KR20170032748A - System, method and computer program for mobile electronic payment considering user's intention - Google Patents

System, method and computer program for mobile electronic payment considering user's intention Download PDF

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KR20170032748A
KR20170032748A KR1020150130534A KR20150130534A KR20170032748A KR 20170032748 A KR20170032748 A KR 20170032748A KR 1020150130534 A KR1020150130534 A KR 1020150130534A KR 20150130534 A KR20150130534 A KR 20150130534A KR 20170032748 A KR20170032748 A KR 20170032748A
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user
terminal
purchase
information
behavior
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KR1020150130534A
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Korean (ko)
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신종호
이예리
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주식회사 케이티
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/34—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using cards, e.g. integrated circuit [IC] cards or magnetic cards
    • G06Q20/353—Payments by cards read by M-devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]

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Abstract

The present invention relates to a mobile electronic payment method, a system and a computer program, and more particularly, to a mobile electronic payment method, a system and a computer program. More specifically, The present invention relates to a mobile electronic payment method, system, and computer program capable of providing a more convenient electronic payment service to a user by providing an electronic payment procedure and further increasing a user's purchase probability.
The present invention provides a user information collecting method comprising the steps of: collecting user information about a behavior or a state of a terminal user in a product selling place; A purchase decision step of inputting information on the behavior or the state of the user to a deep learning neural network circuit to determine the purchase intention of the user; And a terminal authentication step of automatically performing an authentication procedure for the terminal of the user when it is determined that the user is willing to purchase a commodity.

Description

[0001] The present invention relates to a mobile electronic payment method, a system, and a computer program considering a user's intention,

The present invention relates to a mobile electronic payment method, a system and a computer program, and more particularly, to a mobile electronic payment method, a system and a computer program. More specifically, The present invention relates to a mobile electronic payment method, system, and computer program capable of providing a more convenient electronic payment service to a user by providing an electronic payment procedure and further increasing a user's purchase probability.

Recently, as e-commerce becomes popular, various electronic payment technologies are used in online and mobile environments. For example, in an online environment using a personal computer (PC), electronic payment technology using ActiveX has been widely used. Recently, Fintech using a mobile terminal such as a smart phone has spread rapidly As a result, various electronic payment services have been attempted.

However, the conventional electronic payment technologies have a problem in that the authentication process for the user and the terminal is difficult and the payment process after the authentication is complicated, so that the user who performs the electronic settlement can be very troublesome.

Further, the conventional electronic settlement techniques are limited to passively perform predetermined authentication and payment procedures, and it is possible to provide an electronic settlement procedure suitable for a user's situation by grasping a situation or an intention of the user in advance, But it does not provide an active electronic payment service that can help to carry out electronic payment procedures. In particular, when the user's intention is grasped in advance and a more convenient electronic settlement procedure is provided, it is possible to enhance the purchase probability of the user.

Accordingly, it is required to provide a more convenient electronic settlement procedure to the user, furthermore, a measure to grasp the purchase intention of the user and to provide a more convenient electronic settlement procedure by reflecting the purchase intention. However, There is no solution yet.

Korean Patent Laid-Open Publication No. 10-2014-0034367 (published on Mar. 20, 2014)

SUMMARY OF THE INVENTION It is an object of the present invention to provide a mobile electronic settlement method, system, and computer program that can provide a user with an easier electronic settlement procedure.

It is another object of the present invention to provide a mobile electronic payment method, system, and computer program capable of identifying a purchase intention of a user and reflecting the purchase intention to provide a more convenient electronic payment procedure.

According to an aspect of the present invention, there is provided an electronic payment method, comprising: collecting user information about a behavior or a state of a terminal user at a merchandise selling place; A purchase decision step of inputting information on the behavior or the state of the user to a deep learning neural network circuit to determine the purchase intention of the user; And a terminal authentication step of automatically performing an authentication procedure for the user terminal when it is determined that the user intends to purchase a commodity.

At this time, in the terminal authentication step, the terminal and the server can automatically perform an authentication procedure using a smart card provided in the terminal.

