KR102652693B1 - Artificial intelligence-based makeup kit recommendation system - Google Patents

Abstract

The present invention relates to an artificial intelligence-based makeup kit recommendation system that uses artificial intelligence to determine a makeup combination based on a user's request and recommends makeup products based on the determined makeup combination.

Description

Artificial intelligence-based makeup kit recommendation system}

The present invention relates to an artificial intelligence-based makeup kit recommendation system, and more specifically, to artificial intelligence-based makeup that uses artificial intelligence to determine makeup combinations based on user requests and recommends makeup products based on the determined makeup combinations. It is about a kit recommendation system.

People have various skin types depending on genetic factors, and their skin types undergo various changes depending on environmental factors. In addition, the colors and tones of skin required are becoming more diverse according to trends that emphasize taste.

Therefore, the recent beauty market requires a service that recommends personalized cosmetics to respect individuality and express one's own identity, taking into account the individual's personal color and request information rather than recommending standardized cosmetics. It is becoming.

However, in the case of the conventional cosmetics recommendation service, it focuses on the function of suggesting customized makeup considering only the user's face shape and recommending only cosmetics based on this, so there is a problem that there is a limit to the recommendation of the user's customized makeup and cosmetics. .

Meanwhile, the above-mentioned background technology is technical information that the inventor possessed for deriving the present invention or acquired in the process of deriving the present invention, and cannot necessarily be said to be known technology disclosed to the general public before filing the application for the present invention. .

Korean Patent No. 10-2360993

One aspect of the present invention provides an artificial intelligence-based makeup kit recommendation system that uses artificial intelligence to determine a makeup combination based on a user's request and recommends makeup products based on the determined makeup combination.

The technical problem of the present invention is not limited to the technical problem mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

The artificial intelligence-based makeup kit recommendation system according to an embodiment of the present invention uses artificial intelligence to determine a makeup combination based on the user's request and recommends makeup products based on the determined makeup combination.

The artificial intelligence-based makeup kit recommendation system is,

A data collection unit that receives an image of the user's face and makeup setting information from the user terminal;

a makeup kit combination unit that determines the user's skin tone based on the image and the makeup setting information using an artificial neural network, generates a list of makeup products necessary to embody the determined skin tone, and determines a customized makeup combination for the user;

A delivery management department that delivers the determined makeup kit to the user; and

and a prediction service management unit that predicts a date for repurchasing products of the same type as the makeup products constituting the makeup kit based on user information received from the user terminal and information about the makeup kit purchase date.

The prediction service management department,

The date for repurchasing a specific product is predicted using Equation 1 below, and a guidance message for repurchasing the specific product is transmitted to the user terminal based on the predicted date.

[Equation 1]

Here, d _e is the predicted date of repurchase of a specific product, p is the number of days elapsed from the previous purchase date of a specific product, n is the number of products included in the list of multiple makeup kit recommendations requested from the user terminal, and s is the makeup kit recommendation. The seasonal index of this requested date, a is a weight set differently for each type of product, and m is the average number of makeup times per week by age.

According to one aspect of the present invention described above, by setting a makeup tone optimized for the situation set by the user, such as the user's occupation, event, clothing color, worn accessories, combination, etc., and recommending a makeup kit to implement the set makeup tone. The convenience and reliability of user-tailored makeup can be improved.

Figure 1 is a diagram illustrating the schematic configuration of an artificial intelligence-based makeup kit recommendation system according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating the specific configuration of the makeup kit recommendation server shown in FIG. 1.

The detailed description of the present invention described below refers to the accompanying drawings, which show by way of example specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different from one another but are not necessarily mutually exclusive. For example, specific shapes, structures and characteristics described herein may be implemented in one embodiment without departing from the spirit and scope of the invention. Additionally, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the detailed description that follows is not intended to be taken in a limiting sense, and the scope of the invention is limited only by the appended claims, together with all equivalents to what those claims assert, if properly described. Similar reference numbers in the drawings refer to identical or similar functions across various aspects.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

Figure 1 is a diagram illustrating the schematic configuration of an artificial intelligence-based makeup kit recommendation system according to an embodiment of the present invention.

The purpose of the artificial intelligence-based makeup kit recommendation system according to the present invention is to use artificial intelligence to determine a makeup combination based on the user's request and to provide a customized service that recommends makeup products based on the determined makeup combination.

