US20230342439A1 - Disambiguation and authentication of device users - Google Patents
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Definitions
- the present application relates generally to identifying and providing content to specifically and dynamically identified mobile devices.
- Systems often require substantial inputs to accurately identify a user and content to provide to the user.
- the users may access such systems using portable communication devices, which have limited resources.
- the device may be a smartphone, which may have a small screen and limited input mechanisms as compared to, for example, a desktop computer.
- Such devices may also have power, bandwidth, processor time, or other limitations on the ability to process data.
- Each additional element to be processed by a mobile device may be accompanied by an incremental drain on such resources.
- the methods and devices described herein may include one or more of several aspects, no single one of which is solely responsible for the desirable attributes of a particular embodiment. Without limiting the scope of this disclosure, some features will now be discussed briefly. After considering this discussion, and particularly after reading the section entitled “Detailed Description” one will understand how the features described provide advantages that include identifying and providing content to specifically and dynamically identified mobile devices, among other advantages.
- PII personally identifiable information
- the present disclosure describes improvements to, and useful applications of, various computer systems and software that does not require collection, transmission, and processing of as much personal information of the user as in current identification and/or content provisioning systems.
- various embodiments of the present disclosure are inextricably tied to computer technology.
- various systems and methods discussed herein provide monitoring of electronic databases, processing of large volumes of data items, generation and transmission of electronic notifications, and the like.
- Such features and others are intimately tied to, and enabled by, computer technology, and would not exist except for computer technology.
- the implementation of the various embodiments of the present disclosure via computer technology enables many of the advantages described herein, including more efficient processing of various types of electronic data.
- FIG. 1 is a block diagram showing an example environment for providing personalized content to a mobile device.
- FIG. 2 is a flow diagram showing an example method of behavior initiated content provisioning.
- FIG. 3 is a flow diagram showing an example method of user requested content provisioning.
- FIG. 4 is a flow diagram showing an example method of identifying a user.
- FIG. 5 is a messaging diagram showing example communications for providing personalized content to a mobile device.
- FIG. 6 A is a pictorial diagram showing example user interface transitions for requesting and receiving personalized content on a mobile device.
- FIG. 6 B is a pictorial diagram showing another example user interface transitions for requesting and receiving personalized content on a mobile device.
- FIG. 7 is a block diagram showing example components of a location or behavior based content provisioning computing system.
- FIG. 8 is a block diagram showing example components of an identity service for identifying a user of a mobile device.
- the system and methods described may also collect personal information that may be used to determine whether an offer for credit should be provided to the user, such as from credit data of the user, and generate the offer of credit for transmission to the user, such as in real-time while the user is at a location where the credit may be used.
- Various embodiments may also pre-populate an online application for credit, initiate review of the credit application, and/or provide the user with information regarding the approved credit request.
- additional features are included for confirming the predicted identity by asking a dynamically identified, limited set of questions that can solicit responses usable to uniquely identify the user.
- An entity such as an individual or a group of individuals associated with some device, such as a computing device.
- a user that is typically an individual consumer (e.g., a first consumer is John Doe) that operates a particular computing device (e.g., John Doe has an iPhone).
- users may be groups of persons (e.g., a household of individuals, a married couple, etc.) associated with other computing devices or groups of computing devices (e.g., an entire household of mobile and stationary computing devices).
- a content provisioning entity (also referred to herein as a “content provisioning system”, or simply “the system”) is generally an entity and/or associated computing systems that maintain and/or have access to regulated user data that is usable to identify users that qualify for a particular product or service.
- the content provisioning entity manages a computing systems and/or modules that receive publication criteria indicating user attributes that qualify users for an offer for a product or service from the offer provider.
- the regulated user data may include credit data regulated by the Fair Credit Reporting Act (FCRA), which restricts sharing of the credit data from the content provisioning entity.
- FCRA Fair Credit Reporting Act
- the content provisioning entity is a credit bureau, a business unit of a credit bureau that maintains the credit data and enforces the FCRA access regulations, and/or another entity that is authorized to access the credit data.
- the regulated user data may include other types of data, such as healthcare data regulated by the Health Insurance Portability and Accountability Act (HIPAA) or other data that is subject to external regulations (such as set by a government agency) that restrict access to user data that is useful in screening users for certain goods or services.
- HIPAA Health Insurance Portability and Accountability Act
- the content provisioning entity is configured to limit dissemination of any regulated and/or non-regulated user data, such as to reduce the risk that the regulated user data is used in a manner that may not meet the relevant regulations.
- credit data regulated user data
- FCRA access restrictions of the FCRA that restrict use of user credit data in a different manner than regulations and/or sharing restrictions that may be imposed on marketing data of a content provisioning entity.
- the pre-validation system may limit regulated user data provided to the content provisioning entity to reduce risks associate with data loss (whether intentional or fraudulent) at the content provisioning entity (which may not have the same level of data protection as the content provisioning entity).
- pre-validation processes discussed herein may include one or more prescreen and/or pre-qualification processes.
- An offer provider (also referred to as an “offer provider entity”, “offer computing system”, or “offer system”) is generally an organization that offers goods or services to users (e.g., users), such as goods or services that introduce some risk of loss to the offer provider if the user fails to comply with terms of the agreement between the offer provider and the user.
- the offer provider may be a credit card issuer that offers and issues credit cards to individuals.
- the credit card issuer Prior to offering such financial services to users, the credit card issuer typically analyzes (or receives information from another entity regarding) financial information of the user to determine risk associated with offering a credit card to the user. For example, a risk score, such as a credit score, may be used as an indicator of risk that the user defaults on the credit card.
- the offer provider may be an insurance provider, a mortgage broker, a product or service supplier/manufacturer/distributer, an intermediary for services and products, a marketing or public relations firm, or any other entity that offers products or services.
- the offer provider identifies publication criteria that must be met for the offer provider to approve providing product or service offers to users. For example, a credit card issuer offer provider may determine publication criteria (associated with providing a firm offer of credit to consumers) indicating a minimum credit score (e.g., above 700) that is required for the credit card issuer to offer a particular credit card to a user.
- a user device generally includes any device of a user, such as an electronic device through which an offer from an offer provider may be displayed (e.g., via software and/or a site of a digital display entity).
- User devices may include, for example, desktop computer workstation, a smart phone such as an Apple iPhone or an Android phone, a computer laptop, a tablet such as an iPad, Kindle, or Android tablet, a video game console, other handheld computing devices, smart watch, another wearable device, etc.
- the user device is a mobile device, such as a smart phone; however, other user devices (e.g., laptops, kiosks, etc.,) may be used.
- Regulated user data generally includes information regarding users that is stored by an entity (e.g., a content provisioning entity) and is subject to external regulations (such as set by a government agency) that restrict how the user information may be used (e.g., accessed, updated, shared, etc.) outside of the storing entity.
- Regulated user data generally is useful in validating users to receive offers for certain goods or services, but may include sensitive user data that should be protected to a greater degree than publicly available user information.
- dissemination, sharing, and/or any other access to regulated user data may be controlled closely by the storing entity to reduce risks associated with improper use of the regulated data, such as any sharing of regulated user data that violates the relevant regulations.
- regulated user data may be optimal for determining certain characteristics or propensities of users, such as determining risks associated with issuing a credit account to users, sharing of regulated user data with offer providers, digital display entities, and/or others that might be involved in related marketing or communications to the user may be limited to include only the minimum required regulated user data or no regulated user data.
- regulated user data is credit data that is regulated by the Fair Credit Reporting Act (FCRA), which restricts use of the credit data, such as by limiting how credit data of users may be shared with marketing entities.
- FCRA Fair Credit Reporting Act
- the content provisioning entity is a credit bureau, a business unit of a credit bureau that maintains the credit data and enforces the FCRA access regulations, and/or another entity that is authorized to access the credit data. Accordingly, in such an embodiment, the content provisioning entity may limit the use or sharing of user credit data to reduce risk of disclosure or other use of the credit data outside of FCRA regulations, whether intentionally, inadvertently, or fraudulently.
- Regulated user data may include various regulated user attributes, such as information regarding lines of credit, debt, bankruptcy indicators, judgments, suits, liens, wages, collection items, mortgage loans, other loans, retail accounts, checking/savings/transaction data, late or missed payment data, other credit attributes, and/or derivatives/scores/ratings based on at least the credit information.
- regulated user attributes such as information regarding lines of credit, debt, bankruptcy indicators, judgments, suits, liens, wages, collection items, mortgage loans, other loans, retail accounts, checking/savings/transaction data, late or missed payment data, other credit attributes, and/or derivatives/scores/ratings based on at least the credit information.
- PII Personally identifiable information
- PII includes any information regarding a user that alone may be used to uniquely identify a particular user to third parties.
- PII may include first and/or last name, middle name, address, email address, social security number, IP address, passport number, vehicle registration plate number, credit card numbers, date of birth, telephone number for home/work/mobile.
- User identifiers that are used to identify a user within a particular database, but that are not usable by third parties (e.g., other entities) to uniquely identify the user may not be considered PII.
- PII user IDs that would be very difficult to associate with particular users
- PII user IDs that would be very difficult to associate with particular users
- the IDs are unique to corresponding users.
- Facebook digital IDs of users may be considered PII to Facebook and to third parties.
- Data Store Any computer readable storage medium and/or device (or collection of data storage mediums and/or devices). Examples of data stores include, but are not limited to, optical disks (e.g., CD-ROM, DVD-ROM, etc.), magnetic disks (e.g., hard disks, floppy disks, etc.), memory circuits (e.g., solid state drives, random-access memory (RAM), etc.), and/or the like.
- optical disks e.g., CD-ROM, DVD-ROM, etc.
- magnetic disks e.g., hard disks, floppy disks, etc.
- memory circuits e.g., solid state drives, random-access memory (RAM), etc.
- RAM random-access memory
- Another example of a data store is a hosted storage environment that includes a collection of physical data storage devices that may be remotely accessible and may be rapidly provisioned as needed (commonly referred to as “cloud” storage).
- Database Any data structure (and/or combinations of multiple data structures) for storing and/or organizing data, including, but not limited to, relational databases (e.g., Oracle databases, mySQL databases, etc.), non-relational databases (e.g., NoSQL databases, etc.), in-memory databases, spreadsheets, as comma separated values (CSV) files, eXtendible markup language (XML) files, TeXT (TXT) files, flat files, spreadsheet files, and/or any other widely used or proprietary format for data storage.
- CSV comma separated values
- XML eXtendible markup language
- TXT TeXT
- flat files eXT files
- User Input (also referred to herein simply as “input”): Any type of input provided by a user that is intended to be received and/or stored by the system, to cause an update to data that is displayed and/or stored by the system, to cause an update to the way that data is displayed and/or stored by the system, and/or the like.
- user inputs include keyboard inputs, mouse inputs, digital pen inputs, voice inputs, finger touch inputs (e.g., via touch sensitive display), gesture inputs (e.g., hand movements, finger movements, arm movements, movements of any other appendage, and/or body movements), and/or the like.
- user inputs to the system may include inputs via tools and/or other objects manipulated by the user.
- user inputs may include motion, position, rotation, angle, alignment, orientation, configuration (e.g., fist, hand flat, one finger extended, etc.), and/or the like.
- user inputs may comprise a position, orientation, and/or motion of a hand and/or a 3D mouse.
- API Application Programming Interface
- An API defines a standardized set of operations, inputs, outputs, and underlying types, such that functionality is accessible via the API in an efficient way.
- a system provides an API by which a third party may access functionality of the system. Accordingly, the system advantageously abstracts away (from the third party’s perspective), much of the complexity that may be involved in the functionality of the system, and enables the third party to quickly and efficiently leverage functionality of the system to build other systems and services.
- Ensuring the proper content is presented to the right person at the right time can often mean the difference between getting a message to the person and being ignored in the avalanche of data received by the person.
- Examples of technologies for identifying the location of an electronic device include global positioning service (GPS), beacon, or access point location (e.g., WiFi presence detection; cellular tower connectivity; etc.).
- Geofencing allows provisioning of content based on proximity to a location. As a device enters a geofenced area, an information exchange may be performed whereby the device provides location information to the geofencing system and the geofencing system may provide information to the device if the location is associated with a geofenced area.
- the area may be defined by one or more identified geospatial points.
- the points may be identified using positioning technologies such as global positioning service (GPS), beacon, or access point location (e.g., WiFi presence detection; cellular tower connectivity; etc.).
- GIMBALTM is a commercially available beacon system that may be user in certain embodiments discussed herein to provide location based content offerings.
- other proximity sensors may be used to provide information regarding location of a user (or, more particularly, a user’s mobile device) in relation to a designated area associated with a publication rule.
- Bluetooth communication may detect a near range proximity of a user device and, accordingly, trigger processing of publication rules for a user of the device.
- geolocation information such as from global positioning system (GPS) sensors of a mobile device, triangulation of data associated with cellular towers accessible by a user device, and/or location information determined based on proximity of the user device to one or more Wi-Fi nodes, may be used to determine location of a user, which may then be compared to a mapping of geofenced areas.
- GPS global positioning system
- a pre-screening process may occur in real-time without the user necessarily knowing that their credit is being accessed (without authorizing access to the user’s credit data from one or more credit bureaus), while a pre-qualification process may be performed in response to a user request for information regarding qualification for one or more credit products (and authorizing access to the user’s credit data from one or more credit bureaus).
- Such pre-screening and pre-qualification processes are generally referred to herein as pre-validation processes or prescreening processes. For instance, an entity may request pre-validation of a specific user for one or more credit products.
- a credit file for the specific user may be pulled, the credit data assessed based on publication criteria (or factors) and the requesting entity notified if the specific user met the publication criteria.
- Such pre-validation processes work well for providing offers (e.g., credit card offers) to known individuals.
