KR102499800B1 - Artificial intelligence-based customized aesthetics curation system, device and method using questionnaire - Google Patents

Abstract

The present invention relates to an artificial intelligence-based customized aesthetic curation system using a questionnaire, and a device and a method thereof, which can provide commercial information on the basis of statistical data. The artificial intelligence-based customized aesthetic curation system comprises: a communication circuit; a memory; and a processor for receiving survey data from an external server, obtaining store information, and transmitting evaluation information to a user terminal.

Description

AI-based customized aesthetic curation system, device and method using questionnaire {ARTIFICIAL INTELLIGENCE-BASED CUSTOMIZED AESTHETICS CURATION SYSTEM, DEVICE AND METHOD USING QUESTIONNAIRE}

The following embodiments relate to an artificial intelligence-based customer-customized aesthetic curation system, device, and method using a questionnaire.

Companies that sell products and provide services to customers are introducing a methodology of customer relationship management (CRM) to attract customers and maintain continued relationships by increasing satisfaction with existing customers.

CRM refers to a customer-centered management technique that indexes customers' purchase-related behavior by organizing and analyzing information data on current and potential customers and converting them into marketing information, and develops, realizes, and revises marketing programs based on this. .

This CRM has developed into a geographic customer relationship management (Geographic-CRM: GCRM) system, and attempts are being made to combine Geographical Information System (GIS) and CRM. Recently, as a support technology for marketing and start-up such as regional commercial area, regional economic analysis, and site selection, a service that analyzes and provides a commercial area using a statistical analysis method is becoming more common.

The commercial area is a range of trading rights, and may mean a spatial range in which customers who are likely to use the store live or work. The commercial area is the range of the influence area in which effective customers are distributed based on a specific store, and the location may mean a location where a specific store is located.

Background demand and lifestyle can be important in choosing the optimal location. The background demand refers to the potential demand that can be realized as a profit because the product can be used directly or indirectly. Life circulation is a passage to reach the background demand and the location of the store, and it can be an important consideration in determining the optimal location.

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(Prior Document 0001) Korean Patent Publication No. 10-2022-0120829 (Prior Document 0002) Korea Patent Registration No. 10-2433635

The commercial district information system currently being provided meets the actual needs of consumers, such as geographical information such as population distribution, public institutions, and industrial facilities in a specific area, card usage amount used in existing commercial districts, and simple movement information collected through mobile devices. There is a limit to providing commercial area information based only on statistical data that cannot be grasped.

In addition, the conventional commercial district information system focuses on analyzing commercial districts according to demand based on the number of floating populations, and there is a problem in that an incorrect judgment may be made in determining a location where a user can obtain stable and regular profits.

The commercial district information system currently being provided meets the actual needs of consumers, such as geographical information such as population distribution, public institutions, and industrial facilities in a specific area, card usage amount used in existing commercial districts, and simple movement information collected through mobile devices. We provide commercial information based only on statistical data that cannot be grasped, and after starting a business according to the consulting service designed based on this information, there is a limit in that in reality, there are frequent cases where the business is closed with huge losses due to a gap with market demand. .

An artificial intelligence-based customized aesthetic curation system using a questionnaire may include a communication circuit, memory, and processor. Based on the user input, the processor collects survey data on the area of interest, desired location, target customer base, location information of a specific store to be benchmarked, brand, item and price of cosmetics, expected number of employed workers, and target sales. Receiving or receiving the survey data from an external server using the communication circuit, searching for a store located in the area of interest based on a user input or data received through the communication circuit, and having the same type of business as the desired type of business Competing stores are extracted, store information including location and elevation information of the competing stores is acquired, information on demand behind a residential complex, commercial complex or business complex in an area of interest is obtained, and the store information of competing stores is obtained. Collecting location information based on at least one of the shortest distance with the background demand, altitude difference, and access road type, and generating data for commercial district information based on store information, information on background demand, and location information, Send to the artificial intelligence learning model, and based on the user's area of interest, desired industry, and desired location information on the user's survey data, at least one store exposed based on GPS and competitive stores including a specific store subject to benchmarking It can be determined and transmitted to the artificial intelligence learning model.

