KR102520597B1 - Product matching method considering market analysis and company needs - Google Patents

Abstract

The present invention relates to a product matching method considering market analysis and company needs. The product matching method comprises the operations of: receiving survey information about skin type from a user; obtaining skin composition information; determining an ingredient that the user lacks or needs and determining a first group of cosmetics in which the determined ingredient exceeds a specified level; determining an ingredient that may cause an allergy in the user and determining, among the first group, a second group of cosmetics that do not contain the determined ingredient; assigning scores to the second group of cosmetics; and recommending the second group of cosmetics to the user in order of highest score. According to the present invention, products containing less trouble-causing elements can be recommended to suit individuals.

Description

Product matching method considering market analysis and company needs {PRODUCT MATCHING METHOD CONSIDERING MARKET ANALYSIS AND COMPANY NEEDS}

The following embodiments relate to a product matching method in consideration of market analysis and company needs.

A marketing method is used in which customers diagnose their skin type and condition through a diagnostic tool, and recommend products or inform results based on the analysis results.

Since each person's skin condition is different, it is necessary to provide cosmetics suitable for each individual's skin characteristics. However, it is practically impossible to manufacture and sell cosmetics suitable for each user's skin characteristics in a mass production method. In order to solve these problems, a method for providing cosmetics customized to consumers may be required. However, it may be difficult for consumers without professional knowledge about cosmetics to select all ingredients of cosmetics.

Conventional methods of providing customized cosmetics give consumers excessive choices so that they can select all the ingredients of cosmetics, and it may be difficult for consumers to select products.

In addition, with the recent development of communication technology and the popularization of smart phones, the use of the Internet has become increasingly active. In accordance with such changes in the Internet environment, a vast amount of cosmetic product-related information is being accumulated on the Internet. When many people want to purchase cosmetics products, they refer to reviews posted on the Internet by other people or information related to purchasing cosmetics products posted by cosmetics product sales companies, such as brand name, price, product appearance, packaging, usage, and manufacturing of cosmetics products. It is a trend to search for the ingredients used in the market and select the cosmetics you want. However, since there are various brand names, numerous product types, and similar products in the cosmetic product market, the market itself is very large and cosmetic product types and information about them are also diverse, making it difficult for users to select a product.

In addition, wholesalers and retailers operating in the cosmetics sales business must also identify users' needs so that they can stock and sell products (e.g., cosmetics) that are suitable for users. There is a problem that is difficult to prepare in advance.

Korean Patent Registration No. 10-0708319 etc. proposed a method of directly manufacturing cosmetics according to the result of measuring the customer's skin condition in order to solve this problem. However, unlike companies that specialize in cosmetics that go through strict certification procedures, companies that manufacture small amounts of cosmetics suitable for customers may not go through these certification procedures, which can cause more skin problems to users. there is

In addition, the method of visiting a store, using samples, and purchasing products also has limitations in using all of the hundreds of cosmetic samples, and in the process of using cosmetics directly, it is difficult to wipe cosmetics to use several cosmetics in a short time. There is a problem that irritation occurs on the skin due to repetitive work, which may cause skin trouble.

The product matching method according to this document has the purpose of providing product matching in consideration of problematic components by pre-obtaining component information that may be problematic in cosmetics while considering the user's characteristics.

The product matching method according to this document searches for customized cosmetics based on information obtained from users, and provides information about this to cosmetics sellers (e.g., wholesalers and retailers) to increase sales volume of cosmetics sellers and satisfy consumer needs. has the purpose of fulfilling