- offers e.g., credit card offers
- opportunities to provide offers to users via their mobile devices, such as at the time/location where the user really wants to receive a credit offer (e.g., for purchase of an automobile) are limited largely because of the technical difficulties in uniquely identifying (e.g., to the user level) particular users with enough certainty to provide pre-validated offers while the user is still at the desired location.
- a wireless beacon may transmit a message identifying the beacon to the user’s mobile device (or the beacon may be continuously transmitting a broadcast signal requesting responses from devices within the geofenced area).
- the message may be a probe message alerting the mobile device to the presence of the beacon or an identifier for the beacon that can be used to look up a location and/or content associated therewith.
- the mobile device may respond to the beacon or another system with a message including the beacon identifier, an identifier of the mobile device, the user, and/or an application executing on the mobile device such as a standalone application or a context based application such as a web-browser or network content viewing application or a native application in the operating system of the device such as a payment application or wallet application.
- the content provisioning system may in turn identify content to provide to the mobile device.
- the particular content may be identified (from a collection of a plurality - thousands or millions - of available content items) and customized for the user based on attributes associated with the user that are determined based on the initial communication with a geofencing system or the content provisioning system.
- the user attributes may include attributes such as information from a mobile carrier (e.g., Verizon, Sprint, AT&T, etc.) regarding the user, information known to the offer provider (e.g., a financial institution that wants to provide credit offers to qualifying users) about the user, and/or information guarding the user that is accessed from one or more third party systems (e.g., credit data of the user that is received from a credit bureau).
- the content may include content which only prequalified users may receive.
- a credit providing entity may transmit a credit offer specific to the user.
- credit offers may be stored in a data storage medium accessible by the content provisioning system until the user is detected within a geofence.
- the offer may be quickly transmitted to the user’s mobile device so that the user may, in some embodiments, immediately complete the credit application process and receive credit approval that may be used to complete a purchase at the location of the geofence (e.g., within a retail establishment).
- a credit providing entity may transmit publication criteria indicating requirements for various types of credit that may be available to pre-validated users (e.g., users that meet the publication criteria).
- the publication criteria may be stored in a data storage medium accessible by the content provisioning system for application to user attributes of a user when the user is detected within an indicated geofence area (such as may also be included in the publication criteria provided by the offer entity).
- the publication criteria may be re-applied to the user data (e.g., based on a fresh pull of credit data of the user) to determine whether an offer should be transmitted to the user’s mobile device.
- the geofencing system or the content provisioning system may provide a message to the mobile device to invite a specific interaction.
- the geofencing system may provide a message to cause the mobile device to execute an application on the mobile device.
- the application may provide a more secure and controlled environment to communicate with the user of the mobile device.
- the publication criteria may also include timing requirements for provision of offers to users. For example, a user that enters one or more particular geo-fenced areas (e.g., similar retail establishments) for a third time within one week may be provided a content offer (if the user also needs any other publication criteria), whereas the user may not be provided the content offer the first and second times the user entered the particular geofenced areas.
- the geofencing system may detect a mobile device circling a specific car in an auto dealership, which triggers transmission of a message to the user’s mobile device to initiate interactions with the user that may result in an offer of credit to the user for purchase of an automobile.
- the geofencing system or the content provisioning system may provide a message such as “You’d look great in that car. Text 12345 for a top tier credit offer,” to the user device.
- the content provisioning system may store content in conjunction with publication criteria.
- the publication criteria may include time periods when the content may be presented, specific users or groups of users to whom the content can be presented, preconditions for presenting the content (e.g., contact with a different beacon within the geofencing system; specific application on the mobile device; specific mobile device type; etc.), or other conditional logic to afford control over when, where, and to who the content is provided.
- a content provider may provide publication criteria indicating that users with a credit score of above 750, income level above $80,000, and are currently within a geofence of a particular car dealership and have visited that particular car dealership previously within the last 72 hours, are to be offered an automobile line of credit with certain terms.
- a predictive system can be included to accept location information for the target and predict the identity of the target.
- a mobile device of a target may provide geo-location information identifying an address of an office, an address of a school, and an address of a grocery store. A historical record of location information may determine that these three locations are regularly visited by a target and, perhaps, in a specific sequence.
- the predictive system may identify the locations for the mobile device that correspond to the historical record and thus infer the identity of the target.
- a reference data store may be included that stores locations for a device and durations of time at respective locations.
- a pattern of locations may be identified such as extended durations during evening hours at a first location (e.g., home) and extended durations during daytime hours at a second location on weekdays (e.g., work). These two locations may help identify a user of the device who works at the second location and resides at the first location.
- the content provisioning system advantageously optimizes communications with user devices that are needed to reach a required authentication confidence level for a user of the user device (e.g., to be able to determine that the user of the device really is who they say they are). For example, the types and/or quantity of PII requested of a user may be reduced by the content provisioning system, when compared to conventional similar authentication requirements for offers of credit.
- Such optimizations may reduce the communication bandwidth and/or reduce the quantity of communications with the user device, while making the user experience less intrusive, such as by asking for less PII from the user than would normally be required to authenticate the user for a credit application, and also reducing risk of PII exposure to undesirable entities (e.g., hackers that monitor and intercept communications with mobile devices).
- an optimized user identification and authentication process may be performed in substantially real-time by the content provisioning system in response to communication with a mobile user device. For example, a user may initially see a billboard encouraging the user to initiate communication with the content provisioning system, such as one that reads “text CARLOAN to 12345 to find out if you qualify for 0% financing of your dream car.” In another embodiment, the user may initially be contacted based on the content provisioning system detecting a mobile device entering a geo-fenced area. In other embodiments, communication between a user and the content provisioning system may be initiated in other ways such as through addressable media. For example, a communication device may be configured to present streaming media content.
- the content provisioning system may need to perform some authentication or identification of the user, such as by requesting PII from the user and/or requesting additional information associated with the user or user device from one or more internal or external databases, before the user can be considered for content offers (e.g., credit offers) or before content can be selected for the user.
- content offers e.g., credit offers
- authentication of a user is performed by first determining an initial, narrow, set of potential users associated with the detected user device.
- the system may be configured to identify a minimal set of questions to present via the mobile device of a target to accurately confirm the user’s identity. For example, if the location information (e.g., postal code) narrows the potential users to person A and person B, the system may then look for specific differences in attributes of person A and person B, such as may be stored in records for person A and person B (e.g., credit records of the two users). For example, if person A and Person B reside in different states, the system may determine that obtaining the state of residence from the user is sufficient to uniquely identify the user.
- location information e.g., postal code
- the level of authentication, and the corresponding types of user attributes that may be used to authenticate a user may be based on the content being provided. For example, if the content is informational, security may not be as high of a priority and gender may be used to distinguish person A from person B. As another example, if the content is a credit offer having a predetermined maximum line of credit, more secure identification may be needed to ensure that any credit offers provided are to the intended user. In such situations, a more secure and personally attributable user attribute, e.g., the holder of the user’s mortgage, may be identified as a distinguishing attribute to request from the user.
- a more secure and personally attributable user attribute e.g., the holder of the user’s mortgage
- the intelligent identification of user attribute(s) to request for authentication of a user can be particularly useful in limited resource environments. For example, without the features described, a user may be asked to provide responses to many questions to positively identify the user (before application of credit prequalification rules based on the user’s credit data). Each question requires the user device to expend valuable resources such as power, processing, memory, bandwidth, and the like, to receive, present, and respond to the question. Additionally, the requesting and collection of increased amounts of information may introduce user friction and increase the potential for an error in the requesting or collecting process. In contrast, the systems and methods described herein determine an optimized (e.g., minimal) set of questions that may be used to positively distinguish the actual user of a user device from other possible users of the user device. As discussed, the minimal set may be generated based on the content to be provided and/or a desired level of security.
- one challenge in the mobile environment is to accurately identify an individual user without requiring significant data entry by the user.
- an interactive technology that takes first/last name as its primary input may be provided.
- Analysis of the half billion identities, such as stored in a credit header file, indicates that virtually all identities in the US can be accurately determined by triangulating ZIP code, date of birth, and last four digits of a Social Security number (SSN).
- SSN Social Security number
- the vast majority of individuals can be identified based on 1 or 2 of those elements.
- the real-time, interactive process may begin by parsing the name to identify user records matching or fuzzy matching the name and establishing a minimum question set required to uniquely identify a user if more than one candidate is identified.
- the minimum question set may then be presented to the user, such as via a specifically designed and secure application or interactive messaging such as text messaging or interactive voice recognition, to collect user responses.
- the identity Predicated on the responses, the identity may be verified and specific user information about the identified user may be retrieved using the identity.
- the user information may be included as criteria to request additional information about the user, such from a 3 rd party aggregator that queries the mobile network user data store in real-time (e.g., at the time of determining the identity).
- a user initiated text message from a user’s mobile device allows the system to obtain user attributes such as name, address, and/or ZIP code, from a service provider associated with the user device. For example, these user attributes may be automatically retrieved from a service provider, such as a mobile carrier, Internet service provider, social media provider, etc., based on the mobile device number from which the text message was received (e.g., caller ID information) and/or other device identifying information associated with the transmitted text message.
- the system may search a database which indexes the credit header file (CHF) of credit files for any users who match by name and zip code (or other combinations of user attributes in other embodiments). For example, if a user texts a specific term to a specified number associated with a content provisioning system, the system can receive the input information shown in Table 1 from the mobile carrier (such as via a data sharing arrangement that the content provisioning entity has with the mobile carrier).
- CHF credit header file
- the user may initiate the application of publication criteria, including an initial user authentication process, by transmitting a multimedia message (MMS) that includes media such as audio, an image, or video.
- MMS multimedia message
- the system may include audio processing (e.g., speech recognition) to convert the audio into text for further processing.
- audio processing e.g., speech recognition
- image processing such as object recognition to recognize an item shown in the image or facial recognition to identify the person shown in the image.
- image data includes an item
- the item information may be used to drive offers, terms, rewards, or other content relevant to not only the individual who transmitted the image, but to the image itself.
- the system may identify the specific car shown, a license plate shown, or other vehicle identification information (e.g., vehicle identification number (VIN)) and tailor the offer terms based on the specific car and, in some instances, provide additional information about the car such as mileage, condition, location, model year, trim level, accessories, etc.
- the terms may be based on an existing financing option for the item shown (e.g., an existing mortgage on a home shown in an image, an existing lease or auto loan for a car shown in an image).
- the audio and/or images included in the video input may be processed to obtain additional information about the user and/or item or service of interest.
- the image may include an identifier for the item such as a vehicle identification number, Universal Product Code, text, etc. that can be used as a key to identify additional details about the item.
- Media files may include metadata such as time recorded, author, person who recorded the media, location where the media was recorded, and the like. Such metadata may be extracted from an input media element and used to further distinguish the identity of the user and/or the offer, content, reward, etc. they are interested in. Such metadata may be used to determine a device fingerprint, which may then be matched against a database of device fingerprints associated with fraud to predict fraudulent intentions of the user and take appropriate action.
- the user may initiate the process by scanning an identifier. For example, if the user is browsing a car dealership, a QR code or other scannable identifier (e.g., barcode, radio frequency identification tag, near field communication tag, beacon, etc.) may be scanned or otherwise detected using the user’s mobile device Upon detection, the mobile device may initiate a software application which thereby transmits information to the content provisioning system for application of publication criteria of one or more offer providers, such as a credit pre-qualification process that initially requires user authentication. For example, when a QR code is scanned by a mobile device, the QR code may include information to initiate the SMS feature of the mobile device.
- a QR code or other scannable identifier e.g., barcode, radio frequency identification tag, near field communication tag, beacon, etc.
- the QR code may include information to initiate the SMS feature of the mobile device.
- the decoded QR code information may include a “TO” number and a message to send.
- the message is automatically sent by the user device to the content provisioning system, while in other embodiments the message may be automatically generated and the user only needs to send the pre-populated message (e.g., my pushing a “send” button on the user device).
- a beacon signal may enable an automated (e.g., push) type notification to the mobile device.
- the system may previously know some information about the user of the mobile device by virtue of the presence information (e.g., current location, previous location, duration at a given location, etc.) presented during the interaction between the beacon and the mobile device.
- the QR code may launch a web browser and direct it to a specific web address.
- the QR code may activate an application installed on the mobile device. The features described may be applied using SMS, web browser, installed application, operating system function/application, or other initiation mechanisms that allow the mobile device to transmit at least an identifier for a service accessed by the mobile device of a user thereof.
- the system may query the database of indexed CHFs or other identity information data store to identify a candidate list of individual users matching the already known data attributes, as well as additional attributes about each candidate user.
- This query may be a “fuzzy” matching, which searches for alternatives of the user information, such as expanded to include fuzzy matches such as misspellings, phonetic variations, typographic variation (e.g., “rom” being a typographic variant for “tom” due to proximity of the letter “r” to “t” on a standard keyboard), stemming, nicknames, or the like.
- the level of fuzziness of this initial inquiry may be adjusted depending on the implementation.
- Table 2 shows an example of a response from such a query of the database based on the inputs from Table 1.
- the system may sacrifice specificity in the original query, such as by returning records based on a fuzzy, stem search that matches only a few leading characters from each field (e.g., First Name begins with “J”, Last Name begins with “McC”, ZIP Code begins with 921). Because the quick search does not require detailed analysis of the entire value for a given data field, the results may be obtained faster than if a full analysis were performed, and in those implementations, disambiguation applied to the returned candidate records (such as using the techniques described below) may be used to determine a confidence level in the user’s identity.