The artificial intelligence learning model takes store information, information on the background demand, data on commercial district information generated based on the location information, and data on income declarations of competing stores in the area of interest as inputs to review business feasibility, finance User opening analysis information including loan information, labor contract, and interior is generated as output, and information on competing stores including at least one store exposed based on GPS and a specific store targeted for benchmarking is input as input. Thus, the opening analysis information is output based on the competing stores, and the sales, income, business feasibility of the competitor, whether the store is worthy of benchmarking using the user opening analysis information and the competitor store opening analysis information as inputs, and If you open a business in that location, you can print an analysis report that includes information on expected sales and number of customers.

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 artificial intelligence-based customized aesthetic curation system, device, and method using the questionnaire in this document are based on the area of interest, desired location, target customer base, location information of a specific store targeted for benchmarking, brand, item and price of cosmetics, By receiving survey data on the expected number of employees and target sales, it can help users make accurate decisions in determining where they can earn stable and regular income.

In addition, the artificial intelligence-based customized aesthetic curation system, device, and method using the questionnaire in this document are based on the area of interest, desired location, target customer base, location information of a specific store targeted for benchmarking, brand of cosmetics, item and Commercial area information can be provided based on statistical data that can identify the actual demand of consumers, including price, expected number of employed workers, and target sales.

In addition, the artificial intelligence-based customer-customized aesthetic curation system, device, and method using the questionnaire in this document are used as inputs for data on commercial district information and data on income declarations of competing stores in the area of interest to review business feasibility and provide financial loans. By providing user practice analysis information including means, labor contracts and interiors, it can help users effectively benchmark competing stores.

1 is a diagram for explaining an artificial intelligence-based customer-customized aesthetic curation system using a questionnaire according to an embodiment.
2 is a diagram for explaining learning of a neural network according to an exemplary embodiment.
3 is a diagram showing the configuration of an artificial intelligence model according to an embodiment.
4 is an exemplary diagram of a configuration of an electronic device according to an embodiment.
5 is a flowchart of an artificial intelligence-based customer-customized aesthetic curation method using a questionnaire 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 could 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 a diagram for explaining an artificial intelligence-based customer-customized aesthetic curation system using a questionnaire according to an embodiment.

As shown in FIG. 1, the 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 configured separately from the server 120, but is not limited thereto, and the database 130 may be provided in the server 120. For example, the server 120 may include multiple artificial intelligence 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, ..., a server 120, a database 130, etc. For example, the network may be 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), 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) , RS-232 (recommended standard 232) or POTS (plain old telephone service) may be used to perform wired communication.

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

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 could 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.

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

2, the learning device may train the neural network 123 in order to itemize the review responses received from the plurality of user terminals 110-1, .... In addition, the learning device may train the user's movement. In order to extract user stay details from route information, the neural network 123 may be trained. According to an embodiment, the learning device may be a separate subject different from the server 120, but is not limited thereto.

The neural network 123 includes an input layer 121 into which training samples are input and an output layer 125 into which training outputs are output, and may be learned based on differences between the training outputs and labels. Here, the 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 the connected nodes and an activation function that activates the nodes.

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

The learning device may calculate a training error using a predefined loss function. The loss function may be predefined with labels, outputs and parameters as input variables, where the parameters may be set by weights in the neural network 123. For example, the loss function may be designed in a mean square error (MSE) form, an entropy form, and the like, and various techniques or methods may be employed in an embodiment in which the loss function is designed.

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

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

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

According to an 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 may be employed to extract features.

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

According to an embodiment, the learning device may obtain training outputs by applying the first training feature vectors to the neural network 123. The learning device may train the review item extraction algorithm of the neural network 123 based on the training outputs and the first labels. The learning device calculates training errors corresponding to the training outputs, and optimizes a connection relationship between nodes in the neural network 123 to minimize the training errors, thereby training the review item extraction algorithm of the neural network 123. . The server 120 may extract items from the review response using the first neural network for which learning has been completed. For example, the extracted items may include friendliness, store cleanliness, service satisfaction, etc., but are not limited thereto.

