system

Abstract

We provide the system. [Solution] A means for acquiring skin images from a user and analyzing those skin images to evaluate the condition of the skin, A method for predicting the progress of treatment based on past data, A means of analyzing user emotions and generating positive feedback, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the process of acne treatment, there is a problem that the treatment is often interrupted due to a temporary deterioration in the appearance of the skin. The user feels anxious about the progress of the treatment and the prediction of future improvement, cannot maintain motivation, and as a result, the problem is that effective treatment cannot be continued.

Means for Solving the Problems

[0006] "User" refers to an individual who uses the system based on the present invention and is the entity that provides images of the skin condition and manages the progress of treatment.

[0007] "Skin images" are digital image data provided by users to the system to visualize the condition of their skin during acne treatment.

[0008] "Analysis" is a technical method that digitally processes skin images to evaluate the condition of the skin numerically or visually.

[0009] "Evaluation" is the process of determining the health of the skin and the progress of treatment based on information about the skin condition obtained through analysis.

[0010] "Past data" refers to a collection of information gathered from other users who have undergone similar treatment processes, and serves as reference information for predicting the progress and outcome of treatment.

[0011] "Predicting the progression" is a process of estimating the future skin condition and treatment progress based on the user's current skin condition.

[0012] "Analyzing emotions" is the process of quantifying or evaluating a user's psychological state based on comments and diary data provided by the user.

[0013] "Positive feedback" refers to messages that provide users with positive and reassuring information about the effectiveness of treatment and the expected results. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of the present invention provides support to enable users to accurately understand their own skin condition during the treatment process and to continue effective treatment. Specific embodiments are described below.

[0036] This system consists of users, a server, and a terminal. Users periodically take photos of their skin during treatment and upload them to the system via the terminal. The terminal receives the photos and sends this data to the server.

[0037] The server inputs skin image data received from the user into an AI-powered image analysis module to numerically evaluate the skin's condition. This analysis includes factors such as redness, swelling, and the number of pimples. The analysis results are stored in a database and compared with past data. Based on this comparison, the server uses AI to predict the progress of treatment and assess the likelihood of future skin improvement.

[0038] Next, the server analyzes the treatment diaries and comments uploaded by users using natural language processing technology to quantify the users' emotions. This allows for an understanding of changes in the users' feelings and motivations.

[0039] Based on evaluation and predicted data, along with sentiment analysis, the server generates feedback. This feedback includes positive messages to the user regarding the effectiveness of the treatment and expected future improvements. For example, it might say, "You've seen a clear improvement in your skin after three weeks of treatment. We strongly recommend continuing the care."

[0040] Ultimately, the device notifies and displays the generated feedback to the user. This notification feature allows the user to receive reliable information to continue treatment and helps them maintain motivation.

[0041] As a concrete example, suppose user A uploads photos of their skin every two weeks using this system. The server analyzes these photos and confirms that redness has decreased in the second week. Based on past data, it is predicted that even more significant improvement will be seen from the third week onward, so the server provides user A with feedback such as, "Further improvement can be expected by continuing treatment." In this way, user A can continue to quantitatively confirm the effectiveness of the treatment.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user takes a photo of their skin during treatment and uploads it to the system via their device.

[0045] Step 2:

[0046] The device receives photo data from the user and sends this data to the server.

[0047] Step 3:

[0048] The server inputs the received photo data into an AI-based image analysis module to evaluate the skin condition. Here, information such as redness, swelling, and the number of pimples is analyzed.

[0049] Step 4:

[0050] The server saves the analysis results to a database and performs comparative analysis with past data.

[0051] Step 5:

[0052] The server uses AI to predict the progress of treatment based on similar past data and evaluates the likelihood of future skin improvement.

[0053] Step 6:

[0054] The server analyzes the user's treatment log and comments using natural language processing technology and quantifies the user's emotions.

[0055] Step 7:

[0056] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and expected outcomes.

[0057] Step 8:

[0058] The device notifies and displays the generated feedback to the user. This allows the user to review the feedback and obtain information about the next steps.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In modern society, there is growing concern about skin health, but it is difficult for users to accurately assess their own condition. Furthermore, it is difficult for users to perceive the effects of treatment, making it challenging to maintain motivation. In addition, there is a lack of support that takes users' feelings into consideration, and there is a need to increase their motivation to continue treatment.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for acquiring image information captured by the user and analyzing the image information to numerically evaluate the condition of the skin; means for storing the analyzed condition in a recording device and predicting the progress of treatment based on past information; and means for analyzing the user's descriptive information using language processing technology, quantifying emotions, and generating positive evaluation information. This makes it possible to objectively evaluate the condition of the user's skin, visually confirm the effectiveness of the treatment, and improve the user's motivation for continued care.

[0064] "Image information" refers to photographic data showing the condition of the skin, taken by the user.

[0065] "Numerically evaluating" means converting the condition of the skin into data through image analysis and indicating that condition using specific numerical values.

[0066] A "recording device" refers to a database or storage system installed inside a server to store information.

[0067] "Past information" refers to historical data of skin condition and user descriptions that have been acquired and analyzed to date.

[0068] "Predicting the progression" means estimating the future condition of the skin and the effectiveness of treatment based on past information.

[0069] "Descriptive information" refers to textual information such as diaries and comments recorded by users.

[0070] "Language processing technology" refers to techniques that utilize natural language processing to analyze emotions and intentions from user-generated information.

[0071] "Quantifying emotions" means using language processing technology to represent a user's emotional state numerically.

[0072] "Positive evaluation information" refers to information generated based on analysis results that includes content intended to encourage and motivate users.

[0073] "Continuous care" means regularly monitoring the condition of the skin and repeatedly providing necessary treatments and care.

[0074] The following describes embodiments for carrying out the present invention. This system analyzes the user's skin condition and supports the progress of treatment, and consists of a server, a terminal, and a user.

[0075] The system begins with the user taking a photograph of the skin to be treated using a device such as a smartphone or tablet. The captured image information is uploaded to a server via a dedicated application installed on the device. Data transmission utilizes an internet connection, and SSL / TLS protocols are typically used to ensure security.

[0076] The server analyzes the received image information using deep learning technology. In this case, machine learning frameworks such as TENSORFLOW® or PyTorch are likely to be used to numerically evaluate the condition of the skin. The analyzed data is stored in a database, and the progress of treatment is predicted based on past information. Scalable data storage technologies such as MongoDB or PostgreSQL are used for this purpose.

[0077] Furthermore, the server analyzes descriptive information such as user diaries and comments using natural language processing technology. Specifically, it uses spaCy and NLTK to quantify the user's emotions and generate positive evaluation information. This can help improve motivation for treatment.

[0078] The terminal notifies the user of evaluation information generated by the server and displays it on the application. This allows the user to check the progress of their treatment and maintain motivation to continue appropriate care.

[0079] As a concrete example, consider a scenario where a user uploads skin photos to the system once a week. In this process, the system analyzes each image, allowing for quantitative verification of the treatment's effectiveness. Furthermore, an example of a prompt to be input into the generative AI model is: "Generate feedback to improve the user's skin condition. Based on past data, predict the future skin condition and provide a positive message."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user takes a photograph of their skin using their device. The input is the raw image data captured by the user, and the output is digital image data. Specifically, the user takes a photograph using the camera function of their smartphone or tablet and checks the preview within the app.

[0083] Step 2:

[0084] The device uploads the captured image data to the server. The input is the image data obtained in step 1, and the output is the image file that has been successfully uploaded to the server. This process involves securely transmitting the image data over the internet using the SSL / TLS protocol.

[0085] Step 3:

[0086] The server analyzes the received image data using AI. The input is unanalyzed image data uploaded to the server, and the output is numerical evaluation data indicating the condition of the skin. Specifically, it utilizes deep learning models (e.g., TensorFlow or PyTorch) to identify and quantify redness, swelling, the number of pimples, etc.

[0087] Step 4:

[0088] The server stores the analyzed evaluation data in a database and compares it with historical information. The input is the evaluation data obtained in step 3 and existing database records, and the output is predictive data regarding the progress of treatment. MongoDB or PostgreSQL is used to store the data in the database and perform time-series comparative analysis.

[0089] Step 5:

[0090] The server analyzes user-submitted journal entries and comment data using natural language processing techniques. The input is user text data, and the output is numerical data representing emotions. Specifically, it uses spaCy and NLTK to analyze the text and calculate an emotion score.

[0091] Step 6:

[0092] The server generates feedback based on evaluation data and sentiment data. The input is the result data from steps 4 and 5, and the output is the generation of a positive message directed at the user. A generative AI model is used to create messages that promote future therapeutic effects.

[0093] Step 7:

[0094] The device notifies the user of feedback generated by the server. The input is the feedback message provided by the server, and the output is the feedback display that the user can view on the device. This includes utilizing the device's notification function to display the feedback on the screen.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] For users undergoing treatment for skin conditions, accurately understanding their own skin condition and maintaining motivation is difficult. Furthermore, there is a problem in that access to beauty content best suited to each individual's skin condition is limited.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data and providing various related information; and means for analyzing the user's emotions and selecting beauty content that suits the user's skin condition. As a result, the user can receive appropriate feedback and content based on their skin condition, maintain their motivation for treatment, and obtain optimal beauty information.

[0100] A "user" is an individual who uses the system to receive skin condition assessments and beauty content.

[0101] A "skin image" is digital video data of a user's skin.

[0102] "Analysis" refers to the process of using AI to analyze acquired skin images and quantify the condition of the skin.

[0103] "Progression" refers to the evaluation and prediction of the process of improvement or deterioration of the skin condition.

[0104] "Emotional analysis" is a procedure that uses natural language processing to analyze comments and journal entries submitted by users and quantify their emotional state.

[0105] "Beauty content" refers to information provided to users, such as videos, articles, and product information related to beauty that are tailored to their skin condition.

[0106] "Feedback" refers to improvement suggestions and positive messages generated based on skin analysis and emotional analysis and delivered to users.

[0107] The system that implements this invention consists of a user, a terminal, and a server. The user takes an image of their skin using a smartphone or similar terminal and uploads this image from the terminal to the system. On the terminal, the image data is compressed in an appropriate format and securely transmitted to the server.

[0108] The server boasts high-performance processing capabilities and is equipped with an AI model built in Python. Using the OpenCV library for image processing, it efficiently extracts and analyzes skin areas from acquired images, and then quantifies skin features using a model trained with TensorFlow. Furthermore, the analysis results are compared with historical data and used to predict the user's treatment progress.

[0109] Furthermore, the server analyzes user-entered comments and treatment diaries using natural language processing (NLP) techniques. Here, the NLP library Transformers is used to extract and quantify emotions from the text data. This integrated information allows the server to recommend individually optimized beauty content to each user and generate positive feedback.

[0110] For example, suppose a user posts a journal entry to the system stating that their skin improvement has stalled. In this case, the server considers the emotional analysis results and the current state of the user's skin, and suggests relaxation methods aimed at stress relief, as well as review videos of related beauty products. In this way, users can receive appropriate guidance and support for their ongoing skin care.

[0111] An example of a prompt for a generative AI model is: "Analyze the user's skin condition and recommend the most suitable beauty content. The content should mainly consist of videos and articles, focusing on individual skin problems."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user takes an image of their skin using their smartphone. The captured image is converted to a standard image format on the device and temporarily stored. This image data becomes the input to the system.

[0115] Step 2:

[0116] The device compresses and encrypts the captured image data for efficient processing. The compressed image data is securely transmitted to the server via the internet. This encrypted data is then input to the server.

[0117] Step 3:

[0118] The server decrypts the received encrypted image and passes it to an AI model developed using Python. The server extracts skin regions from the image using OpenCV, and then analyzes them using a TensorFlow model to quantify the skin condition. The quantified data obtained from this analysis process is the output of the process.

