Patent Publication Number: US-2015073906-A1

Title: Method and system for optimizing location-based targeted ads served on a moble device

Description:
FIELD 
     The present disclosure relates to the distribution of optimized offers to a mobile device, specifically the optimization of offers based on identification of an inferred future geographic location based on a current location and a location history of the mobile device. 
     BACKGROUND 
     With a large number of consumers carrying smart phones, merchants, manufacturers, retailers, offer providers, etc. have tried to develop ways to use consumer&#39;s smart phones to foster additional business. One method that has been developed includes distributing offers for nearby merchants to a consumer smart phone based on the geographic location of the smart phone. The goal of such an offer distribution is to entice the consumer to stop by a particular merchant based on the strength of the offer and the consumer&#39;s proximity to the merchant. 
     However, these methods often operate based purely on the current location of a consumer based on their mobile device. As a result, consumers may receive offers for products or merchants that the consumer has little interest in, which may further result in the consumer not being enticed to visit the merchant as a result of the offer. In order to attempt to further influence consumers, some systems have been developed that utilize consumer demographics or spending behaviors to select offers for nearby merchants that have a higher likelihood of success with a particular consumer. 
     However, many consumers who may receive these offers may typically follow a specific plan or routine when shopping. As such, these consumers may have no desire to remain in a particular location or visit an additional merchant nearby, even when provided with an offer to the nearby merchant. In addition, many consumers may also shop within specific budget constraints. Thus, even when presented with a beneficial offer, presenting the offer to the consumer once they are already spending an allotted amount may result in their rejecting of the offer due to the extra expense. 
     Thus, there is a need for a technical solution to distribute offers to consumers that are optimized not based purely on a consumer&#39;s current location, but based on an expected future location based on prior location history of the consumer. 
     SUMMARY 
     The present disclosure provides a description of systems and methods for distributing an optimized offer to a mobile communication device. 
     A method for distributing an optimized offer to a mobile communication device includes: storing, in an offer database, a plurality of offer data entries, wherein each offer data entry includes data related to an offer for the purchase of goods or services including at least offer data and an offer location; storing, in a consumer database, a consumer profile, wherein the consumer profile includes data related to a consumer including at least historical geographic location data of a mobile communication device associated with the related consumer; receiving, by a receiving device, a geographic location of the associated mobile communication device; identifying, by a processing device, at least one inferred future geographic location for the associated mobile communication device based on the received geographic location and the historical geographic location data of the associated mobile communication device; identifying, in the offer database, at least one offer data entry where the included offer location corresponds to one of the identified at least one inferred future geographic location; and transmitting, by a transmitting device, the offer data included in the identified at least one offer data entry to the associated mobile communication device. 
     A system for distributing an optimized offer to a mobile communication device includes an offer database, a consumer database, a receiving device, a processing device, and a transmitting device. The offer database is configured to store a plurality of offer data entries, wherein each offer data entry includes data related to an offer for the purchase of goods or services including at least offer data and an offer location. The consumer database is configured to store a consumer profile, wherein the consumer profile includes data related to a consumer including at least historical geographic location data of a mobile communication device associated with the related consumer. The receiving device is configured to receive a geographic location of the associated mobile communication device. The processing device is configured to: identify at least one inferred future geographic location for the associated mobile communication device based on the received geographic location and the historical geographic location data of the associated mobile communication device; and identify, in the offer database, at least one offer data entry where the included offer location corresponds to one of the identified at least one inferred future geographic location. The transmitting device is configured to transmit the offer data included in the identified at least one offer data entry to the associated mobile communication device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The scope of the present disclosure is best understood from the following detailed description of exemplary embodiments when read in conjunction with the accompanying drawings. Included in the drawings are the following figures: 
         FIG. 1  is a high level architecture illustrating a system for distributing optimized offers to a mobile device in accordance with exemplary embodiments. 
         FIG. 2  is a block diagram illustrating the processing server of  FIG. 1  for the identification and distribution of optimized offers in accordance with exemplary embodiments. 
         FIGS. 3A and 3B  are diagrams illustrating the identification of inferred future geographic locations of a mobile device in accordance with exemplary embodiments. 
         FIG. 4  is a diagram illustrating the identification of an optimized offer for distribution to a mobile device in accordance with exemplary embodiments. 
         FIG. 5  is a flow chart illustrating an exemplary method for distributing an optimized offer to a mobile communication device in accordance with exemplary embodiments. 