According to an embodiment, a product matching method considering market analysis and company needs includes an operation of receiving survey information on skin type from a user through a questionnaire, natural moisturizing factors for a plurality of cosmetics from an external server, hyaluronic acid, glycerol, Acquisition of skin component information including ceramide, cholesterol, fatty acid, melanin, dopachrome, amino acid, lactic acid, and moisture, determining a component lacking or necessary for the user based on survey information on skin type, and determining whether the determined component is Determining cosmetics of a first group exceeding a specified level, determining an ingredient that may cause an allergy to a user based on survey information on skin type, and determining a second group that does not contain the determined ingredient from the first group Operation of determining the cosmetics of the second group, based on review information, evaluation information, price, user's purchase history, whether or not they are sold at cosmetics stores in the area where the user is located, online purchase availability and discount on the cosmetics of the second group It may include an operation of assigning a score, and an operation of recommending to the user in order of highest scores among cosmetics of the second group. The operation of inputting survey information on skin type from the user through the questionnaire includes the operation of receiving information related to the cosmetics currently in use and user feedback information on the cosmetics, inputting information on the brand, item, and price of the cosmetics. Whether the user's skin is oily or dry, sensitive or resistant, pigmented or non-pigmented, or wrinkled Operation of receiving information on whether or not it is tight, operation of contacting the user's skin with a sensor and receiving input of skin components including at least one of moisture, elasticity, pores, wrinkles, pigmentation, sebum, and skin color, using a camera An operation of receiving a photograph of the user's skin and, if there is a dermatological diagnosis record, an operation of receiving dermatological diagnosis information.

The system according to an embodiment may be combined with hardware and controlled by a computer program stored in a medium to execute any one of the methods described above.

The product matching method according to this document determines components that are lacking or necessary for a user based on survey information on skin type, and obtains information on components that may cause trouble to the user's body for a plurality of cosmetics to solve the problem. We can recommend products that contain less of the factors that cause it, tailored to the individual.

In addition, the product matching method according to this document considers personal characteristics through survey information, evaluates the popularity of the product through other users' reviews, evaluation and purchase history, and determines whether online purchase is possible, whether there is a discount, and the user's region. You can recommend products that consider market analysis and user needs by evaluating accessibility through whether they are sold or not.

In addition, the product matching method according to this document can receive information about the user in addition to information about cosmetics, and the user directly inputs the information through a questionnaire, contacts the skin through a sensor, photographs the skin through a camera, and dermatologists. By submitting diagnosis records, it is possible to recommend suitable products considering the characteristics and needs of users from a diversified perspective.

The product matching method according to this document searches for customized cosmetics based on the information obtained from the user, and provides the information to cosmetics sellers (eg, wholesalers and retailers) to increase the sales volume of the cosmetics sellers and customize the cosmetics for users. It is possible to provide the necessary information to provide.

1 is a flowchart of a product matching method in consideration of market analysis and company needs according to an embodiment.
Figure 2 shows a product matching method in consideration of market analysis and company needs according to an embodiment in order.
3 is a flowchart of a product matching method in consideration of market analysis and company needs according to an embodiment.
4 is an exemplary diagram of a configuration of an electronic device according to an embodiment.

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

Although terms such as first or second may be used to describe various components, such terms should only be construed for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element.

It should be understood that when an element is referred to as being “connected” to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle.

Terms used in the examples are used only for descriptive purposes and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't

In addition, in the description with reference to the accompanying drawings, the same reference numerals are given to the same components regardless of reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description will be omitted.

The 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.

The 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, machines learn and judge on their own. The more AI systems are used, the higher the recognition rate and the more accurate understanding of user preferences. Conventional rule-based smart systems are gradually being replaced by deep learning-based AI systems.

Artificial intelligence technology consists of machine learning and element technologies using machine learning. Machine learning is an algorithm technology that classifies/learns the characteristics of input data by itself, and element technology is a technology that uses machine learning algorithms such as deep learning to mimic functions such as recognition and judgment of the human brain. It consists of technical fields such as understanding, inference/prediction, knowledge expression, and motion control.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying/processing human language/characters, including natural language processing, machine translation, dialogue systems, question and answering, voice recognition/synthesis, and the like. Visual understanding is a technology for recognizing and processing objects like human vision, and includes object recognition, object tracking, image search, person recognition, scene understanding, space understanding, image improvement, and the like. Inference prediction is a technique of reasoning and predicting logically by judging information, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendation. 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 for controlling the autonomous driving of vehicles and the movement of robots, and includes motion control (navigation, collision, driving), manipulation control (behavior control), and the like.

In general, in order to apply machine learning algorithms to real life, learning is performed in 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) means a technology that imitates human learning ability, reasoning ability, perception ability, etc., and implements it with a computer, and concepts such as machine learning and symbolic logic can include Machine learning (ML) is an algorithm technology that classifies or learns the characteristics of input data by itself. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the result of the analysis, and can make judgments or predictions based on the result of the learning. In addition, 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 linguistic understanding, visual understanding, inference/prediction, knowledge expression, and motion control may be included.