[0119] Step 4:

[0120] The server compares the analysis results obtained from the user with historical data to predict the progress of treatment. Using machine learning algorithms, predictive data is generated by calculating future states based on specific patterns. The predicted results become the output of this step.

[0121] Step 5:

[0122] The server analyzes user logs and comments using natural language processing techniques. Using the Transformers library, it extracts user emotions from the text data and quantifies that information. This quantified emotion data is the output.

[0123] Step 6:

[0124] The server selects individual beauty content based on skin analysis results, progression predictions, and emotional data. A filtering algorithm is used to select the most appropriate video and article data. A list of content optimized for each user is then generated.

[0125] Step 7:

[0126] The server sends a list of generated beauty content to the user as feedback. Specifically, it sends a push notification to the smartphone app, displaying the list. This sent feedback is the final output, and the user can view it and obtain information to continue their treatment.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention combines an emotional engine with a system that allows users to manage their skin condition during acne treatment and provides support to help them continue treatment. The system consists of a user, a terminal, and a server.

[0129] First, the user periodically takes photos of their skin using a device and uploads them to the system. These photos are used to visualize the user's skin condition. The device sends the photos to a server, where they are stored in a database.

[0130] The server inputs received skin photos into an AI-powered image analysis module to evaluate the skin's condition. This evaluation analyzes factors such as redness, swelling, and the number of pimples, and predicts the progression of the condition by comparing it with past treatment data. Furthermore, the system utilizes an emotion engine to analyze the user's facial expressions and tone of voice, recognizing the user's emotions in real time. This process is carried out using natural language processing technology, and user comments and treatment diaries are also processed as part of the emotion analysis.

[0131] The emotional data recognized by the emotion engine forms the basis for generating feedback, along with predicting the progress of treatment. The server generates optimal positive feedback tailored to the user's emotional state. For example, if the user is showing anxiety, it provides a reassuring message such as, "Treatment is progressing well, and improvement is expected soon."

[0132] The device notifies and displays the generated feedback to the user. This allows the user to check the feedback in real time and obtain specific information about the next steps.

[0133] As a concrete example, suppose User B starts using the system and uploads photos of their skin every two weeks. The server analyzes that skin inflammation has decreased in the second week and predicts further improvement from the third week onwards. The emotion engine detects slight anxiety from User B's facial expression, so the server provides feedback saying, "Treatment is progressing well, and we are seeing significant improvement. Let's continue for a little longer." In this way, User B receives feedback that also takes their emotions into consideration, enabling them to continue treatment effectively.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user takes photos of their skin during treatment, including facial photos and voice recordings, through their device and uploads them to the system.

[0137] Step 2:

[0138] The device receives photo and voice data from the user and sends it to the server.

[0139] Step 3:

[0140] The server sends the received photo data to an AI-powered image analysis module to evaluate the skin condition. Here, redness, swelling, and the number of pimples on the skin are analyzed.

[0141] Step 4:

[0142] The server inputs voice and facial expression data into the emotion engine, which analyzes the user's emotions in real time. The analysis results include emotions such as anxiety, stress, and joy.

[0143] Step 5:

[0144] The server stores the analyzed skin condition and emotional data in a database and predicts the progress of treatment by comparing it with past data.

[0145] Step 6:

[0146] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and the expected improvements.

[0147] Step 7:

[0148] The device notifies and displays the generated feedback to the user, allowing them to obtain information about the next steps.

[0149] Step 8:

[0150] Users review the notified feedback and use it as a guide for continuing treatment.

[0151] (Example 2)

[0152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0153] The present invention aims to realize a system that can improve treatment effectiveness and provide continuous treatment support by responding to changes in skin condition while also considering the user's emotional state. In particular, it is necessary to provide a sense of psychological security through feedback that takes the user's emotions into consideration.

[0154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0155] In this invention, the server includes means for acquiring image information from the user and analyzing the image information to evaluate the surface state, means for predicting the progress based on past information, and means for using a generative AI model that generates optimal feedback based on the analyzed surface state and progress prediction. This enables personalized and continuous treatment support that takes into account the user's emotions.

[0156] "Image information" refers to image data obtained from users, which is used for analysis to evaluate surface conditions.

[0157] "Surface condition" refers to the physical state determined from image information, and includes items such as redness, swelling, and the number of changes.

[0158] A "generative AI model" refers to an algorithm or platform that utilizes artificial intelligence technology to automatically generate optimal feedback based on past information and analysis results.

[0159] "Emotional state" refers to the user's psychological state, and includes emotions inferred from facial expressions, voice, and text information.

[0160] "Optimal feedback" refers to information provided to the user as the most beneficial and positive message, based on analyzed information and the user's emotional state.

[0161] As an embodiment of this invention, the following system is constructed. The system consists of three main components: a server, a terminal, and a user.

[0162] First, the user periodically acquires image information of their skin via their device. This is done using common video acquisition devices such as smartphones and tablets. The acquired image information is then sent from the device to the server.

[0163] The server stores the received image information in a database and prepares it for analysis. This analysis uses machine learning libraries such as TensorFlow as an image analysis module. The server evaluates the surface condition from the image information and quantifies elements such as redness, swelling, and the number of changes.

[0164] Next, the server analyzes emotional information obtained from the user's text information and records. Using natural language processing techniques such as NLTK libraries, it detects the user's emotional state. Based on the analyzed information and emotional state, it utilizes a generative AI model to generate optimal feedback.

[0165] The generated feedback is sent from the server to the terminal, which then notifies the user of its contents. For example, based on the analysis results and emotional information, it might provide feedback such as, "Treatment is progressing well, and significant improvement is being seen."

[0166] As a concrete example, consider a scenario where a user uploads skin image data every two weeks. The server evaluates the skin condition in the second week's analysis and makes a prediction for the third week. If the user's emotional state indicates anxiety through sentiment analysis, the generative AI model provides positive feedback that reflects that information.

[0167] An example of a prompt message is, "I have uploaded skin image data from week 2. Please analyze the skin condition and emotional information to generate appropriate feedback." This system allows users to receive individually tailored feedback, which helps them continue their treatment.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user acquires skin image information using the device. Specifically, they take high-resolution photos using the device's camera function. The input is a photo of the user's skin, and the output is an image file stored on the device.

[0171] Step 2:

[0172] The terminal sends the acquired image information to the server. Here, a secure protocol such as HTTPS is used to transmit the data. The input is an image file, and the output is the data sent to the server.

[0173] Step 3:

[0174] The server stores the received image information in a database. Then, it analyzes the image using an image analysis library such as TensorFlow to quantify the skin's surface condition. Specifically, it measures redness, swelling, and the number of pimples. The input is the image information stored on the server, and the output is numerical data of the analyzed skin condition.

[0175] Step 4:

[0176] The server compares the analyzed skin condition data with historical data and performs data processing to predict the progression of the skin condition. The input is the analyzed skin condition data and historical data, and the output is predicted data on the progression.

[0177] Step 5:

[0178] The server uses natural language processing technology to analyze user-registered comments, journal entries, and facial expression data in order to perform sentiment analysis. The input is user comments and journal entries, and the output is numerical data indicating the emotional state.

[0179] Step 6:

[0180] The server uses a generative AI model to generate optimal feedback based on analyzed skin condition, progression prediction, and emotional data. Specifically, it constructs messages such as "Treatment is progressing well." The input is the entire dataset obtained from past processing, and the output is the feedback message to the user.

[0181] Step 7:

[0182] The server sends the generated feedback message to the terminal, and the terminal notifies the user of it. The input is the feedback message from the server, and the output is the notification displayed on the terminal.

[0183] Through this step, users can receive detailed and personalized feedback based on their skin condition and emotions.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] This invention aims to help users undergoing acne treatment effectively manage their skin condition and support them in selecting the optimal product in physical stores and retail environments. Furthermore, it is necessary to reduce anxiety and improve the customer experience by providing positive feedback that takes into account the user's emotional state.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data; means for analyzing the user's emotions and generating positive feedback; and means for evaluating the user's skin condition in real time in a retail environment and recommending appropriate products. This allows the user to monitor the progress of their treatment, reduce anxiety, and make the best product selection at the point of sale.

[0189] A "user" is an individual who uses the system to manage their own skin condition and receive treatment.

[0190] "Skin images" are photographic data taken to visually capture the condition of a user's skin.

[0191] "Analysis" is the process of evaluating a state based on acquired data and extracting information.

[0192] "Progress" refers to information indicating the changes and degree of improvement in the skin condition over time since the start of treatment.

[0193] "Emotions" refer to the user's psychological state or mood, and are recognized by the system.

[0194] "Feedback" refers to advice and information provided to users based on analysis results and emotional states.

[0195] A "retail environment" refers to the physical space where users and products actually interact, including brick-and-mortar stores and commercial facilities.

[0196] An "appropriate product" refers to a skincare item or cosmetic product that is evaluated as being the best match for the user's current skin condition.

[0197] The system for realizing this invention consists of a user terminal, a server, and an application that operates in a retail environment. The user periodically takes pictures of their skin using smart glasses or a smartphone, and the terminal sends this image data to the server. The server utilizes OpenCV for image processing and TensorFlow for skin condition analysis.

[0198] The server analyzes the received skin images and evaluates the skin condition, such as the presence of redness or acne. Based on this evaluation, it predicts the progress of treatment by comparing it with past data. Furthermore, it uses the natural language processing library NLTK to analyze the user's emotions from their facial expressions and voice data, and generates emotion-responsive feedback using a generative AI model.

[0199] In a retail environment, the user's device provides information about recommended products in real time. For example, suppose user C uses the application in a store and it detects that their skin is dry. At this point, the system provides feedback such as, "Your skin is lacking moisture. Please try a highly moisturizing cream."

[0200] Example prompt: "Analyze the customer's skin photo and generate a feedback message suggesting appropriate skincare products. The message should be reassuring and tailored to the customer's emotional state. Customer C's current skin condition is dry."

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The user takes a photograph of their skin using smart glasses or a smartphone. The input is photographic data of the skin, and the output is an image file stored on the device. The user takes photographs at predetermined time intervals to prepare the data for the next step.

[0204] Step 2:

[0205] The device sends images of the skin it has captured to the server. The input is an image file stored on the device, and the output is an image file transferred to the server. The device uses an internet connection to securely upload the data for analysis on the server.

[0206] Step 3:

[0207] The server uses OpenCV for image processing and TensorFlow to analyze skin condition. The input is the submitted image file, and the output is the analyzed skin condition data. The server identifies skin redness, presence or absence of acne, and other features, and saves this data as visualized information.

[0208] Step 4:

[0209] The server references past database data and uses natural language processing technology to predict the progress of treatment based on the analysis results. The input is analyzed skin condition data, and the output is predicted treatment progress data. The server uses an AI algorithm to extract key elements to suggest the next treatment step.

[0210] Step 5:

[0211] The server analyzes the user's facial expressions and voice data, and uses NLTK to evaluate their emotional state. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state data. In this step, the emotional signs shown by the user are used to generate positive feedback.

[0212] Step 6:

[0213] Using a generative AI model, the server generates positive feedback messages tailored to the user's emotions and skin condition. The input is analyzed emotional state and predicted treatment progress data, while the output is a positive feedback message. The server aims to determine the appropriate message to enhance the user's sense of security.

[0214] Step 7:

[0215] The device notifies the user of the generated feedback message and displays it on the screen. The input is the generated feedback message, and the output is a message that the user can visually confirm. This provides the user with clear guidance on what action to take next.

[0216] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] The system of the present invention provides support to enable users to accurately understand their own skin condition during the treatment process and to continue effective treatment. Specific embodiments are described below.

[0233] This system consists of users, a server, and a terminal. Users periodically take photos of their skin during treatment and upload them to the system via the terminal. The terminal receives the photos and sends this data to the server.

