Systems and methods for providing a direct marketing campaign planning environment

Information

  • Patent Grant
  • 9916596
  • Patent Number
    9,916,596
  • Date Filed
    Friday, November 18, 2016
    8 years ago
  • Date Issued
    Tuesday, March 13, 2018
    6 years ago
Abstract
Embodiments of system are disclosed in which selection strategies for a direct marketing campaign that identify consumers from a credit bureau or other consumer database can be planned, tested, and/or refined on a stable subset of the credit database. In some embodiments, once refined, consumer selection criteria may be used to execute the direct marketing campaign on the full consumer/credit database, which is preferably updated approximately twice weekly. In one preferred embodiment, the data for the test database represents a random sampling of approximately 10% of the full database and the sampling is regenerated approximately weekly in order to provide a stable set of data on which campaign developers may test their campaign. For each consumer in the sampling, the environment may allow a client to access and use both attributes calculated by the credit bureau and proprietary attributes and data owned by the client. The system allows for a plurality of clients to use the system substantially simultaneously while protecting the privacy and integrity of the client's proprietary data and results.
Description
BACKGROUND OF THE INVENTION
Field of the Invention

The disclosure relates to database testing environments, and, in particular, to the creation of a sample consumer information database environment for the testing and refining of direct marketing campaigns based on credit-related and/or non-credit-related data.


Description of the Related Art

Various business entities, including credit providers, perform direct marketing campaigns aimed at selected consumers who are identified, at least in part, based on data available from credit bureaus. In order to improve the cost-effectiveness of such campaigns, careful planning of the selection criteria for identifying the consumers to be targeted is desired. Refinement of the selection criteria often takes place by testing a proposed set of criteria on the database of credit bureau consumer information, or on a subset of the database, and analyzing the resulting set of consumers identified using the criteria.


Typically, marketers are trying to identify consumers with a desired set of characteristics. Effective refinement of the selection criteria is dependent, at least in part, upon accuracy and stability of the test database set upon which the testing is run, especially since current legislation regarding the use of consumer credit data for advertising purposes requires in some cases that marketers commit to making a firm offer of credit to consumers who are contacted. In other types of direct marketing campaigns, rather than making a firm offer of credit, the marketers may issue an “invitation to apply” (known as an ITA). Planning and execution for ITA campaigns may be restricted by law from using credit-related data. Marketers for firm offers of credit, ITA's, and other direct marketing campaigns are very interested in maximizing the success of their direct marketing campaigns by careful selection of consumers to be contacted.


SUMMARY

Due to the importance of direct marketing campaigns, it would be beneficial to improve the manner in which business entities, or marketers working on their behalf, can test and refine their direct marketing campaigns. Embodiments of the present invention allow business entities, referred to for purposes of this disclosure as “clients,” to directly access, without intervention of an intermediary, a “partial snapshot” of a very up-to-date credit bureau or other consumer database in order to test and refine direct marketing campaign selection criteria for campaigns that they intend to run on the full credit bureau database in order to identify targets for a direct marketing campaign. The snapshot preferably comprises data that has been very recently updated and provides for the inclusion of a client's proprietary data and attribute definitions.


Embodiments of the systems and methods described herein disclose a computer database environment in which selection of consumers to be targeted by a direct marketing campaign can be planned, tested, and refined on a subset of a credit database. The subset of the credit database provides data that is both up-to-date and stable. In some embodiments, once refined, the selection of consumers to target for the direct marketing campaign may be carried out on the full credit database, which is preferably updated approximately twice weekly. In one preferred embodiment, the data for the test database represents a random sampling of approximately 10% of a full credit bureau database, which may include both credit and/or non-credit data. In one preferred embodiment, the full credit database is updated approximately twice weekly, and the 10% snapshot is updated only approximately once weekly. In some embodiments, the snapshot is updated less frequently than the full credit database. This provides a stable test environment in which the client can rely on the ability to use one static set of data for at least one week while the client refines the campaign selection strategies. Thus, changes in performance of various test runs within a given week can be attributed with confidence to associated changes in underlying selection strategy. In other embodiments, the test database comprises a different percentage of the full credit bureau database. Furthermore, the test database may be updated at a different advantageous frequency, such as once every two weeks, once every three weeks, once a month, and/or the like.


An embodiment of a system for planning and executing a direct marketing campaign is described. The system includes: a database of consumer credit information comprising records with information about a plurality of consumers; a copy of a subset of the consumer credit information from the database; and a client testing interface that allows a plurality of clients to access the copy of the subset and to individually run one or more tests of prospect selection criteria using the copy of the subset in order to identify a desired set of prospect selection criteria. The system further comprises a campaign interface that is configured to extract consumer information from the database, based, at least in part, on the desired set of prospect selection criteria for use by at least one of the plurality of clients in a direct marketing campaign.


Other embodiments of the system are described which include at least one of the following characteristics: the database of consumer credit information is updated twice weekly; the subset of the consumer credit information from the database comprises information from 10% of the consumer records in the database; and/or the copy of the subset of the database is updated weekly. In one embodiment of the system, the client testing interface further allows a client to use proprietary data that the client owns and that is associated with the subset of consumer credit information in running the one or more tests of the prospect selection criteria.


Embodiments of method are described for generating and maintaining a test environment for identifying desired prospect selection criteria for a direct marketing campaign. The method includes: identifying a sampling of records from a consumer credit data warehouse; calculating a set of generic attributes associated with the records included in the sample; cleansing the set of generic attributes of unwanted attributes; and loading the cleansed set of generic attributes into a test environment. The method further includes, for each of a plurality of clients: calculating, cleansing, and loading into the test environment client-proprietary attributes and client-proprietary data provided by the client that is associated with the records in the sample; and providing the client with access to the generic attributes and to the client-proprietary attributes and data provided by the client in the test environment. The method further includes determining when a lifespan associated with the test environment is completed, and, when the lifespan associated with the test environment is completed, rebuilding the test environment, based at least in part on a new sampling of records from the consumer credit data warehouse.


Embodiments of a method are described for using a sample database environment to test and use selection criteria for a direct marketing campaign on behalf of a client. The method includes: generating a sample database with a specified lifespan from a database of consumer information about a population of consumers, such that the sample database includes data from a portion of a set of consumer records in the database of consumer information; accepting from a client a proposed set of selection criteria for testing; running a test campaign on the sample database using the proposed set of selection criteria; and providing the test campaign results to the client for analysis. The method further includes: repeating the accepting, the running, and the providing with a modified proposed set of selection criteria received from the client until the client indicates it is satisfied with a set of selection criteria or until the lifespan of the sample database is complete; and using the set of selection criteria that satisfies the client to identify a subset of consumers from the database of consumer information for use in a direct marketing campaign.


Embodiments of a computer-readable medium are described, such that the computer-readable medium has stored thereon executable instructions that, when executed by a processor, cause the processor to perform a method for testing and executing a direct marketing campaign. The method includes: generating a sample database from a database of credit information about a population of consumers; accepting from a client a proposed set of selection criteria for testing; running a test campaign on the sample database using the proposed set of selection criteria; and providing the test campaign results to the client for analysis. The method also includes: repeating the accepting, the running, and the providing with a modified proposed set of selection criteria until the client indicates it is satisfied with a set of selection criteria; and using the set of selection criteria to identify a subset of consumers from database of credit information for use in a direct marketing campaign.


An embodiment of a system for testing a proposed direct marketing campaign is described. The system can include a client testing interface configured to access a sample database of depersonalized credit information including records with information about a plurality of consumers. The client testing interface can further be configured to individually run one or more tests of prospect selection criteria using the sample database.


In some embodiments, a system for testing and executing a direct marketing campaign is provided. The system can include a client testing interface configured to access a sample database of depersonalized credit information including records with information about a plurality of consumers. The client testing interface can further be configured to individually run one or more tests of prospect selection criteria using the sample database. The system can further include a campaign interface for use in executing a direct marketing campaign, configured to request that a set of selection criteria be used to identify information from a consumer data warehouse about a set of consumers.


In some embodiments, a system for testing, analyzing, refining, and executing a direct marketing campaign is provided. The system can include a consumer data warehouse database storing consumer population credit-related data including at least one generic attribute. The system can further include a client view including at least one proprietary attribute. The system can further include a sample database of consumer population credit-related data, including a database of core consumer data and a database of client pre-calculated data. The database of core consumer data can include a portion of the consumer data warehouse database. The system can further include an other client data database storing information from past direct marketing campaigns, including at least one response from a consumer to a past direct marketing campaign. The system can further include an extraction and data load module configured to load the at least one generic attribute into the database of core consumer data and the at least one proprietary attribute into the database of client pre-calculated data.


In some embodiments, a method for building a sample database is provided. The method can include receiving and storing in a first database a selection of proprietary attributes from a client. The method can further include receiving and storing in a second database consumer information from past direct marketing campaigns. The method can further include sampling a third database of depersonalized consumer credit information, including records with information about a plurality of consumers. The method can further include extracting and loading a subset of depersonalized credit information from the third database into a fourth database, including copying a subset of the consumer credit information in the third database to the fourth database. The method can further include extracting and loading the first database into the fourth database including copying the proprietary attributes from the first database to the fourth database. The method can further include associating the second database with the fourth database.


In some embodiments, a method for testing, analyzing, refining, and executing a direct marketing campaign is provided. The method can include storing consumer population credit-related data, including at least one generic attribute in a consumer data warehouse database. The method can further include generating a sample database including a portion of the consumer population credit-related data in the consumer data warehouse database. The method can further include receiving a first request from a client to access the sample database to run one or more tests of prospect selection criteria using the sample database. The method can further include receiving a second request including actual selection criteria from a client to execute a direct marketing campaign. The method can further include in response to receiving the second request, accessing the consumer data warehouse database and segmenting a consumer population based on the actual selection criteria.


The disclosure provided in the following pages describe examples of some embodiments of the invention. The designs, figures, and description are non-limiting examples of some embodiments of the invention. Other embodiments of the campaign planning environment may or may not include the features disclosed herein. Moreover, disclosed advantages and benefits may apply to only some embodiments of the invention, and should not be used to limit the invention.





BRIEF DESCRIPTION OF THE DRAWINGS

A general architecture that implements various features of specific embodiments of the invention will now be described with reference to the drawings. The drawings and the associated descriptions are provided to illustrate embodiments of the invention and not to limit the scope of the invention. Throughout the drawings, reference numbers are re-used to indicate correspondence between referenced elements. In addition, the first digit of each reference number indicates the figure in which the element first appears.



FIG. 1A illustrates one embodiment of a system for planning and executing a direct marketing campaign, including typical client components for accessing the system.



FIG. 1B is a block diagram that shows how FIGS. 1B1, 1B2, and 1B3 can be combined to form a single high level block diagram depicting one embodiment of a system for planning and executing a direct marketing campaign.



FIGS. 1B
1, 1B2, and 1B3 are high level block diagrams depicting embodiments of the campaign planning and executing system 100 that improves campaign testing and refining capabilities.



FIG. 2 depicts one embodiment of a partitioning solution that allows a plurality of individual clients to access the same sample database, without corrupting or having access to one another's data.



FIG. 3 is a diagram that depicts one embodiment of a selection of data from the credit database to be included in the “snapshot” sample.



FIG. 4 is a flowchart depicting one embodiment of a process for generating and maintaining a sample database environment.



FIG. 5 is a flowchart that depicts one embodiment of a process 500 for using the sample database environment to assist a client 120 to test, analyze, refine, and execute a marketing campaign.





DETAILED DESCRIPTION

Embodiments of the invention will now be described with reference to the accompanying figures, wherein like numerals refer to like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any limited or restrictive manner, simply because it is being utilized in conjunction with a detailed description of certain specific embodiments of the invention. Furthermore, embodiments of the invention may include several novel features, no single one of which is solely responsible for its desirable attributes or which is essential to practicing the invention herein described.


I. Overview

The present disclosure relates to test environment for planning strategies for direct marketing campaigns. As used herein, a “strategy” refers to a set of selection criteria, also known as selection rules that may be used to form a query for execution on a database of records regarding prospective recipients of a direct marketing offer. The strategy may thus identify one or more attributes associated with the records that may be combined, often in a very complex query, to identify a desired subset of the records in the database.


One example of a database that may be suitable for identifying prospective recipients of a direct marketing offer can be one or more databases of consumer information available from a credit bureau. Such credit bureau databases may comprise both credit-related and non-credit related data, such as demographic data, data from public records, and the like. In addition to credit bureaus, other business entities may provide access to suitable databases of consumer information, that may comprise credit-related and/or non-credit related data.


One example of a direct marketing campaign offer is a firm offer of credit, for which campaign offer recipients may be identified using both credit-related and non-credit related data about consumers. Another example of a direct marketing campaign offer is an offer that is not a firm offer of credit, known as an “Invitation to Apply” (ITA) offer, which may be based on non-credit-related data alone. The systems and methods described herein are contemplated as also being useful for identifying recipients of other types of direct marketing offers that may be based on any of a variety of other types of data. Although, for ease of description, the systems and methods disclosed herein are frequently described as being performed on a credit bureau database and as providing a database environment in which clients can use credit-related data for planning direct marketing campaigns, it is to be understood that, in various embodiments, the campaigns may be planned using either credit-related data, non-credit-related data, or both. Furthermore, the environment may be provided by a credit bureau or other entity providing access to consumer data.


Previous test environments for planning direct marketing campaigns using credit bureau data frequently included a full custom-built copy, known as a “full snapshot” or “100% snapshot,” of the credit database from which consumer names for the final direct marketing campaign are selected. Tasks performed in the 100% snapshot may include some or all of: analysis and campaign development, campaign set-up, audit and reporting on campaign logic, receiving client approval to proceed, and execution of the full direct marketing campaign. Since a credit bureau database may include records for hundreds of millions of consumers, building such a full copy of the database typically involves a significant lag time between initiation of the database snapshot building process and availability of the snapshot of use in testing. Thus, freshness of the data used may be compromised by the time testing begins. This lack of data freshness may be exacerbated when the data in the source database is itself lacking in freshness, due, for example, to infrequent updates.


The lack of data freshness is yet further exacerbated when a direct marketing campaign developer, desiring to test and refine campaign strategies, must submit every new refinement of the campaign selection criteria to a credit bureau representative for running on the credit bureau database and must wait for a credit bureau representative to report on the results. The interjection of a third party into a campaign developer's refinement of a campaign strategy frequently makes the process inordinately cumbersome and time-consuming.


Furthermore, lenders frequently desire to include proprietary data of their own and proprietary attribute definitions for use with the credit bureau data in campaign testing, refining, and finally, execution. The desire to include multiple data sources, including proprietary data for those who can afford the investment, frequently leads to building a proprietary test database for the lender's private use. A proprietary database or snapshot is not only an extremely expensive and time-consuming proposition, both to build initially and to update, but also typically yields a database with data that is out-of-date by the time the database is used for testing and finally executing the campaign strategy.


On the other hand, using a snapshot of a database that is updated very frequently and that cannot be used and stored for re-use during the development of a campaign lessens a campaign developer's confidence that differences in campaign test results obtained from various test runs are the result of changes in the campaign's selection strategies and are not simply, in part or in total, the result of changes between the various snapshots.


Systems and methods are disclosed herein for providing a direct marketing campaign planning environment, such as for marketing campaigns directed to consumers identified using a credit-related database system. Frequently, business entities carrying out a direct marketing campaign first identify a desired set of recipients for one or more marketing offers associated with the campaign, and contact the identified recipients, such as via mail, telephone, email, or the like, with the marketing offer.


For purposes of the present disclosure, a “testing” phase is described in which the business entities may repeatedly test and refine a set of selection criteria for identifying consumers expected to be good prospects for a marketing campaign using a sample database that is a copy of a portion of a large database of consumer records. Once a satisfactory set of selection criteria is obtained, an “execution” phase includes using the selection criteria on the large database of consumer records to identify consumers to be recipients of the direct marketing offer. In some embodiments, contact information for the identified consumers may also be provided. In some embodiments, execution may further comprise using the contact information to contact the identified consumers with the direct marketing offer, and may further include tracking consumer response to the direct marketing campaign.


II. Architecture


FIG. 1A illustrates one embodiment of a system for planning and executing a direct marketing campaign. As depicted in FIG. 1A, the system 100 may comprise a consumer data warehouse 110, which is communication with a credit-related database 102, and a sample database 140. In preferred embodiments, the system 100 comprises one or more server systems (typically comprising multiple physical computers/machines) and associated content that are accessible to a plurality of client computing devices 101 via a network 103. The system 100 can be implemented using physical computer servers that are geographically co-located or that are geographically remote from one another. The system 100 may in some embodiments, include content that spans one or multiple internet domains.


The credit-related database 102 may be configured to receive, update, and store vast amounts of data. For example, in one embodiment, a credit bureau uses the credit-related database 102 for storing data received from a variety of sources for approximately three hundred million consumers. Such data may include, for example, demographic information, credit-related information, and information available from public records. Some or all of the data may be used, among other purposes, to calculate credit scores for the consumers.


The consumer data warehouse 110 may be configured to store a copy or near-copy of the data in the credit-related database 102. In various embodiments, a copy of data from the credit-related database 102 is periodically extracted and reconfigured for updating the consumer data warehouse 110. For example, data from the credit-related database 102 may be processed by a set of ETL (Extract, Transform Load) servers before being transmitted to the consumer data warehouse 110.


After the data has been transformed by the ETL servers, the data may be loaded to the consumer data warehouse 110, such as by way of a high speed server interconnect switch that handles incoming and outgoing communications with the consumer data warehouse 110. As one example, the high speed interconnect switch may be an IBM SP2 switch. In other embodiments, Gig Ethernet, Infiniband, and other high speed interconnects may be used.


Embodiments of an architecture for the consumer data warehouse 110 may be implemented using a Massively Parallel Processing (MPP) hardware infrastructure. In one embodiment, IBM AIX pSeries servers (8-way p655) may act as the MPP building blocks of the consumer data warehouse 110. In other embodiments, other types of servers may act as the MPP building blocks of the system, for example, Linux servers, other types of UNIX servers, and/or Windows Servers. A similar architecture could also be implemented using Symmetric Multi-Processing (SMP) servers, such as IBM P690 32-way server or HP Superdome servers.


In preferred embodiments, a relational database management system (RDBMS), such as a DB2 EEE8.1 system, manages the data in the consumer data warehouse 110.


The system 100 can also include a sample database 140 that stores a temporary copy of a portion of the data in the consumer data warehouse 110, as will be described in greater detail below. The sample database 140 can serve as an environment in which one or more clients may test, refine, and/or validate a proposed marketing campaign before executing the campaign on the full consumer data warehouse 110.



FIG. 1A further illustrates typical client components for accessing the system, in accordance with some embodiments of the invention. As depicted in this drawing, one or more clients can use a general purpose computer or computing device 101 with access to a network 103 to access the system 100.


For example, one or more of the computing devices 101 may be a personal computer that is IBM, Macintosh, or Linux/Unix compatible. In one embodiment, the client computing device 101 includes a central processing unit (CPU), which may include a conventional microprocessor. The computing device 101 further includes a memory, such as random access memory (RAM) for temporary storage of information and a read only memory (ROM) for permanent storage of information, and a mass storage device, such as a hard drive, diskette, or optical media storage device.