         FIG. 6  is a block diagram illustrating a computer system architecture in accordance with exemplary embodiments. 
     
    
    
     Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description of exemplary embodiments are intended for illustration purposes only and are, therefore, not intended to necessarily limit the scope of the disclosure. 
     DETAILED DESCRIPTION 
     Definition of Terms 
     Payment Network—A system or network used for the transfer of money via the use of cash-substitutes. Payment networks may use a variety of different protocols and procedures in order to process the transfer of money for various types of transactions. Transactions that may be performed via a payment network may include product or service purchases, credit purchases, debit transactions, fund transfers, account withdrawals, etc. Payment networks may be configured to perform transactions via cash-substitutes, which may include payment cards, letters of credit, checks, financial accounts, etc. Examples of networks or systems configured to perform as payment networks include those operated by MasterCard®, VISA®, Discover®, American Express®, PayPal®, etc. 
     System for Distributing Optimized Offers to Mobile Devices 
       FIG. 1  illustrates a system  100  for the identification of optimized offers and distribution thereof to a mobile device based on historical and current geographic location data. 
     The system  100  may include a processing server  102 . The processing server  102 , discussed in more detail below, may be configured to identify optimized offers for a consumer based on a current geographic location of the consumer and historical location data of the consumer. The processing server  102  may receive one or more offers for distribution from an offer provider  104 . The offer provider  104  may be a merchant, manufacturer, retailer, offer distribution, or any other entity suitable for performing the functions as disclosed herein. 
     Offers provided to the processing server  102  may be associated with a geographic location and include offer data. The offer data may include an offer amount, offer category, merchant name, merchant category, manufacturer name, manufacturer category, product name, product identifier, product data, offer identifier, offer type, start date, end date, offer quantity, offer terms and conditions, or any other data suitable for performing the functions as disclosed herein. The processing server  102  may receive the offers and store the offer data in an offer database, discussed in more detail below. 
     The processing server  102  may attempt to identify optimized offers to be distributed to a consumer  106 . The consumer  106  may engage in a payment transaction with a merchant at a specific geographic location. The payment transaction may be processed by a payment network  108 . The payment network  108  may transmit transaction data corresponding to the payment transaction to the processing server  102 . The transaction data may include at least a geographic location associated with the payment transaction, which may be identified, for example, in an authorization request generated by a merchant involved in the payment transaction. The geographic location may thus represent the geographic location of the consumer  106  at the time of the transaction. 
     The processing server  102  may then identify one or more offers optimized for the consumer  106  based on the consumer&#39;s geographic location and historical geographic location data. The historical geographic location data may be based on a plurality of geographic locations of a mobile device  110  associated with the consumer  106 . The mobile device  110  may be any type of mobile communication device suitable for performing the functions as disclosed herein as will be apparent to persons having skill with the relevant art, such as a cellular phone, smart phone, tablet computer, laptop computer, etc. 
     The geographic location data of the mobile device  110  may be provided to the processing server  102  by a mobile network operator  112  associated with the mobile device  110 . The mobile network operator  112  may identify a geographic location of the mobile device  110  at a predetermined period of time (e.g., at a specific interval, when requested by the processing server  102 , etc.) using systems and methods that will be apparent to persons having skill in the relevant art. 
     The processing server  102  may store the historical geographic location data in a consumer profile stored in a consumer database, discussed in more detail below. In some embodiments, the processing server  102  may request the location data upon receipt of the transaction data from the payment network  108 . In other embodiments, the processing server  102  may have previously stored a consumer profile including historical geographic location data associated with the consumer  106 . In a further embodiment, the processing server  102  may request updated geographic location information from the mobile network operator  112  prior to the identification of optimized offers. 
     In some instances, the processing server  102  may receive geographic location information directly from the mobile device  110 . For example, the consumer  106  may register with the processing server  102  for a service to receive personalized, optimized offers. As part of the service, the consumer  106  may register the mobile device  110 , which may report its geographic location to the processing server  102  at predetermined periods of time. Additional methods for obtaining historical geographic location data of the mobile device  110  will be apparent to persons having skill in the relevant art. 
