch, smartphone app) to the server. The server reevaluates the user's progress based on the new data and provides feedback on the effectiveness of the improvements. The device (smart mirror or smartphone app) notifies the user of the updated feedback, providing ongoing motivation. For example, feedback such as "skin moisture has improved" or "weight loss of 1 kg" may be provided.

[1607] Prompt Sentence Examples

[1608] "Please tell me how to build a system that collects data on a user's daily diet, exercise, sleep, etc., and uses this data to predict future appearance and suggest self-care methods."

[1609] This system allows users to specifically understand the impact of their lifestyle habits, and to confirm and continuously implement specific actions in real time to maintain a healthy and beautiful appearance in the future.

[1610] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1611] Step 1: Collect user data

[1612] 1. The user enters information about their daily diet, exercise, sleep, etc. into a smartwatch or smartphone app. For example, they record what they had for breakfast, the number of steps they took, and the amount of sleep they received.

[1613] 2. The device (smartwatch or smartphone app) sends the recorded data to a server via the Internet.

[1614] 3. The server stores the received data in a database, including information such as dietary habits, exercise, and sleep duration.

[1615] 4. Input: User-recorded food, exercise, and sleep data.

[1616] Output: User's lifestyle and dietary data stored on the server.

[1617] Step 2: Capture your current appearance

[1618] 1. The user activates the smart mirror and places their face in the mirror.

[1619] 2. The device (smart mirror) captures the user's facial image using its built-in camera.

[1620] 3. The captured image is sent to a server via the Internet.

[1621] 4. The server analyzes the received image using an AI-based facial recognition algorithm.

[1622] 5. Input: User's face image.

[1623] Output: Analyzed facial feature data (skin condition, number of wrinkles, facial contours, etc.).

[1624] Step 3: Predict your future appearance

[1625] 1. The server analyzes the user's stored lifestyle data and facial feature data.

[1626] 2. The server predicts future appearance using statistical or machine learning models (e.g., TensorFlow, PyTorch).

[1627] 3. The server generates an image to visualize the future appearance based on the prediction results.

[1628] 4. Input: User's lifestyle data, facial feature data.

[1629] Output: Predicted image of future appearance.

[1630] Step 4: View the prediction results

[1631] 1. The server sends the generated image of the future appearance to the smart mirror.

[1632] 2. The device (smart mirror) displays an image of the user's future appearance.

[1633] 3. Users can visually see what their future appearance will be.

[1634] 4. Input: Predicted image of future appearance.

[1635] Output: The predicted image to be displayed on the smart mirror.

[1636] Step 5: Self-care and strategy suggestions

[1637] 1. The server suggests self-care methods and measures to the user based on the analysis results.

[1638] 2. Suggestions include plant-based meal plans, recommendations for specific skin care products, and exercise plans.

[1639] 3. The device (smart mirror or smartphone app) notifies the user of the suggestions.

[1640] 4. Input: Analysis results.

[1641] Output: Self-care methods and measures notified to the user.

[1642] Step 6: Real-time feedback

[1643] 1. The user incorporates the suggested self-care methods into their daily lives and records their implementation status using a smartwatch or smartphone app.

[1644] 2. New data is sent from the device (smartwatch, smartphone app) to the server.

[1645] 3. The server reassess the user's progress based on the new data.

[1646] 4. The server provides feedback on the effectiveness of improvements and provides ongoing motivation.

[1647] 5. The device (smart mirror or smartphone app) notifies the user of the feedback.

[1648] 6. Input: Data on the implementation status of self-care methods.

[1649] Output: Feedback notification of the improvement effect.

[1650] (Application example 1)

[1651] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1652] Conventional appearance prediction systems only predict and display future appearance based on a user's lifestyle and dietary data, making it difficult for users to select specific self-care methods based on the results. They also lacked the functionality to accurately suggest what products and services users should use. Furthermore, the lack of real-time feedback made it difficult for users to maintain motivation to continue self-care.

[1653] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1654] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, and means for suggesting in-store care products and services to the user. This allows the user to easily select the self-care methods, products, and services that are best suited to them and to engage in self-care while continuously maintaining motivation.