The client computing device 101 may include one or more commonly available input/output (I/O) devices and interfaces, such as a keyboard, mouse, touchpad, and printer. In one embodiment, the I/O devices and interfaces include one or more display device, such as a monitor, that allows the visual presentation of data to a user. More particularly, a display device provides for the presentation of GUIs, application software data, and multimedia presentations, for example. The client computing device 101 may also include one or more multimedia devices, such as speakers, video cards, graphics accelerators, and microphones, for example.


The network 103 may comprise one or more of a LAN, WAN, or the Internet, for example, via a wired, wireless, or combination of wired and wireless, communication link that may be configured to be secured or unsecured.


As further illustrated in FIG. 1A, the computer 101 of the client can run a campaign management tool 125 and business intelligence tool 126. The campaign management tool 125 and business intelligence tool 126 can be configured to perform research and analysis functions associated with testing and refining the proposed direct marketing campaign.


Although the credit-related database 102 and the client computing devices 101 have been depicted in FIG. 1A as being external to the system 100, in other embodiments, one or more of the credit-related database 102 and/or the client computing devices 101 may be provided as part of the system 100, in which cases, communications may take place using one or more internal communications systems. Additionally or alternatively, rather than being a copy of the credit-related database 102, the consumer data warehouse 110 may receive update data, including, but not limited to demographic information, credit-related information, and/or information available from public records, directly from other sources.


In some embodiments, clients 120 may access the consumer data warehouse 110 and/or may run campaigns directly rather than via the project manager 130.



FIGS. 1B
1, 1B2, and 1B3 are high level block diagrams depicting embodiments of a campaign testing and executing system 100 that improve campaign testing and refining capabilities. FIG. 1B shows how FIGS. 1B1, 1B2, and 1B3 can be combined to form a single high level block diagram depicting one embodiment of the campaign testing and executing system 100. The campaign testing and executing system 100 may be used, at least in part, by lenders, other providers of credit services or other business entities, and/or marketers working on their behalf (referred to, for purposes of this disclosure, as “clients” 120) who wish to use data available from one or more credit bureaus or other provider of consumer data to help identify consumers who may be interested in the services of the clients.


In various embodiments, the campaign testing and executing system 100 may be implemented using a variety of computer hardware and software resources. For example, computer servers, such as web servers, database servers, campaign management tool servers, and business intelligence tool servers, as well as direct access storage device (DASD) capacity accessible by one or more of the above-described types of servers are used in various embodiments. Furthermore, associated software, such as cluster multi-processing software, campaign management software, business intelligence software, network communications software, and database management software (such as DB2, Oracle, or Sybase, for example) can also be used.


In the embodiments depicted in FIGS. 1B1, 1B2, and 1B3, a consumer data warehouse 110 includes data about millions of consumers. In particular, data in the consumer data warehouse 110 may be a copy or near-copy of data from the database of credit-related data 102, which is used for calculating consumer credit ratings, among other uses, and which is updated daily or more frequently. The data in the consumer data warehouse 110 may be organized so as to make batch processing of the data more expedient and may be updated from the database of credit-related data 102 that is used for calculating consumer credit ratings on a regular, preferably frequent, basis, so that data in the consumer data warehouse 110 includes up-to-date changes in the consumers' records. In a preferred embodiment, the consumer data warehouse 110 is updated twice weekly.


In the consumer data warehouse 110, the data may be organized generally as records of data for each consumer. Each record may be divided conceptually into attributes associated with each consumer. Examples of demographic, credit, or custom attributes that may be useful to clients 120 wishing to identify potential customers may include, but are not limited to: number of credit cards held by the consumer, total available credit, total current balance, number of late payments within the last year, number of late payments within the last three years, no tradelines, and the like. In addition, some attributed may be derived from other attributes, such as but not limited to derived attributes that are aggregations of other attributes or that are calculated from other attributes. In many embodiments, the consumer data warehouse 110 may include hundreds of attributes for each consumer. Some attributes, and especially attributes useful to a wide variety of clients using the system 100, may be pre-calculated for the consumer records and may be generally available to the clients as a generic attribute view 111 from the consumer data warehouse 110. Clients 120 may also wish to define custom attributes for their own use to help identify consumers of interest. Instructions for calculating these proprietary attributes may be input to the consumer data warehouse 110 by a custom attribute coder 160 on behalf of individual clients 120 for use by the individual clients. The custom attributes may be stored in a plurality of client views 112, which allow each client 120 to access only their own proprietary attributes.


In some embodiments, in addition to the attributes in views 111, 112, clients may provide other data 113 that may be used together with the data in the consumer data warehouse 110 to identify potentially good prospects for a direct marketing campaign. For example, clients 120 may wish to include historical information identifying consumers who have previously been contacted in one or more direct marketing campaigns, response history information about consumers who have been contacted, information identifying consumers who have requested not to be contacted, and/or the like. Other non-campaign related data may also be included with the other data 113.


Generally, when a client 120 wishes to run a direct marketing campaign, a campaign flowchart 121 is generated to describe a plan for identifying desired consumers from the records in the consumer data warehouse 110. The campaign flowchart 121 is typically a complex plan for using a large number of attributes from the generic attribute view 111 and the client view 112, along with other client data 113, to categorize the consumers and to select consumers whose attributes place them in one or more desired categories. The campaign flowchart 121 is provided to a project manager 130 who, among other tasks, accepts the campaign flowchart 121 for extracting the desired information, often in the form of consumer names and contact information, from the consumer data warehouse 110.


In order to help the client 120 design a campaign flowchart 121 that successfully identifies consumers appropriate for a given direct marketing campaign, the campaign testing and executing system 100 advantageously includes a sample database 140 that serves as a temporarily available environment in which a client may test, refine, and validate a proposed campaign flowchart 121. The sample database 140 preferably includes data from a random or semi-random sampling of the records in the consumer data warehouse 110 so that results obtained from test campaigns run on the sample database 140 will be statistically meaningful indicators of the results that would be obtained using the full consumer data warehouse 110. It is desirable for the sample database 140 to include a sufficiently large sampling of the consumer data warehouse 110 records to provide a statistically meaningful sample while being sufficiently small to allow for quick building of the database 140 and quick execution of test campaigns. Preferably, the sample database 140 includes fewer records than the consumer data warehouse 110. In one embodiment, a sample size of 10% of the full consumer data warehouse 110 is preferred. That is, the sample database includes at least a portion of the data from 10% of the consumer records in the full consumer data warehouse 110. In other embodiments, other preferred sizes may be used, including 1% to 70%, or 5% to 25% of the records in the credit-related database 102. Although embodiments of the systems and methods are described herein with reference to a 10% sample database 140, embodiments of the systems and methods are also contemplated as being used with a sample database 140 that represent a different portion of the full consumer data warehouse 110.


As depicted in FIGS. 1B1, 1B2, and 1B3, once a sample (for example, 10% of the data warehouse records) is selected, generic attributes from the generic view 111 and proprietary attributes in the client view 112 that are associated with consumers in the sample go through an extraction and data load process executed by a data load module 150 to build the sample database 140. Generic attributes may be stored in a repository of consumer core data 141 and proprietary attributes from the client view 112 may be stored in a repository of client pre-calculated data 142. In various embodiments, certain portions of the selected records the consumer data warehouse 110 may be omitted from records in the sample database 140. For example, consumer name and contact information may be deleted from the records to be used for campaign testing in order to comply with various governmental regulations regarding proper use of consumer credit information. Instead, anonymous identifier numbers and general location information may be used to identify the individual records and to allow a geographically representative sample to be selected. As will be familiar to a practitioner of skill in the art, other methods of anonymizing consumer records while retaining their usefulness for campaign development may also be used.


As depicted in FIGS. 1B1, 1B2, and 1B3, a copy of some or all of the other client data 113 may be stored in a repository of other client data 143 for use with the sample database 140.


In one embodiment, to test, analyze, and refine a proposed campaign, the client 120 uses a campaign management tool 125 and/or a business intelligence tool 126 to access a sample client view 144 that includes data from the repository of sampled consumer core data 141, the associated repository of client pre-calculated data 142, and the repository of other client data 143. The campaign management tool 125 and the business intelligence tool 126 are software applications that may be accessed and run using a personal computer (PC) or any of a variety of other commonly available computing devices in order to send queries to the sample database 140, to generate reports based at least in part on information obtained from the sample database 140, and to perform other research and analysis functions associated with testing and refining the proposed direct marketing campaign. In preferred embodiments, the client 120 may access the campaign testing and executing system 100 by way of the Internet or other communications network 103.


In preferred embodiments, the campaign management tool 125, or another aspect of the campaign testing and executing system 100, provides the client 120 with a “layman, user-friendly” data dictionary that describes elements available within the sample database 140. The client 120 is preferably also provided with a “look-up” capability for various available categories of attributes, for example mortgage-related attributes, credit-rating related attributes, or the like. In some embodiments, the client 120 may additionally or alternatively create and use proprietary attributes for use in the direct marketing campaign.


Preferably, the campaign management tool 125 allows the client 120 to be able to conduct high-level campaign development functions, such as segmentation of the consumer population, selection of one or more such segments, and/or suppression of one or more segments or one or more individual consumers from the selection results.


Furthermore, the campaign management tool 125 preferably provides the client 120 with a capability to construct queries for testing and executing the campaign through a graphic user interface (GUI). The campaign query interface allows for basic and advanced logic to be defined and used to construct queries in one or more database query languages, such as Standard Query Language (SQL). In a preferred embodiment, the query interface provides the client 120 with a capability to create SQL queries directly, to view either or both of SQL queries created directly by the user and/or queries generated via the query builder interface, and to edit either or both of SQL queries created directly by the user and/or queries generated via the query builder interface.


The query interface of the campaign management tool 125 preferably allows the client 120 to name query definitions, to save query definitions, to reuse query definitions. Additionally, in a preferred embodiment, the query interface provides the client 120 with an ability to record and modify campaign selection rules for future use. In some embodiments, the query interface allows the client 120 to share query definitions with one or more authorized users.


Furthermore, in a preferred embodiment, the query interface allows the client 120 to test a query, to view query results, and to print the query results. For each query result, the query interface may have the capability to provide a sample of the underlying data.


In one embodiment, the campaign management tool 125 includes a query interface that allows the client 120 to select individuals from the marketing database based upon individual or household criteria. The query interface allows the client 120 to add data sources for the purpose of selection for individual campaigns. The query interface further provides the client 120 with an ability to select records based on a “times mailed” calculation derived from the historical campaign response data. The query interface may provide the capability to identify customer segments. The query interface may additionally or alternatively provide the client 120 with a capability to utilize independent queries for each segment and segmentation trees to split the customer universe into subgroups.


In some embodiments, the same campaign management tool 125 and the business intelligence tool 126 software applications that are used for running direct marketing campaigns on the full consumer data warehouse 110 (the 100% environment) may also provide all functionality needed for allowing clients 120 to directly create and test campaigns on the sample database 140 (10% environment). In some embodiments, separate query interfaces for campaign testing and campaign execution may be provided. In some embodiments, the campaign management tool 125 and the business intelligence tool 126 software applications may provide some, but not all, preferred functionality for providing the systems and methods disclosed herein, in which case supplemental software may be added to or used in conjunction with the campaign management tool 125 and/or the business intelligence tool 126 software to provide the missing functionality.


The client 120 may run and re-run the test campaign on the sample database 40 as desired, performing champion/challenger tests, for example, and observing the effects of modifications on the campaign results. In various embodiments, the data in the sample database 140 remains temporarily static until the sample database 140 is re-built, using a new randomly selected sampling of the credit-related database 102 records which may take place at regular intervals, such as for example, once a week. Thus, the client 120 can have confidence that the various campaign test runs being run during a given week are being run on the same data. In other embodiments, the data in the sample database 140 may be updated according to another schedule, such daily, monthly, upon demand by one or more clients, at random intervals, or the like.


In various embodiments, the client 120 can run various types of reports using the campaign management tool 125 and/or the business intelligence tool 126 software in order to aid in analysis of the data and test results. For example, in one embodiment, the client 120 may run one or more campaign analysis reports that allow the client 120 to predict response to the direct marketing campaign within a segment or group of segments of the targeted population. The client 120 may also use reports to refine future marketing strategies. In some embodiments, the client 120 may specify a preferred output layout for the reports.


In some embodiments, the campaign management tool 125 and the business intelligence tool 126 software do not communicate directly with one another and do not directly share metadata or queries, although the client 120 may manually transfer queries, for example, from one to the other. In other embodiments, the campaign management tool 125 and the business intelligence tool 126 software may be configured to have access to shared metadata and queries.


Once the client 120 has had an opportunity to test and/or refine the campaign strategy and is satisfied with the campaign strategy, the client may provide the campaign flowchart 121, which reflects the desired campaign strategy, to the project manager 130 for running on the full data warehouse 110 environment as currently updated.


As was described above, in preferred embodiments, the consumer data warehouse 110 is updated twice weekly or at another advantageously frequent interval to insure “freshness” of the data. Thus, although the campaign testing may, in some embodiments, have been run on data that was about ten days old, the actual campaign may be run on data that is three days old or newer. In some embodiments, once the client 120 submits the desired campaign strategy in the form of a campaign flowchart 121 to the project manager 130, either directly or via an intermediary, the campaign may be run on the full consumer data warehouse 110 and results returned to the client 120 within as little as three business days or less. In other embodiments, results may be returned to the client 120 within another advantageously short period of time.


In preferred embodiments, the campaign management tool 125 and/or the business intelligence tool 126 may provide a variety of reporting services to the client 120. For example, the campaign management tool 125 may also provide the client 120 with data about consumer responses received in connection with one or more executed direct marketing campaigns. In other embodiments, the client 120 may receive consumer response reports from another source. In one embodiment, a response analysis report may provide an analysis of responses received from a direct marketing campaign executed through the system 100. The response analysis report may summarize results over periods of time with shorter comparison periods in the immediate weeks after a campaign is executed to longer time frames after the campaign has completed. The response analysis report may provide flexibility to perform analysis of various levels and/or categories of summarization, which may include, but are not limited to: customer segments, product line, product campaign, promotion, offer, collateral, media, and/or vendor.


In some embodiments, a client data maintenance service 165 provides the client 120 with an ability to store campaign-related data related to client campaigns. For example, the client data maintenance service 165 may make campaign data accessible for further campaign development purposes, for analysis purposes, and/or in order to update/delete/archive campaign data for client campaigns. The client data maintenance service 165 may provide the ability to receive and store campaign-related data for direct marketing campaigns that may be common to most or all of a client's campaigns and thus may be useful for future campaigns. The client data maintenance service 165 may collect data of individual promotions in order to derive a “times contacted” calculation for use in future campaign development.


In some embodiments, the client data maintenance service 165 may further record updates to identifying information, such as name, address, and/or telephone information received during a direct marketing campaign. The client data maintenance service 165 may record mail disposition updates for individual consumer records, such as whether a direct mailing advertisement was mailed or not mailed, along with associated explanatory reason codes. The client data maintenance service 165 may record telephone contact disposition updates for individual consumer records, such as whether a direct mailing advertisement call was made or not made, along with associated explanatory reason codes. In other embodiments, other types of data may additionally or alternatively be maintained on behalf of the client 120, by the client data maintenance service 165 and/or as part of the campaign management 125 or business intelligence tool 126 services.


In some embodiments the system 100 may be used for planning a variety of types of campaigns, including, for example, both firm offers of credit and ITA offers. In some embodiments, the system 100 may provide access for clients 120 to two or more sample databases 140, including at least one sample database that includes only non-credit related data. This type of non-credit related sample database may be used, for example, for planning campaigns where the use of consumer credit data is not permitted. In other embodiments, the sample database 140 may be configured to include a mix of credit and non-credit information, such that the system 100 may provide clients 120 with access to both the credit and the non-credit information or to only the non-credit information in the sample database 140.


The methods and processes described above and throughout the present disclosure may be embodied in, and fully automated via, software code modules executed by one or more general purpose computers/processors. The code modules may be stored in any type of computer-readable medium or other computer storage device. Some or all of the methods may alternatively be embodied in specialized computer hardware.



FIG. 2 depicts one embodiment of a partitioning solution that allows a plurality of individual clients 120′, 120″, 120′″, 120″″ to access the same sample database 140, without corrupting or having access to one another's proprietary data. As was depicted in FIGS. 1B1, 1B2, and 1B3, the sample database 140 includes a repository of core consumer data 141, available to all clients, which includes data associated with a randomly selected portion, such as 10%, of the records in the full consumer data warehouse 110. In a preferred embodiment, once a week, a new 10% sample of the full consumer data warehouse 110 is randomly selected, extracted from the consumer data warehouse 110, cleansed of undesirable or unnecessary attributes, such as name, address, other contact information, and the like, and is loaded into the repository of core consumer data 141. In addition, pre-calculated attributes associated with the generic attribute view 111 for the selected portion of the consumer records can be calculated and loaded in to the repository of core consumer data 141 that is commonly available to clients using the sample database 140.


Furthermore, the system 100 may provide each client 120 accessing the sample database 140 with additional proprietary data owned by the client. The proprietary data may include custom attributes, as defined in the client view 112 of the consumer data warehouse 110 and/or may be custom attributes defined for the current campaign. The custom attributes can be calculated for the randomly selected portion of the consumer records and are loaded in the repository of client pre-calculated data 142 in the sample database 140. In addition, other client data 143, proprietary to each client, may be made available use for by the associated client. For example, client-specific campaign history data and/or client-specific response history data may be provided to clients 120 using the sample database 140. This test environment which persists for one week, or for another desired span of time, provides a stable environment that is very helpful to campaign developers.


As depicted in FIG. 2, the generic data available to all clients and the proprietary data exclusive to individual clients 120′, 120″, 120′″, 120″″ are depicted as Schema 1, Schema 2, Schema 3, and Schema 4. Each schema may, in various embodiments, include the client pre-calculated data 142 for the 10% or other sized sample and/or the other client data 143 that were described with reference to FIGS. 1B1, 1B2, and 1B3 and may be used with the data in the repository of core customer data 141.



FIG. 2 shows that each schema is accessible only to its respective client 120, and is shielded from access or use by other clients by a system of partition. The partition plan depicted in FIG. 2 allows a plurality of clients 120 to access the sample database 140 simultaneously or nearly simultaneously for testing and refining their respective campaign strategies. In one embodiment, the campaign testing and executing system 100 allows for from one up to one hundred clients 120 to access the same sample database 140, including the shared core consumer data 141 and each client's 120 proprietary data, simultaneously. Clients may pose up to one hundred queries per month and may extract from less than one up to over thirty million names per month. In other embodiments, the system 100 may accommodate more than one hundred clients 120 and/or may allow the clients 120 to pose up to another advantageous number of queries and/or extracted names per given time period. In still further embodiments, the system 100 may provide multiple sample databases 140 for use by one or more clients 120.


Starting at the bottom of FIG. 2, a plurality of clients 120′, 120″, 120′″, 120″″ access the business intelligence 126 and/or campaign management 125 tools for testing and refining their respective campaign strategies. A firewall 210 allows access to the system for clients 120 with approved campaign credentials and protects computers used by the individual clients from improper access by others.


