system

Abstract

Provide a system. A method for collecting image data of a user's skin; means for transmitting the collected image data to a server; means for analyzing image data to evaluate the user's skin condition; A means for recommending nutrients and skin care products required by the user based on the evaluation results; a means for detecting an anomaly from the analysis result and generating a warning when an anomaly is detected; The system includes a means for transmitting the evaluation results and recommendations to the terminal and notifying the user.

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] Conventional skin care methods require users to assess their own skin condition and select appropriate skin care products and nutrients, which can lead to insufficient results if users lack experience or knowledge. Furthermore, it can be difficult to detect skin abnormalities (e.g., early signs of skin cancer) early, posing the risk of serious health problems if detected late. It is necessary to address the difficulty of self-management of skin conditions and the risk of missing opportunities for early detection. [Means for solving the problem]

[0005] This invention relates to a system that continuously collects and analyzes a user's skin data to evaluate the user's skin condition and recommend necessary nutrients and skin care products. The system includes a means for collecting image data of the user's skin, a means for transmitting the collected image data to a server, and a means for analyzing the image data to evaluate the user's skin condition. The system also includes a means for recommending necessary nutrients and skin care products to the user based on the evaluation results, and a means for detecting abnormalities from the analysis results and generating a warning if an abnormality is detected. In this way, the user's skin health can be managed accurately and effectively, enabling early detection of potential health risks.

[0006] "User" refers to an individual or organization using the system.

[0007] "Skin image data" refers to digital image data that visually records the condition of a user's skin surface.

[0008] The term "means" refers to a specific device, program, or process for realizing the functions and methods performed by each component of the present invention.

[0009] "Server" refers to a computer system that provides computational resources for receiving, storing, analyzing, and transmitting data over a network.

[0010] "Analysis" refers to the process of performing computations on the acquired image data to identify and evaluate skin characteristics and abnormal areas.

[0011] "Skin condition" refers to the objective evaluation results regarding the health condition and aesthetic appearance of the user's skin.

[0012] "Nutrients" refers to vitamins, minerals, and other chemicals recommended to improve the health of the user's skin.

[0013] "Skin care products" refers to cosmetic and therapeutic products used for the care and maintenance of the skin.

[0014] "Evaluation results" refers to information about the skin condition obtained after image analysis.

[0015] "Warning" refers to notifications or reports intended to inform users of skin abnormalities or potential health risks.

[0016] A "feedback report" refers to a report that organizes and provides analysis results and recommendations to users.

[0017] "Notification" refers to messages and alerts sent to users via their terminals to inform them of analysis results and warnings.

[0018] "Daily skin data" refers to image data of the skin collected by the user every day, and is used to analyze skin changes based on this data. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. The basic operation of this system is as follows.

[0041] User data collection

[0042] The device (e.g., a smartphone or dedicated skin scanner) periodically collects image data of the user's skin. The data is collected by the user launching the application at a specific time and taking a photo of their skin according to the instructions. At this stage, the device can instruct the user on the appropriate angle and distance for taking the photo.

[0043] Sending and Receiving Data

[0044] The device sends the collected image data to a server using a secure protocol (e.g., HTTPS). The server receives the data and stores it in a database. The stored data is organized by date, user identifier, etc., for later analysis.

[0045] Data analysis and evaluation

[0046] The server uses deep learning algorithms and other image analysis techniques to analyze the stored image data. This extracts the user's skin characteristics (such as blemishes, wrinkles, dryness, and variations in skin tone). This is then compared with past data to identify trends in skin changes. For example, the appearance of new blemishes or increased wrinkles can be detected. Based on the results of this analysis, the system recommends the user's necessary nutrients and appropriate skin care products.

[0047] Feedback and Anomaly Detection

[0048] The server generates a feedback report based on the analysis results, including an assessment of the current state of the skin, recommended nutrients and skin care products, and warnings if abnormalities are detected, such as sudden pigmentation that could signal a risk of skin cancer.

[0049] Sending and notifying feedback

[0050] The server sends the generated feedback report to the device, which then receives it and notifies the user. The notification includes specific messages such as "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. We recommend that you consult a dermatologist immediately."

[0051] Specific examples

[0052] Example 1: Daily skin care recommendations

[0053] The user takes a photo of their skin using a smartphone app in the morning, and the image data is sent to a server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing the user to take appropriate skin care measures.

[0054] Example 2: Early detection of disease

[0055] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. We recommend that you consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[0056] In this way, by using the system of the present invention, users can accurately and effectively manage their daily skin health, enabling early detection of potential health risks.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user launches the smartphone app and takes a picture of their skin. The app then instructs the user on the position and angle of the photo.

[0060] Step 2:

[0061] The device pre-processes the captured image data and converts it into the required format, which includes adjusting the image resolution and removing noise.

[0062] Step 3:

[0063] The device sends the preprocessed image data to the server using a secure protocol (e.g., HTTPS).

[0064] Step 4:

[0065] The server receives the image data sent from the device and stores it in a database. The data is organized by user identifier, date, etc.

[0066] Step 5:

[0067] The server then begins processing the stored image data, using deep learning algorithms to extract skin characteristics such as blemishes, wrinkles, dryness, and variations in skin tone.

[0068] Step 6:

[0069] The server compares the extracted features with past data to identify trends in skin changes, such as the appearance of new blemishes or increased wrinkles.

[0070] Step 7:

[0071] The server evaluates the user's skin condition based on the analysis results, including determining the skin's health and necessary nutrients.

[0072] Step 8:

[0073] The server generates a feedback report summarizing the evaluation results and recommendations, including skin care product recommendations and warnings if any abnormalities are detected.

[0074] Step 9:

[0075] The server transmits the generated feedback report to the terminal.

[0076] Step 10:

[0077] The device displays the feedback report received from the server in the user interface. For example, it may say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid."

[0078] Step 11:

[0079] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution if necessary.

[0080] This series of processes allows users to properly manage their daily skin condition and detect and address health risks early.

[0081] Example 1

[0082] 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."

[0083] Conventional skin care systems make it difficult for users to accurately understand their skin condition and select appropriate skin care products. Furthermore, they lack the means to detect skin abnormalities early, making it difficult to detect potential health risks early. This creates inconvenience for users when managing their daily skin care.

[0084] 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.

[0085] In this invention, the server includes means for collecting image data of a user's skin, means for transmitting the collected image data to the server using a security protocol, and means for analyzing the image data using a deep learning algorithm to extract the user's skin characteristics. This allows the user to accurately understand their skin condition and receive recommendations for appropriate nutrients and skin care products. Furthermore, skin abnormalities can be detected early and appropriate warnings can be received, enabling early detection of health risks.

[0086] A "user" is an individual who uses the system to manage their skin condition.

[0087] "Skin image data" is a digital image file that contains visual information that indicates the condition of a user's skin.

[0088] "Security Protocol" means the technical measures used to protect and secure communications during data transmission, and generally includes HTTPS.

[0089] "Server" means a computing device on a network that receives, stores, and analyzes data.

[0090] A "deep learning algorithm" is a machine learning method that learns and analyzes features from large datasets, and includes, for example, Convolutional Neural Networks (CNNs).

[0091] "Skin characteristics" are attributes that indicate the condition of the user's skin, and include blemishes, wrinkles, dryness, and variations in color tone.

[0092] The "evaluation results" are judgments and conclusions about the user's skin condition obtained based on the analysis of image data.

[0093] "Nutrients" are food ingredients necessary to maintain the health of the user's skin.

[0094] "Skin care products" are cosmetics and skin care items used to improve and maintain the condition of the skin.

[0095] "Warning" is a warning message to notify the user of any abnormalities detected in their skin data.

[0096] "Feedback Report" means a report provided to a User that includes analysis results and recommendations.

[0097] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. This system is an appropriate combination of hardware and software.

[0098] User data collection

[0099] Users periodically collect image data of their skin using a smartphone or a dedicated skin scanner (device). The user launches the application at a specific time and takes a photo of their skin according to the shooting guide (e.g., appropriate shooting angle and distance) displayed on the device. This allows the device to acquire high-quality image data.

[0100] Sending and Receiving Data

[0101] The device sends the captured image data to a server using a security protocol such as HTTPS. The server receives the data and stores it in a database. This data is organized by date and user identifier for later analysis.

[0102] Data analysis and evaluation

[0103] The server uses deep learning algorithms (e.g., Convolutional Neural Networks (CNNs)) and other image analysis techniques to analyze the stored image data. This analysis extracts the user's skin characteristics (such as age spots, wrinkles, dryness, and variations in skin tone). The server then compares the newly analyzed data with past data to evaluate skin trends. For example, the appearance of new age spots or increased wrinkles is analyzed. Based on the results of this analysis, the server recommends the user's necessary nutrients and appropriate skin care products.

[0104] Feedback and Anomaly Detection

[0105] The server generates a feedback report based on the analysis results. The report includes an assessment of the current skin condition, recommended nutrients and skin care products, and a warning if an abnormality is detected. For example, if an abnormality such as sudden pigmentation is detected, the user may be at risk of skin cancer and be advised to seek immediate medical advice from a specialist.

[0106] Sending and notifying feedback

[0107] The server sends the generated feedback report to the device, which receives it and displays a notification to the user. The notification content includes specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[0108] Specific examples

[0109] Example 1: Daily skin care recommendations

[0110] Users use the app to take a photo of their skin in the morning, and the image data is sent to the server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing users to take appropriate skin care measures.

[0111] Example 2: Early detection of disease

[0112] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. Please consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[0113] Example prompts for generative AI models

[0114] "Analyze skin images taken by the user to detect dryness."

[0115] "Check for the appearance of new blemishes and wrinkles and generate a report recommending appropriate skin care products."

[0116] "Detect sudden hyperpigmentation and generate a warning recommending that the user consult a dermatologist."

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] The user launches the application

[0120] How it works: The user launches an application installed on their smartphone or a dedicated skin scanner. The application has the function of sending notifications to the user at specific times.

[0121] Input: The user launches an application during a specific time period.

[0122] Output: The application is launched and the user is shown a guide to taking pictures.

[0123] Step 2:

[0124] The device displays a shooting guide to the user.

[0125] What it does: The device displays a screen to the user showing the frame of the entire face and the appropriate distance and angle, allowing the user to accurately take a photo of their skin.

[0126] Input: Application startup signal

[0127] Output: The shooting guide shown to the user

[0128] Step 3:

[0129] The user takes a photo of their skin

[0130] Specific operation: The user follows the instructions on the device to take a photo of their skin. After taking the photo, the device temporarily stores the image data.

[0131] Input: User takes photos according to the shooting guide

[0132] Output: Image data temporarily saved on the device

[0133] Step 4:

[0134] The device sends the image data to the server.

[0135] Specific operation: The device sends the temporarily stored image data to a server using a security protocol such as HTTPS. An internet connection is required for transmission.

[0136] Input: Temporarily saved image data

[0137] Output: Image data sent to the server via HTTPS protocol

[0138] Step 5:

[0139] The server receives and stores the image data.

[0140] Specific operation: The server receives the transmitted image data and stores it in a database along with the date and user identifier.

[0141] Input: Image data received via the HTTPS protocol

[0142] Output: Image data with date and user identifier stored in a database

[0143] Step 6:

[0144] The server analyzes the data using a deep learning model

[0145] How it works: The server analyzes the stored image data using a deep learning algorithm (e.g., CNN), which extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[0146] Input: Image data stored in a database

[0147] Output: Extracted skin feature data

[0148] Step 7:

[0149] The server compares the data with past data and evaluates it.

[0150] Specific operation: The server compares newly analyzed data with past data to evaluate skin trends, such as the appearance of new blemishes or increased wrinkles.

[0151] Input: Newly analyzed skin feature data and past data

[0152] Output: Evaluation results of fluctuation trends

[0153] Step 8:

[0154] The server generates a feedback report based on the analysis results.

[0155] Specific operation: The server generates a feedback report based on the analysis results, including a current skin condition assessment and recommended nutrients and skin care products.

[0156] Input: Evaluation result of fluctuation trend

[0157] Output: Feedback report

[0158] Step 9:

[0159] The server detects an anomaly and generates a warning

[0160] Specific operation: If the server detects any abnormalities, such as sudden pigmentation, it generates a report containing a warning, such as "There is a risk of skin cancer, so we recommend that you seek medical advice immediately."

[0161] Input: Anomaly detection based on analysis results

[0162] Output: Report with warnings

[0163] Step 10:

[0164] The server sends a feedback report to the device.

[0165] Specific operation: The server sends the generated feedback report to the terminal using a communication protocol (e.g., HTTPS).

[0166] Input: Generated feedback report

[0167] Output: Feedback report sent to the device

[0168] Step 11:

[0169] The device notifies the user

[0170] Specific behavior: The device receives the feedback report and displays a notification to the user, with specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[0171] Input: Feedback reports received

[0172] Output: A notification message that is displayed to the user.

[0173] (Application example 1)

[0174] 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."

[0175] Conventional skin analysis systems have the drawback of making it difficult for users to regularly monitor their skin health and take appropriate measures. They also struggle to respond quickly when abnormalities occur, and lack personalized support, especially in physical stores. Therefore, there is a need for a system that allows customers to check their skin condition in real time and quickly recommend appropriate skin care products and nutrients.

[0176] 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.

[0177] In this invention, the server includes means for collecting image data of the user's skin, means for transmitting the collected image data to the server, means for analyzing the image data to evaluate the user's skin condition, means for recommending nutrients and skin care products necessary for the user based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, means for transmitting the evaluation results and recommendations to a terminal and notifying the user, and means for collecting image data of the user's skin using smart glasses and displaying the evaluation results in real time, thereby enabling the user to check their skin condition in real time and quickly receive recommendations for appropriate skin care products and nutrients.

[0178] "User" refers to an individual who uses the system to analyze their skin condition and receive recommendations for skin care products and nutrients.

[0179] "Skin image data" is an image showing the condition of the skin on the user's face or body, and includes information for evaluating the skin condition by analyzing the image.

[0180] The "server" is a central processing unit that receives, stores, analyzes image data sent by users, and generates recommendations based on the results.

[0181] "Recommendation means" refers to a method or device for suggesting appropriate skin care products and nutrients to a user based on the results of skin analysis.

[0182] The term "means for generating a warning" refers to a method or device for creating information that alerts the user when an abnormality is detected from the analysis results.

[0183] The "notification means" refers to a method or device for transmitting the evaluation results and recommendations to a user terminal and informing the user of them.

[0184] "Smart glasses" refers to a device worn on the head that integrates the user's visual and video information through built-in cameras and displays.

[0185] "Means for displaying evaluation results in real time" refers to a method or device for quickly providing the analyzed results to the user so that they can be checked immediately.

[0186] This invention is a system that continuously collects and analyzes image data of a user's skin to evaluate the user's skin condition and recommend necessary nutrients and skin care products. Furthermore, the present invention uses smart glasses to provide evaluation results in real time, enabling personalized service in physical stores. Specific embodiments for implementing the present invention are described below.

[0187] User data collection

[0188] Smart glasses are used as a terminal to collect image data of the user's skin. The user wears the smart glasses and uses the built-in camera to take a picture of their skin according to specified shooting conditions (e.g., appropriate angle and distance). This allows the user to easily obtain high-quality skin image data.

[0189] Sending and Receiving Data

[0190] Image data collected by the smart glasses is transmitted to a server using a secure protocol (e.g., HTTPS). The server receives the data, organizes it by date and user identifier, and stores it for future analysis and comparison.

[0191] Data analysis and evaluation

[0192] The server analyzes the stored image data using deep learning algorithms and other image analysis technologies (e.g., Keras, OpenCV). This makes it possible to evaluate the user's skin condition (e.g., blemishes, wrinkles, dryness, and changes in skin tone). By comparing the data with past data, it is also possible to understand trends in skin condition.

[0193] Feedback and Anomaly Detection

[0194] Based on the analysis results, the server generates a feedback report that includes an assessment of the skin's current condition, recommended nutrients and skin care products, and alerts if abnormalities are detected, such as a sudden increase in pigmentation that could signal a risk of skin cancer.

[0195] Sending and notifying feedback

[0196] The server sends the generated feedback report to the smart glasses, which notify the user and display the analysis results in real time on the display. The user can receive notifications and check recommended skin care products and nutrients, or take immediate action if a doctor's diagnosis is required.

[0197] Specific examples

[0198] Consider a scenario in which a beauty consultant wears smart glasses and approaches a customer who visits the store in the following manner.

[0199] Beauty consultant: "Hello, I'll check your skin condition and recommend the best skin care products for you."

[0200] Customer: "Yes, please."

[0201] The beauty consultant begins to scan the customer's skin using the smart glasses and analyzes it using the following prompts:

[0202] Prompt Sentence Examples

[0203] "Capture image data of the customer's skin and provide recommended skin care products and an evaluation of the skin condition as analysis results."

[0204] Using this system, customers can check their skin condition in real time and receive prompt recommendations for optimal skin care products and nutrients. If any serious skin abnormalities are detected, they can immediately seek medical advice. This allows for personalized service in physical stores and improves customer satisfaction.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] A user wearing smart glasses takes a picture of their skin under specified shooting conditions (appropriate angle and distance). Skin image data is collected using the smart glasses' camera.

[0208] Input: An image of the user's skin

[0209] Output: Image data of the captured skin

[0210] Step 2:

[0211] The smart glasses transmit the collected image data to a server using a secure protocol (e.g., HTTPS).

[0212] Input: Skin image data

[0213] Output: Image data sent to the server

[0214] Step 3:

[0215] The server saves the received image data, organizes it by date and user identifier, and stores it in a database.

