KR102593868B1 - Methods and systems for providing nail and toenail care information and operating expert matching platform services - Google Patents

Abstract

The fingernail and toenail care information provision and expert matching platform service operating system may include a processor and memory. The processor receives images of the user's hands and/or feet, provides them as input to an artificial intelligence learning model, and uses the user's hand and foot input as input using an artificial intelligence learning model that has learned images of the hands and feet of other users. /or provides foot condition as output, analyzes underlying diseases and health conditions based on the user's hand and/or foot condition output using an artificial intelligence learning model, and determines whether there are any medications the user must take for treatment and, based on user input, receive information about the ingredients of the drug the user is currently taking, and information about the drug ingredients in question based on whether there are ingredients that the user should not take together with the drug the user must take for treatment. can be provided.

Description

Method, device and system for providing fingernail and toenail care information and expert matching platform service {METHODS AND SYSTEMS FOR PROVIDING NAIL AND TOENAIL CARE INFORMATION AND OPERATING EXPERT MATCHING PLATFORM SERVICES}

This document relates to an electronic device, and specifically to a method and device for operating a platform that provides fingernail and toenail care information and matches fingernail and toenail care experts.

Nails are described as 'small mirrors of our body', and there is a record that Donguibogam checks the nails first when treating a patient to determine the patient's health status.

Among the health signals sent from various parts of the body, it is very meaningful to check health and health indicators through 'nails'. Diseases can appear differently depending on the color or shape of the nails. For example, there are studies and statistical data showing that if it is white instead of pink, liver disease and anemia may be suspected, and if it is blue, lung abnormalities and bronchial disease may be suspected. Also, if the middle of the nail is sunken in the shape of a fingernail, there is a high possibility that you have anemia caused by iron deficiency. Conversely, if it is bulging, the function of the respiratory or digestive organs may not be smooth.

In this way, the user's disease can be determined based on the color and shape of the fingernails and toenails.

Republic of Korea Patent Publication No. 10-2015-0115028 Republic of Korea Patent Publication No. 10-2021-0160249

When diagnosing the user's health condition based on fingernail and toenail images and recommending medicine, it may contain ingredients that are difficult to take along with medicine the user is already taking.

In this case, even if the diagnosis of the user's health condition is the same, the type of recommended medicine may be different. The purpose of the fingernail and toenail care information provision and expert matching platform service driving system according to this document is to exclude some ingredients or provide information about other medicine ingredients in consideration of the situation in which the user is already taking medicine.

The fingernail and toenail care information provision and expert matching platform service operating system may include a processor and memory. The processor receives images of the user's hands and/or feet, provides them as input to an artificial intelligence learning model, and uses the user's hand and foot input as input using an artificial intelligence learning model that has learned images of the hands and feet of other users. /or provides foot condition as output, analyzes underlying diseases and health conditions based on the user's hand and/or foot condition output using an artificial intelligence learning model, and determines whether there are any medications the user must take for treatment and, based on user input, receive information about the ingredients of the drug the user is currently taking, and information about the drug ingredients in question based on whether there are ingredients that the user should not take together with the drug the user must take for treatment. can be provided.

The fingernail and toenail care information provision and expert matching platform service driving system according to this document may exclude some ingredients or provide information about other drug ingredients in consideration of the situation in which the user is already taking medicine.

In addition, the fingernail and toenail care information provision and expert matching platform service driving system according to this document can provide convenience by matching users with experts who can provide fingernail and toenail care.

1 is a diagram illustrating a system that provides artificial intelligence-based fingernail and toenail care information and an expert matching platform service according to an embodiment.
Figure 2 is a diagram for explaining learning of a neural network according to an embodiment.
Figure 3 is a diagram showing the configuration of an artificial intelligence model according to an embodiment.
Figure 4 is an exemplary diagram of the configuration of a system that provides platform services according to an embodiment.
Figure 5 shows a flowchart of a method for providing platform services according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, various changes can be made to the embodiments, so the scope of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes for the embodiments are included in the scope of rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Terms such as first or second may be used to describe various components, but these terms should be interpreted only for the purpose of distinguishing one component from another component. For example, a first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected or connected to the other component, but that other components may exist in between.

The terms used in the examples are for descriptive purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the embodiments belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.