The clients 120′, 120″, 120′″, 120″″ access the campaign web application server 220 and are given access to their respective partitions. In some embodiments, the clients 120′, 120″, 120′″, 120″″ can connect using a Virtual Private Network (VPN) and/or can use vendor specific user credentials. In one embodiment, access to the campaign server 240 is controlled by an Access Control List (ACL) 230, such as an ACL that makes use of a password or other identifier to correctly authenticate a client 20 wishing to access the system 100, as will be understood by one of ordinary skill in the art in light of the present disclosure. The campaign server 240 accesses the data stored in the sample database server 140 in order to carry out the queries, tests, report generation, and the like that may be requested by the individual clients 120. Once again, communications between the sample database server 140 and the campaign server 240 is controlled by means of an ACL 250. In some embodiments, the sample database server 140 can be implemented using a relational database, such as IBM DB2, Sybase, Oracle, CodeBase and Microsoft® SQL Server as well as other types of databases such as, for example, a flat file database, an entity-relationship database, and object-oriented database, and/or a record-based database.


Thus, clients gain the benefits typically associated with a custom test and execution database system that includes their own data as well as very up-to-date generic consumer data without a substantial delay for database build time and without the very costly up-front financial investment that are typically associated with proprietary databases.


Furthermore, as was described with reference to FIGS. 1B1, 1B2, and 1B3 above, in some embodiments, in addition to having access to different proprietary data 142, 143, different clients may also be provided with access to different portions of the core consumer data 141 in the sample database 140. For example, one or more clients 120 planning a first type of campaign may be allowed access to all of the data in the core consumer data 141, while clients 120 planning a second type of campaign may be allowed access to a portion of the data in the core consumer data 141. Furthermore, in some embodiments, a given client 120 may be allowed access to all of the data in the core consumer data 141 for a first type of campaign, while the same client 120 may, at the same time, be allowed access to only a portion of the data in the core consumer data 141 for use in planning a second type of campaign. In one embodiment, access control to the data in the core consumer data 141 of the sample database 140 is implemented, at least in part, using the various schemas for the various clients 120, as depicted in FIG. 2.



FIG. 3 is a diagram that depicts one embodiment of a selection of data from the credit database to be included in the “snapshot” sample database 140. In the embodiment shown, the following types of data are included: consumer credit information 310, consumer non-credit information 320, client custom data 330, client contact history data 340, and client response data 350. In some embodiments, the client 20 uses the campaign management software tool 125 for campaign development and/or the business intelligence tool 126 for analysis and reporting. The consumer credit information 310 and the consumer non-credit information 320 both come as part of the 10% sample data load from the generic attributes 111 in the consumer data warehouse 110. Together, they make up the consumer core data 141 depicted in FIGS. 1B1, 1B2, 1B3 and FIG. 2. In this embodiment, each record in the consumer credit information 310 and the consumer non-credit information 320 includes a household identifier (HSN), a personal identifier (PIN), and a selection of consumer attributes. The client custom data 330 is stored in the repository of proprietary attributes 142 depicted in FIGS. 1B1, 1B2, and 1B3. In this embodiment, each record in the client custom data 330 includes a household identifier (HSN), a personal identifier (PIN), and a selection of proprietary consumer attributes. In various embodiments, client custom information may not include consumer name, street address or encrypted PIN information, and does include, for each consumer, an element, such as zip code, that can be matched to data within the sample database 141 and the consumer data warehouse 110. The client contact history data 340 and the client response data 350 may both come from the copy of the other client data 143 in the sample database 140, which includes data about the full population of consumers. These data sources are used, for example, to filter campaign results based on past contact history and client response information. The past contact history and client response information may indicate that if a given consumer appears in the list of campaign results, the consumer should be removed from list, for any of a variety of reasons. In some embodiments, some of the data from the client provided data 330, 340, 350 records may not be used for campaign development, in order to comply with federal and other regulations that control the use of credit-related data for advertising purposes.


In one embodiment, a sample test environment is built that represents data from a random 10% of a consumer credit database. The 10% test environment may be used for analysis, campaign development, campaign set-up, and for executing, auditing and reporting on logic proposed for the campaign. The client 120 may review results of the above and may approve or decline to approve execution of the proposed campaign strategy on the full and most recently updated version of the consumer credit database. If the client 120 declines approval, the client may choose to modify and re-test the campaign strategy one or more times until a desired campaign strategy is achieved. Thus, the full campaign executes the desired campaign strategy in the 100% environment of the full consumer data warehouse 110.


Although the foregoing systems and methods have been described in terms of certain preferred embodiments, other embodiments will be apparent to those of ordinary skill in the art from the disclosure herein. For example, in some embodiments, it may be desirable to add one or more demographic tables to allow for development of Invitation to Apply (ITA) lists (non-credit data lists) for direct marketing campaigns by clients. In some embodiments, credit data from more than one credit bureau may be available for use in connection with the systems and methods described herein.


Furthermore, although the systems and methods disclosed herein have been described by way of embodiments in which the clients 120 are typically credit providers or marketers working to plan direct marketing campaigns on their behalf, other embodiments, in which clients 120 are other types of business entities who wish to make use information from the consumer data warehouse 110, especially for planning direct marketing campaigns, are also envisioned.


IV. Operation


FIG. 4 is a flowchart depicting one embodiment of a process 400 for generating and maintaining a sample database environment 140. In block 410, the system 100 performs a sampling of the records in the credit-related database 102. In preferred embodiments, the sampling includes data about a portion of the records in the credit-related database 102. The data may include some or all of the attributes and other data stored for the sampled records. In some preferred embodiments, the sampling includes data about a percentage or fraction of the records, such as 10% of the records, or another percentage of the records in the range of 5%-25% of the records. In some embodiments, the sampling is a randomly selected portion of the records in the credit-related database 102. In other embodiments, the sampling may be selected partially randomly, such as by segmenting the records in the credit-related database 102 and by randomly selecting a portion of the records from one or more of the segments.


In block 420, the system 100 calculates one or more generic attributes associated with the sampled records. The generic attributes may be calculated from attribute definitions stored in one or more generic attribute views 111 and commonly available to clients 120 of the system 100.


In block 430, the data load module 150 of the system 100 cleanses and loads data from the sampled records and the associated attributes. For example, undesirable or unnecessary attributes, such as name, address, other contact information, and the like may be removed from the sample being used for the sample database 140 in order to comply with rules and regulations that govern the use of credit-related data. Attributes may also be removed from the sample data in order to decrease the size of the sample database 140, so that building and running tests on the sample database 140 may be carried out efficiently and expeditiously.


The processes in blocks 440 and 450 are carried out individually for each client 120 using the sample database 140. In block 440, the data load module 150 of the system 100 cleanses and loads client-proprietary attributes, such as those stored in the client's client view 112, deleting undesirable or unnecessary attributes. The data load module 150 may also load other attributes defined by the client for use in the current campaign strategy and/or may load other client data 143 provided by the client 120.


In block 450, the system 100 provides the client 120 with access to the sample database 140, including the generic 141 and the proprietary 142, 143 data. As was described with reference to FIG. 2, the sample database 140 is advantageously configured, using a system of one or more partitions, firewalls, Access Control Lists (ACLs), and/or other security measures to provide access to the same generic data 141 to a plurality of clients 120, simultaneously or near-simultaneously. Additionally, each client 120 may access and use their own client-proprietary data in conjunction with the generic data, without danger that there client-proprietary data will unintentionally become available to other clients 120. This configuration provides for a shared database 140 that economically and efficiently provides a shared environment for campaign testing, while also providing the benefits of a usually much costlier custom testing environment that also for the secure inclusion of proprietary attributes and other information in the campaign strategy testing.


In block 460 the system 100 determines if a lifespan associated with the current version of the sample database 140 is complete. As one example, in embodiments in which the sample database 120 is updated weekly, the lifespan is one week. If the lifespan associated with the current version of the sample database 140 is not yet complete, the system 100 continues to provide the clients 120′, 120″, 120′″ with access to the sample database 140. If the lifespan associated with the current version of the sample database 140 is complete, the process 400 returns to block 410 where the system 100 creates a new version of the sample database 140 to replace the previous version.



FIG. 5 is a flowchart that depicts one embodiment of a process 500 for using the direct marketing campaign planning environment to assist a client 120 to test, analyze, refine, and execute a marketing campaign, in accordance with some embodiments disclosed herein.


In block 505, the system 100 generates a sample database 140 test environment, as has been described with reference to FIG. 4 and elsewhere throughout the present disclosure.


In block 510, the system 100 accepts from the client 120 a proposed set of campaign selection rules to be tested for implementing a campaign strategy. The goal of the campaign strategy may be to identify good prospects for a direct marketing campaign. The goal of the testing may be to identify selection rules, also known as prospect selection criteria, that can successfully identify from the sample database 140 a desired set of prospects for the campaign and that can thus be predicted to identify from the full database of consumer data 110 a desired set of prospects for the campaign being planned. The selection rules may, in some embodiments, be formatted as a database query based on attributes associated with records in the sample database 140 test environment. In some embodiments, the campaign management tool 125 and the business intelligence tool 126 can be configured to provide the client 120 with a data dictionary that describes various categories of attributes available for segmenting the consumer populations, such as mortgage-related attributes, credit-related attributes, various proprietary attributes, and/or the like.


In block 520, the system 100 runs, on behalf of the client 120, a test campaign on the sample database 140 using the proposed campaign selection rules. In some embodiments, the campaign management tool 125 and the business intelligence tool 126 can be configured to access a sample client view 144 of the sample database 140. The sample client view 144 can be configured to include data from the repository of core consumer data 141, repository of client pre-calculated data 142, and repository of other client data 143. In some embodiments, the campaign management tool 125 and/or business intelligence tool 126 can be configured to provide the client 120 with a GUI that provides a query interface to run, name, construct, save, and/or reuse queries for the sample database 140. The queries can, in some embodiments, correspond to campaign selection rules. In some embodiments, the query interface can be configured to provide the client 120 with the ability to record and modify campaign selection rules for future.


In block 530, the system 100 provides results of the test campaign performed as described in block 520 to the client 120 for analysis. The campaign management tool 125 and/or business intelligence tool 126 can be configured to allow the client 120 to conduct high-level campaign development functions, such as segmentation of the consumer population, selection of one or more segments, and/or suppression of one or more segments or one or more individual consumers from the selection results, using individual, household, and/or other criteria. The campaign management tool 125 and/or business intelligence tool 126 can also be configured to generate reports based at least in part on information obtained from the sample database 140, and/or to perform other research and analysis functions associated with testing and refining the proposed direct marketing campaign. In some embodiments, the campaign management tool 125 and/or business intelligence tool 126 can be configured to generate reports predicting the response to the direct marketing campaign within a segment or group of segments of the targeted population.


In block 540, the system 100 receives from the client 120 an indication as to whether it is satisfied with the campaign strategy. If the client 120 is not satisfied, and if the lifespan of the sample database 140 is not yet expired, then the process 500 returns to block 510, and the testing and refining process can be repeated. The client 120 may modify and update the campaign selection rules and re-run the test campaign using new prospect selection criteria. In some embodiments, if the client 120 is not satisfied, and if the lifespan of the sample database 140 has expired, then the client 120 may continue testing the campaign selection rules once the sample database 140 has been rebuilt using a new randomly selected portion of the consumer data warehouse 110.


Alternatively, if, in block 540, the client 120 is satisfied with the results of the current set of prospect selection criteria, the process 500 moves to block 550 where the client 120 can provide a campaign flowchart 121. The campaign flowchart 121 can be configured to specify the desired campaign strategy.


In block 555, the campaign flowchart 121 is used as a specification for running a direct marketing campaign on the full consumer data warehouse 110 using the selection criteria identified during the testing on the sample database 140. In some embodiments, the project manager 130 accepts the campaign flowchart 121 from the client 120 and causes the campaign to be executed on the full consumer data warehouse 110. In some embodiments, the campaign with the tested selection criteria may be run on full consumer data warehouse 110 directly by the client 120 and/or may be run on another database of consumer information


In block 560, the system 100 may optionally provide the client 120 with one or more consumer response reports associated with the direct marketing campaign. In some embodiments, the campaign management tool 125 and/or business intelligence tool 126 can be configured to provide the client 120 with consumer response reports received in connection with one or more direct marketing campaigns actually carried out. The consumer response reports may provide an analysis of consumer responses received from a direct marketing campaign. In some embodiments, the reports may summarize results over periods of time, including shorter comparison periods in the immediate weeks after a campaign is executed, and/or longer time frames, such as years after the campaign has completed.


The reports may provide flexibility to perform analysis of various levels and/or categories of summarization, which may include, but are not limited to: customer segments, product line, product campaign, promotion, offer, collateral, media, and/or vendor. In some embodiments, a client data maintenance service 165 can also be configured to store campaign-related data from executed campaigns that can, in some embodiments, be utilized for future campaigns. In some embodiments, the other client data 113 and/or repository of other client data 143 can be configured to store the campaign-related data for use in future campaigns.


IV. Various Embodiments of System and Method Implementations

In various embodiments, the systems and methods for providing a direct marketing campaign planning and execution environment may be embodied in part or in whole in software that is running on one or more computing devices. The functionality provided for in the components and modules of the computing device(s), including computing devices included in the system 100, may comprise one or more components and/or modules. For example, the computing device(s) may comprise multiple central processing units (CPUs) and a mass storage device(s), such as may be implemented in an array of servers. In one embodiment, the computing device comprises a server, a laptop computer, a cell phone, a personal digital assistant, a smartphone or other handheld device, a kiosk, or an audio player, for example.


In general, the word “module,” “application”, or “engine,” as used herein, refers to logic embodied in hardware and/or firmware, and/or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, Ruby, Ruby on Rails, Lua, C and/or C++. These may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that modules, applications, and engines may be callable from others and/or from themselves, and/or may be invoked in response to detected events or interrupts. Instructions may be embedded in firmware, such as an EPROM.


It will be further appreciated that hardware may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays or processors. The modules, applications, and engines described herein are in certain applications preferably implemented as software modules, but may be represented in hardware or firmware in other implementations. Generally, the modules, applications, and engines described herein refer to logical modules that may be combined with other modules and/or divided into sub-modules despite their physical organization or storage.


In some embodiments, the computing device(s) communicates with one or more databases that store information, including credit data and/or non-credit data. This database or databases may be implemented using a relational database, such as Sybase, Oracle, CodeBase and Microsoft® SQL Server as well as other types of databases such as, for example, a flat file database, an entity-relationship database, and object-oriented database, and/or a record-based database.


In one embodiment, the computing device is IBM, Macintosh, and/or Linux/Unix compatible. In another embodiment, the computing device comprises a server, a laptop computer, a cell phone, a Blackberry, a personal digital assistant, a kiosk, or an audio player, for example. In one embodiment, the computing device includes one or more CPUs, which may each include microprocessors. The computing device may further include one or more memory devices, such as random access memory (RAM) for temporary storage of information and read only memory (ROM) for permanent storage of information, and one or more mass storage devices, such as hard drives, diskettes, or optical media storage devices. In one embodiment, the modules of the computing are in communication via a standards based bus system, such as bus systems using Peripheral Component Interconnect (PCI), Microchannel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA) architectures, for example. In certain embodiments, components of the computing device communicate via a network, such as a local area network that may be secured.


The computing is generally controlled and coordinated by operating system software, such as the Windows 95, Windows 98, Windows NT, Windows 2000, Windows XP, Windows Vista, Linux, SunOS, Solaris, PalmOS, Blackberry OS, or other compatible operating systems. In Macintosh systems, the operating system may be any available operating system, such as MAC OS X. In other embodiments, the computing device may be controlled by a proprietary operating system. Conventional operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, and I/O services, and provide a user interface, such as a graphical user interface (GUI), among other things.


The computing device may include one or more commonly available input/output (I/O) devices and interfaces, such as a keyboard, mouse, touchpad, microphone, and printer. Thus, in one embodiment the computing device may be controlled using the keyboard and mouse input devices, while in another embodiment the user may provide voice commands to the computing device via a microphone. In one embodiment, the I/O devices and interfaces include one or more display device, such as a monitor, that allows the visual presentation of data to a user. More particularly, a display device provides for the presentation of GUIs, application software data, and multimedia presentations, for example. The computing device may also include one or more multimedia devices, such as speakers, video cards, graphics accelerators, and microphones, for example.


In one embodiment, the I/O devices and interfaces provide a communication interface to various external devices. For example, the computing device may be configured to communicate with one or more networks, such as any combination of one or more LANs, WANs, or the Internet, for example, via a wired, wireless, or combination of wired and wireless, communication links. The network communicates with various computing devices and/or other electronic devices via wired or wireless communication links.


Although the foregoing invention has been described in terms of certain embodiments, other embodiments will be apparent to those of ordinary skill in the art from the disclosure herein. Moreover, the described embodiments have been presented by way of example only, and are not intended to limit the scope of the invention. Indeed, the novel methods and systems described herein may be embodied in a variety of other forms without departing from the spirit thereof. Accordingly, other combinations, omissions, substitutions and modifications will be apparent to the skilled artisan in view of the disclosure herein. For purposes of discussing the invention, certain aspects, advantages and novel features of the invention have been described herein. Of course, it is to be understood that not necessarily all such aspects, advantages or features will be embodied in any particular embodiment of the invention.