According to an 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 an algorithm for obtaining user stay details 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 of nodes in the neural network 123 to minimize the training errors, and trains the algorithm for obtaining user stay details of the neural network 123. can The server 120 may obtain the details of the user's stay from the moving path information by using the second neural network for which learning has been completed.

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

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

An input layer is a layer related to input values input to an artificial intelligence model.

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

The MAC operation may be an operation of multiplying an input value by a corresponding weight and summing the multiplied values.

The activation operation may be an operation of inputting a result of the MAC operation to an activation function and outputting a resultant 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 relu function, a ricky relu function, a maxout function, and/or an elu function.

The hidden layer may consist of at least one layer. For example, when the hidden layer is composed of a first hidden layer and a second hidden layer, the first hidden layer outputs a feature map by performing a MAC operation and an activation operation based on the input value of the input system, and A feature map, which is a result value of the layer, may be an input value of the second hidden layer. The second hidden layer may perform a MAC operation and an activation operation based on the feature map resulting from the first hidden layer.

An output layer may be a layer related to a result value of an operation performed in a hidden layer.

In one embodiment, the learning model automatically learns the boundaries of compound words and object names by learning syllable (letter) patterns that are frequently combined and used in a given corpus, and object information of the first UI source and object information rendered by the browser Integrating to create an object information file for learning, generating learning data for learning a deep learning network using the object information file for learning, receiving data from various domains of the support system, and corresponding to each of the various domains Based on at least one standardization method, standardizes the data of the various domains in an integrated format, learns and infers data of a specific domain, determines information to be transmitted for the standardization in the data of the specific domain, Post processing may be performed on data from the various domains. The first UI source includes an XML file, and the object information file for learning includes an input JSON file for learning features and an output JSON file that is label data during learning, and the output JSON file is a web standard. and the various domains include at least one of RAN (radio access network), transport, or core, and the post-processing is (correlation) function can be included.

4 is an exemplary diagram of a configuration of a system according to an embodiment.

The system 400 according to an embodiment may include a communication circuit 410, a processor 420, and a memory 430, and some of the illustrated components may be omitted or replaced. System 400 according to an embodiment may be a server or a terminal. According to one embodiment, the processor 420 is a component capable of performing calculations or data processing related to control and/or communication of each component of the system 400, and may include one or more processors. The memory 430 may store information related to the above-described method or a program in which the above-described method is implemented. Memory 430 may be volatile memory or non-volatile memory. The memory 430 may store various file data, and the stored file data may be updated according to the operation of the processor 420 .

According to an embodiment, the communication circuit 410 may communicate with an external device through a wireless network under the control of the processor 420 . The communication circuit 410 is hardware and software for transmitting and receiving data from a cellular network (eg, a long term evolution (LTE) network, a 5G network, a new radio (NR) network) and a local area network (eg, Wi-Fi, bluetooth) modules may be included.

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

An artificial intelligence-based customized aesthetic curation system using a questionnaire may include a communication circuit 410, a processor 420, and a memory 430. Based on the user input, the processor 420 determines the region of interest, desired location, target customer base, location information of a specific store to be benchmarked, brand, item and price of cosmetics, expected number of employed workers, and target sales. Receive survey data or receive the survey data from an external server using the communication circuit, search for stores located in the area of interest based on user input or data received through the communication circuit, and enter the same industry as the desired industry Extract competitive stores having , obtain store information including location and elevation information of the competing stores, obtain information on demand behind a residential complex, commercial complex, or business complex in an area of interest, Location information is collected based on at least one of the shortest distance with the background demand, altitude difference, and access road type from the store information, and data on commercial district information is collected based on the store information, information on background demand, and location information. Competitive including at least one store exposed based on GPS and a specific store subject to benchmarking based on user's area of interest, desired industry, and desired location information on the user's survey data. The store can be determined and sent to the artificial intelligence learning model.