- a fuzzy, stem search that matches only a few leading characters from each field (e.g., First Name begins with “J”, Last Name begins with “McC”, ZIP Code begins with 921). Because the quick search does not require detailed analysis of the entire value for a given data field, the results may be obtained faster than if a full analysis were performed, and in those implementations, disambiguation applied to the returned candidate records (such as using the techniques described below) may be used to determine
- the result set returned by the initial query may be processed by the content provisioning system to determine whether there is a perfect match within the result set. For example, if the result set included another Record # 5 for James McCoy in ZIP code 92110, the system may determine that record five is an exact match to the provided user information and further disambiguation (such as using the techniques described below) are not performed. Rather, with a single perfect match identified, the system may confirm identity of the user and provide the appropriate offer content to the user. In situations where a perfect match is not included in the candidate list or to or more perfect matches are included in the candidate list, further disambiguation may be performed.
- the system may then evaluate the candidate list and eliminate candidate users having a probabilistic fuzzy match below a certain threshold. For example, “Jaime McCountinosh” may be eliminated because of the degree of difference between the user attributes received from the mobile carrier and attributes of that particular candidate from the credit header database (and/or other databases in other embodiments). For the remaining candidate users in the candidate list, the system may rank order the candidates based on the similarity to the user attributes from the carrier. In the example provided, there are 2 possible high quality matches in view of the significant match of CHF attributes of the three users with the user attributes from the mobile carrier (e.g., “Jay McCoy” and “Jimmy McCoy”).
- attributes shared by the candidate users may be used to interact with the user in a personalized way.
- the system may present a greeting to the user such as “Hello, Mr. McCoy” but let the user know that they might need to do some additional validation.
- record numbers 1 and 2 would be kept because they are seen as high confidence matches with the first and last name provided by the service provider.
- address information e.g., ZIP code, street address, etc.
- the system may compare the address of any candidate users with and the incoming address information from the service provider.
- the address information from the service provider may be compared to one or more historical addresses of the candidate users from the CHF (and/or other data sources) of the candidate users.
- other attributes provided by the mobile carrier may not only be compared to current attributes of the candidate user from other data sources (e.g. CHF), but also to historical attributes of the candidate user’s.
- the system may be configured to automatically select a single authenticated user if the carrier provided user attributes match only a single user’s CHF data, thereby ending the search.
- the system may apply additional profiling to determine which disambiguation questions can be asked to arrive at a single candidate. Assuming the above example has three unique candidates, the system may determine if asking for the date of birth, month of birth, year of birth, or last four SSN or some combination thereof will uniquely identify only one of the three candidates. In the very rare case where the system cannot distinguish between two or more candidates, even with additional PII provided by the user, the process may provide an indication that the identity cannot be confirmed without further authentication procedures.
- the further authentication procedures may be initiated at that time by the content provisioning system, such as by directing the user’s mobile device to an out of wallet authentication site, such as may be provided by a credit bureau, to ask the user for information regarding particular credit data items (e.g., balances of particular loans/mortgages, previous residence addresses, etc.).
- further processing of publication criteria for offers requiring a higher level of user authentication e.g., credit offers or insurance quotes, may be terminated when the identity of the user cannot be confirmed. This safeguards the system from making an offer to the wrong person.
- SmartPIN Smart Lookup
- device-level fraud analysis may be integrated into the various user identification processes.
- script code e.g., JavaScript
- the script code may be included on a landing page to which the user is directed after initiating an identity validation process (e.g., in response to the user completing a call to action, such as sending a specific text message to a particular telephone number to receive a prequalified offer).
- the script code may be configured to determine characteristics of the mobile device and/or user based on metadata associated with the URL request, and in some implementations by requesting further identification information from the mobile device without notifications to the user.
- the script code may determine a “device fingerprint” that can be compared to one or more lists of device fingerprints associated with high risks of fraud.
- a likelihood of potential fraud by the particular user device may be determined and used to determine how to further interact with the user (e.g., to close the connection with the user device based on a high likelihood of fraud or continue the communication with the user device).
- the ability to uniquely identify a user (from a set of multiple possible users sharing a set of user attributes) based on minimal additional information requests from the user optimizes the user identification process.
- the determination as to whether a given candidate is a “match” presents a non-trivial problem for computer-implemented systems.
- a scoring process may be implemented.
- a minimum cutoff to qualify for consideration may be a 0.4 Jaccard index score (scaled from 0 to 1.0). Equation (1) below shows one expression of a Jaccard index for the similarity between two data sets A and B.
- the system may use a bi-character Jaccard index for the fuzzy matching algorithm. This minimum cutoff is selected to be loose enough to allow for possible name variations.
- the system may implement further constraints to ensure validity.
- a nickname table may be included to enhance the candidate list and prevent the system from missing common name variations.
- the candidate list may be processed for relevant matches to the original input (e.g., from the service provider) as well as nicknames.
- the system may look for candidate records that have at least a Jaccard score of 0.4 or higher. If more than one candidate has a Jaccard score greater than or equal to 0.7, the system may initiate a disambiguation process. The disambiguation process may be skipped if a perfect match for the name is identified based on the Jaccard score and the candidate record has a perfect match to the provided address information.
- the disambiguation process begins.
- the disambiguation may first parse the list of date of births from the remaining candidates and determine if each candidate in the list has a unique birthdate. If every remaining candidate has a unique date of birth (DOB), the system can ask the user for date of birth and, based on this information, one match can be identified. For the candidates shown in Table 2, DOB will provide a unique identification for any one of the candidates. If the DOB received by the system is not one of the candidate DOBs, the system may provide an indication that the identity cannot be confirmed and further processing terminated. The system also may also determine that other candidate information (e.g., Last 4 SSN) is not unique. In such instances, the system would not prompt the user to provide this information.
- other candidate information e.g., Last 4 SSN
- the system may include two or more paths to the identification.
- the identification inputs may be provided automatically via the service provider or directly from the user.
- the system may include features to prevent displaying any information that the user did not provide. For example, the system may require a candidate have at least 0.9 or higher Jaccard score before displaying a candidate’s information simply because it matches to the input name provided by the service provider. Table 3 shows some examples of the Jaccard score for various candidate comparisons.
- Candidate 1 Jaccard score “James Mcoy” “James McCoy” 0.7273 “John Smith” “Jonh Smith” 0.5 “John Smith” “Johhn Smith” 0.9 “Bob Grey” “Bob Gery” 0.4
- the names may be visibly very similar.
- the disambiguation process may increase the clarity on distinguishing between facially related candidate by collecting information using follow-up questions that identify one and only one candidate.
- the system may be configured to prevent further activity until the user provides all the correct information that is required to positively identify the user.
- user authentication based on only credit header file data may be able to get 96% unique identifications from the full name and zip code (such as may be provided by the mobile carrier through which the user sends an initial text message to initiate a prequalification process). Further, if the system also considers the date of birth and/or last four SSN, a unique identification can be identified more than 99.8% of the time. For the remaining 0.2% of the time or if the user is a no-hit (e.g., not found in the data stores accessible by the system), the system may simply default by not making any offer and terminating any further processing (e.g., decline to provide an offer, content, or other good/service).
- the identification process may be applicable in other fields.
- This sign may include an active (e.g., user prompt; QR code; barcode) or passive (e.g., beacon, RFID, NFC) indicator.
- This indicator may cause a mobile device associated with a user interested in the item to send a text message or other initiation message to start the identification process.
- a location indicator may detect presence of a user near a house that is for sale and initiate communications with the user regarding the house.
- the user may not provide any proactive request for the information, but rather the information is provided based on the user’s proximity to the house for sale.
- other criteria may be assessed before initiating communication with the user, such as the user’s predicted interest in purchasing a house.
- the location indicator may detect presence of the user near the house that is for sale and directly communicate to the user device (e.g. via Bluetooth communication) information on how the user can initiate a prequalification process and/or receive further information on the house (and/or related houses for sale or other similar real estate information), such as by providing a short code that can be selected by the user to send a text message to the content provisioning system 150 .
- message content provided to the user may be associated with the specific item (e.g., the particular house for sale) such that the location and specific terms for the process (e.g., mortgage lending, credit lending, insurance rating, etc.) may be identified not just for the user but also for the specific item of interest.
- specific item e.g., the particular house for sale
- specific terms for the process e.g., mortgage lending, credit lending, insurance rating, etc.
- FIG. 1 is a block diagram showing an example environment for providing personalized content to a mobile device.
- the resources available to a mobile device 102 may be limited.
- the limitations may be physical, such as display size or input means for providing information. These limitations can present a barrier to viewing or otherwise interacting with content presented via the mobile device 102 .
- the limitation may be a computing resource such as power, processing speed, network bandwidth, network connectivity, or the like. It may be desirable to conserve these resources to ensure longer operational time for the mobile device 102 .
- Each message transmitted or received requires resources to process. Accordingly, the ability to accurately identify a user of the mobile device 102 in a way that limits the number of resources can improve the overall speed at which the user can be identified but also improve the resource utilization for the mobile device 102 during the identification or content provisioning process.
- the mobile device 102 is shown as a smart phone.
- the mobile device 102 may be implemented as a tablet computer, a wearable device (e.g., smartwatch, smart glasses), or other communications device configured to transmit and receive messages.
- the mobile device 102 may include one or more transceivers (not shown) to transmit and receive such messages.
- the messages may be transmitted to or receive from one or more communication systems 110 such as a satellite 112 , an access point 114 (e.g., cellular access point), or a wide area network router 116 (e.g., WI-FI®).
- a satellite 112 such as a satellite 112 , an access point 114 (e.g., cellular access point), or a wide area network router 116 (e.g., WI-FI®).
- an access point 114 e.g., cellular access point
- a wide area network router 116 e.g., WI-FI®
- the communications may provide data communication path to a network 120 such as the Internet, cellular networks, or combinations of networks.
- the communications may provide services such as location services.
- a beacon may transmit location information that can be received by the mobile device 102 and used to identify a location of the mobile device 102 .
- the geofencing information may include an identifier of the beacon which can then be used to look up a location associated with the beacon.
- the location services may include a global positioning service (GPS).
- GPS global positioning service
- the GPS signals provide a way for the mobile device 102 to identify its current location. Location may also or alternatively be determined based on the access point 114 location or address information (e.g., IP address) of the wide area network router 116 .
- a personalized content provisioning system 150 may be included in the environment 100 .
- the personalized content provisioning system 150 may include an identity confirmation device 155 , a publication manager 160 , and a content generator 170 .
- the identity confirmation device 155 may be included to authenticate the identity of a user, such as using the various systems and methods discussed herein. In some implementations, authentication of a user may be referred to as Smart Lookup.
- the identity may be based on information provided by the mobile device 102 or an application executing thereon. For example, the mobile device 102 may include a device identifier in messages transmitted to the personalized content provisioning system 150 .
- Examples of a device identifier include an IP address, a telephone number, a tax identifier, a media access control (MAC) address, and a mobile equipment identifier (MEID).
- the information may be configuration information for the mobile device 102 such as home address, username, account number, or the like.
- a software application such as an offer identification application, may be installed on the mobile device 102 and used to interact with the content provisioning system to identify any offers for the user.
- the offer identification application such as an application provided by a credit bureau or other entity that has access to regulated data of users, may receive user input values through a user interface such as name, mailing address, email address, birthdate, etc. One or more of the user input values may be included in the information transmitted to the personalized content provisioning system 150 .
- the information provided to the identity confirmation device 155 may not be sufficient to uniquely identify a user of the mobile device 102 .
- the identity confirmation device 155 may be further configured to cause presentation of a user interface to collect one or more additional elements of information to identify the user as described herein.
- the publication manager 160 may be configured to receive content and publication criteria that must be met for the content to be provided to a user.
- the content may include offer content or instructional content.
- the content may be associated with location criteria such that the content can be published to users at or associated with a specified location.
- the content may be associated with user behavior.
- the behavior may include historical locations, time between visits to one or more locations, mobile device behavior (e.g., logging into an application, executing a feature of an application), interactions with the personalized content provisioning system 150 , messages (e.g., text messages) received (e.g., specific message content or specific message destination), or the like.
- the content and criteria may be stored in a publication information data store 184 .
- the content generator 170 may be assessing the publication criteria received by the publication manager to determine, for specific behavior or requests, whether and when to respond to the mobile device 102 .
- a user request processor 172 may be included to process user initiated requests for content. For example, a user may activate a control element on an application executing via his mobile device. The control element may transmit a request to view current content offerings.
- the content generator 170 may include identified information for a user to prefill content such as a form or other interactive interface.
- An identity data types may be mapped to specific fields and, if available for an identified user, included in the content.
- the prefilled content may be provided for display or printing such as via a kiosk.
- the prefill may be performed without presentation to the user. For example, consider an implementation where the user transmits a text message including “more info” to a predetermined destination.
- the user may be identified using information from the mobile carrier and, based on the identification information, additional user information such as address, gender, or the like may be received. This additional user information may be used to transmit a request to produce a physical mail item including the additional information.
- the additional user information may be provided via a machine interface such as an application programming interface configured to receive a device identifier or a limited set of user values (e.g., name and postal code) and return a set of additional user information if a user can be identified.
- the set of information may be accompanied by a confidence score indicating the likelihood that a given set of information is associated with input the user information.
- third parties may leverage the prefill technology using an API that facilities direct communication with the content generator 170 , such as to verify identity of a user based on a minimal set of user information (as discussed thoroughly herein) and then provide back more extensive user PII once the user’s identity is verified (such as from the identity information database 188 and/or other internal or external databases of user information).
- the content may include an authorization token such as an optical scancode (e.g., barcode, QR code).
- an optical scancode e.g., barcode, QR code.
- the mobile device may display an interface including the optical scancode to complete a transaction at a point of sale against the line of credit.
- a behavior detector 174 may be included to generate and transmit content based on user behavior. In some implementations, this may be referred to as a content “push.”
- the behavior detector 174 may receive behavior information associated with the mobile device 102 .
- the information may be received from the mobile device 102 (e.g., included in a message transmitted from the mobile device 102 to the personalized content provisioning system 150 .
