KR102397385B1 - Method for providing online to offline based customized coupon service using storage coupon - Google Patents

Abstract

A method for providing a customized coupon service using an O2O-based storage coupon is provided, comprising the steps of collecting stored stored coupons after use in a user terminal, and checking whether the collected stored coupons are duplicate coupons previously registered in personal data of the user terminal Step, when the stored coupon is a previously registered duplicate coupon, a preset weight is given to the repeated attribute information while adding at least one attribute information of the stored coupon to personal data, and the storage coupon is not a previously registered duplicate coupon , adding at least one attribute information of the stored coupon to personal data, and calculating the personal preference ranking of the user terminal from coupon data corresponding to the at least one attribute information of the storage coupon, and based on the personal preference ranking to the user terminal and providing at least one coupon.

Description

How to provide customized coupon service using O2O-based storage coupons {METHOD FOR PROVIDING ONLINE TO OFFLINE BASED CUSTOMIZED COUPON SERVICE USING STORAGE COUPON}

The present invention relates to a method of providing a customized coupon service using an O2O-based storage coupon. Based on the coupon used by the user, the user's preference is traced back and the business owner is provided with a platform that can provide information on the customer base who will use the issued coupon. do.

In recent years, the rapid growth of the online advertising market has been remarkable in the advertising world, and online advertising, centered on mobile advertising, has overtaken broadcast advertising and occupies the first place in the advertising market share. However, as a result of a user perception survey on online advertising, it was found that more than half of the respondents had negative thoughts about online advertising. for a reason Since negative perceptions of online advertisements can cause users to avoid advertisements, which can greatly reduce the effectiveness of advertisements, new types of online advertisements have been devised to minimize users' avoidance of advertisements and to increase their effectiveness. A typical example of this is online behavioral advertising. Online customized advertisement refers to advertisements that collect and analyze information about online users' activities on the website and provide customized contents to the relevant users. In other words, unlike general online advertisements that are provided randomly, advertisements suitable for users' interests, interests, and tendencies are individually selected and delivered.

At this time, a method of providing a customized coupon by reflecting the user's preference has been researched and developed. In No. 0005187 (published on January 14, 2016), matching coupon banners are selected based on buyer attribute information, transmitted to the online shopping mall server, and the coupon banner inserted in the purchase completion page delivered to the buyer A configuration for receiving a coupon transmission request from the online shopping mall server, transmitting the requested coupon to the buyer as a text message, and performing billing processing for the advertiser based on the number of text messages sent, and the food coupon recommendation agent according to user preference The response information for calculating the weight is received and transmitted to the food coupon recommendation server, and the food coupon recommended by the food coupon recommendation server is displayed, and the food coupon recommendation server stores information about the food coupon, but coupon information through the stored food coupon Calculates the priority based on the priority of the base and the priority based on the coupon evaluation, calculates the final priority by combining the priority based on the coupon information and the priority based on the coupon evaluation, and recommends food coupons according to the calculated final priority. A possible configuration is disclosed.

However, in the former case, user's online activity information is essential in order to provide online customized coupons. In this process, personal information is collected without the user's consent and the information can be used for purposes other than advertisements, and online customized coupons are not available online. Even if it is designed to reduce users' reluctance to interfere with their activities, if the same coupons are followed for each website they visit, they may aggravate fatigue rather than general advertisements, and this fatigue is pointed out as a problem in terms of digital labor. do. In the latter case, based on the specificity of food, even if you eat your favorite food once, you will not eat it for a while if you eat it again. They are more likely to reject online customized coupons because they think they are negative rather than useful. Accordingly, research and development of a platform that can provide coupons is required in a direction to increase acceptance perception without abusing users' personal information and to reduce fatigue of online customized advertisements.

An embodiment of the present invention defines personalization from the user's point of view as perceived personalization rather than actual personalization from the marketer's point of view, so that the effect of the message is measured based on the perceived personalization of the user's point of view. By identifying the user's preference through the coupons the user already has, and determining the matching degree with the coupon to be issued by each franchisee based on the identified preference using decision tree-based machine learning, the franchisee The user can receive the result of calculating the expected user's information, and while minimizing the use of personal information, it is possible to further increase the effect of coupon delivery by delivering a message that the user feels has been personalized from the point of view of the user who is the recipient of the message, It is possible to provide a method of providing a customized coupon service using an O2O-based storage coupon that can zero the fatigue caused by coupon advertisements by estimating consumption patterns with coupon storage and coupon usage patterns to match the timing of coupon provision. However, the technical task to be achieved by the present embodiment is not limited to the technical task as described above, and other technical tasks may exist.

As a technical means for achieving the above-described technical problem, an embodiment of the present invention includes the steps of collecting stored storage coupons after use in the user terminal, whether the collected storage coupons are duplicate coupons previously registered in personal data of the user terminal a step of checking whether or not the stored coupon is a previously registered duplicate coupon, adding at least one attribute information of the stored coupon to personal data, giving a preset weight to the repeated attribute information, and If it is not a duplicate coupon, adding at least one attribute information of the stored coupon to personal data and calculating the personal preference ranking of the user terminal from the coupon data corresponding to the at least one attribute information of the storage coupon, and in the personal preference ranking based on the step of providing at least one coupon to the user terminal.