     The processing server  102  may then use the historical geographic location data and current geographic location data to identify one or more inferred future geographic locations. As discussed in more detail below, the inferred future geographic locations may be estimated locations that the consumer  106  is expected to visit upon leaving their current geographic location. The processing server  102  may then identify offers corresponding to the inferred future geographic locations, and distribute the offers to the consumer  106  and/or the mobile device  110 . Systems and methods for distributing offers to a consumer or a mobile device will be apparent to persons having skill in the relevant art. 
     In some embodiments, the processing server  102  may use action sequences to optimize the selection of offers. Action sequences, discussed in more detail below, may be a sequence of actions performed by a consumer, which may be used to identify an expected future action of a consumer. In some instances, action sequences may be specific to a particular consumer (e.g., the consumer  106 ) based on historical actions of the consumer  106 . The historical actions of the consumer  106  may be based on transaction data, such as transaction data received from the payment network  108  and stored in a transaction database by the processing server  102 , as discussed in more detail below. 
     For example, an action sequence may established for when a consumer arrives in a new city via airport. An action sequence may be established where a consumer arrives at the airport, then rents a car or a taxi, then checks into a hotel, and then visits a restaurant. The processing server  102  may use an action sequence in conjunction with historical geographic location data to further optimize an offer distributed to the consumer  106 . An example of the use of both historical geographic location data and action sequence data is illustrated in  FIG. 4  and discussed in more detail below. 
     The use of historical geographic location data to optimize the identification and distribution of offers to a mobile device may result in the distribution of offers with a higher rate of redemption. By providing the consumer  106  with offers corresponding to where they are going to be, rather than where they are, the consumer  106  may be more enticed to visit a corresponding merchant. In addition, such offers may alter a consumer&#39;s itinerary, such as by influencing the consumer to shop at one store versus another yet still remain with a consumer&#39;s budget as such spending may not yet have occurred, resulting in a sale when there may otherwise not have been one. 
     Processing Device 
       FIG. 2  illustrates an embodiment of the processing server  102  of the system  100 . It will be apparent to persons having skill in the relevant art that the embodiment of the processing server  102  illustrated in  FIG. 2  is provided as illustration only and may not be exhaustive to all possible configurations of the processing server  102  suitable for performing the functions as discussed herein. For example, the computer system  600  illustrated in  FIG. 6  and discussed in more detail below may be a suitable configuration of the processing server  102 . 
     The processing server  102  may include a receiving unit  202 . The receiving unit  202  may be configured to receive data over one or more networks via one or more network protocols, such as the Internet, a mobile cellular network, etc. The receiving unit  202  may receive offer data from the offer provider  104 , transaction data from the payment network  108 , and geographic location data from the mobile network operator  112 , mobile device  110 , or other suitable entity. 
     The processing server  102  may also include a processing unit  204 . The processing unit  204  may be configured to store received data in one or more databases. The processing server  102  may include an offer database  208 , which may include one or more offer data entries  210 , wherein each offer data entry  210  includes data related to an offer for the purchase of goods or services and includes at least offer data and an offer location. The offer location may be represented by latitude and longitude, a postal or zip code, street address, municipality, or any other suitable representation that will be apparent to persons having skill in the relevant art. In some embodiments, each offer data entry  210  may include an offer strength. The offer strength may be a value representative of the effect the offer has on a transaction amount when used. For example, an offer for $5 off a $50 item may have a lower offer strength than an offer for 20% off the same item. 
     The processing server  102  may also include a consumer database  212 . The consumer database  212  may include one or more consumer profiles  214 , wherein each consumer profile  214  includes data related to a consumer  106  including at least historical geographic location data of the mobile device  110  associated with the respective consumer  106 . In some embodiments, the consumer profile  214  may also include merchant preferences, which may correspond to preferences the related consumer  106  may have for one merchant over another for use in optimizing distributed offers. In other embodiments, the consumer profile  214  may include one or more propensities to redeem, which may correspond to a propensity for the related consumer  106  to redeem an offer. Each propensity to redeem may be associated with at least one of: an offer category, merchant, offer type, offer value, offer strength, or other suitable criteria. 
     The consumer profile  214  may also include one or more consumer action sequences or associated consumer actions. The consumer action sequences may be action sequences specific to the related consumer  106 . The associated consumer actions may be actions that are associated with a specific consumer action, similar to an action sequence. The consumer profile  214  may also include a preferred method of distribution. The preferred method of distribution may be a preferred method for the distribution of offers to the consumer  106  and/or the mobile device  110 , such as short message service (SMS) message, multimedia message service (MMS) message, e-mail, etc. 