- the information may be received from a service provider such as an application service provider or communication service provider (e.g., satellite, cellular, internet service provider).
- the behavior information may be stored in a user history data store 182 .
- the behavior detector 174 may monitor the user history data store 182 for user records that include behaviors associated with content. Once a behavior condition is met, the behavior detector 174 may initiate generation of content for the mobile device 102 associated with the behavior.
- generating content may include identifying the user of the mobile device 102 .
- the identification may be performed using the identity confirmation device 155 .
- Generating the content may include customizing the content to include specific values for the user in the content. For example, it may be desirable to insert the user’s first and last name to personalize the message.
- the personal information may be retrieved from an identity information data store 188 .
- the identity information data store 188 may be queried using at least a portion of the user information associated with a request for personalized content.
- an identity information data store is a credit header database (and/or other databases in other embodiments).
- content may include variable features such as a prequalified line of credit. This value may be generated for the specific user based on their personal information.
- a content history data store 186 may be included to store an identifier for a personalized content item provided to the mobile device 102 and/or the authenticated user to which the content item was targeted.
- the content history data store 186 may include an identifier of the mobile device 102 and an expiration date for the content.
- one or more custom values generated for the content may be stored in association with the identifier for the personalized content item (e.g., prequalified line of credit, contact information of agent included for accepting or inquiring about the content, etc.).
- An ID-device binding data store 180 may also be included in the environment 100 .
- the binding data may store an association of device identifiers and an identifier for a specific user.
- the identifier for a specific user may be a personal identifier that uniquely identifies the user within the environment 100 or at least in the context of the personalized content provisioning system 150 .
- FIG. 2 is a flow diagram showing an example method of behavior initiated content provisioning.
- the method 200 may be implemented in whole or in part by the personalized content provisioning system 150 .
- the method 200 illustrates an example method in which user behavior, such as location behavior, can be used to provide personalized content to a user.
- user behavior such as location behavior
- an alert may be transmitted to the mobile device indicating that the user is prequalified for a loan to purchase a car at the dealership.
- the method of FIG. 2 may include fewer or additional blocks and the blocks may be performed in an order that is different than illustrated.
- a location of a device associated with a user may be detected such as by a presence system or a behavior detector.
- the detection may be based on information transmitted from the device.
- a presence detection system may be implemented to identify where a mobile device is located, when it arrived at the location, and, in some implementations, how long it was at the location.
- the identification may be based on transmitting a beacon identifier from the mobile device to the presence detection system.
- the information may be transmitted by a service accessed by the device such as accessing the WI-FI® network at a location.
- the personalized content provisioning system 150 may determine whether the location of the device is associated with content. The determination may include querying a publication information data store for content associated with the location. In some implementations, the association may be based on an area. For example, a content element may be associated with an entire city block. If the location of the device is within that city block, then the location may be deemed associated with the area. In some implementations, a threshold distance may be associated with an area. Thus, if the device location is determined to be within the threshold distance of a geographic area, the device location may be deemed associated with the area.
- the method 200 may return to block 202 to detect another location for the device. If the determination at block 204 is affirmative, then content may be associated with the location.
- the personalized content provisioning system 150 may receive a publication rule for the content associated with the location.
- the publication rule may identify user attributes required for delivery of the content to the user. For example, the publication criteria may indicate that the user has visited a particular location two or more times in one week.
- the personalized content provisioning system 150 applies the remaining publication criteria (e.g., pre-validation criteria for a credit card offer) to user attributes (e.g., from third party data sources and/or directly Received from the user device) to determine if content (e.g., a credit card offer) should be provided to the user.
- the determination may include retrieving behavior data from a user history data store.
- the method 200 may return to block 202 and continue as described above. If the determination at block 208 is affirmative and the behavior meets a publication criterion, the method 200 may continue to block 210 .
- information about the user associated with the device may be received by the personalized content provisioning system 150 .
- Receiving the information may include retrieving user information from the user history data store.
- Receiving information may include receiving information from the device or application executing thereon.
- a message including the content for the user may be generated by the personalized content provisioning system 150 .
- Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor.
- the metric may be a score based on prior transactions. The score may indicate how reliable the user performed one or more prior transactions.
- the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected.
- the content generation may include generating an identifier that uniquely identifies the personalized content provided to the user.
- the content may be associated with an expiration date.
- the expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof.
- the personalized content provisioning system 150 may transmit the message to the device.
- the message including the personalized content may be transmitted via wired, wireless, or hybrid wired and wireless means such as via one or more of the communication systems 110 and/or the network 120 shown in FIG. 1 .
- the message may cause the device to activate and/or initiate an application that is configured to present the content.
- an alert indicating a product offer to a user may be automatically transmitted to the mobile device 102 in response to determining that personalized content should be provide to the user (e.g., in block 214 ), such as in real-time as the user is at the location triggering the content analysis (e.g., at block 204 ).
- Such alert communications may be automatically transmitted to the user in one or more modes of communication, such as, for example, direct to a mobile application, electronic mail, text messaging (e.g., SMS, MMS, or other), to name a few. In certain modes of communication, the communication may be configured to automatically operate on the user’s electronic device.
- a software application installed on the user’s mobile device may be automatically activated to deliver the communication to the user (e.g., a SMS viewer or application may automatically display information from the alert communication when received by the device or when the device is connected to the internet).
- the alert communication may activate a web browser and access a web site to present the alert communication to the entity.
- a communication may be transmitted to the user’s email account and, when received, automatically cause the user’s device, such as a computer, tablet, or the like, to display the transmitted communication or a link to take the entity to a webpage with additional information regarding the selected publication content (e.g., product or service offer).
- FIG. 2 describes a method of providing personalized content based on detected behaviors. In some implementations, it may be desirable to provide personalized content on demand such as in response to a user request.
- FIG. 3 is a flow diagram showing an example method of user requested content provisioning.
- the method 300 may be implemented in whole or in part by the personalized content provisioning system 150 .
- the method 300 illustrates an example method wherein a user can request that information regarding the user be obtained and a determination of eligibility for content be performed.
- a user visits a car dealership while walking home.
- the dealership may include a sign saying “Text ‘credit’ to 333333 to drive home today.”
- the act of submitting a text message with a specific keyword e.g., credit
- the method of FIG. 3 may include fewer or additional blocks and the blocks may be performed in an order that is different than illustrated.
- a message requesting offer content may be received from a user device.
- the message may be received in response to a location alert (e.g., behavior based), such as a message sent to the user device as the user walks into the car dealership and requests a reply back from the user.
- the message may be received without direct solicitation via the user device such as by a user seeing a sign or hearing a radio advertisement, creating the message, and transmitting the message.
- the message may be generated via an application executing on the user device. For example, a bank may provide its users a custom application.
- the custom application may include a user interface element that, when activated, causes transmission of a message to the content provisioning system requesting identification of current credit offers for the use.
- the example method in FIG. 3 discusses credit offers, but the method 300 may be applicable for requesting and providing other types of personal content such as educational content, informational content, bank cards, mortgages, job applications, government forms, auto loans, retail loans, loyalty programs, pre-fillable content, timeshare or other eligibility based content, comparison service, or the like.
- personal content such as educational content, informational content, bank cards, mortgages, job applications, government forms, auto loans, retail loans, loyalty programs, pre-fillable content, timeshare or other eligibility based content, comparison service, or the like.
- the message may include a keyword such as “credit.”
- the message may include an image such as an image of a car the user is thinking about purchasing.
- the message may be transmitted along with information about the user and/or the user device such as a device identifier, phone number, sender email address, IP address of the device, current location of the user device, or other identifying information for the user and/or user device.
- the personalized content provisioning system 150 may determine that the message received at block 302 is associated with content.
- the determination may include querying a publication information data store for content associated with the location of the user device or other attribute included in the request.
- the determination at block 304 may include identifying content associated with the destination number and specific message included.
- a car dealership may be associated with a particular message destination code (e.g., a phone number or other short message code). The same dealership may have different promotions or content that can be served.
- each campaign may be associated with a different keyword.
- the code may be associated with date information indicating when the code is “active” for providing content. Table 1 below provides one example of how codes may be associated with different content (e.g., different campaigns, different credit levels (e.g., standard versus preferred), or different user types (e.g., individual versus business)).
- the method 300 may return to block 302 to receive another message from the user device. In the negative case, it may be that the destination number and/or code received at block 302 are no longer valid or are not yet active. If the determination at block 304 is affirmative, then content may be available for the user.
- information about the user associated with the device may be received by the personalized content provisioning system 150 .
- Receiving the information may include retrieving user information from the user history data store.
- Receiving information may include receiving information from the device or application executing thereon.
- information may be received from a service provider that provides a service to the user or the device. Before requesting such information, the user may be prompted such as via a graphical user interface to consent to data gathering. A response confirming the consent may be received to permit requesting and receipt of this information.
- the personalized content provisioning system 150 may receive a publication rule for the available content.
- the publication rule may identify a user behavior to provide the content. For example, the user behavior may include visiting the location two or more times in one week.
- the publication rule may include consideration of user information. For example, demographic information (e.g., mailing address, age, gender, etc.) may be used to determine whether content is available for the user.
- the method 300 may return to block 302 and continue as described above. If the determination at block 308 is affirmative and the user information or behavior meets a publication criterion, the method 300 may continue to block 310 .
- a message including the content for the user may be generated by the personalized content provisioning system 150 .
- Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor.
- the metric may be a score based on prior transactions. The score may indicate how reliable the user performed one or more prior transactions.
- the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected.
- the content generation may include generating an identifier that uniquely identifies the personalized content provided to the user.
- the content may be associated with an expiration date.
- the expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof.
- the personalized content provisioning system 150 may generate a message including offer content for the user and then at block 314 transmit the message to the device.
- the message including the personalized content may be transmitted via wired, wireless, or hybrid wired and wireless means such as via one or more of the communication systems 110 and/or the network 120 shown in FIG. 1 .
- the message may cause the device to activate and/or initiate an application that is configured to present the content.
- FIG. 4 is a flow diagram showing an example method of identifying a user. Some implementations, such as the methods shown in FIGS. 2 and 3 , may include uniquely identifying a user based on limited information. FIG. 4 provides one way to efficiently identify a user of a device. Depending on the embodiment, the method of FIG. 4 may include fewer or additional blocks and the blocks may be performed in an order that is different than illustrated.
- the method 400 may begin at block 402 by receiving, e.g., at the identity confirmation device 155 of the content provisioning system 150 ( FIG. 1 ), first information identifying a user of a device from a service accessed by the device.
- the first information may be user input to an application executing on the device.
- the first information may be a phone number or device identifier assigned by a service provider (e.g., mobile network operator) to the device, or even user information associated with the phone number provided by the service provider. If the first information is a phone number, a reverse lookup may be performed to identity user data associated with the phone number.
- the user data may include first name, last name, and mailing address information such as a ZIP code.
- the user data may include historical user data such as previous addresses or names (e.g., due to a change in marital or adoption status).
- the system may query a user data source using at least a portion of the first information. For example, a credit data base including header files associated with different users may be queried using the first name, last name, and ZIP code identified by the first information.
- a set of user data for different users corresponding to the first information may be received at block 405 .
- some users may have the same first name or last name.
- Such user records may correspond to the first information.
- the correspondence may be based on fuzzy logic or phonetic matching. Similarity may also be based on current user information or historical user information (e.g., prior address, name, etc.). Because there may be discrepancies between the received first information and the specific users identified from the user data source, the method 400 may further process the received records to identify a record for a unique user included in the set of users.
- the system may forgo the remainder of the matching process by identifying that user as the authenticated user. In some embodiments, this short-circuiting of the matching process may only be performed if a single perfect match exists in the returned candidate identity records, where a perfect match may be defined as exact matching of all user provided data (e.g., exact match of first name, last name, ZIP code provided by the user), or in some embodiments could include variants of certain identity data (e.g., a perfect match could be identified for a candidate record for “Joe Jones” in response to the user providing the name of “Joseph Jones”), or as defined by the offer provider (e.g., based on the needed level of identity verification).
- a perfect match may be defined as exact matching of all user provided data (e.g., exact match of first name, last name, ZIP code provided by the user), or in some embodiments could include variants of certain identity data (e.g., a perfect match could be identified for a candidate record for “Joe Jones” in response to
- the system may generate a similarity metric identifying, for each of the candidate users, how closely the user data for a respective candidate user matches the first information.
- a similarity metric is a Jaccard score. The degree of relatedness may be used to identify whether any candidate users may be identified as the user and to what level of confidence.
- a weight may be applied to discount values occurring further from the current time. For example, if a user moved in the last six months, the prior address may be considered with a higher weight than a previous address for a person who moved thirteen years ago.
- the weighting may be dynamically specified for each data type (e.g., a first weight may be applied for names while a second weight may be used for ZIP code). The weighting applied for a data type may be identified in a configuration accessible by the system.
- the system may determine whether at least one of the candidate users is associated with a metric that meets or exceeds a matching threshold. For example, it may be desirable to identify a “match” only when the Jaccard score exceeds a minimum threshold, such as to ensure that credit offers provided to a user are based on that same user’s unique credit data that qualifies the user for the credit offers. This may ensure a minimal degree of relatedness. If the determination at block 408 is negative, the user data store may not include records with sufficient level of relatedness to identify the user. In such instances, the method 400 may proceed to block 430 to identify a default user. Identifying a default user may include collecting generic user information that can be presented to any user.
- the user may be provided with generic invitations to apply for credit, rather than being provided with an offer of credit that may be provided only to qualifying authenticated users. This may include identifying no user information and providing a request to provide a more robust set of information than provided via the first information.
