KR102543570B1 - User location-based pet beauty shop reservation method, device and system - Google Patents

Abstract

An electronic device according to an embodiment may include a memory and a processor.
The processor receives information about the beauty shop from the server and provides the user with information about the desired beauty shop location, time, and desired service type. It determines the time and type of desired service, transmits the determined information to the server, requests payment of a deposit to receive the service, and provides service at the time desired by the user among a plurality of beauty shops desired by the user from the server. receive and display information about whether the user can provide the service, whether the user wants the service, whether the location of the beauty shovel is within the specified level from the user's location, and the price condition, and display the user's location Based on this, the location of a plurality of beauty shops is displayed on the map, and if the user selects at least one of the beauty shops mapped and displayed on the navigation map, whether the service can be provided at the time the user wants, the user It provides information on whether the desired service can be provided, the distance from the user's current location to the beauty shop, traffic information and price conditions, displays a guide asking whether or not to make a reservation at the selected beauty shop, and selects the user Determines the beauty shop to make a reservation based on, receives the cost for the service the user wants from the network linked to the determined beauty shop, receives the deposit deposited by the user, and transmits information asking whether or not to confirm the reservation and confirms the reservation based on receiving the signal instructing the user to receive the deposit and confirm the reservation on the network linked with the beauty shop, and displays the reservation information to the user, and on the network linked with the beauty shop, the user Displays a guide to the user to choose whether or not to confirm the reservation by adding a deposit based on receiving a signal indicating an insufficient deposit has been made and based on user input choosing to confirm the reservation by adding a deposit Based on receiving a signal instructing the user to receive the deposit additionally deposited and confirm the reservation on the network linked with the beauty shop, The reservation can be confirmed and the reservation information can be displayed to the user.

Description

Companion animal beauty shop reservation method, device and system based on user location {USER LOCATION-BASED PET BEAUTY SHOP RESERVATION METHOD, DEVICE AND SYSTEM}

The following embodiments relate to a method, apparatus, and system for reserving a companion animal beauty shop based on a user's location.

Pets have been raised for intimate exchanges, emotional sharing, and relief from loneliness. As the number of single-person households or elderly households living alone increases, the demand for pets is gradually increasing. In addition, pets have been changed to the name of companion animals in the sense of life companions or family members.

Currently, various beauty shops are operating that provide various types of beauty services such as makeup, hair styling, massage, nail care, etc. to dogs, and these beauty shops are actively attracting customers through advertisements.

In general, most of these beauty shops provide services to users who have visited the beauty shops, and it takes a considerable amount of time to decorate the users, so most of the beauty shops make service reservations essential to minimize the waiting time of users.

Due to the nature of the beauty shop that makes reservations for these services, if the user makes a reservation for the beauty shop and determines that the service conditions, including the price or service content provided by the beauty shop, are different from the conditions requested by the user, even if the user visits the beauty shop. Cases in which reservations are canceled without receiving the service frequently occur, and as a result, users who cancel service reservations may fail to attract other customers. are being carried out concurrently. Therefore, beauty shops are actively conducting consultations to attract customers that lead to sales, and executing various advertisements through online and offline to secure customers.

(Prior Document 0001) KR Publication No. 10-2018-0043236 (Prior Document 0002) KR Patent Publication No. 10-2018-0081934

On the side of the beauty shop, the cost of advertising can be a burden, and since the advertisement is provided to all people who have not been authenticated, there is a problem in that it is not reliable whether users who made a reservation after viewing the advertisement will appear at the beauty shop at the promised time.

From the user's point of view, it may be inconvenient to have to contact the beauty shop directly because it is not known whether the desired service can be provided at the desired time slot before contacting the beauty shop directly after seeing the advertisement.

The method, device, and system for making a reservation for a companion animal beauty shop based on the user's location according to this document use the deposit to prevent damage to the no-show of the booker on the beauty shop side, and the user side can reserve the desired time slot, and can not make a reservation right away. In this case, if there is a vacant seat later in the corresponding time zone, the purpose is to increase user convenience by quickly providing the corresponding news to the user.

An electronic device according to an embodiment may include a memory and a processor.