According to any one of the above-described problem solving means of the present invention, the effect of the message is defined as perceived personalization rather than actual personalization from the marketer's point of view as the user's point of view. Decision tree-based machine learning to determine the user's preference through the coupons the user already has, and match the coupon to be issued by each franchisee based on the identified preference so that it can be measured based on personalization. By understanding this, the franchisee can receive the result of calculating the expected user's information, and while minimizing the use of personal information, the message that the user feels personalized is delivered from the perspective of the user who is the message recipient, thereby further enhancing the effect of coupon delivery. It can further increase, and by estimating consumption patterns with coupon storage and coupon usage patterns, it is possible to match the timing of coupon provision to zero fatigue from coupon advertisements. Effective local advertising can be done, and consumers can effectively receive desired benefits.

1 is a view for explaining a system for providing a customized coupon service using an O2O-based storage coupon according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating a customized coupon service providing server included in the system of FIG. 1 .
3 and 4 are diagrams for explaining an embodiment in which a customized coupon service using an O2O-based storage coupon according to an embodiment of the present invention is implemented.
5 is an operation flowchart illustrating a method of providing a customized coupon service using an O2O-based storage coupon according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement them. However, the present invention may be embodied in several different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is "connected" with another part, this includes not only the case of being "directly connected" but also the case of being "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated, and one or more other features However, it is to be understood that the existence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded in advance.

The terms "about", "substantially", etc. to the extent used throughout the specification are used in or close to the numerical value when manufacturing and material tolerances inherent in the stated meaning are presented, and are intended to enhance the understanding of the present invention. To help, precise or absolute figures are used to prevent unfair use by unconscionable infringers of the stated disclosure. As used throughout the specification of the present invention, the term "step of (to)" or "step of" does not mean "step for".

In this specification, a "part" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. In addition, one unit may be implemented using two or more hardware, and two or more units may be implemented by one hardware. Meanwhile, '~ unit' is not limited to software or hardware, and '~ unit' may be configured to be in an addressable storage medium or to reproduce one or more processors. Thus, as an example, '~' denotes components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. The functions provided in the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'. In addition, components and '~ units' may be implemented to play one or more CPUs in a device or secure multimedia card.

Some of the operations or functions described as being performed by the terminal, apparatus, or device in the present specification may be performed instead of by a server connected to the terminal, apparatus, or device. Similarly, some of the operations or functions described as being performed by the server may also be performed in a terminal, apparatus, or device connected to the server.

In this specification, some of the operations or functions described as mapping or matching with the terminal means mapping or matching the terminal's unique number or personal identification information, which is the identification data of the terminal. can be interpreted as

In the present specification, a customized coupon service using a stored coupon is based on a location based service (LBS) based on the user's location, or may be an LBS-based service.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a view for explaining a system for providing a customized coupon service using an O2O-based storage coupon according to an embodiment of the present invention. Referring to FIG. 1 , the system 1 for providing a customized coupon service using an O2O-based storage coupon includes at least one user terminal 100 , a customized coupon service providing server 300 and at least one affiliated store terminal 400 . can do. However, since the customized coupon service providing system 1 using the O2O-based storage coupon of FIG. 1 is only an embodiment of the present invention, the present invention is not limitedly interpreted through FIG. 1 .

At this time, each component of FIG. 1 is generally connected through a network 200 . For example, as shown in FIG. 1 , at least one user terminal 100 may be connected to the customized coupon service providing server 300 through the network 200 . In addition, the customized coupon service providing server 300 may be connected to at least one user terminal 100 and at least one affiliate store terminal 400 through the network 200 . In addition, the at least one affiliated store terminal 400 may be connected to the customized coupon service providing server 300 through the network 200 .

Here, the network refers to a connection structure in which information exchange is possible between each node, such as a plurality of terminals and servers, and an example of such a network includes a local area network (LAN), a wide area network (WAN: Wide Area Network), the Internet (WWW: World Wide Web), wired and wireless data communication networks, telephone networks, wired and wireless television networks, and the like. Examples of wireless data communication networks include 3G, 4G, 5G, 3rd Generation Partnership Project (3GPP), 5th Generation Partnership Project (5GPP), Long Term Evolution (LTE), World Interoperability for Microwave Access (WIMAX), Wi-Fi (Wi-Fi) , Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth (Bluetooth) network, NFC ( Near-Field Communication) networks, satellite broadcast networks, analog broadcast networks, Digital Multimedia Broadcasting (DMB) networks, and the like are included, but are not limited thereto.

In the following, the term at least one is defined as a term including the singular and the plural, and even if the term at least one does not exist, each element may exist in the singular or plural, and it is evident that the singular or the plural may be implied. something to do. In addition, that each component is provided in singular or plural may be changed according to embodiments.

At least one user terminal 100 searches for a coupon using a web page, an app page, a program or an application related to a customized coupon service using an O2O-based storage coupon, or provides the coupon of the day from the customized coupon service providing server 300 It may be a terminal of a buyer, a consumer, a user, a user, or a customer that receives and stores the coupon used in the at least one affiliated store terminal 400 as a stored coupon, and provides a review for the affiliated store where the stored coupon is used. At this time, the user terminal 100 may transmit a location corresponding to the GPS signal or a location input from the user terminal 100 to the customized coupon service providing server 300 , and the customized coupon service providing server 300 may provide a location or A coupon corresponding to the input location may be provided.

Here, the at least one user terminal 100 may be implemented as a computer that can access a remote server or terminal through a network. Here, the computer may include, for example, navigation, a laptop equipped with a web browser, a desktop, and a laptop. In this case, the at least one user terminal 100 may be implemented as a terminal capable of accessing a remote server or terminal through a network. At least one user terminal 100 is, for example, as a wireless communication device that guarantees portability and mobility, navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS(Personal Handyphone System), PDA(Personal Digital Assistant), IMT(International Mobile Telecommunication)-2000, CDMA(Code Division Multiple Access)-2000, W-CDMA(W-Code Division Multiple Access), Wibro(Wireless Broadband Internet) ) terminal, a smart phone, a smart pad, a tablet PC, etc. may include all kinds of handheld-based wireless communication devices.