     The processing server  102  may also include a transaction database  216 . The transaction database  216  may be configured to store a plurality of transaction data entries  218 , wherein each transaction data entry  218  may include data related to a payment transaction including at least a consumer identifier and transaction data. The consumer identifier may be an identifier associated with a consumer profile  214  related to a consumer  106  involved in the related payment transaction. The transaction data may include any data corresponding to the related payment transaction suitable for performing the functions as disclosed herein, such as a transaction amount, merchant data, product data, geographic location, transaction time and/or date, redeemed offer data, etc. 
     In some embodiments, the processing server  102  may also include an action database. The action database may be configured to store a plurality of action sequence data entries, which may be related to an action sequence and include at least a consumer action and an inferred future consumer action. In some instances, each action sequence data entry may be associated with one or more consumer profiles  214 . In some embodiments, an action sequence data entry may include multiple inferred future consumer actions. In a further embodiment, the multiple inferred future consumer actions may be weighted. 
     The processing unit  204  of the processing server  102  may be configured to utilize the data stored in the database to identify at least one inferred future geographic location for the consumer  106  and/or the mobile device  110 . The at least one inferred future geographic location may be identified based on the current geographic location of the consumer  106  and the historical geographic location data stored in the consumer profile  214  related to the consumer  106 . The processing unit  204  may then identify at least one offer data entry  210  in the offer database  208  wherein the included offer location corresponds to the at least one inferred future geographic location. 
     The processing server  102  may include a transmitting unit  206 . The transmitting unit  206  may be configured to transmit data over one or more networks via one or more network protocols, including transmitting the offer data corresponding to the identified at least one offer data entry  210  to the consumer  106  and/or the mobile device  110 . In some embodiments, the processing unit  204  may be configured to identify a single offer data entry  210  for transmission to the consumer  106  and/or the mobile device  110 . 
     In such an embodiment, the processing unit  204  may identify the single offer data entry  210  based on offer strength, consumer merchant preferences, consumer propensities to redeem, consumer previous transaction history, consumer action sequence data, and/or action sequence data entries stored in the action database. The transmitting unit  206  may also distribute the at least one or single offer data entry  210  to the consumer  106  and/or mobile device  110  based on a preferred method of distribution in the corresponding consumer profile  214 . 
     Identification of Inferred Future Geographic Locations 
       FIGS. 3A and 3B  illustrate the identification of future geographic locations based on historical geographic location data. 
     As illustrated in  FIG. 3A , the mobile device  110  of the consumer  106  may be identified at a plurality of geographic locations  304  in a geographic area  302 . The geographic locations  304  may represent historical geographic location data of the consumer  106 . The geographic locations  304  may be identified at predetermined periods of time related to: time intervals (e.g., every hour), changes in location (e.g., locations at which the mobile device  110  has stopped for a predetermined length of time), when payment transactions occur, etc. 
     Based on times at which the mobile device  110  is identified at each geographic location  304 , the processing server  102  may identify a path  306  of the consumer  106 . The path  306  may represent a path of travel of the consumer  106  based on the geographic locations  304 . The processing server  102  may use the path  306 , as well as the geographic locations  304 , to identify inferred future geographic locations of the consumer  106 . 
     As illustrated in  FIG. 3B , the processing server  102  may identify a current geographic location  308  of the consumer  106 , such as based on transaction data received from the payment network  108  or geographic location data received from the mobile network operator  112  and/or the mobile device  110 . The processing server  102  may then identify an inferred path  310  based on the previous path  306  of the consumer leaving from the current geographic location  308 . The processing server  102  may then identify a plurality of inferred future geographic locations  312  based on the inferred path  310  and the historical geographic location data. 
     Optimization of Offers for Distribution 
       FIG. 4  illustrates the selection of an optimized offer for distribution to the consumer  106  based on consumer historical geographic location data. 
     The offer database  208  of the processing server  102  may store offer data entries  210  related to a plurality of offers, such as offers 1, 2, 3, 4, 5, and 6 illustrated in  FIG. 4 . Each of the offers may include an offer location, which is represented in  FIG. 4  as a zip code associated with each offer. For example, offer 1 is associated with zip code 22314. 