[1655] "Lifestyle data" is information relating to the activities and behaviors of the user in their daily lives.

[1656] "Dietary data" is information about the types, amounts, and frequency of food and beverage intake by a user.

[1657] A "face image" is image data of the user's face.

[1658] "Visualization" is the visual display of data or information.

[1659] A "self-care method" is a method for health management or beauty care that the user himself or herself carries out.

[1660] "Countermeasures" are specific measures or methods taken to address a specific problem or issue.

[1661] "Real-time feedback" refers to instantly evaluating the results of the actions and measures taken by the user and returning this information to the user immediately.

[1662] A "server" is a computer system that manages and processes data.

[1663] A "smart device" is a digital device equipped with internet connectivity and sensors.

[1664] A "statistical model" is a mathematical model that numerically describes and predicts phenomena based on data.

[1665] A "machine learning model" is a model that uses algorithms to learn patterns and knowledge from data and make predictions and classifications.

[1666] "In-store care products and services" are products and services related to health management and beauty care provided in stores.

[1667] The system that realizes this application example consists of multiple hardware and software components. First, smart devices (e.g., smartwatches and smartphone apps) are used to collect the user's lifestyle and dietary data. These devices record the user's daily data, such as diet, exercise, and sleep, in real time and send it to a server via the Internet.

[1668] The server stores the received data in a database and manages it with a unique ID for each user, which makes it possible to accurately track the data of each individual user.

[1669] Next, the user stands in front of the smart mirror and reflects their face. The smart mirror uses its built-in camera to capture the user's face and sends the image to a server. The server then analyzes the received image using a facial recognition algorithm to extract facial features. This step could use OpenCV, an open-source software for facial recognition.

[1670] The server then analyzes the user's stored lifestyle and dietary data using statistical or machine learning models to predict the user's future appearance. These models use generative AI models to predict future changes in skin condition and body shape based on the user's data patterns.

[1671] The future appearance image generated by the predictive model is visualized by the server and sent to the smart mirror, allowing the user to visually confirm their future appearance.

[1672] Furthermore, based on the analysis results, the server will suggest appropriate self-care methods and measures to the user. Suggestions include vegetable-based meal plans, specific skin care products, exercise plans, etc. The smart mirror and smartphone app will notify the user of these suggestions and encourage them to use in-store care products and services.

[1673] Finally, the user incorporates the suggested self-care methods and measures into their daily lives and records their progress using a smartphone app or smartwatch. The server then reevaluates their progress based on the new data and provides real-time feedback on the effectiveness. This allows users to constantly check their progress and maintain motivation while engaging in self-care.

[1674] As a concrete example, consider a scenario in which the system is used at a beauty salon. The user stands in front of the salon's smart mirror and projects their face onto the camera. The smart mirror sends the user's lifestyle data and current facial image to a server, which then uses this data to predict their future appearance. The smart mirror then displays the predicted future appearance and suggests specific beauty care products and services. This allows the user to select the best care method for themselves and make more effective use of the salon's services.

[1675] Examples of prompts to input to a generative AI model might include the following:

[1676] "Write a program to create an app that predicts future appearance. Include a function to send the user's face image and lifestyle data to a server, and retrieve and display the predicted future appearance."

[1677] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1678] Processing Steps

[1679] Step 1:

[1680] Users use smartphone apps or smartwatches to record daily life data such as diet, exercise, and sleep.

[1681] Input: Lifestyle and dietary data entered by users into their smart devices.

[1682] Data processing: These data are temporarily stored in the smart device.

[1683] Output: Lifestyle and dietary data are ready to be sent to the server.

[1684] Step 2:

[1685] The smart device transmits the recorded data to a server via the Internet.

[1686] Input: Lifestyle and dietary data recorded on a smart device.

[1687] Data computation: Data is sent to a server via the Internet as an HTTP request.

[1688] Output: Each user's lifestyle and dietary data is sent to the server.

[1689] Step 3:

[1690] The server stores the received data in a database and manages it with a unique ID for each user.

[1691] Input: Lifestyle and dietary data sent to the server.

[1692] Data processing: Data is stored in a database and managed with a unique ID to identify and accumulate data for each user.