In particular, according to the present invention, an appropriate skin tone is set based on the situation (event) set by the user, and a makeup kit containing a combination of makeup products to implement the set skin tone is recommended to the user, so that the user can wear makeup and prepare for the event. Satisfaction can be improved by receiving recommendations through artificial intelligence for the makeup tone that best matches the type, occupation, clothes and accessories worn.

Specifically, the artificial intelligence-based makeup kit recommendation system according to the present invention includes a user terminal 100 and a makeup kit recommendation server 200.

The user terminal 100 is an electronic device owned by a user who wishes to receive the artificial intelligence-based makeup kit recommendation service according to the present invention, such as a smartphone, PC, It may be in the form of a laptop, tablet PC, wearable device, kiosk, etc.

The user connects to the makeup kit recommendation server 200 through the user terminal 100 and receives the artificial intelligence-based makeup kit recommendation service according to the present invention from the makeup kit recommendation server 200.

The makeup kit recommendation server 200 is connected to a plurality of user terminals 100 through wired or wireless communication and provides customized services for each user terminal 100.

The makeup kit recommendation server 200 can store information necessary to operate a user cosmetics recommendation service. The information required to operate the user cosmetics recommendation service may include information on each cosmetic product that can be provided by the service operator for providing the service. Additionally, the makeup kit recommendation server 200 can obtain and store cosmetics usage information of other existing buyers. Information on the use of cosmetics by other buyers may include purchase reviews, period of use, frequency of use, return status, etc.

The makeup kit recommendation server 200 acquires the user's face image through the user terminal 200, determines the user's skin characteristics through the acquired user's face image, and recommends cosmetics to the user based on the determined skin characteristics. can decide. The makeup kit recommendation server 200 may generate a recommendation list of the determined cosmetics and transmit the generated recommendation list to the user terminal 200.

Figure 2 is a diagram showing the specific configuration of this makeup kit recommendation server 200.

As shown, the makeup kit recommendation server 200 includes a data collection unit 210, a makeup kit combination unit 220, a delivery management unit 230, and a prediction service management unit 240.

The data collection unit 210 receives an image of the user's face and makeup setting information from the user terminal.

Here, the makeup setting information may include information about the user's age, gender, occupation, type of event, and type and color of clothes and accessories to be worn at the event.

The makeup kit combination unit 220 determines the user's skin tone based on the image and the makeup setting information using an artificial neural network, generates a list of makeup products necessary to embody the determined skin tone, and determines a customized makeup combination for the user. .

Specifically, the makeup kit combination unit 220 captures the event information set by the user (date, location, occupation, type of event, type and color of clothes and accessories to be worn) and the user's face received from the user terminal. The facial image is input as an input value to a pre-trained artificial neural network, and a skin tone optimized for the event set by the user is set based on the output value of the artificial neural network.

For this purpose, the artificial neural network is constructed based on CNN (Convolution Neural Network), which includes a convolution layer that extracts feature information about facial photos input as an input layer, and the colors included in the feature map generated through this. It can be composed of a hidden layer consisting of a convolutional layer that extracts makeup information based on brightness, and an output layer that outputs the user's makeup characteristic information through a fully connected layer at the last stage.

In addition, the makeup kit combination unit 220 learns learning data using the Word2Vec algorithm to understand the meaning of information in the form of text received from the user terminal and builds a neural network that extracts context information for the input data. .

The Word2Vec algorithm may include a neural network language model (NNLM). A neural network language model is basically a neural network consisting of an input layer, projection layer, hidden layer, and output layer. Neural network language models are used to vectorize words. Since the neural network language model is a known technology, a more detailed description will be omitted.

The Word2vec algorithm is for text mining and is an algorithm that determines proximity by looking at the front and back relationships between each word. The Word2vec algorithm is an unsupervised learning algorithm. As the name indicates, the Word2vec algorithm can be a quantitative technique that expresses the meaning of words in vector form. The Word2vec algorithm can express each word as a vector in a space of about 200 dimensions. Using the Word2vec algorithm, you can obtain the vector corresponding to the word for each word.

The Word2vec algorithm can enable dramatic improvements in precision in the field of natural language processing compared to other conventional algorithms. Word2vec can learn the meaning of words using the relationships between words and adjacent words in sentences in the input corpus. The Word2vec algorithm is based on an artificial neural network and starts from the premise that words with the same context have close meanings. The Word2vec algorithm learns through text documents, and for one word, other words that appear nearby (about 5 to 10 words before or after it) are taught to the artificial neural network as related words. Because words with related meanings are likely to appear close together in a document, two words may have increasingly closer vectors during repeated learning.