Machine learning may refer to processing that trains a neural network model using experience of processing data. Through machine learning, computer software can mean improving its own data processing capabilities. A neural network model is constructed by modeling a correlation between data, and the correlation may be expressed by a plurality of parameters. The neural network model extracts and analyzes features from given data to derive a correlation between the data. Machine learning is to optimize the parameters of the neural network model by repeating this process. For example, a neural network model may learn a mapping (correlation) between an input and an output with respect to data given as an input/output pair. Alternatively, even when only input data is given, the neural network model may learn the relationship by deriving regularities between given data.

An artificial intelligence learning model or a neural network model may be designed to implement a human brain structure on a computer, and may include a plurality of network nodes having weights while simulating neurons of a human neural network. A plurality of network nodes may have a connection relationship between them by simulating synaptic activities of neurons in which neurons exchange signals through synapses. In the artificial intelligence learning model, a plurality of network nodes can exchange data according to the convolution connection relationship while being located in layers of different depths. The artificial intelligence learning model may be, for example, an artificial neural network model, a convolutional neural network model (CNN), and the like. As an embodiment, the 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.

Of these, CNNs are a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolutional layers and general artificial neural network layers placed on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize the two-dimensional structure of the input data. 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 are easier to train than other feedforward artificial neural network techniques and have the advantage of using fewer parameters.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increasing size of available training data and the availability of computing power, combined with algorithmic advances such as piecewise linear units and dropout training, have greatly improved many computer vision tasks. With huge 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. To this end, a distributed, scalable implementation of deep neural networks can be used.

1 is an exemplary diagram of a configuration of a system for performing market analysis and product matching in consideration of company needs according to an embodiment.

The system 100 according to an embodiment may include a processor 120 and a memory 130, and some of the illustrated components may be omitted or replaced. System 100 according to an embodiment may be a server or a terminal. According to one embodiment, the processor 120 is a component capable of performing calculations or data processing related to control and/or communication of each component of the system 100, and may include one or more processors. The memory 130 may store information related to the above-described method or a program in which the above-described method is implemented. Memory 130 may be volatile memory or non-volatile memory. The memory 130 may store various file data, and the stored file data may be updated according to the operation of the processor 120 .

According to one embodiment, the processor 120 may execute a program and control the system 100 . Program codes executed by the processor 120 may be stored in the memory 130 . Operations of the processor 120 may be performed by loading instructions stored in the memory 130 . The system 100 may be connected to an external device (eg, a personal computer or network) through an input/output device (not shown) and exchange data.

According to an embodiment, the processor 120 inputs data on commercial district information generated based on shop information selling cosmetics, information on background demand, and location information, and data on income declarations of competing shops in the area of interest. As a result, user opening analysis information including business feasibility review, financial loan information, labor contract, and interior can be generated as output. The processor 120 may determine how much sales the user can achieve when opening a store based on the data on the income report of competing stores and the trade area information, and review business feasibility. The processor 120 may output data indicating how much loan is required when opening a business based on the data on the income report. The processor 120 may calculate the number of employees necessary for opening a store based on information on the number of employees employed and commercial districts of competing stores, and output data on labor contracts. The processor 120 may obtain an image of the interior environment of a competitor store on a map application or a search application, and output a type of interior, a cost estimate, and information on interior design companies in the surrounding area.

The processor 120 may take information about competitive shops including at least one shop exposed based on GPS and a specific shop to be benchmarked as input and output business analysis information based on the competing shops. there is. The business opening analysis information may include information about expected sales, estimated costs, expected number of customers per day, and business feasibility upon opening. The user business opening analysis information is expected business opening analysis information when the user opens a store, and the competing shop opening analysis information may refer to business opening analysis information based on data of competing stores. For example, the processor 120 may determine that the business feasibility is insufficient based on the location where the user can open the business when the user opens the business, but may determine that the business feasibility is sufficient because the competing store exists in another location.

The processor 120 uses the user opening analysis information and the competitor store opening analysis information as inputs to determine whether the competitor's sales, income, business feasibility, whether the store is worthy of benchmarking, and expected sales when opening a business in the corresponding location , an analysis report containing information on the number of customers can be generated as an output. For example, the processor 120 may determine that only stores whose sales exceed a specified level (eg, 100 million won per month) are stores worthy of benchmarking. This is just one example, and the criterion for determining that the store is worthy of benchmarking may vary depending on the setting. The processor 420 may provide a user with an analysis report, provide analysis information tailored to the customer, and provide aesthetic curation.