[1693] Output: Each user's lifestyle and dietary data is stored in a database.

[1694] Step 4:

[1695] The user stands in front of the smart mirror and reflects their face in the mirror, which then captures the user's face using its built-in camera.

[1696] Input: Current face image of the user.

[1697] Data processing: Facial image data captured by the smart mirror's built-in camera.

[1698] Output: A face image is captured by the smart mirror.

[1699] Step 5:

[1700] The smart mirror sends the captured facial image to a server.

[1701] Input: A face image captured by a smart mirror.

[1702] Data calculation: The smart mirror sends the facial image data to the server as an HTTP request.

[1703] Output: The facial image data arrives at the server.

[1704] Step 6:

[1705] The server analyzes the received facial image using a facial recognition algorithm to extract the user's facial features.

[1706] Input: Facial image data sent to the server.

[1707] Data processing: Analyze and extract facial features using a facial recognition algorithm (e.g., OpenCV).

[1708] Output: Extracted facial feature data.

[1709] Step 7:

[1710] The server uses a statistical model or machine learning model to predict the user's future appearance based on the stored lifestyle and dietary data.

[1711] Input: Lifestyle and dietary data in the database, extracted facial feature data.

[1712] Data computation: Predict future appearance using statistical and machine learning models (generative AI models).

[1713] Output: Image of future appearance.

[1714] Step 8:

[1715] The server sends the predicted future appearance image to the smart mirror.

[1716] Input: Server-generated image of future appearance.

[1717] Data calculation: Send the future appearance image to the smart mirror as an HTTP response.

[1718] Output: An image of your future appearance is sent to the smart mirror.

[1719] Step 9:

[1720] The smart mirror displays an image of the user's future appearance.

[1721] Input: Future appearance image sent from the server.

[1722] Data processing: Displaying an image of your future appearance on the smart mirror display.

[1723] Output: The user can visually confirm the future appearance image.

[1724] Step 10:

[1725] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1726] Input: Lifestyle data, dietary data, and future appearance prediction results.

[1727] Data calculation: Self-care methods and measures are generated using the proposed algorithm.

[1728] Output: Suggested self-care methods and measures.

[1729] Step 11:

[1730] Smart mirrors and smartphone apps will notify users of the suggestions.

[1731] Input: Self-care methods and measures suggested by the server.

[1732] Data processing: The proposed content is displayed on the screen of a smart mirror or smartphone app.

[1733] Output: The user can check the suggestions and put the self-care methods and measures into practice.

[1734] Step 12:

[1735] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1736] Input: Data on self-care methods and measures taken by the user.

[1737] Data processing: Smart devices record and temporarily store new data.

[1738] Output: New lifestyle and dietary data is generated.

[1739] Step 13:

[1740] The smart device sends new data to the server, which then re-evaluates it.

[1741] Input: New lifestyle and dietary data.

[1742] Data calculation: The server uses a re-evaluation algorithm to analyze your progress and generate feedback.

[1743] Output: Real-time feedback results.

[1744] Step 14:

[1745] Smart mirrors and smartphone apps provide updated feedback to users and provide ongoing motivation.

[1746] Input: The feedback result sent by the server.

[1747] Data processing: The feedback content is displayed on the screen of a smart mirror or smartphone app.

[1748] Output: The user receives real-time feedback and is motivated to continue the action.

[1749] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1750] MODE FOR CARRYING OUT THE INVENTION

[1751] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. It also has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[1752] System Configuration

[1753] The system consists of the following major components:

[1754] 1. User data collection devices (e.g., smartwatches, smartphone apps)

[1755] 2. Server

[1756] 3. Display devices (e.g. smart mirrors)

[1757] 4. Emotion Engine

[1758] Program processing

[1759] User Data Collection

[1760] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1761] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1762] The server stores the received data in a database and manages it with a unique ID for each user.

[1763] Capture your current appearance

[1764] The user activates the smart mirror and reflects their face in the mirror.

[1765] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[1766] The device (smart mirror) processes the captured facial image at runtime and transmits it to a server via the Internet.