Here, the terminal authentication step may include: the server transmitting an authentication procedure code to the terminal; Receiving authentication information of the smart card encrypted using the authentication procedure code from the terminal; And automatically performing the authentication procedure for the terminal using the authentication information of the smart card.

The purchase decision step may include a waiting step of waiting for a point of time when the predetermined purchase decision start condition is satisfied; And determining whether the user intends to purchase the product if the purchase decision process start condition is satisfied.

Here, in the determination waiting step, the user may wait for a time point when the user is located in a specific area within the merchandise selling place.

At this time, the specific area within the merchandise sale place can be calculated through machine learning on the information about the behavior or the state of the terminal user in the merchandise selling place.

Also, the deep learning neural network circuit may determine a purchase intention for the user by using a user purchase pseudo model that models a purchase intention of the user.

At this time, the user purchase pseudo-model may include a deep learning process in consideration of the information on the behavior or the state of a plurality of kinds of users, considering the spatial relevance to the product sale place, the temporal relevance, ≪ / RTI >

The method may further include a payment confirmation step of providing a screen asking whether the electronic payment is to be made to the user terminal if it is determined that the user is willing to purchase a product.

In addition, when it is determined that the user is willing to purchase a commodity, considering information on the behavior or the state of the user to the deep learning neural network circuit, And a product recommendation step of recommending the product.

According to another aspect of the present invention, there is provided an electronic payment method, comprising: a user information collection step of collecting information on a behavior or a state of a terminal user in a product sale place; A purchase decision step of inputting information on the behavior or the state of the user to a deep learning neural network circuit to determine the purchase intention of the user; And a terminal authentication step of automatically performing an authentication procedure for the user terminal if it is determined that the user intends to purchase a commodity.

A computer program according to another aspect of the present invention is characterized by being a computer program recorded on a computer-readable medium for executing each step of the above-described method in a computer.

According to another aspect of the present invention, there is provided an electronic payment system comprising: a user information collection unit for collecting information on a behavior or a state of a terminal user at a merchandise sale place; A purchase decision unit for inputting information on the behavior or state of the user to a deep learning neural network circuit to determine the purchase intention of the user; And a terminal authentication unit for automatically performing an authentication procedure for the user terminal when it is determined that the user intends to purchase a commodity.

The mobile electronic payment method, system, and computer program according to an embodiment of the present invention can provide a user with a more convenient electronic payment procedure by using a smart card such as a USIM provided in a mobile terminal.

The mobile electronic payment method, system, and computer program according to an embodiment of the present invention recognize an action or a state of a terminal user and identify a purchase intention of the user based on the behavior or state, So that the user can more conveniently perform the electronic settlement procedure.

In addition, the mobile electronic payment method, system, and computer program according to an embodiment of the present invention can increase the purchase probability of a user by providing a more convenient purchase procedure reflecting a user's intention.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.
FIG. 1 is a flowchart of an electronic settlement method from a server perspective according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating information collection about a behavior or a state of a terminal user at a merchandise selling place according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating information collection about a behavior or a state of a terminal user at a merchandise selling place according to another embodiment of the present invention.
4 is a view for explaining a user purchasing pseudo model by deep learning that reflects a weight according to an embodiment of the present invention.
5 is an explanatory diagram of a learning process by deep learning according to an embodiment of the present invention.
FIG. 6 is a flowchart illustrating an operation of a user's decision to purchase a product using a user purchase pseudo-model according to an embodiment of the present invention.
7 is a flowchart illustrating an automatic authentication procedure according to an exemplary embodiment of the present invention.
Figure 8 is an illustration of product recommendations for a user in accordance with an embodiment of the present invention.
9 is a flowchart of an electronic settlement method in terms of a terminal according to an embodiment of the present invention.
10 is a configuration diagram of an electronic settlement system according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments will be described in detail below with reference to the accompanying drawings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

The terms first, second, etc. may be used to describe various components, but the components are not limited by the terms, and the terms are used only for the purpose of distinguishing one component from another Is used.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Exemplary embodiments of a mobile electronic payment method, system and computer program considering the intention of a voice user according to the present invention will now be described in detail with reference to the accompanying drawings.