- the determination at block 410 will match at least one user. Based on experiments, it has been determined that many users may be uniquely identified on the basis of first name, last name, and ZIP code. Accordingly, the default identification at block 410 may apply to a small percentage of the users submitted for identification.
- the method 400 may continue to block 412 to determine whether only one user exceeds the threshold.
- the determination at block 412 may be affirmative when only one user is identified with a similarity metric above the matching threshold.
- the method 400 may proceed to block 440 to positively identify the user as the candidate user associated with the metric.
- the positive identification may include providing additional information about the user retrieved from the user data source.
- One example of such additional information may include user information for processing credit inquiries.
- the method 400 may compare the remaining candidate users’ information to identify a data type or field (e.g., first name, date of birth, last four of the Social Security Number, previous street address, etc.) that may be used to distinguish all candidate users. For example, consider the three example candidate user records shown in Table 4 below.
- the example candidates have the same first name, so the data type or field associated with first name cannot distinguish the candidate users.
- the data type or field associated with ZIP code cannot distinguish all of the candidate users as two the three candidates are associated with the same ZIP code.
- the street address information can distinguish the candidate users as each user can be uniquely identified based on the house number. Accordingly, the data type or field associated with house number may be identified for the example shown in Table 4.
- the system may transmit a request to the user device requesting a value for the data type or field identified at block 412 . It will be appreciated that the method 400 requires only this single value for distinguishing the candidate users. This ensures a limited collection and exchange of information between the system and the device to attain a unique identification.
- the request transmitted at block 414 may cause the display of a data collection user interface to receive and transmit a value for the requested data type or field to the system.
- the value may be received from the user device.
- the value may be compared to the candidate user values to ensure the received value is in fact associated with one of the candidate users.
- the method 400 may proceed to block 440 as described. However, it is expected that the received value will match one of the candidate users thereby providing a unique identification of the user.
- user information for the user may be provided for additional processing.
- personalized content may be provided based on the identity of a user.
- personalization may require specific user information to identify or generate the content.
- One specific example is a prequalified credit offer.
- a model may be included. The model may accept a set of input values representing specific user information such as age, income, credit score, etc. These values may only be provided if the user is uniquely identified such as via the method 400 .
- Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor. For example, the metric may be a score based on prior transactions.
- the score may indicate how reliable the user performed one or more prior transactions.
- the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected.
- the content generation may include generating an identifier that uniquely identifies the personalized content provided to the user. For example, it may be desirable to maintain a record of the terms or conditions included in the content.
- the content may be associated with an expiration date. The expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof.
- FIG. 5 is a messaging diagram showing example communications for providing personalized content to a mobile device.
- FIG. 5 shows messages exchanged between a user device 502 , a short code provider 506 , a decisioning service 508 , and a service operator 510 (e.g., mobile carrier, internet service provider, application service provider, etc.).
- the messages in FIG. 5 are shown as being exchanged directly between entities.
- intermediaries such as security devices, routers, gateways, network switches, and the like may be included. These intermediaries have been omitted to focus the reader on certain features of providing personalized content. Additionally, in some embodiments functionality of two or more devices illustrated in FIG. 5 may be performed by a single entity and/or single device.
- a user of the user device 502 may receive a call to action.
- the user device 502 may transmit a message including device information, such as a location of the user device 502 or a signal detected by the user device 502 such as a beacon identifier.
- the user device 502 may receive a message including a short code.
- the message may be a text message indicating that the short code may be used to request personalized content.
- the message may include instructions to adjust a graphical user interface of the user device 502 to display the short code for requesting personalized content.
- the message may include a URL that directs the user to a web portal (e.g., by launching a browser on the user’s mobile device) to provide further identity verification information and/or perform other actions to prequalify for an offer.
- the content provisioning system 150 includes a URL shortening logic that generates short URLs (e.g., ⁇ 20 characters) that are redirected to the corresponding full URL (e.g., 80+ characters). Longer URLs may not work on certain mobile devices (e.g., may result in an error code on the user device) and/or are not user-friendly (e.g., may fill the entire mobile device display with irrelevant information to the user).
- the URL shortening logic may include code, e.g., python code that generates the shortened URLs and stores them in association with the full URL on a web services server (e.g., Amazon’s web services or AWS).
- a web services server e.g., Amazon’s web services or AWS.
- an external URL shortening service may be called to provide shortened URLs that may then be included in message content by the content provisioning system 150 .
- the user of the user device 502 may decide to request the content offered by activating a control element on the user device 502 to transmit a short code message 524 to the short code provider 506 .
- the short code message 524 may be manually input by the user such as based on information collected from a sign or other promotional material.
- the short code provider 506 may translate the short code received in the short code message 524 to identify a process or service to respond to the request.
- the short code message 524 may include information identifying the user device 502 such as a phone number, mobile equipment identifier, username, or account number. This identifying information may be transmitted in a message 526 to the decisioning service 508 .
- the decisioning service 508 may implement the personalized content selection and generation features described such as in FIGS. 2 , 3 , or 4 .
- the decisioning service 508 may request consent from the user of the user device 502 to retrieve additional information about the user such as performing a reverse lookup for personal information about the user based on the device identifier.
- a consent request message 528 may be transmitted to the user device 502 .
- the consent request message 528 may include a link that can be activated via the user device 502 to provide consent and continue the process.
- the link may include a token or other temporary credential to associate the consent with a particular session.
- the consent request message 528 may request return of a password, personal access number, or other verification information.
- the consent may be provided in the form of a short code message 530 .
- the message 530 is received by the short code provider 506 and provided in turn to the decisioning service 508 via message 532 .
- the message 532 may include consent information such as a password or token along with the device identifier.
- the decisioning service 508 may then associate the consent with a particular device and thus set of personal information.
- the decisioning service 508 may transmit a message 534 to the service provider 510 to retrieve additional user information for the user associated with the user device 502 .
- the service provider 510 may be a cellular service provider for the user device 502 .
- additional information about the user account associated with the phone number may be retrieved via message 536 .
- the user data collected, or a portion thereof, may be transmitted via message 538 to the decisioning service 508 .
- the service provider 510 may be an application service provider, credit service provider, merchant, retailer, government, or other entity to whom users have identified themselves.
- more than one service provider 510 may be included and queried for user information.
- the user information received from respective providers may be different or may be the same, in which case, a comparison may be performed between values for a given data field to generate a confidence in the identification result.
- the service provider 510 may be a social media or other online media provider.
- the content provisioning system 150 may request user PII from a social media server in response to the user indicating a desire to be prequalified for a particular offer (e.g., for an auto loan).
- a wireless service provider e.g., Verizon, AT&T, Sprint, etc.
- initial user identification information e.g., name and ZIP Code
- a social media server may provide similar and/or additional information regarding users that may then be used in a user verification process and determination of eligibility for the particular offer.
- a call to action may be presented via a social media service such as via a textual or image post.
- An interaction with the call to action e.g., click, like, download, display, button press
- a user may see content on their social media feed (on a mobile, portable, or desktop device) regarding prequalification for credit (which may be provided to the user based on analysis of the user’s profile and recent posts that indicate the user is searching for a new car, house, etc.) that, when selected, initiates any of the identity verification processes discussed herein.
- the decisioning service 508 has at least two sources of information to help identify the user of the user device 502 namely the information provided by the user device 502 during the communication session and the information provided by the service provider 510 .
- the decisioning service 508 may identify the user based on the collected information. In some instances, the identification may include querying an additional user data source such as credit header files or other personal information data stores. In some instances, the collected data may not be sufficient to uniquely identify a specific user. In such instances, messaging 542 may be included to request and receive additional identity data to distinguish amongst multiple candidate users, such as discussed with reference to Table 4 above.
- the personalized content may be generated.
- Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor.
- the metric may be a score based on prior transactions. The score may indicate how reliable the user performed one or more prior transactions.
- the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected.
- the content generation may include generating an identifier that uniquely identifies the personalized content provided to the user.
- the content may be associated with an expiration date.
- the expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof.
- the decisioning service 508 may transmit a message 548 including the content or information that the user device 502 may use to access the content such as a network location (e.g., URL) of the content.
- FIG. 6 A is a pictorial diagram showing user interface transitions for requesting and receiving personalized content on a mobile device.
- a welcome interface 602 provides a welcome message.
- the welcome message may be generated by an application executing on the user device, such as one provided by a bank, a car dealership, a car manufacturer, or an online retailer or a general purpose application such as a web-browser, dynamic media streaming application, or other network content viewing application.
- the welcome interface 602 may include personalized content such as the user’s name “PAT SMITH.”
- the welcome interface 602 includes a textual control element (“Not me?”) that, when activated, may cause the user interface to collect user information for a different user.
- the welcome interface 602 includes a button control element (“Get Offer Content”) that, when activate, transmits a message to request content. The request may be transmitted to a personalized content provisioning system.
- the interface may receive information to for a first transition 610 from the welcome interface 602 to a content presentation interface 608 .
- the content presentation interface 608 may include personalized content selected based on the user data received in the message as well as any user information identified from one or more service providers.
- the content presentation interface 608 may include navigation arrows to allow viewing of different content for which the user is qualified to receive.
- the content shown on the content presentation interface 608 may include a personalized message.
- the personalized message may include name (“PAT SMITH”) or custom generated values (“$25,000”) based on the user data.
- the content presentation interface 608 also shows an expiration date for the content and a unique identifier for the content.
- the content presentation interface 608 also includes a button control element (“Begin Enrollment”) that, when activated, transmits a message to a network service to begin a process associated with the content.
- the process may be an enrollment process to accept the prequalified credit offer described in the received content.
- the message may include the offer identifier to facilitate expedited collection of the required information for the process.
- a second transition 612 may present a clarification interface 604 .
- the clarification interface 608 may be generated to collect one or more elements of identity data that can uniquely distinguish the limited set of candidate users. As discussed, there may be one data type (e.g., month of birth, house number, street name, numeric day of birth, previous city of residence, etc.) that can distinguish all users in the limited set. In such implementations, a value for this data type may be the only requested information. As shown in FIG. 6 A , the clarification interface 604 is requesting only the last four digits of the user’s social security number.
- the clarification interface 604 may include input control elements (shown as boxes) for receiving user input values such as respective digits of a social security number.
- the clarification interface 604 also includes a control element (“submit” button) that, when activated, causes transmission of the input values to the system. If the provided information narrows the identity of the user to a unique user, transition 620 may provide the personalized content via the content presentation interface 608 as described above. If the provided information fails to uniquely identify a user, transition 622 may cause presentation of a user information input interface 606 .
- the user information input interface 606 may include one or more input control elements to receive respective values for a set of data types such as first name, last name, last four digits of the social security number, home address ZIP code, and the like.
- An input control element may be prepopulated with known values, such as a first name common to the set of candidate users identified based on the initial request.
- the user information input interface 606 may include a control element such as a “submit” button that, when activated, causes transmission of the input values to the system.
- the interfaces shown in FIG. 6 A may be provided within an application such as a web browser or custom built application. Upon submission of valid values, transition 624 may cause presentation of the content presentation interface 608 as described above.
- Transition 614 from the welcome interface 602 to the user information input interface 606 may be initiated when the candidate list of users cannot be distinguished based on a single data type or field or no candidate users are found.
- the user information input interface 606 may prepopulate some fields with user values common to members of the candidate list and leave fields empty for those values which cannot be distinguished.
- the user identification and offer provision systems as discussed herein, such as with reference to FIG. 6 A may be implemented in an online marketplace environment, wherein the user may be provided with multiple offers for which they are prequalified, rather than a single particular offer.
- the user may be directed to a marketplace (e.g. via a shortened URL to the marketplace that is sent to the user’s mobile device as a text message or application-direct message) that displays multiple offers for exploration by the user (e.g. in a browser or standalone application provided by the content provisioning system 150 ).
- the marketplace may identify multiple (two, three, four, five, or more) credit card offers for which the user is prequalified that are perhaps sorted to identify the prequalified offers with the most advantageous terms (e.g., lowest interest rates, best rewards programs, etc.) -or to display up all of the prequalified offers in some embodiments.
- the user may then be given the opportunity to filter the prequalified offers (e.g., by interest rate, rewards program features, etc.) and identify a particular offer for which the user would like to complete an application.
- the offers may be selected from a plurality of offer providers, such that the user could potentially be provided with prequalified offers from multiple offer providers.
- credit card offers this means that are user could be provided with credit card offers from multiple financial institutions (e.g., a first credit card from big bank A, a second credit card from big bank B, etc.).
- example implementations such as in particular vertical markets (e.g., credit card offers) are provided for ease of explanation and do not limit the scope of any associated systems and methods to other vertical markets.
- FIG. 6 B is a pictorial diagram showing another example user interface transitions for requesting and receiving personalized content on a mobile device.
- An eligibility initiation interface 652 may be presented on a user device.
- the eligibility initiation interface 652 may be presented in response to a message received from the personalized content provisioning system 150 .
- the message may cause execution of an application on the user device to present the eligibility initiation interface 652 .
- the application may be executed by the user via the user device.
- the eligibility initiation interface 652 may be presented without remote communication (e.g., to the personalized content provisioning system 150 ).
- the eligibility initiation interface 652 may include input control elements (shown as boxes) for receiving user input values such as first name, last name, or postcode.
- the application presenting the eligibility initiation interface 652 may pre-populate the fields with a value for one or more of the input control elements. For example, if the user previously provided name or postcode information as a configuration for the application, these values may be retrieved from the configuration storage and displayed in respective input control elements.
- the eligibility initiation interface 652 may include additional control elements such as a checkbox to indicate user’s consent to process his or her personal information.
- an additional control element may be provided that, when activated, shows additional details about the processing, what information may be collected, how the information may be used, etc.
- the eligibility initiation interface 652 shown in FIG. 6 B also includes a control element (e.g., button “check eligibility”) that, when activated, submits the user input values for further processing.