[1767] Face Recognition and Feature Extraction

[1768] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[1769] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[1770] Predicting future appearance

[1771] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[1772] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[1773] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[1774] Capturing and analyzing user emotions

[1775] Users express their emotions through facial expressions and voice in front of the smart mirror.

[1776] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[1777] The device processes the captured data in real time and transmits it to a server over the Internet.

[1778] The server uses an emotion engine to analyze the received data and determine the user's emotional state.

[1779] Displaying prediction results

[1780] The server sends the generated future appearance image to the smart mirror.

[1781] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[1782] Self-care and countermeasure suggestions

[1783] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1784] The server uses an emotion engine to tailor the optimal care regimen based on the user's emotional state.

[1785] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[1786] Real-time feedback

[1787] Users incorporate the suggested self-care methods and measures into their daily lives, and then record their progress using a smartphone app or smartwatch.

[1788] The device (smartwatch, smartphone app) sends the collected new data to the server.

[1789] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[1790] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[1791] Specific examples

[1792] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[1793] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[1794] The processing flow will be explained below.

[1795] Program processing

[1796] Step 1: Collect user data

[1797] Users use smartwatches and smartphone apps to record data such as diet, exercise, and sleep in their daily lives.

[1798] The device (smartwatch, smartphone app) sends the recorded data to a server via the Internet.

[1799] The server stores the received data in a database and manages it with a unique ID for each user.

[1800] Step 2: Capture your current appearance

[1801] The user activates the smart mirror and reflects their face in the mirror.

[1802] The device (smart mirror) captures the user's face with its built-in camera, a process that takes a few seconds to obtain a high-resolution image.

[1803] The device (smart mirror) processes the captured facial images in real time and transmits them to a server via the Internet.

[1804] Step 3: Face recognition and feature extraction

[1805] The server analyzes the received image with a facial recognition algorithm, which identifies the user's key facial features, such as the eyes, nose, and mouth.

[1806] The server extracts facial feature data and stores it in a database for subsequent analysis and prediction.

[1807] Step 4: Predict your future appearance

[1808] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models.

[1809] The server uses statistical and machine learning models to predict the user's future appearance, including changes in skin condition and body shape.

[1810] The server generates an image to visualize the user's future appearance based on the prediction results. This image shows what the user's future appearance will look like if they maintain their current lifestyle habits.

[1811] Step 5: Capturing and analyzing user emotions

[1812] Users express their emotions through facial expressions and voice in front of the smart mirror.

[1813] The device (smart mirror) uses a built-in camera and microphone to capture the user's facial expressions and voice.

[1814] The device processes the captured data in real time and transmits it to a server over the Internet.

[1815] The server uses an emotion engine to analyze the received data and identify the user's emotional state. For example, it classifies emotions such as "happiness," "sadness," "anger," and "surprise" based on facial expressions captured by a camera, and also analyzes emotions from voice.

[1816] Step 6: View the prediction results

[1817] The server sends the generated future appearance image to the smart mirror.

[1818] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes by looking at this image.

[1819] Step 7: Self-care and strategy suggestions

[1820] Based on the analysis results, the server suggests appropriate self-care methods and measures to the user.

[1821] The server uses an emotion engine to tailor optimal care methods based on the user's emotional state. For example, if the user is feeling stressed, it will suggest relaxation and mindfulness techniques.

[1822] The device (smart mirror or smartphone app) will notify the user of the suggestions, either in the form of a visual pop-up message or periodic reminders.

[1823] Step 8: Real-time feedback

[1824] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1825] The device (smartwatch, smartphone app) sends the collected new data to the server.

[1826] The server analyzes the new data and reassess the user's progress, providing real-time feedback on how their improvements are paying off.

[1827] The device (smart mirror or smartphone app) notifies the user with the latest feedback and provides continuous motivation.

[1828] Specific examples

[1829] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin or weight gain, in five years. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's (smart mirror's) emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques or relaxation products to help them relax.

[1830] This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. In addition, by using an emotion engine, the system provides optimal suggestions that correspond to the user's emotional state.