[0234] The server inputs skin image data received from the user into an AI-powered image analysis module to numerically evaluate the skin's condition. This analysis includes factors such as redness, swelling, and the number of pimples. The analysis results are stored in a database and compared with past data. Based on this comparison, the server uses AI to predict the progress of treatment and assess the likelihood of future skin improvement.

[0235] Next, the server analyzes the treatment diaries and comments uploaded by users using natural language processing technology to quantify the users' emotions. This allows for an understanding of changes in the users' feelings and motivations.

[0236] Based on evaluation and predicted data, along with sentiment analysis, the server generates feedback. This feedback includes positive messages to the user regarding the effectiveness of the treatment and expected future improvements. For example, it might say, "You've seen a clear improvement in your skin after three weeks of treatment. We strongly recommend continuing the care."

[0237] Ultimately, the device notifies and displays the generated feedback to the user. This notification feature allows the user to receive reliable information to continue treatment and helps them maintain motivation.

[0238] As a concrete example, suppose user A uploads photos of their skin every two weeks using this system. The server analyzes these photos and confirms that redness has decreased in the second week. Based on past data, it is predicted that even more significant improvement will be seen from the third week onward, so the server provides user A with feedback such as, "Further improvement can be expected by continuing treatment." In this way, user A can continue to quantitatively confirm the effectiveness of the treatment.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user takes a photo of their skin during treatment and uploads it to the system via their device.

[0242] Step 2:

[0243] The device receives photo data from the user and sends this data to the server.

[0244] Step 3:

[0245] The server inputs the received photo data into an AI-based image analysis module to evaluate the skin condition. Here, information such as redness, swelling, and the number of pimples is analyzed.

[0246] Step 4:

[0247] The server saves the analysis results to a database and performs comparative analysis with past data.

[0248] Step 5:

[0249] The server uses AI to predict the progress of treatment based on similar past data and evaluates the likelihood of future skin improvement.

[0250] Step 6:

[0251] The server analyzes the user's treatment log and comments using natural language processing technology and quantifies the user's emotions.

[0252] Step 7:

[0253] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and expected outcomes.

[0254] Step 8:

[0255] The device notifies and displays the generated feedback to the user. This allows the user to review the feedback and obtain information about the next steps.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In modern society, there is growing concern about skin health, but it is difficult for users to accurately assess their own condition. Furthermore, it is difficult for users to perceive the effects of treatment, making it challenging to maintain motivation. In addition, there is a lack of support that takes users' feelings into consideration, and there is a need to increase their motivation to continue treatment.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for acquiring image information captured by the user and analyzing the image information to numerically evaluate the condition of the skin; means for storing the analyzed condition in a recording device and predicting the progress of treatment based on past information; and means for analyzing the user's descriptive information using language processing technology, quantifying emotions, and generating positive evaluation information. This makes it possible to objectively evaluate the condition of the user's skin, visually confirm the effectiveness of the treatment, and improve the user's motivation for continued care.

[0261] "Image information" refers to photographic data showing the condition of the skin, taken by the user.

[0262] "Numerically evaluating" means converting the condition of the skin into data through image analysis and indicating that condition using specific numerical values.

[0263] A "recording device" refers to a database or storage system installed inside a server to store information.

[0264] "Past information" refers to historical data of skin condition and user descriptions that have been acquired and analyzed to date.

[0265] "Predicting the progression" means estimating the future condition of the skin and the effectiveness of treatment based on past information.

[0266] "Descriptive information" refers to textual information such as diaries and comments recorded by users.

[0267] "Language processing technology" refers to techniques that utilize natural language processing to analyze emotions and intentions from user-generated information.

[0268] "Quantifying emotions" means using language processing technology to represent a user's emotional state numerically.

[0269] "Positive evaluation information" refers to information generated based on analysis results that includes content intended to encourage and motivate users.

[0270] "Continuous care" means regularly monitoring the condition of the skin and repeatedly providing necessary treatments and care.

[0271] The following describes embodiments for carrying out the present invention. This system analyzes the user's skin condition and supports the progress of treatment, and consists of a server, a terminal, and a user.

[0272] The system begins with the user taking a photograph of the skin to be treated using a device such as a smartphone or tablet. The captured image information is uploaded to a server via a dedicated application installed on the device. Data transmission utilizes an internet connection, and SSL / TLS protocols are typically used to ensure security.

[0273] The server analyzes the received image information using deep learning technology. In this case, machine learning frameworks such as TensorFlow and PyTorch are likely to be used to numerically evaluate the condition of the skin. The analyzed data is stored in a database, and the progress of treatment is predicted based on past information. Scalable data storage technologies such as MongoDB and PostgreSQL are used for this purpose.

[0274] Furthermore, the server analyzes descriptive information such as user diaries and comments using natural language processing technology. Specifically, it uses spaCy and NLTK to quantify the user's emotions and generate positive evaluation information. This can help improve motivation for treatment.

[0275] The terminal notifies the user of evaluation information generated by the server and displays it on the application. This allows the user to check the progress of their treatment and maintain motivation to continue appropriate care.

[0276] As a concrete example, consider a scenario where a user uploads skin photos to the system once a week. In this process, the system analyzes each image, allowing for quantitative verification of the treatment's effectiveness. Furthermore, an example of a prompt to be input into the generative AI model is: "Generate feedback to improve the user's skin condition. Based on past data, predict the future skin condition and provide a positive message."

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user takes a photo of the skin using a terminal. The input is the raw image data taken by the user, and digital-formatted image data is obtained as the output. As a specific operation, the camera function of a smartphone or tablet is used to take a photo, and a preview is checked within the app.

[0280] Step 2:

[0281] The terminal uploads the taken image data to the server. The input is the image data obtained in Step 1, and the output is the image file that has been successfully uploaded to the server. Here, the process of securely transmitting the image data using the SSL / TLS protocol via the Internet is included.

[0282] Step 3:

[0283] The server analyzes the received image data using AI. The input is the unanalyzed image data uploaded to the server, and the output is numerical evaluation data indicating the skin condition. Specifically, a deep learning model (e.g., using TensorFlow or PyTorch) is utilized to perform identification and quantification of redness, swelling, the number of acne, etc.

[0284] Step 4:

[0285] The server saves the analyzed evaluation data in the database and compares it with past information. The input is the evaluation data obtained in Step 3 and the existing database records, and the output is prediction data regarding the progress of treatment. MongoDB or PostgreSQL is utilized to save data in the database and perform comparative analysis in a time series.

[0286] Step 5:

[0287] The server analyzes user-submitted journal entries and comment data using natural language processing techniques. The input is user text data, and the output is numerical data representing emotions. Specifically, it uses spaCy and NLTK to analyze the text and calculate an emotion score.

[0288] Step 6:

[0289] The server generates feedback based on evaluation data and sentiment data. The input is the result data from steps 4 and 5, and the output is the generation of a positive message directed at the user. A generative AI model is used to create messages that promote future therapeutic effects.

[0290] Step 7:

[0291] The device notifies the user of feedback generated by the server. The input is the feedback message provided by the server, and the output is the feedback display that the user can view on the device. This includes utilizing the device's notification function to display the feedback on the screen.

[0292] (Application Example 1)

[0293] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0294] For users undergoing treatment for skin conditions, accurately understanding their own skin condition and maintaining motivation is difficult. Furthermore, there is a problem in that access to beauty content best suited to each individual's skin condition is limited.

[0295] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data and providing various related information; and means for analyzing the user's emotions and selecting beauty content that suits the user's skin condition. As a result, the user can receive appropriate feedback and content based on their skin condition, maintain their motivation for treatment, and obtain optimal beauty information.

[0297] A "user" is an individual who uses the system to receive skin condition assessments and beauty content.

[0298] A "skin image" is digital video data of a user's skin.

[0299] "Analysis" refers to the process of using AI to analyze acquired skin images and quantify the condition of the skin.

[0300] "Progression" refers to the evaluation and prediction of the process of improvement or deterioration of the skin condition.

[0301] "Emotional analysis" is a procedure that uses natural language processing to analyze comments and journal entries submitted by users and quantify their emotional state.

[0302] "Beauty content" refers to information provided to users, such as videos, articles, and product information related to beauty that are tailored to their skin condition.

[0303] "Feedback" refers to improvement suggestions and positive messages generated based on skin analysis and emotional analysis and delivered to users.

[0304] The system that implements this invention consists of a user, a terminal, and a server. The user takes an image of their skin using a smartphone or similar terminal and uploads this image from the terminal to the system. On the terminal, the image data is compressed in an appropriate format and securely transmitted to the server.

[0305] The server has high-performance processing capabilities and is equipped with an AI model built in Python. By using the OpenCV library for image processing, it efficiently extracts and analyzes the skin part from the acquired images, and quantifies the skin features using a model trained with TensorFlow. Also, the analysis results are compared with past data and used to predict the user's treatment progress.

[0306] Furthermore, the server analyzes the comments and treatment logs input by the user using natural language processing technology. Here, by using the Transformers, an NLP library, it extracts and quantifies the emotions from the text data. By integrating this information, the server makes recommendations for beauty content optimized individually for the user and generates positive feedback.

[0307] For example, assume that a certain user posts a log stating that the improvement of their skin has stalled in the system. In this case, the server considers the result of the sentiment analysis and the current skin condition, and proposes relaxation methods for stress relief and review videos of related beauty products. In this way, the user can receive appropriate guidance and support in continuous skin care.

[0308] As an example of the prompt text for the generative AI model, "Analyze the user's skin condition and recommend the optimal beauty content. The content mainly consists of videos and articles, focusing on individual skin problems." can be cited.

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The user takes a picture of their skin using a smartphone. The taken image is converted into a standard image format in the terminal and temporarily saved. This image data becomes the input to the system.

[0312] Step 2:

[0313] The device compresses and encrypts the captured image data for efficient processing. The compressed image data is securely transmitted to the server via the internet. This encrypted data is then input to the server.

[0314] Step 3:

[0315] The server decrypts the received encrypted image and passes it to an AI model developed using Python. The server extracts skin regions from the image using OpenCV, and then analyzes them using a TensorFlow model to quantify the skin condition. The quantified data obtained from this analysis process is the output of the process.

[0316] Step 4:

[0317] The server compares the analysis results obtained from the user with historical data to predict the progress of treatment. Using machine learning algorithms, predictive data is generated by calculating future states based on specific patterns. The predicted results become the output of this step.

[0318] Step 5:

[0319] The server analyzes user logs and comments using natural language processing techniques. Using the Transformers library, it extracts user emotions from the text data and quantifies that information. This quantified emotion data is the output.

[0320] Step 6:

[0321] The server selects individual beauty content based on skin analysis results, progression predictions, and emotional data. A filtering algorithm is used to select the most appropriate video and article data. A list of content optimized for each user is then generated.

[0322] Step 7:

[0323] The server sends a list of generated beauty content to the user as feedback. Specifically, it sends a push notification to the smartphone app, displaying the list. This sent feedback is the final output, and the user can view it and obtain information to continue their treatment.

[0324] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0325] This invention combines an emotional engine with a system that allows users to manage their skin condition during acne treatment and provides support to help them continue treatment. The system consists of a user, a terminal, and a server.

[0326] First, the user periodically takes photos of their skin using a device and uploads them to the system. These photos are used to visualize the user's skin condition. The device sends the photos to a server, where they are stored in a database.

[0327] The server inputs received skin photos into an AI-powered image analysis module to evaluate the skin's condition. This evaluation analyzes factors such as redness, swelling, and the number of pimples, and predicts the progression of the condition by comparing it with past treatment data. Furthermore, the system utilizes an emotion engine to analyze the user's facial expressions and tone of voice, recognizing the user's emotions in real time. This process is carried out using natural language processing technology, and user comments and treatment diaries are also processed as part of the emotion analysis.