     In step  402 , the processing server  102  may filter the plurality of offers based on inferred future geographic locations of the consumer  106  based on the historical geographic location data of the consumer  106 . As illustrated in  FIG. 4 , the processing server  102  may identify an inferred future geographic location of the consumer  106  to be in zip code 22314. As a result, the processing server  102  may filter out each of the offers not associated with zip code 22314, resulting in remaining offers 1, 3, 4, and 6. 
     Each of the remaining offers may also be associated with a specific consumer action. As illustrated offer 1 may be for gas, offer 3 may be for a retail store, offer 4 may be for a restaurant, and offer 6 may be for a hotel. The processing server  102  may, in step  404 , filter the offers based on an action sequence. For example, the consumer  106  may have just traveled by air and arrived at an airport. The processing server  102  may identify action sequences including a consumer traveling by air to an airport or future inferred consumer actions following travel by air to an airport, to identify future actions that the consumer  106  may take. In some embodiments, the processing server  102  may identify action sequences for consumers in general, action sequences for the specific consumer  106 , action sequences for similar consumers (e.g., having similar demographic characteristics or behaviors, etc.) or a combination thereof. 
     In the example illustrated in  FIG. 4 , the processing server  102  may identify getting gas (e.g., for a rental car or to fill up a car when returning home), eating, or visiting a hotel as actions commonly following arrival at an airport. The processing server  102  may identify visiting a retail store as not being a common action following arrival at an airport. As a result, in step  404  the processing server  102  may filter out offer 3, which may leave offers 1, 4, and 6 eligible for distribution to the consumer  106 . 
     In step  406 , the processing server  102  may use offer strength to select a single offer for distribution to the consumer  106 . As illustrated in  FIG. 4 , offer 1 may be for 5% off, offer 4 may be for 25% off, and offer 6 may be for 10% off. Accordingly, the processing server  102  may identify offer 4 as being the strongest offer (e.g., and therefore potentially the most likely to be redeemed) and may identify offer 4 for distribution to the consumer  106 . 
     It will be apparent to persons having skill in the relevant art that an offer with the highest percentage discount may not necessarily be the strongest offer. For example, 25% off at an inexpensive restaurant may represent a much smaller discount than 10% off an expensive hotel, and may, in some instances, be considered a weaker offer. In some embodiments, offer strengths may be weighted based on information included in the consumer profile  214 . For example, the consumer  106  may have merchant preferences, action preferences, etc. In some instances, the processing server  102  may select, optimize, filter, etc. offers based on demographic information associated with the consumer  106 . Methods and systems for further filtering or selecting an offer to be distributed to a consumer  106  will be apparent to persons having skill in the relevant art. 
     Additional criteria for offer strength may include season (e.g., higher strength for warm beverages in winter, higher strength for ice cream in summer, etc.), geographic area, overall redemption of the offer (e.g., of all consumers to whom the offer has been distributed), quantity of the offer (e.g., perceived rarity of the offer), availability period of the offer, popularity of an associated merchant or product, and other criteria that will be apparent to persons having skill in the relevant art. Weighting of offers may be performed after the filtering of offers, or may be combined with other methods of filtering, such as action sequences. For example, offers may have weights based on both the additional criteria and an action sequence (e.g., hotel offers may be weighed more heavily than restaurant offers following arrival at an airport), but an offer may be selected based on a combined weight, such that an offer with an abnormally high value for a restaurant may be selected over an offer for a hotel of a significantly lesser value. 
     Method for Distributing an Optimized Offer to a Mobile Communication Device 
       FIG. 5  illustrates a method  500  for distributing an optimized offer to a mobile device based on historical geographic location data. 
     In step  502 , a plurality of offer data entries (e.g., offer data entries  210 ) may be stored in an offer database (e.g., the offer database  208 ), wherein each offer data entry  210  includes data related to an offer for the purchase of goods or services including at least offer data and an offer location. In some embodiments, the offer data may include at least one of: offer amount, offer category, merchant name, merchant category, manufacturer name, manufacturer category, product name, product identifier, product data, offer identifier, offer type, start date, end date, offer quantity, and offer terms and conditions. 
     In step  504 , a consumer profile (e.g., the consumer profile  214 ) may be stored, in a consumer database (e.g., the consumer database  212 ), wherein the consumer profile  214  includes data related to a consumer (e.g., the consumer  106 ) including at least historical geographic location data of a mobile communication device (e.g., the mobile device  110 ) associated with the related consumer  106 . In step  506 , a geographic location (e.g., geographic location  308 ) of the associated mobile communication device  110  may be received by a receiving device (e.g., the receiving unit  202 ). In one embodiment, the geographic location may be represented by latitude and longitude. 