[1831] Example 2

[1832] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1833] Conventional systems could easily predict a user's future appearance based on their lifestyle and dietary data and suggest self-care methods, but they were unable to provide optimal care methods based on the user's emotional state. Furthermore, they lacked real-time feedback on changes in the user's lifestyle, making it difficult to maintain the user's motivation. This resulted in the challenge of making it difficult to achieve long-term health improvements.

[1834] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1835] In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for capturing and analyzing the user's emotional state, and means for suggesting an optimal self-care method based on the emotional state. This allows for more effective health improvement by suggesting an optimal care method according to the user's emotional state and providing continuous motivation.

[1836] "User" refers to a person who uses the system to record their own lifestyle and dietary data and receives self-care methods and feedback provided by the system.

[1837] "Lifestyle data" refers to information such as exercise, sleep, and activity level in the user's daily life.

[1838] "Dietary data" refers to information about the food and beverages a user regularly consumes.

[1839] "Facial image" refers to image data of a photograph of the user's face.

[1840] "Smart devices" refers to electronic devices used to collect users' lifestyle and dietary data, including smartwatches and smartphone apps.

[1841] "Means of capturing" refers to a method of acquiring an image or data of a user using a camera or sensor.

[1842] "Predictive methods" refer to methods that use statistical models or machine learning models to estimate future situations based on collected data.

[1843] "Means for visualization and display" refers to a method for displaying the calculated prediction results as images or graphs so that the user can visually confirm them.

[1844] "Self-care methods" refer to specific actions and measures that users take to maintain and improve their own health.

[1845] "Real-time feedback" refers to methods that provide immediate progress information and advice based on user behavior and data.

[1846] "Means for capturing and analyzing emotional state" refers to a method for capturing a user's facial expressions and voice and analyzing that data to identify emotions.

[1847] The "means for suggesting an optimal self-care method" refers to a method for providing the most effective self-care method for the user based on the analyzed emotional state.

[1848] The present invention is a system that collects a user's lifestyle and dietary data, predicts the user's future appearance based on that data, and visualizes and presents the prediction results to the user. Furthermore, it has the function of providing the user with specific self-care methods and measures, and providing real-time feedback on the effectiveness of the measures taken. Furthermore, by combining it with an emotion engine, it proposes optimal care methods tailored to the user's emotional state. Details of this system are described below.

[1849] System Configuration

[1850] The system consists of the following major components:

[1851] 1. User data collection devices (smartwatches, smartphone apps)

[1852] 2. Server

[1853] 3. Display device (smart mirror)

[1854] 4. Emotion Engine

[1855] User Data Collection

[1856] Users use smartwatches or smartphone apps in their daily lives to record data such as diet, exercise, and sleep. The collected data is sent from the device (smartwatch, smartphone app) to a server via the Internet. The server stores the received data in a database and manages it with a unique ID for each user.

[1857] Capture your current appearance

[1858] A user activates the smart mirror and looks at their face. The device (smart mirror) captures the user's face using its built-in camera. After capturing high-resolution images, these images are processed in runtime and sent over the internet to a server.

[1859] Face Recognition and Feature Extraction

[1860] The server analyzes the received facial images using a facial recognition algorithm (e.g., Dlib, OpenCV), identifying key facial features such as the user's eyes, nose, and mouth, and storing this data in a database.

[1861] Predicting future appearance

[1862] The server uses statistical and machine learning models (e.g., TensorFlow, PyTorch) to analyze the user's stored lifestyle and dietary data, predicting the user's future appearance and generating predicted results including changes in skin condition and body shape. Furthermore, it uses a generative adversarial network (GAN) to create a visualized image of the user's future appearance.

[1863] Capturing and analyzing user emotions

[1864] Users express their emotions through facial expressions and voice in front of the smart mirror. The device (smart mirror) captures the user's facial expressions and voice using a built-in camera and microphone, processes them in real time, and then sends them to a server via the Internet. The server then uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state.

[1865] Displaying prediction results

[1866] The server sends the generated image of the future appearance to the smart mirror, which then displays the image to the user, allowing the user to visually confirm the future changes.