Claims
  • 1. A computer system for generating and maintaining a test environment architecture for developing custom attributes, comprising: a data warehouse comprising data records corresponding to millions of consumers, each data record associated with one or more consumer data values;a campaign server comprising one or more processors which when programmed execute instructions, comprising: accessing the data warehouse;partitioning the campaign server into at least a first virtual electronic partition allowing access to a first user, and a second virtual electronic partition allowing access to a second user;receiving, from a first user device associated with the first user, a first indication specifying first definitions for a first set of custom attributes and a first request to access the campaign server;receiving, from a second user device associated with the second user, a second indication specifying second definitions for a second set of custom attributes and a second request to access the campaign server;authenticating the first and second user devices requesting access to the campaign server;generating a first sample data set comprising a first random or semi-random sampling of the data records of the data warehouse based on the first definitions;generating a second sample data set comprising a second random or semi-random sampling of the data records of the data warehouse based on the second definitions;accessing a first custom data set associated with the first user and the first virtual electronic partition;accessing a second custom data set associated with the second user and the second virtual electronic partition;via the first virtual electronic partition, allowing the first user to query and test for a first marketing campaign test using the first sample data set and the first custom data set, while restricting access by the first user to the second virtual electronic partition and the second custom data set, wherein querying and testing for the first marketing campaign test comprises generating values for at least a portion of the first set of custom attributes using the first sample data set and the first custom data set; andvia the second virtual electronic partition, allowing the second user to query and test for a second marketing campaign test using the second sample data set and the second custom data set, while restricting access by the second user to first virtual electronic partition and the first custom data set, wherein querying and testing for the second marketing campaign test comprises generating values for at least a portion of the second set of custom attributes using the second sample data set and the second custom data set.
  • 2. The computer system of claim 1, wherein the one or more processors are further configured to: determine whether a preset lifespan associated with the first sample data set is completed; andin response to a determination that the preset lifespan has completed, regenerate the first sample data set based upon the data records of the data warehouse.
  • 3. The computer system of claim 2, wherein the lifespan associated with the first sample data set is longer than a period of time between updates performed on the data warehouse.
  • 4. The computer system of claim 1, wherein the first sample data set is the same as the second sample data set.
  • 5. The computer system of claim 1, wherein the first sample data set corresponds to a data set size of between 5% and 25% of the data records of the data warehouse.
  • 6. The computer system of claim 1, wherein the data warehouse further comprises first proprietary data and second proprietary data corresponding to the plurality of customers, and wherein the first user may, via the first virtual electronic partition, access the first proprietary data but not the second proprietary data, and the second user may, via the second virtual electronic partition, access the second proprietary data but not the first proprietary data.
  • 7. The computer system of claim 1, wherein generating the first data sets comprises cleansing the sampled data records of the data warehouse.
  • 8. The computer system of claim 1, wherein the one or more processors are further programmed to: receive a first campaign strategy from the first user;receive a second campaign strategy from the second user;provide a first user interface configured to allow the first user query and test for the first marketing campaign test via the first virtual electronic partition using the first campaign strategy; andprovide a second user interface configured to allow the second user to query and test for the second marketing campaign test via the second virtual electronic partition using the second campaign strategy.
  • 9. A computer-implemented method for generating and maintaining a test environment architecture for developing custom attributes, comprising: accessing, via a campaign server, a data warehouse comprising data records corresponding to millions of consumers, each data record associated with one or more consumer data values;partitioning a campaign server into at least a first virtual electronic partition allowing access to a first user, and a second virtual electronic partition allowing access to a second user;receiving, from a first user device associated with the first user, a first indication specifying first definitions for a first set of custom attributes and a first request to access the campaign server;receiving, from a second user device associated with the second user, a second indication specifying second definitions for a second set of custom attributes and a second request to access the campaign server;authenticating the first and second user devices requesting access to the campaign server;generating a first sample data set comprising a first random or semi-random sampling of the data records of the data warehouse based on the first definitions;generating a second sample data set comprising a second random or semi-random sampling of the data records of the data warehouse based on the second definitions;accessing a first custom data set associated with the first user and the first virtual electronic partition;accessing a second custom data set associated with the second user and the second virtual electronic partition;via the first virtual electronic partition, allowing the first user to query and test for a first marketing campaign test using the first sample data set and the first custom data set, while restricting access by the first user to the second virtual electronic partition and the second custom data set, wherein querying and testing for the first marketing campaign test comprises generating values for at least a portion of the first set of custom attributes using the first sample data set and the first custom data set; andvia the second virtual electronic partition, allowing the second user to query and test for a second marketing campaign test using the second sample data set and the second custom data set, while restricting access by the second user to first virtual electronic partition and the first custom data set, wherein querying and testing for the second marketing campaign test comprises generating values for at least a portion of the second set of custom attributes using the second sample data set and the second custom data set.
  • 10. The computer-implemented method of claim 9, further comprising: determining whether a preset lifespan associated with the first sample data set is completed; andin response to a determination that the preset lifespan has completed, regenerating the first sample data set based upon the data records of the data warehouse.
  • 11. The computer-implemented method of claim 9, wherein the first sample data set is the same as the second sample data set.
  • 12. The computer-implemented method of claim 9, wherein the first sample data set corresponds to a data set size of between 5% and 25% of the data records of the data warehouse.
  • 13. The computer-implemented method of claim 9, wherein the data warehouse further comprises first proprietary data and second proprietary data corresponding to the plurality of customers, and wherein the first user may, via the first virtual electronic partition, access the first proprietary data but not the second proprietary data, and the second user may, via the second virtual electronic partition, access the second proprietary data but not the first proprietary data.
  • 14. The computer-implemented method of claim 9, further comprising: receiving a first campaign strategy from the first user;receiving a second campaign strategy from the second user;providing a first user interface configured to allow the first user query and test for the first marketing campaign test via the first virtual electronic partition using the first campaign strategy; andproviding a second user interface configured to allow the second user to query and test for the second marketing campaign test via the second virtual electronic partition using the second campaign strategy.
  • 15. A non-transitory computer storage medium which stores a client application comprising executable code, the executable code, when executed by the computing device, causing the computing device to perform operations for generating and maintaining a test environment architecture for developing custom attributes, comprising: accessing, via a campaign server, a data warehouse comprising data records corresponding to a plurality of consumers, each data record associated with one or more consumer data values;partitioning a campaign server into at least a first virtual electronic partition allowing access to a first user, and a second virtual electronic partition allowing access to a second user;receiving, from a first user device associated with the first user, a first indication specifying first definitions for a first set of custom attributes and a first request to access the campaign server;receiving, from a second user device associated with the second user, a second indication specifying second definitions for a second set of custom attributes and a second request to access the campaign server;authenticating the first and second user devices requesting access to the campaign server;generating a first sample data set comprising a first random or semi-random sampling of the data records of the data warehouse based on the first definitions;generating a second sample data set comprising a second random or semi-random sampling of the data records of the data warehouse based on the second definitions;accessing a first custom data set associated with the first user and the first virtual electronic partition;accessing a second custom data set associated with the second user and the second virtual electronic partition;via the first virtual electronic partition, allowing the first user to query and test for a first marketing campaign test using the first sample data set and the first custom data set, while restricting access by the first user to the second virtual electronic partition and the second custom data set, wherein querying and testing for the first marketing campaign test comprises generating values for at least a portion of the first set of custom attributes using the first sample data set and the first custom data set; andvia the second virtual electronic partition, allowing the second user to query and test for a second marketing campaign test using the second sample data set and the second custom data set, while restricting access by the second user to first virtual electronic partition and the first custom data set, wherein querying and testing for the second marketing campaign test comprises generating values for at least a portion of the second set of custom attributes using the second sample data set and the second custom data set.
  • 16. The non-transitory computer storage medium of claim 15, wherein the operations further comprise: determining whether a preset lifespan associated with the first sample data set is completed;in response to a determination that the preset lifespan has completed, regenerating the first sample data set based upon the data records of the data warehouse.
  • 17. The non-transitory computer storage medium of claim 15, wherein the first sample data set is the same as the second sample data set.
  • 18. The non-transitory computer storage medium of claim 15, wherein the first sample data set corresponds to a data set size of between 5% and 25% of the data records of the data warehouse.
  • 19. The non-transitory computer storage medium of claim 15, wherein the data warehouse further comprises first proprietary data and second proprietary data corresponding to the plurality of customers, and wherein the first user may, via the first virtual electronic partition, access the first proprietary data but not the second proprietary data, and the second user may, via the second virtual electronic partition, access the second proprietary data but not the first proprietary data.
  • 20. The non-transitory computer storage medium of claim 15, the operations further comprising: receiving a first campaign strategy from the first user;receiving a second campaign strategy from the second user; andproviding a first user interface configured to allow the first user query and test for the first marketing campaign test via the first virtual electronic partition using the first campaign strategy; andproviding a second user interface configured to allow the second user to query and test for the second marketing campaign test via the second virtual electronic partition using the second campaign strategy.
PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No. 14/090,834 filed on Nov. 26, 2013, issued as U.S. Pat. No. 9,508,092, entitled “SYSTEMS AND METHODS FOR PROVIDING A DIRECT MARKETING CAMPAIGN PLANNING ENVIRONMENT,” which is a divisional of U.S. patent application Ser. No. 12/022,874 filed on Jan. 30, 2008, issued as U.S. Pat. No. 8,606,626, entitled “SYSTEMS AND METHODS FOR PROVIDING A DIRECT MARKETING CAMPAIGN PLANNING ENVIRONMENT,” which claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 60/887,521, filed Jan. 31, 2007, and entitled “SYSTEMS AND METHODS FOR PROVIDING A DIRECT MARKETING CAMPAIGN PLANNING ENVIRONMENT,” all of the entire contents of which are hereby incorporated by reference in their entirety herein and should be considered a part of this specification.