The learning methods of the Word2vec algorithm include the CBOW (Continuous Bag Of Words) method and the skip-gram method. The CBOW method predicts the target word using the context created by surrounding words. The skip-gram method predicts words that may come nearby based on one word. The skip-gram method is known to be more accurate in large datasets.

Therefore, in the embodiment of the present invention, the Word2vec algorithm using the skip-gram method is used. For example, if learning is successfully completed through the Word2vec algorithm, similar words can be located nearby in a high-dimensional space. According to the Word2vec algorithm as described above, the closer the distribution of surrounding words in a learning document is, the more similar the calculated vector values can be, and words with similar calculated vector values can be considered similar. Since the Word2vec algorithm is a known technology, detailed descriptions related to vector value calculation will be omitted.

The makeup kit combination unit 220 may input data received from the user terminal into a neural network and extract an evaluation result vector value representing context information.

The makeup kit combination unit 220 may calculate the similarity between the evaluation result vector value and each of the plurality of reference vector values, and extract the reference vector value with the highest similarity to the evaluation result vector value among the plurality of reference vector values. At this time, Euclidean distance, cosine similarity, Tanimoto coefficient, etc. may be adopted as similarity calculation methods.

The makeup kit combination unit 220 may extract the word corresponding to the reference vector value with the highest similarity to the evaluation result vector value as the word corresponding to the recognized text.

Additionally, the makeup kit combination unit 220 can train an artificial neural network, and can also use a trained artificial neural network. The processor can train or execute an artificial neural network stored in memory, and the memory can store a trained artificial neural network. The electronic device that trains the artificial neural network and the electronic device that uses it may be the same, but may also be separate. Artificial intelligence is a computer system that implements some of the functions of the human brain and can learn, guess, and make decisions on its own. As learning progresses, the probability of extracting an answer may increase. Artificial intelligence can be composed of learning and elemental technologies using it. Artificial intelligence learning is an algorithmic technology that classifies and learns features based on input data, and elemental technologies may be technologies that implement some of the functions of the human brain using learning algorithms.

Artificial intelligence is a technology that makes it easy to approach problems that can have multiple answers probabilistically, and can logically and probabilistically infer the optimal cycle, method, and plan according to any input data. Artificial intelligence's inference technology can include judging input data, optimization predictions, knowledge and probability-based reasoning, and preference-based planning.

Artificial neural network is one of the learning algorithms in the machine learning field and is a program that implements the connection between neurons and synapses in the brain. Artificial neural networks can be created through a program to create a neural network structure and then learn it to have the desired function. Although there may be errors, it is possible to learn based on huge data and output appropriate output data with input data. It has the advantage of being able to obtain output data with statistically good results and being similar to human reasoning.

The makeup kit combination unit 220 can build a query/metric dataset required for learning using an artificial intelligence algorithm built on big data, and may include a number of artificial neural networks that have been trained in advance for this purpose. .

In this embodiment, the server may include a number of artificial neural networks that have been trained in advance to perform machine learning algorithms. With machine learning, you can output output data based on input data and use the results to learn on your own, which can improve your own data processing ability. Artificial neural networks can extract features based on input data, infer regularities, and output result data. As this process accumulates, the reliability of the result data increases.

In this embodiment, the artificial neural network may be an algorithm that outputs text data from at least one of the feature data of the shape, length, number, and elevation difference of an object recognized as text. Artificial neural networks can infer the best output data using big data as input data or after going through a processing process to organize unnecessary data.

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering 등이 사용될 수 있다.Depending on the type of learning, artificial intelligence machine learning models include Super Viser Learning, UnSuper Viser Learning, Semisupervised Learning, and Reinforcement Learning. And machine learning algorithms include Decision Tree, K-Nearest Nearest Bor, Artificial Neural Network, Support Vector Machine, Ensemble Learning, Gradient Descent, Na

ve Bayes Classifier, Hidden Markov Model, K-Means Clustering, etc. can be used.

The artificial neural network may be pre-trained on various input values that may be included in the input data. An artificial neural network may be an artificial neural network that is learned according to reinforcement learning, one of the learning methods. Reinforcement learning is a method of gradually increasing the probability of obtaining the correct result by setting rewards and limits. Artificial neural networks can also be modeled based on Convelutional Neural Network (CNN) or Recurrent Neural Network (RNN).