[0216] Input: Image data sent

[0217] Output: Organized database entries

[0218] Step 4:

[0219] The server analyzes the stored image data using deep learning algorithms and image analysis techniques (e.g., Keras, OpenCV), thereby evaluating the user's skin condition (blemishes, wrinkles, dryness, color variations, etc.).

[0220] Input: Organized image data

[0221] Output: Analysis results (skin condition evaluation)

[0222] Step 5:

[0223] The server compares the data with past data to identify trends in skin condition, and compares past and current data to extract points of change.

[0224] Input: Current and past analysis results

[0225] Output: Fluctuation trend data

[0226] Step 6:

[0227] The server generates a feedback report based on the analysis results, which includes a current skin assessment, recommended nutrients and skin care products, and alerts if any abnormalities are detected.

[0228] Input: Analysis results and fluctuation trend data

[0229] Output: Feedback report

[0230] Step 7:

[0231] The server sends the generated feedback report to the smart glasses, which receive the report and notify the user.

[0232] Input: Feedback Report

[0233] Output: Informed assessment findings and recommendations

[0234] Step 8:

[0235] The smart glasses display the analysis results to the user in real time, and the user can view the display on the smart glasses, including recommended skin care products and nutrients, as well as warnings if any abnormalities are detected.

[0236] Input: Informed assessment findings and recommendations

[0237] Output: Evaluation results and recommendations displayed on the screen

[0238] In this way, users can check their skin condition in real time, receive prompt recommendations for appropriate skin care products and nutrients, and can immediately seek medical advice if any serious skin abnormalities are detected.

[0239] 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.

[0240] This invention combines a system that continuously collects and analyzes a user's skin data to evaluate the user's skin condition and recommend necessary nutrients and skin care products with an emotion engine that recognizes the user's emotions. The basic operation of this system is as follows.

[0241] User data collection

[0242] The device (e.g., a smartphone or a dedicated skin scanner) periodically collects image data of the user's skin and emotional data. The user launches the application and follows the instructions to take a photo of their skin, while simultaneously acquiring emotional data using the user's facial expressions and voice.

[0243] Sending and Receiving Data

[0244] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS). The server receives the data, associates the image data with the emotion data, and stores them in a database. The stored data is organized by user identifier, date, etc., for later analysis.

[0245] Data analysis and evaluation

[0246] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.). At the same time, it uses an emotion engine to analyze the user's emotional data and recognize their current emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[0247] Generate feedback

[0248] The server generates a feedback report based on the analysis results, including an assessment of the skin's health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the user is stressed, the server may recommend skin care products or lifestyle changes that will help reduce stress.

[0249] Sending and notifying feedback

[0250] The server sends the generated feedback report to the device, which then receives it and displays it on the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0251] Specific examples

[0252] Example 1: Daily skin care and emotional management

[0253] Users use the app to take photos of their skin in the morning and simultaneously collect emotional data. The server detects dryness and stress levels and recommends moisturizing skin care products and provides advice on how to reduce stress. This allows users to care for both their skin and their emotions.

[0254] Example 2: Early disease detection and response

[0255] The user scans their skin every day and collects emotional data. The server detects sudden pigmentation and abnormalities in the user's emotional state, and determines that they are at risk of skin cancer. The server notifies the user, saying, "Abnormal pigmentation has been detected in a part of your skin. We recommend that you consult a dermatologist immediately. We also recommend that you remain calm." The user can then seek a specialist's diagnosis early and take appropriate measures to deal with the emotional issues.

[0256] In this way, by using the system of the present invention, users can comprehensively manage their daily skin health and emotional state, enabling them to detect and address health risks early.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] The user launches the smartphone app and takes a photo of their skin. The app then instructs the user on the correct position and angle to capture accurate image data.

[0260] Step 2:

[0261] The terminal simultaneously collects the captured image data and the user's emotional data (for example, facial expressions and voice data).

[0262] Step 3:

[0263] The device performs pre-processing to convert the skin image data into the required format, including adjusting the image resolution and removing noise.

[0264] Step 4:

[0265] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS).

[0266] Step 5:

[0267] The server receives the image data and emotion data sent from the device and stores them in a database. The data is organized by user identifier, date, etc.

[0268] Step 6:

[0269] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[0270] Step 7:

[0271] The server uses an emotion engine to analyze the emotion data and recognize the user's emotional state (e.g., happiness, sadness, stress, etc.).

[0272] Step 8:

[0273] Based on the analysis results, the server comprehensively evaluates the user's skin condition and emotional state, including determining the necessary nutrients and skin care products according to the skin's health and emotional state.

[0274] Step 9:

[0275] The server generates a feedback report summarizing the assessment results and recommendations, including a skin health assessment, recommended nutrients and skin care products, and advice based on emotional state.

[0276] Step 10:

[0277] The server transmits the generated feedback report to the terminal.

[0278] Step 11:

[0279] The device displays the feedback report received from the server in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0280] Step 12:

[0281] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution as necessary. The user can also take actions to reduce stress (e.g., practicing relaxation techniques).

[0282] This series of processes allows users to comprehensively manage both their skin health and their emotions, enabling them to detect health risks early and take appropriate measures.

[0283] Example 2

[0284] 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."

[0285] Previous skin care systems focused only on analyzing the user's skin condition and were unable to provide comprehensive advice that took into account the user's emotional state. Furthermore, they ignored the impact of changes in emotional state on the skin, resulting in a lack of specific guidelines for users to practice better skin care. As a result, it was difficult for users to select optimal products and improve their lifestyle habits, making it difficult to maintain healthy skin.

[0286] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data to evaluate the user's skin condition and the emotional data to recognize the user's emotional state, means for recommending nutrients and skin care products necessary for the user based on the evaluation results and the recognition results and generating advice based on the emotional state, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This makes it possible to comprehensively manage the user's skin health condition and emotional state and recommend optimal skin care products and lifestyle improvements.

[0287] "Skin image data" refers to photographs or image data acquired to record the user's skin condition.

[0288] "Emotional data" refers to data such as voice and facial expressions that are collected to express the user's emotional state.

[0289] A "terminal" is an electronic device used by a user, such as a smartphone or a dedicated skin scanner.

[0290] A "server" is a computer system for analyzing collected data and processing results.

[0291] A "deep learning algorithm" is an artificial intelligence technology that learns using large amounts of data and performs highly accurate analysis.

[0292] An "emotion engine" is software that analyzes collected emotion data and recognizes the user's emotional state.

[0293] A "feedback report" is a report generated based on the analysis results that includes evaluations and advice regarding the user's skin health and emotional state.

[0294] A "secure protocol" is a communication method that prevents unauthorized access by third parties when transmitting data.

[0295] "Nutrients" are substances such as vitamins and minerals that need to be ingested to maintain human health.

[0296] "Skin care products" are cosmetic and medicated products used to protect and improve the skin.

[0297] "Abnormal" refers to skin problems or changes in emotional state that are different from normal.

[0298] "Notification" is a function that notifies the user of information via the terminal.

[0299] This invention is a system that continuously collects and analyzes a user's skin and emotional data to evaluate the user's skin condition and emotional state, and provides advice on necessary nutrients, skin care products, and lifestyle habits. Detailed embodiments of this system are described below.

[0300] First, the user launches a dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The user takes the photo of their skin, and the device acquires the image data. At the same time, the device collects emotional data from the user's voice, facial expressions, etc. This is done using the smartphone's camera and microphone functions.

[0301] The device then preprocesses the collected skin image data and emotion data. This preprocessing includes denoising the images and converting them to a suitable format. The preprocessed data is then sent to a server using the secure HTTPS protocol. The server then stores the received data in a database, organized by user identifier, date, and other information.

[0302] The server applies a deep learning algorithm to the stored image data to analyze skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[0303] The server generates a feedback report based on the analysis results. The report includes an assessment of the user's skin health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the server determines that the user is under stress and has dry skin, it will recommend lifestyle changes that will help reduce stress and skin care products rich in moisturizing ingredients. If the analysis detects any abnormalities (e.g., sudden pigmentation), it will generate a warning recommending that the user consult a dermatologist.

[0304] Finally, the server sends the generated feedback report to the device. The device receives it and displays it on the user interface. For example, the device may display a message in a format that is easy for the user to understand, such as, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0305] Prompt Sentence Examples

[0306] "We will provide appropriate feedback by collecting data on the user's skin condition and emotions and analyzing it using deep learning and an emotion engine. For example, if the user has dry skin and is feeling stressed, we will recommend skin care products with high moisturizing ingredients and advice on stress reduction. Please list the recommended products and advice in this case."

[0307] This system allows users to comprehensively manage their skin health and emotional state, enabling them to implement optimal skin care products and lifestyle improvements.

[0308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0309] Step 1: Data collection

[0310] The user launches the dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The device then uses its camera function to acquire image data of the skin captured by the user. At the same time, the device uses its microphone function to collect emotion data from the user's voice and facial expressions. This data is equipped with an emotion engine.

[0311] Input: Smartphone or dedicated skin scanner, photo of user's skin, voice

[0312] Output: Skin image data, emotion data

[0313] Specific operation: The device displays "The camera has been activated. Please take a photo of your skin." The user takes a photo. At the same time, the device records audio.

[0314] Step 2: Preprocessing the data

[0315] The device performs preprocessing on the collected skin image data and emotion data. Image data preprocessing includes noise removal and image format conversion. Emotion data preprocessing includes audio noise removal and sample rate conversion. The device generates preprocessed data.

[0316] Input: Skin image data, emotion data

[0317] Output: Preprocessed image data, preprocessed emotion data

[0318] Specific operation: The device displays "Starting noise removal from image data" and notifies you that "Preprocessing completed" when the process is complete.

[0319] Step 3: Sending data

[0320] The device transmits the preprocessed image data and emotion data to the server using a secure protocol (HTTPS).

[0321] Input: Preprocessed image data, preprocessed emotion data

[0322] Output: Data sent to the server

[0323] Specific operation: The device will display "Sending data..." and after the transmission is complete, will notify you that "Data transmission completed."

[0324] Step 4: Receiving and storing data

[0325] The server receives the preprocessed image data and emotion data and stores them in a database, organized by user identifier and date.

[0326] Input: Preprocessed image data, preprocessed emotion data

[0327] Output: Data stored in the database

[0328] Specific operation: The server records in the log "Data received. Saving to database", and when the saving process is complete, it records "Saving completed".

[0329] Step 5: Analyze the data

[0330] The server uses a deep learning algorithm to analyze the image data stored in the database, extracting skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state.

[0331] Input: Preprocessed image data stored in a database, preprocessed emotion data

[0332] Output: Skin feature data, emotional state data

[0333] Specific operation: The server records in the log "Starting analysis of skin data using deep learning" and generates the analysis results.

[0334] Step 6: Generate ratings and feedback

[0335] The server uses the analysis results to comprehensively assess the user's skin health and emotional state, then generates a feedback report containing skin health assessment, recommended nutrients and skin care products, and advice based on the emotional state, and generates a warning if any abnormalities are detected.

[0336] Input: Skin feature data, emotional state data

[0337] Output: Feedback report, warnings

[0338] Specific behavior: The server logs "Generating feedback report" and saves the generated report.

[0339] Step 7: Submit your feedback

[0340] The server transmits the generated feedback report to the terminal.

[0341] Input: Feedback Report

[0342] Output: Feedback report sent to the device

[0343] Specific operation: The server logs "Sending feedback report to terminal", and after the transmission is complete, logs "Sending completed".

[0344] Step 8: Notification and display of feedback

[0345] The device receives the feedback report and displays it in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0346] Input: Feedback Report

[0347] Output: Feedback given to the user

[0348] Specific behavior: The device notifies the user that "You have a new feedback report," and the user opens the app to view the feedback.

[0349] Through these steps, the system of the present invention can comprehensively manage the user's skin health and emotional state, and provide optimal skin care products and lifestyle improvements.

[0350] (Application example 2)

[0351] 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."

[0352] Conventional skin care systems specialize in analyzing the user's skin condition and recommending skin care products based on that analysis, but they have the problem of being unable to provide more comprehensive health management because they do not take the user's emotional state into account.In particular, in situations where real-time personalization is required, such as shopping in a physical store, comprehensive advice that includes the user's emotional state is required.

[0353] The identification processing 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 image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data and emotional data to evaluate the user's skin condition and emotional state, means for recommending nutrients and skin care products necessary for the user and emotional advice based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This enables users to receive comprehensive health management and personalized product selection that takes into account their skin condition and emotional state, even in physical stores.

[0354] "User's skin image data" is image information that digitally records the user's skin condition.

[0355] "Emotion data" is information that represents the user's emotional state, and is data extracted from facial expressions, voice, and the like.

[0356] The "collection means" refers to a device or system for acquiring image data of the user's skin and emotion data.

[0357] "Server" refers to a computer system for receiving, storing, and analyzing collected data.

[0358] "Means of transmission" refers to the communication functions and protocols used to transfer collected data to the server.

[0359] "Means for analyzing and evaluating" refers to algorithms or programs that process the collected image data and emotional data to determine the user's skin condition and emotional state.

[0360] "Recommendation means" refers to a device or program that provides appropriate nutritional and skin care products, as well as emotional advice, to a user based on the analysis results.

[0361] "Means for detecting anomalies" refers to systems or programs that identify abnormal conditions from the analysis results and generate warnings.

[0362] "Means of notification" refers to communication means and software for displaying the evaluation results and recommendations on the user's terminal.

[0363] This invention relates to a system that comprehensively evaluates a user's skin condition and emotional state, and recommends appropriate nutrients, skin care products, and emotional advice. To implement this invention, the following hardware and software are mainly used:

[0364] Hardware and software used

[0365] Hardware:

[0366] Smartphone built-in camera: Used to capture image data of the user's skin.

[0367] Smartphone: Runs applications and sends and receives data.

[0368] Server: Stores and analyzes data.

[0369] software:

[0370] Python: Used to write the entire program.

[0371] OpenCV: Used to capture and preprocess skin image data.

[0372] Keras: Used to run skin data analysis models.

[0373] Transformers library: Used to run the emotion recognition engine.

[0374] Overview of the invention

[0375] 1. User data collection

[0376] The user launches a smartphone application to collect skin image data and emotion data. Skin images are captured using the smartphone's built-in camera. Emotion data is collected through the user's voice and text input.

[0377] 2. Sending and Receiving Data

[0378] The application sends the collected image data and emotion data to a server using a secure protocol (e.g., HTTPS), which then associates the image data with the emotion data and stores them in a database.

[0379] 3. Data analysis and evaluation

[0380] The server analyzes the stored image data and emotional data using a deep learning algorithm and an emotion recognition engine. From the image data, it extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.), and from the emotional data, it recognizes the user's emotional state.

[0381] 4. Generate feedback

[0382] Based on the analysis results, the server generates a feedback report that includes an assessment of skin health, recommended nutrients and skin care products, and advice based on emotional state.

[0383] 5. Sending Feedback and Notifications

[0384] The generated feedback report is sent from the server to the user's device, where the user can check the feedback through a smartphone application.

[0385] Specific examples

[0386] For example, suppose a user uses the app to take a photo of their skin in the morning and inputs, "I've been busy and tired lately." In this case, the server detects that the user's skin is dry and stressed, recommends using skin care products with high moisturizing ingredients, and provides advice on how to reduce stress. Specifically, the server notifies the user, "Your skin appears to be dry. We recommend using skin care products with high moisturizing ingredients. Also, you appear to be feeling stressed recently. We recommend that you adopt a relaxation technique."

[0387] Example prompt sentence:

[0388] I've been busy and tired lately.

[0389] In this way, by using the system of the present invention, users can evaluate their skin and emotional condition in real time even in a physical store, and obtain the most suitable products and advice.

[0390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0391] Step 1:

[0392] User data collection

[0393] The user launches a smartphone application and collects skin image data and emotion data on the device. Specifically, the smartphone's built-in camera is used to take a skin image, and emotion data is simultaneously collected through voice or text input. The input data is the user's skin image and text or voice data. The output data is the collected image data and emotion data.

[0394] Step 2:

[0395] Sending and Receiving Data

[0396] The device sends the collected image data and emotion data to the server using a secure protocol (e.g., HTTPS). Specifically, the device encodes the data and sends it to the server through a secure channel. The input data is the image data and emotion data collected in step 1. The output data is the image data and emotion data transferred to the server.

[0397] Step 3:

[0398] Data storage

[0399] The server associates the received image data and emotion data and stores them in a database. Specifically, it organizes the data using user identifiers and timestamps for later analysis. The input data is the image data and emotion data sent to the server in step 2. The output data is the stored database entry.

[0400] Step 4:

[0401] Data analysis

[0402] The server analyzes the stored image data and emotion data using a deep learning algorithm and emotion recognition engine. Specifically, it extracts skin features (blemishes, wrinkles, dryness, color variations, etc.) from the image data and determines the emotional state using the emotion recognition engine. The input data are the image data and emotion data obtained from the database. The output data are the skin evaluation results and emotion evaluation results.

[0403] Step 5:

[0404] Generate feedback

[0405] The server generates a feedback report based on the skin assessment results and emotion assessment results. Specifically, it creates a report containing advice on appropriate nutrients, skin care products, and emotions. The input data are the skin assessment results and emotion assessment results obtained in step 4. The output data is the feedback report to be sent to the user.

[0406] Step 6:

[0407] Sending and notifying feedback

[0408] The server sends the generated feedback report to the terminal and notifies the user. Specifically, the terminal receives the feedback report and displays it on the user interface. The input data is the feedback report generated in step 5. The output data is the feedback report displayed on the terminal and the notification to the user.

[0409] 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.

[0410] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0411] 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.

[0412] [Second embodiment]

[0413] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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. 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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."