US Referenced Citations (1035)
Number Name Date Kind
3316395 Lavin et al. Apr 1967 A
4305059 Benton Dec 1981 A
4371739 Lewis et al. Feb 1983 A
4398055 Ijaz et al. Aug 1983 A
4578530 Zeidler Mar 1986 A
4617195 Mental Oct 1986 A
4672149 Yoshikawa et al. Jun 1987 A
4736294 Gill Apr 1988 A
4754544 Hanak Jul 1988 A
4774664 Campbell et al. Sep 1988 A
4775935 Yourick Oct 1988 A
4876592 Von Kohorn Oct 1989 A
4895518 Arnold Jan 1990 A
4947028 Gorog Aug 1990 A
4982346 Girouard et al. Jan 1991 A
5025138 Cuervo Jun 1991 A
5025373 Keyser, Jr. et al. Jun 1991 A
5034807 Von Kohorn Jul 1991 A
5060153 Nakagawa Oct 1991 A
5148365 Dembo Sep 1992 A
5201010 Deaton Apr 1993 A
5220501 Lawlor et al. Jun 1993 A
5259766 Sack Nov 1993 A
5262941 Saladin Nov 1993 A
5274547 Zoffel et al. Dec 1993 A
5283731 Lalonde et al. Feb 1994 A
5297031 Gutterman et al. Mar 1994 A
5305195 Murphy Apr 1994 A
5325509 Lautzenheiser Jun 1994 A
5336870 Hughes et al. Aug 1994 A
5454030 De Oliveira et al. Sep 1995 A
5459306 Stein et al. Oct 1995 A
5468988 Glatfelter et al. Nov 1995 A
5504675 Cragun et al. Apr 1996 A
5515098 Carles May 1996 A
5521813 Fox et al. May 1996 A
5563783 Stolfo et al. Oct 1996 A
5583763 Atcheson et al. Dec 1996 A
5590038 Pitroda Dec 1996 A
5592560 Deaton et al. Jan 1997 A
5611052 Dykstra et al. Mar 1997 A
5615408 Johnson Mar 1997 A
5621201 Langhans et al. Apr 1997 A
5627973 Armstrong et al. May 1997 A
5629982 Micali May 1997 A
5630127 Moore et al. May 1997 A
5640577 Scharmer Jun 1997 A
5649114 Deaton et al. Jul 1997 A
5659731 Gustafson Aug 1997 A
5661516 Carles Aug 1997 A
5679176 Tsuzuki et al. Oct 1997 A
5689651 Lozman Nov 1997 A
5692107 Simoudis et al. Nov 1997 A
5696907 Tom Dec 1997 A
5704029 Wright, Jr. Dec 1997 A
5724521 Dedrick Mar 1998 A
5732400 Mandler Mar 1998 A
5740549 Reilly et al. Apr 1998 A
5745654 Titan Apr 1998 A
5771562 Harvey et al. Jun 1998 A
5774868 Cragun et al. Jun 1998 A
5774883 Andersen Jun 1998 A
5793972 Shane Aug 1998 A
5802142 Browne Sep 1998 A
5819226 Gopinathan et al. Oct 1998 A
5822750 Jou et al. Oct 1998 A
5828833 Belville Oct 1998 A
5835915 Carr et al. Nov 1998 A
5844218 Kawan et al. Dec 1998 A
5848396 Gerace Dec 1998 A
5857175 Day et al. Jan 1999 A
5864830 Armetta et al. Feb 1999 A
5870721 Norris Feb 1999 A
5873068 Beaumont et al. Feb 1999 A
5875236 Jankowitz Feb 1999 A
5878403 DeFrancesco Mar 1999 A
5881131 Farris Mar 1999 A
5884287 Edesess Mar 1999 A
5884289 Anderson et al. Mar 1999 A
5893090 Friedman Apr 1999 A
5912839 Ovshinsky et al. Jun 1999 A
5924082 Silverman et al. Jul 1999 A
5926800 Baronowski et al. Jul 1999 A
5930764 Melchione et al. Jul 1999 A
5930774 Chennault Jul 1999 A
5930776 Dykstra Jul 1999 A
5933813 Teicher et al. Aug 1999 A
5940812 Tengel et al. Aug 1999 A
5944790 Levy Aug 1999 A
5950172 Klingman Sep 1999 A
5953707 Huang et al. Sep 1999 A
5956693 Geerlings Sep 1999 A
5966695 Melchione et al. Oct 1999 A
5974396 Anderson et al. Oct 1999 A
5991735 Gerace Nov 1999 A
5995947 Fraser et al. Nov 1999 A
6014688 Venkatraman et al. Jan 2000 A
6021362 Maggard et al. Feb 2000 A
6026368 Brown et al. Feb 2000 A
6029139 Cunningham et al. Feb 2000 A
6029149 Dykstra et al. Feb 2000 A
6029154 Pettitt Feb 2000 A
6038551 Barlow et al. Mar 2000 A
6044357 Garg Mar 2000 A
6058375 Park May 2000 A
6061658 Chou et al. May 2000 A
6061691 Fox May 2000 A
6064973 Smith et al. May 2000 A
6064987 Walker May 2000 A
6070141 Houvener May 2000 A
6070142 McDonough et al. May 2000 A
6070147 Harms et al. May 2000 A
6073140 Morgan et al. Jun 2000 A
6088686 Walker et al. Jul 2000 A
6094643 Anderson et al. Jul 2000 A
6101486 Roberts et al. Aug 2000 A
6105007 Norris Aug 2000 A
6115690 Wong Sep 2000 A
6115693 McDonough et al. Sep 2000 A
6119103 Basch et al. Sep 2000 A
6128599 Walker Oct 2000 A
6128603 Dent Oct 2000 A
6134548 Gottsman et al. Oct 2000 A
6144948 Walker et al. Nov 2000 A
6178442 Yamazaki Jan 2001 B1
6198217 Suzuki et al. Mar 2001 B1
6202053 Christiansen et al. Mar 2001 B1
6208979 Sinclair Mar 2001 B1
6223171 Chaudhuri et al. Apr 2001 B1
6233566 Levine et al. May 2001 B1
6236977 Verba et al. May 2001 B1
6239352 Luch May 2001 B1
6249770 Erwin et al. Jun 2001 B1
6254000 Degen et al. Jul 2001 B1
6263334 Fayyad Jul 2001 B1
6263337 Fayyad Jul 2001 B1
6269325 Lee et al. Jul 2001 B1
6278055 Forrest et al. Aug 2001 B1
6285983 Jenkins Sep 2001 B1
6289318 Barber Sep 2001 B1
6298330 Gardenswartz et al. Oct 2001 B1
6307958 Deaton et al. Oct 2001 B1
6311169 Duhon Oct 2001 B2
6317752 Lee et al. Nov 2001 B1
6321205 Eder Nov 2001 B1
6324524 Lent et al. Nov 2001 B1
6324566 Himmel et al. Nov 2001 B1
6330546 Gopinathan et al. Dec 2001 B1
6330575 Moore et al. Dec 2001 B1
6334110 Walter et al. Dec 2001 B1
6345300 Bakshi Feb 2002 B1
6366903 Agrawal et al. Apr 2002 B1
6385592 Angles et al. May 2002 B1
6385594 Lebda et al. May 2002 B1
6393406 Eder May 2002 B1
6405173 Honarvar Jun 2002 B1
6405181 Lent et al. Jun 2002 B2
6412012 Bieganski et al. Jun 2002 B1
6418436 Degen et al. Jul 2002 B1
6424956 Werbos Jul 2002 B1
6430539 Lazarus et al. Aug 2002 B1
6442577 Britton et al. Aug 2002 B1
6456979 Flagg Sep 2002 B1
6457012 Jatkowski Sep 2002 B1
6460036 Herz Oct 2002 B1
6505168 Rothman et al. Jan 2003 B1
6513018 Culhane Jan 2003 B1
6542894 Lee et al. Apr 2003 B1
6567791 Lent et al. May 2003 B2
6597775 Lawyer et al. Jul 2003 B2
6601234 Bowman-Amuah Jul 2003 B1
6611816 Lebda et al. Aug 2003 B2
6615193 Kingdon et al. Sep 2003 B1
6615247 Murphy Sep 2003 B1
6622266 Goddard et al. Sep 2003 B1
6623529 Lakritz Sep 2003 B1
6640215 Galperin et al. Oct 2003 B1
6654727 Tilton Nov 2003 B2
6658393 Basch et al. Dec 2003 B1
6665715 Houri Dec 2003 B1
6687713 Mattson et al. Feb 2004 B2
6708166 Dysart Mar 2004 B1
6714918 Hillmer et al. Mar 2004 B2
6735572 Landesmann May 2004 B2
6748426 Shaffer et al. Jun 2004 B1
6757740 Parekh et al. Jun 2004 B1
6782390 Lee et al. Aug 2004 B2
6804346 Mewhinney Oct 2004 B1
6810356 Garcia-Franco et al. Oct 2004 B1
6823319 Lynch et al. Nov 2004 B1
6836764 Hucal Dec 2004 B1
6839682 Blume et al. Jan 2005 B1
6839690 Foth et al. Jan 2005 B1
6850606 Lawyer et al. Feb 2005 B2
6859785 Case Feb 2005 B2
6865566 Serrano-Morales et al. Mar 2005 B2
6873979 Fishman et al. Mar 2005 B2
6901406 Nabe et al. May 2005 B2
6910624 Natsuno Jun 2005 B1
6925441 Jones, III et al. Aug 2005 B1
6959281 Freeling et al. Oct 2005 B1
6965889 Serrano-Morales et al. Nov 2005 B2
6983478 Grauch et al. Jan 2006 B1
6985887 Sunstein et al. Jan 2006 B1
6991159 Zenou Jan 2006 B2
6993493 Galperin et al. Jan 2006 B1
7003504 Angus et al. Feb 2006 B1
7003792 Yuen Feb 2006 B1
7028052 Chapman et al. Apr 2006 B2
7031945 Donner Apr 2006 B1
7033792 Zhong et al. Apr 2006 B2
7039176 Borodow et al. May 2006 B2
7050986 Vance et al. May 2006 B1
7050989 Hurt et al. May 2006 B1
7054828 Heching et al. May 2006 B2
7072853 Shkedi Jul 2006 B2
7072963 Anderson et al. Jul 2006 B2
7076462 Nelson et al. Jul 2006 B1
7076475 Honarvar et al. Jul 2006 B2
7082435 Guzman et al. Jul 2006 B1
7085734 Grant et al. Aug 2006 B2
7117172 Black Oct 2006 B1
7133935 Hedy Nov 2006 B2
7136448 Venkataperumal et al. Nov 2006 B1
7143063 Lent Nov 2006 B2
7150030 Eldering et al. Dec 2006 B1
7152018 Wicks Dec 2006 B2
7152053 Serrano-Morales et al. Dec 2006 B2
7152237 Flickinger et al. Dec 2006 B2
7165036 Kruk et al. Jan 2007 B2
7165037 Lazarus et al. Jan 2007 B2
7185353 Schlack Feb 2007 B2
7200602 Jonas Apr 2007 B2
7212995 Schulkins May 2007 B2
7234156 French et al. Jun 2007 B2
7240059 Bayliss et al. Jul 2007 B2
7249048 O'Flaherty Jul 2007 B1
7249114 Burchetta et al. Jul 2007 B2
7263506 Lee et al. Aug 2007 B2
7275083 Seibel et al. Sep 2007 B1
7277875 Serrano-Morales et al. Oct 2007 B2
7283974 Katz et al. Oct 2007 B2
7296734 Pliha Nov 2007 B2
7313618 Braemer Dec 2007 B2
7314166 Anderson et al. Jan 2008 B2
7314167 Kiliccote Jan 2008 B1
7324962 Valliani et al. Jan 2008 B1
7328169 Temares et al. Feb 2008 B2
7337133 Bezos et al. Feb 2008 B1
7343149 Benco Mar 2008 B2
7346573 Cobrinik et al. Mar 2008 B1
7360251 Spalink et al. Apr 2008 B2
7366694 Lazerson Apr 2008 B2
7373324 Engin et al. May 2008 B1
7376603 Mayr et al. May 2008 B1
7376618 Anderson et al. May 2008 B1
7376714 Gerken May 2008 B1
7379880 Pathria et al. May 2008 B1
7383215 Navarro et al. Jun 2008 B1
7386786 Davis et al. Jun 2008 B2
7392203 Edison et al. Jun 2008 B2
7392216 Palmgren et al. Jun 2008 B1
7409362 Calabria Aug 2008 B2
7424439 Fayyad et al. Sep 2008 B1
7428509 Klebanoff Sep 2008 B2
7428526 Miller et al. Sep 2008 B2
7433855 Gavan et al. Oct 2008 B2
7444302 Hu et al. Oct 2008 B2
7451095 Bradley et al. Nov 2008 B1
7458508 Shao et al. Dec 2008 B1
7467401 Cicchitto Dec 2008 B2
7472088 Taylor et al. Dec 2008 B2
7499868 Galperin et al. Mar 2009 B2
7505938 Lang et al. Mar 2009 B2
7509117 Yum Mar 2009 B2
7512221 Toms Mar 2009 B2
7516149 Motwani et al. Apr 2009 B2
7529698 Joao May 2009 B2
7542993 Satterfield et al. Jun 2009 B2
7546266 Beirne et al. Jun 2009 B2
7546619 Anderson et al. Jun 2009 B2
7548886 Kirkland et al. Jun 2009 B2
7552089 Bruer et al. Jun 2009 B2
7556192 Wokaty, Jr. Jul 2009 B2
7562184 Henmi et al. Jul 2009 B2
7571139 Giordano et al. Aug 2009 B1
7575157 Barnhardt et al. Aug 2009 B2
7580856 Pliha Aug 2009 B1
7581112 Brown et al. Aug 2009 B2
7584126 White Sep 2009 B1
7584146 Duhon Sep 2009 B1
7590589 Hoffberg Sep 2009 B2
7593893 Ladd et al. Sep 2009 B1
7596716 Frost et al. Sep 2009 B2
7610216 May et al. Oct 2009 B1
7610243 Haggerty et al. Oct 2009 B2
7610257 Abrahams Oct 2009 B1
7620592 O'Mara et al. Nov 2009 B2
7620596 Knudson et al. Nov 2009 B2
7623844 Herrmann et al. Nov 2009 B2
7624068 Heasley et al. Nov 2009 B1
7653592 Flaxman et al. Jan 2010 B1
7657471 Sankaran et al. Feb 2010 B1
7668769 Baker et al. Feb 2010 B2
7668840 Bayliss et al. Feb 2010 B2
7672865 Kumar et al. Mar 2010 B2
7676751 Allen et al. Mar 2010 B2
7686214 Shao et al. Mar 2010 B1
7689494 Torre et al. Mar 2010 B2
7689504 Warren et al. Mar 2010 B2
7689505 Kasower Mar 2010 B2
7690032 Peirce Mar 2010 B1
7698236 Cox et al. Apr 2010 B2
7702550 Perg et al. Apr 2010 B2
7707059 Reed et al. Apr 2010 B2
7707102 Rothstein Apr 2010 B2
7708190 Brandt et al. May 2010 B2
7711635 Steele et al. May 2010 B2
7725300 Pinto et al. May 2010 B2
7730509 Boulet et al. Jun 2010 B2
7734523 Cui et al. Jun 2010 B1
7734539 Ghosh et al. Jun 2010 B2
7742982 Chaudhuri et al. Jun 2010 B2
7747480 Agresta et al. Jun 2010 B1
7747559 Leitner et al. Jun 2010 B2
7752236 Williams et al. Jul 2010 B2
7761384 Madhogarhia Jul 2010 B2
7778885 Semprevivo et al. Aug 2010 B1
7783515 Kumar et al. Aug 2010 B1
7783562 Ellis Aug 2010 B1
7788147 Haggerty et al. Aug 2010 B2
7788152 Haggerty et al. Aug 2010 B2
7792732 Haggerty et al. Sep 2010 B2
7793835 Coggeshall et al. Sep 2010 B1
7801811 Merrell et al. Sep 2010 B1
7802104 Dickinson Sep 2010 B2
7805345 Abrahams et al. Sep 2010 B2
7805362 Merrell et al. Sep 2010 B1
7814004 Haggerty et al. Oct 2010 B2
7822665 Haggerty et al. Oct 2010 B2
7827115 Weller et al. Nov 2010 B2
7835983 Lefner et al. Nov 2010 B2
7836111 Shan Nov 2010 B1
7840484 Haggerty et al. Nov 2010 B2
7844534 Haggerty et al. Nov 2010 B2
7848987 Haig Dec 2010 B2
7853518 Cagan Dec 2010 B2
7853998 Blaisdell et al. Dec 2010 B2
7856397 Whipple et al. Dec 2010 B2
7856494 Kulkarni Dec 2010 B2
7870078 Clark et al. Jan 2011 B2
7877320 Downey Jan 2011 B1
7890420 Haggerty et al. Feb 2011 B2
7912770 Haggerty et al. Mar 2011 B2
7912865 Akerman et al. Mar 2011 B2
7925549 Looney et al. Apr 2011 B2
7925582 Kornegay et al. Apr 2011 B1
7930285 Abraham et al. Apr 2011 B2
7941363 Tanaka et al. May 2011 B2
7958126 Schachter Jun 2011 B2
7962404 Metzger, II et al. Jun 2011 B1
7966255 Wong et al. Jun 2011 B2
7970676 Feinstein Jun 2011 B2
7983976 Nafeh et al. Jul 2011 B2
7991666 Haggerty et al. Aug 2011 B2
7991677 Haggerty et al. Aug 2011 B2
7991689 Brunzell et al. Aug 2011 B1
7996521 Chamberlain et al. Aug 2011 B2
8005759 Hirtenstein et al. Aug 2011 B2
8006261 Haberman et al. Aug 2011 B1
8015045 Galperin et al. Sep 2011 B2
8024245 Haggerty et al. Sep 2011 B2
8024264 Chaudhuri et al. Sep 2011 B2
8027871 Willams et al. Sep 2011 B2
8036979 Torrez et al. Oct 2011 B1
8060424 Kasower Nov 2011 B2
8065234 Liao et al. Nov 2011 B2
8073752 Haggerty et al. Dec 2011 B2
8073768 Haggerty et al. Dec 2011 B2
8078524 Crawford et al. Dec 2011 B2
8082202 Weiss Dec 2011 B2
8086509 Haggerty et al. Dec 2011 B2
8086524 Craig et al. Dec 2011 B1
8103530 Quiring et al. Jan 2012 B2
8121918 Haggerty et al. Feb 2012 B2
8126805 Sulkowski et al. Feb 2012 B2
8127982 Casey et al. Mar 2012 B1
8131614 Haggerty et al. Mar 2012 B2
8131639 Haggerty et al. Mar 2012 B2
8135607 Willams et al. Mar 2012 B2
8145754 Chamberlain et al. Mar 2012 B2
8160960 Fei et al. Apr 2012 B1
8161104 Tomkow Apr 2012 B2
8170938 Haggerty et al. May 2012 B2
8175945 Haggerty et al. May 2012 B2
8180654 Berkman et al. May 2012 B2
8185408 Baldwin, Jr. et al. May 2012 B2
8204774 Chwast et al. Jun 2012 B2
8209250 Bradway et al. Jun 2012 B2
8214238 Fairfield et al. Jul 2012 B1
8214262 Semprevivo et al. Jul 2012 B1
8271313 Williams et al. Sep 2012 B2
8271378 Chaudhuri et al. Sep 2012 B2
8280805 Abrahams et al. Oct 2012 B1
8285577 Galperin et al. Oct 2012 B1
8285656 Chang et al. Oct 2012 B1
8290840 Kasower Oct 2012 B2
8296229 Yellin et al. Oct 2012 B1
8301574 Kilger et al. Oct 2012 B2
8315942 Haggerty et al. Nov 2012 B2
8315943 Torrez et al. Nov 2012 B2
8326671 Haggerty et al. Dec 2012 B2
8326672 Haggerty et al. Dec 2012 B2
8352343 Haggerty et al. Jan 2013 B2
8386377 Xiong et al. Feb 2013 B1
8392334 Hirtenstein et al. Mar 2013 B2
8401889 Chwast et al. Mar 2013 B2
8458074 Showalter Jun 2013 B2
8468198 Tomkow Jun 2013 B2
8478673 Haggerty et al. Jul 2013 B2
8510184 Imrev et al. Aug 2013 B2
8515862 Zhang et al. Aug 2013 B2
8533322 Chamberlain et al. Sep 2013 B2
8560434 Morris et al. Oct 2013 B2
8560666 Low Oct 2013 B2
8595101 Daukas et al. Nov 2013 B1
8600854 Mayr et al. Dec 2013 B2
8606626 DeSoto et al. Dec 2013 B1
8606666 Courbage et al. Dec 2013 B1
8626563 Williams et al. Jan 2014 B2
8626646 Torrez et al. Jan 2014 B2
8630929 Haggerty et al. Jan 2014 B2
8639920 Stack et al. Jan 2014 B2
8694390 Imrey et al. Apr 2014 B2
8706596 Cohen et al. Apr 2014 B2
8732004 Ramos et al. May 2014 B1
8738515 Chaudhuri et al. May 2014 B2
8775299 Achanta et al. Jul 2014 B2
8781953 Kasower Jul 2014 B2
8825544 Imrey et al. Sep 2014 B2
9508092 De Soto et al. Nov 2016 B1
9563916 Torrez et al. Feb 2017 B1
9619579 Courbage et al. Apr 2017 B1
9652802 Kasower May 2017 B1
20010011245 Duhon Aug 2001 A1
20010013011 Day et al. Aug 2001 A1
20010014868 Herz et al. Aug 2001 A1
20010016833 Everling et al. Aug 2001 A1
20010027413 Bhutta Oct 2001 A1
20010039523 Iwamoto Nov 2001 A1
20020019804 Sutton Feb 2002 A1
20020023051 Kunzle et al. Feb 2002 A1
20020023143 Stephenson Feb 2002 A1
20020035511 Haji et al. Mar 2002 A1
20020046096 Srinivasan et al. Apr 2002 A1
20020046099 Frengut et al. Apr 2002 A1
20020049626 Mathis et al. Apr 2002 A1
20020049701 Nabe et al. Apr 2002 A1
20020049738 Epstein Apr 2002 A1
20020049968 Wilson et al. Apr 2002 A1
20020052836 Galperin et al. May 2002 A1
20020055869 Hegg May 2002 A1
20020055906 Katz et al. May 2002 A1
20020069122 Yun et al. Jun 2002 A1
20020077964 Brody et al. Jun 2002 A1
20020082892 Raffel et al. Jun 2002 A1
20020087460 Hornung Jul 2002 A1
20020095360 Joao Jul 2002 A1
20020099641 Mills et al. Jul 2002 A1
20020099649 Lee et al. Jul 2002 A1
20020099824 Bender et al. Jul 2002 A1
20020111845 Chong Aug 2002 A1
20020123904 Amengual et al. Sep 2002 A1
20020123928 Eldering et al. Sep 2002 A1
20020128960 Lambiotte et al. Sep 2002 A1
20020128962 Kasower Sep 2002 A1
20020129368 Schlack et al. Sep 2002 A1
20020133404 Pedersen Sep 2002 A1
20020133444 Sankaran et al. Sep 2002 A1
20020138331 Hosea et al. Sep 2002 A1
20020138333 DeCotiis et al. Sep 2002 A1
20020138334 DeCotiis et al. Sep 2002 A1
20020138417 Lawrence Sep 2002 A1
20020143661 Tumulty et al. Oct 2002 A1
20020147623 Rifaat Oct 2002 A1
20020147669 Taylor et al. Oct 2002 A1
20020147695 Khedkar et al. Oct 2002 A1
20020161664 Shaya et al. Oct 2002 A1
20020161711 Sartor et al. Oct 2002 A1
20020165757 Lisser Nov 2002 A1
20020169747 Chapman et al. Nov 2002 A1
20020184255 Edd Dec 2002 A1
20020188544 Wizon et al. Dec 2002 A1
20020194099 Weiss Dec 2002 A1
20020194103 Nabe Dec 2002 A1
20020194140 Makuck Dec 2002 A1
20020198824 Cook Dec 2002 A1
20030000568 Gonsiorawski Jan 2003 A1
20030002639 Huie Jan 2003 A1
20030004787 Tripp et al. Jan 2003 A1
20030004855 Dutta et al. Jan 2003 A1
20030004865 Kinoshita Jan 2003 A1
20030009368 Kitts Jan 2003 A1
20030009393 Norris et al. Jan 2003 A1
20030009418 Green et al. Jan 2003 A1
20030009426 Ruiz-Sanchez Jan 2003 A1
20030018549 Fei Jan 2003 A1
20030018769 Foulger et al. Jan 2003 A1
20030023489 McGuire et al. Jan 2003 A1
20030033242 Lynch et al. Feb 2003 A1
20030033261 Knegendorf Feb 2003 A1
20030036996 Lazerson Feb 2003 A1