In this way, the makeup kit combination unit 220 can use big data and an artificial neural network to extract feature information for input into the artificial neural network from data received from the user terminal.

The delivery management unit 230 delivers the determined makeup kit to the user.

The prediction service management unit 240 predicts a date for repurchasing products of the same type as the makeup products constituting the makeup kit based on user information received from the user terminal and information on the makeup kit purchase date.

In one embodiment, the prediction service management unit predicts the date for repurchasing a specific product using Equation 1 below, and transmits a guidance message for repurchasing the specific product to the user terminal based on the predicted date.

[Equation 1]

Here, d _e is the predicted date of repurchase of a specific product, p is the number of days elapsed from the previous purchase date of a specific product, n is the number of products included in the list of multiple makeup kit recommendations requested from the user terminal, and s is the makeup kit recommendation. The seasonal index of this requested date, a is a weight set differently for each type of product, and m is the average number of makeup times per week by age.

In this way, the prediction service management unit 230 can improve service satisfaction by accurately predicting the repurchase time of previously purchased products using the above-mentioned mathematical equations, allowing users to continuously use the product without interruption. .

In some other embodiments, the makeup kit combination unit 220 may set a recommended skin tone in consideration of a currently popular makeup style.

To this end, the makeup kit combination unit 220 calculates a trend score using Equation 2 below.

[Equation 2]

Here, TS is the trend score, d is the first constant value set according to the brightness of the currently popular skin tone, u is the weight set differentially by age group, h is the second constant set according to body size, and o is the same as the user. This is a trend score calculated from users of different age groups.

For example, if d is 3, u is 5, h is 5, and o is 15, the trend score TS is calculated as approximately 17 points (rounded to the nearest decimal point). In this case, the makeup kit combination unit 220 sets the makeup tone previously matched to the 16 points as the recommended makeup tone.

In this way, the makeup kit combination unit 220 sets a customized makeup tone according to the trend score calculated through Equation 2 above, thereby recommending a makeup tone that comprehensively reflects the user's age and the makeup style of users of the same age. can do.

As such, the technology according to the present invention may be implemented as an application or in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination.

The program instructions recorded on the computer-readable recording medium may be those specifically designed and configured for the present invention, or may be known and usable by those skilled in the computer software field.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. media), and hardware devices specifically configured to store and perform program instructions, such as ROM, RAM, flash memory, etc.

Examples of program instructions include not only machine language code such as that created by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the invention and vice versa.

Although the above has been described with reference to embodiments, those skilled in the art will understand that various modifications and changes can be made to the present invention without departing from the spirit and space of the present invention as set forth in the following patent claims. You will be able to.

210: data collection unit
220: Makeup kit assembly unit
230: Delivery management department
240: Predictive service management department

Claims (3)

In an artificial intelligence-based makeup kit recommendation system that uses artificial intelligence to determine a makeup combination based on the user's request and recommends makeup products based on the determined makeup combination,
The artificial intelligence-based makeup kit recommendation system is,
A data collection unit that receives an image of the user's face and makeup setting information from the user terminal;
a makeup kit combination unit that determines the user's skin tone based on the image and the makeup setting information using an artificial neural network, generates a list of makeup products necessary to embody the determined skin tone, and determines a customized makeup combination for the user;
a delivery management department that delivers the determined makeup kit to the user; and
It includes a prediction service management unit that predicts the date for repurchasing products of the same type as the makeup products constituting the makeup kit based on user information received from the user terminal and information on the makeup kit purchase date,
The prediction service management department,
Predict the date for repurchasing a specific product using Equation 1 below, and transmit a guidance message for repurchasing the specific product to the user terminal based on the predicted date,
The makeup kit combination unit,
An artificial intelligence-based makeup kit recommendation system in which the makeup kit combination unit calculates a trend score using Equation 2 below and sets a customized makeup tone based on the calculated trend score.

[Equation 1]

Here, R is the predicted date of repurchase of a specific product, p is the number of days elapsed from the previous purchase date of a specific product, n is the number of products included in the list of multiple makeup kit recommendations requested from the user terminal, and s is the makeup kit recommendation. The seasonal index of the requested date, a is a weight set differently for each type of product, and m is the average number of makeup times per week by age.

[Equation 2]

Here, TS is the trend score, d is the first constant value set according to the brightness of the currently popular skin tone, u is the weight set differentially by age group, h is the second constant set according to body size, and o is the same as the user. This is a trend score calculated from users of different age groups.
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