The processor receives information about the beauty shop from the server and provides the user with information about the desired beauty shop location, time, and desired service type. It determines the time and type of desired service, transmits the determined information to the server, requests payment of a deposit to receive the service, and provides service at the time desired by the user among a plurality of beauty shops desired by the user from the server. receive and display information about whether the user can provide the service, whether the user wants the service, whether the location of the beauty shovel is within the specified level from the user's location, and the price condition, and display the user's location Based on this, the location of a plurality of beauty shops is displayed on the map, and if the user selects at least one of the beauty shops mapped and displayed on the navigation map, whether the service can be provided at the time the user wants, the user It provides information on whether the desired service can be provided, the distance from the user's current location to the beauty shop, traffic information and price conditions, displays a guide asking whether or not to make a reservation at the selected beauty shop, and selects the user Determines the beauty shop to make a reservation based on, receives the cost for the service the user wants from the network linked to the determined beauty shop, receives the deposit deposited by the user, and transmits information asking whether or not to confirm the reservation and confirms the reservation based on receiving the signal instructing the user to receive the deposit and confirm the reservation on the network linked with the beauty shop, and displays the reservation information to the user, and on the network linked with the beauty shop, the user Displays a guide to the user to choose whether or not to confirm the reservation by adding a deposit based on receiving a signal indicating an insufficient deposit has been made and based on user input choosing to confirm the reservation by adding a deposit Based on receiving a signal instructing the user to receive the deposit additionally deposited and confirm the reservation on the network linked with the beauty shop, The reservation can be confirmed and the reservation information displayed to the user.

An electronic device 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.

A method, apparatus, and system for making a reservation for a companion animal beauty shop based on a user's location according to an embodiment can provide convenience so that a user can make a reservation at a desired time slot without directly contacting the beauty shop. In addition, even if it is not possible to make a reservation at the desired time slot, if there is a vacant seat later, the news is quickly provided to the user so that the beauty shop can be operated without vacant time, and the user can receive services at the desired time slot.

A method, apparatus, and system for making a reservation for a companion animal beauty shop based on a user's location according to an embodiment of the present invention prevent a no-show that does not appear even after making a reservation by setting a deposit, and transmit a record of the no-show to the server to beautician. The side can also provide a function to know the level of trust or verification of the booker.

1 is a diagram for explaining an AI-based beauty shop reservation platform service providing system 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 illustrating a method for making a reservation for a companion animal beauty shop based on a user's location 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 AI-based beauty shop reservation platform service providing system according to an embodiment.

As shown in FIG. 1, the AI-based beauty shop reservation platform service providing system 100 may include a plurality of user terminals 110-1, ..., a server 120, and a database 130. According to one embodiment, the database 130 is shown as 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).

A plurality of user terminals 110-1, ..., 110-n are used by users who want to reserve a beauty shop through the AI-based beauty shop reservation platform service providing system 100 and use the beauty shop. and a second user terminal (eg 110-n) used by beauty shops subscribed to the AI-based beauty shop reservation platform service. . For example, the first user terminal 110 - 1 may form real-time location information of the user by including a Global Positioning System (GPS) or the like.

The server 120 may receive a beauty shop reservation request from the first user terminal 110-1. According to an embodiment, the server 120 may receive a beauty shop reservation request from the first user terminal 110-1 used by a user who wants to use the beauty shop through the network N. For example, the beauty shop reservation request may include the user's real-time location information, movement route information, service item information, etc., but is not limited thereto.

The server 120 may form a first list including location information of beauty shops available for reservation. According to an embodiment, the server 120 may form a first list including location information of beauty shops that are currently available for reservation among beauty shops registered to the AI-based beauty shop reservation platform service.

The server 120 rearranges the beauty shops included in the first list in order closer to the user using the location information of the beauty shops included in the first list and the user's real-time location information to form a modified first list. can do. According to an embodiment, the server 120 provides location information (eg, first latitude and first longitude) of beauty shops included in the first list and real-time location information (eg, second latitude and longitude) of the user. second longitude) to calculate the distance between the beauty shops included in the first list and the user's current location, and rearrange the beauty shops included in the first list in order of proximity to the user using the calculated distance Thus, a first modified list may be formed.

The server 120 may transmit the first modified list to the first user terminal. According to an embodiment, the server 120 may transmit the formed first modified list to the first user terminal 110-1 through the network N.