According to one embodiment, the processor 420 determines a score for the opening analysis information based on business feasibility, financial loan status, sales, sales level of nearby competitors, the number of nearby competing stores, and the expected number of customers, Based on the score of the opening analysis information on the analysis report exceeding a specified level, it is determined that the probability of success is higher than the probability of failure when opening a business, and information on a real estate agent having data on stores in the desired area may be displayed.

According to an embodiment, the processor 420 receives social information about a competitive store through a social network service using the communication circuit 410, and the competitive store is displayed at the top of a related search word of the social network service on the social information. Information on the number of items sold, score evaluation of competitive stores, and qualitative evaluation of competitive stores may be obtained and transmitted to an artificial intelligence learning model. The artificial intelligence learning model outputs the ratio of positive and negative evaluations by taking the words included in the information on the qualitative evaluation of competing stores as input, and the positive evaluation ratio exceeds the specified level compared to the negative evaluation ratio. In this case, the competing store may be designated as a benchmarking store, and the analysis report for the benchmarking store may be output.

According to an embodiment, the artificial intelligence learning model is based on the number of merchants in the commercial district, the concentration of the commercial district, the number of transportation facilities in the commercial district, the number of floating people in the commercial district, the number of people behind the commercial district, the number of working people near the commercial district, and the number of households behind the commercial district. Information on sales, rent, and probability of business closure can be output by inputting information on average income, total number of houses in the vicinity of the commercial district, and area of the commercial district. The processor 420 may evaluate business feasibility based on information on sales, rent, and probability of business closure, and transmit the evaluation information to the user terminal.

According to an exemplary embodiment, the processor 420 may perform commercial district information data including the percentage of online sales for competing stores, the percentage of major sales items in sales, and the percentage of sales and sales by time slot extracted from card sales records of the competing stores. Creates, based on commercial district information data, determines the expected sales, profit and loss, and the number of visiting customers by time zone when opening in the area of interest, and provides information on tax accounting including tax based on sales and losses, , determine the number of workers required based on the number of visiting customers per hour, and provide information on worker contracts based on the determined number of workers.

According to an embodiment, calculation and data processing functions that the processor 120 can implement on an electronic device will not be limited, but hereinafter, a function of matching a product in consideration of market analysis and company needs will be described.

2 is a flowchart of a product matching method in consideration of market analysis and company needs according to an embodiment.

Although process steps, method steps, algorithms, etc. are described in a sequential order in the flowchart of FIG. 2, 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 the various embodiments of the invention need not be performed in the order described herein. Also, although some steps are described as being performed asynchronously, in other embodiments, some of these steps may be performed concurrently. Further, illustration of a process by depiction in the drawings does not mean that the illustrated process is exclusive of other changes and modifications thereto, and that any of the illustrated process or steps thereof may be one of various embodiments of the present invention. It is not meant to be essential to one or more, and it does not imply that the illustrated process is preferred.

In operation 210, the system (eg, the system 100 of FIG. 1) may perform an operation of receiving survey information about skin type from the user through a questionnaire.

In operation 220, the system 100 may perform an operation of obtaining skin component information on a plurality of cosmetic products from an external server. Skin component information may include natural moisturizing factors for veterinary cosmetics, hyaluronic acid, glycerol, ceramides, cholesterol, fatty acids, melanin, dopachrome, amino acids, lactic acid and water. The system 100 may obtain skin component information from an external server.

In operation 230, the system 100 may perform an operation of determining an ingredient lacking or necessary for the user based on the questionnaire information on the skin type and classifying cosmetics. The system 100 may determine an ingredient lacking or necessary for the user based on the questionnaire information about the skin type, and may determine cosmetics of a first group in which the determined ingredient exceeds a specified level (eg, 50%). Ingredients lacking or required by the user may be determined based on questionnaire information on skin type obtained from the user in operation 210 . The specified level for determining the first group of cosmetics is not fixed and may vary according to settings. The system 100 may determine an ingredient that may cause an allergy to the user based on the questionnaire information on the skin type, and may determine a second group of cosmetics that do not contain the determined ingredient from among the first group. Ingredients that may cause allergies may be determined differently for each user based on questionnaire information on skin type obtained from the user in operation 210 .