     In step  508 , a processing device (e.g., the processing unit  204 ) may identify at least one inferred future geographic location (e.g., future geographic location  312 ) for the associated mobile communication device  110  based on the received geographic location  308  and the historical geographic location data of the associated mobile communication device  110 . In some embodiments, the at least one future inferred geographic location may include a plurality of inferred future geographic locations corresponding to an identified inferred future path of travel. 
     In step  510 , at least one offer data entry  210  may be identified, in the offer database  208 , where the included offer location corresponds to one of the identified at least one inferred future geographic location  312 . In one embodiment, the consumer profile  214  may include merchant preferences, each offer data entry  210  may further include a merchant category, and step  510  may include identifying at least one offer data entry  210  where the included merchant category corresponds to the merchant preferences. In some embodiments, each offer data entry  210  may further include an offer distance, and identifying the at least one offer data entry  210  may include identifying at least one offer data entry  210  where the included offer location is within the included offer distance of the identified at least one inferred future geographic location. 
     In step  512 , the offer data included in the identified at least one offer data entry  210  may be transmitted, by a transmitting device (e.g., the transmitting unit  206 ), to the associated mobile communication device  110 . In one embodiment, the consumer profile  214  may further include a preferred method of distribution, and transmitting the offer data may further include transmitting the offer data to the associated mobile communication device  110  via the preferred method of distribution. In a further embodiment, the preferred method of distribution is at least one of: SMS message, MMS message, e-mail, near field communication, and an application program executed by the associated mobile communication device  110 . 
     In one embodiment, each offer data entry  210  may further include an offer strength, and the method  500  may further include identifying, by the processing device  204 , a specific offer data entry  210  of the at least one offer data entry  210  based on the included offer strength, wherein transmitting the offer data to the associated mobile communication device  110  includes transmitting the offer data included in the identified specific offer data entry  210 . In a further embodiment, the offer strength may be based on at least one of: discount amount, merchant category, offer availability, and product or service data. 
     In another embodiment, the consumer profile  214  may further include a plurality of propensities to redeem, each propensity to redeem being associated with one or more coupon categories, and wherein the method  500  further includes identifying, by the processing device  204 , a specific offer data entry  210  of the at least one offer data entry  210  based on the included offer data and the plurality of propensities to redeem, wherein transmitting the offer data to the associated mobile communication device  110  includes transmitting the offer data included in the identified specific offer data entry  210 . 
     In yet another embodiment, each offer data entry  210  may further include a corresponding consumer action, the received geographic location may further include an associated consumer action, and the method  500  may further include: storing, in an action database, a plurality of consumer action data entries, wherein each consumer action data entry includes at least a current consumer action and an inferred following consumer action; identifying, by the processing device  204 , a specific offer data entry  210  of the at least one offer data entry  210  where the included corresponding consumer action corresponds to the inferred following consumer action included in the identified specific action sequence data entry, wherein transmitting the offer data to the associated mobile communication device  110  includes transmitting the offer data included in the identified specific offer data entry. In a further embodiment, the consumer action may include at least one of: transportation by train, transportation by air, transportation by boat, renting a car, purchasing gas, dining out, purchasing a hotel room, checking into a hotel, renting a taxi, and visiting an attraction. 
     Computer System Architecture 
       FIG. 6  illustrates a computer system  600  in which embodiments of the present disclosure, or portions thereof, may be implemented as computer-readable code. For example, the processing server  102  of  FIG. 1  may be implemented in the computer system  600  using hardware, software, firmware, non-transitory computer readable media having instructions stored thereon, or a combination thereof and may be implemented in one or more computer systems or other processing systems. Hardware, software, or any combination thereof may embody modules and components used to implement the methods of  FIGS. 4 and 5 . 
     If programmable logic is used, such logic may execute on a commercially available processing platform or a special purpose device. A person having ordinary skill in the art may appreciate that embodiments of the disclosed subject matter can be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device. For instance, at least one processor device and a memory may be used to implement the above described embodiments. 