[0425] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. The basic operation of this system is as follows.

[0426] User data collection

[0427] The device (e.g., a smartphone or dedicated skin scanner) periodically collects image data of the user's skin. The data is collected by the user launching the application at a specific time and taking a photo of their skin according to the instructions. At this stage, the device can instruct the user on the appropriate angle and distance for taking the photo.

[0428] Sending and Receiving Data

[0429] The device sends the collected image data to a server using a secure protocol (e.g., HTTPS). The server receives the data and stores it in a database. The stored data is organized by date, user identifier, etc., for later analysis.

[0430] Data analysis and evaluation

[0431] The server uses deep learning algorithms and other image analysis techniques to analyze the stored image data. This extracts the user's skin characteristics (such as blemishes, wrinkles, dryness, and variations in skin tone). This is then compared with past data to identify trends in skin changes. For example, the appearance of new blemishes or increased wrinkles can be detected. Based on the results of this analysis, the system recommends the user's necessary nutrients and appropriate skin care products.

[0432] Feedback and Anomaly Detection

[0433] The server generates a feedback report based on the analysis results, including an assessment of the current state of the skin, recommended nutrients and skin care products, and warnings if abnormalities are detected, such as sudden pigmentation that could signal a risk of skin cancer.

[0434] Sending and notifying feedback

[0435] The server sends the generated feedback report to the device, which then receives it and notifies the user. The notification includes specific messages such as "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. We recommend that you consult a dermatologist immediately."

[0436] Specific examples

[0437] Example 1: Daily skin care recommendations

[0438] The user takes a photo of their skin using a smartphone app in the morning, and the image data is sent to a server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing the user to take appropriate skin care measures.

[0439] Example 2: Early detection of disease

[0440] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. We recommend that you consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[0441] In this way, by using the system of the present invention, users can accurately and effectively manage their daily skin health, enabling early detection of potential health risks.

[0442] The processing flow will be explained below.

[0443] Step 1:

[0444] The user launches the smartphone app and takes a picture of their skin. The app then instructs the user on the position and angle of the photo.

[0445] Step 2:

[0446] The device pre-processes the captured image data and converts it into the required format, which includes adjusting the image resolution and removing noise.

[0447] Step 3:

[0448] The device sends the preprocessed image data to the server using a secure protocol (e.g., HTTPS).

[0449] Step 4:

[0450] The server receives the image data sent from the device and stores it in a database. The data is organized by user identifier, date, etc.

[0451] Step 5:

[0452] The server then begins processing the stored image data, using deep learning algorithms to extract skin characteristics such as blemishes, wrinkles, dryness, and variations in skin tone.

[0453] Step 6:

[0454] The server compares the extracted features with past data to identify trends in skin changes, such as the appearance of new blemishes or increased wrinkles.

[0455] Step 7:

[0456] The server evaluates the user's skin condition based on the analysis results, including determining the skin's health and necessary nutrients.

[0457] Step 8:

[0458] The server generates a feedback report summarizing the evaluation results and recommendations, including skin care product recommendations and warnings if any abnormalities are detected.

[0459] Step 9:

[0460] The server transmits the generated feedback report to the terminal.

[0461] Step 10:

[0462] The device displays the feedback report received from the server in the user interface. For example, it may say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid."

[0463] Step 11:

[0464] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution if necessary.

[0465] This series of processes allows users to properly manage their daily skin condition and detect and address health risks early.

[0466] Example 1

[0467] 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."

[0468] Conventional skin care systems make it difficult for users to accurately understand their skin condition and select appropriate skin care products. Furthermore, they lack the means to detect skin abnormalities early, making it difficult to detect potential health risks early. This creates inconvenience for users when managing their daily skin care.

[0469] 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.

[0470] In this invention, the server includes means for collecting image data of a user's skin, means for transmitting the collected image data to the server using a security protocol, and means for analyzing the image data using a deep learning algorithm to extract the user's skin characteristics. This allows the user to accurately understand their skin condition and receive recommendations for appropriate nutrients and skin care products. Furthermore, skin abnormalities can be detected early and appropriate warnings can be received, enabling early detection of health risks.

[0471] A "user" is an individual who uses the system to manage their skin condition.

[0472] "Skin image data" is a digital image file that contains visual information that indicates the condition of a user's skin.

[0473] "Security Protocol" means the technical measures used to protect and secure communications during data transmission, and generally includes HTTPS.

[0474] "Server" means a computing device on a network that receives, stores, and analyzes data.

[0475] A "deep learning algorithm" is a machine learning method that learns and analyzes features from large datasets, and includes, for example, Convolutional Neural Networks (CNNs).

[0476] "Skin characteristics" are attributes that indicate the condition of the user's skin, and include blemishes, wrinkles, dryness, and variations in color tone.

[0477] The "evaluation results" are judgments and conclusions about the user's skin condition obtained based on the analysis of image data.

[0478] "Nutrients" are food ingredients necessary to maintain the health of the user's skin.

[0479] "Skin care products" are cosmetics and skin care items used to improve and maintain the condition of the skin.

[0480] "Warning" is a warning message to notify the user of any abnormalities detected in their skin data.

[0481] "Feedback Report" means a report provided to a User that includes analysis results and recommendations.

[0482] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. This system is an appropriate combination of hardware and software.

[0483] User data collection

[0484] Users periodically collect image data of their skin using a smartphone or a dedicated skin scanner (device). The user launches the application at a specific time and takes a photo of their skin according to the shooting guide (e.g., appropriate shooting angle and distance) displayed on the device. This allows the device to acquire high-quality image data.

[0485] Sending and Receiving Data

[0486] The device sends the captured image data to a server using a security protocol such as HTTPS. The server receives the data and stores it in a database. This data is organized by date and user identifier for later analysis.

[0487] Data analysis and evaluation

[0488] The server uses deep learning algorithms (e.g., Convolutional Neural Networks (CNNs)) and other image analysis techniques to analyze the stored image data. This analysis extracts the user's skin characteristics (such as age spots, wrinkles, dryness, and variations in skin tone). The server then compares the newly analyzed data with past data to evaluate skin trends. For example, the appearance of new age spots or increased wrinkles is analyzed. Based on the results of this analysis, the server recommends the user's necessary nutrients and appropriate skin care products.

[0489] Feedback and Anomaly Detection

[0490] The server generates a feedback report based on the analysis results. The report includes an assessment of the current skin condition, recommended nutrients and skin care products, and a warning if an abnormality is detected. For example, if an abnormality such as sudden pigmentation is detected, the user may be at risk of skin cancer and be advised to seek immediate medical advice from a specialist.

[0491] Sending and notifying feedback

[0492] The server sends the generated feedback report to the device, which receives it and displays a notification to the user. The notification content includes specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[0493] Specific examples

[0494] Example 1: Daily skin care recommendations

[0495] Users use the app to take a photo of their skin in the morning, and the image data is sent to the server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing users to take appropriate skin care measures.

[0496] Example 2: Early detection of disease

[0497] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. Please consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[0498] Example prompts for generative AI models

[0499] "Analyze skin images taken by the user to detect dryness."

[0500] "Check for the appearance of new blemishes and wrinkles and generate a report recommending appropriate skin care products."

[0501] "Detect sudden hyperpigmentation and generate a warning recommending that the user consult a dermatologist."

[0502] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0503] Step 1:

[0504] The user launches the application

[0505] How it works: The user launches an application installed on their smartphone or a dedicated skin scanner. The application has the function of sending notifications to the user at specific times.

[0506] Input: The user launches an application during a specific time period.

[0507] Output: The application is launched and the user is shown a guide to taking pictures.

[0508] Step 2:

[0509] The device displays a shooting guide to the user.

[0510] What it does: The device displays a screen to the user showing the frame of the entire face and the appropriate distance and angle, allowing the user to accurately take a photo of their skin.

[0511] Input: Application startup signal

[0512] Output: The shooting guide shown to the user

[0513] Step 3:

[0514] The user takes a photo of their skin

[0515] Specific operation: The user follows the instructions on the device to take a photo of their skin. After taking the photo, the device temporarily stores the image data.

[0516] Input: User takes photos according to the shooting guide

[0517] Output: Image data temporarily saved on the device

[0518] Step 4:

[0519] The device sends the image data to the server.

[0520] Specific operation: The device sends the temporarily stored image data to a server using a security protocol such as HTTPS. An internet connection is required for transmission.

[0521] Input: Temporarily saved image data

[0522] Output: Image data sent to the server via HTTPS protocol

[0523] Step 5:

[0524] The server receives and stores the image data.

[0525] Specific operation: The server receives the transmitted image data and stores it in a database along with the date and user identifier.

[0526] Input: Image data received via the HTTPS protocol

[0527] Output: Image data with date and user identifier stored in a database

[0528] Step 6:

[0529] The server analyzes the data using a deep learning model

[0530] How it works: The server analyzes the stored image data using a deep learning algorithm (e.g., CNN), which extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[0531] Input: Image data stored in a database

[0532] Output: Extracted skin feature data

[0533] Step 7:

[0534] The server compares the data with past data and evaluates it.

[0535] Specific operation: The server compares newly analyzed data with past data to evaluate skin trends, such as the appearance of new blemishes or increased wrinkles.

[0536] Input: Newly analyzed skin feature data and past data

[0537] Output: Evaluation results of fluctuation trends

[0538] Step 8:

[0539] The server generates a feedback report based on the analysis results.

[0540] Specific operation: The server generates a feedback report based on the analysis results, including a current skin condition assessment and recommended nutrients and skin care products.

[0541] Input: Evaluation result of fluctuation trend

[0542] Output: Feedback report

[0543] Step 9:

[0544] The server detects an anomaly and generates a warning

[0545] Specific operation: If the server detects any abnormalities, such as sudden pigmentation, it generates a report containing a warning, such as "There is a risk of skin cancer, so we recommend that you seek medical advice immediately."

[0546] Input: Anomaly detection based on analysis results

[0547] Output: Report with warnings

[0548] Step 10:

[0549] The server sends a feedback report to the device.

[0550] Specific operation: The server sends the generated feedback report to the terminal using a communication protocol (e.g., HTTPS).

[0551] Input: Generated feedback report

[0552] Output: Feedback report sent to the device

[0553] Step 11:

[0554] The device notifies the user

[0555] Specific behavior: The device receives the feedback report and displays a notification to the user, with specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[0556] Input: Feedback reports received

[0557] Output: A notification message that is displayed to the user.

[0558] (Application example 1)

[0559] 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."

[0560] Conventional skin analysis systems have the drawback of making it difficult for users to regularly monitor their skin health and take appropriate measures. They also struggle to respond quickly when abnormalities occur, and lack personalized support, especially in physical stores. Therefore, there is a need for a system that allows customers to check their skin condition in real time and quickly recommend appropriate skin care products and nutrients.

[0561] 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.

[0562] In this invention, the server includes means for collecting image data of the user's skin, means for transmitting the collected image data to the server, means for analyzing the image data to evaluate the user's skin condition, means for recommending nutrients and skin care products necessary for the user based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, means for transmitting the evaluation results and recommendations to a terminal and notifying the user, and means for collecting image data of the user's skin using smart glasses and displaying the evaluation results in real time, thereby enabling the user to check their skin condition in real time and quickly receive recommendations for appropriate skin care products and nutrients.

[0563] "User" refers to an individual who uses the system to analyze their skin condition and receive recommendations for skin care products and nutrients.

[0564] "Skin image data" is an image showing the condition of the skin on the user's face or body, and includes information for evaluating the skin condition by analyzing the image.

[0565] The "server" is a central processing unit that receives, stores, analyzes image data sent by users, and generates recommendations based on the results.

[0566] "Recommendation means" refers to a method or device for suggesting appropriate skin care products and nutrients to a user based on the results of skin analysis.

[0567] The term "means for generating a warning" refers to a method or device for creating information that alerts the user when an abnormality is detected from the analysis results.

[0568] The "notification means" refers to a method or device for transmitting the evaluation results and recommendations to a user terminal and informing the user of them.

[0569] "Smart glasses" refers to a device worn on the head that integrates the user's visual and video information through built-in cameras and displays.

[0570] "Means for displaying evaluation results in real time" refers to a method or device for quickly providing the analyzed results to the user so that they can be checked immediately.

[0571] This invention is a system that continuously collects and analyzes image data of a user's skin to evaluate the user's skin condition and recommend necessary nutrients and skin care products. Furthermore, the present invention uses smart glasses to provide evaluation results in real time, enabling personalized service in physical stores. Specific embodiments for implementing the present invention are described below.

[0572] User data collection

[0573] Smart glasses are used as a terminal to collect image data of the user's skin. The user wears the smart glasses and uses the built-in camera to take a picture of their skin according to specified shooting conditions (e.g., appropriate angle and distance). This allows the user to easily obtain high-quality skin image data.

[0574] Sending and Receiving Data

[0575] Image data collected by the smart glasses is transmitted to a server using a secure protocol (e.g., HTTPS). The server receives the data, organizes it by date and user identifier, and stores it for future analysis and comparison.

[0576] Data analysis and evaluation

[0577] The server analyzes the stored image data using deep learning algorithms and other image analysis technologies (e.g., Keras, OpenCV). This makes it possible to evaluate the user's skin condition (e.g., blemishes, wrinkles, dryness, and changes in skin tone). By comparing the data with past data, it is also possible to understand trends in skin condition.

[0578] Feedback and Anomaly Detection

[0579] Based on the analysis results, the server generates a feedback report that includes an assessment of the skin's current condition, recommended nutrients and skin care products, and alerts if abnormalities are detected, such as a sudden increase in pigmentation that could signal a risk of skin cancer.

[0580] Sending and notifying feedback

[0581] The server sends the generated feedback report to the smart glasses, which notify the user and display the analysis results in real time on the display. The user can receive notifications and check recommended skin care products and nutrients, or take immediate action if a doctor's diagnosis is required.

[0582] Specific examples

[0583] Consider a scenario in which a beauty consultant wears smart glasses and approaches a customer who visits the store in the following manner.

[0584] Beauty consultant: "Hello, I'll check your skin condition and recommend the best skin care products for you."

[0585] Customer: "Yes, please."

[0586] The beauty consultant begins to scan the customer's skin using the smart glasses and analyzes it using the following prompts:

[0587] Prompt Sentence Examples

[0588] "Capture image data of the customer's skin and provide recommended skin care products and an evaluation of the skin condition as analysis results."

[0589] Using this system, customers can check their skin condition in real time and receive prompt recommendations for optimal skin care products and nutrients. If any serious skin abnormalities are detected, they can immediately seek medical advice. This allows for personalized service in physical stores and improves customer satisfaction.

[0590] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0591] Step 1:

[0592] A user wearing smart glasses takes a picture of their skin under specified shooting conditions (appropriate angle and distance). Skin image data is collected using the smart glasses' camera.

[0593] Input: An image of the user's skin

[0594] Output: Image data of the captured skin

[0595] Step 2:

[0596] The smart glasses transmit the collected image data to a server using a secure protocol (e.g., HTTPS).

[0597] Input: Skin image data

[0598] Output: Image data sent to the server

[0599] Step 3:

[0600] The server saves the received image data, organizes it by date and user identifier, and stores it in a database.

[0601] Input: Image data sent

[0602] Output: Organized database entries

[0603] Step 4:

[0604] The server analyzes the stored image data using deep learning algorithms and image analysis techniques (e.g., Keras, OpenCV), thereby evaluating the user's skin condition (blemishes, wrinkles, dryness, color variations, etc.).

[0605] Input: Organized image data

[0606] Output: Analysis results (skin condition evaluation)

[0607] Step 5:

[0608] The server compares the data with past data to identify trends in skin condition, and compares past and current data to extract points of change.

[0609] Input: Current and past analysis results

[0610] Output: Fluctuation trend data

[0611] Step 6:

[0612] The server generates a feedback report based on the analysis results, which includes a current skin assessment, recommended nutrients and skin care products, and alerts if any abnormalities are detected.

[0613] Input: Analysis results and fluctuation trend data

[0614] Output: Feedback report

[0615] Step 7:

[0616] The server sends the generated feedback report to the smart glasses, which receive the report and notify the user.

[0617] Input: Feedback Report

[0618] Output: Informed assessment findings and recommendations

[0619] Step 8:

[0620] The smart glasses display the analysis results to the user in real time, and the user can view the display on the smart glasses, including recommended skin care products and nutrients, as well as warnings if any abnormalities are detected.

[0621] Input: Informed assessment findings and recommendations

[0622] Output: Evaluation results and recommendations displayed on the screen

[0623] In this way, users can check their skin condition in real time, receive prompt recommendations for appropriate skin care products and nutrients, and can immediately seek medical advice if any serious skin abnormalities are detected.

[0624] 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.

[0625] This invention combines a system that continuously collects and analyzes a user's skin data to evaluate the user's skin condition and recommend necessary nutrients and skin care products with an emotion engine that recognizes the user's emotions. The basic operation of this system is as follows.

[0626] User data collection

[0627] The device (e.g., a smartphone or a dedicated skin scanner) periodically collects image data of the user's skin and emotional data. The user launches the application and follows the instructions to take a photo of their skin, while simultaneously acquiring emotional data using the user's facial expressions and voice.

[0628] Sending and Receiving Data

[0629] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS). The server receives the data, associates the image data with the emotion data, and stores them in a database. The stored data is organized by user identifier, date, etc., for later analysis.

[0630] Data analysis and evaluation

[0631] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.). At the same time, it uses an emotion engine to analyze the user's emotional data and recognize their current emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[0632] Generate feedback

[0633] The server generates a feedback report based on the analysis results, including an assessment of the skin's health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the user is stressed, the server may recommend skin care products or lifestyle changes that will help reduce stress.