In addition, when describing with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiments, if it is determined that detailed descriptions of related known technologies may unnecessarily obscure the gist of the embodiments, the detailed descriptions are omitted.

Embodiments may be implemented in various types of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

An artificial intelligence (AI) system is a computer system that implements human-level intelligence, and unlike existing rule-based smart systems, it is a system in which machines learn and make decisions on their own. As artificial intelligence systems are used, their recognition rates improve and they can more accurately understand user preferences, and existing rule-based smart systems are gradually being replaced by deep learning-based artificial intelligence systems.

Artificial intelligence technology consists of machine learning and element technologies using machine learning. Machine learning is an algorithmic technology that classifies/learns the characteristics of input data on its own, and elemental technology is a technology that utilizes machine learning algorithms such as deep learning to mimic the functions of the human brain such as recognition and judgment, including linguistic understanding and visual It consists of technical areas such as understanding, reasoning/prediction, knowledge expression, and motion control.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology that recognizes and applies/processes human language/characters and includes natural language processing, machine translation, conversation systems, question and answer, and voice recognition/synthesis. Visual understanding is a technology that recognizes and processes objects as if they were human vision, and includes object recognition, object tracking, image search, person recognition, scene understanding, spatial understanding, and image improvement. Inferential prediction is a technology that judges information to make logical inferences and predictions, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendations. Knowledge expression is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data creation/classification) and knowledge management (data utilization). Motion control is a technology that controls the autonomous driving of vehicles and the movement of robots, and includes motion control (navigation, collision, driving), operation control (behavior control), etc.

Generally, in order to apply machine learning algorithms to real life, learning is performed using a trial and error method due to the nature of the basic methodology of machine learning. In particular, deep learning requires hundreds of thousands of iterations. It is impossible to execute this in an actual physical external environment, so instead, the actual physical external environment is virtually implemented on a computer and learning is performed through simulation.

In the present invention, artificial intelligence (AI) refers to technology that imitates human learning ability, reasoning ability, perception ability, etc. and implements this with a computer, and includes concepts such as machine learning and symbolic logic. It may include. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and makes judgments or predictions based on the results of the learning. Additionally, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms can also be understood as the category of artificial intelligence. For example, technical fields such as verbal understanding, visual understanding, reasoning/prediction, knowledge representation, and motion control may be included.

Machine learning can refer to the process of training a neural network model using experience processing data. Machine learning can mean that computer software improves its own data processing capabilities. A neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data, and repeating this process to optimize the parameters of the neural network model can be called machine learning. For example, a neural network model can learn the mapping (correlation) between input and output for data given as input-output pairs. Alternatively, a neural network model can learn the relationships by deriving regularities between given data even when only input data is given.

An artificial intelligence learning model or neural network model may be designed to implement the human brain structure on a computer, and may include a plurality of network nodes with weights that simulate neurons of a human neural network. A plurality of network nodes may have a connection relationship with each other by simulating the synaptic activity of neurons in which neurons exchange signals through synapses. In an artificial intelligence learning model, multiple network nodes are located in layers of different depths and can exchange data according to convolutional connection relationships. The artificial intelligence learning model may be, for example, an artificial neural network model (Artificial Neural Network), a convolution neural network (CNN) model, etc. As an example, an artificial intelligence learning model may be machine-learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, and Ada-boost. , Perceptron, Genetic Programming, Clustering, etc. can be used.

Among them, CNN is a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolution layers and general artificial neural network layers on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize input data with a two-dimensional structure. Compared to other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained via standard back propagation. CNNs have the advantage of being easier to train and using fewer parameters than other feedforward artificial neural network techniques.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increase in the size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have led to significant improvements in many computer vision tasks. For extremely large data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. For this purpose, distributed, scalable implementations of deep neural networks can be used.

1 is a diagram illustrating a system that provides artificial intelligence-based fingernail and toenail care information and an expert matching platform service according to an embodiment.

As shown in FIG. 1, the artificial intelligence-based platform service providing system 100 may include a plurality of user terminals 110-1,??, a server 120, and a database 130. According to one embodiment, the database 130 is shown as being configured separately from the server 120, but the present invention is not limited thereto, and the database 130 may be provided within the server 120. For example, the server 120 may include multiple artificial intelligence systems for performing machine learning algorithms. According to one embodiment, a plurality of user terminals 110-1, the server 120, and the database 130 may be connected to communicate with each other through a network N.