[0328] The emotional data recognized by the emotion engine forms the basis for generating feedback, along with predicting the progress of treatment. The server generates optimal positive feedback tailored to the user's emotional state. For example, if the user is showing anxiety, it provides a reassuring message such as, "Treatment is progressing well, and improvement is expected soon."

[0329] The device notifies and displays the generated feedback to the user. This allows the user to check the feedback in real time and obtain specific information about the next steps.

[0330] As a concrete example, suppose User B starts using the system and uploads photos of their skin every two weeks. The server analyzes that skin inflammation has decreased in the second week and predicts further improvement from the third week onwards. The emotion engine detects slight anxiety from User B's facial expression, so the server provides feedback saying, "Treatment is progressing well, and we are seeing significant improvement. Let's continue for a little longer." In this way, User B receives feedback that also takes their emotions into consideration, enabling them to continue treatment effectively.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The user takes photos of their skin during treatment, including facial photos and voice recordings, through their device and uploads them to the system.

[0334] Step 2:

[0335] The device receives photo and voice data from the user and sends it to the server.

[0336] Step 3:

[0337] The server sends the received photo data to an AI-powered image analysis module to evaluate the skin condition. Here, redness, swelling, and the number of pimples on the skin are analyzed.

[0338] Step 4:

[0339] The server inputs voice and facial expression data into the emotion engine, which analyzes the user's emotions in real time. The analysis results include emotions such as anxiety, stress, and joy.

[0340] Step 5:

[0341] The server stores the analyzed skin condition and emotional data in a database and predicts the progress of treatment by comparing it with past data.

[0342] Step 6:

[0343] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and the expected improvements.

[0344] Step 7:

[0345] The device notifies and displays the generated feedback to the user, allowing them to obtain information about the next steps.

[0346] Step 8:

[0347] Users review the notified feedback and use it as a guide for continuing treatment.

[0348] (Example 2)

[0349] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0350] The present invention aims to realize a system that can improve treatment effectiveness and provide continuous treatment support by responding to changes in skin condition while also considering the user's emotional state. In particular, it is necessary to provide a sense of psychological security through feedback that takes the user's emotions into consideration.

[0351] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0352] In this invention, the server includes means for acquiring image information from the user and analyzing the image information to evaluate the surface state, means for predicting the progress based on past information, and means for using a generative AI model that generates optimal feedback based on the analyzed surface state and progress prediction. This enables personalized and continuous treatment support that takes into account the user's emotions.

[0353] "Image information" refers to image data obtained from users, which is used for analysis to evaluate surface conditions.

[0354] "Surface condition" refers to the physical state determined from image information, and includes items such as redness, swelling, and the number of changes.

[0355] A "generative AI model" refers to an algorithm or platform that utilizes artificial intelligence technology to automatically generate optimal feedback based on past information and analysis results.

[0356] "Emotional state" refers to the user's psychological state, and includes emotions inferred from facial expressions, voice, and text information.

[0357] "Optimal feedback" refers to information provided to the user as the most beneficial and positive message, based on analyzed information and the user's emotional state.

[0358] As an embodiment of this invention, the following system is constructed. The system consists of three main components: a server, a terminal, and a user.

[0359] First, the user periodically acquires image information of their skin via their device. This is done using common video acquisition devices such as smartphones and tablets. The acquired image information is then sent from the device to the server.

[0360] The server stores the received image information in a database and prepares it for analysis. This analysis uses machine learning libraries such as TensorFlow as an image analysis module. The server evaluates the surface condition from the image information and quantifies elements such as redness, swelling, and the number of changes.

[0361] Next, the server analyzes emotional information obtained from the user's text information and records. Using natural language processing techniques such as NLTK libraries, it detects the user's emotional state. Based on the analyzed information and emotional state, it utilizes a generative AI model to generate optimal feedback.

[0362] The generated feedback is sent from the server to the terminal, which then notifies the user of its contents. For example, based on the analysis results and emotional information, it might provide feedback such as, "Treatment is progressing well, and significant improvement is being seen."

[0363] As a concrete example, consider a scenario where a user uploads skin image data every two weeks. The server evaluates the skin condition in the second week's analysis and makes a prediction for the third week. If the user's emotional state indicates anxiety through sentiment analysis, the generative AI model provides positive feedback that reflects that information.

[0364] An example of a prompt message is, "I have uploaded skin image data from week 2. Please analyze the skin condition and emotional information to generate appropriate feedback." This system allows users to receive individually tailored feedback, which helps them continue their treatment.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The user acquires skin image information using the device. Specifically, they take high-resolution photos using the device's camera function. The input is a photo of the user's skin, and the output is an image file stored on the device.

[0368] Step 2:

[0369] The terminal sends the acquired image information to the server. Here, a secure protocol such as HTTPS is used to transmit the data. The input is an image file, and the output is the data sent to the server.

[0370] Step 3:

[0371] The server stores the received image information in a database. Then, it analyzes the image using an image analysis library such as TensorFlow to quantify the skin's surface condition. Specifically, it measures redness, swelling, and the number of pimples. The input is the image information stored on the server, and the output is numerical data of the analyzed skin condition.

[0372] Step 4:

[0373] The server compares the analyzed skin condition data with historical data and performs data processing to predict the progression of the skin condition. The input is the analyzed skin condition data and historical data, and the output is predicted data on the progression.

[0374] Step 5:

[0375] The server uses natural language processing technology to analyze user-registered comments, journal entries, and facial expression data in order to perform sentiment analysis. The input is user comments and journal entries, and the output is numerical data indicating the emotional state.

[0376] Step 6:

[0377] The server uses a generative AI model to generate optimal feedback based on analyzed skin condition, progression prediction, and emotional data. Specifically, it constructs messages such as "Treatment is progressing well." The input is the entire dataset obtained from past processing, and the output is the feedback message to the user.

[0378] Step 7:

[0379] The server sends the generated feedback message to the terminal, and the terminal notifies the user of it. The input is the feedback message from the server, and the output is the notification displayed on the terminal.

[0380] Through this step, users can receive detailed and personalized feedback based on their skin condition and emotions.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0383] This invention aims to help users undergoing acne treatment effectively manage their skin condition and support them in selecting the optimal product in physical stores and retail environments. Furthermore, it is necessary to reduce anxiety and improve the customer experience by providing positive feedback that takes into account the user's emotional state.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data; means for analyzing the user's emotions and generating positive feedback; and means for evaluating the user's skin condition in real time in a retail environment and recommending appropriate products. This allows the user to monitor the progress of their treatment, reduce anxiety, and make the best product selection at the point of sale.

[0386] A "user" is an individual who uses the system to manage their own skin condition and receive treatment.

[0387] "Skin images" are photographic data taken to visually capture the condition of a user's skin.

[0388] "Analysis" is the process of evaluating a state based on acquired data and extracting information.

[0389] "Progress" refers to information indicating the changes and degree of improvement in the skin condition over time since the start of treatment.

[0390] "Emotions" refer to the user's psychological state or mood, and are recognized by the system.

[0391] "Feedback" refers to advice and information provided to users based on analysis results and emotional states.

[0392] A "retail environment" refers to the physical space where users and products actually interact, including brick-and-mortar stores and commercial facilities.

[0393] An "appropriate product" refers to a skincare item or cosmetic product that is evaluated as being the best match for the user's current skin condition.

[0394] The system for realizing this invention consists of a user terminal, a server, and an application that operates in a retail environment. The user periodically takes pictures of their skin using smart glasses or a smartphone, and the terminal sends this image data to the server. The server utilizes OpenCV for image processing and TensorFlow for skin condition analysis.

[0395] The server analyzes the received skin images and evaluates the skin condition, such as the presence of redness or acne. Based on this evaluation, it predicts the progress of treatment by comparing it with past data. Furthermore, it uses the natural language processing library NLTK to analyze the user's emotions from their facial expressions and voice data, and generates emotion-responsive feedback using a generative AI model.

[0396] In a retail environment, the user's device provides information about recommended products in real time. For example, suppose user C uses the application in a store and it detects that their skin is dry. At this point, the system provides feedback such as, "Your skin is lacking moisture. Please try a highly moisturizing cream."

[0397] Example prompt: "Analyze the customer's skin photo and generate a feedback message suggesting appropriate skincare products. The message should be reassuring and tailored to the customer's emotional state. Customer C's current skin condition is dry."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The user takes a photograph of their skin using smart glasses or a smartphone. The input is photographic data of the skin, and the output is an image file stored on the device. The user takes photographs at predetermined time intervals to prepare the data for the next step.

[0401] Step 2:

[0402] The device sends images of the skin it has captured to the server. The input is an image file stored on the device, and the output is an image file transferred to the server. The device uses an internet connection to securely upload the data for analysis on the server.

[0403] Step 3:

[0404] The server uses OpenCV for image processing and TensorFlow to analyze skin condition. The input is the submitted image file, and the output is the analyzed skin condition data. The server identifies skin redness, presence or absence of acne, and other features, and saves this data as visualized information.

[0405] Step 4:

[0406] The server references past database data and uses natural language processing technology to predict the progress of treatment based on the analysis results. The input is analyzed skin condition data, and the output is predicted treatment progress data. The server uses an AI algorithm to extract key elements to suggest the next treatment step.

[0407] Step 5:

[0408] The server analyzes the user's facial expressions and voice data, and uses NLTK to evaluate their emotional state. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state data. In this step, the emotional signs shown by the user are used to generate positive feedback.

[0409] Step 6:

[0410] Using a generative AI model, the server generates positive feedback messages tailored to the user's emotions and skin condition. The input is analyzed emotional state and predicted treatment progress data, while the output is a positive feedback message. The server aims to determine the appropriate message to enhance the user's sense of security.

[0411] Step 7:

[0412] The device notifies the user of the generated feedback message and displays it on the screen. The input is the generated feedback message, and the output is a message that the user can visually confirm. This provides the user with clear guidance on what action to take next.

[0413] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0414] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0415] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0419] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0420] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0421] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0422] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0423] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0424] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0425] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0426] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0427] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0428] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0429] The system of the present invention provides support to enable users to accurately understand their own skin condition during the treatment process and to continue effective treatment. Specific embodiments are described below.

[0430] This system consists of users, a server, and a terminal. Users periodically take photos of their skin during treatment and upload them to the system via the terminal. The terminal receives the photos and sends this data to the server.

[0431] The server inputs skin image data received from the user into an AI-powered image analysis module to numerically evaluate the skin's condition. This analysis includes factors such as redness, swelling, and the number of pimples. The analysis results are stored in a database and compared with past data. Based on this comparison, the server uses AI to predict the progress of treatment and assess the likelihood of future skin improvement.

[0432] Next, the server analyzes the treatment diaries and comments uploaded by users using natural language processing technology to quantify the users' emotions. This allows for an understanding of changes in the users' feelings and motivations.

[0433] Based on evaluation and predicted data, along with sentiment analysis, the server generates feedback. This feedback includes positive messages to the user regarding the effectiveness of the treatment and expected future improvements. For example, it might say, "You've seen a clear improvement in your skin after three weeks of treatment. We strongly recommend continuing the care."

[0434] Ultimately, the device notifies and displays the generated feedback to the user. This notification feature allows the user to receive reliable information to continue treatment and helps them maintain motivation.

[0435] As a concrete example, suppose user A uploads photos of their skin every two weeks using this system. The server analyzes these photos and confirms that redness has decreased in the second week. Based on past data, it is predicted that even more significant improvement will be seen from the third week onward, so the server provides user A with feedback such as, "Further improvement can be expected by continuing treatment." In this way, user A can continue to quantitatively confirm the effectiveness of the treatment.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user takes a photo of their skin during treatment and uploads it to the system via their device.