- a control element e.g., button “check eligibility”
- the user input values may be transmitted via a network for further processing such as to the personalized content provisioning system 150 .
- a transition 660 may cause presentation of a status interface 654 .
- the status interface 654 may provide an indicator of the progress of the eligibility processing.
- the indicator shown in the status interface 654 comprises a progress bar.
- the progress bar may be updated based on one or more messages received from the system processing the eligibility request such as the personalized content provisioning system 150 .
- the result of the processing may be that the user is identified and is eligible for particular content.
- a transition 662 may cause presentation of a content offer interface 656 .
- the content offer interface 656 may include a personalized greeting (e.g., “Pat” is the name of the user and is used in the message shown).
- the content offer interface 656 may include an estimate of a likelihood of obtaining the content referenced on the content offer interface 656 .
- the prequalification decision may not be a firm offer of credit, but rather an indicator of what credit terms might be offered to the user.
- An actual offer may be contingent on additional verification or information not available at the prequalification stage.
- the content may be generated from a content template which includes a field for the likelihood information.
- the likelihood may be generated by the content generator 170 .
- the content offer interface 656 may include additional text summarizing the terms of the offer.
- the content offer interface 656 may include an additional control element, such as hyperlinked text or a button, that, when activated, causes presentation of the full terms of the offer.
- the presentation may include retrieving the terms from a network location or displaying additional information received from the source system.
- the content offer interface 656 may include a control element (e.g., button), that, when activated, begins the enrollment process.
- the control element may submit a session identifier to an enrollment system for continued processing.
- the session identifier may be used by the enrollment system to access user information collected by the prequalification process and/or the content offer identified.
- the approved offer information may be automatically provided to the offer provider, such as by pre-populating the offer provider’s online eligibility service with the user’s information.
- the user may be provided with an option to perform a further approval process by selecting a link to the offer provider’s online eligibility service, which will be pre-populated (or auto-populated as the service is opened) with the user’s information.
- the user information may be provided to other applications or entities.
- the credit card information may be automatically (or after authorization by the user) provided to a mobile wallet on the user’s mobile device.
- Mobile wallets may be included as part of an operating system (e.g., Apple pay on iPhones) or third-party mobile payment applications (e.g., Google pay).
- an operating system e.g., Apple pay on iPhones
- third-party mobile payment applications e.g., Google pay.
- a credit card could be applied for, approved, and available for use by a user within a significantly shortened time, and without the user ever receiving a physical credit card in some implementations.
- the user is presented with an option to receive a follow-up text or email (e.g., if they supply address) at some future time (e.g., 1 day, 1 week, or 1 month later - which may be determined automatically by the system or may be selectable by the user in some embodiments), that includes the link to the digital application process (e.g., either the same link as already provided or a modified link for identifying the user visit as responsive to a follow-up message).
- a follow-up text or email e.g., if they supply address
- some future time e.g., 1 day, 1 week, or 1 month later
- some future time e.g., 1 day, 1 week, or 1 month later
- the link to the digital application process e.g., either the same link as already provided or a modified link for identifying the user visit as responsive to a follow-up message.
- Another result of the processing may be that the user cannot be positively identified.
- one or more additional interfaces may be presented to receive user input values to identify and distinguish the user from other candidate users. Examples of these interfaces are shown in FIG. 6 A , such as the interfaces 604 and 606 .
- a content offer may not be identified.
- the failure to identify an offer may be because the user cannot be identified, because the user cannot be distinguished from other candidate users, because the user does not qualify for any available content offers, or because of a system failure (e.g., unavailable data source, system, etc.).
- a transition 664 may cause the status interface 654 to be replaced with an error interface 658 .
- the error interface 658 may include a description of the error identifying what caused the eligibility check to fail.
- the present disclosure describes various embodiments of interactive and dynamic user interfaces, such as the mobile user interfaces discussed above with reference to FIGS. 6 A- 6 B , that are the result of significant development.
- This non-trivial development has resulted in the user interfaces described herein which may provide significant cognitive and ergonomic efficiencies and advantages over previous systems.
- the interactive and dynamic user interfaces include improved human-computer interactions that may provide reduced mental workloads, improved decision-making, reduced work stress, and/or the like, for a user.
- user interaction with the interactive user interface via the inputs described herein may provide an optimized display of, and interaction with, image data (including medical images) and may enable a user to more quickly and accurately access, navigate, assess, and digest the image data than previous systems.
- FIG. 7 is a block diagram showing example components of a location or behavior based content provisioning computing system 150 .
- the computing system 700 includes, for example, a personal computer that is IBM, Macintosh, or Linux/Unix compatible or a server or workstation.
- the computing system 700 comprises a server, a laptop computer, a smart phone, a personal digital assistant, a kiosk, or a media player, for example.
- the exemplary computing system 700 includes one or more central processing unit (“CPU”) 705 , which may each include a conventional or proprietary microprocessor.
- CPU central processing unit
- the computing system 700 further includes one or more memory 732 , such as random access memory (“RAM”) for temporary storage of information, one or more read only memory (“ROM”) for permanent storage of information, and one or more mass storage device 722 , such as a hard drive, diskette, solid state drive, or optical media storage device.
- RAM random access memory
- ROM read only memory
- mass storage device 722 such as a hard drive, diskette, solid state drive, or optical media storage device.
- the components of the computing system 700 are connected to the computer using a standard based bus system 790 .
- the standard based bus system could be implemented in Peripheral Component Interconnect (“PCI”), Microchannel, Small Computer System Interface (“SCSI”), Industrial Standard Architecture (“ISA”) and Extended ISA (“EISA”) architectures, for example.
- PCI Peripheral Component Interconnect
- SCSI Microchannel
- ISA Industrial Standard Architecture
- EISA Extended ISA
- the functionality provided for in the components and modules of computing system 700 may be combined into fewer components and modules or
- the computing system 700 is generally controlled and coordinated by operating system software, such as Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS, or other compatible operating systems.
- operating system software such as Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS, or other compatible operating systems.
- the operating system may be any available operating system, such as MAC OS X.
- the computing system 700 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, I/O services, and provide a user interface, such as a graphical user interface (“GUI”), among other things.
- GUI graphical user interface
- the exemplary computing system 700 may include one or more commonly available input/output (I/O) devices and interfaces 712 , such as a keyboard, mouse, touchpad, and printer.
- the I/O devices and interfaces 712 include one or more display devices, 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 system 700 may also include one or more multimedia devices 742 , such as speakers, video cards, graphics accelerators, and microphones, for example.
- the I/O devices and interfaces 712 provide a communication interface to various external devices.
- the computing system 700 is electronically coupled to one or more networks, which comprise one or more of a LAN, WAN, and/or the Internet, for example, via a wired, wireless, or combination of wired and wireless, communication link.
- the networks communicate with various computing devices and/or other electronic devices via wired or wireless communication links, such as the credit bureau data source and financial information data sources.
- information may be provided to the computing system 700 over a network from one or more data sources.
- the data sources may include one or more internal and/or external data sources that provide transaction data, such as credit issuers (e.g., financial institutions that issue credit cards), transaction processors (e.g., entities that process credit card swipes at points of sale), and/or transaction aggregators.
- the data sources may include internal and external data sources which store, for example, credit bureau data (for example, credit bureau data from File One(SM)) and/or other user data.
- credit issuers e.g., financial institutions that issue credit cards
- transaction processors e.g., entities that process credit card swipes at points of sale
- transaction aggregators e.g., transaction aggregators.
- the data sources may include internal and external data sources which store, for example, credit bureau data (for example, credit bureau data from File One(SM)) and/or other user data.
- SM File One
- one or more of the databases or data sources 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, an object-oriented database, solr database, and/or a record-based database.
- a relational database such as Sybase, Oracle, CodeBase and Microsoft® SQL Server
- other types of databases such as, for example, a flat file database, an entity-relationship database, an object-oriented database, solr database, and/or a record-based database.
- the content data storage 708 may be included to support the identification of users and/or provisioning of content to a user.
- the content data may include content templates, content models, content publication rules, and the like.
- module refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, Lua, C or C++.
- a software module 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 software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts.
- Software modules configured for execution on computing devices may be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, or any other tangible medium.
- Such software code may be stored, partially or fully, on a memory device of the executing computing device, such as the computing system 700 , for execution by the computing device.
- Software instructions may be embedded in firmware, such as an EPROM.
- hardware modules 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 described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage.
- the modules 710 may be configured for execution by the CPU 705 to perform any or all of the processes discussed above with reference to FIGS. 2 - 4 , the messaging shown in FIG. 5 , or to generate and/or present the interfaces shown in FIG. 6 A or FIG. 6 B .
- certain processes, or in the processes, or groups of processes discussed herein may be performed by multiple devices, such as multiple computing system similar to computing system 700 .
- certain of the processes described herein may be performed by a computing system that identifies content, while other processes are performed an identification system that determines a user’s identity.
- identity detection e.g., Smart Lookup
- the identity detection features may be used in additional or alternative implementations where an efficient and accurate identification of a user based at least in part on a communication device associated with the user is desired.
- Such implementations may include test taking systems, personal account systems (e.g., banking, insurance, email, or loyalty programs), inquiry systems (e.g., credit inquiry, education record inquiry, government record inquiry), content prefilling services, monitoring services (e.g., credit monitoring), login/authentication services, or the like.
- FIG. 8 is a block diagram showing example components of an identity confirmation device for identifying a user of a mobile device.
- a request processor 802 included in the identity confirmation device 155 may receive an access request message from the user device 102 .
- the access request message may be received directly from the user device 102 or from a service that is being accessed or used by the user device 102 .
- the access request message may include user information such as first name, last name, ZIP code or postcode, or an identifier that can be used to obtain the user information such from a service provider.
- the request processor 802 may present a machine interface such as an application programming interface or web service interface to receive messages from other services requesting identity confirmation and/or information.
- the interface may receive machine readable messages such as access request messages and return user information related thereto.
- the request processor 802 may retrieve digest data from a user data digest data store 804 .
- the digest data store 804 may store a limited set of user information for user and an associated custom identifier (e.g., PIN) for the user.
- the digest data store 804 may be a look up table indexed by the user information expected to be included in the access request. This provides a quick lookup of the custom identifier and other user information for the user.
- the access request may include first name, last name, and postal code.
- the digest data store 804 may include a record for a user with the same first name, last name, and postal code. The record may also include address information (e.g., street address), date of birth, and the custom identifier.
- the digest data store 804 may be generated based on information that does not include consideration of events that occurred since the last digest was generated. When dealing with hundreds of thousands of users who each may be associated with hundreds or thousands of events, it is not uncommon for a digest to lag behind current events by several days. Such events may impact identity decisioning or other downstream modeling based on user value inputs.
- the digest data may be provided to a real-time user data retrieval 806 .
- the real-time user data retrieval 806 may receive all or a portion of the digest data retrieved for the access request.
- the real-time user data retrieval 806 may initiate a separate request for user information from a real-time user data storage 808 .
- the real-time nature of the real-time user data storage 808 indicates that the user information included in the real-time user data storage 808 is based on events as processed by the system rather than a digested snapshot from a past point in time.
- the real-time data may include first name, last name, postal code, address information (e.g., street address), date of birth, and a custom identifier.
- An identity reconciler 810 may compare the real-time data with the digest data to confirm the identity of the user. The confirmation may include comparing the custom identifiers (e.g., PINs) included in the real-time data and the digest data.
- the identity reconciler 810 may transmit a clarification request to a clarification collector 812 .
- the clarification collector 812 may be configured to identify what information is needed to identify a single user. For example, the clarification collector 812 may identify an identity data type that distinguishes all the candidate users. The clarification collector 812 may then retrieve the clarification data. Retrieving the clarification data may include causing presentation of an interface on the user device 102 to receive a user input including the clarification data. Retrieving the clarification data may include querying an additional service or data store for the clarification data.
- the service that is to be accessed by the user device 102 may store user profiles which may include additional information that can be used to clarify the identity of the user of the user device 102 .
- the received clarification data may be provided to the identity reconciler 810 for further consideration and analysis to identify the user.
- the identity reconciler 810 may confirm the identity of the user by transmitting identity data.
- the identity data may include transmitting additional user information such as address, date of birth, or other user information received from the real-time user data storage 808 .
- the identity data may include a binary result indicating that the user is identified. This may be an efficient way to indicate to a service whether the user of the mobile device is authenticated and/or authorized to access the service. The identity information may then be used, in certain implementations, to automatically initiate additional processes based on the additional information regarding the now-authenticated user.
- a form fill process may be initiated using information pulled from one or more databases accessible by the content provisioning system, such as PII included in a credit report (e.g., full residence address, age, credit score, etc.) of the authenticated user.
- the user information may be encoded and transmitted to the user device for auto population of a form (e.g., a house/apartment rental application, online account registration, car rental application, credit application, etc.), or in some embodiments may be transmitted directly to the offer provider.
- the user information may be included with the initial data indicating results of the identity verification process or may be included in a subsequent data transmission to the requesting entity.
- this identity validation process may be used by any third-party entity that desires identity determination and/or identity verification.
- Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware.
- the code modules may be stored on any type of non-transitory computer-readable medium or computer storage device, such as hard drives, solid state memory, optical disc, and/or the like.
- the systems and modules may also be transmitted as generated data signals (for example, as part of a carrier wave or other analog or digital propagated signal) on a variety of computer-readable transmission mediums, including wireless-based and wired/cable-based mediums, and may take a variety of forms (for example, as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames).
- the processes and algorithms may be implemented partially or wholly in application-specific circuitry.
- the results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of non-transitory computer storage such as, for example, volatile or non-volatile storage
- Conditional language such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
- determining may include calculating, computing, processing, deriving, generating, obtaining, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like via a hardware element without user intervention.
- determining may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like via a hardware element without user intervention.