The network (N) may perform wireless or wired communication between a plurality of user terminals 110-1, ??, server 120, database 130, etc. For example, the network may be configured to perform LTE (long- term evolution), LTE-A (LTE Advanced), CDMA (code division multiple access), WCDMA (wideband CDMA), WiBro (Wireless BroadBand), WiFi (wireless fidelity), Bluetooth, NFC (near field communication), Wireless communication can be performed according to a method such as GPS (Global Positioning System) or GNSS (global navigation satellite system), etc. For example, the network (N) is USB (universal serial bus), HDMI (high definition multimedia interface), etc. ), RS-232 (recommended standard 232), or POTS (plain old telephone service).

The database 130 can store various data. Data stored in the database 130 is data acquired, processed, or used by at least one component of the multiple user terminals 110-1 and server 120, and is software (e.g. : program) The database 130 may include volatile and/or non-volatile memory.

In the present invention, artificial intelligence (AI) refers to technology that imitates human learning ability, reasoning ability, perception ability, etc. and implements it with a computer, and includes concepts such as machine learning and symbolic logic. may include. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and makes judgments or predictions based on the results of the learning. Additionally, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms can also be understood as the category of artificial intelligence. For example, technical fields such as verbal understanding, visual understanding, reasoning/prediction, knowledge representation, and motion control may be included.

Machine learning can refer to the process of training a neural network model using experience processing data. Machine learning can mean that computer software improves its own data processing capabilities. A neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data, and repeating this process to optimize the parameters of the neural network model can be called machine learning. For example, a neural network model can learn the mapping (correlation) between input and output for data given as input-output pairs. Alternatively, a neural network model can learn the relationships by deriving regularities between given data even when only input data is given.

An artificial intelligence learning model or neural network model may be designed to implement the human brain structure on a computer, and may include a plurality of network nodes with weights that simulate neurons of a human neural network. A plurality of network nodes may have a connection relationship with each other by simulating the synaptic activity of neurons in which neurons exchange signals through synapses. In an artificial intelligence learning model, multiple network nodes are located in layers of different depths and can exchange data according to convolutional connection relationships. The artificial intelligence learning model may be, for example, an artificial neural network model (Artificial Neural Network), a convolution neural network (CNN) model, etc. As an example, an artificial intelligence learning model may be machine-learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, and Ada-boost. , Perceptron, Genetic Programming, Clustering, etc. can be used.

Among them, CNN is a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolution layers and general artificial neural network layers on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize input data with a two-dimensional structure. Compared to other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained via standard back propagation. CNNs have the advantage of being easier to train and using fewer parameters than other feedforward artificial neural network techniques.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increase in the size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have led to significant improvements in many computer vision tasks. For extremely large data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. For this purpose, distributed, scalable implementations of deep neural networks can be used.

Figure 2 is a diagram for explaining learning of a neural network according to an embodiment.

As shown in FIG. 2, the learning device can train the neural network 123 to classify review responses received from a plurality of user terminals 110-1,??, by item. Additionally, the learning device can train the neural network 123 to extract the user's stay details from the user's movement path information. According to one embodiment, the learning device may be a separate entity from the server 120, but is not limited thereto.

The neural network 123 includes an input layer 121 through which training samples are input and an output layer 125 through which training outputs are output, and can be learned based on the difference between training outputs and labels. Here, labels may be defined based on items corresponding to review responses and based on user stay details corresponding to movement path information. The neural network 123 is connected to a group of a plurality of nodes, and is defined by weights between connected nodes and an activation function that activates the nodes.

The learning device may learn the neural network 123 using a gradient descent (GD) technique or a stochastic gradient descent (SGD) technique. The learning device can use a loss function designed by the outputs and labels of the neural network.

The learning device can calculate the training error using a predefined loss function. The loss function may be predefined with a label, output, and parameter as input variables, where the parameter may be set by weights in the neural network 123. For example, the loss function may be designed in the form of MSE (Mean Square Error), entropy, etc., and various techniques or methods may be employed in embodiments in which the loss function is designed.

The learning device can use the backpropagation technique to find weights that affect the training error. Here, the weights are relationships between nodes in the neural network 123. The learning device can use the SGD technique using labels and outputs to optimize the weights found through the backpropagation technique. For example, the learning device can update the weights of the loss function defined based on the labels, outputs, and weights using the SGD technique.