[0634] Sending and notifying feedback

[0635] The server sends the generated feedback report to the device, which then receives it and displays it on the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0636] Specific examples

[0637] Example 1: Daily skin care and emotional management

[0638] Users use the app to take photos of their skin in the morning and simultaneously collect emotional data. The server detects dryness and stress levels and recommends moisturizing skin care products and provides advice on how to reduce stress. This allows users to care for both their skin and their emotions.

[0639] Example 2: Early disease detection and response

[0640] The user scans their skin every day and collects emotional data. The server detects sudden pigmentation and abnormalities in the user's emotional state, and determines that they are at risk of skin cancer. The server notifies the user, saying, "Abnormal pigmentation has been detected in a part of your skin. We recommend that you consult a dermatologist immediately. We also recommend that you remain calm." The user can then seek a specialist's diagnosis early and take appropriate measures to deal with the emotional issues.

[0641] In this way, by using the system of the present invention, users can comprehensively manage their daily skin health and emotional state, enabling them to detect and address health risks early.

[0642] The processing flow will be explained below.

[0643] Step 1:

[0644] The user launches the smartphone app and takes a photo of their skin. The app then instructs the user on the correct position and angle to capture accurate image data.

[0645] Step 2:

[0646] The terminal simultaneously collects the captured image data and the user's emotional data (for example, facial expressions and voice data).

[0647] Step 3:

[0648] The device performs pre-processing to convert the skin image data into the required format, including adjusting the image resolution and removing noise.

[0649] Step 4:

[0650] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS).

[0651] Step 5:

[0652] The server receives the image data and emotion data sent from the device and stores them in a database. The data is organized by user identifier, date, etc.

[0653] Step 6:

[0654] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[0655] Step 7:

[0656] The server uses an emotion engine to analyze the emotion data and recognize the user's emotional state (e.g., happiness, sadness, stress, etc.).

[0657] Step 8:

[0658] Based on the analysis results, the server comprehensively evaluates the user's skin condition and emotional state, including determining the necessary nutrients and skin care products according to the skin's health and emotional state.

[0659] Step 9:

[0660] The server generates a feedback report summarizing the assessment results and recommendations, including a skin health assessment, recommended nutrients and skin care products, and advice based on emotional state.

[0661] Step 10:

[0662] The server transmits the generated feedback report to the terminal.

[0663] Step 11:

[0664] The device displays the feedback report received from the server in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0665] Step 12:

[0666] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution as necessary. The user can also take actions to reduce stress (e.g., practicing relaxation techniques).

[0667] This series of processes allows users to comprehensively manage both their skin health and their emotions, enabling them to detect health risks early and take appropriate measures.

[0668] Example 2

[0669] 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."

[0670] Previous skin care systems focused only on analyzing the user's skin condition and were unable to provide comprehensive advice that took into account the user's emotional state. Furthermore, they ignored the impact of changes in emotional state on the skin, resulting in a lack of specific guidelines for users to practice better skin care. As a result, it was difficult for users to select optimal products and improve their lifestyle habits, making it difficult to maintain healthy skin.

[0671] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data to evaluate the user's skin condition and the emotional data to recognize the user's emotional state, means for recommending nutrients and skin care products necessary for the user based on the evaluation results and the recognition results and generating advice based on the emotional state, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This makes it possible to comprehensively manage the user's skin health condition and emotional state and recommend optimal skin care products and lifestyle improvements.

[0672] "Skin image data" refers to photographs or image data acquired to record the user's skin condition.

[0673] "Emotional data" refers to data such as voice and facial expressions that are collected to express the user's emotional state.

[0674] A "terminal" is an electronic device used by a user, such as a smartphone or a dedicated skin scanner.

[0675] A "server" is a computer system for analyzing collected data and processing results.

[0676] A "deep learning algorithm" is an artificial intelligence technology that learns using large amounts of data and performs highly accurate analysis.

[0677] An "emotion engine" is software that analyzes collected emotion data and recognizes the user's emotional state.

[0678] A "feedback report" is a report generated based on the analysis results that includes evaluations and advice regarding the user's skin health and emotional state.

[0679] A "secure protocol" is a communication method that prevents unauthorized access by third parties when transmitting data.

[0680] "Nutrients" are substances such as vitamins and minerals that need to be ingested to maintain human health.

[0681] "Skin care products" are cosmetic and medicated products used to protect and improve the skin.

[0682] "Abnormal" refers to skin problems or changes in emotional state that are different from normal.

[0683] "Notification" is a function that notifies the user of information via the terminal.

[0684] This invention is a system that continuously collects and analyzes a user's skin and emotional data to evaluate the user's skin condition and emotional state, and provides advice on necessary nutrients, skin care products, and lifestyle habits. Detailed embodiments of this system are described below.

[0685] First, the user launches a dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The user takes the photo of their skin, and the device acquires the image data. At the same time, the device collects emotional data from the user's voice, facial expressions, etc. This is done using the smartphone's camera and microphone functions.

[0686] The device then preprocesses the collected skin image data and emotion data. This preprocessing includes denoising the images and converting them to a suitable format. The preprocessed data is then sent to a server using the secure HTTPS protocol. The server then stores the received data in a database, organized by user identifier, date, and other information.

[0687] The server applies a deep learning algorithm to the stored image data to analyze skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[0688] The server generates a feedback report based on the analysis results. The report includes an assessment of the user's skin health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the server determines that the user is under stress and has dry skin, it will recommend lifestyle changes that will help reduce stress and skin care products rich in moisturizing ingredients. If the analysis detects any abnormalities (e.g., sudden pigmentation), it will generate a warning recommending that the user consult a dermatologist.

[0689] Finally, the server sends the generated feedback report to the device. The device receives it and displays it on the user interface. For example, the device may display a message in a format that is easy for the user to understand, such as, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0690] Prompt Sentence Examples

[0691] "We will provide appropriate feedback by collecting data on the user's skin condition and emotions and analyzing it using deep learning and an emotion engine. For example, if the user has dry skin and is feeling stressed, we will recommend skin care products with high moisturizing ingredients and advice on stress reduction. Please list the recommended products and advice in this case."

[0692] This system allows users to comprehensively manage their skin health and emotional state, enabling them to implement optimal skin care products and lifestyle improvements.

[0693] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0694] Step 1: Data collection

[0695] The user launches the dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The device then uses its camera function to acquire image data of the skin captured by the user. At the same time, the device uses its microphone function to collect emotion data from the user's voice and facial expressions. This data is equipped with an emotion engine.

[0696] Input: Smartphone or dedicated skin scanner, photo of user's skin, voice

[0697] Output: Skin image data, emotion data

[0698] Specific operation: The device displays "The camera has been activated. Please take a photo of your skin." The user takes a photo. At the same time, the device records audio.

[0699] Step 2: Preprocessing the data

[0700] The device performs preprocessing on the collected skin image data and emotion data. Image data preprocessing includes noise removal and image format conversion. Emotion data preprocessing includes audio noise removal and sample rate conversion. The device generates preprocessed data.

[0701] Input: Skin image data, emotion data

[0702] Output: Preprocessed image data, preprocessed emotion data

[0703] Specific operation: The device displays "Starting noise removal from image data" and notifies you that "Preprocessing completed" when the process is complete.

[0704] Step 3: Sending data

[0705] The device transmits the preprocessed image data and emotion data to the server using a secure protocol (HTTPS).

[0706] Input: Preprocessed image data, preprocessed emotion data

[0707] Output: Data sent to the server

[0708] Specific operation: The device will display "Sending data..." and after the transmission is complete, will notify you that "Data transmission completed."

[0709] Step 4: Receiving and storing data

[0710] The server receives the preprocessed image data and emotion data and stores them in a database, organized by user identifier and date.

[0711] Input: Preprocessed image data, preprocessed emotion data

[0712] Output: Data stored in the database

[0713] Specific operation: The server records in the log "Data received. Saving to database", and when the saving process is complete, it records "Saving completed".

[0714] Step 5: Analyze the data

[0715] The server uses a deep learning algorithm to analyze the image data stored in the database, extracting skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state.

[0716] Input: Preprocessed image data stored in a database, preprocessed emotion data

[0717] Output: Skin feature data, emotional state data

[0718] Specific operation: The server records in the log "Starting analysis of skin data using deep learning" and generates the analysis results.

[0719] Step 6: Generate ratings and feedback

[0720] The server uses the analysis results to comprehensively assess the user's skin health and emotional state, then generates a feedback report containing skin health assessment, recommended nutrients and skin care products, and advice based on the emotional state, and generates a warning if any abnormalities are detected.

[0721] Input: Skin feature data, emotional state data

[0722] Output: Feedback report, warnings

[0723] Specific behavior: The server logs "Generating feedback report" and saves the generated report.

[0724] Step 7: Submit your feedback

[0725] The server transmits the generated feedback report to the terminal.

[0726] Input: Feedback Report

[0727] Output: Feedback report sent to the device

[0728] Specific operation: The server logs "Sending feedback report to terminal", and after the transmission is complete, logs "Sending completed".

[0729] Step 8: Notification and display of feedback

[0730] The device receives the feedback report and displays it in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[0731] Input: Feedback Report

[0732] Output: Feedback given to the user

[0733] Specific behavior: The device notifies the user that "You have a new feedback report," and the user opens the app to view the feedback.

[0734] Through these steps, the system of the present invention can comprehensively manage the user's skin health and emotional state, and provide optimal skin care products and lifestyle improvements.

[0735] (Application example 2)

[0736] 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."

[0737] Conventional skin care systems specialize in analyzing the user's skin condition and recommending skin care products based on that analysis, but they have the problem of being unable to provide more comprehensive health management because they do not take the user's emotional state into account.In particular, in situations where real-time personalization is required, such as shopping in a physical store, comprehensive advice that includes the user's emotional state is required.

[0738] The identification processing 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 image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data and emotional data to evaluate the user's skin condition and emotional state, means for recommending nutrients and skin care products necessary for the user and emotional advice based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This enables users to receive comprehensive health management and personalized product selection that takes into account their skin condition and emotional state, even in physical stores.

[0739] "User's skin image data" is image information that digitally records the user's skin condition.

[0740] "Emotion data" is information that represents the user's emotional state, and is data extracted from facial expressions, voice, and the like.

[0741] The "collection means" refers to a device or system for acquiring image data of the user's skin and emotion data.

[0742] "Server" refers to a computer system for receiving, storing, and analyzing collected data.

[0743] "Means of transmission" refers to the communication functions and protocols used to transfer collected data to the server.

[0744] "Means for analyzing and evaluating" refers to algorithms or programs that process the collected image data and emotional data to determine the user's skin condition and emotional state.

[0745] "Recommendation means" refers to a device or program that provides appropriate nutritional and skin care products, as well as emotional advice, to a user based on the analysis results.

[0746] "Means for detecting anomalies" refers to systems or programs that identify abnormal conditions from the analysis results and generate warnings.

[0747] "Means of notification" refers to communication means and software for displaying the evaluation results and recommendations on the user's terminal.

[0748] This invention relates to a system that comprehensively evaluates a user's skin condition and emotional state, and recommends appropriate nutrients, skin care products, and emotional advice. To implement this invention, the following hardware and software are mainly used:

[0749] Hardware and software used

[0750] Hardware:

[0751] Smartphone built-in camera: Used to capture image data of the user's skin.

[0752] Smartphone: Runs applications and sends and receives data.

[0753] Server: Stores and analyzes data.

[0754] software:

[0755] Python: Used to write the entire program.

[0756] OpenCV: Used to capture and preprocess skin image data.

[0757] Keras: Used to run skin data analysis models.

[0758] Transformers library: Used to run the emotion recognition engine.

[0759] Overview of the invention

[0760] 1. User data collection

[0761] The user launches a smartphone application to collect skin image data and emotion data. Skin images are captured using the smartphone's built-in camera. Emotion data is collected through the user's voice and text input.

[0762] 2. Sending and Receiving Data

[0763] The application sends the collected image data and emotion data to a server using a secure protocol (e.g., HTTPS), which then associates the image data with the emotion data and stores them in a database.

[0764] 3. Data analysis and evaluation

[0765] The server analyzes the stored image data and emotional data using a deep learning algorithm and an emotion recognition engine. From the image data, it extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.), and from the emotional data, it recognizes the user's emotional state.

[0766] 4. Generate feedback

[0767] Based on the analysis results, the server generates a feedback report that includes an assessment of skin health, recommended nutrients and skin care products, and advice based on emotional state.

[0768] 5. Sending Feedback and Notifications

[0769] The generated feedback report is sent from the server to the user's device, where the user can check the feedback through a smartphone application.

[0770] Specific examples

[0771] For example, suppose a user uses the app to take a photo of their skin in the morning and inputs, "I've been busy and tired lately." In this case, the server detects that the user's skin is dry and stressed, recommends using skin care products with high moisturizing ingredients, and provides advice on how to reduce stress. Specifically, the server notifies the user, "Your skin appears to be dry. We recommend using skin care products with high moisturizing ingredients. Also, you appear to be feeling stressed recently. We recommend that you adopt a relaxation technique."

[0772] Example prompt sentence:

[0773] I've been busy and tired lately.

[0774] In this way, by using the system of the present invention, users can evaluate their skin and emotional condition in real time even in a physical store, and obtain the most suitable products and advice.

[0775] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0776] Step 1:

[0777] User data collection

[0778] The user launches a smartphone application and collects skin image data and emotion data on the device. Specifically, the smartphone's built-in camera is used to take a skin image, and emotion data is simultaneously collected through voice or text input. The input data is the user's skin image and text or voice data. The output data is the collected image data and emotion data.

[0779] Step 2:

[0780] Sending and Receiving Data

[0781] The device sends the collected image data and emotion data to the server using a secure protocol (e.g., HTTPS). Specifically, the device encodes the data and sends it to the server through a secure channel. The input data is the image data and emotion data collected in step 1. The output data is the image data and emotion data transferred to the server.

[0782] Step 3:

[0783] Data storage

[0784] The server associates the received image data and emotion data and stores them in a database. Specifically, it organizes the data using user identifiers and timestamps for later analysis. The input data is the image data and emotion data sent to the server in step 2. The output data is the stored database entry.

[0785] Step 4:

[0786] Data analysis

[0787] The server analyzes the stored image data and emotion data using a deep learning algorithm and emotion recognition engine. Specifically, it extracts skin features (blemishes, wrinkles, dryness, color variations, etc.) from the image data and determines the emotional state using the emotion recognition engine. The input data are the image data and emotion data obtained from the database. The output data are the skin evaluation results and emotion evaluation results.

[0788] Step 5:

[0789] Generate feedback

[0790] The server generates a feedback report based on the skin assessment results and emotion assessment results. Specifically, it creates a report containing advice on appropriate nutrients, skin care products, and emotions. The input data are the skin assessment results and emotion assessment results obtained in step 4. The output data is the feedback report to be sent to the user.

[0791] Step 6:

[0792] Sending and notifying feedback

[0793] The server sends the generated feedback report to the terminal and notifies the user. Specifically, the terminal receives the feedback report and displays it on the user interface. The input data is the feedback report generated in step 5. The output data is the feedback report displayed on the terminal and the notification to the user.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] [Third embodiment]

[0798] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0799] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0800] 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).

[0801] 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.

[0802] 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.

[0803] 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).

[0804] 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. 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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."

[0810] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. The basic operation of this system is as follows.

[0811] User data collection

[0812] The device (e.g., a smartphone or dedicated skin scanner) periodically collects image data of the user's skin. The data is collected by the user launching the application at a specific time and taking a photo of their skin according to the instructions. At this stage, the device can instruct the user on the appropriate angle and distance for taking the photo.

[0813] Sending and Receiving Data

[0814] The device sends the collected image data to a server using a secure protocol (e.g., HTTPS). The server receives the data and stores it in a database. The stored data is organized by date, user identifier, etc., for later analysis.

[0815] Data analysis and evaluation

[0816] The server uses deep learning algorithms and other image analysis techniques to analyze the stored image data. This extracts the user's skin characteristics (such as blemishes, wrinkles, dryness, and variations in skin tone). This is then compared with past data to identify trends in skin changes. For example, the appearance of new blemishes or increased wrinkles can be detected. Based on the results of this analysis, the system recommends the user's necessary nutrients and appropriate skin care products.

[0817] Feedback and Anomaly Detection

[0818] The server generates a feedback report based on the analysis results, including an assessment of the current state of the skin, recommended nutrients and skin care products, and warnings if abnormalities are detected, such as sudden pigmentation that could signal a risk of skin cancer.

[0819] Sending and notifying feedback

[0820] The server sends the generated feedback report to the device, which then receives it and notifies the user. The notification includes specific messages such as "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. We recommend that you consult a dermatologist immediately."

[0821] Specific examples

[0822] Example 1: Daily skin care recommendations

[0823] The user takes a photo of their skin using a smartphone app in the morning, and the image data is sent to a server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing the user to take appropriate skin care measures.

[0824] Example 2: Early detection of disease

[0825] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. We recommend that you consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[0826] In this way, by using the system of the present invention, users can accurately and effectively manage their daily skin health, enabling early detection of potential health risks.

[0827] The processing flow will be explained below.

[0828] Step 1:

[0829] The user launches the smartphone app and takes a picture of their skin. The app then instructs the user on the position and angle of the photo.

[0830] Step 2:

[0831] The device pre-processes the captured image data and converts it into the required format, which includes adjusting the image resolution and removing noise.

[0832] Step 3:

[0833] The device sends the preprocessed image data to the server using a secure protocol (e.g., HTTPS).