First, FIG. 1 illustrates a flowchart of an electronic settlement method in terms of a server according to an embodiment of the present invention. 1, the electronic payment method according to an embodiment of the present invention may include user information collection step S110, purchase decision step S120, and terminal authentication step S130, And may further include recommendation step S140. Hereinafter, an electronic settlement method according to an embodiment of the present invention will be described in detail for each step.

First, in the user information collection step (S110), the server collects information on the behavior or the state of the terminal user in the merchandise sale place. Here, the merchandise selling place may be various places where the user can purchase the merchandise. For example, not only stores for displaying and selling merchandise but also digital signage including a merchandise selling function are located And public places such as streets.

In this step S110, the server senses the location and movement of the user using communication (NFC, Beacon, WiFi, etc.) with the terminal of the user, or transmits information on the operation of the user detected by the terminal Or information about the behavior or the state of the user can be collected by using a sensor provided at the merchandise selling place or the like.

For example, FIG. 2 illustrates a case where information on the behavior of a terminal user at a coffee shop is collected. As shown in FIG. 2, the server can recognize that the user of the terminal stays for a predetermined time in front of the counter via NFC (Near Field Communication), beacon, and WiFi communication with the terminal . Accordingly, the server can determine that the user has an intention to order a predetermined commodity such as coffee when the user stays in front of the cash register for a predetermined time (for example, one minute).

Further, the server may provide a list of goods recommending purchase to the terminal or a message to confirm purchase intention.

In addition, the server may receive information on the operation of the user detected by the terminal. For example, when the user enters the coffee shop and searches for contents of coffee using the terminal, the terminal may transmit the information about the search to the server so as to be used to determine the purchase intention of the user have. Further, the terminal may detect information on the movement, position, and status of the user and transmit the information to the server.

As another example, FIG. 3 illustrates the case of sensing a user's behavior in a table screen (FIG. 3 (a)) and a digital signage (FIG. 3 (b)). As shown in FIG. 3, the user may be provided with a guidance message for purchase of goods, and a method of detecting whether the user performs an action according to the guidance message may be used. In addition, the electronic settlement procedure can be easily performed at the user terminal.

Further, when the user senses approaching within a predetermined distance by a table screen, a digital signage, or the like, an appropriate guidance message may be presented according to the characteristics or circumstances of the user so as to assist the user in purchasing.

In addition, the purchase decision step (S110) may include a step of waiting for a time to satisfy a predetermined purchase decision start condition and a step of determining the purchase intention of the user if the purchase decision start condition is satisfied .

Since a considerable computational resource may be consumed in the operation of determining the purchase intention of the user by using the deep learning neural network circuit, To be limited. Accordingly, it is possible to greatly reduce the amount of computer resources required in the server or the like. Therefore, it is possible to efficiently process information on a plurality of users. Further, in some cases, do.

As an embodiment of the present invention, it is also possible to set, as the purchase decision start condition, a state in which the user waits for a time point at which the user is located in a specific area within the merchandise sales place in the determination wait step. For example, in FIG. 2, after the user sets the area in front of the cash register in the coffee shop, the user may wait until the user moves to the area before the cash register to determine the purchase intention of the user.

Further, the specific area within the merchandise sales place may be determined in advance by the system operator, or the purchasing intention of users is calculated through machine learning on the information on the behavior or the state of the terminal user in the merchandise selling place And then select all or some of them.

Next, in the purchase decision step S120, information on the behavior or the state of the user is input to a deep learning neural network circuit to determine the purchase intention of the user.

In one embodiment of the present invention, the deep learning neural network circuit may determine a purchase intention for the user using a user purchase pseudo model that models a purchase intention of the user.

At this time, the user intension model collects information on behaviors and states of a plurality of users and generates a structured database having a structured data structure and a user database Based on a feature set extracted from an unstructured DB having an informal data structure constituted from information on the characteristics of the objects. For example, the user purchase pseudo-model may be implemented using a neural network and a Bayesian network. In the deep learning process, parameter tuning is performed, .