The customized coupon service providing server 300 may be a server that provides a customized coupon service web page, app page, program or application using an O2O-based storage coupon. In addition, the customized coupon service providing server 300 collects categories or locations from at least one user terminal 100 and provides a corresponding today's coupon (coupon list), or when a keyword is searched, keywords, locations and After searching for a coupon corresponding to a category, it may be a server that similarly provides today's coupon. To this end, the customized coupon service providing server 300 may be a server that receives a coupon from at least one affiliated store terminal 400 and generates coupon data using attribute information in the registered coupon. In this case, the coupon data may be data that is then combined with personal information to form personal data. That is, it may be a server that collects personal information from coupon information selected by the individual, rather than actually collecting personal information from the individual. For example, if user A used coupon B at merchant C, by creating personal data such as [User A-C merchant location-purchased product category-coupon type], instead of actually using personal information, the user As it is a method that completes personal data by tracing backwards through coupons, it does not violate the GDPR (General Data Protection Regulation) as well as Korea's Personal Information Protection Act. In this case, the category of the purchased product may be an affiliate store and/or a category of the purchased product.

The customized coupon service providing server 300 builds the personal data accumulated in this way into big data, and inputs the coupon information of each affiliated store terminal 400, that is, when a query is made, the expected consumer (user) who is likely to find each affiliated store It may be a server that extracts information so that each affiliate store can identify information of customers to be targeted. In addition, the customized coupon service providing server 300 receives a review from at least one user terminal 100. In addition, each affiliate store terminal 400 can give a rating to the account of the user terminal 100, To solve the problem of Gapjil, which has become a recent problem. However, when a low rating is given from each affiliated store terminal 400 to the user terminal 100, the reason may be to attach CCTV or testimony, but this does not exclude giving the rating the same as the user. It is not limited to an Example. In addition, the customized coupon service providing server 300 may be a server that categorizes and clusters big data through collection, pre-processing, analysis, and the like, and then learns the big data. In addition, the customized coupon service providing server 300 performs data learning using a deep learning artificial neural network algorithm for tagging the identifier from the image data and image data in order to extract the identifier from the image data or image data, which are unstructured data. It can be a server that In addition, the customized coupon service providing server 300 may be a server that tags or extracts an identifier from an image, image, etc. input later according to a learning result.

Here, the customized coupon service providing server 300 may be implemented as a computer capable of accessing a remote server or terminal through a network. Here, the computer may include, for example, navigation, a laptop equipped with a web browser, a desktop, and a laptop.

The at least one merchant terminal 400 may be a terminal of an online or offline merchant using a web page, an app page, a program or an application related to a customized coupon service using an O2O-based storage coupon. At this time, the type of business of the affiliated store is not limited, and anything may be possible. In addition, the at least one affiliate store terminal 400 may be a terminal that registers an affiliate store by uploading a trade name, category, location, photo, menu, coupon type, and the like. In addition, the at least one affiliated store terminal 400 may be a terminal that obtains information on the prospective customer base by using a coupon uploaded by the affiliated store as an input value. In addition, the at least one affiliate store terminal 400 may be a terminal that can give a rating to a user who uses a coupon at the affiliate store. The rating of the affiliate store and the rating of the user may be set to be shared with each other.

Here, the at least one affiliated store terminal 400 may be implemented as a computer that can access a remote server or terminal through a network. Here, the computer may include, for example, navigation, a laptop equipped with a web browser, a desktop, and a laptop. In this case, the at least one affiliated store terminal 400 may be implemented as a terminal capable of accessing a remote server or terminal through a network. At least one affiliated store terminal 400 is, for example, a wireless communication device that guarantees portability and mobility, such as navigation, Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), PHS(Personal Handyphone System), PDA(Personal Digital Assistant), IMT(International Mobile Telecommunication)-2000, CDMA(Code Division Multiple Access)-2000, W-CDMA(W-Code Division Multiple Access), Wibro(Wireless Broadband Internet) ) terminal, a smart phone, a smart pad, a tablet PC, etc. may include all kinds of handheld-based wireless communication devices.

Figure 2 is a block diagram for explaining a customized coupon service providing server included in the system of Figure 1, Figures 3 and 4 is a customized coupon service using an O2O-based storage coupon according to an embodiment of the present invention is implemented It is a diagram for explaining an embodiment.

Referring to FIG. 2 , the customized coupon service providing server 300 includes a collection unit 310 , a confirmation unit 320 , an addition unit 330 , a provision unit 340 , a budget customer calculation unit 350 , and a coupon register. (360) and may include a review management unit (370).

The customized coupon service providing server 300 or other server (not shown) that operates in conjunction with at least one user terminal 100 and at least one affiliate store terminal 400 according to an embodiment of the present invention provides O2O-based storage coupons When transmitting a customized coupon service application, program, app page, web page, etc. using You can install or open programs, app pages, web pages, and more. In addition, a service program may be driven in at least one user terminal 100 and at least one affiliate store terminal 400 by using a script executed in a web browser. Here, the web browser is a program that enables the use of a web (WWW: World Wide Web) service, and refers to a program that receives and displays hypertext written in HTML (Hyper Text Mark-up Language), for example, Netscape. , Explorer, Chrome, and the like. In addition, the application means an application on the terminal, for example, includes an app (App) executed in a mobile terminal (smartphone).