The first user terminal 110-1 receives a selection reservation request for the first beauty shop, which is a specific beauty shop included in the first modified list, from the user who has checked the first modified list, and the server ( 120) can be transmitted.

The server 120 may receive a reservation request for a selection of a first beauty shop from among beauty shops included in the first modified list from the first user terminal. According to an embodiment, the server 120 receives a reservation request for a selection of a first beauty shop among beauty shops included in the modified first list from the first user terminal 110-1 through the network N. can

The server 120 may use the user's real-time location information to calculate the time required for the user to arrive at the first beauty shop. According to an embodiment, the server 120 may calculate the distance between the first beauty shop and the user's current location using Equation 1, and the calculated distance is the user's moving speed (for example, , 1 minute/100 m) to calculate the required time.

The server 120 may transmit a selection reservation request to the second user terminal used in the first beauty shop in consideration of the required time. According to an embodiment, the server 120 transmits a selection reservation request together with the time required for the user to arrive at the first beauty shop to the second user terminal 110-n through the network N, The beauty shop may prepare in advance to provide services according to the user's arrival time.

The second user terminal 110 - n may receive a reservation completion guide from the user of the first beauty shop who has confirmed the selection request and transmit the information to the server 120 . For example, the reservation completion information may include information about items that need to be prepared for use of the first beauty shop, usage fees, and the like.

The server 120 may receive a reservation completion guide from the second user terminal. According to one embodiment, the server 120 may receive a reservation completion guide from the second user terminal 110-n through the network N.

The server 120 may form shortest path information for arriving at the first beauty shop from the user's current location.

According to an embodiment, the server 120 may form shortest path information for arriving at the first beauty shop from the user's current location using real-time location information of the user. For example, the server 120 displays the user's current location and the location of the first beauty shop using a map application, and images the shortest path information for arriving at the first beauty shop from the user's current location. so that it can be displayed in the map application.

The server 120 may transmit reservation completion information and shortest path information to the first user terminal. According to one embodiment, the server 120 is formed by using the reservation completion guide and map application received from the second user terminal 110-n to the first user terminal 110-1 through the network (N). Shortest path information can be transmitted.

The server 120 may use real-time location information of the first user terminal to calculate an estimated time for the user to arrive at the first beauty shop. According to an embodiment, the server 120 may use real-time location information of the first user terminal 110-1 to calculate an estimated time for the user to arrive at the first beauty shop from the current location.

The server 120 may transmit the expected time to the second user terminal. According to an embodiment, the server 120 may transmit the calculated expected time to the second user terminal 110-n through the network N.

The server 120 may generate a user arrival notification signal when the user is located within a predetermined distance from the first beauty shop using real-time location information of the first user terminal. According to an embodiment, the server 120 uses real-time location information of the first user terminal 110-1 so that the user is located within a predetermined distance (eg, 500m, 300m, 100m, etc.) from the first beauty shop. In this case, a user arrival notification signal may be formed.

The server 120 may transmit a user arrival notification signal to the second user terminal. According to an embodiment, the server 120 may transmit the formed user arrival notification signal to the second user terminal 110-n through the network N.

The server 120 may use the real-time location information of the first user terminal to form a user review request and transmit the request to the first user terminal when the user is away from the first beauty shop by more than a predetermined distance. According to an embodiment, the server 120 uses the real-time location information of the first user terminal 110-1 when the user is more than a predetermined distance (eg, 500m, 1km, etc.) from the first beauty shop, When it is determined that the user has finished using the first beauty shop, a user review request may be formed and transmitted to the first user terminal 110-1.

After receiving the user review request from the server 120, the first user terminal 110-1 may receive a review response according to the user review request from the user and transmit the received review response to the server 120 through the network N.

The server 120 may transmit preset points to the first user terminal according to reception of a review response. According to an embodiment, when receiving a review response from the first user terminal 110-1, the server 120 provides preset points as a reward for participation in the review to the first user terminal 110-1. can be sent

The server 120 may classify and store review responses by item. According to an embodiment, the server 120 may receive a review response from the first user terminal 110-1, classify the review response by item, and store the review response. For example, the server 120 may classify and store the received review responses by item using a machine learning algorithm such as deep learning.