[0439] Step 2:

[0440] The device receives photo data from the user and sends this data to the server.

[0441] Step 3:

[0442] The server inputs the received photo data into an AI-based image analysis module to evaluate the skin condition. Here, information such as redness, swelling, and the number of pimples is analyzed.

[0443] Step 4:

[0444] The server saves the analysis results to a database and performs comparative analysis with past data.

[0445] Step 5:

[0446] The server uses AI to predict the progress of treatment based on similar past data and evaluates the likelihood of future skin improvement.

[0447] Step 6:

[0448] The server analyzes the user's treatment log and comments using natural language processing technology and quantifies the user's emotions.

[0449] Step 7:

[0450] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and expected outcomes.

[0451] Step 8:

[0452] The device notifies and displays the generated feedback to the user. This allows the user to review the feedback and obtain information about the next steps.

[0453] (Example 1)

[0454] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] In modern society, there is growing concern about skin health, but it is difficult for users to accurately assess their own condition. Furthermore, it is difficult for users to perceive the effects of treatment, making it challenging to maintain motivation. In addition, there is a lack of support that takes users' feelings into consideration, and there is a need to increase their motivation to continue treatment.

[0456] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0457] In this invention, the server includes means for acquiring image information captured by the user and analyzing the image information to numerically evaluate the condition of the skin; means for storing the analyzed condition in a recording device and predicting the progress of treatment based on past information; and means for analyzing the user's descriptive information using language processing technology, quantifying emotions, and generating positive evaluation information. This makes it possible to objectively evaluate the condition of the user's skin, visually confirm the effectiveness of the treatment, and improve the user's motivation for continued care.

[0458] "Image information" refers to photographic data showing the condition of the skin, taken by the user.

[0459] "Numerically evaluating" means converting the condition of the skin into data through image analysis and indicating that condition using specific numerical values.

[0460] A "recording device" refers to a database or storage system installed inside a server to store information.

[0461] "Past information" refers to historical data of skin condition and user descriptions that have been acquired and analyzed to date.

[0462] "Predicting the progression" means estimating the future condition of the skin and the effectiveness of treatment based on past information.

[0463] "Descriptive information" refers to textual information such as diaries and comments recorded by users.

[0464] "Language processing technology" refers to techniques that utilize natural language processing to analyze emotions and intentions from user-generated information.

[0465] "Quantifying emotions" means using language processing technology to represent a user's emotional state numerically.

[0466] "Positive evaluation information" refers to information generated based on analysis results that includes content intended to encourage and motivate users.

[0467] "Continuous care" means regularly monitoring the condition of the skin and repeatedly providing necessary treatments and care.

[0468] The following describes embodiments for carrying out the present invention. This system analyzes the user's skin condition and supports the progress of treatment, and consists of a server, a terminal, and a user.

[0469] The system begins with the user taking a photograph of the skin to be treated using a device such as a smartphone or tablet. The captured image information is uploaded to a server via a dedicated application installed on the device. Data transmission utilizes an internet connection, and SSL / TLS protocols are typically used to ensure security.

[0470] The server analyzes the received image information using deep learning technology. In this case, machine learning frameworks such as TensorFlow and PyTorch are likely to be used to numerically evaluate the condition of the skin. The analyzed data is stored in a database, and the progress of treatment is predicted based on past information. Scalable data storage technologies such as MongoDB and PostgreSQL are used for this purpose.

[0471] Furthermore, the server analyzes descriptive information such as user diaries and comments using natural language processing technology. Specifically, it uses spaCy and NLTK to quantify the user's emotions and generate positive evaluation information. This can help improve motivation for treatment.

[0472] The terminal notifies the user of evaluation information generated by the server and displays it on the application. This allows the user to check the progress of their treatment and maintain motivation to continue appropriate care.

[0473] As a concrete example, consider a scenario where a user uploads skin photos to the system once a week. In this process, the system analyzes each image, allowing for quantitative verification of the treatment's effectiveness. Furthermore, an example of a prompt to be input into the generative AI model is: "Generate feedback to improve the user's skin condition. Based on past data, predict the future skin condition and provide a positive message."

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The user takes a photograph of their skin using their device. The input is the raw image data captured by the user, and the output is digital image data. Specifically, the user takes a photograph using the camera function of their smartphone or tablet and checks the preview within the app.

[0477] Step 2:

[0478] The device uploads the captured image data to the server. The input is the image data obtained in step 1, and the output is the image file that has been successfully uploaded to the server. This process involves securely transmitting the image data over the internet using the SSL / TLS protocol.

[0479] Step 3:

[0480] The server analyzes the received image data using AI. The input is unanalyzed image data uploaded to the server, and the output is numerical evaluation data indicating the condition of the skin. Specifically, it utilizes deep learning models (e.g., TensorFlow or PyTorch) to identify and quantify redness, swelling, the number of pimples, etc.

[0481] Step 4:

[0482] The server stores the analyzed evaluation data in a database and compares it with historical information. The input is the evaluation data obtained in step 3 and existing database records, and the output is predictive data regarding the progress of treatment. MongoDB or PostgreSQL is used to store the data in the database and perform time-series comparative analysis.

[0483] Step 5:

[0484] The server analyzes user-submitted journal entries and comment data using natural language processing techniques. The input is user text data, and the output is numerical data representing emotions. Specifically, it uses spaCy and NLTK to analyze the text and calculate an emotion score.

[0485] Step 6:

[0486] The server generates feedback based on evaluation data and sentiment data. The input is the result data from steps 4 and 5, and the output is the generation of a positive message directed at the user. A generative AI model is used to create messages that promote future therapeutic effects.

[0487] Step 7:

[0488] The device notifies the user of feedback generated by the server. The input is the feedback message provided by the server, and the output is the feedback display that the user can view on the device. This includes utilizing the device's notification function to display the feedback on the screen.

[0489] (Application Example 1)

[0490] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] For users undergoing treatment for skin conditions, accurately understanding their own skin condition and maintaining motivation is difficult. Furthermore, there is a problem in that access to beauty content best suited to each individual's skin condition is limited.

[0492] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0493] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data and providing various related information; and means for analyzing the user's emotions and selecting beauty content that suits the user's skin condition. As a result, the user can receive appropriate feedback and content based on their skin condition, maintain their motivation for treatment, and obtain optimal beauty information.

[0494] A "user" is an individual who uses the system to receive skin condition assessments and beauty content.

[0495] A "skin image" is digital video data of a user's skin.

[0496] "Analysis" refers to the process of using AI to analyze acquired skin images and quantify the condition of the skin.

[0497] "Progression" refers to the evaluation and prediction of the process of improvement or deterioration of the skin condition.

[0498] "Emotional analysis" is a procedure that uses natural language processing to analyze comments and journal entries submitted by users and quantify their emotional state.

[0499] "Beauty content" refers to information provided to users, such as videos, articles, and product information related to beauty that are tailored to their skin condition.

[0500] "Feedback" refers to improvement suggestions and positive messages generated based on skin analysis and emotional analysis and delivered to users.

[0501] The system that implements this invention consists of a user, a terminal, and a server. The user takes an image of their skin using a smartphone or similar terminal and uploads this image from the terminal to the system. On the terminal, the image data is compressed in an appropriate format and securely transmitted to the server.

[0502] The server boasts high-performance processing capabilities and is equipped with an AI model built in Python. Using the OpenCV library for image processing, it efficiently extracts and analyzes skin areas from acquired images, and then quantifies skin features using a model trained with TensorFlow. Furthermore, the analysis results are compared with historical data and used to predict the user's treatment progress.

[0503] Furthermore, the server analyzes user-entered comments and treatment diaries using natural language processing (NLP) techniques. Here, the NLP library Transformers is used to extract and quantify emotions from the text data. This integrated information allows the server to recommend individually optimized beauty content to each user and generate positive feedback.

[0504] For example, suppose a user posts a journal entry to the system stating that their skin improvement has stalled. In this case, the server considers the emotional analysis results and the current state of the user's skin, and suggests relaxation methods aimed at stress relief, as well as review videos of related beauty products. In this way, users can receive appropriate guidance and support for their ongoing skin care.

[0505] An example of a prompt for a generative AI model is: "Analyze the user's skin condition and recommend the most suitable beauty content. The content should mainly consist of videos and articles, focusing on individual skin problems."

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The user takes an image of their skin using their smartphone. The captured image is converted to a standard image format on the device and temporarily stored. This image data becomes the input to the system.

[0509] Step 2:

[0510] The device compresses and encrypts the captured image data for efficient processing. The compressed image data is securely transmitted to the server via the internet. This encrypted data is then input to the server.

[0511] Step 3:

[0512] The server decrypts the received encrypted image and passes it to an AI model developed using Python. The server extracts skin regions from the image using OpenCV, and then analyzes them using a TensorFlow model to quantify the skin condition. The quantified data obtained from this analysis process is the output of the process.

[0513] Step 4:

[0514] The server compares the analysis results obtained from the user with historical data to predict the progress of treatment. Using machine learning algorithms, predictive data is generated by calculating future states based on specific patterns. The predicted results become the output of this step.

[0515] Step 5:

[0516] The server analyzes user logs and comments using natural language processing techniques. Using the Transformers library, it extracts user emotions from the text data and quantifies that information. This quantified emotion data is the output.

[0517] Step 6:

[0518] The server selects individual beauty content based on skin analysis results, progression predictions, and emotional data. A filtering algorithm is used to select the most appropriate video and article data. A list of content optimized for each user is then generated.

[0519] Step 7:

[0520] The server sends a list of generated beauty content to the user as feedback. Specifically, it sends a push notification to the smartphone app, displaying the list. This sent feedback is the final output, and the user can view it and obtain information to continue their treatment.

[0521] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0522] This invention combines an emotional engine with a system that allows users to manage their skin condition during acne treatment and provides support to help them continue treatment. The system consists of a user, a terminal, and a server.

[0523] First, the user periodically takes photos of their skin using a device and uploads them to the system. These photos are used to visualize the user's skin condition. The device sends the photos to a server, where they are stored in a database.

[0524] The server inputs received skin photos into an AI-powered image analysis module to evaluate the skin's condition. This evaluation analyzes factors such as redness, swelling, and the number of pimples, and predicts the progression of the condition by comparing it with past treatment data. Furthermore, the system utilizes an emotion engine to analyze the user's facial expressions and tone of voice, recognizing the user's emotions in real time. This process is carried out using natural language processing technology, and user comments and treatment diaries are also processed as part of the emotion analysis.

[0525] The emotional data recognized by the emotion engine forms the basis for generating feedback, along with predicting the progress of treatment. The server generates optimal positive feedback tailored to the user's emotional state. For example, if the user is showing anxiety, it provides a reassuring message such as, "Treatment is progressing well, and improvement is expected soon."

[0526] The device notifies and displays the generated feedback to the user. This allows the user to check the feedback in real time and obtain specific information about the next steps.

[0527] As a concrete example, suppose User B starts using the system and uploads photos of their skin every two weeks. The server analyzes that skin inflammation has decreased in the second week and predicts further improvement from the third week onwards. The emotion engine detects slight anxiety from User B's facial expression, so the server provides feedback saying, "Treatment is progressing well, and we are seeing significant improvement. Let's continue for a little longer." In this way, User B receives feedback that also takes their emotions into consideration, enabling them to continue treatment effectively.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The user takes photos of their skin during treatment, including facial photos and voice recordings, through their device and uploads them to the system.

[0531] Step 2:

[0532] The device receives photo and voice data from the user and sends it to the server.

[0533] Step 3:

[0534] The server sends the received photo data to an AI-powered image analysis module to evaluate the skin condition. Here, redness, swelling, and the number of pimples on the skin are analyzed.