[0834] Step 4:

[0835] The server receives the image data sent from the device and stores it in a database. The data is organized by user identifier, date, etc.

[0836] Step 5:

[0837] The server then begins processing the stored image data, using deep learning algorithms to extract skin characteristics such as blemishes, wrinkles, dryness, and variations in skin tone.

[0838] Step 6:

[0839] The server compares the extracted features with past data to identify trends in skin changes, such as the appearance of new blemishes or increased wrinkles.

[0840] Step 7:

[0841] The server evaluates the user's skin condition based on the analysis results, including determining the skin's health and necessary nutrients.

[0842] Step 8:

[0843] The server generates a feedback report summarizing the evaluation results and recommendations, including skin care product recommendations and warnings if any abnormalities are detected.

[0844] Step 9:

[0845] The server transmits the generated feedback report to the terminal.

[0846] Step 10:

[0847] The device displays the feedback report received from the server in the user interface. For example, it may say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid."

[0848] Step 11:

[0849] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution if necessary.

[0850] This series of processes allows users to properly manage their daily skin condition and detect and address health risks early.

[0851] Example 1

[0852] 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."

[0853] Conventional skin care systems make it difficult for users to accurately understand their skin condition and select appropriate skin care products. Furthermore, they lack the means to detect skin abnormalities early, making it difficult to detect potential health risks early. This creates inconvenience for users when managing their daily skin care.

[0854] 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.

[0855] In this invention, the server includes means for collecting image data of a user's skin, means for transmitting the collected image data to the server using a security protocol, and means for analyzing the image data using a deep learning algorithm to extract the user's skin characteristics. This allows the user to accurately understand their skin condition and receive recommendations for appropriate nutrients and skin care products. Furthermore, skin abnormalities can be detected early and appropriate warnings can be received, enabling early detection of health risks.

[0856] A "user" is an individual who uses the system to manage their skin condition.

[0857] "Skin image data" is a digital image file that contains visual information that indicates the condition of a user's skin.

[0858] "Security Protocol" means the technical measures used to protect and secure communications during data transmission, and generally includes HTTPS.

[0859] "Server" means a computing device on a network that receives, stores, and analyzes data.

[0860] A "deep learning algorithm" is a machine learning method that learns and analyzes features from large datasets, and includes, for example, Convolutional Neural Networks (CNNs).

[0861] "Skin characteristics" are attributes that indicate the condition of the user's skin, and include blemishes, wrinkles, dryness, and variations in color tone.

[0862] The "evaluation results" are judgments and conclusions about the user's skin condition obtained based on the analysis of image data.

[0863] "Nutrients" are food ingredients necessary to maintain the health of the user's skin.

[0864] "Skin care products" are cosmetics and skin care items used to improve and maintain the condition of the skin.

[0865] "Warning" is a warning message to notify the user of any abnormalities detected in their skin data.

[0866] "Feedback Report" means a report provided to a User that includes analysis results and recommendations.

[0867] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. This system is an appropriate combination of hardware and software.

[0868] User data collection

[0869] Users periodically collect image data of their skin using a smartphone or a dedicated skin scanner (device). The user launches the application at a specific time and takes a photo of their skin according to the shooting guide (e.g., appropriate shooting angle and distance) displayed on the device. This allows the device to acquire high-quality image data.

[0870] Sending and Receiving Data

[0871] The device sends the captured image data to a server using a security protocol such as HTTPS. The server receives the data and stores it in a database. This data is organized by date and user identifier for later analysis.

[0872] Data analysis and evaluation

[0873] The server uses deep learning algorithms (e.g., Convolutional Neural Networks (CNNs)) and other image analysis techniques to analyze the stored image data. This analysis extracts the user's skin characteristics (such as age spots, wrinkles, dryness, and variations in skin tone). The server then compares the newly analyzed data with past data to evaluate skin trends. For example, the appearance of new age spots or increased wrinkles is analyzed. Based on the results of this analysis, the server recommends the user's necessary nutrients and appropriate skin care products.

[0874] Feedback and Anomaly Detection

[0875] The server generates a feedback report based on the analysis results. The report includes an assessment of the current skin condition, recommended nutrients and skin care products, and a warning if an abnormality is detected. For example, if an abnormality such as sudden pigmentation is detected, the user may be at risk of skin cancer and be advised to seek immediate medical advice from a specialist.

[0876] Sending and notifying feedback

[0877] The server sends the generated feedback report to the device, which receives it and displays a notification to the user. The notification content includes specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[0878] Specific examples

[0879] Example 1: Daily skin care recommendations

[0880] Users use the app to take a photo of their skin in the morning, and the image data is sent to the server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing users to take appropriate skin care measures.

[0881] Example 2: Early detection of disease

[0882] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. Please consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[0883] Example prompts for generative AI models

[0884] "Analyze skin images taken by the user to detect dryness."

[0885] "Check for the appearance of new blemishes and wrinkles and generate a report recommending appropriate skin care products."

[0886] "Detect sudden hyperpigmentation and generate a warning recommending that the user consult a dermatologist."

[0887] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0888] Step 1:

[0889] The user launches the application

[0890] How it works: The user launches an application installed on their smartphone or a dedicated skin scanner. The application has the function of sending notifications to the user at specific times.

[0891] Input: The user launches an application during a specific time period.

[0892] Output: The application is launched and the user is shown a guide to taking pictures.

[0893] Step 2:

[0894] The device displays a shooting guide to the user.

[0895] What it does: The device displays a screen to the user showing the frame of the entire face and the appropriate distance and angle, allowing the user to accurately take a photo of their skin.

[0896] Input: Application startup signal

[0897] Output: The shooting guide shown to the user

[0898] Step 3:

[0899] The user takes a photo of their skin

[0900] Specific operation: The user follows the instructions on the device to take a photo of their skin. After taking the photo, the device temporarily stores the image data.

[0901] Input: User takes photos according to the shooting guide

[0902] Output: Image data temporarily saved on the device

[0903] Step 4:

[0904] The device sends the image data to the server.

[0905] Specific operation: The device sends the temporarily stored image data to a server using a security protocol such as HTTPS. An internet connection is required for transmission.

[0906] Input: Temporarily saved image data

[0907] Output: Image data sent to the server via HTTPS protocol

[0908] Step 5:

[0909] The server receives and stores the image data.

[0910] Specific operation: The server receives the transmitted image data and stores it in a database along with the date and user identifier.

[0911] Input: Image data received via the HTTPS protocol

[0912] Output: Image data with date and user identifier stored in a database

[0913] Step 6:

[0914] The server analyzes the data using a deep learning model

[0915] How it works: The server analyzes the stored image data using a deep learning algorithm (e.g., CNN), which extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[0916] Input: Image data stored in a database

[0917] Output: Extracted skin feature data

[0918] Step 7:

[0919] The server compares the data with past data and evaluates it.

[0920] Specific operation: The server compares newly analyzed data with past data to evaluate skin trends, such as the appearance of new blemishes or increased wrinkles.

[0921] Input: Newly analyzed skin feature data and past data

[0922] Output: Evaluation results of fluctuation trends

[0923] Step 8:

[0924] The server generates a feedback report based on the analysis results.

[0925] Specific operation: The server generates a feedback report based on the analysis results, including a current skin condition assessment and recommended nutrients and skin care products.

[0926] Input: Evaluation result of fluctuation trend

[0927] Output: Feedback report

[0928] Step 9:

[0929] The server detects an anomaly and generates a warning

[0930] Specific operation: If the server detects any abnormalities, such as sudden pigmentation, it generates a report containing a warning, such as "There is a risk of skin cancer, so we recommend that you seek medical advice immediately."

[0931] Input: Anomaly detection based on analysis results

[0932] Output: Report with warnings

[0933] Step 10:

[0934] The server sends a feedback report to the device.

[0935] Specific operation: The server sends the generated feedback report to the terminal using a communication protocol (e.g., HTTPS).

[0936] Input: Generated feedback report

[0937] Output: Feedback report sent to the device

[0938] Step 11:

[0939] The device notifies the user

[0940] Specific behavior: The device receives the feedback report and displays a notification to the user, with specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[0941] Input: Feedback reports received

[0942] Output: A notification message that is displayed to the user.

[0943] (Application example 1)

[0944] 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."

[0945] Conventional skin analysis systems have the drawback of making it difficult for users to regularly monitor their skin health and take appropriate measures. They also struggle to respond quickly when abnormalities occur, and lack personalized support, especially in physical stores. Therefore, there is a need for a system that allows customers to check their skin condition in real time and quickly recommend appropriate skin care products and nutrients.

[0946] 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.

[0947] In this invention, the server includes means for collecting image data of the user's skin, means for transmitting the collected image data to the server, means for analyzing the image data to evaluate the user's skin condition, means for recommending nutrients and skin care products necessary for the user based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, means for transmitting the evaluation results and recommendations to a terminal and notifying the user, and means for collecting image data of the user's skin using smart glasses and displaying the evaluation results in real time, thereby enabling the user to check their skin condition in real time and quickly receive recommendations for appropriate skin care products and nutrients.

[0948] "User" refers to an individual who uses the system to analyze their skin condition and receive recommendations for skin care products and nutrients.

[0949] "Skin image data" is an image showing the condition of the skin on the user's face or body, and includes information for evaluating the skin condition by analyzing the image.

[0950] The "server" is a central processing unit that receives, stores, analyzes image data sent by users, and generates recommendations based on the results.

[0951] "Recommendation means" refers to a method or device for suggesting appropriate skin care products and nutrients to a user based on the results of skin analysis.

[0952] The term "means for generating a warning" refers to a method or device for creating information that alerts the user when an abnormality is detected from the analysis results.

[0953] The "notification means" refers to a method or device for transmitting the evaluation results and recommendations to a user terminal and informing the user of them.

[0954] "Smart glasses" refers to a device worn on the head that integrates the user's visual and video information through built-in cameras and displays.

[0955] "Means for displaying evaluation results in real time" refers to a method or device for quickly providing the analyzed results to the user so that they can be checked immediately.

[0956] This invention is a system that continuously collects and analyzes image data of a user's skin to evaluate the user's skin condition and recommend necessary nutrients and skin care products. Furthermore, the present invention uses smart glasses to provide evaluation results in real time, enabling personalized service in physical stores. Specific embodiments for implementing the present invention are described below.

[0957] User data collection

[0958] Smart glasses are used as a terminal to collect image data of the user's skin. The user wears the smart glasses and uses the built-in camera to take a picture of their skin according to specified shooting conditions (e.g., appropriate angle and distance). This allows the user to easily obtain high-quality skin image data.

[0959] Sending and Receiving Data

[0960] Image data collected by the smart glasses is transmitted to a server using a secure protocol (e.g., HTTPS). The server receives the data, organizes it by date and user identifier, and stores it for future analysis and comparison.

[0961] Data analysis and evaluation

[0962] The server analyzes the stored image data using deep learning algorithms and other image analysis technologies (e.g., Keras, OpenCV). This makes it possible to evaluate the user's skin condition (e.g., blemishes, wrinkles, dryness, and changes in skin tone). By comparing the data with past data, it is also possible to understand trends in skin condition.

[0963] Feedback and Anomaly Detection

[0964] Based on the analysis results, the server generates a feedback report that includes an assessment of the skin's current condition, recommended nutrients and skin care products, and alerts if abnormalities are detected, such as a sudden increase in pigmentation that could signal a risk of skin cancer.

[0965] Sending and notifying feedback

[0966] The server sends the generated feedback report to the smart glasses, which notify the user and display the analysis results in real time on the display. The user can receive notifications and check recommended skin care products and nutrients, or take immediate action if a doctor's diagnosis is required.

[0967] Specific examples

[0968] Consider a scenario in which a beauty consultant wears smart glasses and approaches a customer who visits the store in the following manner.

[0969] Beauty consultant: "Hello, I'll check your skin condition and recommend the best skin care products for you."

[0970] Customer: "Yes, please."

[0971] The beauty consultant begins to scan the customer's skin using the smart glasses and analyzes it using the following prompts:

[0972] Prompt Sentence Examples

[0973] "Capture image data of the customer's skin and provide recommended skin care products and an evaluation of the skin condition as analysis results."

[0974] Using this system, customers can check their skin condition in real time and receive prompt recommendations for optimal skin care products and nutrients. If any serious skin abnormalities are detected, they can immediately seek medical advice. This allows for personalized service in physical stores and improves customer satisfaction.

[0975] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0976] Step 1:

[0977] A user wearing smart glasses takes a picture of their skin under specified shooting conditions (appropriate angle and distance). Skin image data is collected using the smart glasses' camera.

[0978] Input: An image of the user's skin

[0979] Output: Image data of the captured skin

[0980] Step 2:

[0981] The smart glasses transmit the collected image data to a server using a secure protocol (e.g., HTTPS).

[0982] Input: Skin image data

[0983] Output: Image data sent to the server

[0984] Step 3:

[0985] The server saves the received image data, organizes it by date and user identifier, and stores it in a database.

[0986] Input: Image data sent

[0987] Output: Organized database entries

[0988] Step 4:

[0989] The server analyzes the stored image data using deep learning algorithms and image analysis techniques (e.g., Keras, OpenCV), thereby evaluating the user's skin condition (blemishes, wrinkles, dryness, color variations, etc.).

[0990] Input: Organized image data

[0991] Output: Analysis results (skin condition evaluation)

[0992] Step 5:

[0993] The server compares the data with past data to identify trends in skin condition, and compares past and current data to extract points of change.

[0994] Input: Current and past analysis results

[0995] Output: Fluctuation trend data

[0996] Step 6:

[0997] The server generates a feedback report based on the analysis results, which includes a current skin assessment, recommended nutrients and skin care products, and alerts if any abnormalities are detected.

[0998] Input: Analysis results and fluctuation trend data

[0999] Output: Feedback report

[1000] Step 7:

[1001] The server sends the generated feedback report to the smart glasses, which receive the report and notify the user.

[1002] Input: Feedback Report

[1003] Output: Informed assessment findings and recommendations

[1004] Step 8:

[1005] The smart glasses display the analysis results to the user in real time, and the user can view the display on the smart glasses, including recommended skin care products and nutrients, as well as warnings if any abnormalities are detected.

[1006] Input: Informed assessment findings and recommendations

[1007] Output: Evaluation results and recommendations displayed on the screen

[1008] In this way, users can check their skin condition in real time, receive prompt recommendations for appropriate skin care products and nutrients, and can immediately seek medical advice if any serious skin abnormalities are detected.

[1009] 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.

[1010] This invention combines a system that continuously collects and analyzes a user's skin data to evaluate the user's skin condition and recommend necessary nutrients and skin care products with an emotion engine that recognizes the user's emotions. The basic operation of this system is as follows.

[1011] User data collection

[1012] The device (e.g., a smartphone or a dedicated skin scanner) periodically collects image data of the user's skin and emotional data. The user launches the application and follows the instructions to take a photo of their skin, while simultaneously acquiring emotional data using the user's facial expressions and voice.

[1013] Sending and Receiving Data

[1014] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS). The server receives the data, associates the image data with the emotion data, and stores them in a database. The stored data is organized by user identifier, date, etc., for later analysis.

[1015] Data analysis and evaluation

[1016] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.). At the same time, it uses an emotion engine to analyze the user's emotional data and recognize their current emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[1017] Generate feedback

[1018] The server generates a feedback report based on the analysis results, including an assessment of the skin's health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the user is stressed, the server may recommend skin care products or lifestyle changes that will help reduce stress.

[1019] Sending and notifying feedback

[1020] The server sends the generated feedback report to the device, which then receives it and displays it on the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1021] Specific examples

[1022] Example 1: Daily skin care and emotional management

[1023] Users use the app to take photos of their skin in the morning and simultaneously collect emotional data. The server detects dryness and stress levels and recommends moisturizing skin care products and provides advice on how to reduce stress. This allows users to care for both their skin and their emotions.

[1024] Example 2: Early disease detection and response

[1025] The user scans their skin every day and collects emotional data. The server detects sudden pigmentation and abnormalities in the user's emotional state, and determines that they are at risk of skin cancer. The server notifies the user, saying, "Abnormal pigmentation has been detected in a part of your skin. We recommend that you consult a dermatologist immediately. We also recommend that you remain calm." The user can then seek a specialist's diagnosis early and take appropriate measures to deal with the emotional issues.

[1026] In this way, by using the system of the present invention, users can comprehensively manage their daily skin health and emotional state, enabling them to detect and address health risks early.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] The user launches the smartphone app and takes a photo of their skin. The app then instructs the user on the correct position and angle to capture accurate image data.

[1030] Step 2:

[1031] The terminal simultaneously collects the captured image data and the user's emotional data (for example, facial expressions and voice data).

[1032] Step 3:

[1033] The device performs pre-processing to convert the skin image data into the required format, including adjusting the image resolution and removing noise.

[1034] Step 4:

[1035] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS).

[1036] Step 5:

[1037] The server receives the image data and emotion data sent from the device and stores them in a database. The data is organized by user identifier, date, etc.

[1038] Step 6:

[1039] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[1040] Step 7:

[1041] The server uses an emotion engine to analyze the emotion data and recognize the user's emotional state (e.g., happiness, sadness, stress, etc.).

[1042] Step 8:

[1043] Based on the analysis results, the server comprehensively evaluates the user's skin condition and emotional state, including determining the necessary nutrients and skin care products according to the skin's health and emotional state.

[1044] Step 9:

[1045] The server generates a feedback report summarizing the assessment results and recommendations, including a skin health assessment, recommended nutrients and skin care products, and advice based on emotional state.

[1046] Step 10:

[1047] The server transmits the generated feedback report to the terminal.