Referring to FIG. 2 , the collection unit 310 may collect stored coupons after being used in the user terminal 100 . In this case, the stored coupon means a coupon stored in the user terminal 100 after being used in the affiliate store. In general, storing a coupon means storing a coupon before use, but in an embodiment of the present invention, storing a coupon means a coupon used after use. At this time, the user terminal 100 controls to store the coupons after being used, and the reason for collecting the stored coupons in the collection unit 310 is that if the user only downloads and does not use the coupon, the user's preference This is because there is a possibility that an error may occur in accurately figuring it out.

Accordingly, in an embodiment of the present invention, the coupon used in the user terminal 100, that is, the coupon that generated the payment is stored in the user terminal 100 as a storage coupon, and the collection unit 310 collects it. Afterwards, the user's preferences and user's personal information can be tracked in reverse. For example, assuming that user A uses coupon B at coffee shop B, coupon C at yoga center C, and coupon D at supermarket D, collect user A's consumption log to prevent excessive privacy violations. By tracing the coupon usage log without triggering, it is possible to conversely understand user A's preference.

At this time, since each affiliate store (coffee shop, yoga studio, supermarket) includes location information, user A's action radius can also be traced backwards. In addition, since the type of coupon, that is, the coupon type is also determined, by analyzing the type of coupon used, it is possible to reversely determine whether a discount is desired or an additional service is desired. In addition, when the coupon is used and extracted together in synchronization, the user's behavioral pattern or route can be tracked according to time. Accordingly, the collection unit 310 collects the stored coupons that have already been used in the user terminal 100 .

The check unit 320 may check whether the collected stored coupons are duplicate coupons previously registered in the personal data of the user terminal 100 .

The adding unit 330, if the storage coupon is a pre-registered duplicate coupon, adds at least one attribute information of the stored coupon to personal data and gives a preset weight to the repeated attribute information, and the stored coupon is pre-registered. If it is not a duplicate coupon, at least one attribute information of the stored coupon may be added to the personal data. When the stored coupon is a pre-registered duplicate coupon, when a preset weight is given to the repeated attribute information while adding at least one attribute information of the stored coupon to personal data, the preset weight is repeated as many times as the repeated attribute information For example, a number greater than 1 may be assigned a weight. However, the weight is not limited to the above-described numerical value and may be changed according to embodiments.

The at least one attribute information may include a location of an affiliated store of an affiliated store that issued the storage coupon, a category of products sold by the affiliated store, and a coupon type that is a type of service provided by the stored coupon. In addition, the type of service provided by the storage coupon may be a discount or additional service. And, the personal preference ranking is a percentage proportional to the number of stored coupons, and the affiliate store location, category, and coupon type are expressed as a percentage, and the personal preference ranking can be given by arranging in the order of increasing the number expressed as a percentage, an example of may be as shown in Table 1 below. However, the present invention is not limited thereto, and Table 1 is merely a simple example for convenience of explanation, and it will be apparent that an embodiment of the present invention is not limited thereto.

Privacy Merchant Location category Coupon type preference ranking blush
female
31 years old Gangnam-gu 70% Food 70%
Culture 20%
market 10% discount 70%
Extra 30% Location - Gangnam-gu
Category - Food
type-discount Yongsan-gu 20% Culture 70%
Food 20%
market 10% Extra 70%
discount 30% Seocho-gu 10% Market 70%
Food 20%
market 10% Extra 70%
discount 30% Hong Gil Dong
male
25 years old Songpa-gu 60% Food 80%
Market 20% Extra 95%
discount 5% Location - Songpa-gu
Category - Food
type-add Gangdong-gu 30% Food 90%
Culture 10% discount 60%
Extra 40% Geumcheon-gu 10% Market 90%
Clothing 10% discount 99%
Extra 1%

At this time, personal information is extracted from the user terminal 100, but the affiliate store location, category, and coupon type are extracted from the stored coupon. Accordingly, the user's preference can be inferred by analyzing the coupon used without using the user's personal information. If the save coupon has already been registered, that is, if the user went to coffee shop A with a coupon and used the same coupon at coffee shop A, it means that the user frequently visits coffee shop A and likes it. A weight may be assigned to reflect , and a number of weights greater than 1 may be given as described above. The providing unit 340 calculates a personal preference ranking of the user terminal 100 from coupon data corresponding to at least one attribute information of the stored coupon, and based on the personal preference ranking, at least one coupon to the user terminal 100 can provide The providing unit 340 calculates a personal preference ranking of the user terminal 100 from coupon data corresponding to at least one attribute information of the stored coupon, and based on the personal preference ranking, at least one coupon to the user terminal 100 When providing , at least one coupon may be provided to the user terminal 100 based on a matching rate that matches the keyword when the user terminal 100 searches for a keyword.

Meanwhile, the user terminal 100 may be at least one user terminal 100 belonging to different subjects. That is, assuming that there are a plurality of users, it is possible to model the following big data and desired output values.

The expected customer calculation unit 350, when the stored coupon is a pre-registered duplicate coupon in the adding unit 330, adds a preset weight to the repeated attribution information while adding at least one attribute information of the stored coupon to personal data And, if the storage coupon is not a previously registered duplicate coupon, after adding at least one attribute information of the storage coupon to personal data, the personal data collected from at least one user terminal 100 can be collected as big data. In addition, by performing machine learning based on a decision tree on the collected big data, coupon data of the affiliated store terminal 400 may be input as a query. In addition, the expected customer calculation unit 350, based on the similarity between the coupon data of the affiliated store terminal 400 and the personal data of the at least one user terminal 100, at least one personal data of the at least one user terminal 100 may be extracted, and expected user information of the affiliated store of the affiliated store terminal 400 may be calculated based on the extracted at least one piece of personal data. In this case, the at least one piece of personal data may include an age group and gender, and the coupon data of the affiliated store terminal 400 may include an affiliated store location, a category, and a coupon type. At this time, the modeling of the decision tree and the artificial intelligence algorithm is the same as the known technology, so it will not be described in detail herein.