[1867] Self-care and countermeasure suggestions

[1868] The server then suggests appropriate self-care methods and measures to the user based on the analysis results. It uses an emotion engine to tailor the advice based on the user's emotional state. The device (smart mirror or smartphone app) then provides the suggestions to the user as visual pop-up messages or periodic reminders.

[1869] Real-time feedback

[1870] The user incorporates the suggested self-care methods and measures into their daily life and records their progress using a smartphone app or smartwatch. The device (smartwatch, smartphone app) sends the collected new data to the server. The server analyzes the new data and reassess the user's progress. The latest feedback is sent to the user via the smart mirror or smartphone app, providing ongoing motivation.

[1871] Specific examples

[1872] For example, if a user regularly eats a high-fat diet and exercises insufficiently, this data is collected via a smartwatch or smartphone app. The server analyzes this data and predicts the likelihood of the user developing beauty and health problems, such as sagging skin and weight gain, five years from now. This prediction result is then visualized and displayed on the smart mirror. When the user shows their facial expression in front of the smart mirror, the device's emotion engine identifies the user's emotional state and suggests optimal care methods based on this state. For example, if the user is tired, it will suggest stretching techniques and relaxation products to help them relax. This system allows users to specifically understand the impact of their lifestyle habits and take action to achieve a healthy and beautiful future. The emotion engine also provides optimal suggestions based on the user's emotional state.

[1873] Prompt Sentence Examples

[1874] For example, if a user stands in front of a smart mirror and says, "Show me what I'll look like in five years," the system will visualize and display their future appearance. Similarly, if a user says, "Tell me the best self-care method to suit my mood today," the emotion engine will analyze the user's current mood and suggest the best self-care method.

[1875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1876] Step 1: Collect user data

[1877] Users use smartwatches or smartphone apps in their daily lives to input data on diet, exercise, sleep, etc. This input data includes calorie intake, number of steps, exercise time, and sleep time.

[1878] The device (smartwatch, smartphone app) automatically records data using various sensors and transmits the collected data to a server via the Internet. For example, step count data is obtained from an acceleration sensor, and heart rate data is obtained from a heart rate sensor.

[1879] The server stores the received data in a database and manages it with a unique ID for each user. When saving the data, preprocessing is performed to eliminate duplicate data and check the consistency of the data.

[1880] Input: Data collected from smartwatches and smartphone apps

[1881] Output: Consistent lifestyle data for each user is saved in the database.

[1882] Step 2: Capture your current appearance

[1883] The user activates the smart mirror and reflects their face in the mirror, adjusting the position of their face accordingly.

[1884] The device (smart mirror) uses a built-in camera to capture the user's face in high resolution, adapting to lighting conditions and correcting for facial angles during the capture process.

[1885] The device (smart mirror) compresses the captured facial image in real time and transmits it to a server via the Internet.

[1886] Input: User's face image

[1887] Output: High-resolution face image sent to the server

[1888] Step 3: Face recognition and feature extraction

[1889] The server analyzes the received facial images using an open-source facial recognition library (e.g., Dlib, OpenCV).

[1890] The server uses a facial recognition algorithm to identify and extract key facial features such as the user's eyes, nose, and mouth, which includes identifying facial landmark points.

[1891] The server stores the extracted facial feature data in a database for subsequent analysis and prediction processing.

[1892] Input: High-resolution face image

[1893] Output: Facial feature data is saved in a database

[1894] Step 4: Predict your future appearance

[1895] The server analyzes the user's stored lifestyle and dietary data using statistical and machine learning models (e.g., TensorFlow, PyTorch), which includes data normalization and feature engineering.

[1896] The server uses machine learning models to predict the user's future appearance, based on data pattern and trend analysis.

[1897] The server uses a generative adversarial network (GAN) to visualize the prediction results and generate future appearance images. In the process, a digital image is generated based on the predicted features.

[1898] Input: User's lifestyle data, dietary data, facial feature data

[1899] Output: An image is generated that visualizes the future appearance.

[1900] Step 5: Capturing and analyzing user emotions

[1901] The user expresses facial expressions and sounds in front of the smart mirror. For example, the user speaks to the smart mirror or makes facial expressions.