According to one embodiment, the learning device extracts first objects from the review response, obtains first labels that are items corresponding to the first objects, applies the first objects to the first neural network, and selects the first object. First training outputs corresponding to the first training outputs may be generated, and a first neural network may be trained based on the first training outputs and first labels.

The learning device extracts second objects from the movement path information, obtains second labels that are user stay details corresponding to the second objects, applies the second objects to the second neural network, and applies the second objects to the second objects. Corresponding second training outputs may be generated, and a second neural network may be trained based on the second training outputs and second labels.

According to one embodiment, the learning device may generate first training feature vectors based on configuration features, location features, and pattern features of the review response. Various methods can be employed to extract features.

According to one embodiment, the learning device may generate second training feature vectors based on the configuration features, length features, and pattern features of the movement path information. Various methods can be employed to extract features.

According to one embodiment, the learning device may obtain training outputs by applying the first training feature vectors to the neural network 123. The learning device may learn the review item extraction algorithm of the neural network 123 based on the training outputs and the first labels. The learning device can learn the review item extraction algorithm of the neural network 123 by calculating training errors corresponding to the training outputs and optimizing the connection relationships of nodes in the neural network 123 to minimize the training errors. . The server 120 may extract items from the review response using the first neural network on which learning has been completed. For example, extracted items may include, but are not limited to, friendliness, store cleanliness, service satisfaction, etc.

According to one embodiment, the learning device may obtain training outputs by applying the second training feature vectors to the neural network 123. The learning device may learn the user residence history acquisition algorithm of the neural network 123 based on the training outputs and the second labels. The learning device calculates training errors corresponding to the training outputs, optimizes the connection relationship between nodes in the neural network 123 to minimize the training errors, and learns the user stay history acquisition algorithm of the neural network 123. there is. The server 120 can obtain user stay details from movement path information using the second neural network on which learning has been completed.

Figure 3 is a diagram showing the configuration of an artificial intelligence model according to an embodiment.

An artificial intelligence model according to one embodiment may include an input layer, a hidden layer, and an output layer.

The input layer is a layer related to the input values input to the artificial intelligence model.

In the hidden layer, a feature map can be output by performing MAC operation (multiply-accumulate) and activation operation on the input value.

The MAC operation may be an operation that multiplies the input value and the corresponding weight, and adds the multiplied values.

The activation operation may be an operation that inputs the result of the MAC operation into an activation function and outputs a result value. Activation functions can be of various types. For example, the activation function may include, but is not limited to, a sigmoid function, a tangent function, a Lelu function, a Ricky Lelu function, a MaxOut function, and/or an Elu function.

The hidden layer may consist of at least one layer. For example, if the hidden layer consists of a first hidden layer and a second hidden layer, the first hidden layer performs a MAC operation and an activation operation based on the input value of the input system to output a feature map, and the first hidden layer performs a MAC operation and an activation operation based on the input value of the input system. The feature map, which is the result of the layer, can be the input value to the second hidden layer. The second hidden layer may perform MAC operation and activation operation based on the feature map that is the result of the first hidden layer.

The output layer may be a layer related to the result value of the operation performed in the hidden layer.

In one embodiment, the learning model automatically learns the boundaries of compound words and entity names by learning syllable (letter) patterns frequently used in combination in a given corpus, and combines object information from the first UI source and object information rendered by the browser. Integrate to create a learning object information file, use the learning object information file to generate learning data for learning a deep learning network, receive data from various domains of the support system, and generate a learning object information file corresponding to each of the various domains. Based on at least one standardization method, standardize data of the various domains into an integrated format, learn and infer data of a specific domain, and determine, from the data of the specific domain, information to be conveyed for the standardization, Post processing can be performed on data from the various domains. The first UI source includes an XML file, the learning object information file includes an input JSON file for learning features and an output JSON file that is the correct answer (Label) data during learning, and the output JSON file is a web standard Contains a file containing DOM Tree information of HTML implemented in compliance with the above, and the various domains include at least one of RAN (radio access network), transport, or core, and the post-processing is related to (correlation) function may be included.

Figure 4 is an exemplary diagram of the configuration of a system that provides platform services according to an embodiment.