4, the user purchase pseudo-model includes information on a behavior or a state of a plurality of kinds of users, a weighting value according to a spatial relation with the product sale place, a temporal relevance, and a relevance to the user The user can be updated in real time or periodically by taking a deep learning process and the information on the behavior or the state of the user can be accumulated thereby improving the accuracy of the purchase decision of the user.

For example, when updating the user purchase pseudo-model for user A, the weight is set to "up" for information about the behavior or state generated by user A within a predetermined time range and distance range And assigns a weight to "medium" when it is generated by the user A but deviates from the predetermined time range or distance range, and transmits a structured DB or web (Web) composed of information on a plurality of users, ) And the information of the unstructured DB that is collected and constructed can be updated by updating the user purchasing pseudo model by assigning a weight to the information.

As a more specific example, the case where the weight is given as "upper ", may be information obtained by the user A purchasing the product at the specific B store or information retrieved by the user A at the particular B store. Accordingly, information on the behavior of the user A in the particular store B (purchase, search for goods, SNS, time to stay in a specific area of a store, etc.) is weighted differently according to information listed in time order or the like Can be updated.

Even if the user A purchases at the specific B store, the purchasing information that has elapsed for a predetermined period of time (for example, one week) or the information details retrieved from the user B outside the specific B store can be given a weighted value. For example, information about a product that the user A searched before arriving at the particular store B, or information that the user A displayed "likes" in the social network (SNS) .

Furthermore, the range of time and space can be set in various ways in consideration of various circumstances, and the weight according to the time and space can be given variously according to need.

If the information is not generated by the user A, the weight can be given as "lower ". For example, when a user purchasing pseudo-model is created for the first time, there may be no information for each user. Therefore, a structured database constructed from information on a large number of users, an unstructured database unstructured DB), it is possible to generate a user purchase pseudo model for a general case, and to update the user purchase pseudo model by collecting information generated by the user A and considering the weight. For example, an unstructured DB can be constructed using information clustering based on a user profile on postings on SNS, information on news sites, and the like, and a weight can be assigned to the unstructured DB. have.

In addition, FIG. 5 illustrates a learning process by deep learning according to a set of stepwise features according to an embodiment of the present invention. That is, as shown in FIG. 5, after dividing a set of features necessary for recognizing the user's intention into a large class, a middle class, and a small class, and deep-running combinations thereof, And the depth learning learning is repeatedly updated according to the weighting index of Equation (1) as time elapses, so that the performance can be continuously improved.

Figure pat00001

(w: weight, v: value, h: transfer value, recon: recognized result, data: actual data,

At this time, information on the behavior or state of the user collected by the server can be used as data used as a feature set used for the deep learning. The information on the behavior or the state of the user includes information on the location or movement of the user, which is obtained through NFC, beacon or Wi-Fi communication between the user terminal and the server, a search phrase input by the user to the terminal, Posting, and the like, information on the behavior or state of the user collected by the sensor included in the merchandise selling place, and the like.

Accordingly, when the user stands in front of the counter for a predetermined period of time (for example, one minute) or when a specific gesture operation is performed such as touching the terminal at a specific point, shaking the terminal, It is possible to deduce that there is an intention to purchase a commodity.

The user purchase pseudo model generated or updated by the deep learning and the database of information on the user behavior and the state can be separately stored and can be updated in real time or periodically.

FIG. 6 is a flowchart illustrating an operation of a user's decision to purchase a product using a user purchase pseudo-model according to an embodiment of the present invention. As shown in FIG. 6, structured DBs and unstructured DBs are constructed and data sets are formed by using data mining and deep learning. A user intention model is constructed. The server can detect the user's behavior and store it in a database (Behavior patterns DB). Information on the behavior of the user can be used to determine whether the user is willing to purchase using the user purchase pseudo-model. If it is determined that the user is willing to purchase, the server and the terminal can automatically perform authentication for the terminal, and then provide a screen for asking whether the electronic settlement is to be made to the terminal of the user, The user can recommend one or more products to the user terminal in consideration of the purchase intentions for each product calculated using information on the behavior of the user. Then, the electronic settlement procedure is performed using the terminal. Also, the information on which the user has purchased the product may be used for updating the user purchase pseudo-model again.