The artificial intelligence learning model takes store information, information on the background demand, data on commercial district information generated based on the location information, and data on income declarations of competing stores in the area of interest as inputs to review business feasibility, finance User opening analysis information including loan information, labor contract, and interior is generated as output, and information on competing stores including at least one store exposed based on GPS and a specific store targeted for benchmarking is input as input. Thus, the opening analysis information is output based on the competing stores, and the sales, income, business feasibility of the competitor, whether the store is worthy of benchmarking using the user opening analysis information and the competitor store opening analysis information as inputs, and If you open a business in that location, you can print an analysis report that includes information on expected sales and number of customers.

According to one embodiment, there will be no limitations on the calculation and data processing functions that the processor 420 can implement on the system 400, but hereinafter, the function of providing customized aesthetic curation based on artificial intelligence using a questionnaire will be explained

5 is a flowchart of an artificial intelligence-based customer-customized aesthetic curation method using a questionnaire according to an embodiment.

Although process steps, method steps, algorithms, etc. are described in a sequential order in the flowchart 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 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 510, a processor (eg, the processor 420 of FIG. 4) may receive survey data. Based on the user input, the processor 420 determines the region of interest, desired location, target customer base, location information of a specific store to be benchmarked, brand, item and price of cosmetics, expected number of employed workers, and target sales. The survey data may be received or the survey data may be received from an external server using a communication circuit. Survey data may be generated by user input, and the system 400 may receive survey data by directly receiving a user input, or may receive survey data from an external server.

In operation 520, the processor 420 searches for stores located in the area of interest and extracts information on competing stores. The processor 420 may identify a region of interest of the user on the received survey data. The processor 420 may search for stores within a predetermined distance based on GPS in the area of interest, and determine competing stores having the same type of business as the user's desired type of business from among the searched stores. The processor 420 may obtain store information including location and altitude information of competing stores using an external server or a map application.

In operation 530, the processor 420 may collect shop information, information on demand behind the scenes, and location information. The processor 420 may obtain information on demand behind a residential complex, commercial complex, or business complex in an area of interest by using an external server or a map application. The background demand refers to the potential demand that can be realized as a profit because the product can be used directly or indirectly. The system according to this document may perform calculations on sales and expected number of customers based on information on demand behind it, and provide specific numerical information to the user.

In operation 540, the processor 420 may determine a competitive store and transmit the result to the artificial intelligence learning model. The processor 420 may collect location information based on at least one of a shortest distance to the background demand, an altitude difference, and an access road type from the store information of competing stores. The processor 420 may generate data on commercial area information based on store information, information on demand behind the scenes, and location information, and transmit the data to an artificial intelligence learning model. The processor 420 determines a competitor store including at least one store exposed based on GPS and a specific store to be benchmarked based on information on the area of interest, desired industry, and desired location in the user's survey data, and determines artificial It can be transferred to an intelligent learning model.

In operation 550, the processor 420 may receive individual analysis information and an analysis report from the artificial intelligence learning model.

The artificial intelligence learning model (not shown) reviews business feasibility by taking store information, information on background demand, data on commercial district information generated based on the above location information, and data on income declarations of competing stores in the area of interest as input , financial loan information, labor contract, and user opening analysis information including interior can be generated as output. The artificial intelligence learning model can determine how much sales a user can achieve when opening a business and review business feasibility based on data on income declarations and commercial district information of competing stores. The artificial intelligence learning model can output data on how much loan is needed when opening a business based on data on income reporting. The artificial intelligence learning model can calculate the number of employees required to open a business based on the number of employees and trade area information of competing stores, and output data on labor contracts. The artificial intelligence learning model acquires an image of the interior environment of a competitor store on a map application or a search application, and outputs information on interior types, cost estimates, and interior design companies in the surrounding area.