[1902] The device (smart mirror) uses a built-in camera and microphone to capture facial expressions and voice, and pre-processes them on an edge device (e.g., NVIDIA Jetson).

[1903] The terminal transmits the processed data to a server via the Internet.

[1904] The server uses an emotion engine (e.g., IBM Watson, Microsoft Azure Cognitive Services) to analyze the user's emotional state using facial expression recognition and voice analysis technologies.

[1905] Input: User's facial expression data, voice data

[1906] Output: User emotional state data is identified

[1907] Step 6: View the prediction results

[1908] The server sends the generated future appearance image to the smart mirror.

[1909] The device (smart mirror) displays an image of the user's future appearance, allowing the user to visually confirm future changes.

[1910] The images displayed include changes in skin condition and body shape, providing a realistic depiction of what the user will look like in the future if they maintain their current lifestyle habits.

[1911] Input: Image of future appearance

[1912] Output: Image of future appearance displayed in a smart mirror

[1913] Step 7: Self-care and strategy suggestions

[1914] Based on the analysis results and emotional state data, the server will suggest appropriate self-care methods and measures to the user, including specific methods such as improving diet, recommending exercise, and managing stress.

[1915] Based on the analysis results of the emotion engine, the server adjusts the care method that is optimized for the user's emotional state.

[1916] The device (smart mirror or smartphone app) will notify the user of the suggestions, either as a visual pop-up message or periodic reminder.

[1917] Input: Analysis results and emotional state data

[1918] Output: Self-care suggestions displayed on a smart mirror or smartphone app

[1919] Step 8: Real-time feedback

[1920] Users incorporate the suggested self-care methods and measures into their daily lives and record their progress using a smartphone app or smartwatch.

[1921] The device (smartwatch, smartphone app) sends the collected new data to a server via the Internet, including new step count data, meal details, etc.

[1922] The server analyzes the new data and reassess the user's progress, for example, to see if the user is achieving the suggested exercise volume.

[1923] The device (smart mirror or smartphone app) provides ongoing motivation by providing users with up-to-date feedback, including progress and additional advice.

[1924] Input: New lifestyle data

[1925] Output: The latest feedback given to the user

[1926] (Application example 2)

[1927] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1928] In modern society, individual lifestyles and poor dietary habits have a significant impact on beauty and health, but it is difficult to specifically understand how one's lifestyle will affect the future. In particular, many people who lead busy lives often lack the time or knowledge to continuously manage their health and beauty status. As a result, lifestyle changes are delayed, leading to endless serious health and beauty concerns. Furthermore, current systems and tools on the market lack the ability to propose self-care and countermeasures that take into account the user's emotional state. There is a need for an effective and comprehensive system to solve these issues.

[1929] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting lifestyle and dietary data of a user, means for capturing a facial image of the user, means for predicting the user's future appearance based on the collected lifestyle and dietary data, means for visualizing the predicted future appearance and displaying it to the user, means for providing self-care methods and measures based on the prediction, means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures, means for scanning the user's facial image using a smartphone, visualizing the user's future appearance and suggesting measures, and means for analyzing the user's emotional state using an emotion engine and suggesting optimal care methods and products based on the results. This allows the user to specifically understand how their lifestyle will affect their future and to receive optimal self-care and product recommendations tailored to their emotional state.

[1930] "User's lifestyle data" is data that reflects the user's daily life, and includes dietary details, exercise habits, sleep patterns, and the like.

[1931] "Dietary data" refers to information such as the types, amounts, and times of consumption of foods and beverages that a user consumes on a daily basis.

[1932] "Capture" is the act of using a digital device to obtain a user's facial image or other visual data.

[1933] "Prediction" is the act of estimating future conditions or events based on collected data.

[1934] "Visualization" is the act of converting digital data into a visual format such as a diagram or image and displaying it.

[1935] A "self-care method" is a health or beauty care method that a user carries out by himself or herself.

[1936] A "countermeasure" is an action or measure taken in response to a specific problem or risk.

[1937] "Real-time feedback" is a process of instantly evaluating the effectiveness of the self-care methods and measures implemented by the user and immediately returning the results to the user.

[1938] A "smartphone" is a portable information terminal that can connect to the Internet and has a camera and various applications.