The server 120, using the real-time location information of the first user terminal, when the user is located within a predetermined distance from the first beauty shop, provides a coupon list including information on coupons usable at the first beauty shop to the first user terminal. can be sent to According to an embodiment, the server 120 uses the real-time location information of the first user terminal 110-1 when the user is located within a predetermined distance (eg, 300m, 100m, etc.) from the first beauty shop. A coupon list including information on coupons usable in one beauty shop may be transmitted to the first user terminal 110-1 through the network N.

The server 120 may perform a first payment of the estimated cost of using the first beauty shop by applying a first coupon having the highest discount rate among coupons in the coupon list. According to an embodiment, the server 120 may perform a first payment of an estimated cost of using the first beauty shop by applying a first coupon having the highest discount rate among coupons usable at the first beauty shop. For example, since the first payment corresponds to a provisional payment, information on the payment request is not transmitted to the card company server, etc., and the server 120 can perform a formal payment after the user's use of the first beauty shop is completed. there is. Accordingly, it is possible to prevent a no-show after the user makes a reservation for the first beauty shop.

The server 120 may use real-time location information of the first user terminal to receive service information provided by the user from the first beauty shop from the second user terminal when the user moves away from the first beauty shop by more than a predetermined distance. . According to an embodiment, the server 120 uses the real-time location information of the first user terminal 110-1 to determine if the user is more than a predetermined distance (eg, 50m, 100m, etc.) from the first beauty shop. 1 The user may receive service information provided by the first beauty shop through the network N from the second user terminal 110-n used in the beauty shop.

The server 120 may form second payment details according to the use of the first beauty shop using the service information and the first coupon, and transmit the information to the first user terminal. According to an embodiment, the server 120 forms second payment details according to the user's use of the first beauty shop by using the service information provided by the user from the first beauty shop and the first coupon having the highest discount rate, and , it can be transmitted to the first user terminal 110-1 through the network N.

The first user terminal 110-1 receives the second payment details from the server 120, receives the user's approval request for this, and transmits the approval request to the server 120 through the network N. there is.

The server 120 may receive an approval request for the second payment details from the first user terminal. According to an embodiment, the server 120 may receive an approval request for second payment details from the first user terminal 110-1 through the network N.

The server 120 may perform the second payment by transmitting the second payment details to the card company server. According to an embodiment, the server 120 may perform the second payment by transmitting the second payment details to the card company server through the network N.

The server 120 may receive and store advertisement information of multiple beauty shops. According to an embodiment, the server 120 receives advertisement information of corresponding beauty shops from the second user terminal 110-n used by beauty shops through the network N, and stores the advertisement information in the database 130. You can control it.

The server 120 may extract a predetermined number of second beauty shop information located within a predetermined distance from the user's movement path by using the movement path information. According to an embodiment, the server 120 uses the moving path information of the first user terminal 110-1 to set a predetermined number (eg, 50m, 30m, etc.) located within a predetermined distance from the user's moving path. For example, 5, 10, etc.) of second beauty shop information may be extracted.

The server 120 may extract a third beauty shop matching the service item information extracted from the reservation request for selection among the second beauty shops. According to an embodiment, the server 120 may extract a third beauty shop matching the service item information extracted from the selected reservation request received from the first user terminal 110-1 from among the second beauty shops.

The server 120 may transmit advertisement information of the third beauty shop to the first user terminal. According to an embodiment, the server 120 may retrieve the extracted advertisement information of the third beauty shop from the database 130 and transmit it to the first user terminal 110-1 through the network N. That is, the server 120 extracts only the beauty shops related to the service the user wants to receive from among the beauty shops located around the user, and transmits advertisements of the beauty shops to the first user terminal 110-1 so that the user can provide helpful information for making a reservation for a beauty shop, and can improve the advertising effect of beauty shops.

The server 120 obtains the user's stay history through the travel route information included in the beauty shop reservation request, and based on the user's stay history, checks the stay time for each user, and calculates the user's average stay time The standard value is set according to the length of the user's average stay time, the minimum time allowed for the user is calculated by subtracting the standard value from the user's average stay time, and the standard value is added to the user's average stay time. As one value, the maximum time of the user permissible range can be calculated, and the user permissible range can be set based on the minimum time of the user permissible range and the maximum time of the user permissible range.