In the terminal authentication step (S130), if it is determined that the user intends to purchase a commodity, the terminal automatically performs the authentication procedure for the terminal.

FIG. 7 illustrates a flowchart of an automatic authentication procedure according to an exemplary embodiment of the present invention. As shown in FIG. 7, if the user's purchase intention is confirmed through step S120, the server transmits an authentication procedure code to the terminal. The terminal encrypts the authentication information of the smart card for terminal authentication such as a USIM using the authentication procedure code, and transmits the encrypted information to the server. The server decrypts information received from the terminal and performs authentication on the terminal from a communication company or the like. Through the above-described process, the server can automatically perform the authentication procedure for the terminal, and the server can then proceed with the settlement procedure for the goods using the terminal.

1, an electronic payment method according to an exemplary embodiment of the present invention includes a step of inputting information on an action or a state of the user into a deep learning neural network circuit, And recommending one or more products to the user (S140). At this time, the server may further provide a payment confirmation screen for providing a screen asking whether the electronic payment is made to the user terminal.

FIG. 8 illustrates a case where a recommended product is presented to a user terminal according to an embodiment of the present invention. As can be seen from FIG. 8, the server can recommend one or more products to the user in consideration of the calculated purchase intention score for each product, and can provide an electronic settlement procedure therefor. If the user selects one of the recommended items and proceeds with the settlement procedure, the user can provide the electronic settlement environment used in the past, and if there is no electronic settlement environment used in the past, the settlement environment can be set. A history of purchasing the product or the product purchased by the user but not purchasing it may be used for updating the user purchase pseudo model again.

In addition, various methods for improving the convenience of the user such as SSO (Single Sigh On) may be applied together with the present invention.

FIG. 9 illustrates a flowchart of an electronic settlement method in terms of a terminal according to another embodiment of the present invention. 9, an electronic settlement method according to another embodiment of the present invention includes a user information collection step of collecting information on a behavior or a state of a terminal user in a product sale place S210), a purchase decision step (S220) of inputting information on the behavior or state of the user to a deep learning neural network circuit to determine the purchase intention of the user, and a step of determining The terminal authentication step S230 for automatically performing an authentication procedure for the terminal of the user may be performed. Further, information on the behavior or the state of the user may be input to a deep learning neural network circuit The recommendation step (S240) of recommending one or more products to the user in consideration of the calculated purchase intention score for each product It may be.

The electronic settlement method from the terminal perspective is somewhat different from the electronic settlement method in terms of servers in that each step is performed at the terminal of the user, and the operation principle and operation are similar to each other, Therefore, a detailed description will be omitted and the electronic settlement method from the server side described above can be referred to.

A computer program according to another aspect of the present invention is characterized by being a computer program recorded on a computer-readable medium for executing each step of the electronic payment method described above on a computer.

The medium on which the computer-readable program is recorded may include program commands, data files, data structures, and the like, alone or in combination. The program instructions recorded on the computer readable medium may be those specially designed and constructed for the present invention or may be known and used by those of ordinary skill in the computer software arts. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROM and DVD, magneto-optical media such as floptical disks, medium and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like, and may be recorded in a storage device of a terminal such as a smart phone or a PC. Examples of program instructions include high-level language code that can be executed by a computer using an interpreter, as well as machine accords such as those produced by a compiler. The hardware device may be modified into one or more software modules for performing the processing according to the present invention, and vice versa.