20030041031 Hedy Feb 2003 A1
20030041050 Smith et al. Feb 2003 A1
20030046222 Bard et al. Mar 2003 A1
20030046223 Crawford Mar 2003 A1
20030061132 Yu et al. Mar 2003 A1
20030061163 Durfield Mar 2003 A1
20030065563 Elliott et al. Apr 2003 A1
20030078877 Beirne et al. Apr 2003 A1
20030093311 Knowlson May 2003 A1
20030093366 Halper et al. May 2003 A1
20030097320 Gordon May 2003 A1
20030105696 Kalotay et al. Jun 2003 A1
20030110111 Nalebuff et al. Jun 2003 A1
20030110293 Friedman et al. Jun 2003 A1
20030113727 Girn et al. Jun 2003 A1
20030115080 Kasravi et al. Jun 2003 A1
20030115133 Bian Jun 2003 A1
20030120591 Birkhead et al. Jun 2003 A1
20030135451 O'Brien et al. Jul 2003 A1
20030139986 Roberts Jul 2003 A1
20030144950 O'Brien et al. Jul 2003 A1
20030149610 Rowan et al. Aug 2003 A1
20030158751 Suresh et al. Aug 2003 A1
20030164497 Carcia et al. Sep 2003 A1
20030167218 Field et al. Sep 2003 A1
20030182214 Taylor Sep 2003 A1
20030195830 Merkoulovitch et al. Oct 2003 A1
20030195859 Lawrence Oct 2003 A1
20030205845 Pichler et al. Nov 2003 A1
20030208428 Raynes et al. Nov 2003 A1
20030212618 Keyes et al. Nov 2003 A1
20030216965 Libman Nov 2003 A1
20030217003 Weinflash et al. Nov 2003 A1
20030225656 Aberman et al. Dec 2003 A1
20030225692 Bosch et al. Dec 2003 A1
20030233278 Marshall Dec 2003 A1
20030233323 Bilski et al. Dec 2003 A1
20030233655 Gutta et al. Dec 2003 A1
20030236738 Lange et al. Dec 2003 A1
20040002916 Timmerman et al. Jan 2004 A1
20040006536 Kawashima et al. Jan 2004 A1
20040010443 May et al. Jan 2004 A1
20040019518 Abraham et al. Jan 2004 A1
20040023637 Johnson et al. Feb 2004 A1
20040024692 Turbeville et al. Feb 2004 A1
20040029311 Snyder et al. Feb 2004 A1
20040030649 Nelson et al. Feb 2004 A1
20040030667 Xu et al. Feb 2004 A1
20040033375 Mori Feb 2004 A1
20040034570 Davis et al. Feb 2004 A1
20040039681 Cullen et al. Feb 2004 A1
20040039686 Klebanoff Feb 2004 A1
20040039688 Sulkowski et al. Feb 2004 A1
20040044615 Xue et al. Mar 2004 A1
20040044617 Lu Mar 2004 A1
20040046497 Shaepkens et al. Mar 2004 A1
20040049452 Blagg Mar 2004 A1
20040054619 Watson et al. Mar 2004 A1
20040059653 Verkuylen et al. Mar 2004 A1
20040062213 Koss Apr 2004 A1
20040078248 Altschuler Apr 2004 A1
20040078809 Drazin Apr 2004 A1
20040088221 Katz et al. May 2004 A1
20040093278 Burchetta et al. May 2004 A1
20040098625 Lagadec et al. May 2004 A1
20040102197 Dietz May 2004 A1
20040103147 Flesher et al. May 2004 A1
20040107123 Haffner et al. Jun 2004 A1
20040107125 Guheen et al. Jun 2004 A1
20040111305 Gavan et al. Jun 2004 A1
20040111358 Lange et al. Jun 2004 A1
20040111363 Trench et al. Jun 2004 A1
20040117235 Shacham Jun 2004 A1
20040122730 Tucciarone et al. Jun 2004 A1
20040122735 Meshkin Jun 2004 A1
20040128193 Brice et al. Jul 2004 A1
20040128230 Oppenheimer et al. Jul 2004 A1
20040128232 Descloux Jul 2004 A1
20040128236 Brown et al. Jul 2004 A1
20040139025 Coleman Jul 2004 A1
20040139035 Wang Jul 2004 A1
20040143526 Monasterio et al. Jul 2004 A1
20040143546 Wood et al. Jul 2004 A1
20040153330 Miller et al. Aug 2004 A1
20040153448 Cheng et al. Aug 2004 A1
20040158520 Noh Aug 2004 A1
20040163101 Swix Aug 2004 A1
20040167793 Masuoka et al. Aug 2004 A1
20040176995 Fusz Sep 2004 A1
20040177030 Shoham Sep 2004 A1
20040177046 Ogram Sep 2004 A1
20040193535 Barazesh Sep 2004 A1
20040199456 Flint et al. Oct 2004 A1
20040199458 Ho Oct 2004 A1
20040199462 Starrs Oct 2004 A1
20040199584 Kirshenbaum et al. Oct 2004 A1
20040205157 Bibelnieks et al. Oct 2004 A1
20040212299 Ishikawa et al. Oct 2004 A1
20040220896 Finlay Nov 2004 A1
20040225586 Woods et al. Nov 2004 A1
20040225594 Nolan, III et al. Nov 2004 A1
20040230448 Schaich Nov 2004 A1
20040230459 Dordick et al. Nov 2004 A1
20040230527 Hansen et al. Nov 2004 A1
20040230820 Hui Hsu et al. Nov 2004 A1
20040243518 Clifton et al. Dec 2004 A1
20040261116 Mckeown et al. Dec 2004 A1
20050015330 Beery et al. Jan 2005 A1
20050021397 Cui et al. Jan 2005 A1
20050021476 Candella et al. Jan 2005 A1
20050027632 Zeitoun et al. Feb 2005 A1
20050027983 Klawon Feb 2005 A1
20050033734 Chess et al. Feb 2005 A1
20050038726 Salomon et al. Feb 2005 A1
20050050027 Yeh et al. Mar 2005 A1
20050058262 Timmins et al. Mar 2005 A1
20050065874 Lefner et al. Mar 2005 A1
20050086261 Mammone Apr 2005 A1
20050091164 Varble Apr 2005 A1
20050097039 Kulcsar et al. May 2005 A1
20050102206 Savasoglu et al. May 2005 A1
20050102226 Oppenheimer et al. May 2005 A1
20050125350 Tidwell et al. Jun 2005 A1
20050130704 McParland et al. Jun 2005 A1
20050137963 Ricketts et al. Jun 2005 A1
20050144067 Farahat et al. Jun 2005 A1
20050144641 Lewis Jun 2005 A1
20050154664 Guy et al. Jul 2005 A1
20050154665 Kerr Jul 2005 A1
20050159996 Lazaraus et al. Jul 2005 A1
20050177489 Neff et al. Aug 2005 A1
20050192008 Desai Sep 2005 A1
20050197953 Broadbent et al. Sep 2005 A1
20050197954 Maitland et al. Sep 2005 A1
20050201272 Wang et al. Sep 2005 A1
20050209922 Hofmeister Sep 2005 A1
20050222900 Fuloria et al. Oct 2005 A1
20050222906 Chen Oct 2005 A1
20050228692 Hodgon Oct 2005 A1
20050251820 Stefanik et al. Nov 2005 A1
20050256809 Sadri Nov 2005 A1
20050262014 Fickes Nov 2005 A1
20050262158 Sauermann Nov 2005 A1
20050273442 Bennett et al. Dec 2005 A1
20050278246 Friedman et al. Dec 2005 A1
20050278542 Pierson et al. Dec 2005 A1
20050278743 Flickinger et al. Dec 2005 A1
20050279824 Anderson et al. Dec 2005 A1
20050288954 McCarthy et al. Dec 2005 A1
20050288998 Verma et al. Dec 2005 A1
20050289003 Thompson et al. Dec 2005 A1
20060004731 Seibel et al. Jan 2006 A1
20060010055 Morita et al. Jan 2006 A1
20060014129 Coleman et al. Jan 2006 A1
20060031158 Orman Feb 2006 A1
20060031747 Wada et al. Feb 2006 A1
20060032909 Seegar Feb 2006 A1
20060041443 Horvath Feb 2006 A1
20060041464 Powers et al. Feb 2006 A1
20060059073 Walzak Mar 2006 A1
20060059110 Madhok et al. Mar 2006 A1
20060074986 Mallalieu et al. Apr 2006 A1
20060080126 Greer et al. Apr 2006 A1
20060080230 Freiberg Apr 2006 A1
20060080233 Mendelovich et al. Apr 2006 A1
20060080251 Fried et al. Apr 2006 A1
20060080263 Willis et al. Apr 2006 A1
20060089842 Medawar Apr 2006 A1
20060095363 May May 2006 A1
20060095923 Novack et al. May 2006 A1
20060100954 Schoen May 2006 A1
20060122921 Comerford et al. Jun 2006 A1
20060129428 Wennberg Jun 2006 A1
20060129481 Bhatt et al. Jun 2006 A1
20060131390 Kim Jun 2006 A1
20060136330 DeRoy Jun 2006 A1
20060149674 Cook Jul 2006 A1
20060155624 Schwartz Jul 2006 A1
20060155639 Lynch et al. Jul 2006 A1
20060161435 Atef et al. Jul 2006 A1
20060173726 Hall et al. Aug 2006 A1
20060173772 Hayes et al. Aug 2006 A1
20060173776 Shalley et al. Aug 2006 A1
20060178957 LeClaire Aug 2006 A1
20060178971 Owen et al. Aug 2006 A1
20060178983 Nice et al. Aug 2006 A1
20060195390 Rusk et al. Aug 2006 A1
20060202012 Grano et al. Sep 2006 A1
20060204051 Holland, IV Sep 2006 A1
20060206379 Rosenberg Sep 2006 A1
20060206416 Farias Sep 2006 A1
20060212350 Ellis et al. Sep 2006 A1
20060212353 Roslov et al. Sep 2006 A1
20060218069 Aberman et al. Sep 2006 A1
20060218079 Goldblatt et al. Sep 2006 A1
20060224696 King et al. Oct 2006 A1
20060229943 Mathias et al. Oct 2006 A1
20060229961 Lyftogt et al. Oct 2006 A1
20060229996 Keithley Oct 2006 A1
20060239512 Petrillo Oct 2006 A1
20060242046 Haggerty et al. Oct 2006 A1
20060242047 Haggerty et al. Oct 2006 A1
20060242048 Haggerty et al. Oct 2006 A1
20060242050 Haggerty et al. Oct 2006 A1
20060253323 Phan et al. Nov 2006 A1
20060253328 Kohli et al. Nov 2006 A1
20060253358 Delgrosso et al. Nov 2006 A1
20060259364 Strock et al. Nov 2006 A1
20060262929 Vatanen et al. Nov 2006 A1
20060265243 Racho et al. Nov 2006 A1
20060265323 Winter et al. Nov 2006 A1
20060267999 Cash et al. Nov 2006 A1
20060271456 Romain et al. Nov 2006 A1
20060271457 Romain et al. Nov 2006 A1
20060271552 McChesney et al. Nov 2006 A1
20060277102 Agliozzo Dec 2006 A1
20060277141 Palmer Dec 2006 A1
20060282328 Gerace et al. Dec 2006 A1
20060282359 Nobili et al. Dec 2006 A1
20060287915 Boulet et al. Dec 2006 A1
20060287919 Rubens et al. Dec 2006 A1
20060293921 McCarthy et al. Dec 2006 A1
20060293932 Cash et al. Dec 2006 A1
20060293954 Anderson et al. Dec 2006 A1
20060293955 Wilson et al. Dec 2006 A1
20060293979 Cash et al. Dec 2006 A1
20060294199 Bertholf Dec 2006 A1
20070005508 Chiang Jan 2007 A1
20070011020 Martin Jan 2007 A1
20070011026 Higgins et al. Jan 2007 A1
20070011039 Oddo Jan 2007 A1
20070011083 Bird et al. Jan 2007 A1
20070016500 Chatterji et al. Jan 2007 A1
20070016501 Chatterji et al. Jan 2007 A1
20070016518 Atkinson et al. Jan 2007 A1
20070016522 Wang Jan 2007 A1
20070022032 Anderson et al. Jan 2007 A1
20070022141 Singleton et al. Jan 2007 A1
20070027778 Schellhammer et al. Feb 2007 A1
20070027791 Young et al. Feb 2007 A1
20070030282 Cash et al. Feb 2007 A1
20070033227 Gaito et al. Feb 2007 A1
20070038483 Wood Feb 2007 A1
20070038516 Apple et al. Feb 2007 A1
20070043654 Libman Feb 2007 A1
20070055598 Arnott et al. Mar 2007 A1
20070055599 Arnott Mar 2007 A1
20070061195 Liu et al. Mar 2007 A1
20070061243 Ramer et al. Mar 2007 A1
20070067207 Haggerty et al. Mar 2007 A1
20070067208 Haggerty et al. Mar 2007 A1
20070067209 Haggerty et al. Mar 2007 A1
20070067285 Blume et al. Mar 2007 A1
20070067297 Kublickis Mar 2007 A1
20070067437 Sindambiwe Mar 2007 A1
20070072190 Aggarwal Mar 2007 A1
20070078741 Haggerty et al. Apr 2007 A1
20070078835 Donnelli Apr 2007 A1
20070078985 Shao et al. Apr 2007 A1
20070083460 Bachenheimer Apr 2007 A1
20070093234 Willis et al. Apr 2007 A1
20070094137 Phillips et al. Apr 2007 A1
20070106582 Baker et al. May 2007 A1
20070112667 Rucker May 2007 A1
20070118393 Rosen et al. May 2007 A1
20070156515 Hasselback et al. Jul 2007 A1
20070157110 Gandhi et al. Jul 2007 A1
20070168246 Haggerty et al. Jul 2007 A1
20070168267 Zimmerman et al. Jul 2007 A1
20070169189 Crespo et al. Jul 2007 A1
20070174122 Howard et al. Jul 2007 A1
20070192165 Haggerty et al. Aug 2007 A1
20070192248 West Aug 2007 A1
20070192409 Kleinstern et al. Aug 2007 A1
20070205266 Carr et al. Sep 2007 A1
20070208619 Branam et al. Sep 2007 A1
20070208653 Murphy Sep 2007 A1
20070214076 Robida et al. Sep 2007 A1
20070220611 Socolow et al. Sep 2007 A1
20070226093 Chan et al. Sep 2007 A1
20070226114 Haggerty et al. Sep 2007 A1
20070226130 Haggerty et al. Sep 2007 A1
20070233857 Cheng et al. Oct 2007 A1
20070244732 Chatterji et al. Oct 2007 A1
20070244807 Andringa et al. Oct 2007 A1
20070271178 Davis et al. Nov 2007 A1
20070271582 Ellis et al. Nov 2007 A1
20070282684 Prosser et al. Dec 2007 A1
20070282730 Carpenter et al. Dec 2007 A1
20070282736 Conlin et al. Dec 2007 A1
20070288271 Klinkhammer Dec 2007 A1
20070288355 Roland et al. Dec 2007 A1
20070288559 Parsadayan Dec 2007 A1
20070288950 Downey et al. Dec 2007 A1
20070288953 Sheeman et al. Dec 2007 A1
20070294126 Maggio Dec 2007 A1
20070294163 Harmon et al. Dec 2007 A1
20070299759 Kelly Dec 2007 A1
20070299771 Brody Dec 2007 A1
20080004957 Hildreth et al. Jan 2008 A1
20080005313 Flake et al. Jan 2008 A1
20080010206 Coleman Jan 2008 A1
20080010687 Gonen et al. Jan 2008 A1
20080015887 Drabek et al. Jan 2008 A1
20080021802 Pendleton Jan 2008 A1
20080021804 Deckoff Jan 2008 A1
20080028067 Berkhin et al. Jan 2008 A1
20080033852 Megdal et al. Feb 2008 A1
20080059317 Chandran et al. Mar 2008 A1
20080059449 Webster et al. Mar 2008 A1
20080065774 Keeler Mar 2008 A1
20080066188 Kwak Mar 2008 A1
20080071882 Hering et al. Mar 2008 A1
20080077526 Arumugam Mar 2008 A1
20080086368 Bauman et al. Apr 2008 A1
20080091463 Shakamuri Apr 2008 A1
20080091535 Heiser et al. Apr 2008 A1
20080097768 Godshalk Apr 2008 A1
20080097928 Paulson Apr 2008 A1
20080103800 Domenikos et al. May 2008 A1
20080103972 Lanc May 2008 A1
20080120155 Pliha May 2008 A1
20080120569 Mann et al. May 2008 A1
20080126233 Hogan May 2008 A1
20080126476 Nicholas et al. May 2008 A1
20080133322 Kalia et al. Jun 2008 A1
20080133325 De et al. Jun 2008 A1
20080134042 Jankovich Jun 2008 A1
20080140476 Anand et al. Jun 2008 A1
20080140507 Hamlisch et al. Jun 2008 A1
20080140549 Eder Jun 2008 A1
20080147454 Walker et al. Jun 2008 A1
20080167883 Thavildar Khazaneh Jul 2008 A1
20080167936 Kapoor Jul 2008 A1
20080175360 Schwarz et al. Jul 2008 A1
20080177836 Bennett Jul 2008 A1
20080183564 Tien et al. Jul 2008 A1
20080184289 Cristofalo et al. Jul 2008 A1
20080195425 Haggerty et al. Aug 2008 A1
20080195600 Deakter Aug 2008 A1
20080208548 Metzger et al. Aug 2008 A1
20080208631 Morita et al. Aug 2008 A1
20080208788 Merugu et al. Aug 2008 A1
20080215470 Sengupta et al. Sep 2008 A1
20080221934 Megdal et al. Sep 2008 A1
20080221947 Megdal et al. Sep 2008 A1
20080221970 Megdal et al. Sep 2008 A1
20080221971 Megdal et al. Sep 2008 A1
20080221972 Megdal et al. Sep 2008 A1
20080221973 Megdal et al. Sep 2008 A1
20080221990 Megdal et al. Sep 2008 A1
20080222016 Megdal et al. Sep 2008 A1
20080222027 Megdal et al. Sep 2008 A1
20080228538 Megdal et al. Sep 2008 A1
20080228539 Megdal et al. Sep 2008 A1
20080228540 Megdal et al. Sep 2008 A1
20080228541 Megdal et al. Sep 2008 A1
20080228556 Megdal et al. Sep 2008 A1
20080228606 Megdal et al. Sep 2008 A1
20080228635 Megdal et al. Sep 2008 A1
20080243680 Megdal et al. Oct 2008 A1
20080244008 Wilkinson et al. Oct 2008 A1
20080255897 Megdal et al. Oct 2008 A1
20080255992 Lin Oct 2008 A1
20080276271 Anderson et al. Nov 2008 A1
20080281737 Fajardo Nov 2008 A1
20080288382 Smith et al. Nov 2008 A1
20080294540 Celka et al. Nov 2008 A1
20080294546 Flannery Nov 2008 A1
20080301016 Durvasula Dec 2008 A1
20080301727 Cristofalo et al. Dec 2008 A1
20090006185 Stinson Jan 2009 A1
20090006475 Udezue et al. Jan 2009 A1
20090012889 Finch Jan 2009 A1
20090018996 Hunt et al. Jan 2009 A1
20090024505 Patel et al. Jan 2009 A1
20090037247 Quinn Feb 2009 A1
20090037323 Feinstein et al. Feb 2009 A1
20090043637 Eder Feb 2009 A1
20090044246 Sheehan et al. Feb 2009 A1
20090044279 Crawford et al. Feb 2009 A1
20090076883 Kilger et al. Mar 2009 A1
20090089190 Girulat Apr 2009 A1
20090089205 Bayne Apr 2009 A1
20090094640 Anderson et al. Apr 2009 A1
20090106150 Pelegero et al. Apr 2009 A1
20090112650 Iwane Apr 2009 A1
20090119169 Chandratillake et al. May 2009 A1
20090119199 Salahi May 2009 A1
20090125369 Kloostra et al. May 2009 A1
20090132347 Anderson et al. May 2009 A1
20090132559 Chamberlain et al. May 2009 A1
20090133058 Kouritzin et al. May 2009 A1
20090144102 Lopez Jun 2009 A1
20090144160 Haggerty et al. Jun 2009 A1
20090144201 Gierkink et al. Jun 2009 A1
20090177480 Chen et al. Jul 2009 A1
20090182653 Zimiles Jul 2009 A1
20090199264 Lang Aug 2009 A1
20090210886 Bhojwani et al. Aug 2009 A1
20090222308 Zoldi et al. Sep 2009 A1
20090222373 Choudhuri et al. Sep 2009 A1
20090222374 Choudhuri et al. Sep 2009 A1
20090222375 Choudhuri et al. Sep 2009 A1
20090222376 Choudhuri et al. Sep 2009 A1
20090222377 Choudhuri et al. Sep 2009 A1
20090222378 Choudhuri et al. Sep 2009 A1
20090222379 Choudhuri et al. Sep 2009 A1
20090222380 Choudhuri et al. Sep 2009 A1
20090228918 Rolff et al. Sep 2009 A1
20090234775 Whitney et al. Sep 2009 A1
20090240609 Cho et al. Sep 2009 A1
20090248567 Haggerty et al. Oct 2009 A1
20090248568 Haggerty et al. Oct 2009 A1
20090248569 Haggerty et al. Oct 2009 A1
20090248570 Haggerty et al. Oct 2009 A1
20090248571 Haggerty et al. Oct 2009 A1
20090248572 Haggerty et al. Oct 2009 A1
20090248573 Haggerty et al. Oct 2009 A1
20090254476 Sharma et al. Oct 2009 A1
20090288109 Downey et al. Nov 2009 A1
20090313163 Wang et al. Dec 2009 A1
20090319648 Dutta et al. Dec 2009 A1
20090327120 Eze et al. Dec 2009 A1
20100009320 Wilkelis Jan 2010 A1
20100010935 Shelton Jan 2010 A1
20100017300 Bramlage et al. Jan 2010 A1
20100030649 Ubelhor Feb 2010 A1
20100037255 Sheehan et al. Feb 2010 A1
20100043055 Baumgart Feb 2010 A1
20100094758 Chamberlain et al. Apr 2010 A1
20100094768 Miltonberger Apr 2010 A1
20100100945 Ozzie et al. Apr 2010 A1
20100107225 Spencer et al. Apr 2010 A1
20100114744 Gonen May 2010 A1
20100121767 Coulter et al. May 2010 A1
20100130172 Vendrow et al. May 2010 A1
20100138290 Zschocke et al. Jun 2010 A1
20100145836 Baker et al. Jun 2010 A1
20100169264 O'Sullivan Jul 2010 A1
20100205662 Ibrahim et al. Aug 2010 A1
20100211445 Bodington Aug 2010 A1
20100217837 Ansari et al. Aug 2010 A1
20100223168 Haggerty et al. Sep 2010 A1
20100228657 Kagarlis Sep 2010 A1
20100229245 Singhal Sep 2010 A1
20100248681 Phills Sep 2010 A1
20100250364 Song et al. Sep 2010 A1
20100250434 Megdal et al. Sep 2010 A1
20100250469 Megdal et al. Sep 2010 A1
20100268660 Ekdahl Oct 2010 A1
20100274739 Haggerty et al. Oct 2010 A1
20100293114 Khan et al. Nov 2010 A1
20100312717 Haggerty et al. Dec 2010 A1
20110004498 Readshaw Jan 2011 A1
20110016042 Cho et al. Jan 2011 A1
20110023115 Wright Jan 2011 A1
20110029388 Kendall et al. Feb 2011 A1
20110035333 Haggerty et al. Feb 2011 A1