Coupon registration unit 360, before collecting the stored coupon after using in the user terminal 100 in the collection unit 310, at least one affiliated store terminal 400 from at least one affiliated store terminal 400 issued by Coupon data can be collected and stored to be mapped. After going through this process, when the preference of the user terminal 100 is analyzed, it becomes possible to provide a coupon.

The review management unit 370, after collecting the stored coupon after being used in the user terminal 100 in the collecting unit 310, when leaving a review for the affiliate store using the storage coupon in the user terminal 100, for the review Reward points may be accumulated in the account of the user terminal 100 . In addition, the review management unit 370, when the points accumulated in the user terminal 100 exceeds a preset cumulative number, may be converted into universal coupons usable in all affiliated stores and transmitted to the user terminal 100 . Universal coupons are coupons that can be used at any merchant. In this case, the coupon type may be a coupon that provides a discount, but does not exclude the coupon type that provides an additional service.

In addition, when the review management unit 370 leaves a review for the affiliate store using the storage coupon in the user terminal 100, after accumulating points, which is a reward for the review, to the account of the user terminal 100, the user terminal 100 When there are a plurality of reviews, a review list may be provided, and the user terminal 100 may be designated as a reviewer to calculate a reviewer ranking. At this time, the reviewer ranking is based on the absolute number of reviews corresponding to the total number of reviews written in the user terminal 100 and the relative number of reviews converted by the ratio of the total number of reviews to the number of coupons downloaded from the user terminal 100 can be calculated based on In this case, since the user of the user terminal 100 can be evaluated in each affiliated store terminal 400 as well, when the user uploads a review, the user's rating can also be uploaded. For example, let's assume that the rating of 2 out of 5 is because the malicious commenter Hong Gil-dong pours out complaints and complaints to all the franchisees and acts as a true customer even when he goes to the franchise. At this time, assuming that the rating of "Starbucks at Yeoksam Station" written by Gil-dong Hong is 3 points, the rating of 2 points, which is the rating of Gil-dong Hong, is also displayed. In addition, by providing statistical processing of all the ratings given by Gil-dong Hong, it is possible to know that the Starbucks at Yeoksam Station is not three-pointed, but that the ratings of Mr.

For example, at least one review is an evaluation scale in which a rating is given with a star rating or a preset scoring system, and after calculating statistics for different ratings for the same parameter, which is the same variable, based on the average value, at least one A characteristic tag of at least one user terminal 100 may be extracted with the rating given by the user terminal 100 . That is, the same conditions exist, and the user's responses will be different, but if the arithmetic average of these statistics is obtained, the responses most people feel can converge to one. Nevertheless, it can be explained that a person who shows a reaction outside of these statistics shows a characteristic of that person. For example, with respect to main menu B in restaurant A, most people say that it is delicious (4 points) or very tasty (5), and the rating of main menu B is 4.5. For example, user E who gave 1 point , Since B dislikes the main menu itself, NOT "menu B" may be added to the characteristic tag to exclude food with ingredients similar to main menu B or food with recipes or similar menus. Alternatively, it may be the case that menu B is okay but restaurant A's cooking method is not good, so that restaurant A is not recommended again as NOT "restaurant A" in the characteristic tag, ie, it can be removed.

In addition, the method according to an embodiment of the present invention monitors a situation in which a user is stressed at school or work by using the proposition "consumption is an emotion", and extracts food that the user liked in a stressful situation. , it is possible to extract other food menus except for the food the user recently ate, and transmit a coupon of a merchant selling the corresponding food menu to the user terminal 100 . At this time, if the user terminal 100 is linked with social media or at least one type of messenger, for example, Facebook, Instagram, KakaoTalk, etc., "quickly", "until today" If there are many prompt words such as ", "within XX hours", "priority processing", etc. In the event that the number of messages exceeding the threshold value is received by checking the frequency and period of factors that may cause a sudden increase in the stress index or cause discomfort, such as in the case of there is. In this case, it is possible to identify words such as "quickly", "until today", "annoying", "ㅠㅠ", etc., and check the frequency or period of use.

In this case, a context-aware-based algorithm may be further applied. A method of changing the preference when a specific situation occurs based on a situation that can occur when using the user terminal 100 and the user terminal 100 usage record can be used, and each preference can be scored or the priority of a coupon can be scored there is. For context recognition in mobile, a sensor value can be input through a sensor that can be collected from a smartphone, and the situation can be inferred by using it as a proof value of the Bayesian network through a pre-processing process. The degree of busyness indicating how busy the user is can be configured based on the number of unread messages and the number of unreceived calls existing in the user terminal 100 of the user. And it can be inferred by using the number of contacts from unfamiliar users together based on the discomfort index. The user's behavior can be inferred using the time and place information by classifying indoors/outdoors based on the values received from the GPS sensor of the smartphone.