[1048] Step 11:

[1049] The device displays the feedback report received from the server in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1050] Step 12:

[1051] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution as necessary. The user can also take actions to reduce stress (e.g., practicing relaxation techniques).

[1052] This series of processes allows users to comprehensively manage both their skin health and their emotions, enabling them to detect health risks early and take appropriate measures.

[1053] Example 2

[1054] 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."

[1055] Previous skin care systems focused only on analyzing the user's skin condition and were unable to provide comprehensive advice that took into account the user's emotional state. Furthermore, they ignored the impact of changes in emotional state on the skin, resulting in a lack of specific guidelines for users to practice better skin care. As a result, it was difficult for users to select optimal products and improve their lifestyle habits, making it difficult to maintain healthy skin.

[1056] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data to evaluate the user's skin condition and the emotional data to recognize the user's emotional state, means for recommending nutrients and skin care products necessary for the user based on the evaluation results and the recognition results and generating advice based on the emotional state, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This makes it possible to comprehensively manage the user's skin health condition and emotional state and recommend optimal skin care products and lifestyle improvements.

[1057] "Skin image data" refers to photographs or image data acquired to record the user's skin condition.

[1058] "Emotional data" refers to data such as voice and facial expressions that are collected to express the user's emotional state.

[1059] A "terminal" is an electronic device used by a user, such as a smartphone or a dedicated skin scanner.

[1060] A "server" is a computer system for analyzing collected data and processing results.

[1061] A "deep learning algorithm" is an artificial intelligence technology that learns using large amounts of data and performs highly accurate analysis.

[1062] An "emotion engine" is software that analyzes collected emotion data and recognizes the user's emotional state.

[1063] A "feedback report" is a report generated based on the analysis results that includes evaluations and advice regarding the user's skin health and emotional state.

[1064] A "secure protocol" is a communication method that prevents unauthorized access by third parties when transmitting data.

[1065] "Nutrients" are substances such as vitamins and minerals that need to be ingested to maintain human health.

[1066] "Skin care products" are cosmetic and medicated products used to protect and improve the skin.

[1067] "Abnormal" refers to skin problems or changes in emotional state that are different from normal.

[1068] "Notification" is a function that notifies the user of information via the terminal.

[1069] This invention is a system that continuously collects and analyzes a user's skin and emotional data to evaluate the user's skin condition and emotional state, and provides advice on necessary nutrients, skin care products, and lifestyle habits. Detailed embodiments of this system are described below.

[1070] First, the user launches a dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The user takes the photo of their skin, and the device acquires the image data. At the same time, the device collects emotional data from the user's voice, facial expressions, etc. This is done using the smartphone's camera and microphone functions.

[1071] The device then preprocesses the collected skin image data and emotion data. This preprocessing includes denoising the images and converting them to a suitable format. The preprocessed data is then sent to a server using the secure HTTPS protocol. The server then stores the received data in a database, organized by user identifier, date, and other information.

[1072] The server applies a deep learning algorithm to the stored image data to analyze skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[1073] The server generates a feedback report based on the analysis results. The report includes an assessment of the user's skin health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the server determines that the user is under stress and has dry skin, it will recommend lifestyle changes that will help reduce stress and skin care products rich in moisturizing ingredients. If the analysis detects any abnormalities (e.g., sudden pigmentation), it will generate a warning recommending that the user consult a dermatologist.

[1074] Finally, the server sends the generated feedback report to the device. The device receives it and displays it on the user interface. For example, the device may display a message in a format that is easy for the user to understand, such as, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1075] Prompt Sentence Examples

[1076] "We will provide appropriate feedback by collecting data on the user's skin condition and emotions and analyzing it using deep learning and an emotion engine. For example, if the user has dry skin and is feeling stressed, we will recommend skin care products with high moisturizing ingredients and advice on stress reduction. Please list the recommended products and advice in this case."

[1077] This system allows users to comprehensively manage their skin health and emotional state, enabling them to implement optimal skin care products and lifestyle improvements.

[1078] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1079] Step 1: Data collection

[1080] The user launches the dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The device then uses its camera function to acquire image data of the skin captured by the user. At the same time, the device uses its microphone function to collect emotion data from the user's voice and facial expressions. This data is equipped with an emotion engine.

[1081] Input: Smartphone or dedicated skin scanner, photo of user's skin, voice

[1082] Output: Skin image data, emotion data

[1083] Specific operation: The device displays "The camera has been activated. Please take a photo of your skin." The user takes a photo. At the same time, the device records audio.

[1084] Step 2: Preprocessing the data

[1085] The device performs preprocessing on the collected skin image data and emotion data. Image data preprocessing includes noise removal and image format conversion. Emotion data preprocessing includes audio noise removal and sample rate conversion. The device generates preprocessed data.

[1086] Input: Skin image data, emotion data

[1087] Output: Preprocessed image data, preprocessed emotion data

[1088] Specific operation: The device displays "Starting noise removal from image data" and notifies you that "Preprocessing completed" when the process is complete.

[1089] Step 3: Sending data

[1090] The device transmits the preprocessed image data and emotion data to the server using a secure protocol (HTTPS).

[1091] Input: Preprocessed image data, preprocessed emotion data

[1092] Output: Data sent to the server

[1093] Specific operation: The device will display "Sending data..." and after the transmission is complete, will notify you that "Data transmission completed."

[1094] Step 4: Receiving and storing data

[1095] The server receives the preprocessed image data and emotion data and stores them in a database, organized by user identifier and date.

[1096] Input: Preprocessed image data, preprocessed emotion data

[1097] Output: Data stored in the database

[1098] Specific operation: The server records in the log "Data received. Saving to database", and when the saving process is complete, it records "Saving completed".

[1099] Step 5: Analyze the data

[1100] The server uses a deep learning algorithm to analyze the image data stored in the database, extracting skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state.

[1101] Input: Preprocessed image data stored in a database, preprocessed emotion data

[1102] Output: Skin feature data, emotional state data

[1103] Specific operation: The server records in the log "Starting analysis of skin data using deep learning" and generates the analysis results.

[1104] Step 6: Generate ratings and feedback

[1105] The server uses the analysis results to comprehensively assess the user's skin health and emotional state, then generates a feedback report containing skin health assessment, recommended nutrients and skin care products, and advice based on the emotional state, and generates a warning if any abnormalities are detected.

[1106] Input: Skin feature data, emotional state data

[1107] Output: Feedback report, warnings

[1108] Specific behavior: The server logs "Generating feedback report" and saves the generated report.

[1109] Step 7: Submit your feedback

[1110] The server transmits the generated feedback report to the terminal.

[1111] Input: Feedback Report

[1112] Output: Feedback report sent to the device

[1113] Specific operation: The server logs "Sending feedback report to terminal", and after the transmission is complete, logs "Sending completed".

[1114] Step 8: Notification and display of feedback

[1115] The device receives the feedback report and displays it in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1116] Input: Feedback Report

[1117] Output: Feedback given to the user

[1118] Specific behavior: The device notifies the user that "You have a new feedback report," and the user opens the app to view the feedback.

[1119] Through these steps, the system of the present invention can comprehensively manage the user's skin health and emotional state, and provide optimal skin care products and lifestyle improvements.

[1120] (Application example 2)

[1121] 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."

[1122] Conventional skin care systems specialize in analyzing the user's skin condition and recommending skin care products based on that analysis, but they have the problem of being unable to provide more comprehensive health management because they do not take the user's emotional state into account.In particular, in situations where real-time personalization is required, such as shopping in a physical store, comprehensive advice that includes the user's emotional state is required.

[1123] The identification processing 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 image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data and emotional data to evaluate the user's skin condition and emotional state, means for recommending nutrients and skin care products necessary for the user and emotional advice based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This enables users to receive comprehensive health management and personalized product selection that takes into account their skin condition and emotional state, even in physical stores.

[1124] "User's skin image data" is image information that digitally records the user's skin condition.

[1125] "Emotion data" is information that represents the user's emotional state, and is data extracted from facial expressions, voice, and the like.

[1126] The "collection means" refers to a device or system for acquiring image data of the user's skin and emotion data.

[1127] "Server" refers to a computer system for receiving, storing, and analyzing collected data.

[1128] "Means of transmission" refers to the communication functions and protocols used to transfer collected data to the server.

[1129] "Means for analyzing and evaluating" refers to algorithms or programs that process the collected image data and emotional data to determine the user's skin condition and emotional state.

[1130] "Recommendation means" refers to a device or program that provides appropriate nutritional and skin care products, as well as emotional advice, to a user based on the analysis results.

[1131] "Means for detecting anomalies" refers to systems or programs that identify abnormal conditions from the analysis results and generate warnings.

[1132] "Means of notification" refers to communication means and software for displaying the evaluation results and recommendations on the user's terminal.

[1133] This invention relates to a system that comprehensively evaluates a user's skin condition and emotional state, and recommends appropriate nutrients, skin care products, and emotional advice. To implement this invention, the following hardware and software are mainly used:

[1134] Hardware and software used

[1135] Hardware:

[1136] Smartphone built-in camera: Used to capture image data of the user's skin.

[1137] Smartphone: Runs applications and sends and receives data.

[1138] Server: Stores and analyzes data.

[1139] software:

[1140] Python: Used to write the entire program.

[1141] OpenCV: Used to capture and preprocess skin image data.

[1142] Keras: Used to run skin data analysis models.

[1143] Transformers library: Used to run the emotion recognition engine.

[1144] Overview of the invention

[1145] 1. User data collection

[1146] The user launches a smartphone application to collect skin image data and emotion data. Skin images are captured using the smartphone's built-in camera. Emotion data is collected through the user's voice and text input.

[1147] 2. Sending and Receiving Data

[1148] The application sends the collected image data and emotion data to a server using a secure protocol (e.g., HTTPS), which then associates the image data with the emotion data and stores them in a database.

[1149] 3. Data analysis and evaluation

[1150] The server analyzes the stored image data and emotional data using a deep learning algorithm and an emotion recognition engine. From the image data, it extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.), and from the emotional data, it recognizes the user's emotional state.

[1151] 4. Generate feedback

[1152] Based on the analysis results, the server generates a feedback report that includes an assessment of skin health, recommended nutrients and skin care products, and advice based on emotional state.

[1153] 5. Sending Feedback and Notifications

[1154] The generated feedback report is sent from the server to the user's device, where the user can check the feedback through a smartphone application.

[1155] Specific examples

[1156] For example, suppose a user uses the app to take a photo of their skin in the morning and inputs, "I've been busy and tired lately." In this case, the server detects that the user's skin is dry and stressed, recommends using skin care products with high moisturizing ingredients, and provides advice on how to reduce stress. Specifically, the server notifies the user, "Your skin appears to be dry. We recommend using skin care products with high moisturizing ingredients. Also, you appear to be feeling stressed recently. We recommend that you adopt a relaxation technique."

[1157] Example prompt sentence:

[1158] I've been busy and tired lately.

[1159] In this way, by using the system of the present invention, users can evaluate their skin and emotional condition in real time even in a physical store, and obtain the most suitable products and advice.

[1160] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1161] Step 1:

[1162] User data collection

[1163] The user launches a smartphone application and collects skin image data and emotion data on the device. Specifically, the smartphone's built-in camera is used to take a skin image, and emotion data is simultaneously collected through voice or text input. The input data is the user's skin image and text or voice data. The output data is the collected image data and emotion data.

[1164] Step 2:

[1165] Sending and Receiving Data

[1166] The device sends the collected image data and emotion data to the server using a secure protocol (e.g., HTTPS). Specifically, the device encodes the data and sends it to the server through a secure channel. The input data is the image data and emotion data collected in step 1. The output data is the image data and emotion data transferred to the server.

[1167] Step 3:

[1168] Data storage

[1169] The server associates the received image data and emotion data and stores them in a database. Specifically, it organizes the data using user identifiers and timestamps for later analysis. The input data is the image data and emotion data sent to the server in step 2. The output data is the stored database entry.

[1170] Step 4:

[1171] Data analysis

[1172] The server analyzes the stored image data and emotion data using a deep learning algorithm and emotion recognition engine. Specifically, it extracts skin features (blemishes, wrinkles, dryness, color variations, etc.) from the image data and determines the emotional state using the emotion recognition engine. The input data are the image data and emotion data obtained from the database. The output data are the skin evaluation results and emotion evaluation results.

[1173] Step 5:

[1174] Generate feedback

[1175] The server generates a feedback report based on the skin assessment results and emotion assessment results. Specifically, it creates a report containing advice on appropriate nutrients, skin care products, and emotions. The input data are the skin assessment results and emotion assessment results obtained in step 4. The output data is the feedback report to be sent to the user.

[1176] Step 6:

[1177] Sending and notifying feedback

[1178] The server sends the generated feedback report to the terminal and notifies the user. Specifically, the terminal receives the feedback report and displays it on the user interface. The input data is the feedback report generated in step 5. The output data is the feedback report displayed on the terminal and the notification to the user.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] [Fourth embodiment]

[1183] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1184] 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.

[1185] 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).

[1186] 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.

[1187] 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.

[1188] 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).

[1189] 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. 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.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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.

[1195] 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."

[1196] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. The basic operation of this system is as follows.

[1197] User data collection

[1198] The device (e.g., a smartphone or dedicated skin scanner) periodically collects image data of the user's skin. The data is collected by the user launching the application at a specific time and taking a photo of their skin according to the instructions. At this stage, the device can instruct the user on the appropriate angle and distance for taking the photo.

[1199] Sending and Receiving Data

[1200] The device sends the collected image data to a server using a secure protocol (e.g., HTTPS). The server receives the data and stores it in a database. The stored data is organized by date, user identifier, etc., for later analysis.

[1201] Data analysis and evaluation

[1202] The server uses deep learning algorithms and other image analysis techniques to analyze the stored image data. This extracts the user's skin characteristics (such as blemishes, wrinkles, dryness, and variations in skin tone). This is then compared with past data to identify trends in skin changes. For example, the appearance of new blemishes or increased wrinkles can be detected. Based on the results of this analysis, the system recommends the user's necessary nutrients and appropriate skin care products.

[1203] Feedback and Anomaly Detection

[1204] The server generates a feedback report based on the analysis results, including an assessment of the current state of the skin, recommended nutrients and skin care products, and warnings if abnormalities are detected, such as sudden pigmentation that could signal a risk of skin cancer.

[1205] Sending and notifying feedback

[1206] The server sends the generated feedback report to the device, which then receives it and notifies the user. The notification includes specific messages such as "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. We recommend that you consult a dermatologist immediately."

[1207] Specific examples

[1208] Example 1: Daily skin care recommendations

[1209] The user takes a photo of their skin using a smartphone app in the morning, and the image data is sent to a server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing the user to take appropriate skin care measures.

[1210] Example 2: Early detection of disease

[1211] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. We recommend that you consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[1212] In this way, by using the system of the present invention, users can accurately and effectively manage their daily skin health, enabling early detection of potential health risks.

[1213] The processing flow will be explained below.

[1214] Step 1:

[1215] The user launches the smartphone app and takes a picture of their skin. The app then instructs the user on the position and angle of the photo.

[1216] Step 2:

[1217] The device pre-processes the captured image data and converts it into the required format, which includes adjusting the image resolution and removing noise.

[1218] Step 3:

[1219] The device sends the preprocessed image data to the server using a secure protocol (e.g., HTTPS).

[1220] Step 4:

[1221] The server receives the image data sent from the device and stores it in a database. The data is organized by user identifier, date, etc.

[1222] Step 5:

[1223] The server then begins processing the stored image data, using deep learning algorithms to extract skin characteristics such as blemishes, wrinkles, dryness, and variations in skin tone.

[1224] Step 6:

[1225] The server compares the extracted features with past data to identify trends in skin changes, such as the appearance of new blemishes or increased wrinkles.

[1226] Step 7:

[1227] The server evaluates the user's skin condition based on the analysis results, including determining the skin's health and necessary nutrients.

[1228] Step 8:

[1229] The server generates a feedback report summarizing the evaluation results and recommendations, including skin care product recommendations and warnings if any abnormalities are detected.

[1230] Step 9:

[1231] The server transmits the generated feedback report to the terminal.

[1232] Step 10:

[1233] The device displays the feedback report received from the server in the user interface. For example, it may say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E or hyaluronic acid."

[1234] Step 11:

[1235] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution if necessary.

[1236] This series of processes allows users to properly manage their daily skin condition and detect and address health risks early.

[1237] Example 1

[1238] 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."

[1239] Conventional skin care systems make it difficult for users to accurately understand their skin condition and select appropriate skin care products. Furthermore, they lack the means to detect skin abnormalities early, making it difficult to detect potential health risks early. This creates inconvenience for users when managing their daily skin care.

[1240] 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.

[1241] In this invention, the server includes means for collecting image data of a user's skin, means for transmitting the collected image data to the server using a security protocol, and means for analyzing the image data using a deep learning algorithm to extract the user's skin characteristics. This allows the user to accurately understand their skin condition and receive recommendations for appropriate nutrients and skin care products. Furthermore, skin abnormalities can be detected early and appropriate warnings can be received, enabling early detection of health risks.

[1242] A "user" is an individual who uses the system to manage their skin condition.

[1243] "Skin image data" is a digital image file that contains visual information that indicates the condition of a user's skin.

[1244] "Security Protocol" means the technical measures used to protect and secure communications during data transmission, and generally includes HTTPS.

[1245] "Server" means a computing device on a network that receives, stores, and analyzes data.

[1246] A "deep learning algorithm" is a machine learning method that learns and analyzes features from large datasets, and includes, for example, Convolutional Neural Networks (CNNs).

[1247] "Skin characteristics" are attributes that indicate the condition of the user's skin, and include blemishes, wrinkles, dryness, and variations in color tone.