     A processor device as discussed herein may be a single processor, a plurality of processors, or combinations thereof. Processor devices may have one or more processor “cores.” The terms “computer program medium,” “non-transitory computer readable medium,” and “computer usable medium” as discussed herein are used to generally refer to tangible media such as a removable storage unit  618 , a removable storage unit  622 , and a hard disk installed in hard disk drive  612 . 
     Various embodiments of the present disclosure are described in terms of this example computer system  600 . After reading this description, it will become apparent to a person skilled in the relevant art how to implement the present disclosure using other computer systems and/or computer architectures. Although operations may be described as a sequential process, some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multi-processor machines. In addition, in some embodiments the order of operations may be rearranged without departing from the spirit of the disclosed subject matter. 
     Processor device  604  may be a special purpose or a general purpose processor device. The processor device  604  may be connected to a communication infrastructure  606 , such as a bus, message queue, network, multi-core message-passing scheme, etc. The network may be any network suitable for performing the functions as disclosed herein and may include a local area network (LAN), a wide area network (WAN), a wireless network (e.g., WiFi), a mobile communication network, a satellite network, the Internet, fiber optic, coaxial cable, infrared, radio frequency (RF), or any combination thereof. Other suitable network types and configurations will be apparent to persons having skill in the relevant art. The computer system  600  may also include a main memory  608  (e.g., random access memory, read-only memory, etc.), and may also include a secondary memory  610 . The secondary memory  610  may include the hard disk drive  612  and a removable storage drive  614 , such as a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. 
     The removable storage drive  614  may read from and/or write to the removable storage unit  618  in a well-known manner. The removable storage unit  618  may include a removable storage media that may be read by and written to by the removable storage drive  614 . For example, if the removable storage drive  614  is a floppy disk drive, the removable storage unit  618  may be a floppy disk. In one embodiment, the removable storage unit  618  may be non-transitory computer readable recording media. 
     In some embodiments, the secondary memory  610  may include alternative means for allowing computer programs or other instructions to be loaded into the computer system  600 , for example, the removable storage unit  622  and an interface  620 . Examples of such means may include a program cartridge and cartridge interface (e.g., as found in video game systems), a removable memory chip (e.g., EEPROM, PROM, etc.) and associated socket, and other removable storage units  622  and interfaces  620  as will be apparent to persons having skill in the relevant art. 
     Data stored in the computer system  600  (e.g., in the main memory  608  and/or the secondary memory  610 ) may be stored on any type of suitable computer readable media, such as optical storage (e.g., a compact disc, digital versatile disc, Blu-ray disc, etc.) or magnetic tape storage (e.g., a hard disk drive). The data may be configured in any type of suitable database configuration, such as a relational database, a structured query language (SQL) database, a distributed database, an object database, etc. Suitable configurations and storage types will be apparent to persons having skill in the relevant art. 
     The computer system  600  may also include a communications interface  624 . The communications interface  624  may be configured to allow software and data to be transferred between the computer system  600  and external devices. Exemplary communications interfaces  624  may include a modem, a network interface (e.g., an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via the communications interface  624  may be in the form of signals, which may be electronic, electromagnetic, optical, or other signals as will be apparent to persons having skill in the relevant art. The signals may travel via a communications path  626 , which may be configured to carry the signals and may be implemented using wire, cable, fiber optics, a phone line, a cellular phone link, a radio frequency link, etc. 
     Computer program medium and computer usable medium may refer to memories, such as the main memory  608  and secondary memory  610 , which may be memory semiconductors (e.g. DRAMs, etc.). These computer program products may be means for providing software to the computer system  600 . Computer programs (e.g., computer control logic) may be stored in the main memory  608  and/or the secondary memory  610 . Computer programs may also be received via the communications interface  624 . Such computer programs, when executed, may enable computer system  600  to implement the present methods as discussed herein. In particular, the computer programs, when executed, may enable processor device  604  to implement the methods illustrated by  FIGS. 4 and 5 , as discussed herein. Accordingly, such computer programs may represent controllers of the computer system  600 . Where the present disclosure is implemented using software, the software may be stored in a computer program product and loaded into the computer system  600  using the removable storage drive  614 , interface  620 , and hard disk drive  612 , or communications interface  624 . 
     Techniques consistent with the present disclosure provide, among other features, systems and methods for providing characteristic payments data. While various exemplary embodiments of the disclosed system and method have been described above it should be understood that they have been presented for purposes of example only, not limitations. It is not exhaustive and does not limit the disclosure to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the disclosure, without departing from the breadth or scope.