[1939] An "emotion engine" is software that analyzes a user's facial expressions and voice data to identify the user's emotional state.

[1940] "Product suggestion" is the act of recommending an appropriate product based on the user's condition and needs.

[1941] The system for implementing this invention collects lifestyle and dietary data of a user, predicts future appearance based on this data, visualizes and displays it to the user, and also provides self-care methods and measures, providing feedback on their effectiveness in real time.

[1942] Hardware and software used

[1943] Smartphones: Used to collect data from everyday life and capture facial images.

[1944] Cloud server: Data analysis and processing is performed using AWS Lambda, Google Cloud Functions, etc.

[1945] Facial Recognition Algorithm: Uses OpenCV and AWS Rekognition to identify the user's facial features.

[1946] Emotion Engine: Analyzes the user's emotional state using Azure Emotion API.

[1947] Database: Use Firebase Database or Amazon DynamoDB to manage collected data.

[1948] System Operation

[1949] 1. Collection of User Data:

[1950] A smartphone app collects data about the user's daily life (diet, exercise, sleep), and sends this data to a cloud server.

[1951] 2. Capture your current appearance:

[1952] A user scans a face image using the smartphone camera and sends this image data to a cloud server.

[1953] 3. Face Recognition and Feature Extraction:

[1954] The cloud server uses a facial recognition algorithm to identify the user's facial features and store them in a database.

[1955] 4. Predicting your future appearance:

[1956] The cloud server uses machine learning models to predict future appearance based on collected lifestyle and facial feature data.

[1957] 5. Emotion capture and analysis:

[1958] The smartphone's camera and microphone are used to capture the user's emotions from facial expressions and voice, and the emotions are then analyzed on a cloud server.

[1959] 6. Displaying prediction results and countermeasures:

[1960] The prediction results are visualized, and self-care methods and products tailored to the user's emotional state are displayed to the user on their smartphone.

[1961] 7. Real-time feedback:

[1962] It records the progress of the user's self-care, sends new data to a cloud server for analysis, and provides immediate feedback.

[1963] Specific examples

[1964] For example, if a user regularly eats high-fat meals and exercises insufficiently, a smartphone app records this data and sends it to a cloud server. The cloud server analyzes this data and predicts the likelihood that the user will be overweight and have poor skin condition in five years. When the user scans their face with their smartphone camera, a prediction of their future appearance is displayed, and additional suggestions are added, such as, "If you continue like this, you will easily gain weight. Here are some recommended diet plans and skin care products." If the emotion engine detects stress from the user's facial expression, it also makes additional suggestions, such as, "We also recommend a yoga program and aromatherapy products to help you relax."

[1965] Prompt Sentence Examples

[1966] "Build an app that uses a user's lifestyle data and current facial image to predict their appearance in five years' time, and uses an emotion engine to suggest the best skincare products."

[1967] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1968] Step 1:

[1969] Users use a smartphone app to input lifestyle and dietary data, including dietary habits, exercise habits, and sleep patterns. This data is sent from the smartphone to a cloud server. The smartphone application organizes the input data and stores it on the cloud server as a series of numerical and text data. This allows the user's daily lifestyle to be recorded in detail.

[1970] Step 2:

[1971] The user captures a current facial image using the smartphone camera. The captured image is sav...

Claims

1. means for collecting lifestyle and dietary data of a user; means for capturing a facial image of a user; a means for predicting the user's future appearance based on the collected lifestyle habit data and dietary habit data; a means for visualizing the predicted future appearance and displaying it to a user; means for providing self-care methods and measures based on said prediction; A system including a means for providing real-time feedback on changes resulting from the implementation of the self-care methods and measures.

2. The system of claim 1 , wherein the lifestyle and dietary data is collected from a smart device.

3. The system of claim 1 , wherein the means for predicting future appearance utilizes a statistical model or a machine learning model.

4. The system of claim 1 , wherein the self-care methods and measures include suggestions for improving diet, recommendations for skin care products, and exercise plans.

5. The system of claim 1 , wherein the real-time feedback reassess the user's progress based on new user data and provides feedback periodically.

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