In operation 240, the system 100 may perform an operation of assigning scores by evaluating popularity and accessibility in addition to personal information. Popularity may be determined by evaluation of review information, evaluation information, price, and user's purchase history. Accessibility may be determined by whether cosmetics are sold at a cosmetics store in the region where the user is located, whether online purchase is possible, and whether a discount is given.

In operation 250, the system 100 may perform a recommendation operation to the user according to the score.

According to an embodiment, in operation 210, the operation of receiving survey information on skin type from the user through a questionnaire includes an operation of receiving information related to cosmetics currently in use and user feedback information on the cosmetics, and brand of cosmetics. , operation of receiving information on items and prices, whether the user's skin is oily or dry, sensitive or resistant, pigmented or non-pigmented (Non-pigmented) Pigmented, wrinkled, or tight, contacting the user's skin with a sensor to detect at least one of moisture, elasticity, pores, wrinkles, pigmentation, sebum, and skin color. An operation of receiving a skin component, an operation of receiving a picture of the user's skin taken by a camera, and an operation of receiving dermatological diagnosis information if there is a dermatological diagnosis record. The product matching method in consideration of market analysis and company needs according to this document can receive information about users in addition to information about cosmetics. , and by submitting a dermatological diagnosis record, it is possible to recommend a suitable product considering the characteristics and needs of users from a diversified perspective.

Figure 3 shows a product matching method in consideration of market analysis and company needs according to an embodiment in order.

In operation 310, the system (eg, the system 100 of FIG. 1) may separate the stratum corneum from the user's skin and obtain skin component information. The skin component information may include a natural moisturizing factor, hyaluronic acid, glycerol, ceramide, cholesterol, fatty acid, melanin, dopachrome, amino acid, lactic acid, and moisture. The system 100 may contact the user's skin with a sensor to receive input of skin components including at least one of moisture, elasticity, pores, wrinkles, pigmentation, sebum, and skin color. The system 100 separates the stratum corneum from the user's skin, and from the separated stratum corneum, skin component information including natural moisturizing factors, hyaluronic acid, glycerol, ceramide, cholesterol, fatty acids, melanin, dopachrome, amino acids, lactic acid, and moisture is obtained. can be obtained

In operation 320, the system 100 may perform an operation of receiving at least one captured face image and obtaining skin analysis information. The skin analysis information may include information on skin tone, pores, elasticity, oiliness or dryness, and wrinkles. The system 100 may receive an input of a user's skin picture taken by a camera. The system 100 may receive at least one face image captured by the user terminal and analyze the face image to obtain skin analysis information about skin tone, pores, elasticity, oiliness or dryness, and wrinkles.

In operation 330, the system 100 may perform an operation of determining the user's personal color based on the skin component information and the skin analysis information. The personal color may include a color composed of at least one of warm tones and cool tones according to a combination of at least one of spring, summer, fall, and winter in response to the customer's skin tone.

In operation 340, the system 100 may perform an operation of recommending cosmetics based on the user's personal color.

4 is a flowchart of a product matching method in consideration of market analysis and company needs according to an embodiment.

In operation 410, the system (eg, the system 100 of FIG. 1) may perform an operation of collecting photographic data, survey response data, user attribute information, and skin environment information after interworking with the user terminal. The photographing data refers to image data of a user photographed using a camera. Survey response data refers to data based on the user's survey response in operation 210 of FIG. 2 above. User attribute information includes whether the user's skin is oily or dry, sensitive or resistant, pigmented or non-pigmented, and wrinkled. Analyzing skin component information and facial images including information on whether or not they are tight, natural moisturizing factors, hyaluronic acid, glycerol, ceramide, cholesterol, fatty acids, melanin, dopachrome, amino acids, lactic acid, and moisture in the separated stratum corneum It may include skin analysis information about skin tone, pores, elasticity, oiliness or dryness, and wrinkles obtained through the above process. The skin environment information may include information about the temperature, humidity, and sunlight exposure time of the environment in which the user lives.