[0535] Step 4:

[0536] The server inputs voice and facial expression data into the emotion engine, which analyzes the user's emotions in real time. The analysis results include emotions such as anxiety, stress, and joy.

[0537] Step 5:

[0538] The server stores the analyzed skin condition and emotional data in a database and predicts the progress of treatment by comparing it with past data.

[0539] Step 6:

[0540] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and the expected improvements.

[0541] Step 7:

[0542] The device notifies and displays the generated feedback to the user, allowing them to obtain information about the next steps.

[0543] Step 8:

[0544] Users review the notified feedback and use it as a guide for continuing treatment.

[0545] (Example 2)

[0546] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] The present invention aims to realize a system that can improve treatment effectiveness and provide continuous treatment support by responding to changes in skin condition while also considering the user's emotional state. In particular, it is necessary to provide a sense of psychological security through feedback that takes the user's emotions into consideration.

[0548] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0549] In this invention, the server includes means for acquiring image information from the user and analyzing the image information to evaluate the surface state, means for predicting the progress based on past information, and means for using a generative AI model that generates optimal feedback based on the analyzed surface state and progress prediction. This enables personalized and continuous treatment support that takes into account the user's emotions.

[0550] "Image information" refers to image data obtained from users, which is used for analysis to evaluate surface conditions.

[0551] "Surface condition" refers to the physical state determined from image information, and includes items such as redness, swelling, and the number of changes.

[0552] A "generative AI model" refers to an algorithm or platform that utilizes artificial intelligence technology to automatically generate optimal feedback based on past information and analysis results.

[0553] "Emotional state" refers to the user's psychological state, and includes emotions inferred from facial expressions, voice, and text information.

[0554] "Optimal feedback" refers to information provided to the user as the most beneficial and positive message, based on analyzed information and the user's emotional state.

[0555] As an embodiment of this invention, the following system is constructed. The system consists of three main components: a server, a terminal, and a user.

[0556] First, the user periodically acquires image information of their skin via their device. This is done using common video acquisition devices such as smartphones and tablets. The acquired image information is then sent from the device to the server.

[0557] The server stores the received image information in a database and prepares it for analysis. This analysis uses machine learning libraries such as TensorFlow as an image analysis module. The server evaluates the surface condition from the image information and quantifies elements such as redness, swelling, and the number of changes.

[0558] Next, the server analyzes emotional information obtained from the user's text information and records. Using natural language processing techniques such as NLTK libraries, it detects the user's emotional state. Based on the analyzed information and emotional state, it utilizes a generative AI model to generate optimal feedback.

[0559] The generated feedback is sent from the server to the terminal, which then notifies the user of its contents. For example, based on the analysis results and emotional information, it might provide feedback such as, "Treatment is progressing well, and significant improvement is being seen."

[0560] As a concrete example, consider a scenario where a user uploads skin image data every two weeks. The server evaluates the skin condition in the second week's analysis and makes a prediction for the third week. If the user's emotional state indicates anxiety through sentiment analysis, the generative AI model provides positive feedback that reflects that information.

[0561] An example of a prompt message is, "I have uploaded skin image data from week 2. Please analyze the skin condition and emotional information to generate appropriate feedback." This system allows users to receive individually tailored feedback, which helps them continue their treatment.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] The user acquires skin image information using the device. Specifically, they take high-resolution photos using the device's camera function. The input is a photo of the user's skin, and the output is an image file stored on the device.

[0565] Step 2:

[0566] The terminal sends the acquired image information to the server. Here, a secure protocol such as HTTPS is used to transmit the data. The input is an image file, and the output is the data sent to the server.

[0567] Step 3:

[0568] The server stores the received image information in a database. Then, it analyzes the image using an image analysis library such as TensorFlow to quantify the skin's surface condition. Specifically, it measures redness, swelling, and the number of pimples. The input is the image information stored on the server, and the output is numerical data of the analyzed skin condition.

[0569] Step 4:

[0570] The server compares the analyzed skin condition data with historical data and performs data processing to predict the progression of the skin condition. The input is the analyzed skin condition data and historical data, and the output is predicted data on the progression.

[0571] Step 5:

[0572] The server uses natural language processing technology to analyze user-registered comments, journal entries, and facial expression data in order to perform sentiment analysis. The input is user comments and journal entries, and the output is numerical data indicating the emotional state.

[0573] Step 6:

[0574] The server uses a generative AI model to generate optimal feedback based on analyzed skin condition, progression prediction, and emotional data. Specifically, it constructs messages such as "Treatment is progressing well." The input is the entire dataset obtained from past processing, and the output is the feedback message to the user.

[0575] Step 7:

[0576] The server sends the generated feedback message to the terminal, and the terminal notifies the user of it. The input is the feedback message from the server, and the output is the notification displayed on the terminal.

[0577] Through this step, users can receive detailed and personalized feedback based on their skin condition and emotions.

[0578] (Application Example 2)

[0579] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0580] This invention aims to help users undergoing acne treatment effectively manage their skin condition and support them in selecting the optimal product in physical stores and retail environments. Furthermore, it is necessary to reduce anxiety and improve the customer experience by providing positive feedback that takes into account the user's emotional state.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0582] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data; means for analyzing the user's emotions and generating positive feedback; and means for evaluating the user's skin condition in real time in a retail environment and recommending appropriate products. This allows the user to monitor the progress of their treatment, reduce anxiety, and make the best product selection at the point of sale.

[0583] A "user" is an individual who uses the system to manage their own skin condition and receive treatment.

[0584] "Skin images" are photographic data taken to visually capture the condition of a user's skin.

[0585] "Analysis" is the process of evaluating a state based on acquired data and extracting information.

[0586] "Progress" refers to information indicating the changes and degree of improvement in the skin condition over time since the start of treatment.

[0587] "Emotions" refer to the user's psychological state or mood, and are recognized by the system.

[0588] "Feedback" refers to advice and information provided to users based on analysis results and emotional states.

[0589] A "retail environment" refers to the physical space where users and products actually interact, including brick-and-mortar stores and commercial facilities.

[0590] An "appropriate product" refers to a skincare item or cosmetic product that is evaluated as being the best match for the user's current skin condition.

[0591] The system for realizing this invention consists of a user terminal, a server, and an application that operates in a retail environment. The user periodically takes pictures of their skin using smart glasses or a smartphone, and the terminal sends this image data to the server. The server utilizes OpenCV for image processing and TensorFlow for skin condition analysis.

[0592] The server analyzes the received skin images and evaluates the skin condition, such as the presence of redness or acne. Based on this evaluation, it predicts the progress of treatment by comparing it with past data. Furthermore, it uses the natural language processing library NLTK to analyze the user's emotions from their facial expressions and voice data, and generates emotion-responsive feedback using a generative AI model.

[0593] In a retail environment, the user's device provides information about recommended products in real time. For example, suppose user C uses the application in a store and it detects that their skin is dry. At this point, the system provides feedback such as, "Your skin is lacking moisture. Please try a highly moisturizing cream."

[0594] Example prompt: "Analyze the customer's skin photo and generate a feedback message suggesting appropriate skincare products. The message should be reassuring and tailored to the customer's emotional state. Customer C's current skin condition is dry."

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The user takes a photograph of their skin using smart glasses or a smartphone. The input is photographic data of the skin, and the output is an image file stored on the device. The user takes photographs at predetermined time intervals to prepare the data for the next step.

[0598] Step 2:

[0599] The device sends images of the skin it has captured to the server. The input is an image file stored on the device, and the output is an image file transferred to the server. The device uses an internet connection to securely upload the data for analysis on the server.

[0600] Step 3:

[0601] The server uses OpenCV for image processing and TensorFlow to analyze skin condition. The input is the submitted image file, and the output is the analyzed skin condition data. The server identifies skin redness, presence or absence of acne, and other features, and saves this data as visualized information.

[0602] Step 4:

[0603] The server references past database data and uses natural language processing technology to predict the progress of treatment based on the analysis results. The input is analyzed skin condition data, and the output is predicted treatment progress data. The server uses an AI algorithm to extract key elements to suggest the next treatment step.

[0604] Step 5:

[0605] The server analyzes the user's facial expressions and voice data, and uses NLTK to evaluate their emotional state. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state data. In this step, the emotional signs shown by the user are used to generate positive feedback.

[0606] Step 6:

[0607] Using a generative AI model, the server generates positive feedback messages tailored to the user's emotions and skin condition. The input is analyzed emotional state and predicted treatment progress data, while the output is a positive feedback message. The server aims to determine the appropriate message to enhance the user's sense of security.

[0608] Step 7:

[0609] The device notifies the user of the generated feedback message and displays it on the screen. The input is the generated feedback message, and the output is a message that the user can visually confirm. This provides the user with clear guidance on what action to take next.

[0610] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0611] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0612] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0616] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0617] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0618] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0619] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0620] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0621] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0622] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0623] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0624] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0625] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0626] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] The system of the present invention provides support to enable users to accurately understand their own skin condition during the treatment process and to continue effective treatment. Specific embodiments are described below.

[0628] This system consists of users, a server, and a terminal. Users periodically take photos of their skin during treatment and upload them to the system via the terminal. The terminal receives the photos and sends this data to the server.

[0629] The server inputs skin image data received from the user into an AI-powered image analysis module to numerically evaluate the skin's condition. This analysis includes factors such as redness, swelling, and the number of pimples. The analysis results are stored in a database and compared with past data. Based on this comparison, the server uses AI to predict the progress of treatment and assess the likelihood of future skin improvement.

[0630] Next, the server analyzes the treatment diaries and comments uploaded by users using natural language processing technology to quantify the users' emotions. This allows for an understanding of changes in the users' feelings and motivations.

[0631] Based on evaluation and predicted data, along with sentiment analysis, the server generates feedback. This feedback includes positive messages to the user regarding the effectiveness of the treatment and expected future improvements. For example, it might say, "You've seen a clear improvement in your skin after three weeks of treatment. We strongly recommend continuing the care."

[0632] Ultimately, the device notifies and displays the generated feedback to the user. This notification feature allows the user to receive reliable information to continue treatment and helps them maintain motivation.

[0633] As a concrete example, suppose user A uploads photos of their skin every two weeks using this system. The server analyzes these photos and confirms that redness has decreased in the second week. Based on past data, it is predicted that even more significant improvement will be seen from the third week onward, so the server provides user A with feedback such as, "Further improvement can be expected by continuing treatment." In this way, user A can continue to quantitatively confirm the effectiveness of the treatment.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] The user takes a photo of their skin during treatment and uploads it to the system via their device.

[0637] Step 2:

[0638] The device receives photo data from the user and sends this data to the server.

[0639] Step 3:

[0640] The server inputs the received photo data into an AI-based image analysis module to evaluate the skin condition. Here, information such as redness, swelling, and the number of pimples is analyzed.

[0641] Step 4:

[0642] The server saves the analysis results to a database and performs comparative analysis with past data.

[0643] Step 5:

[0644] The server uses AI to predict the progress of treatment based on similar past data and evaluates the likelihood of future skin improvement.

[0645] Step 6:

[0646] The server analyzes the user's treatment log and comments using natural language processing technology and quantifies the user's emotions.

[0647] Step 7:

[0648] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and expected outcomes.

[0649] Step 8:

[0650] The device notifies and displays the generated feedback to the user. This allows the user to review the feedback and obtain information about the next steps.

[0651] (Example 1)

[0652] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] In modern society, there is growing concern about skin health, but it is difficult for users to accurately assess their own condition. Furthermore, it is difficult for users to perceive the effects of treatment, making it challenging to maintain motivation. In addition, there is a lack of support that takes users' feelings into consideration, and there is a need to increase their motivation to continue treatment.