The electronic device 400 according to one embodiment may include a processor 420 and a memory 430, and some of the illustrated components may be omitted or replaced. The electronic device 400 according to one embodiment may be a server or a terminal. According to one embodiment, the processor 420 is a component capable of performing operations or data processing related to control and/or communication of each component of the electronic device 400 and may be composed of one or more processors. The memory 430 may store information related to the above-described method or store a program implementing the above-described method. Memory 430 may be volatile memory or non-volatile memory. The memory 430 can store various file data, and the stored file data can be updated according to the operation of the processor 120.

According to one embodiment, the processor 420 can execute a program and control the device 400. The code of the program executed by the processor 120 may be stored in the memory 430. Operations of the processor 420 may be performed by loading instructions stored in the memory 430. The electronic device 400 is connected to an external device (eg, a personal computer or a network) through an input/output device (not shown) and can exchange data.

According to one embodiment, there will be no limitation to the calculation and data processing functions that the processor 420 can implement on the electronic device, but the functions of providing fingernail and toenail care information and expert matching information will be described below.

According to one embodiment, the processor 420 receives images of the user's hands and/or feet, provides them as input to an artificial intelligence learning model, and learns artificial intelligence by learning about images of the hands and feet of other users. The user's hand and/or foot condition input using the model is provided as output, and the underlying disease and health condition are analyzed based on the user's hand and/or foot condition output using an artificial intelligence learning model, and the user receives treatment. Determine if there are any medications the user should take for treatment, receive information about the components of the medication the user is currently taking based on user input, and based on whether there are any components that the user should not take with the medications the user should take for the treatment. This can provide information about the drug ingredient in question.

According to one embodiment, the processor 420 links an external device capable of photographing the state of the user's hands and feet with a communication circuit on the system and transmits a signal to provide a guide for photographing the state of the hand and feet on the external device. And, using an external device, the user receives the captured image based on the state of the hand and foot, and sends a signal requesting rephotography to the external device based on the fact that the resolution of the captured image is below a specified level. Using an artificial intelligence learning model, the underlying disease and health status are analyzed according to the shape, color, and oxygen saturation of the fingernails in the received image, and based on the output of the underlying disease and health condition, the necessary conditions for treatment are analyzed. You can decide on medication. The processor 420 selects at least one drug from among the determined drugs by user input, and images the hands and feet to which the treatment effect is applied to show how the user's hands and feet can be treated when the selected drug is applied to the user's hands and feet. can be displayed. Images with treatment effects can be created using other users' data (e.g., reviews of related products).

According to one embodiment, the processor 420 uses the artificial intelligence learning model based on receiving images of the user's hands and/or feet to determine underlying diseases and health conditions according to the shape, color, and oxygen saturation of the fingernails. Analyzes and assigns a score based on oxygen saturation according to the shape, color, and color of the nails, determines that care is needed for the hand or foot based on the score being below a specified level, provides care information, and provides user account information. Determines the user's care consultation information based on the service provision record and input consultation information between the account and the care expert's account, determines the user's skin information and the user's health information based on the user input, and reproduces by the user Determine the user's search information based on beauty content for fingernails and toenails or search records related to fingernail and toenail care on an Internet application, and provide care consultation information, the search information, the user's skin information, and the user's health information Based on this, at least one care product suitable for the user, a care service, and an expert who can provide the service may be determined, and information about the determined expert may be provided to the user's terminal. The processor 420 may display an image of the user's hands and feet with a treatment effect showing how the user's hands and feet can be treated when at least one care product or care service suitable for the user is applied to the user's hands and feet. Images of hands and feet to which at least one care product or care service is applied may be generated using data (eg, product reviews) of other users who have received at least one care product or care service.

According to one embodiment, the processor 420 displays a guide requesting information about the remaining capacity of the recommended care product or other care product based on the passage of a specified period of time from the time the recommended care product or other care product was purchased. Request a photo of the purchased product, determine the remaining capacity of the purchased product based on the captured product photo, and provide shopping information for recommended care products based on the remaining capacity of the purchased product being below a specified level. You can display a guide asking whether to purchase or not.