In operation 420, the system 100 may generate a curation message based on each collected data and build a database. The system 100 may determine a facial skin image analysis result and a skin environmental influence factor measured through analysis of at least one or more photographing data and questionnaire response data. The system 100 may determine the user's lifestyle and skin concern item values based on the survey response data. The system 100 may determine a skin type value based on skin image analysis values measured through analysis of photographic data and self-recognized skin characteristic information in the collected survey response data. The system 100 may generate a curation message in consideration of the user's lifestyle, worry item values, and skin type values. The system 100 may build a database based on an image analysis result of at least one piece of photographing data, a skin environment influence factor in skin environment information, and survey response data.

In operation 430, the system 100 may perform an operation of providing a service suitable for the user's facial skin type based on the database. The system 100 generates facial skin type data of the user through analysis of data stored in a database, and recommends a skin health diet service, skin health care recommendation service, customized cosmetics recommendation service, and skin suitable for the user's facial skin type. Trouble expert consultation service can be provided.

In operation 440, the system 100 may perform an operation of receiving satisfaction information with respect to the provided service and determining recommended cosmetics necessary for the user based on the satisfaction level. The system 100 may receive satisfaction information about a healthy skin diet recommendation service, a skin health care recommendation service, a customized cosmetics recommendation service, and a skin trouble expert consultation service based on a user input. The system 100 may collect satisfaction information of a plurality of users and determine recommended cosmetics required by the user based on the collected plurality of satisfaction information.

According to an embodiment, an operation of determining a component lacking or necessary for a user based on survey information on skin type and determining cosmetics of a first group in which the determined component exceeds a designated level is performed by the user based on the user's selection. The method may further include receiving information about the type of cosmetics preferred by the user and receiving information about elements of the skin component information among cosmetics preferred by the user from an external server. The information on the type of cosmetic preferred by the user includes information on the color, texture, brand, and price of the cosmetic, and the color of the cosmetic is at least one of red color, orange color, coral color, burgundy color, or neutral color. The cosmetic texture includes at least one of matte, semi-matte, velvet, glossy, and water products, and the cosmetic brand is determined by the user's actual purchase information, the user's search information, and the user's selection. can

According to one embodiment, the operation of recommending to the user in the order of high scores among cosmetics of the second group may further include an operation of providing location and altitude information of stores selling cosmetics with the highest scores. The operation of contacting the user's skin with the sensor to receive input of skin components including at least one of moisture, elasticity, pores, wrinkles, pigmentation, sebum, and skin color measures the amount of moisture, transepidermal water loss, or skin oil on the user's skin More actions may be included. The amount of moisture and the amount of transepidermal water loss can be measured by capacitance method, and the amount of skin oil can be measured by photometric method.

According to an embodiment, the operation of recommending to the user in the order of high scores among the cosmetics of the second group is the information on the skin type input from the user through the questionnaire and the user determined according to the scores among the cosmetics of the second group. Transmitting information on customized cosmetics to the cosmetics seller's terminal and sending the wholesale price, retail price, seller's phone number, and seller's location information for the customized cosmetics determined according to the score to the cosmetics seller's terminal may further include.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (3)