[0654] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0655] In this invention, the server includes means for acquiring image information captured by the user and analyzing the image information to numerically evaluate the condition of the skin; means for storing the analyzed condition in a recording device and predicting the progress of treatment based on past information; and means for analyzing the user's descriptive information using language processing technology, quantifying emotions, and generating positive evaluation information. This makes it possible to objectively evaluate the condition of the user's skin, visually confirm the effectiveness of the treatment, and improve the user's motivation for continued care.

[0656] "Image information" refers to photographic data showing the condition of the skin, taken by the user.

[0657] "Numerically evaluating" means converting the condition of the skin into data through image analysis and indicating that condition using specific numerical values.

[0658] A "recording device" refers to a database or storage system installed inside a server to store information.

[0659] "Past information" refers to historical data of skin condition and user descriptions that have been acquired and analyzed to date.

[0660] "Predicting the progression" means estimating the future condition of the skin and the effectiveness of treatment based on past information.

[0661] "Descriptive information" refers to textual information such as diaries and comments recorded by users.

[0662] "Language processing technology" refers to techniques that utilize natural language processing to analyze emotions and intentions from user-generated information.

[0663] "Quantifying emotions" means using language processing technology to represent a user's emotional state numerically.

[0664] "Positive evaluation information" refers to information generated based on analysis results that includes content intended to encourage and motivate users.

[0665] "Continuous care" means regularly monitoring the condition of the skin and repeatedly providing necessary treatments and care.

[0666] The following describes embodiments for carrying out the present invention. This system analyzes the user's skin condition and supports the progress of treatment, and consists of a server, a terminal, and a user.

[0667] The system begins with the user taking a photograph of the skin to be treated using a device such as a smartphone or tablet. The captured image information is uploaded to a server via a dedicated application installed on the device. Data transmission utilizes an internet connection, and SSL / TLS protocols are typically used to ensure security.

[0668] The server analyzes the received image information using deep learning technology. In this case, machine learning frameworks such as TensorFlow and PyTorch are likely to be used to numerically evaluate the condition of the skin. The analyzed data is stored in a database, and the progress of treatment is predicted based on past information. Scalable data storage technologies such as MongoDB and PostgreSQL are used for this purpose.

[0669] Furthermore, the server analyzes descriptive information such as user diaries and comments using natural language processing technology. Specifically, it uses spaCy and NLTK to quantify the user's emotions and generate positive evaluation information. This can help improve motivation for treatment.

[0670] The terminal notifies the user of evaluation information generated by the server and displays it on the application. This allows the user to check the progress of their treatment and maintain motivation to continue appropriate care.

[0671] As a concrete example, consider a scenario where a user uploads skin photos to the system once a week. In this process, the system analyzes each image, allowing for quantitative verification of the treatment's effectiveness. Furthermore, an example of a prompt to be input into the generative AI model is: "Generate feedback to improve the user's skin condition. Based on past data, predict the future skin condition and provide a positive message."

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] The user takes a photograph of their skin using their device. The input is the raw image data captured by the user, and the output is digital image data. Specifically, the user takes a photograph using the camera function of their smartphone or tablet and checks the preview within the app.

[0675] Step 2:

[0676] The device uploads the captured image data to the server. The input is the image data obtained in step 1, and the output is the image file that has been successfully uploaded to the server. This process involves securely transmitting the image data over the internet using the SSL / TLS protocol.

[0677] Step 3:

[0678] The server analyzes the received image data using AI. The input is unanalyzed image data uploaded to the server, and the output is numerical evaluation data indicating the condition of the skin. Specifically, it utilizes deep learning models (e.g., TensorFlow or PyTorch) to identify and quantify redness, swelling, the number of pimples, etc.

[0679] Step 4:

[0680] The server stores the analyzed evaluation data in a database and compares it with historical information. The input is the evaluation data obtained in step 3 and existing database records, and the output is predictive data regarding the progress of treatment. MongoDB or PostgreSQL is used to store the data in the database and perform time-series comparative analysis.

[0681] Step 5:

[0682] The server analyzes user-submitted journal entries and comment data using natural language processing techniques. The input is user text data, and the output is numerical data representing emotions. Specifically, it uses spaCy and NLTK to analyze the text and calculate an emotion score.

[0683] Step 6:

[0684] The server generates feedback based on evaluation data and sentiment data. The input is the result data from steps 4 and 5, and the output is the generation of a positive message directed at the user. A generative AI model is used to create messages that promote future therapeutic effects.

[0685] Step 7:

[0686] The device notifies the user of feedback generated by the server. The input is the feedback message provided by the server, and the output is the feedback display that the user can view on the device. This includes utilizing the device's notification function to display the feedback on the screen.

[0687] (Application Example 1)

[0688] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0689] For users undergoing treatment for skin conditions, accurately understanding their own skin condition and maintaining motivation is difficult. Furthermore, there is a problem in that access to beauty content best suited to each individual's skin condition is limited.

[0690] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0691] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data and providing various related information; and means for analyzing the user's emotions and selecting beauty content that suits the user's skin condition. As a result, the user can receive appropriate feedback and content based on their skin condition, maintain their motivation for treatment, and obtain optimal beauty information.

[0692] A "user" is an individual who uses the system to receive skin condition assessments and beauty content.

[0693] A "skin image" is digital video data of a user's skin.

[0694] "Analysis" refers to the process of using AI to analyze acquired skin images and quantify the condition of the skin.

[0695] "Progression" refers to the evaluation and prediction of the process of improvement or deterioration of the skin condition.

[0696] "Emotional analysis" is a procedure that uses natural language processing to analyze comments and journal entries submitted by users and quantify their emotional state.

[0697] "Beauty content" refers to information provided to users, such as videos, articles, and product information related to beauty that are tailored to their skin condition.

[0698] "Feedback" refers to improvement suggestions and positive messages generated based on skin analysis and emotional analysis and delivered to users.

[0699] The system that implements this invention consists of a user, a terminal, and a server. The user takes an image of their skin using a smartphone or similar terminal and uploads this image from the terminal to the system. On the terminal, the image data is compressed in an appropriate format and securely transmitted to the server.

[0700] The server boasts high-performance processing capabilities and is equipped with an AI model built in Python. Using the OpenCV library for image processing, it efficiently extracts and analyzes skin areas from acquired images, and then quantifies skin features using a model trained with TensorFlow. Furthermore, the analysis results are compared with historical data and used to predict the user's treatment progress.

[0701] Furthermore, the server analyzes user-entered comments and treatment diaries using natural language processing (NLP) techniques. Here, the NLP library Transformers is used to extract and quantify emotions from the text data. This integrated information allows the server to recommend individually optimized beauty content to each user and generate positive feedback.

[0702] For example, suppose a user posts a journal entry to the system stating that their skin improvement has stalled. In this case, the server considers the emotional analysis results and the current state of the user's skin, and suggests relaxation methods aimed at stress relief, as well as review videos of related beauty products. In this way, users can receive appropriate guidance and support for their ongoing skin care.

[0703] An example of a prompt for a generative AI model is: "Analyze the user's skin condition and recommend the most suitable beauty content. The content should mainly consist of videos and articles, focusing on individual skin problems."

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The user takes an image of their skin using their smartphone. The captured image is converted to a standard image format on the device and temporarily stored. This image data becomes the input to the system.

[0707] Step 2:

[0708] The device compresses and encrypts the captured image data for efficient processing. The compressed image data is securely transmitted to the server via the internet. This encrypted data is then input to the server.

[0709] Step 3:

[0710] The server decrypts the received encrypted image and passes it to an AI model developed using Python. The server extracts skin regions from the image using OpenCV, and then analyzes them using a TensorFlow model to quantify the skin condition. The quantified data obtained from this analysis process is the output of the process.

[0711] Step 4:

[0712] The server compares the analysis results obtained from the user with historical data to predict the progress of treatment. Using machine learning algorithms, predictive data is generated by calculating future states based on specific patterns. The predicted results become the output of this step.

[0713] Step 5:

[0714] The server analyzes user logs and comments using natural language processing techniques. Using the Transformers library, it extracts user emotions from the text data and quantifies that information. This quantified emotion data is the output.

[0715] Step 6:

[0716] The server selects individual beauty content based on skin analysis results, progression predictions, and emotional data. A filtering algorithm is used to select the most appropriate video and article data. A list of content optimized for each user is then generated.

[0717] Step 7:

[0718] The server sends a list of generated beauty content to the user as feedback. Specifically, it sends a push notification to the smartphone app, displaying the list. This sent feedback is the final output, and the user can view it and obtain information to continue their treatment.

[0719] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0720] This invention combines an emotional engine with a system that allows users to manage their skin condition during acne treatment and provides support to help them continue treatment. The system consists of a user, a terminal, and a server.

[0721] First, the user periodically takes photos of their skin using a device and uploads them to the system. These photos are used to visualize the user's skin condition. The device sends the photos to a server, where they are stored in a database.

[0722] The server inputs received skin photos into an AI-powered image analysis module to evaluate the skin's condition. This evaluation analyzes factors such as redness, swelling, and the number of pimples, and predicts the progression of the condition by comparing it with past treatment data. Furthermore, the system utilizes an emotion engine to analyze the user's facial expressions and tone of voice, recognizing the user's emotions in real time. This process is carried out using natural language processing technology, and user comments and treatment diaries are also processed as part of the emotion analysis.

[0723] The emotional data recognized by the emotion engine forms the basis for generating feedback, along with predicting the progress of treatment. The server generates optimal positive feedback tailored to the user's emotional state. For example, if the user is showing anxiety, it provides a reassuring message such as, "Treatment is progressing well, and improvement is expected soon."

[0724] The device notifies and displays the generated feedback to the user. This allows the user to check the feedback in real time and obtain specific information about the next steps.

[0725] As a concrete example, suppose User B starts using the system and uploads photos of their skin every two weeks. The server analyzes that skin inflammation has decreased in the second week and predicts further improvement from the third week onwards. The emotion engine detects slight anxiety from User B's facial expression, so the server provides feedback saying, "Treatment is progressing well, and we are seeing significant improvement. Let's continue for a little longer." In this way, User B receives feedback that also takes their emotions into consideration, enabling them to continue treatment effectively.

[0726] The following describes the processing flow.

[0727] Step 1:

[0728] The user takes photos of their skin during treatment, including facial photos and voice recordings, through their device and uploads them to the system.

[0729] Step 2:

[0730] The device receives photo and voice data from the user and sends it to the server.

[0731] Step 3:

[0732] The server sends the received photo data to an AI-powered image analysis module to evaluate the skin condition. Here, redness, swelling, and the number of pimples on the skin are analyzed.

[0733] Step 4:

[0734] The server inputs voice and facial expression data into the emotion engine, which analyzes the user's emotions in real time. The analysis results include emotions such as anxiety, stress, and joy.

[0735] Step 5:

[0736] The server stores the analyzed skin condition and emotional data in a database and predicts the progress of treatment by comparing it with past data.

[0737] Step 6:

[0738] The server generates personalized feedback for the user based on progress predictions and sentiment analysis results. This feedback includes positive messages about the effectiveness of the treatment and the expected improvements.

[0739] Step 7:

[0740] The device notifies and displays the generated feedback to the user, allowing them to obtain information about the next steps.

[0741] Step 8:

[0742] Users review the notified feedback and use it as a guide for continuing treatment.

[0743] (Example 2)

[0744] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] The present invention aims to realize a system that can improve treatment effectiveness and provide continuous treatment support by responding to changes in skin condition while also considering the user's emotional state. In particular, it is necessary to provide a sense of psychological security through feedback that takes the user's emotions into consideration.

[0746] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0747] In this invention, the server includes means for acquiring image information from the user and analyzing the image information to evaluate the surface state, means for predicting the progress based on past information, and means for using a generative AI model that generates optimal feedback based on the analyzed surface state and progress prediction. This enables personalized and continuous treatment support that takes into account the user's emotions.