According to one embodiment, the processor 420 determines the first point in time at which care services were recently provided based on care consultation information, and determines the first point in time at which care services were recently provided to the user based on the specified period exceeding the first point in time. Afterwards, information indicating that the specified period has been exceeded is provided, and the user has recently received services from at least one expert determined based on care consultation information, the search information, the user's skin information, and the user's health information. Afterwards, information indicating that the specified period has been exceeded is provided, and information about the expert and care is provided to the user based on receiving information from the expert's terminal indicating that the care service recently received by the user can be provided. Information about services can be provided. Information about the expert may include the expert's previous consultation history and other users' service evaluation scores.

Figure 5 shows a flowchart of a method for providing platform services according to an embodiment. Although the process steps, method steps, algorithms, etc. are depicted in a sequential order in the flow chart of FIG. 5, such processes, methods, and algorithms may be configured to operate in any suitable order. In other words, the steps of the processes, methods and algorithms described in various embodiments of the invention do not need to be performed in the order described herein. Additionally, although some steps are described as being performed asynchronously, in other embodiments, some such steps may be performed concurrently. Additionally, illustration of a process by depiction in the drawings is not intended to imply that the illustrated process excludes other variations and modifications thereto, and that any of the illustrated process or steps thereof may be incorporated into any of the various embodiments of the invention. It does not imply that more than one is required, nor does it imply that the illustrated process is desirable.

In operation 510, a processor (e.g., processor 420 in FIG. 4) may receive an image of the user's hands and/or feet and provide the image as input to an artificial intelligence learning model.

In operation 520, the processor 420 may provide the input user's hand and/or foot condition as output using an artificial intelligence learning model learned about hand and foot images of other users.

In operation 530, the processor 420 analyzes the underlying disease and health condition based on the user's hand and/or foot condition output using an artificial intelligence learning model and determines whether there is any medicine the user must take for treatment. there is.

In operation 540, the processor 420 may receive information about the drug ingredient that the user is currently taking based on the user input.

In operation 550, the processor 420 may provide information about the drug ingredient in question based on whether there is an ingredient that should not be taken with the drug the user must take for treatment.

Claims (3)

In the fingernail and toenail care information provision and expert matching platform service operation system,
processor; and
contains memory,
The processor is
Receive images of the user's hands and/or feet and provide them as input to the artificial intelligence learning model,
The input user's hand and/or foot condition is provided as output using an artificial intelligence learning model that learned about other users' hand and foot images.
Analyzing the underlying disease and health condition based on the user's hand and/or foot condition printed using the artificial intelligence learning model, and determining whether there are any medications the user must take for treatment,
Receive information about the ingredients of the drug the user is currently taking based on the user input,
Provides information about problematic drug ingredients based on whether there are ingredients that the user should not take together with the medicine that the user must take for treatment,
Selecting at least one drug among the determined drugs by user input, displaying an image of the hand and foot with the treatment effect showing how the user's hand and foot can be treated when the selected drug is applied to the user's hand and foot,
Determine the user's care consultation information based on the service provision record and input consultation information between the user account and the care expert's account,
Determine the user's skin information and the user's health information based on the user input,
Determining the user's search information based on beauty content for fingernails and toenails reproduced by the user or search history related to fingernail and toenail care on an Internet application,
Determining at least one care product, care service, and expert who can provide the service suitable for the user based on the care consultation information, the search information, the user's skin information, and the user's health information,
Displaying images of hands and feet with treatment effects showing how the user's hands and feet can be treated when at least one care product or care service suitable for the user is applied to the user's hands and feet,
Based on the fact that a specified period has passed since the recommended care product or other care product was purchased, a guide is displayed requesting information about the remaining capacity of the purchased product, or a guide is requested to take a picture of the purchased product, and the pictured product is taken. Determine the remaining capacity of the purchased product based on the product photo,
Based on the remaining capacity of the purchased product being less than a specified level, displaying shopping information for the recommended care product and displaying a guide asking whether to purchase it;
Information indicating that the first point in time at which care services were recently provided to the user is determined based on the care consultation information, and that the designated period since the service was recently provided to the user has been exceeded based on the specified period exceeding the first point in time. provide,
Provide information indicating that a specified period of time has elapsed since the user recently received the service to at least one expert determined based on the care consultation information, the search information, the user's skin information, and the user's health information; ,
Provides information about the expert and information about the care service to the user based on receiving information from the expert's terminal indicating that the user can provide the recently received care service,
Information about the above experts
A system that includes the expert's previous consultation history and other users' service evaluation scores.



delete delete