The server 120 may obtain details of the user's stay from movement route information. According to an embodiment, the server 120 may extract user sojourn details from movement route information using a machine learning algorithm such as deep learning. For example, the server 120 may extract places in which the latitude and longitude of the user do not change for a predetermined time or more from movement route information as user sojourn details.

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. As an embodiment, the database 130 may include data received from the first user terminal 110-1. A beauty shop reservation request and a selective reservation request, a reservation completion guide received from the second user terminal 110-n, a first list formed in the server 120, a modified first list, and shortest path information may be stored.

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 an electronic device according to an embodiment.

The electronic device 400 according to an embodiment may include a processor 420 and a memory 430, and some of the illustrated components may be omitted or replaced. The electronic device 400 according to an embodiment may be a server or a terminal. According to an 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 electronic device 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 120 .

According to one embodiment, the processor 420 may execute a program and control the device 400 . Program codes executed by the processor 120 may be stored in the memory 430 . Operations of the processor 420 may be performed by loading instructions stored in the memory 430 . The electronic device 400 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 420 may receive beauty shop information from the server and provide the user with information about the desired beauty shop location, time, and desired service type.

The processor 420 determines the location of a plurality of beauty shops desired by the user, the time desired to receive service, and the desired service type based on the user selection, transmits the determined information to the server, and deposits money for receiving the service. may be requested to pay.

The processor 420 determines whether the service can be provided at the time desired by the user among the plurality of beauty shops desired by the user from the server, whether the service desired by the user can be provided, and whether the location of the beauty shovel is designated from the location of the user. It may receive information about whether it is within the level and the price condition and display it to the user.

The processor 420 displays the locations of a plurality of beauty shops on a map based on the user's location, and when the user selects at least one of the beauty shops mapped and displayed on the navigation map, the user selects at a desired time. Whether the service can be provided, whether the user can provide the desired service, the distance from the user's current location to the beauty shovel, information on traffic information and price conditions, and whether to make a reservation at the selected beauty shovel A guide may be displayed asking whether

The processor 420 determines a beauty shop to make a reservation based on the user selection, receives a cost for a service desired by the user from a network linked to the determined beauty shop, receives a deposit deposited by the user, and confirms the reservation. You can send information asking whether or not it is.

The processor 420 may confirm the reservation and display reservation information to the user based on receiving a signal instructing the user to receive a deposit and to confirm the reservation on the network linked with the beauty shop.

The processor 420 displays a guide for the user to select whether to confirm a reservation by adding a deposit based on receiving a signal indicating that the deposit deposited by the user is insufficient on the network linked with the beauty shop, , Information on the added deposit is transmitted over the network linked with the beauty shop based on the input of the user who selected to confirm the reservation by adding the deposit, and the deposit additionally deposited by the user on the network linked with the beauty shop Reservation can be confirmed based on receiving a signal instructing to confirm the reservation and display reservation information to the user.

According to one embodiment, the processor 420 requests an additional amount when the size of the service cost exceeds the user's deposit amount, and if the size of the service cost is smaller than the user's deposit amount, the processor 420 transfers the difference to the designated user's deposit amount. Payment can be made to the account, evaluation of the provided service can be requested, and the user's evaluation can be registered on the server.

According to an embodiment, the processor 420 provides the user with classification information based on the dog hairdresser's activity area, time, and service from the server, and when the user selects a dog hairdresser based on the classification information, the desired service type or The time and service are transmitted to the server along with text information including at least one of notices or requests, and a desired cost for the corresponding service is input using an application, and the dog hairdresser selected by the user pays the corresponding cost. information about the service is transmitted, and an amount corresponding to about 50% of the desired cost for the service is requested to be deposited as a deposit into a designated account or server, the user's deposit is confirmed, and the final result is received from the selected dog hairdresser. Once your selection is confirmed, you can confirm your reservation.

According to an embodiment, the processor 420 stores information on the user's identity authentication information, beauty shop use history, beauty shop use reviews, evaluation ratings, and no-show history in the memory and transmits the information to the server. Based on receiving information from the server indicating that the user did not appear at the beauty shop beyond the specified time after the reservation was confirmed, notification information including the reservation time and reservation information is displayed to the user, and notification information is displayed After that, based on the user's decision that there is no response from the user for a specified time or cancellation of the reservation, the user's deposit may be controlled to be remitted to the selected beauty shop or dog hairdresser.