The artificial intelligence learning model (not shown) takes information about competing stores including at least one store exposed based on GPS and a specific store to be benchmarked as an input, and provides business analysis information based on the competing stores. can output 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, an artificial intelligence learning model (not shown) may determine that business feasibility is insufficient based on a location where a user can open a business when opening a business, but may determine that a competing store exists in a different location and has sufficient business feasibility. there is.

The artificial intelligence learning model (not shown) uses the user opening analysis information and the competitor store opening analysis information as input to determine whether the competitor's sales, income, business feasibility, whether the store is worthy of benchmarking, and when opening a business in that location An analysis report containing information on expected sales and number of customers can be generated as output. For example, an artificial intelligence learning model (not shown) may determine that only stores whose sales exceed a specified level (eg, 100 million won per month) are benchmarkable stores. 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.

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 an artificial intelligence-based customer-customized aesthetic curation system using a questionnaire,
communication circuit;
processor; and
contains memory;
The processor
Based on the user input, receive survey data about the area of interest, desired location, target customer base, location information of a specific store to be benchmarked, item to be sold, expected price and target sales of the item, or use the communication circuit Receive the survey data from an external server using
Based on the user input or data received through the communication circuit, shops located in the area of interest are searched for, competing shops having the same type of business as the item to be sold are extracted, and location and elevation information of the competing shops are included. get store information;
Calculating demand for the item to be sold based on the size and floating population of at least one of a residential complex, a commercial complex, or a business complex in the area of interest;
Collecting location information based on at least one of a shortest distance from the region of interest, an altitude difference, or a size of an access road based on location and altitude information of the competing stores;
Transmits information about the commercial district including the store information, calculated demand, and location information to the artificial intelligence learning model;

Evaluate the business feasibility based on the information on sales, rent, and closure probability output from the artificial intelligence learning model, and transmit the evaluation information to the user terminal,
Generate commercial district information data including the share of online sales for competing stores, the share of major sales items in sales, sales by time slot extracted from card sales records of the competing stores, and sales share, and based on the commercial district information data, When opening a business in an area of interest, determine the expected turnover, profit and loss, and number of visiting customers per hour; determine the number of workers, provide information on worker contracts based on the determined number of workers;
The artificial intelligence learning model is
Includes business feasibility review, financial loan information, labor contract and interior by inputting the store information, information on calculated demand, commercial district information including the location information, and data on the income declaration of competing stores in the area of interest as input Generates user practice analysis information as output,
Taking information about competing stores based on GPS as an input, and outputting analysis information based on the competing stores;
Based on the user opening analysis information and the competitor store opening analysis information as input, sales, income, business feasibility, whether the store is worth benchmarking, and expected sales and expected customers when opening a business in that location Generates an analysis report containing information about the number as output,
The number of franchisees by business type in the commercial district, the concentration of industries in 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, the average income of households behind the commercial district, the total number of houses behind the commercial district and the commercial district A system that outputs information on sales, rent, and probability of business closure by taking information on area as input.
According to claim 1,
The processor
Determine a score for business opening analysis information based on business feasibility, financial loans, sales, sales level of nearby competitors, number of nearby competing stores, and expected number of customers;
A system for determining that the probability of success is higher than the probability of failure at opening based on the score of the opening analysis information on the analysis report exceeding a specified level, and displaying information about a realtor having data of a store located in the desired location.

According to claim 2,
The processor
Receiving social information through a social network service for the competitive store using the communication circuit;
Information on the number of exposures of the competitive store on the top of the related search word of the social network service, a score evaluation of the competitive store, and a qualitative evaluation of the competitive store on the social information are obtained and transmitted to the artificial intelligence learning model. and
The artificial intelligence learning model is
Outputting a ratio of positive and negative evaluations by taking words included in information on the qualitative evaluation of the competing stores as input;
designate the competitor store as a benchmarking store if the positive review rate exceeds a specified level compared to the negative review rate;
A system that outputs the analysis report for the benchmarking store.

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