Finally, FIG. 10 illustrates a configuration diagram of an electronic payment system 100 according to an embodiment of the present invention. 10, the electronic payment system 100 according to an embodiment of the present invention includes a user information collecting unit 110 for collecting information on a behavior or a state of a terminal user at a merchandise selling place, (120) for inputting information on the behavior or state of the user to the deep learning neural network circuit to determine the purchase intention of the user, and a controller And a terminal authentication unit 130 for automatically performing an authentication procedure for the terminal of the user terminal 100. Further, the terminal authentication unit 130 may be configured to input information on the behavior or state of the user to a deep learning neural network circuit, And a product recommendation unit 140 for recommending one or more products to the user in consideration of purchase intention scores.

More specifically, the electronic settlement system 100 according to an embodiment of the present invention may be implemented as a server, or may be configured to operate on a user's terminal. In some cases, Or may be configured to function separately.

The electronic settlement system 100 according to the embodiment of the present invention is similar to the electronic settlement method described above in operation principle and operation and can be easily implemented and implemented by a typical technician, You can refer to the payment method.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments described in the present invention are not intended to limit the technical spirit of the present invention but to illustrate the present invention. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents thereof should be construed as being included in the scope of the present invention.

100: Electronic payment system
110: User information collecting unit
120: Purchase decision unit
130:
140: Goods

Claims (13)

A user information collecting step of collecting information on a behavior or a state of a terminal user at a merchandise selling place;
A purchase decision step of inputting information on the behavior or the state of the user to a deep learning neural network circuit to determine the purchase intention of the user; And
And a terminal authentication step of automatically performing an authentication procedure for the terminal of the user when it is determined that the user intends to purchase a commodity. The method according to claim 1,
In the terminal authentication step,
Wherein the terminal and the server automatically perform an authentication procedure using a smart card provided in the terminal. 3. The method of claim 2,
The terminal authentication step includes:
The server transmitting a code for an authentication procedure to the terminal;
Receiving authentication information of the smart card encrypted using the authentication procedure code from the terminal; And
And automatically performing an authentication procedure for the terminal using the authentication information of the smart card. The method according to claim 1,
Wherein the purchase decision step comprises:
A judgment waiting step of waiting for a point of time when a predetermined purchase decision judgment start condition is satisfied; And
And judging whether the user intends to purchase a commodity if the purchase decision process start condition is satisfied. 5. The method of claim 4,
In the judgment waiting step,
And waits for a time point when the user is located in a specific area within the merchandise selling place. 6. The method of claim 5,
The specific area within the merchandise sales place is,
Wherein the information is calculated through machine learning on information on the behavior or state of the terminal user at the merchandise selling place. The method according to claim 1,
The deep learning neural network circuit comprises:
Wherein the user is determined to purchase the product using the user purchase pseudo-model that models the purchase intention of the user. 8. The method of claim 7,
The user purchase pseudo-model includes:
For information on the behavior or state of a plurality of types of users,
Taking into account the weighting factors according to the spatial relevance, the temporal relevance, and the relevance to the user,
And then updated through a deep learning process. The method according to claim 1,
If it is determined that the user is willing to purchase a commodity,
Further comprising a payment confirmation step of providing a screen asking whether the electronic payment is to be made to the terminal of the user. The method according to claim 1,
If it is determined that the user is willing to purchase a commodity,
Taking into consideration the product purchase intention score calculated by inputting information on the behavior or state of the user to a deep learning neural network circuit,
And recommending one or more products to the user. A user information collection step in which the terminal collects information on a behavior or a state of a terminal user in a product sale place;
A purchase decision step of inputting information on the behavior or the state of the user to a deep learning neural network circuit to determine the purchase intention of the user; And
And a terminal authentication step of automatically performing an authentication procedure for the terminal of the user when it is determined that the user intends to purchase a commodity. A computer program recorded on a computer-readable medium for executing the steps of any one of claims 1 to 11 in a computer. A user information collecting unit for collecting information on a behavior or a state of a terminal user at a merchandise selling place;
A purchase decision unit for inputting information on the behavior or state of the user to a deep learning neural network circuit to determine the purchase intention of the user; And
And a terminal authentication unit for automatically performing an authentication procedure for the terminal of the user when it is determined that the user intends to purchase a commodity.
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