In the product matching method in consideration of market analysis and company needs performed by the system 100,
receiving survey information about a skin type from a user through a questionnaire;
obtaining skin component information including natural moisturizing factor, hyaluronic acid, glycerol, ceramide, cholesterol, fatty acid, melanin, dopachrome, amino acid, lactic acid, and water for a plurality of cosmetic products from an external server;
determining an ingredient lacking or necessary for the user based on questionnaire information about skin type, and determining a first group of cosmetic products in which the determined ingredient exceeds a designated level;
determining an ingredient that may cause an allergy to a user based on questionnaire information about skin type, and determining cosmetics of a second group that do not contain the determined ingredient from among the first group;
An operation of assigning a score to the cosmetics of the second group based on review information, evaluation information, price, user's purchase history, whether they are sold at a cosmetics store in the area where the user is located, whether online purchase is possible, and whether there is a discount or not ;
Including an operation of recommending to a user in order of highest score among cosmetics of the second group,
The operation of receiving survey information on the skin type from the user through the questionnaire
an operation of receiving information related to a previously used cosmetic product and feedback information of a user related to the cosmetic product;
Operation of receiving information about the brand, item and price of cosmetics,
Whether the user's skin is oily or dry, sensitive or resistant, pigmented or non-pigmented, wrinkled or tight ) operation of receiving information on recognition;
contacting the user's skin with the sensor to receive an input of skin components including at least one of moisture, elasticity, pores, wrinkles, pigmentation, sebum, and skin color;
An operation of receiving an input of a user's skin picture taken by a camera; and
If there is a dermatology diagnosis record, including an operation of receiving dermatology diagnosis information,
An operation of determining a component lacking or necessary for the user based on the questionnaire information on the skin type, and determining cosmetics of a first group in which the determined component exceeds a designated level.
Receiving information about the type of cosmetics preferred by the user based on the user's selection; and
Further comprising receiving, from an external server, information on elements of the skin component information among cosmetics preferred by the user,
Information on the type of cosmetics preferred by users
Contains information on the color, texture, brand and price of cosmetics;
The color of the cosmetic
At least one of red color, orange color, coral color, burgundy color, or neutral color,
The texture of the cosmetic
Including at least one or more of matte, semi-matte, velvet, glossy and water products,
The cosmetic brand
determined by the user's actual purchase information, the user's search information, and the user's selection;
The operation of recommending to the user in the order of high scores among the cosmetics of the second group is
Further comprising providing location and altitude information of stores selling cosmetics with the highest score,
The operation of contacting the user's skin with the sensor to receive an input of skin components including at least one of moisture, elasticity, pores, wrinkles, pigmentation, sebum, and skin color
Further comprising the operation of measuring the amount of moisture, the amount of transepidermal water loss or the amount of skin oil on the user's skin,
The amount of water and the amount of transepidermal water loss are measured by a capacitance method,
The skin oil amount is measured by a photometric method.
According to claim 1,
The operation of recommending to the user in the order of high scores among the cosmetics of the second group is
transmitting, to a terminal of a cosmetic seller, information on a skin type received from a user through a questionnaire and information on a user-customized cosmetic determined according to a score among the second group of cosmetics;
Further comprising transmitting wholesale price, retail price, seller's phone number, and seller's location information for the customized cosmetics determined according to the score to the terminal of the cosmetics seller,
The operation of contacting the user's skin with the sensor to receive input of skin components including at least one of moisture, elasticity, pores, wrinkles, pigmentation, sebum, and skin color
Separating the stratum corneum from the user's skin, and acquiring skin component information including natural moisturizing factors, hyaluronic acid, glycerol, ceramide, cholesterol, fatty acids, melanin, dopachrome, amino acids, lactic acid, and moisture from the separated stratum corneum do,
The operation of receiving the user's skin picture taken by the camera
Receiving at least one face image captured by a user terminal, and analyzing the face image to obtain skin analysis information on skin tone, pores, elasticity, oiliness or dryness, and wrinkles,
The product matching method considering the market analysis and company needs is
determining a user's personal color based on the skin component information and the skin analysis information;
Further comprising determining customized recommended cosmetics based on the determined personal color of the user,
The personal color is
A method including a color composed of at least one of warm tones and cool tones according to a combination of at least one season of spring, summer, fall, and winter in response to a customer's skin tone.
According to claim 1,
The operation of receiving survey information on the skin type from the user through the questionnaire
After interworking with the user terminal, collecting at least one or more photographic data of the user's facial skin, questionnaire response data for questionnaire contents, user attribute information, and skin environment information;
At least one or more photographing data and questionnaire response data are analyzed to determine the measured facial skin image analysis result and skin environmental factors, and to determine the user's lifestyle and skin concern item values based on the questionnaire response data, After determining the skin type value based on the skin image analysis value measured through data analysis and the self-recognized skin characteristic information in the collected survey response data, curation is performed in consideration of the user's lifestyle, worry item value, and skin type value generating a message;
An operation of building a database based on image analysis results for at least one or more photographing data, skin environment influence factors in skin environment information, and survey response data.
The user's facial skin type data is generated through analysis of data stored in the database, and a skin health diet recommendation service, skin health management recommendation service, customized cosmetics recommendation service, and skin trouble expert consultation service suitable for the user's facial skin type are generated. action to provide;
Receiving satisfaction information on the skin health diet recommendation service, skin health care recommendation service, customized cosmetics recommendation service, and skin trouble expert consultation service; and
The method further comprising an operation of collecting satisfaction information of a plurality of users and determining a recommended cosmetics required for the user based on the collected plurality of satisfaction information.

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