In addition, when the user terminal 100 has a wearable device, for example, a smart watch or a smart band, the biosignals that change when stressed include heart rate, respiration, electrocardiogram, electrocardiogram variation, and body temperature. , sweat, etc. can be collected in real time and monitored in real time. Stress is also influenced by environmental factors. Different levels of stress occur depending on environmental information such as temperature, humidity, illuminance, and noise. Therefore, it is possible to classify factors affecting stress through machine learning based on the collection of biosignals and environmental information. Accuracy improvement for stress indexing is achieved through machine learning. Intelligent situation recognition includes not only learned bio-signals, but also environmental information, facial expression recognition, and voice recognition, and complex correlation analysis of the level of stress the user receives in real time, based on this, the final stress quantification can be inferred. The indexation of the stress inferred in this way can be collected as big data, and the user's individual stress status and stress status by module or group, day of the week, and time of day can be grasped. If a specific user causes a sudden change and the stress index increases, a franchisee to take a break, for example, a spa use ticket, a sauna use coupon, a yoga studio discount coupon, etc. can be provided to the user, and the user terminal 100 can take a break to be able to take

It is assumed that access to personalized data is permitted from the user terminal 100 . At this time, the personalized data including web traffic and keystroke logging is based on at least one preset contextual information that affects the time of accessing the main page to selecting and purchasing an advertisement product. It may be used as data for providing a coupon to a user. Accordingly, it is possible to control to use at least one type of context-aware recommender algorithm when setting the exposure order based on the personalized data. At this time, mobile context data (personalization data) is, for example, whether the client continues to view one page, whether the number of scrolling is slow (if interested), or how quickly a page is turned off when it is output, or scrolling You can collect all of them (if they are not interested) and how long it took from order to payment (hesitation time ∝ preference is not high). Of course, computing resources and networking resources must be supported, but this can be solved by setting that processing speed and delay can be lowered by using a distributed parallel system in the server and processing each node separately and using a GPU instead of a CPU. In this case, the distributed parallel system may use, for example, Hadoop, but is not limited thereto. Contextual Pre-Filtering, Contextual Post-Filtering, and Contextual Modeling may be used depending on the reflection method of the situation.

On the other hand, users who receive coupons think that they will receive less service as much as they use this coupon. For example, if you order 10 chickens, you get one, even though it is actually a 10% discount, when you exchange 10 coupons for 1 chicken, people say that the chicken is something strange, or that they reuse the discarded chicken. , there are rumors circulating that the dropped chicken is collected and given away, and there are also reviews that say that something will not taste good or that the quantity is small after receiving it. In other words, consumers who have experienced that they were getting the full price rather than a discount even at department stores that offer so-called high-end services, as cases and precedents and cases of illegal sales of anomalous sales frequently appear in the news, even when they receive coupons, they can no longer receive coupons. not happy Accordingly, in one embodiment of the present invention, by providing the user with the result of comparing the lowest price for the industrial product sold offline and the industrial product sold online, a discount is not provided with the intention of deceiving, but actually a sale This may provide a further way to emphasize that it provides an opportunity to buy at the lowest price. At this time, in terms of actually having a sale, benefits provided by offline franchisees, ie, operators, may be couponed, like discounts registered on flyers for general local advertisements.

In this case, the results proving the lowest price can be collected using a web crawler. A web crawler is a computer program that browses the World Wide Web in an organized and automated way. The work of a web crawler is called web crawling or spidering, and it is a type of bot or software agent. There are two main types of web crawlers: general web crawlers and distributed web crawlers. When explaining the basic operation of a web crawler, it starts by fetching a URL from the URL Frontier module and fetching a web page of the URL using the HTTP protocol. Then, the Fetch module stores the web page in temporary storage, the Parser module extracts the text and links, and the text is sent to the Indexer. In the case of a link, whether it should be added to the URL frontier is determined by going through the Content Seen, URL Filter, and Duplication URL Element modules. At this time, since it is virtually impossible to crawl all web documents with a general web crawler, a distributed web crawler can be further used.

Distributed web crawler is largely divided into two types, one of which is centralized and the other is P2P (or Fully-Distributed). A centralized distributed web crawler is a structure in which the URL manager acts like a server and the crawler acts as a client. After downloading the document from the crawler, extracting the OutLink URL and passing it to the URL manager, the URL manager checks whether the URL of the downloaded document is the URL and removes the URL duplication. In other words, the URL manager takes over the URL duplication and URL management in the general web crawler. On the other hand, in the P2P method, each crawler has a completely independent structure. In the P2P method, each crawler operates like a general web crawler. Each crawler downloads documents, extracts OutLink URLs, and removes duplicate URLs. Each crawler operates independently. To do this, the list of downloaded URLs managed by each crawler must be mutually exclusive. Otherwise, the phenomenon of downloading the same document from different crawlers will occur. As a way to solve this, each crawler can manage the download URL domains by dividing them exclusively. In other words, it is a method of managing only what belongs to the download domain and passing the rest of the URLs to other crawlers. If this method is used, each crawler can operate independently.

Next, web content needs to be extracted. The web content extraction technology provides a function to automatically extract the product name, price, publication date, text, and detailed information in the text, which are content to be used for information analysis from a web document. The web content extraction system is a device that extracts only content by automatically producing rules for extracting content, a rule generator that automatically creates content extraction rules, and a navigation content eliminator that removes navigation content from a given web document. , it may be composed of a content extractor (Core Context Extractor) that extracts content through comparison of keyword similarity with content extraction rules. Of course, it is possible to collect real-time data by various methods other than the above-described crawling method, and is not limited to the listed ones, and is not excluded for reasons not listed.