According to an embodiment, the processor 420 may store information including location information of a plurality of beauty shops, operation time information of the plurality of beauty shops, reservation information, and a list of pet hairdressers working at the plurality of beauty shops from the server. and when the reservation is confirmed based on the user input, real-time traffic information is received and the shortest route from the current location of the electronic device to the selected beauty shop, public transportation information, and fare information are provided, and the beauty shop is provided based on traffic conditions. If the expected arrival time to the shop is later than the reservation time, information related to the selected beauty shop is provided, and selection information on whether to cancel the reservation at the beauty shop on the server or wait for the user to arrive and provide the service can be received and displayed to the user.

According to an embodiment, the processor 420 determines beauty consultation information of the first user based on a service provision record between a first user account and a first designer account and input consultation information, and based on a user input, the processor 420 determines the beauty consultation information of the first user. determining pet dog information of 1 user and lifestyle information of the first user, and determining beauty search information of the first user based on beauty content reproduced by the first user or a beauty-related search record on an Internet application; The beauty consultation information, the beauty search information, the first user's dog information, and the first user's lifestyle information are input to a first artificial neural network, an output is obtained from the first artificial neural network, and Determine at least one beauty product, beauty service, and a designer capable of providing the corresponding service suitable for the pet dog of the first user based on the output, and a designer capable of providing the at least one beauty product, beauty service, and corresponding service Information on may be provided to the first user.

According to an embodiment, the processor 420 determines a first point in time at which a service has recently been provided based on beauty consultation information, and provides the latest service to the first user based on an expiration of a specified period from the first point in time. Provides information indicating that a specified period of time has elapsed since receiving, and provides information indicating that the first user has recently received a service to at least one designer suitable for the dog of the first user determined based on the output of the first artificial neural network. Provide information indicating that the period has exceeded, and at least one designer can provide a service to the first user based on receiving information indicating that the first user can provide a recently received service. Information on at least one designer who answered yes may be provided.

According to an embodiment, if at least one beauty product suitable for the pet dog of the first user is determined based on the output of the first artificial neural network, the processor 420 transmits information on a website where the beauty product can be purchased and price information to the user. and searches a memory for whether the first user has a history of purchasing the beauty product or another beauty product, and recommends beauty products based on the output of the first artificial neural network to the first user. Displays shopping information about beauty products recommended by the first user based on the output of the first artificial neural network and displays a guide asking whether or not to purchase the product or other beauty products based on the lapse of a specified period from the time of purchasing the product or other beauty products can do.

According to an embodiment, the processor 420 determines the value of the purchased product based on the lapse of a specified period from the time when the first user purchases the recommended beauty product or other beauty product based on the output of the first artificial neural network. Displays a guide requesting information on the remaining capacity, requests to take a picture of the purchased product, determines the remaining capacity of the purchased product based on the photographed product picture, and determines the remaining capacity of the purchased product as the first If the level is exceeded, after the first period, display shopping information for the beauty product recommended by the first user based on the output of the first artificial neural network, display a guide asking whether or not to purchase, and display the remaining capacity of the purchased product If is less than the first level, after a second period that is relatively shorter than the first period, the first user displays shopping information for a beauty product recommended based on the output of the first artificial neural network and asks whether or not to purchase it. guides can be displayed.

According to an embodiment, the processor 420 requests to photograph the purchased product, and takes at least one photograph of the photographed product determined to be suitable for the pet dog of the first user determined based on the output of the first artificial neural network. Provided to the designer, input to the first artificial neural network based on the first user's beauty consultation progress record, the first user's beauty search record, or the first user's dog information received, At least one designer determined to be suitable for the dog of the first user may be transmitted.

According to an embodiment, the first artificial neural network is predefined based on at least one of the first user's beauty consultation progress record, the first user's beauty search record, and the first user's dog information. At least one text corresponding to a language may be extracted, and the extracted text may be provided as an input to the second artificial neural network.