[1248] The "evaluation results" are judgments and conclusions about the user's skin condition obtained based on the analysis of image data.

[1249] "Nutrients" are food ingredients necessary to maintain the health of the user's skin.

[1250] "Skin care products" are cosmetics and skin care items used to improve and maintain the condition of the skin.

[1251] "Warning" is a warning message to notify the user of any abnormalities detected in their skin data.

[1252] "Feedback Report" means a report provided to a User that includes analysis results and recommendations.

[1253] This invention is a system that continuously collects and analyzes skin data from a user to evaluate the user's skin condition and recommend necessary nutrients and skin care products. This system is an appropriate combination of hardware and software.

[1254] User data collection

[1255] Users periodically collect image data of their skin using a smartphone or a dedicated skin scanner (device). The user launches the application at a specific time and takes a photo of their skin according to the shooting guide (e.g., appropriate shooting angle and distance) displayed on the device. This allows the device to acquire high-quality image data.

[1256] Sending and Receiving Data

[1257] The device sends the captured image data to a server using a security protocol such as HTTPS. The server receives the data and stores it in a database. This data is organized by date and user identifier for later analysis.

[1258] Data analysis and evaluation

[1259] The server uses deep learning algorithms (e.g., Convolutional Neural Networks (CNNs)) and other image analysis techniques to analyze the stored image data. This analysis extracts the user's skin characteristics (such as age spots, wrinkles, dryness, and variations in skin tone). The server then compares the newly analyzed data with past data to evaluate skin trends. For example, the appearance of new age spots or increased wrinkles is analyzed. Based on the results of this analysis, the server recommends the user's necessary nutrients and appropriate skin care products.

[1260] Feedback and Anomaly Detection

[1261] The server generates a feedback report based on the analysis results. The report includes an assessment of the current skin condition, recommended nutrients and skin care products, and a warning if an abnormality is detected. For example, if an abnormality such as sudden pigmentation is detected, the user may be at risk of skin cancer and be advised to seek immediate medical advice from a specialist.

[1262] Sending and notifying feedback

[1263] The server sends the generated feedback report to the device, which receives it and displays a notification to the user. The notification content includes specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[1264] Specific examples

[1265] Example 1: Daily skin care recommendations

[1266] Users use the app to take a photo of their skin in the morning, and the image data is sent to the server. The server detects the dryness level and recommends skin care products containing vitamin E and hyaluronic acid, allowing users to take appropriate skin care measures.

[1267] Example 2: Early detection of disease

[1268] The user scans their skin every day and sends the data to a server. The server detects any sudden pigmentation and determines that there is a risk of skin cancer. The user is notified that "abnormal pigmentation has been detected in a part of the skin. Please consult a dermatologist immediately." The user can then receive an early diagnosis from a specialist and take appropriate measures.

[1269] Example prompts for generative AI models

[1270] "Analyze skin images taken by the user to detect dryness."

[1271] "Check for the appearance of new blemishes and wrinkles and generate a report recommending appropriate skin care products."

[1272] "Detect sudden hyperpigmentation and generate a warning recommending that the user consult a dermatologist."

[1273] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1274] Step 1:

[1275] The user launches the application

[1276] How it works: The user launches an application installed on their smartphone or a dedicated skin scanner. The application has the function of sending notifications to the user at specific times.

[1277] Input: The user launches an application during a specific time period.

[1278] Output: The application is launched and the user is shown a guide to taking pictures.

[1279] Step 2:

[1280] The device displays a shooting guide to the user.

[1281] What it does: The device displays a screen to the user showing the frame of the entire face and the appropriate distance and angle, allowing the user to accurately take a photo of their skin.

[1282] Input: Application startup signal

[1283] Output: The shooting guide shown to the user

[1284] Step 3:

[1285] The user takes a photo of their skin

[1286] Specific operation: The user follows the instructions on the device to take a photo of their skin. After taking the photo, the device temporarily stores the image data.

[1287] Input: User takes photos according to the shooting guide

[1288] Output: Image data temporarily saved on the device

[1289] Step 4:

[1290] The device sends the image data to the server.

[1291] Specific operation: The device sends the temporarily stored image data to a server using a security protocol such as HTTPS. An internet connection is required for transmission.

[1292] Input: Temporarily saved image data

[1293] Output: Image data sent to the server via HTTPS protocol

[1294] Step 5:

[1295] The server receives and stores the image data.

[1296] Specific operation: The server receives the transmitted image data and stores it in a database along with the date and user identifier.

[1297] Input: Image data received via the HTTPS protocol

[1298] Output: Image data with date and user identifier stored in a database

[1299] Step 6:

[1300] The server analyzes the data using a deep learning model

[1301] How it works: The server analyzes the stored image data using a deep learning algorithm (e.g., CNN), which extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[1302] Input: Image data stored in a database

[1303] Output: Extracted skin feature data

[1304] Step 7:

[1305] The server compares the data with past data and evaluates it.

[1306] Specific operation: The server compares newly analyzed data with past data to evaluate skin trends, such as the appearance of new blemishes or increased wrinkles.

[1307] Input: Newly analyzed skin feature data and past data

[1308] Output: Evaluation results of fluctuation trends

[1309] Step 8:

[1310] The server generates a feedback report based on the analysis results.

[1311] Specific operation: The server generates a feedback report based on the analysis results, including a current skin condition assessment and recommended nutrients and skin care products.

[1312] Input: Evaluation result of fluctuation trend

[1313] Output: Feedback report

[1314] Step 9:

[1315] The server detects an anomaly and generates a warning

[1316] Specific operation: If the server detects any abnormalities, such as sudden pigmentation, it generates a report containing a warning, such as "There is a risk of skin cancer, so we recommend that you seek medical advice immediately."

[1317] Input: Anomaly detection based on analysis results

[1318] Output: Report with warnings

[1319] Step 10:

[1320] The server sends a feedback report to the device.

[1321] Specific operation: The server sends the generated feedback report to the terminal using a communication protocol (e.g., HTTPS).

[1322] Input: Generated feedback report

[1323] Output: Feedback report sent to the device

[1324] Step 11:

[1325] The device notifies the user

[1326] Specific behavior: The device receives the feedback report and displays a notification to the user, with specific messages such as "Your current skin condition is dry. Please use skin care products containing vitamin E or hyaluronic acid" or "Abnormal pigmentation has been detected in some areas of your skin. Please consult a dermatologist immediately."

[1327] Input: Feedback reports received

[1328] Output: A notification message that is displayed to the user.

[1329] (Application example 1)

[1330] 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."

[1331] Conventional skin analysis systems have the drawback of making it difficult for users to regularly monitor their skin health and take appropriate measures. They also struggle to respond quickly when abnormalities occur, and lack personalized support, especially in physical stores. Therefore, there is a need for a system that allows customers to check their skin condition in real time and quickly recommend appropriate skin care products and nutrients.

[1332] 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.

[1333] In this invention, the server includes means for collecting image data of the user's skin, means for transmitting the collected image data to the server, means for analyzing the image data to evaluate the user's skin condition, means for recommending nutrients and skin care products necessary for the user based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, means for transmitting the evaluation results and recommendations to a terminal and notifying the user, and means for collecting image data of the user's skin using smart glasses and displaying the evaluation results in real time, thereby enabling the user to check their skin condition in real time and quickly receive recommendations for appropriate skin care products and nutrients.

[1334] "User" refers to an individual who uses the system to analyze their skin condition and receive recommendations for skin care products and nutrients.

[1335] "Skin image data" is an image showing the condition of the skin on the user's face or body, and includes information for evaluating the skin condition by analyzing the image.

[1336] The "server" is a central processing unit that receives, stores, analyzes image data sent by users, and generates recommendations based on the results.

[1337] "Recommendation means" refers to a method or device for suggesting appropriate skin care products and nutrients to a user based on the results of skin analysis.

[1338] The term "means for generating a warning" refers to a method or device for creating information that alerts the user when an abnormality is detected from the analysis results.

[1339] The "notification means" refers to a method or device for transmitting the evaluation results and recommendations to a user terminal and informing the user of them.

[1340] "Smart glasses" refers to a device worn on the head that integrates the user's visual and video information through built-in cameras and displays.

[1341] "Means for displaying evaluation results in real time" refers to a method or device for quickly providing the analyzed results to the user so that they can be checked immediately.

[1342] This invention is a system that continuously collects and analyzes image data of a user's skin to evaluate the user's skin condition and recommend necessary nutrients and skin care products. Furthermore, the present invention uses smart glasses to provide evaluation results in real time, enabling personalized service in physical stores. Specific embodiments for implementing the present invention are described below.

[1343] User data collection

[1344] Smart glasses are used as a terminal to collect image data of the user's skin. The user wears the smart glasses and uses the built-in camera to take a picture of their skin according to specified shooting conditions (e.g., appropriate angle and distance). This allows the user to easily obtain high-quality skin image data.

[1345] Sending and Receiving Data

[1346] Image data collected by the smart glasses is transmitted to a server using a secure protocol (e.g., HTTPS). The server receives the data, organizes it by date and user identifier, and stores it for future analysis and comparison.

[1347] Data analysis and evaluation

[1348] The server analyzes the stored image data using deep learning algorithms and other image analysis technologies (e.g., Keras, OpenCV). This makes it possible to evaluate the user's skin condition (e.g., blemishes, wrinkles, dryness, and changes in skin tone). By comparing the data with past data, it is also possible to understand trends in skin condition.

[1349] Feedback and Anomaly Detection

[1350] Based on the analysis results, the server generates a feedback report that includes an assessment of the skin's current condition, recommended nutrients and skin care products, and alerts if abnormalities are detected, such as a sudden increase in pigmentation that could signal a risk of skin cancer.

[1351] Sending and notifying feedback

[1352] The server sends the generated feedback report to the smart glasses, which notify the user and display the analysis results in real time on the display. The user can receive notifications and check recommended skin care products and nutrients, or take immediate action if a doctor's diagnosis is required.

[1353] Specific examples

[1354] Consider a scenario in which a beauty consultant wears smart glasses and approaches a customer who visits the store in the following manner.

[1355] Beauty consultant: "Hello, I'll check your skin condition and recommend the best skin care products for you."

[1356] Customer: "Yes, please."

[1357] The beauty consultant begins to scan the customer's skin using the smart glasses and analyzes it using the following prompts:

[1358] Prompt Sentence Examples

[1359] "Capture image data of the customer's skin and provide recommended skin care products and an evaluation of the skin condition as analysis results."

[1360] Using this system, customers can check their skin condition in real time and receive prompt recommendations for optimal skin care products and nutrients. If any serious skin abnormalities are detected, they can immediately seek medical advice. This allows for personalized service in physical stores and improves customer satisfaction.

[1361] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1362] Step 1:

[1363] A user wearing smart glasses takes a picture of their skin under specified shooting conditions (appropriate angle and distance). Skin image data is collected using the smart glasses' camera.

[1364] Input: An image of the user's skin

[1365] Output: Image data of the captured skin

[1366] Step 2:

[1367] The smart glasses transmit the collected image data to a server using a secure protocol (e.g., HTTPS).

[1368] Input: Skin image data

[1369] Output: Image data sent to the server

[1370] Step 3:

[1371] The server saves the received image data, organizes it by date and user identifier, and stores it in a database.

[1372] Input: Image data sent

[1373] Output: Organized database entries

[1374] Step 4:

[1375] The server analyzes the stored image data using deep learning algorithms and image analysis techniques (e.g., Keras, OpenCV), thereby evaluating the user's skin condition (blemishes, wrinkles, dryness, color variations, etc.).

[1376] Input: Organized image data

[1377] Output: Analysis results (skin condition evaluation)

[1378] Step 5:

[1379] The server compares the data with past data to identify trends in skin condition, and compares past and current data to extract points of change.

[1380] Input: Current and past analysis results

[1381] Output: Fluctuation trend data

[1382] Step 6:

[1383] The server generates a feedback report based on the analysis results, which includes a current skin assessment, recommended nutrients and skin care products, and alerts if any abnormalities are detected.

[1384] Input: Analysis results and fluctuation trend data

[1385] Output: Feedback report

[1386] Step 7:

[1387] The server sends the generated feedback report to the smart glasses, which receive the report and notify the user.

[1388] Input: Feedback Report

[1389] Output: Informed assessment findings and recommendations

[1390] Step 8:

[1391] The smart glasses display the analysis results to the user in real time, and the user can view the display on the smart glasses, including recommended skin care products and nutrients, as well as warnings if any abnormalities are detected.

[1392] Input: Informed assessment findings and recommendations

[1393] Output: Evaluation results and recommendations displayed on the screen

[1394] In this way, users can check their skin condition in real time, receive prompt recommendations for appropriate skin care products and nutrients, and can immediately seek medical advice if any serious skin abnormalities are detected.

[1395] 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.

[1396] This invention combines a system that continuously collects and analyzes a user's skin data to evaluate the user's skin condition and recommend necessary nutrients and skin care products with an emotion engine that recognizes the user's emotions. The basic operation of this system is as follows.

[1397] User data collection

[1398] The device (e.g., a smartphone or a dedicated skin scanner) periodically collects image data of the user's skin and emotional data. The user launches the application and follows the instructions to take a photo of their skin, while simultaneously acquiring emotional data using the user's facial expressions and voice.

[1399] Sending and Receiving Data

[1400] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS). The server receives the data, associates the image data with the emotion data, and stores them in a database. The stored data is organized by user identifier, date, etc., for later analysis.

[1401] Data analysis and evaluation

[1402] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.). At the same time, it uses an emotion engine to analyze the user's emotional data and recognize their current emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[1403] Generate feedback

[1404] The server generates a feedback report based on the analysis results, including an assessment of the skin's health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the user is stressed, the server may recommend skin care products or lifestyle changes that will help reduce stress.

[1405] Sending and notifying feedback

[1406] The server sends the generated feedback report to the device, which then receives it and displays it on the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1407] Specific examples

[1408] Example 1: Daily skin care and emotional management

[1409] Users use the app to take photos of their skin in the morning and simultaneously collect emotional data. The server detects dryness and stress levels and recommends moisturizing skin care products and provides advice on how to reduce stress. This allows users to care for both their skin and their emotions.

[1410] Example 2: Early disease detection and response

[1411] The user scans their skin every day and collects emotional data. The server detects sudden pigmentation and abnormalities in the user's emotional state, and determines that they are at risk of skin cancer. The server notifies the user, saying, "Abnormal pigmentation has been detected in a part of your skin. We recommend that you consult a dermatologist immediately. We also recommend that you remain calm." The user can then seek a specialist's diagnosis early and take appropriate measures to deal with the emotional issues.

[1412] In this way, by using the system of the present invention, users can comprehensively manage their daily skin health and emotional state, enabling them to detect and address health risks early.

[1413] The processing flow will be explained below.

[1414] Step 1:

[1415] The user launches the smartphone app and takes a photo of their skin. The app then instructs the user on the correct position and angle to capture accurate image data.

[1416] Step 2:

[1417] The terminal simultaneously collects the captured image data and the user's emotional data (for example, facial expressions and voice data).

[1418] Step 3:

[1419] The device performs pre-processing to convert the skin image data into the required format, including adjusting the image resolution and removing noise.

[1420] Step 4:

[1421] The device sends the preprocessed image data and emotion data to the server using a secure protocol (e.g., HTTPS).

[1422] Step 5:

[1423] The server receives the image data and emotion data sent from the device and stores them in a database. The data is organized by user identifier, date, etc.

[1424] Step 6:

[1425] The server analyzes the stored image data using a deep learning algorithm to extract the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.).

[1426] Step 7:

[1427] The server uses an emotion engine to analyze the emotion data and recognize the user's emotional state (e.g., happiness, sadness, stress, etc.).

[1428] Step 8:

[1429] Based on the analysis results, the server comprehensively evaluates the user's skin condition and emotional state, including determining the necessary nutrients and skin care products according to the skin's health and emotional state.

[1430] Step 9:

[1431] The server generates a feedback report summarizing the assessment results and recommendations, including a skin health assessment, recommended nutrients and skin care products, and advice based on emotional state.

[1432] Step 10:

[1433] The server transmits the generated feedback report to the terminal.

[1434] Step 11:

[1435] The device displays the feedback report received from the server in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1436] Step 12:

[1437] The user checks the notification on the device and takes appropriate skin care measures based on the feedback report. If an abnormality is detected, the user should consult a medical institution as necessary. The user can also take actions to reduce stress (e.g., practicing relaxation techniques).

[1438] This series of processes allows users to comprehensively manage both their skin health and their emotions, enabling them to detect health risks early and take appropriate measures.

[1439] Example 2

[1440] 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."

[1441] Previous skin care systems focused only on analyzing the user's skin condition and were unable to provide comprehensive advice that took into account the user's emotional state. Furthermore, they ignored the impact of changes in emotional state on the skin, resulting in a lack of specific guidelines for users to practice better skin care. As a result, it was difficult for users to select optimal products and improve their lifestyle habits, making it difficult to maintain healthy skin.

[1442] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data to evaluate the user's skin condition and the emotional data to recognize the user's emotional state, means for recommending nutrients and skin care products necessary for the user based on the evaluation results and the recognition results and generating advice based on the emotional state, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This makes it possible to comprehensively manage the user's skin health condition and emotional state and recommend optimal skin care products and lifestyle improvements.

[1443] "Skin image data" refers to photographs or image data acquired to record the user's skin condition.

[1444] "Emotional data" refers to data such as voice and facial expressions that are collected to express the user's emotional state.

[1445] A "terminal" is an electronic device used by a user, such as a smartphone or a dedicated skin scanner.