20110047071 Choudhuri et al. Feb 2011 A1
20110060905 Stack et al. Mar 2011 A1
20110066495 Ayloo et al. Mar 2011 A1
20110078073 Annappindi et al. Mar 2011 A1
20110093383 Haggerty et al. Apr 2011 A1
20110112958 Haggerty et al. May 2011 A1
20110126275 Anderson et al. May 2011 A1
20110137789 Kortina et al. Jun 2011 A1
20110164746 Nice et al. Jul 2011 A1
20110173116 Yan et al. Jul 2011 A1
20110178899 Huszar Jul 2011 A1
20110184851 Megdal et al. Jul 2011 A1
20110211445 Chen Sep 2011 A1
20110213641 Metzger, II et al. Sep 2011 A1
20110218826 Birtel et al. Sep 2011 A1
20110219421 Ullman et al. Sep 2011 A1
20110238566 Santos Sep 2011 A1
20110258050 Chan et al. Oct 2011 A1
20110258142 Haggerty et al. Oct 2011 A1
20110264581 Clyne Oct 2011 A1
20110270618 Banerjee et al. Nov 2011 A1
20110270779 Showalter Nov 2011 A1
20110282779 Megdal et al. Nov 2011 A1
20110307397 Benmbarek Dec 2011 A1
20120005070 McFall et al. Jan 2012 A1
20120011056 Ward et al. Jan 2012 A1
20120029956 Ghosh et al. Feb 2012 A1
20120035980 Haggerty et al. Feb 2012 A1
20120047219 Feng et al. Feb 2012 A1
20120054592 Jaffe et al. Mar 2012 A1
20120066065 Switzer Mar 2012 A1
20120066106 Papadimitriou Mar 2012 A1
20120084230 Megdal et al. Apr 2012 A1
20120101938 Kasower Apr 2012 A1
20120101939 Kasower Apr 2012 A1
20120106801 Jackson May 2012 A1
20120116807 Hane et al. May 2012 A1
20120123968 Megdal et al. May 2012 A1
20120124498 Santoro et al. May 2012 A1
20120136763 Megdal et al. May 2012 A1
20120143637 Paradis et al. Jun 2012 A1
20120173339 Flynt et al. Jul 2012 A1
20120179536 Kalb et al. Jul 2012 A1
20120191479 Gupta et al. Jul 2012 A1
20120265661 Megdal et al. Oct 2012 A1
20120290660 Rao et al. Nov 2012 A1
20130085902 Chew Apr 2013 A1
20130103571 Chung et al. Apr 2013 A1
20130132151 Stibel et al. May 2013 A1
20130173359 Megdal et al. Jul 2013 A1
20130218638 Kilger et al. Aug 2013 A1
20130268324 Megdal et al. Oct 2013 A1
20130275331 Megdal et al. Oct 2013 A1
20130293363 Plymouth Nov 2013 A1
20140012633 Megdal et al. Jan 2014 A1
20140019331 Megdal et al. Jan 2014 A1
20140025815 Low Jan 2014 A1
20140032265 Paprocki et al. Jan 2014 A1
20140032384 Megdal et al. Jan 2014 A1
20140096249 Dupont et al. Apr 2014 A1
20140156501 Howe Jun 2014 A1
20140164112 Kala Jun 2014 A1
20140244353 Winters Aug 2014 A1
20170278182 Kasower Sep 2017 A1
Foreign Referenced Citations (53)
Number Date Country
1290373 Apr 2001 CN
1290372 May 2001 CN
91 08 341 Oct 1991 DE
0 350 907 Jan 1990 EP
0 468 440 Jan 1992 EP
0 554 083 Aug 1993 EP
0 566 736 Aug 1993 EP
0 749 081 Dec 1996 EP
0 869 652 Oct 1998 EP
0 913 789 May 1999 EP
1 028 401 Aug 2000 EP
1 122 664 Aug 2001 EP
2 392 748 Mar 2004 GB
10-293732 Nov 1998 JP
2001-282957 Oct 2001 JP
2002-163449 Jun 2002 JP
2003-016261 Jan 2003 JP
2003-316950 Nov 2003 JP
256569 Jun 2006 TW
WO 94006103 Mar 1994 WO
WO 97023838 Jul 1997 WO
WO 98049643 Nov 1998 WO
WO 99004350 Jan 1999 WO
WO 99022328 May 1999 WO
WO 99033012 Jul 1999 WO
WO 99046710 Sep 1999 WO
WO 00055789 Sep 2000 WO
WO 00055790 Sep 2000 WO
WO 01010090 Feb 2001 WO
WO 01011522 Feb 2001 WO
WO 01016896 Mar 2001 WO
WO 01025896 Apr 2001 WO
WO 01039090 May 2001 WO
WO 01039589 Jun 2001 WO
WO 01041083 Jun 2001 WO
WO 01057720 Aug 2001 WO
WO 01075754 Oct 2001 WO
WO 01080053 Oct 2001 WO
WO 01084281 Nov 2001 WO
WO 02027610 Apr 2002 WO
WO 03071388 Aug 2003 WO
WO 03101123 Dec 2003 WO
WO 2004046882 Jun 2004 WO
WO 2004114160 Dec 2004 WO
WO 2007149941 Dec 2007 WO
WO 2008022289 Feb 2008 WO
WO 2008054403 May 2008 WO
WO 2008127288 Oct 2008 WO
WO 2009117518 Sep 2009 WO
WO 2009132114 Oct 2009 WO
WO 2010062537 Jun 2010 WO
WO 2010132492 Nov 2010 WO
WO 2014018900 Jan 2014 WO
Non-Patent Literature Citations (198)
Entry
“A Google Health update,” Google Official Blog, Sep. 15, 2010 in 4 pages, http://googleblog.blogspot.com/2010/09/google-health-update.html.
Corepoint Health, “The Continuity of Care Document—Changing the Landscape of Healthcare Information Exchange,” Jan. 2009, pp. 9.
Dé, Andy, “Will mHealth Apps and Devices Empower ePatients for Wellness and Disease Management? A Case Study,” Jan. 10, 2011 in 6 pages, http://www.healthsciencestrategy.com/2011/04/will-mhealth-apps-and-devices-empower-epatients-for-wellness-and-disease-management-a-case-study-2/.
Equifax; “White Paper: Driving Safe Growth in a Fluid Economy”, http://www.equifax.com/assets/USCIS/efx—safeGrowth—wp.pdf, Oct. 2012 in 14 pages.
Equifax; “True In-Market Propensity Scores™”, http://www.equifax.com/assets/USCIS/efx-00174-11-13—efx—tips.pdf, Nov. 2013 in 1 page.
“Experian Helps Verify the Identity of Patients and Provide Secure Enrollment to Healthcare Portals by Integrating with Major Electronic Medical Records Platform,” http://press.experian.com/United- States/Press-Release/experian-helps-verify-the-identity-of-patients-and-provide-secure-enrollment-to-healthcare.aspx?&p=1, Dec. 19, 2013, pp. 2.
Experian; “Case study: SC Telco Federal Credit Union”, http://annualcreditreport.experian.com/assets/consumer-information/case-studies/sc-telco-case-study.pdf, Jun. 2011 in 2 pages.
Experian; “In the Market ModelsSM”, http://www.experian.com/assets/consumer-information/product-sheets/in-the-market-models.pdf, Sep. 2013 in 2 pages.
Fisher, Joseph, “Access to Fair Credit Reports: Current Practices and Proposed Legislation,” American Business Law Journal, Fall 1981, vol. 19, No. 3, p. 319.
Glenn, Brandon, “Multi-provider patient portals get big boost with ONC ruling”, Feb. 25, 2013, http://medicaleconomics.modernmedicine.com/medical-economics/news/user-defined-tags/meaningful-use/multi-provider-patient-portals-get-big-boost in 2 pages.
Healow.com, Various screenshots from page titled “Health and Online Wellness,” https://healow.com/apps/jsp/webview/index.jsp printed Aug. 19, 2013 in 4 pages.
Healthspek.com, “How Good Are We?” http://healthspek.com/how-good-are-we/ printed Jan. 21, 2014 in 2 pages.
“Healthspek Users Can Now Import Their Doctors' Records into Their Personal Health Record,” PRWeb, Nashville, TN, Jan. 14, 2014, pp. 1 http://www.prweb.com/releases/2014/01/prweb11485346.htm.
HealthVault, “Share Health Information,” https://account.healthvault.com/sharerecord.aspx, printed Feb. 20, 2013 in 2 pages.
HealthVault, “What Can you do with HealthVault?” https://www.healthvault.com/us/en/overview, http://www.eweek.com/mobile/diversinet-launches-mobihealth-wallet-for-patient-data-sharing/, printed Feb. 20, 2013 in 2 pages.
Horowitz, Brian T., “Diversinet Launches MobiHealth Wallet for Patient Data Sharing,” eWeek, Dec. 4, 2012, http://www.eweek.com/mobile/diversinet-launches-mobihealth-wallet-for-patient-data-sharing/.
IgiHealth.com, “Orbit® PHR: Personal Health Record (PHR),” http://www.igihealth.com/consumers/orbit—phr.html, printed Jan. 21, 2014 in 2 pages.
InsightsOne.com, “Healthcare,” http://insightsone.com/healthcare-predictive-analytics/ printed Mar. 6, 2014 in 5 pages.
LifeLock, http://web.archive.org/web/20110724011010/http://www.lifelock.com/? as archived Jul. 24, 2011 in 1 page.
MergePower, Inc., “Attribute Pro”, http://web.archive.org/web/20060520135324/http://www.mergepower.com/attribute—pro.html, dated May 20, 2006 in 1 page.
Pagano, et al., “Information Sharing in Credit Markets,” Dec. 1993, The Journal of Finance, vol. 48, No. 5, pp. 1693-1718.
Partnoy, Frank, Rethinking Regulation of Credit Rating Agencies: An Institutional Investor Perspective, Council of Institutional Investors, Apr. 2009, pp. 21.
PrivacyGuard, http://web.archive.org/web/20110728114049/http://www.privacyguard.com/ as archived Jul. 28, 2011 in 1 page.
Tennant, Don, “How a Health Insurance Provider Uses Big Data to Predict Patient Needs,” http://www.itbusinessedge.com/blogs/from-under-the-rug/how-a-health-insurance-provider-uses-big-data-to-predict-patient-needs.html, printed Mar. 6, 2014 in 2 pages.
U.S. Appl. No. 12/022,874, U.S. Pat. No. 8,606,626, Systems and Methods for Providing a Direct Marketing Campaign Planning Environment, filed Jan. 30, 2008.
U.S. Appl. No. 14/090,834, U.S. Pat. No. 9,508,092, Systems and Methods for Providing a Direct Marketing Campaign Planning Environment, filed Nov. 26, 2013.
U.S. Appl. No. 12/220,320, filed Jul. 23, 2008, Brunzell et al.
U.S. Appl. No. 12/705,489, filed Feb. 12, 2010, Bargoli et al.
U.S. Appl. No. 12/705,511, filed Feb. 12, 2010, Bargoli et al.
“Accenture Launches Media Audit and Optimization Service to Help U.S. Companies Measure Return on Investment in Advertising,” Business Wire, May 22, 2006, 2 pages, http://findarticles.com/p/articles/mi—m0EIN/is—2006—May—22/ai—n16374159.
“Accenture Newsroom: Accenture Completes Acquisition of Media Audits: Acquisition Expands Company's Marketing Sciences and Data Services Capabilities,” Accenture.com, Dec. 12, 2005, 2 pages, http://accenture.tekgroup.com/article—display.cfm?article—id=428.
AKL, Selim G., “Digital Signatures: A Tutorial Survey,” Computer, Feb. 1983, pp. 15-24.
“Atlas on Demand, Concurrent, and Everstream Strike Video-On-Demand Advertising Alliance”, www.atlassolutions.com, Jul. 13, 2006, 3 pages.
“Arbitron 2006 Black Consumers,” Arbitron Inc., LVTSG.com, Jul. 8, 2006, 2 pages, http://www.lvtsg.com/news/publish/Factoids/article—3648.shtml.
“Atlas on Demand and C-COR Join Forces to Offer Advertising Management Solution for on Demand TV: Global Provider of on Demand Systems Partners with Atlas to Develop and Market Comprehensive VOD Advertising Solution,” www.atlassolutions.com, Jul. 25, 2005, 3 pages.
“Atlas on Demand and Tandberg Television Join Forces to Enhance Dynamic Ad Placement for On-Demand Television: Combined End-to End Solution to Provide Media Buying and Selling Communities with New Tools for Dynamic Advertising that Eliminate Technical Bar” Jun. 22, 2006—3 pages, http://www.atlassolutions.com/news—20060622.aspx.
Adzilla, Press Release, “Zillacasting Technology Approved and Patent Pending,” http://www.adzilla.com/newsroom/pdf/patent—051605.pdf, May 16, 2005, pp. 2.
AISG's National Underwriting Database, A-Plus, is Now the Largest in the Industry, Business Wire, Aug. 7, 1997.
AFX New Limited—AFX International Focus, “Nielsen moving to measure off-TV viewing,” Jun. 14, 2006, 1 page.
Announcing TrueProfiler, http://web.archive.org/web/20021201123646/http://www.truecredit.com/index.asp, dated Dec. 1, 2002, 2 pages.
Applied Geographic Solutions, “What is MOSAIC™”, as captured Feb. 15, 2004 from http://web.archive.org/web/20040215224329/http://www.appliedgeographic.com/mosaic.html in 2 pages.
“AT&T Expected to Turn Up Heat in Card Wars”, American Banker, May 27, 1993, vol. 158, No. 101, pp. 3.
Bachman, Katy, “Arbitron, VNU Launch Apollo Project,” Mediaweek.com Jan. 17, 2006, 3 pages, http://www.mediaweek.com/mw/search/article—display.jsp?schema=&vnu—content—id=1001847353.
“Balance Transfers Offer Opportunities”, Risk Credit Risk Management Report, Jan. 29, 1996, vol. 6, No. 2, pp. 2.
“Bank of America Direct Web-Based Network Adds Core Functionality to Meet Day-To-Day Treasury Needs”, Business Wire, Oct. 25, 1999. pp. 2.
Bitran et al., “Mailing Decisions in Catalog Sales Industry”, Management Science (JSTOR), vol. 42, No. 9, pp. 1364-1381, Sep. 1996.
Brown et al., “ALCOD IDSS:Assisting the Australian Stock Market Surveillance Team's Review Process,” Applied Artificial Intelligence Journal, Dec. 1, 1996, pp. 625-641.
Bult et al., “Optimal Selection for Direct Mail,” Marketing Science, 1995, vol. 14, No. 4, pp. 378-394.
Burr Ph.D., et al., “Utility Payments as Alternative Credit Data: A Reality Check”, Asset Builders of America, Inc., Oct. 5, 2006, pp. 1-18, Washington, D.C.
“Cable Solution Now, The Industry Standard for Information Management: Strata's Tim.net Crosses Important Threshold Dominant Solution for All Top 20 TV Markets,” Stratag.com, Apr. 28, 2006, 1 page, http://stratag.com/news/cablepress042806.html.
Caliendo, et al., “Some Practical Guidance for the Implementation of Propensity Score Matching”, IZA:Discussion Paper Series, No. 1588, Germany, May 2005, pp. 32.
Cantor, R. and Packer, F., “The Credit Rating Industry,” FRBNY Quarterly Review, Summer-Fall, 1994, pp. 1-24.
Card Marketing, Use the Latest CRM Tools and Techniques, www.cardforum.com, vol. 5 No. 10, Dec. 2001.
ChannelWave.com, PRM Central—About PRM, http://web.archive.org/web/20000510214859/http://www.channelwave.com as printed on Jun. 21, 2006, May 2000 Archive.
“Chase Gets Positive,” Bank Technology News, May 6, 2000, vol. 14, No. 5, p. 33.
Chatterjee et al., “Expenditure Patterns and Aggregate Consumer Behavior, Some Experiments with Australian and New Zealand Data”, The Economic Record, vol. 70, No. 210, Sep. 1994, pp. 278-291.
Chen, et al., “Modeling Credit Card ‘Share of Wallet’: Solving the Incomplete Information Problem”, New York University: Kauffman Management Center, http://www.rhsmith.unid.edu/marketing/pdfs—docs/seminarsspr05/abstract%20-%20chen.pdf , Spring 2005, 48 pages.
“Claritas Forms Life Insurance Consortium with Worldwide Financial Services Association: Initiative with LIMRA International is First of its Kind to Provide Actual Sales Information at Small Geographic Areas,” Feb. 9, 2006, 3 pages, http://www.claritas.com/claritas/Default/jsp?ci=5&si=1&pn=limra.
“Claritas Introduces PRIZM NE Consumer Electronic Monitor Profiles: New Information Product Provides Insight Into the Public's Purchasing Behaviors of Consumer Electronics,” May 30, 2006, 3 pages.
“Cole Taylor Bank Chooses Integrated E-Banking/E-Payments/Reconciliation Solution From Fundtech”, Business Wire, Oct. 21, 1999, pp. 2.
CreditAnalyst, Digital Matrix Systems, as printed out Mar. 4, 2008, pp. 2.
CreditXpert, http://www.creditxpert.com/Products/individuals.asp printed Oct. 12, 2012 in 1 page.
Click Z, “ISPs Collect User Data for Behavioral Ad Targeting,” dated Jan. 3, 2008, printed from http://www.clickz.com/showPage.html?page=clickz Apr. 16, 2008.
Cnet News.com, “Target me with your ads, please,” dated Dec. 5, 2007, printed from http://www.news.com/2102-1024—3-6221241.html?tag+st.util.print Mar. 18, 2008.
ComScore Networks Launches Business Unit to Help Credit Card Marketers Master Online and Multi-Channel Strategies—Solutions Provide Unprecedented Insight Into Customer Acquisition and Usage Opportunities, Reston, VA, Oct. 11, 2001, 2 pages.
Cowie, Norman, “Warning Bells & ‘The Bust-Out’”, Business Credit, Jul. 1, 2000, pp. 5.
Creamer, Matthew; Consulting in marketing; Accenture, Others Playing Role in Firms' Processes, Crain's Chicago Business, Jun. 12, 2006, 2 pages.
Credit Card Management, “Neural Nets Shoot for Jackpot,” Dec. 1995, pp. 1-6.
Credit Risk Management Report, Potomac, Mar. 9, 1998, vol. 8, No. 4.
CreditXpert Inc., CreditXpert 3-Bureau Comparison™, 2002, pp. 5, http://web.archive.org/web/20030608171018/http://creditxpert.com/CreditXpert%203-Burea%20Comparison(TM)%20sample.pdf.
CreditXpert Inc., CreditXpert Credit Score & Analysis™, Jan. 11, 2000, pp. 6, http://web.archive.org/web/20030611070058/http://www.creditxpert.com/CreditXpert%20Score%20&%20Analysis%20and%20Credit%20Wizard%20sample.pdf.
CreditXpert Inc., CreditXpert Essentials™, Advisor View-Experian on Jul. 7, 2003, http://www.creditxpert.com/cx—ess—app.pdf.
CreditXpert Inc., CreditXpert Essentials™, Advisor View-TransUnion on Oct. 10, 1999, pp. 6, http://web.archive.org/web/20041211052543/http://creditxpert.com/cx—ess—app.pdf.
CreditXpert Inc., CreditXpert Essentials™, Applicant View-TransUnion on Oct. 10, 1999, pp. 6, http://www.creditxpert.com/cx—ess—app.pdf.
CreditXpert Inc., CreditXpert What-If Simulator™, 2002, pp. 8, http://web.archive.org/web/20030630132914/http://creditxpert.com/CreditXpert%20What-If%20Simulator(TM)%20sample.pdf.
Dataman Group, “Summarized Credit Statistics,” Aug. 22, 2001, http://web.archive.org/web/20010822113446/http://www.datamangroup.com/summarized—credit.asp.
David, Alexander, “Controlling Information Premia by Repackaging Asset-Backed Securities,” The Journal of Risk and Insurance, Dec. 1997, 26 pages.
Davies, Donald W., “Applying the RSA Digital Signature to Electronic Mail,” Computer, Feb. 1983, pp. 55-62.
deGruchy, et al., “Geodemographic Profiling Benefits Stop-Smoking Service;” The British Journal of Healthcare Computing & Information Management; Feb. 2007; 24, 7; pp. 29-31.
Delany et al., “Firm Mines Offline Data to Target Online”, http://web.archive.org/web/20071117140456/http://www.commercialalert.org/news/archive/2007/10/firm-mines-offline-data-to-target-online-ads, Commercial Alert, Oct. 17, 2007, pp. 3.
Demographicsnow.com, sample reports, “Age Rank Report”, Jul. 17, 2006, 3 pages.
Demographicsnow.com, sample reports, “Consumer Expenditure Summary Report”, Jul. 17, 2006, 3 pages.
Demographicsnow.com, sample reports, “Income Comparison Report”, Jul. 17, 2006, 4 pages.
Dillon et al., “Good Science”, Marketing Research: A Magazine of Management & ApplicationsTM, Winter 1997, vol. 9, No. 4; pp. 11.