According to an embodiment, the second artificial neural network classifies the input text as one of beauty, fashion, pet dog, puppy, companion animal, beauty shop, beauty service, beauty product, and reservation, and the extracted text is classified into a category Among them, a ratio of each category into which the text is classified is determined, a characteristic of the first user is determined based on the determined ratio, and at least one beauty product, a beauty service, and a corresponding beauty product expected to be suitable for the first user are determined. At least one of the designers capable of providing the service may be recommended.

According to an embodiment, the processor 420 receives a face image of a dog, measures at least one of a ratio, length, or skin color of a facial part on the face image of the dog to generate dog information, and visits a beauty shop. Customer's dog information, treatment history or service history at the beauty shop, customer review score of the beauty shop, number of customer reviews, customer satisfaction level, distance information based on the current location of the electronic device, and location specified by the user Distance information, advertisement information, and recent use information can be transmitted to an artificial neural network (ANN).

According to an embodiment, the artificial neural network receives information about a customer's dog who visited the beauty shop, treatment history or service history at the beauty shop, customer review score of the beauty shop, number of customer reviews, customer satisfaction level, and user satisfaction of the electronic device. Based on distance information based on the current location, distance information based on a place designated by the user, advertisement information, and recent usage information, the dog's face image is analyzed, and at least one beauty product or beauty service suitable for the dog is provided. It is recommended, and an image to which a beauty product or beauty service is virtually applied can be created.

According to an embodiment, the artificial neural network may include a beauty consultation progress record of a beauty shop, a beauty search record of the first user or pet dog information of the first user, pet dog information of a customer who visited the beauty shop, and treatment history at the beauty shop. or service history, customer review score of the beauty shop, number of customer reviews, customer satisfaction level, distance information based on the current location of the electronic device, distance information based on a place designated by the user, advertisement information, and recent use information The big data analysis result is used as an input layer, and at least one beauty product or beauty service suitable for the dog is used as an output layer, and a communication connection is made to an API (application programming interface) provided by an external site. In addition, shopping information for purchasing at least one beauty product output from the output layer or information on a beauty designer capable of providing at least one beauty service output from the output layer may be generated.

According to an embodiment, the processor 420 is a beauty designer capable of providing shopping information for purchasing at least one beauty product output from the output layer generated by an artificial neural network or at least one beauty service output from the output layer. information can be displayed to the user.

According to an embodiment, there will be no limitations on calculation and data processing functions that the processor 420 can implement on the electronic device, but hereinafter, functions for implementing a method for making a reservation for a companion animal beauty shop based on a user's location will be described.

5 is a flowchart illustrating a method for making a reservation for a companion animal beauty shop based on a user's location 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, the processor (eg, the processor 420 of FIG. 4 ) may receive beauty shop information from the server and provide the user with information about the desired beauty shop location, time, and desired service type.

In operation 520, the processor 420 determines the location of the plurality of beauty shops desired by the user, the desired time to receive the service, and the desired service type based on the user's selection, transmits the determined information to the server, and provides the service. You may request to pay a deposit to receive the offer.

In operation 530, the processor 420 determines whether a service can be provided at a time desired by the user among a plurality of beauty shops desired by the user from the server, whether a service desired by the user can be provided, and a beauty shop from the location of the user. Information about whether the position of the is within a specified level and price conditions may be received and displayed to the user.

In operation 540, the processor 420 displays the locations of a plurality of beauty shops on the map based on the location of the user, and when the user selects at least one of the beauty shops mapped and displayed on the navigation map, the user Provide information on whether the service can be provided at the desired time, whether the service can be provided to the user, the distance from the user's current location to the beauty shop, traffic information and price conditions, and make a reservation at the selected beauty shop You can display a guide asking whether or not to run

In operation 550, the processor 420 determines a beauty shop to make a reservation based on the user selection, receives a cost for a service desired by the user from a network associated with the determined beauty shop, receives a deposit deposited by the user, and Information asking whether or not to confirm the reservation may be transmitted.

In operation 560, the processor 420 may confirm the reservation and display reservation information to the user based on receiving a signal instructing to receive a deposit deposited by the user and to confirm the reservation on the network linked with the beauty shop.