[1446] A "server" is a computer system for analyzing collected data and processing results.

[1447] A "deep learning algorithm" is an artificial intelligence technology that learns using large amounts of data and performs highly accurate analysis.

[1448] An "emotion engine" is software that analyzes collected emotion data and recognizes the user's emotional state.

[1449] A "feedback report" is a report generated based on the analysis results that includes evaluations and advice regarding the user's skin health and emotional state.

[1450] A "secure protocol" is a communication method that prevents unauthorized access by third parties when transmitting data.

[1451] "Nutrients" are substances such as vitamins and minerals that need to be ingested to maintain human health.

[1452] "Skin care products" are cosmetic and medicated products used to protect and improve the skin.

[1453] "Abnormal" refers to skin problems or changes in emotional state that are different from normal.

[1454] "Notification" is a function that notifies the user of information via the terminal.

[1455] This invention is a system that continuously collects and analyzes a user's skin and emotional data to evaluate the user's skin condition and emotional state, and provides advice on necessary nutrients, skin care products, and lifestyle habits. Detailed embodiments of this system are described below.

[1456] First, the user launches a dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The user takes the photo of their skin, and the device acquires the image data. At the same time, the device collects emotional data from the user's voice, facial expressions, etc. This is done using the smartphone's camera and microphone functions.

[1457] The device then preprocesses the collected skin image data and emotion data. This preprocessing includes denoising the images and converting them to a suitable format. The preprocessed data is then sent to a server using the secure HTTPS protocol. The server then stores the received data in a database, organized by user identifier, date, and other information.

[1458] The server applies a deep learning algorithm to the stored image data to analyze skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state. Based on these analysis results, it comprehensively evaluates the user's skin and emotional condition.

[1459] The server generates a feedback report based on the analysis results. The report includes an assessment of the user's skin health, recommended nutrients and skin care products, and advice based on the user's emotional state. For example, if the server determines that the user is under stress and has dry skin, it will recommend lifestyle changes that will help reduce stress and skin care products rich in moisturizing ingredients. If the analysis detects any abnormalities (e.g., sudden pigmentation), it will generate a warning recommending that the user consult a dermatologist.

[1460] Finally, the server sends the generated feedback report to the device. The device receives it and displays it on the user interface. For example, the device may display a message in a format that is easy for the user to understand, such as, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1461] Prompt Sentence Examples

[1462] "We will provide appropriate feedback by collecting data on the user's skin condition and emotions and analyzing it using deep learning and an emotion engine. For example, if the user has dry skin and is feeling stressed, we will recommend skin care products with high moisturizing ingredients and advice on stress reduction. Please list the recommended products and advice in this case."

[1463] This system allows users to comprehensively manage their skin health and emotional state, enabling them to implement optimal skin care products and lifestyle improvements.

[1464] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1465] Step 1: Data collection

[1466] The user launches the dedicated application on a device such as a smartphone or a dedicated skin scanner. The application prompts the user to take a photo of their skin. The device then uses its camera function to acquire image data of the skin captured by the user. At the same time, the device uses its microphone function to collect emotion data from the user's voice and facial expressions. This data is equipped with an emotion engine.

[1467] Input: Smartphone or dedicated skin scanner, photo of user's skin, voice

[1468] Output: Skin image data, emotion data

[1469] Specific operation: The device displays "The camera has been activated. Please take a photo of your skin." The user takes a photo. At the same time, the device records audio.

[1470] Step 2: Preprocessing the data

[1471] The device performs preprocessing on the collected skin image data and emotion data. Image data preprocessing includes noise removal and image format conversion. Emotion data preprocessing includes audio noise removal and sample rate conversion. The device generates preprocessed data.

[1472] Input: Skin image data, emotion data

[1473] Output: Preprocessed image data, preprocessed emotion data

[1474] Specific operation: The device displays "Starting noise removal from image data" and notifies you that "Preprocessing completed" when the process is complete.

[1475] Step 3: Sending data

[1476] The device transmits the preprocessed image data and emotion data to the server using a secure protocol (HTTPS).

[1477] Input: Preprocessed image data, preprocessed emotion data

[1478] Output: Data sent to the server

[1479] Specific operation: The device will display "Sending data..." and after the transmission is complete, will notify you that "Data transmission completed."

[1480] Step 4: Receiving and storing data

[1481] The server receives the preprocessed image data and emotion data and stores them in a database, organized by user identifier and date.

[1482] Input: Preprocessed image data, preprocessed emotion data

[1483] Output: Data stored in the database

[1484] Specific operation: The server records in the log "Data received. Saving to database", and when the saving process is complete, it records "Saving completed".

[1485] Step 5: Analyze the data

[1486] The server uses a deep learning algorithm to analyze the image data stored in the database, extracting skin characteristics (e.g., blemishes, wrinkles, dryness, and variations in skin tone). At the same time, it uses an emotion engine to analyze the emotional data and recognize the user's emotional state.

[1487] Input: Preprocessed image data stored in a database, preprocessed emotion data

[1488] Output: Skin feature data, emotional state data

[1489] Specific operation: The server records in the log "Starting analysis of skin data using deep learning" and generates the analysis results.

[1490] Step 6: Generate ratings and feedback

[1491] The server uses the analysis results to comprehensively assess the user's skin health and emotional state, then generates a feedback report containing skin health assessment, recommended nutrients and skin care products, and advice based on the emotional state, and generates a warning if any abnormalities are detected.

[1492] Input: Skin feature data, emotional state data

[1493] Output: Feedback report, warnings

[1494] Specific behavior: The server logs "Generating feedback report" and saves the generated report.

[1495] Step 7: Submit your feedback

[1496] The server transmits the generated feedback report to the terminal.

[1497] Input: Feedback Report

[1498] Output: Feedback report sent to the device

[1499] Specific operation: The server logs "Sending feedback report to terminal", and after the transmission is complete, logs "Sending completed".

[1500] Step 8: Notification and display of feedback

[1501] The device receives the feedback report and displays it in the user interface. For example, it might say, "Your current skin condition is a little dry. We recommend using skin care products containing vitamin E and hyaluronic acid. Also, your stress level seems to be increasing, so we recommend that you adopt relaxation techniques."

[1502] Input: Feedback Report

[1503] Output: Feedback given to the user

[1504] Specific behavior: The device notifies the user that "You have a new feedback report," and the user opens the app to view the feedback.

[1505] Through these steps, the system of the present invention can comprehensively manage the user's skin health and emotional state, and provide optimal skin care products and lifestyle improvements.

[1506] (Application example 2)

[1507] 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."

[1508] Conventional skin care systems specialize in analyzing the user's skin condition and recommending skin care products based on that analysis, but they have the problem of being unable to provide more comprehensive health management because they do not take the user's emotional state into account.In particular, in situations where real-time personalization is required, such as shopping in a physical store, comprehensive advice that includes the user's emotional state is required.

[1509] The identification processing 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 image data and emotional data of the user's skin, means for transmitting the collected image data and emotional data to the server, means for analyzing the image data and emotional data to evaluate the user's skin condition and emotional state, means for recommending nutrients and skin care products necessary for the user and emotional advice based on the evaluation results, means for detecting abnormalities from the analysis results and generating an alert if an abnormality is detected, and means for transmitting the evaluation results and recommendations to a terminal and notifying the user. This enables users to receive comprehensive health management and personalized product selection that takes into account their skin condition and emotional state, even in physical stores.

[1510] "User's skin image data" is image information that digitally records the user's skin condition.

[1511] "Emotion data" is information that represents the user's emotional state, and is data extracted from facial expressions, voice, and the like.

[1512] The "collection means" refers to a device or system for acquiring image data of the user's skin and emotion data.

[1513] "Server" refers to a computer system for receiving, storing, and analyzing collected data.

[1514] "Means of transmission" refers to the communication functions and protocols used to transfer collected data to the server.

[1515] "Means for analyzing and evaluating" refers to algorithms or programs that process the collected image data and emotional data to determine the user's skin condition and emotional state.

[1516] "Recommendation means" refers to a device or program that provides appropriate nutritional and skin care products, as well as emotional advice, to a user based on the analysis results.

[1517] "Means for detecting anomalies" refers to systems or programs that identify abnormal conditions from the analysis results and generate warnings.

[1518] "Means of notification" refers to communication means and software for displaying the evaluation results and recommendations on the user's terminal.

[1519] This invention relates to a system that comprehensively evaluates a user's skin condition and emotional state, and recommends appropriate nutrients, skin care products, and emotional advice. To implement this invention, the following hardware and software are mainly used:

[1520] Hardware and software used

[1521] Hardware:

[1522] Smartphone built-in camera: Used to capture image data of the user's skin.

[1523] Smartphone: Runs applications and sends and receives data.

[1524] Server: Stores and analyzes data.

[1525] software:

[1526] Python: Used to write the entire program.

[1527] OpenCV: Used to capture and preprocess skin image data.

[1528] Keras: Used to run skin data analysis models.

[1529] Transformers library: Used to run the emotion recognition engine.

[1530] Overview of the invention

[1531] 1. User data collection

[1532] The user launches a smartphone application to collect skin image data and emotion data. Skin images are captured using the smartphone's built-in camera. Emotion data is collected through the user's voice and text input.

[1533] 2. Sending and Receiving Data

[1534] The application sends the collected image data and emotion data to a server using a secure protocol (e.g., HTTPS), which then associates the image data with the emotion data and stores them in a database.

[1535] 3. Data analysis and evaluation

[1536] The server analyzes the stored image data and emotional data using a deep learning algorithm and an emotion recognition engine. From the image data, it extracts the user's skin characteristics (blemishes, wrinkles, dryness, color variations, etc.), and from the emotional data, it recognizes the user's emotional state.

[1537] 4. Generate feedback

[1538] Based on the analysis results, the server generates a feedback report that includes an assessment of skin health, recommended nutrients and skin care products, and advice based on emotional state.

[1539] 5. Sending Feedback and Notifications

[1540] The generated feedback report is sent from the server to the user's device, where the user can check the feedback through a smartphone application.

[1541] Specific examples

[1542] For example, suppose a user uses the app to take a photo of their skin in the morning and inputs, "I've been busy and tired lately." In this case, the server detects that the user's skin is dry and stressed, recommends using skin care products with high moisturizing ingredients, and provides advice on how to reduce stress. Specifically, the server notifies the user, "Your skin appears to be dry. We recommend using skin care products with high moisturizing ingredients. Also, you appear to be feeling stressed recently. We recommend that you adopt a relaxation technique."

[1543] Example prompt sentence:

[1544] I've been busy and tired lately.

[1545] In this way, by using the system of the present invention, users can evaluate their skin and emotional condition in real time even in a physical store, and obtain the most suitable products and advice.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] User data collection

[1549] The user launches a smartphone application and collects skin image data and emotion data on the device. Specifically, the smartphone's built-in camera is used to take a skin image, and emotion data is simultaneously collected through voice or text input. The input data is the user's skin image and text or voice data. The output data is the collected image data and emotion data.

[1550] Step 2:

[1551] Sending and Receiving Data

[1552] The device sends the collected image data and emotion data to the server using a secure protocol (e.g., HTTPS). Specifically, the device encodes the data and sends it to the server through a secure channel. The input data is the image data and emotion data collected in step 1. The output data is the image data and emotion data transferred to the server.

[1553] Step 3:

[1554] Data storage

[1555] The server associates the received image data and emotion data and stores them in a database. Specifically, it organizes the data using user identifiers and timestamps for later analysis. The input data is the image data and emotion data sent to the server in step 2. The output data is the stored database entry.

[1556] Step 4:

[1557] Data analysis

[1558] The server analyzes the stored image data and emotion data using a deep learning algorithm and emotion recognition engine. Specifically, it extracts skin features (blemishes, wrinkles, dryness, color variations, etc.) from the image data and determines the emotional state using the emotion recognition engine. The input data are the image data and emotion data obtained from the database. The output data are the skin evaluation results and emotion evaluation results.

[1559] Step 5:

[1560] Generate feedback

[1561] The server generates a feedback report based on the skin assessment results and emotion assessment results. Specifically, it creates a report containing advice on appropriate nutrients, skin care products, and emotions. The input data are the skin assessment results and emotion assessment results obtained in step 4. The output data is the feedback report to be sent to the user.

[1562] Step 6:

[1563] Sending and notifying feedback

[1564] The server sends the generated feedback report to the terminal and notifies the user. Specifically, the terminal receives the feedback report and displays it on the user interface. The input data is the feedback report generated in step 5. The output data is the feedback report displayed on the terminal and the notification to the user.

[1565] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1566] 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.

[1567] 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 robot 414.

[1568] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1569] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1570] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1571] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1572] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1573] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1574] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1575] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1576] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1577] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1578] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1579] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1580] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1581] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1582] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1583] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1584] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1585] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1586] The following is further disclosed regarding the above embodiment.

[1587] (Claim 1)

[1588] means for collecting image data of a user's skin;

[1589] means for transmitting the collected image data to a server;

[1590] means for analyzing image data to evaluate the user's skin condition;

[1591] A means for recommending nutrients and skin care products required by the user based on the evaluation results;

[1592] a means for detecting an anomaly from the analysis result and generating a warning when an anomaly is detected;

[1593] The system includes a means for transmitting the evaluation results and recommendations to the terminal and notifying the user.

[1594] (Claim 2)

[1595] 10. The system of claim 1, further comprising means for comparing the user's daily skin data and analyzing variations from past data.

[1596] (Claim 3)

[1597] 10. The system of claim 1, further comprising means for displaying to the user a feedback report generated based on the analyzed skin condition.

[1598] "Example 1"

[1599] (Claim 1)

[1600] means for collecting image data of a user's skin;

[1601] means for transmitting the collected image data to a server using a security protocol;

[1602] A means for analyzing the image data using a deep learning algorithm to extract the user's skin characteristics;

[1603] A means for identifying skin trends by comparing the analysis results with past data and recommending nutrients and skin care products necessary for the user;

[1604] a means for detecting an anomaly from the analysis result and generating a warning when an anomaly is detected;

[1605] The system includes means for transmitting evaluation results and recommendations to the terminal and notifying the user of specific messages.

[1606] (Claim 2)

[1607] 10. The system of claim 1, further comprising means for comparing the user's daily skin data and analyzing variations from past data.

[1608] (Claim 3)

[1609] 10. The system of claim 1, further comprising means for notifying the user of a feedback report generated based on the analyzed skin condition.

[1610] "Application Example 1"

[1611] (Claim 1)

[1612] means for collecting image data of a user's skin;

[1613] means for transmitting the collected image data to a server;

[1614] means for analyzing image data to evaluate the user's skin condition;

[1615] A means for recommending nutrients and skin care products required by the user based on the evaluation results;

[1616] a means for detecting an anomaly from the analysis result and generating a warning when an anomaly is detected;

[1617] a means for transmitting the evaluation results and recommendations to the terminal and notifying the user;

[1618] A system including means for collecting image data of a user's skin using smart glasses and displaying assessment results in real time.

[1619] (Claim 2)

[1620] 10. The system of claim 1, further comprising means for comparing the user's daily skin data and analyzing variations from past data.

[1621] (Claim 3)

[1622] 10. The system of claim 1, further comprising means for displaying to the user a feedback report generated based on the analyzed skin condition.

[1623] "Example 2: Combining Emotion Engines"

[1624] (Claim 1)

[1625] means for collecting skin image data and emotion data of a user;

[1626] means for transmitting the collected image data and emotion data to a server;

[1627] means for analyzing the image data to evaluate the user's skin condition and for analyzing the emotion data to recognize the user's emotional state;

[1628] means for recommending nutrients and skin care products to the user based on the evaluation and recognition results, and generating advice based on the user's emotional state;

[1629] a means for detecting an anomaly from the analysis result and generating a warning when an anomaly is detected;

[1630] The system includes a means for transmitting the evaluation results and recommendations to the terminal and notifying the user.

[1631] (Claim 2)

[1632] 10. The system of claim 1, further comprising means for comparing the user's daily skin data and emotion data and analyzing variations from past data.

[1633] (Claim 3)

[1634] 10. The system of claim 1, further comprising means for displaying to the user a feedback report generated based on the analyzed skin condition and emotional state.

[1635] "Application example 2 when combining emotion engines"

[1636] (Claim 1)

[1637] means for collecting skin image data and emotion data of a user;

[1638] means for transmitting the collected image data and emotion data to a server;

[1639] means for analyzing image data and emotional data to assess a user's skin condition and emotional state;

[1640] A means for recommending necessary nutrients and skin care products to the user based on the evaluation results, as well as emotion-based advice;

[1641] a means for detecting an anomaly from the analysis result and generating a warning when an anomaly is detected;

[1642] The system includes a means for transmitting the evaluation results and recommendations to the terminal and notifying the user.

[1643] (Claim 2)

[1644] 10. The system of claim 1, further comprising means for comparing the user's daily skin data and emotion data and analyzing variations from past data.

[1645] (Claim 3)

[1646] 10. The system of claim 1, further comprising means for displaying to the user a feedback report generated based on the analyzed skin condition and emotional state. [Explanation of symbols]

[1647] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting image data of a user's skin; means for transmitting the collected image data to a server; means for analyzing image data to evaluate the user's skin condition; A means for recommending nutrients and skin care products required by the user based on the evaluation results; a means for detecting an anomaly from the analysis result and generating a warning when an anomaly is detected; The system includes a means for transmitting the evaluation results and recommendations to the terminal and notifying the user.

2. 2. The system of claim 1, further comprising means for comparing the user's daily skin data and analyzing variations from past data.

3. The system according to claim 1 , further comprising means for displaying to the user a feedback report generated based on the analyzed skin condition.

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