Dymi, Amilda, Need for Leads Spurs Some Upgrades, Origination News-Special Report, May 1, 2008, vol. vol. 17, Issue No. 8, Pages p. 24, Atlanta, Copyright 2008 SourceMedia, Inc.
eFunds Corporation, “Data & Decisioning: Debit Report” printed Apr. 1, 2007, http://www.efunds.com/web/industry-solutions/financial-services/frm-debit-report/htm in 1 page.
Egol, Len; “What's New in Database Marketing Software,” Direct, Aug. 1994, vol. 6, No. 8, pp. 39.
“Epsilon Leads Discussion on Paradigm Shift in TV Advertising,” Epsilon.com, Jun. 24, 2004, 2 pages, http://www.epsilon.com/who-pr—tvad040624.html.
Ettorre, “Paul Kahn on Exceptional Marketing,” Management Review, vol. 83, No. 11, Nov. 1994, pp. 48-51.
“Equifax and FICO Serve Consumers”, Mortgage Servicing News, Mar. 2001, vol. 5, No. 3, p. 19.
Experian Announces PLUS Score; Experian Press Release dated Oct. 16, 2003; Experian Global Press Office.
Experian and AGS Select SRC to Deliver Complete Marketing Solutions; Partnership First to Marketplace with Census2000 Data. PR Newswire. New York: Mar. 21, 2001. p. 1.
“Experian Launches Portfolio Monitor—Owner NoticesSM”, News Release, Feb. 2003, Costa Mesa, CA.
Experian-Scorex Announces New Credit Simulation Tool, PR Newswire, Costa Mesa, CA, Jun. 13, 2005.
Experian Information Solutions, Inc., Credit Trends: Access Credit Trending Information Instantly, http://kewaneecreditbureau.com/Credit.Trends.pdf, Aug. 2000, pp. 4.
Fair Isaac Announces Integrated, End-to-End Collection and Recovery Solution, Business Wire, New York, Sep. 2, 2004, p. 1.
“Fair Isaac Introduces Falcon One System to Combat Fraud at Every Customer Interaction”, Business Wire, May 5, 2005, pp. 3.
“Fair Isaac Offers New Fraud Tool”, National Mortgage News & Source Media, Inc., Jun. 13, 2005, pp. 2.
Fanelli, Marc, “Building a Holistic Customer View”, MultiChannel Merchant, Jun. 26, 2006, pp. 2.
Fickenscher, Lisa, “Merchant American Express Seeks to Mine its Data on Cardholder Spending Patterns,” American Banker, vol. 162, Issue 56, Mar. 24, 1997, pp. 1-2.
“FinExtra, Basepoint Analytics Introduces Predictive Technology for Mortgage Fraud”, Oct. 5, 2005, pp. 3.
Frontporch, “Ad Networks-Partner with Front Porch!,” www.frontporch.com printed Apr. 2008 in 2 pages.
Frontporch, “New Free Revenue for Broadband ISPs!”, http://www.frontporch.com/html/bt/FPBroadbandISPs.pdf printed May 28, 2008 in 2 pages.
“FTC Testifies: Identity Theft on the Rise”, FTC News Release, Mar. 7, 2000, pp. 3.
GAO-03-661, Best Practices: Improved Knowledge of DOD Service Contracts Could Reveal Significant Savings, GAO, Jun. 2003.
Gilje, Shelby, “Keeping Tabs on Businesses That Keep Tabs on Us”, NewsRoom, The Seattle Times, Section: Scene, Apr. 19, 1995, pp. 4.
Gonul, et al., “Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models”, 14 pages, Management Science, vol. 44, No. 9, Sep. 1998.
Halliday, Jean, “Ford Recruits Accenture for Marketing Plan,” Automotive News Feb. 13, 2006, 2 pages, Crain Communications.
Haughton et al., “Direct Marketing Modeling with CART and CHAID”, Journal of Direct Marketing, Fall 1997, vol. 11, No. 4, pp. 42-52.
Helm, Burt, “Nielsen's New Ratings Yardstick,” Businessweek.com, Jun. 20, 2006, 3 pages, http://www.businessweek.com/technology/content/jun2006/tc20060620—054223.htm.
Hill, Kerry, “Identity Theft Your Social Security Number Provides Avenue for Thieves”, NewsRoom, Wisconsin State Journal, Sep. 13, 1998, pp. 4.
Hinman, Donald P., “The Perfect Storm: Response Metrics and Digital TV,” Chiefmarketer.com, May 17, 2006, 2 pages, http://www.chiefmarketer.com/crm—loop/roi/perfect-storm-051706/index.html.
“ID Thieves These Days Want Your Number, Not Your Name”, The Columbus Dispatch, Columbus, Ohio, http://www.dispatch.com/content/stories/business/2014/08/03/id-thieves-these-days-want-your-number-not-your-name.html, Aug 3, 2014 in 2 pages.
Ideon, Credit-Card Registry that Bellyflopped this Year, Is Drawing some Bottom-Fishers, The Wall Street Journal, Aug. 21, 1995, pp. C2.
“Impac Funding Introduces Enhanced Website for Static Pool Tracking of MBS Transactions,” Waltham, MA; Webpage printed out from http://www.lewtan.com/press/1208044—Impac-Lewtan.htm on Mar. 20, 2008.
“Industry News, New Technology Identifies Mortgage Fraud: Basepoint Analytics Launches FraudMark”, Inman News, American Land Title Association, Oct. 5, 2005, pp. 1.
Information Resources, Inc. and Navic Networks Form Joint Relationship to Support Next Generation of Technology for Advertising Testing, IRI Expands BehaviorScan® Solution to Meet Digital and On-demand Needs, Feb. 27, 2006, http://us.infores.com/page/news/pr/pr—archive?mode=single&pr—id=117, printed Oct. 4, 2007 in 2 pages.
Instant Access to Credit Reports Now Available Online with DMS' CreditBrowser-based system also Simplifies Credit Decisioning and Offers a Central Point of Control, Business Wire, Dallas, May 23, 2000, p. 0264.
“Intelligent Miner Applications Guide”, IBM Corp., Apr. 2, 1999, Chapters 4-7, pp. 33-132.
Internal Revenue Service Data Book 2000, Issued Aug. 2001, Revised May 2003.
“IRI and Acxiom Introduce More Efficient and Actionable Approach to Consumer Segmentation and Targeted Marketing,” EU-Marketingportal.de, Jan. 26, 2006, 2 pages, http://www.eu-markertingportal.de.
Jacob et al., A Case Study of Checking Account Inquiries and Closures in Chicago, The Center for Financial Services Innovation, Nov. 2006.
Jost, Allen; Neural Networks, Credit World, Mar./Apr. 1993, vol. 81, No. 4, pp. 26-33.
“JPMorgan Worldwide Securities Services to Acquire Paloma's Middle and Back Office Operations,” Webpage printed from http://www.jpmorgan.com on Apr. 1, 2009.
Lamons, Bob, “Be Smart: Offer Inquiry Qualification Services,” Marketing News, ABI/Inform Global, Nov. 6, 1995, vol. 29, No. 23, pp. 13.
Lee, W.A.; “Experian, on Deal Hunt, Nets Identity Theft Insurer”, American Banker: The Financial Services Daily, Jun. 4, 2003, New York, NY, 1 page.
LifeLock, “How LifeLock Works,” http://www.lifelock.com/lifelock-for-people printed Mar. 14, 2008 in 1 page.
LifeLock, “LifeLock Launches First ID Theft Prevention Program for the Protection of Children,” Press Release, Oct. 14, 2005, http://www.lifelock.com/about-us/press-room/2005-press-releases/lifelock-protection-for-children.
LifeLock, Various Pages, www.lifelock.com/, 2007.
Longo, Tracey, “Managing Money: Your Family Finances”, Kiplinger's Personal Finance Magazine, Jun. 1, 1995, vol. 49, No. 6, pp. 4.
McManus et al.; “Street Wiser,” American Demographics; ABI/Inform Global; Jul./Aug. 2003; 25, 6; pp. 32-35.
McNamara, Paul, “Start-up's pitch: The Envelope, please,” Network World, Apr. 28, 1997, vol. 14, No. 17, p. 33.
“Mediamark Research Inc. Releases Findings From Mobile Marketing Consumer Study; Outback Steakhouse and Royal Caribbean Cruise Lines Among Brands Participating in Mobile Marketing Research,” www.thefreelibrary.com, May 9, 2006, 4 pages.
Merugu, et al.; “A New Multi-View Regression Method with an Application to Customer Wallet Estimation,” The 12th International Conference on Knowledge Discovery and Data Mining, Aug. 20-23, 2006, Philadelphia, PA.
Morrissey, Brian, “Aim High: Ad Targeting Moves to the Next Level”, ADWEEK, dated Jan. 21, 2008 as downloaded from http://www.adweek.com/aw/magazine/article—display.isp?vnu on Apr. 16, 2008.
Muus, et al., “A Decision Theoretic Framework for Profit Maximization in Direct Marketing”, Sep. 1996, pp. 20.
NebuAd, “Venture Capital: What's New—The Latest on Technology Deals From Dow Jones VentureWire”, Press Release, http://www.nebuad.com/company/media—coverage/media—10—22—07.php, Oct. 22, 2007, pp. 2.
“New Privista Product Provides Early Warning System to Combat Identity Theft”, PR Newswire, Oct. 24, 2000, PR Newswire Association, Inc., New York.
Occasional CF Newsletter; http://www.halhelms.com/index.cfm?fuseaction=newsletters.oct1999; Oct. 1999.
Office of Integrated Analysis and Forecasting, DOE/EIA-M065(2004), Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, EIA, Washington DC, Feb. 2004.
Otter, et al., “Direct Mail Selection by Joint Modeling of the Probability and Quantity of Response”, Jun. 1997, pp. 14.
“Parse”, Definition from PC Magazine Encyclopedia, http://www/pcmag.com/encyclopedia—term—0,2542,t=parse&i=48862,00.asp as downloaded Mar. 5, 2012.
Perlich et al., “High Quantile Modeling for Customer Wallet Estimation with Other Applications,” The 13th International Conference on Knowledge Discovery and Data Mining, Aug. 12-15, 2007, San Jose, CA.
Phorm, “BT PLC TalkTalk and Virgin Media Inc. confirm exclusive agreements with Phorm”, Press Release, http://www.phorm.com/about/launch—agreement.php, Feb. 14, 2008, pp. 2.
Phorm, “The Open Internet Exchange, ‘Introducing the OIX’”, http://www.phorm.com/oix/ printed May 29, 2008 in 1 page.
Polatoglu et al., “Theory and Methodology, Probability Distributions of Cost, Revenue and Profit over a Warranty Cycle”, European Journal of Operational Research, Jul. 1998, vol. 108, Issue 1, pp. 170-183.
“PostX to Present at Internet Showcase”, PR Newswire, Apr. 28, 1997, pp. 2.
PostX, “PostX® Envelope and ActiveView”, http://web.archive.org/web/19970714203719/http://www.postx.com/priducts—fm.html, Jul. 14, 1997 (retrieved Nov. 7, 2013) in 2 pages.
Powerforms: Declarative Client-Side for Field Validation, ISSN 1386-145x, Dec. 2000.
Predictive Behavioral Targeting http://www.predictive-behavioral-targeting.com/index.php.Main—Page as printed Mar. 28, 2008 in 4 pages.
“PremierGuide Announces Release 3.0 of Local Search Platform”, Business Wire, Mar. 4, 2004, Palo Alto, CA, p. 5574.
Rap Interactive, Inc. and Web Decisions: Proudly Presents Live Decisions, A Powerful New Information and Technology Resource that Revolutionizes Interactive Marketing, downloaded from www.webdecisions.com/pdf/LiveDecisions—Bro.pdf, as printed on Aug. 13, 2007.
Reinbach, Andrew; MCIF aids banks in CRA Compliance, Bank Systems & Technology, Aug. 1995, vol. vol. 32, Issue No. 8, Pages pp. 27.
Rosset et al., “Wallet Estimation Models”, IBM TJ Watson Research Center, 2005, Yorktown Heights, NY, pp. 12.
Saunders, A., “Data Goldmine,” Management Today, London: Mar. 1, 2004, 6 pages.
Schmittlein et al., “Customer Base Analysis: An Industrial Purchase Process Application”, Marketing Science, vol. 13, No. 1, Winter 1994, pp. 41-67.
Singletary, Michelle, “Score One for Open Credit Ratings”, The Washington Post, Washington DC, Jun. 18, 2000, 3 pages.
Smith, Richard M., “The Web Bug FAQ”, Nov. 11, 1999, Version 1.0, pp. 4.
“SRC Announces Free Dashups to Mashups Adding Geographic Business Intelligence at Web Speed to the Enterprise on www.FreeDemographics.com/API,” Directionsmag.com, Jun. 12, 2006, 3 page, http://www.directionsmag.com/press.releases/index.php?duty=Show&id=1
“SRC Delivers Industry's First Drive Time Engine Developed to Follow Actual Road Networks,” Thomasnet.com, May 21, 2006, 4 pages, http://news.thomasnet.com/companystory/485722.
Stanton, T.H., “Credit Scoring and Loan Scoring as Tools for Improved Management of Federal Credit Programs”, Financier, Philadelphia, Summer 1999, vol. 6, 36 pages.
Stein, Benchmarking Default Prediction Models: Pitfalls and Remedies in Model Validation, Moody's KMV, Revised Jun. 13, 2002, Technical Report #020305; New York.
Sumner, Anthony, “Tackling the Issue of Bust-Out Fraud”, Retail Banker International, Jul. 24, 2007, pp. 4.
Sweat, Jeff; “Know Your Customers,” Information Week, Nov. 30, 1998, pp. 20.
Tao, Lixin, “Shifting Paradigms with the Application Service Provider Model”; Concordia University, IEEE, Oct. 2001, Canada.
Thoemmes, Felix, “Propensity Score Matching in SPSS”, Center for Educational Science and Psychology, University of Tübingen, Jan. 2012.
Truston, “Checking if your Child is an ID Theft Victim can be Stressful,” as posted by Michelle Pastor on Jan. 22, 2007 at http://www.mytruston.com/blog/credit/checking—if—your—child—is—an—id—theft—vi.html.
UPI, “Nielsen Media Research goes electronic,” Jun. 14, 2006, 1 page.
Van Collie, Shimon, “The Road to Better Credit-Card Marketing,” Bank Technology News, Sep. 1995, pp. 4.
Verstraeten, Geert, Ph.D.; Issues in predictive modeling of individual customer behavior: Applications in targeted marketing and consumer credit scoring; Universiteit Gent (Belgium) 2005.
“VOD Integration Now Available in Strata: Buyers / Sellers Benefit from VOD Component on Popular Platform,” Stratag.com, Feb. 21, 2006, 1 page, http://www.stratag.com/news/mediapress022106.html.
Watts, Craig, “Consumers Now Can Know What Loan Rate Offers to Expect Based on Their FICO Credit Score at MyFICO.com,” Mar. 6, 2002, pp. 2, http://www.myfico.com/PressRoom/PressReleases/2002—03—06.aspx.
Watts, Craig, “Fair, Isaac and Equifax Give Consumers New Score Power Tools Offering Greater Insights for Managing Their Credit Health,” May 21, 2002, pp. 3, http://www.myfico.com/PressRoom/PressReleases/2002—05—21.aspx.
Webber, Richard, “The Relative Power of Geodemographics vis a vis Person and Household Level Demographic Variables as Discriminators of Consumer Behavior,” CASA:Working Paper Series, http://www.casa.ucl.ac.uk/working—papers/paper84.pdf, Oct. 2004, pp. 17.
Webpage printed out from http://www.jpmorgan.com/cm/ContentServer?c=TS—Content&pagename=jpmorgan%2Fts%FTS—Content%2FGeneral&cid=1139403950394 on Mar. 20, 2008, Feb. 13, 2006, New York, NY.
Webpage printed out from http://www.fairisaac.com/NR/rdonlyres/AC4C2F79-4160-4E44-B0CB-5C899004879A/0/ScoreNetnetworkBR.pdf on Mar. 4, 2008.
Whitney, Daisy; Atlas Positioning to Shoulder VOD Ads; Campaign Management Tools Optimize Inventory, TelevisionWeek, May 23, 2005, 3 pages.
Wyatt, Craig, “Usage Models just for Merchants,” Credit Card Management, Sep. 1995, vol. 8, No. 6, pp. 4.
Yücesan et al., “Distributed Web-Based Simulation Experiments for Optimization”, Simulation Practice and Theory 9, 2001, pp. 73-90.
Zimmerman et al., “A Web-Based Platform for Experimental Investigation of Electric Power Auctions,” Decision Support Systems, 1999, vol. 24, pp. 193-205.
Declaration of Paul Clark, DSc. for Inter Partes Review of U.S. Pat. No. 8,504,628 (Symantec Corporation, Petitioner), dated Jan. 15, 2014 in 76 pages.
Exhibit D to Joint Claim Construction Statement, filed in Epsilon Data Management, LLC, No. 2:12-cv-00511-JRG (E.D. Tex.) (combined for pretrial purposes with RPost Holdings. Inc., et al. v. Experian Marketing Solutions. Inc., No. 2:12-cv-00513-JRG (E.D. Tex.)) filed Jan. 14, 2014 in 9 pages.
First Amended Complaint in Civil Action No. 2:12-cv-511-JRG (Rpost Holdings, Inc. and Rpost Communications Limited V. Constant Contact, Inc.; et al.) filed Feb. 11, 2013 in 14 pages.
First Amended Complaint in Civil Action No. 2:12-cv-511-JRG (Rpost Holdings, Inc. and Rpost Communications Limited V. Epsilon Data Management, LLC.) filed Sep. 13, 2013 in 9 pages.
First Amended Complaint in Civil Action No. 2:12-cv-513-JRG (Rpost Holdings, Inc. and Rpost Communications Limited V. Experian Marketing Solutions, Inc.) filed Aug. 30, 2013 in 9 pages.
Petition for Covered Business Method Patent Review in U.S. Pat. No. 8,161,104 (Experian Marketing Solutions, Inc., Epsilon Data Management, LLC, and Constant Contact, Inc., v. Rpost Communications Limited) dated Jan. 29, 2014 in 90 pages.
Source Code Appendix attached to U.S. Appl. No. 08/845,722 by Venkatraman et al., Exhibit A, Part 1 & 2, pp. 32.
International Search Report and Written Opinion for Application No. PCT/US2007/06070, dated Nov. 10, 2008.
International Search Report and Written Opinion for Application No. PCT/US2007/021815, dated Sep. 5, 2008.
International Search Report and Written Opinion for Application No. PCT/US2008/064594, dated Oct. 30, 2008.
International Preliminary Report and Written Opinion in PCT/US2008/064594, dated Dec. 10, 2009.
International Search Report and Written Opinion in PCT/US08/083939, dated Jan. 29, 2009.
International Search Report and Written Opinion for Application No. PCT/US09/60393, dated Dec. 23, 2009.
International Search Report and Written Opinion for Application No. PCT/US2010/034434, dated Jun. 23, 2010.
International Preliminary Report on Patentability for Application No. PCT/US2010/034434, dated Feb. 4, 2014.
International Search Report and Written Opinion for Application No. PCT/US2007/063822, dated Sep. 11, 2007.
International Search Report and Written Opinion for Application No. PCT/US2007/063823, dated Oct. 24, 2007.
International Search Report and Written Opinion for Application No. PCT/US2007/063824, dated Oct. 3, 2007.
Provisional Applications (1)
Number Date Country
60887521 Jan 2007 US
Divisions (1)
Number Date Country
Parent 12022874 Jan 2008 US
Child 14090834 US
Continuations (1)
Number Date Country
Parent 14090834 Nov 2013 US
Child 15356415 US