- determining may include resolving, selecting, choosing, establishing, and the like via a hardware element without user intervention.
- the terms “provide” or “providing” encompass a wide variety of actions.
- “providing” may include storing a value in a location of a storage device for subsequent retrieval, transmitting a value directly to the recipient via at least one wired or wireless communication medium, transmitting or storing a reference to a value, and the like.
- “Providing” may also include encoding, decoding, encrypting, decrypting, validating, verifying, and the like via a hardware element.
- a message encompasses a wide variety of formats for communicating (e.g., transmitting or receiving) information.
- a message may include a machine readable aggregation of information such as an XML document, fixed field message, comma separated message, or the like.
- a message may, in some implementations, include a signal utilized to transmit one or more representations of the information. While recited in the singular, it will be understood that a message may be composed, transmitted, stored, received, etc. in multiple parts.
- receiving may include transmitting a request message for the information.
- the request message may be transmitted via a network as described above.
- the request message may be transmitted according to one or more well-defined, machine readable standards which are known in the art.
- the request message may be stateful in which case the requesting device and the device to which the request was transmitted maintain a state between requests.
- the request message may be a stateless request in which case the state information for the request is contained within the messages exchanged between the requesting device and the device serving the request.
- One example of such state information includes a unique token that can be generated by either the requesting or serving device and included in messages exchanged.
- the response message may include the state information to indicate what request message caused the serving device to transmit the response message.
- “generate” or “generating” may include specific algorithms for creating information based on or using other input information. Generating may include retrieving the input information such as from memory or as provided input parameters to the hardware performing the generating. Once obtained, the generating may include combining the input information. The combination may be performed through specific circuitry configured to provide an output indicating the result of the generating. The combination may be dynamically performed such as through dynamic selection of execution paths based on, for example, the input information, device operational characteristics (e.g., hardware resources available, power level, power source, memory levels, network connectivity, bandwidth, and the like). Generating may also include storing the generated information in a memory location. The memory location may be identified as part of the request message that initiates the generating. In some implementations, the generating may return location information identifying where the generated information can be accessed. The location information may include a memory location, network locate, file system location, or the like.
- activate may refer to causing or triggering a mechanical, electronic, or electro-mechanical state change to a device.
- Activation of a device may cause the device, or a feature associated therewith, to change from a first state to a second state.
- activation may include changing a characteristic from a first state to a second state such as, for example, changing the viewing state of a lens of stereoscopic viewing glasses.
- Activating may include generating a control message indicating the desired state change and providing the control message to the device to cause the device to change state.
- a tangible computer-readable medium is a data storage device that can store data that is readable by a computer system. Examples of computer-readable mediums include read-only memory, random-access memory, other volatile or non-volatile memory devices, CD-ROMs, magnetic tape, flash drives, and optical data storage devices.
Abstract
Features are described for efficiently and accurately identifying a user of an electronic device with limited user interaction. The features include receiving a mobile device identifier from the mobile device. The features include transmitting the mobile device identifier to a service provider associated with the mobile device. The features include receiving information identifying the user from the service provider. The features include identifying a set of candidates associated with at least a portion of the information. The features include generating a metric for the candidates included in the set of candidates. An individual metric indicates a degree of relatedness between a value for the user for the at least one data field and a value for a candidate for the at least one data field. The features include identifying the user as a specific candidate included in the set of candidates based on the metric corresponding to a threshold.
Description
- This application is a continuation of U.S. Application No. 16/864,443, filed 1 May 2020 and entitled “Disambiguation and Authentication of Device Users,” which is a continuation of U.S. Application No. 15/684,590, filed 23 Aug. 2017 and entitled “Disambiguation and Authentication of Device Users,” which are hereby incorporated in their entirety herein. This application claims a priority benefit to U.S. Provisional Application No. 62/379,019, filed 24 Aug. 2016 and entitled “System and Method of Providing Mobile Location Based Content.” This application also claims a priority benefit to U.S. Provisional Application No. 62/459,919, filed 16 Feb. 2017 and entitled “System and Method of Providing Mobile Location Based Content.” Each of the foregoing applications and appendices thereto are hereby expressly incorporated by reference in their entirety. Furthermore, any and all priority claims identified in the Application Data Sheet, or any correction thereto, are hereby incorporated by reference under 37 C.F.R. § 1.57.
- The present application relates generally to identifying and providing content to specifically and dynamically identified mobile devices.
- Systems often require substantial inputs to accurately identify a user and content to provide to the user. In many cases, the users may access such systems using portable communication devices, which have limited resources. For example, the device may be a smartphone, which may have a small screen and limited input mechanisms as compared to, for example, a desktop computer. Such devices may also have power, bandwidth, processor time, or other limitations on the ability to process data. Each additional element to be processed by a mobile device may be accompanied by an incremental drain on such resources.
- As more services are deployed and seek to interface with mobile devices, improvements in how users and content are identified are desirable.
- The methods and devices described herein may include one or more of several aspects, no single one of which is solely responsible for the desirable attributes of a particular embodiment. Without limiting the scope of this disclosure, some features will now be discussed briefly. After considering this discussion, and particularly after reading the section entitled “Detailed Description” one will understand how the features described provide advantages that include identifying and providing content to specifically and dynamically identified mobile devices, among other advantages.
- Various embodiments of the present disclosure provide improvements to various technologies and technological fields. For example, existing authentication systems require significant personally identifiable information (“PII”) from a user to uniquely verify the identity of the user. Among other things, the present disclosure describes improvements to, and useful applications of, various computer systems and software that does not require collection, transmission, and processing of as much personal information of the user as in current identification and/or content provisioning systems.
- Additionally, various embodiments of the present disclosure are inextricably tied to computer technology. In particular, various systems and methods discussed herein provide monitoring of electronic databases, processing of large volumes of data items, generation and transmission of electronic notifications, and the like. Such features and others are intimately tied to, and enabled by, computer technology, and would not exist except for computer technology. Further, the implementation of the various embodiments of the present disclosure via computer technology enables many of the advantages described herein, including more efficient processing of various types of electronic data.
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FIG. 1 is a block diagram showing an example environment for providing personalized content to a mobile device. -
FIG. 2 is a flow diagram showing an example method of behavior initiated content provisioning. -
FIG. 3 is a flow diagram showing an example method of user requested content provisioning. -
FIG. 4 is a flow diagram showing an example method of identifying a user. -
FIG. 5 is a messaging diagram showing example communications for providing personalized content to a mobile device. -
FIG. 6A is a pictorial diagram showing example user interface transitions for requesting and receiving personalized content on a mobile device. -
FIG. 6B is a pictorial diagram showing another example user interface transitions for requesting and receiving personalized content on a mobile device. -
FIG. 7 is a block diagram showing example components of a location or behavior based content provisioning computing system. -
FIG. 8 is a block diagram showing example components of an identity service for identifying a user of a mobile device. - Features are described for using mobile device location history to identify an individual and provide content such as an offer of credit to the identified individual. In some embodiments, this includes predicting the identity of the user based at least in part on the location history for the user. Once a user is uniquely identified, the system and methods described may also collect personal information that may be used to determine whether an offer for credit should be provided to the user, such as from credit data of the user, and generate the offer of credit for transmission to the user, such as in real-time while the user is at a location where the credit may be used. Various embodiments may also pre-populate an online application for credit, initiate review of the credit application, and/or provide the user with information regarding the approved credit request. In certain embodiments discussed herein, additional features are included for confirming the predicted identity by asking a dynamically identified, limited set of questions that can solicit responses usable to uniquely identify the user.
- To facilitate an understanding of the systems and methods discussed herein, certain terms are defined below. The terms defined below, as well as other terms used herein, should be construed to include the provided definitions, the ordinary and customary meaning of the terms, and/or any other implied meaning for the respective terms. Thus, the definitions below do not limit the meaning of these terms, but only provide exemplary definitions.
- User: An entity, such as an individual or a group of individuals associated with some device, such as a computing device. For example, the various systems and methods discussed herein often refer to a user that is typically an individual consumer (e.g., a first consumer is John Doe) that operates a particular computing device (e.g., John Doe has an iPhone). In other embodiments, however, users may be groups of persons (e.g., a household of individuals, a married couple, etc.) associated with other computing devices or groups of computing devices (e.g., an entire household of mobile and stationary computing devices).
- A content provisioning entity (also referred to herein as a “content provisioning system”, or simply “the system”) is generally an entity and/or associated computing systems that maintain and/or have access to regulated user data that is usable to identify users that qualify for a particular product or service. The content provisioning entity manages a computing systems and/or modules that receive publication criteria indicating user attributes that qualify users for an offer for a product or service from the offer provider.
- Depending on the embodiment, the regulated user data may include credit data regulated by the Fair Credit Reporting Act (FCRA), which restricts sharing of the credit data from the content provisioning entity. Thus, in some embodiments the content provisioning entity is a credit bureau, a business unit of a credit bureau that maintains the credit data and enforces the FCRA access regulations, and/or another entity that is authorized to access the credit data. In other embodiments, the regulated user data may include other types of data, such as healthcare data regulated by the Health Insurance Portability and Accountability Act (HIPAA) or other data that is subject to external regulations (such as set by a government agency) that restrict access to user data that is useful in screening users for certain goods or services.
- In the examples discussed herein, the content provisioning entity is configured to limit dissemination of any regulated and/or non-regulated user data, such as to reduce the risk that the regulated user data is used in a manner that may not meet the relevant regulations. For example, credit data (regulated user data) is subject to access restrictions of the FCRA that restrict use of user credit data in a different manner than regulations and/or sharing restrictions that may be imposed on marketing data of a content provisioning entity. Thus, even if the content provisioning entity is authorized to use the regulated user data, the pre-validation system may limit regulated user data provided to the content provisioning entity to reduce risks associate with data loss (whether intentional or fraudulent) at the content provisioning entity (which may not have the same level of data protection as the content provisioning entity). Depending on the embodiment, pre-validation processes discussed herein may include one or more prescreen and/or pre-qualification processes.
- An offer provider (also referred to as an “offer provider entity”, “offer computing system”, or “offer system”) is generally an organization that offers goods or services to users (e.g., users), such as goods or services that introduce some risk of loss to the offer provider if the user fails to comply with terms of the agreement between the offer provider and the user. For example, the offer provider may be a credit card issuer that offers and issues credit cards to individuals. Prior to offering such financial services to users, the credit card issuer typically analyzes (or receives information from another entity regarding) financial information of the user to determine risk associated with offering a credit card to the user. For example, a risk score, such as a credit score, may be used as an indicator of risk that the user defaults on the credit card. In other embodiments, the offer provider may be an insurance provider, a mortgage broker, a product or service supplier/manufacturer/distributer, an intermediary for services and products, a marketing or public relations firm, or any other entity that offers products or services. In some embodiments, the offer provider identifies publication criteria that must be met for the offer provider to approve providing product or service offers to users. For example, a credit card issuer offer provider may determine publication criteria (associated with providing a firm offer of credit to consumers) indicating a minimum credit score (e.g., above 700) that is required for the credit card issuer to offer a particular credit card to a user.
- A user device (also referred to herein as a “user device”) generally includes any device of a user, such as an electronic device through which an offer from an offer provider may be displayed (e.g., via software and/or a site of a digital display entity). User devices may include, for example, desktop computer workstation, a smart phone such as an Apple iPhone or an Android phone, a computer laptop, a tablet such as an iPad, Kindle, or Android tablet, a video game console, other handheld computing devices, smart watch, another wearable device, etc. In many embodiments discussed herein, the user device is a mobile device, such as a smart phone; however, other user devices (e.g., laptops, kiosks, etc.,) may be used.
- Regulated user data generally includes information regarding users that is stored by an entity (e.g., a content provisioning entity) and is subject to external regulations (such as set by a government agency) that restrict how the user information may be used (e.g., accessed, updated, shared, etc.) outside of the storing entity. Regulated user data generally is useful in validating users to receive offers for certain goods or services, but may include sensitive user data that should be protected to a greater degree than publicly available user information. Thus, in some embodiments, dissemination, sharing, and/or any other access to regulated user data may be controlled closely by the storing entity to reduce risks associated with improper use of the regulated data, such as any sharing of regulated user data that violates the relevant regulations. Accordingly, while regulated user data may be optimal for determining certain characteristics or propensities of users, such as determining risks associated with issuing a credit account to users, sharing of regulated user data with offer providers, digital display entities, and/or others that might be involved in related marketing or communications to the user may be limited to include only the minimum required regulated user data or no regulated user data.
- In one embodiment, regulated user data is credit data that is regulated by the Fair Credit Reporting Act (FCRA), which restricts use of the credit data, such as by limiting how credit data of users may be shared with marketing entities. Thus, in some embodiments the content provisioning entity is a credit bureau, a business unit of a credit bureau that maintains the credit data and enforces the FCRA access regulations, and/or another entity that is authorized to access the credit data. Accordingly, in such an embodiment, the content provisioning entity may limit the use or sharing of user credit data to reduce risk of disclosure or other use of the credit data outside of FCRA regulations, whether intentionally, inadvertently, or fraudulently. Regulated user data may include various regulated user attributes, such as information regarding lines of credit, debt, bankruptcy indicators, judgments, suits, liens, wages, collection items, mortgage loans, other loans, retail accounts, checking/savings/transaction data, late or missed payment data, other credit attributes, and/or derivatives/scores/ratings based on at least the credit information.
- Personally identifiable information (also referred to herein as “PII”) includes any information regarding a user that alone may be used to uniquely identify a particular user to third parties. Depending on the embodiment, and on the combination of user data that might be provided to a third party, PII may include first and/or last name, middle name, address, email address, social security number, IP address, passport number, vehicle registration plate number, credit card numbers, date of birth, telephone number for home/work/mobile. User identifiers that are used to identify a user within a particular database, but that are not usable by third parties (e.g., other entities) to uniquely identify the user may not be considered PII. However, in some embodiments even user IDs that would be very difficult to associate with particular users might be considered PII, such as if the IDs are unique to corresponding users. For example, Facebook’s digital IDs of users may be considered PII to Facebook and to third parties.