[0748] "Image information" refers to image data obtained from users, which is used for analysis to evaluate surface conditions.

[0749] "Surface condition" refers to the physical state determined from image information, and includes items such as redness, swelling, and the number of changes.

[0750] A "generative AI model" refers to an algorithm or platform that utilizes artificial intelligence technology to automatically generate optimal feedback based on past information and analysis results.

[0751] "Emotional state" refers to the user's psychological state, and includes emotions inferred from facial expressions, voice, and text information.

[0752] "Optimal feedback" refers to information provided to the user as the most beneficial and positive message, based on analyzed information and the user's emotional state.

[0753] As an embodiment of this invention, the following system is constructed. The system consists of three main components: a server, a terminal, and a user.

[0754] First, the user periodically acquires image information of their skin via their device. This is done using common video acquisition devices such as smartphones and tablets. The acquired image information is then sent from the device to the server.

[0755] The server stores the received image information in a database and prepares it for analysis. This analysis uses machine learning libraries such as TensorFlow as an image analysis module. The server evaluates the surface condition from the image information and quantifies elements such as redness, swelling, and the number of changes.

[0756] Next, the server analyzes emotional information obtained from the user's text information and records. Using natural language processing techniques such as NLTK libraries, it detects the user's emotional state. Based on the analyzed information and emotional state, it utilizes a generative AI model to generate optimal feedback.

[0757] The generated feedback is sent from the server to the terminal, which then notifies the user of its contents. For example, based on the analysis results and emotional information, it might provide feedback such as, "Treatment is progressing well, and significant improvement is being seen."

[0758] As a concrete example, consider a scenario where a user uploads skin image data every two weeks. The server evaluates the skin condition in the second week's analysis and makes a prediction for the third week. If the user's emotional state indicates anxiety through sentiment analysis, the generative AI model provides positive feedback that reflects that information.

[0759] An example of a prompt message is, "I have uploaded skin image data from week 2. Please analyze the skin condition and emotional information to generate appropriate feedback." This system allows users to receive individually tailored feedback, which helps them continue their treatment.

[0760] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0761] Step 1:

[0762] The user acquires skin image information using the device. Specifically, they take high-resolution photos using the device's camera function. The input is a photo of the user's skin, and the output is an image file stored on the device.

[0763] Step 2:

[0764] The terminal sends the acquired image information to the server. Here, a secure protocol such as HTTPS is used to transmit the data. The input is an image file, and the output is the data sent to the server.

[0765] Step 3:

[0766] The server stores the received image information in a database. Then, it analyzes the image using an image analysis library such as TensorFlow to quantify the skin's surface condition. Specifically, it measures redness, swelling, and the number of pimples. The input is the image information stored on the server, and the output is numerical data of the analyzed skin condition.

[0767] Step 4:

[0768] The server compares the analyzed skin condition data with historical data and performs data processing to predict the progression of the skin condition. The input is the analyzed skin condition data and historical data, and the output is predicted data on the progression.

[0769] Step 5:

[0770] The server uses natural language processing technology to analyze user-registered comments, journal entries, and facial expression data in order to perform sentiment analysis. The input is user comments and journal entries, and the output is numerical data indicating the emotional state.

[0771] Step 6:

[0772] The server uses a generative AI model to generate optimal feedback based on analyzed skin condition, progression prediction, and emotional data. Specifically, it constructs messages such as "Treatment is progressing well." The input is the entire dataset obtained from past processing, and the output is the feedback message to the user.

[0773] Step 7:

[0774] The server sends the generated feedback message to the terminal, and the terminal notifies the user of it. The input is the feedback message from the server, and the output is the notification displayed on the terminal.

[0775] Through this step, users can receive detailed and personalized feedback based on their skin condition and emotions.

[0776] (Application Example 2)

[0777] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0778] This invention aims to help users undergoing acne treatment effectively manage their skin condition and support them in selecting the optimal product in physical stores and retail environments. Furthermore, it is necessary to reduce anxiety and improve the customer experience by providing positive feedback that takes into account the user's emotional state.

[0779] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0780] In this invention, the server includes means for acquiring skin images from the user and analyzing the skin images to evaluate the condition of the skin; means for predicting the progress of treatment based on past data; means for analyzing the user's emotions and generating positive feedback; and means for evaluating the user's skin condition in real time in a retail environment and recommending appropriate products. This allows the user to monitor the progress of their treatment, reduce anxiety, and make the best product selection at the point of sale.

[0781] A "user" is an individual who uses the system to manage their own skin condition and receive treatment.

[0782] "Skin images" are photographic data taken to visually capture the condition of a user's skin.

[0783] "Analysis" is the process of evaluating a state based on acquired data and extracting information.

[0784] "Progress" refers to information indicating the changes and degree of improvement in the skin condition over time since the start of treatment.

[0785] "Emotions" refer to the user's psychological state or mood, and are recognized by the system.

[0786] "Feedback" refers to advice and information provided to users based on analysis results and emotional states.

[0787] A "retail environment" refers to the physical space where users and products actually interact, including brick-and-mortar stores and commercial facilities.

[0788] An "appropriate product" refers to a skincare item or cosmetic product that is evaluated as being the best match for the user's current skin condition.

[0789] The system for realizing this invention consists of a user terminal, a server, and an application that operates in a retail environment. The user periodically takes pictures of their skin using smart glasses or a smartphone, and the terminal sends this image data to the server. The server utilizes OpenCV for image processing and TensorFlow for skin condition analysis.

[0790] The server analyzes the received skin images and evaluates the skin condition, such as the presence of redness or acne. Based on this evaluation, it predicts the progress of treatment by comparing it with past data. Furthermore, it uses the natural language processing library NLTK to analyze the user's emotions from their facial expressions and voice data, and generates emotion-responsive feedback using a generative AI model.

[0791] In a retail environment, the user's device provides information about recommended products in real time. For example, suppose user C uses the application in a store and it detects that their skin is dry. At this point, the system provides feedback such as, "Your skin is lacking moisture. Please try a highly moisturizing cream."

[0792] Example prompt: "Analyze the customer's skin photo and generate a feedback message suggesting appropriate skincare products. The message should be reassuring and tailored to the customer's emotional state. Customer C's current skin condition is dry."

[0793] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0794] Step 1:

[0795] The user takes a photograph of their skin using smart glasses or a smartphone. The input is photographic data of the skin, and the output is an image file stored on the device. The user takes photographs at predetermined time intervals to prepare the data for the next step.

[0796] Step 2:

[0797] The device sends images of the skin it has captured to the server. The input is an image file stored on the device, and the output is an image file transferred to the server. The device uses an internet connection to securely upload the data for analysis on the server.

[0798] Step 3:

[0799] The server uses OpenCV for image processing and TensorFlow to analyze skin condition. The input is the submitted image file, and the output is the analyzed skin condition data. The server identifies skin redness, presence or absence of acne, and other features, and saves this data as visualized information.

[0800] Step 4:

[0801] The server references past database data and uses natural language processing technology to predict the progress of treatment based on the analysis results. The input is analyzed skin condition data, and the output is predicted treatment progress data. The server uses an AI algorithm to extract key elements to suggest the next treatment step.

[0802] Step 5:

[0803] The server analyzes the user's facial expressions and voice data, and uses NLTK to evaluate their emotional state. The input is the user's facial expressions and voice data, and the output is the analyzed emotional state data. In this step, the emotional signs shown by the user are used to generate positive feedback.

[0804] Step 6:

[0805] Using a generative AI model, the server generates positive feedback messages tailored to the user's emotions and skin condition. The input is analyzed emotional state and predicted treatment progress data, while the output is a positive feedback message. The server aims to determine the appropriate message to enhance the user's sense of security.

[0806] Step 7:

[0807] The device notifies the user of the generated feedback message and displays it on the screen. The input is the generated feedback message, and the output is a message that the user can visually confirm. This provides the user with clear guidance on what action to take next.

[0808] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0809] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0810] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0811] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0812] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0813] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0814] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0815] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0816] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0817] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0818] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0819] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0820] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0821] 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.

[0822] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0823] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0824] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0825] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0826] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0827] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0828] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] A means for acquiring skin images from a user and analyzing those skin images to evaluate the condition of the skin,

[0832] A method for predicting the progress of treatment based on past data,

[0833] A means of analyzing user emotions and generating positive feedback,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, which notifies the user of the analyzed skin condition and progression prediction, thereby guiding them to reduce anxiety.

[0837] (Claim 3)

[0838] The system according to claim 1, which analyzes user comments and diaries to quantify emotions and provides specific advice to support the continuation of treatment.

[0839] "Example 1"

[0840] (Claim 1)

[0841] A means for acquiring image information taken from a user, analyzing the image information, and numerically evaluating the condition of the skin,

[0842] A means of storing the analyzed state in a recording device and predicting the progress of treatment based on past information,

[0843] A means of analyzing user-generated information using language processing technology, quantifying emotions, and generating positive evaluation information,

[0844] By notifying users of evaluation information, this can be a means of supporting their motivation to continue treatment.

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which displays numerical information of the analyzed skin condition and a progression prediction to help the user feel at ease.

[0848] (Claim 3)

[0849] The system according to claim 1, which analyzes text information entered by the user, quantifies emotions, and provides concrete guidance information useful for continuing treatment.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] A means for acquiring skin images from a user and analyzing those skin images to evaluate the condition of the skin,

[0853] A means of predicting the progress of treatment based on past data and providing various related information,

[0854] A method for analyzing user emotions and selecting beauty content that suits the user's skin condition,

[0855] A means of generating personalized positive feedback for users based on analysis results and providing content that focuses on skin problems,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, which delivers individually selected beauty content to the user based on the analyzed skin condition and progression prediction, thereby guiding the user to alleviate anxiety.

[0859] (Claim 3)

[0860] The system according to claim 1, which analyzes user comments and diaries to quantify emotions, provides specific advice along with relevant beauty content, and proposes promotional materials to support the continuation of treatment.

[0861] "Example 2 of combining an emotion engine"

[0862] (Claim 1)

[0863] A means for acquiring image information from a user and analyzing said image information to evaluate the surface state,

[0864] A means of predicting the progress based on past information,

[0865] A means of using a generative AI model that generates optimal feedback based on the analyzed surface state and progression prediction,

[0866] A means for detecting the user's emotional state and adjusting the feedback content considering the detected emotions,

[0867] A means of quantifying emotional information by analyzing users' text information and records,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, which notifies the user of the analyzed surface state and progression prediction, and provides feedback to promote a sense of security.

[0871] (Claim 3)

[0872] The system according to claim 1, which analyzes the user's emotions based on recorded information and provides detailed guidance based on the results of that analysis.

[0873] "Application example 2 when combining with an emotional engine"

[0874] (Claim 1)

[0875] A means for acquiring skin images from a user and analyzing those skin images to evaluate the condition of the skin,

[0876] A method for predicting the progress of treatment based on past data,

[0877] A means of analyzing user emotions and generating positive feedback,

[0878] In a retail environment, a means to evaluate the user's skin condition in real time and recommend appropriate products,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, which notifies the user of the analyzed skin condition and progression prediction, guides the user to alleviate anxiety, and suggests recommended products.

[0882] (Claim 3)

[0883] The system according to claim 1, which analyzes user comments and diaries to quantify emotions, provides specific advice to support continued treatment, and assists in product selection at retail locations. [Explanation of symbols]

[0884] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring skin images from a user and analyzing those skin images to evaluate the condition of the skin, A method for predicting the progress of treatment based on past data, A means of analyzing user emotions and generating positive feedback, A system that includes this.

2. The system according to claim 1, which notifies the user of the analyzed skin condition and progression prediction, and helps to alleviate anxiety.

3. The system according to claim 1, which analyzes user comments and diaries to quantify emotions and provides specific advice to support the continuation of treatment.

Images