In operation 570, the processor 420 provides a guide for the user to select whether to confirm a reservation by adding a deposit based on receiving a signal indicating that the deposit deposited by the user is insufficient on the network linked with the beauty shop. and transmits information on the deposit added based on the user input that selects to confirm the reservation by adding the deposit to the network linked with the beauty shop, and on the network linked with the beauty shop, the user additionally Reservation may be confirmed and reservation information may be displayed to the user based on receiving a deposited deposit and receiving a signal instructing reservation to be confirmed.

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. You can command the device. 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 (5)

In electronic devices,
memory; and
contains a processor;
The processor
Receives beauty shop information from the server and provides information about the desired beauty shop location, time, and desired service type to the user,
Based on the user's selection, the location of the plurality of beauty shops desired by the user, the time desired to receive the service, and the desired service type are determined, the determined information is transmitted to the server, and a request to pay a deposit for the service is provided. and
Whether the service can be provided from the server at the time the user desires among the plurality of beauty shops desired by the user, whether the service desired by the user can be provided, whether the location of the beauty shovel from the user's location is within a specified level and receive information about pricing terms and display them to the user;
Locations of a plurality of beauty shops are displayed on a map based on the location of the user, and when the user selects at least one of the beauty shops mapped and displayed on the navigation map, the service can be provided at the time the user desires. A guide asks whether or not the user wants to provide the desired service, provides information about the distance from the user's current location to the beauty shovel, traffic information and price conditions, and executes a reservation at the selected beauty shovel. display,
Information that determines the beauty shop to make a reservation based on user selection, receives the cost for the service the user wants from the network linked with the determined beauty shop, and asks whether or not to confirm the reservation after receiving the deposit deposited by the user. send,
Receiving a deposit from a user on a network linked to the beauty shop and receiving a signal instructing to confirm the reservation confirms the reservation and displays reservation information to the user;
Based on receiving a signal indicating that the user's deposit is insufficient on the network linked to the beauty shop, a guide is displayed to the user to select whether to confirm the reservation by adding a deposit, and by adding the deposit, Information on the deposit added based on the user input to confirm the reservation is transmitted over the network linked with the beauty shop, and the user receives the deposit additionally deposited on the network linked with the beauty shop to confirm the reservation. Based on receiving a signal instructing what to do, confirm the reservation and display the reservation information to the user;
If the size of the cost for the service exceeds the user's deposit, an additional amount is requested, and if the size of the cost for the service is smaller than the user's deposit, the difference is paid to the designated user's account,
Request an evaluation of the services provided;
Register the user's evaluation on the server,
Provides classification information from the server to the user based on the dog hairdresser's activity area, time and service;
When the user selects a dog hairdresser based on the classification information, the time and service are transmitted to the server along with text information including at least one of the desired service type, precautions, and requests,
Enter the desired cost for the service using the application, transmit information on the cost to the dog groomer selected by the user, and deposit an amount equivalent to 50% of the desired cost for the service to a designated account or server ask for a deposit,
The electronic device for confirming the reservation when the user's deposit is confirmed and the final selection is confirmed by the selected dog hairdresser.
delete delete According to claim 1,
The processor
Storing information on the user's identity authentication information, beauty shop use history, beauty shop use reviews, evaluation ratings, and no-show history in the memory and transmitting it to the server;
Based on receiving information indicating that the user has not appeared at the beauty shop for more than a specified time after the reservation is confirmed from the server, notification information including the reservation time and reservation details is displayed to the user,
An electronic device for controlling remittance of the user's deposit to a selected beauty shop or pet hairdresser based on the user's determination that there is no response from the user for a specified time after displaying the notification information or that the user cancels the reservation.
According to claim 1,
The processor
Receiving information including location information of a plurality of beauty shops, operation time information of the plurality of beauty shops, reservation information, and a list of dog hairdressers working at the plurality of beauty shops from the server;
When the reservation is confirmed based on the user input, real-time traffic information is received and the shortest route from the current location of the electronic device to the selected beauty shop, public transportation information, and fare information are provided;
If the expected arrival time to the beauty shop is later than the reservation time due to traffic conditions, information related to the selected beauty shop is provided,
An electronic device that receives, on the server, selection information on whether to cancel a reservation related to the beauty shovel or waits for the user to arrive and provides the service, and displays the selected information to the user.

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