Hereinafter, a profit model according to an embodiment of the present invention will be described. If the name of the service according to an embodiment of the present invention is "coupon bank", the revenue model of the coupon bank may be the main revenue source for the generation of revenue from premium members and advertisements for affiliated stores (business operators). The premium membership system is based on local search advertisements. When you sign up for a monthly subscription, you can offer discounts on search advertisement CPC (Cost Per Click) costs and referral registration benefits. In addition, it is possible to provide search priority priority for non-members, and it can be differentiated by a cost different from the premium member advertisement cost, such as CPC = 1,000 won. In addition, the local band or display advertisement may be exposed to a certain number of users by the amount paid in the CPM (Cost Per Mile) method. In addition, by applying location-based data of users and affiliates, pop-up advertisements to which CPM or CPC etc. are applied, and local advertisements to which mail or SMS advertisements are applied to consumers who have agreed to receive mail and SMS can also be carried out. For advertisements registered by all affiliated stores (business operators), the position data of the affiliated stores can be transmitted to the database to expose advertisements according to the user's location and search keyword target. Furthermore, by utilizing the collected data information such as search keyword traffic and coupon type, it is possible to provide services such as providing different advertising registration costs for each search keyword, and keyword advertising costs with high search traffic are higher than keyword advertising costs with low traffic. Ad registration fees may be different, such as setting, etc. Table 2 summarizes them as follows.

division level monthly amount CPC CPM Benefits (subject to change) Search Ads (General) - - N won - Search priority priority, recommendation registration, etc.
Search Ads (Premium) One X circle M won - search priority, etc. 2 Y Won M won - Priority in search ranking, free recommendation registration possible n times a month, etc. 3 Z circle M won W won Priority in search ranking, free recommendation registration possible n times a month, CPM discount for band ads, etc. Strip/Display Advertising - - - V circle Main page, my information page strip/banner advertisement, etc.

Hereinafter, an operation process according to the configuration of the customized coupon service providing server of FIG. 2 will be described in detail with reference to FIGS. 3 and 4 as an example. However, it will be apparent that the embodiment is only one of various embodiments of the present invention and is not limited thereto.

Referring to FIG. 3 , it is assumed that a 23-year-old male user named Gildong Hong exists. In this case, the name is not important, and since it is unique identification information, it is okay to exclude it. The customized coupon service providing server 300 extracts personal information from the user terminal 100 only two types of data, "male" and "23 years old", and traces the rest from the coupon used by the user backwards to determine the user's preference. estimate At this time, the user searches for "red bean bingsu", and the corresponding B coupon can be downloaded (not a save coupon). Here, (b) assuming that the user used the coupon B at the red bean shop A, the data [User-A red bean shop-B coupon use] is accumulated, and this becomes the stored coupon. (c) In this way, the coupons used by the user are combined with personal information to accumulate data that can estimate individual preferences, and can be created as [personal data = personal information + coupon information]. (d) When such user data is accumulated, patbingsu shop A can know the age, gender, favorite coupon type, and action radius of the customer base using its store.

4A is a flowchart illustrating a method for providing a customized coupon service according to an embodiment of the present invention, and FIG. 4B is a diagram illustrating a process of block A1 in FIG. 4A. Referring to FIG. 4A , after registering as a member, a user (consumer) may select a category and location on the user page, search by keyword, and select a coupon matching the category, location, and keyword. At this time, when the coupon output from the user terminal 100 is used in the affiliate store terminal 400 , it is stored as a storage coupon of the user terminal 100 and is used as input data for configuring the above-described configuration. On the other hand, after registering as a member in the affiliated store (business owner) terminal 400, and registering the affiliated store name, category, location, photo, menu, description, coupon benefit content and coupon type, etc., the user who wants the coupon provided by himself/herself information can be obtained. Referring to FIG. 4B, it is asked whether it is a previously used duplicate coupon, and if it is positive, a weight is given. In addition, it is possible to provide the user with a coupon list reflecting the user's desired preference.

The matters not described with respect to the method of providing a customized coupon service using the O2O-based storage coupon of FIGS. 2 to 4 are the same as those described for the method of providing a customized coupon service using the O2O-based storage coupon through FIG. 1 above, or Since it can be easily inferred from the described content, the following description will be omitted.

5 is a diagram illustrating a process in which data is transmitted/received between components included in the system for providing a customized coupon service using the O2O-based storage coupon of FIG. 1 according to an embodiment of the present invention. Hereinafter, an example of a process in which data is transmitted and received between each component will be described with reference to FIG. 5, but the present application is not limited to such an embodiment, and the example shown in FIG. 5 according to various embodiments described above will be described. It is apparent to those skilled in the art that the data transmission/reception process may be changed.

Referring to FIG. 5 , the customized coupon service providing server collects stored coupons after being used in the user terminal ( S5100 ).

And, the customized coupon service providing server checks whether the collected stored coupon is a duplicate coupon previously registered in the personal data of the user terminal (S5200), and if the stored coupon is a previously registered duplicate coupon, at least the stored coupon A preset weight is given to repeated attribution information while adding one piece of attribution information to personal data, and when the storage coupon is not a previously registered duplicate coupon, at least one attribution information of the storage coupon is added to the personal data (S5300) ).

In addition, the customized coupon service providing server calculates the personal preference ranking of the user terminal from coupon data corresponding to at least one attribute information of the stored coupon, and provides at least one coupon to the user terminal based on the personal preference ranking ( S5400).

The order between the above-described steps ( S5100 to S5400 ) is merely an example and is not limited thereto. That is, the order between the above-described steps ( S5100 to S5400 ) may be mutually changed, and some of these steps may be simultaneously executed or deleted.

Matters not described with respect to the method of providing a customized coupon service using the O2O-based storage coupon of FIG. 5 are the same as those described for the method of providing a customized coupon service using the O2O-based storage coupon through FIGS. 1 to 4 above, or Since it can be easily inferred from the described content, the following description will be omitted.

The method of providing a customized coupon service using an O2O-based storage coupon according to an embodiment described through FIG. 5 is also in the form of a recording medium including instructions executable by a computer, such as an application or program module executed by a computer. can be implemented. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. Also, computer-readable media may include all computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

The method for providing a customized coupon service using the O2O-based storage coupon according to the embodiment of the present invention described above includes an application basically installed in a terminal (which may include a program included in a platform or operating system, etc. that is basically installed in the terminal) may be executed by the application store server, an application or an application (ie, a program) directly installed in the master terminal through an application providing server such as a web server related to the application or the corresponding service by the user. In this sense, the method for providing a customized coupon service using the O2O-based storage coupon according to the embodiment of the present invention described above is implemented as an application (ie, program) installed by default in the terminal or directly installed by the user, and the like. It may be recorded on a computer-readable recording medium.

The above description of the present invention is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a dispersed form, and likewise components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

Claims (11)

In the method for providing a customized coupon service that is executed on a server for providing a customized coupon service,
collecting stored coupons after use in the user terminal;
checking whether the collected stored coupons are duplicate coupons previously registered in the personal data of the user terminal;
When the storage coupon is a previously registered duplicate coupon, a preset weight is given to the repeated attribute information while adding at least one attribute information of the storage coupon to the personal data, and the duplicate coupon in which the storage coupon is previously registered if not, adding at least one attribute information of the storage coupon to the personal data; and
Calculating a personal preference ranking of the user terminal from coupon data corresponding to at least one attribute information of the stored coupon, and providing at least one coupon to the user terminal based on the personal preference ranking,
The at least one attribute information includes a location of an affiliated store of an affiliated store that issued the storage coupon, a category of products sold by the affiliated store, and a coupon type that is a type of service provided by the stored coupon,
The type of service provided by the storage coupon is a discount or additional service,
The personal preference ranking is a value proportional to the number of stored coupons, and the affiliate store location, category, and coupon type are expressed as a percentage, and the personal preference ranking is given by arranging in the order of increasing the number indicated by the percentage,
When the stored coupon is a previously registered duplicate coupon, when a preset weight is given to the repeated attribute information while adding at least one attribute information of the stored coupon to the personal data, the preset weight is the number of repetitions A method of providing a customized coupon service using an O2O (Online to Offline)-based stored coupon, characterized in that a multiple of a preset weight is given to the repeated attribute information.
delete delete The method of claim 1,
The user terminal is at least one user terminal belonging to different subjects,
When the storage coupon is a previously registered duplicate coupon, a preset weight is given to the repeated attribute information while adding at least one attribute information of the storage coupon to the personal data, and the duplicate coupon in which the storage coupon is previously registered If not, after adding at least one attribute information of the storage coupon to the personal data,
collecting personal data collected from the at least one user terminal as big data;
performing decision tree-based machine learning on the collected big data, and inputting coupon data of a merchant terminal as a query;
extracting at least one personal data of at least one user terminal based on the similarity between the coupon data of the affiliated store terminal and the personal data of the at least one user terminal;
calculating expected user information of an affiliated store of the affiliated store terminal based on the at least one extracted personal data;
A method of providing a customized coupon service using an O2O-based storage coupon that further comprises.
5. The method of claim 4,
The at least one personal data includes an age group and gender,
The coupon data of the affiliated store terminal, including the affiliate store location, category, and coupon type, a method of providing a customized coupon service using an O2O-based storage coupon.
The method of claim 1,
Before the step of collecting the stored coupons after use in the user terminal,
collecting and storing coupon data issued by the at least one affiliated store terminal from at least one affiliated store terminal to be mapped;
further comprising,
The coupon data is a method of providing a customized coupon service using an O2O-based storage coupon, which includes the merchant location, category, and coupon type.
The method of claim 1,
Calculating the personal preference ranking of the user terminal from coupon data corresponding to at least one attribute information of the stored coupon, and providing at least one coupon to the user terminal based on the personal preference ranking,
providing at least one coupon to the user terminal based on a matching rate matching the keyword when the user terminal searches for a keyword;
A method of providing a customized coupon service using an O2O-based storage coupon that further comprises.
The method of claim 1,
After collecting the stored coupons after using them in the user terminal,
when the user terminal leaves a review on the affiliate store using the storage coupon, accumulating points as a reward for the review to the user terminal account;
converting the points accumulated in the user terminal into universal coupons usable at all affiliated stores when the accumulated points exceed a preset number of points and transmitting the converted points to the user terminal;
A method of providing a customized coupon service using an O2O-based storage coupon that further comprises.
9. The method of claim 8,
When the user terminal leaves a review on the affiliate store using the storage coupon, after the step of accumulating points, which is a reward for the review, into the user terminal account,
providing a review list when there are a plurality of reviews of the user terminal, and calculating a reviewer ranking by designating the user terminal as a reviewer;
further comprising,
The reviewer ranking is calculated based on the absolute number of reviews corresponding to the total number of reviews written in the user terminal and the relative number of reviews converted by the ratio of the total number of reviews to the number of coupons downloaded from the user terminal A method of providing customized coupon service using in- and O2O-based storage coupons.
The method of claim 1,
Before the step of collecting the stored coupons after use in the user terminal,
receiving a premium service application or advertisement request from at least one affiliated store terminal;
A method of providing a customized coupon service using an O2O-based storage coupon that further comprises.
delete

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