- Data Store: Any computer readable storage medium and/or device (or collection of data storage mediums and/or devices). Examples of data stores include, but are not limited to, optical disks (e.g., CD-ROM, DVD-ROM, etc.), magnetic disks (e.g., hard disks, floppy disks, etc.), memory circuits (e.g., solid state drives, random-access memory (RAM), etc.), and/or the like. Another example of a data store is a hosted storage environment that includes a collection of physical data storage devices that may be remotely accessible and may be rapidly provisioned as needed (commonly referred to as “cloud” storage).
- Database: Any data structure (and/or combinations of multiple data structures) for storing and/or organizing data, including, but not limited to, relational databases (e.g., Oracle databases, mySQL databases, etc.), non-relational databases (e.g., NoSQL databases, etc.), in-memory databases, spreadsheets, as comma separated values (CSV) files, eXtendible markup language (XML) files, TeXT (TXT) files, flat files, spreadsheet files, and/or any other widely used or proprietary format for data storage. Databases are typically stored in one or more data stores. Accordingly, each database referred to herein (e.g., in the description herein and/or the figures of the present application) is to be understood as being stored in one or more data stores.
- User Input (also referred to herein simply as “input”): Any type of input provided by a user that is intended to be received and/or stored by the system, to cause an update to data that is displayed and/or stored by the system, to cause an update to the way that data is displayed and/or stored by the system, and/or the like. Non-limiting examples of such user inputs include keyboard inputs, mouse inputs, digital pen inputs, voice inputs, finger touch inputs (e.g., via touch sensitive display), gesture inputs (e.g., hand movements, finger movements, arm movements, movements of any other appendage, and/or body movements), and/or the like. Additionally, user inputs to the system may include inputs via tools and/or other objects manipulated by the user. For example, the user may move an object, such as a tool, stylus, or wand, to provide inputs. Further, user inputs may include motion, position, rotation, angle, alignment, orientation, configuration (e.g., fist, hand flat, one finger extended, etc.), and/or the like. For example, user inputs may comprise a position, orientation, and/or motion of a hand and/or a 3D mouse.
- Application Programming Interface (API): A set of routines, protocols, and/or tools for building a software application. Generally, an API defines a standardized set of operations, inputs, outputs, and underlying types, such that functionality is accessible via the API in an efficient way. A system provides an API by which a third party may access functionality of the system. Accordingly, the system advantageously abstracts away (from the third party’s perspective), much of the complexity that may be involved in the functionality of the system, and enables the third party to quickly and efficiently leverage functionality of the system to build other systems and services.
- Ensuring the proper content is presented to the right person at the right time can often mean the difference between getting a message to the person and being ignored in the avalanche of data received by the person. Examples of technologies for identifying the location of an electronic device include global positioning service (GPS), beacon, or access point location (e.g., WiFi presence detection; cellular tower connectivity; etc.). Geofencing allows provisioning of content based on proximity to a location. As a device enters a geofenced area, an information exchange may be performed whereby the device provides location information to the geofencing system and the geofencing system may provide information to the device if the location is associated with a geofenced area. The area may be defined by one or more identified geospatial points. The points may be identified using positioning technologies such as global positioning service (GPS), beacon, or access point location (e.g., WiFi presence detection; cellular tower connectivity; etc.).
- GIMBAL™ is a commercially available beacon system that may be user in certain embodiments discussed herein to provide location based content offerings. In other embodiments, other proximity sensors may be used to provide information regarding location of a user (or, more particularly, a user’s mobile device) in relation to a designated area associated with a publication rule. For example, Bluetooth communication may detect a near range proximity of a user device and, accordingly, trigger processing of publication rules for a user of the device. In another example, geolocation information, such as from global positioning system (GPS) sensors of a mobile device, triangulation of data associated with cellular towers accessible by a user device, and/or location information determined based on proximity of the user device to one or more Wi-Fi nodes, may be used to determine location of a user, which may then be compared to a mapping of geofenced areas.
- Currently, there are several methods of processing user information in order to determine risk of lending to the user. For example, a pre-screening process may occur in real-time without the user necessarily knowing that their credit is being accessed (without authorizing access to the user’s credit data from one or more credit bureaus), while a pre-qualification process may be performed in response to a user request for information regarding qualification for one or more credit products (and authorizing access to the user’s credit data from one or more credit bureaus). Such pre-screening and pre-qualification processes are generally referred to herein as pre-validation processes or prescreening processes. For instance, an entity may request pre-validation of a specific user for one or more credit products. A credit file for the specific user may be pulled, the credit data assessed based on publication criteria (or factors) and the requesting entity notified if the specific user met the publication criteria. Such pre-validation processes work well for providing offers (e.g., credit card offers) to known individuals. However, in the world of mobile devices and rapid movement of mobile device users, opportunities to provide offers to users via their mobile devices, such as at the time/location where the user really wants to receive a credit offer (e.g., for purchase of an automobile) are limited largely because of the technical difficulties in uniquely identifying (e.g., to the user level) particular users with enough certainty to provide pre-validated offers while the user is still at the desired location.
- For example, if a user enters a geofenced area associated with a brick-and-mortar store, a wireless beacon may transmit a message identifying the beacon to the user’s mobile device (or the beacon may be continuously transmitting a broadcast signal requesting responses from devices within the geofenced area). The message may be a probe message alerting the mobile device to the presence of the beacon or an identifier for the beacon that can be used to look up a location and/or content associated therewith. The mobile device may respond to the beacon or another system with a message including the beacon identifier, an identifier of the mobile device, the user, and/or an application executing on the mobile device such as a standalone application or a context based application such as a web-browser or network content viewing application or a native application in the operating system of the device such as a payment application or wallet application. The content provisioning system may in turn identify content to provide to the mobile device. The particular content may be identified (from a collection of a plurality - thousands or millions - of available content items) and customized for the user based on attributes associated with the user that are determined based on the initial communication with a geofencing system or the content provisioning system. The user attributes may include attributes such as information from a mobile carrier (e.g., Verizon, Sprint, AT&T, etc.) regarding the user, information known to the offer provider (e.g., a financial institution that wants to provide credit offers to qualifying users) about the user, and/or information guarding the user that is accessed from one or more third party systems (e.g., credit data of the user that is received from a credit bureau). The content may include content which only prequalified users may receive. For example, a credit providing entity may transmit a credit offer specific to the user. In one embodiment, credit offers may be stored in a data storage medium accessible by the content provisioning system until the user is detected within a geofence. Once the user is detected by the geofencing system, the offer may be quickly transmitted to the user’s mobile device so that the user may, in some embodiments, immediately complete the credit application process and receive credit approval that may be used to complete a purchase at the location of the geofence (e.g., within a retail establishment).
- As another example, a credit providing entity may transmit publication criteria indicating requirements for various types of credit that may be available to pre-validated users (e.g., users that meet the publication criteria). The publication criteria may be stored in a data storage medium accessible by the content provisioning system for application to user attributes of a user when the user is detected within an indicated geofence area (such as may also be included in the publication criteria provided by the offer entity). Once the user is detected by the geofencing system, the publication criteria may be re-applied to the user data (e.g., based on a fresh pull of credit data of the user) to determine whether an offer should be transmitted to the user’s mobile device.
- As another example, the geofencing system or the content provisioning system may provide a message to the mobile device to invite a specific interaction. For example, the geofencing system may provide a message to cause the mobile device to execute an application on the mobile device. The application may provide a more secure and controlled environment to communicate with the user of the mobile device. The publication criteria may also include timing requirements for provision of offers to users. For example, a user that enters one or more particular geo-fenced areas (e.g., similar retail establishments) for a third time within one week may be provided a content offer (if the user also needs any other publication criteria), whereas the user may not be provided the content offer the first and second times the user entered the particular geofenced areas. As another example, the geofencing system may detect a mobile device circling a specific car in an auto dealership, which triggers transmission of a message to the user’s mobile device to initiate interactions with the user that may result in an offer of credit to the user for purchase of an automobile. For example, the geofencing system or the content provisioning system may provide a message such as “You’d look great in that car. Text 12345 for a top tier credit offer,” to the user device.
- The content provisioning system may store content in conjunction with publication criteria. The publication criteria may include time periods when the content may be presented, specific users or groups of users to whom the content can be presented, preconditions for presenting the content (e.g., contact with a different beacon within the geofencing system; specific application on the mobile device; specific mobile device type; etc.), or other conditional logic to afford control over when, where, and to who the content is provided.
- To identify the proper content to present to a user, the particular user should be identified. For example, a content provider may provide publication criteria indicating that users with a credit score of above 750, income level above $80,000, and are currently within a geofence of a particular car dealership and have visited that particular car dealership previously within the last 72 hours, are to be offered an automobile line of credit with certain terms. In one aspect, a predictive system can be included to accept location information for the target and predict the identity of the target. For example, a mobile device of a target may provide geo-location information identifying an address of an office, an address of a school, and an address of a grocery store. A historical record of location information may determine that these three locations are regularly visited by a target and, perhaps, in a specific sequence. The predictive system may identify the locations for the mobile device that correspond to the historical record and thus infer the identity of the target. For example, a reference data store may be included that stores locations for a device and durations of time at respective locations. A pattern of locations may be identified such as extended durations during evening hours at a first location (e.g., home) and extended durations during daytime hours at a second location on weekdays (e.g., work). These two locations may help identify a user of the device who works at the second location and resides at the first location.
- As discussed elsewhere herein, the content provisioning system advantageously optimizes communications with user devices that are needed to reach a required authentication confidence level for a user of the user device (e.g., to be able to determine that the user of the device really is who they say they are). For example, the types and/or quantity of PII requested of a user may be reduced by the content provisioning system, when compared to conventional similar authentication requirements for offers of credit. Such optimizations may reduce the communication bandwidth and/or reduce the quantity of communications with the user device, while making the user experience less intrusive, such as by asking for less PII from the user than would normally be required to authenticate the user for a credit application, and also reducing risk of PII exposure to undesirable entities (e.g., hackers that monitor and intercept communications with mobile devices).
- In some implementations, an optimized user identification and authentication process may be performed in substantially real-time by the content provisioning system in response to communication with a mobile user device. For example, a user may initially see a billboard encouraging the user to initiate communication with the content provisioning system, such as one that reads “text CARLOAN to 12345 to find out if you qualify for 0% financing of your dream car.” In another embodiment, the user may initially be contacted based on the content provisioning system detecting a mobile device entering a geo-fenced area. In other embodiments, communication between a user and the content provisioning system may be initiated in other ways such as through addressable media. For example, a communication device may be configured to present streaming media content. During streaming of the media content, it may be desirable to present personalized content to the specific communication device and/or user of the communication device. However, the content provisioning system may need to perform some authentication or identification of the user, such as by requesting PII from the user and/or requesting additional information associated with the user or user device from one or more internal or external databases, before the user can be considered for content offers (e.g., credit offers) or before content can be selected for the user.
- In one embodiment, authentication of a user is performed by first determining an initial, narrow, set of potential users associated with the detected user device. In such instances, the system may be configured to identify a minimal set of questions to present via the mobile device of a target to accurately confirm the user’s identity. For example, if the location information (e.g., postal code) narrows the potential users to person A and person B, the system may then look for specific differences in attributes of person A and person B, such as may be stored in records for person A and person B (e.g., credit records of the two users). For example, if person A and Person B reside in different states, the system may determine that obtaining the state of residence from the user is sufficient to uniquely identify the user.
- The level of authentication, and the corresponding types of user attributes that may be used to authenticate a user, may be based on the content being provided. For example, if the content is informational, security may not be as high of a priority and gender may be used to distinguish person A from person B. As another example, if the content is a credit offer having a predetermined maximum line of credit, more secure identification may be needed to ensure that any credit offers provided are to the intended user. In such situations, a more secure and personally attributable user attribute, e.g., the holder of the user’s mortgage, may be identified as a distinguishing attribute to request from the user.
- The intelligent identification of user attribute(s) to request for authentication of a user can be particularly useful in limited resource environments. For example, without the features described, a user may be asked to provide responses to many questions to positively identify the user (before application of credit prequalification rules based on the user’s credit data). Each question requires the user device to expend valuable resources such as power, processing, memory, bandwidth, and the like, to receive, present, and respond to the question. Additionally, the requesting and collection of increased amounts of information may introduce user friction and increase the potential for an error in the requesting or collecting process. In contrast, the systems and methods described herein determine an optimized (e.g., minimal) set of questions that may be used to positively distinguish the actual user of a user device from other possible users of the user device. As discussed, the minimal set may be generated based on the content to be provided and/or a desired level of security.
- Thus, one challenge in the mobile environment is to accurately identify an individual user without requiring significant data entry by the user. To accomplish this, an interactive technology that takes first/last name as its primary input may be provided. Analysis of the half billion identities, such as stored in a credit header file, indicates that virtually all identities in the US can be accurately determined by triangulating ZIP code, date of birth, and last four digits of a Social Security number (SSN). Moreover, the vast majority of individuals can be identified based on 1 or 2 of those elements.
- The real-time, interactive process may begin by parsing the name to identify user records matching or fuzzy matching the name and establishing a minimum question set required to uniquely identify a user if more than one candidate is identified. The minimum question set may then be presented to the user, such as via a specifically designed and secure application or interactive messaging such as text messaging or interactive voice recognition, to collect user responses. Predicated on the responses, the identity may be verified and specific user information about the identified user may be retrieved using the identity. For example, the user information may be included as criteria to request additional information about the user, such from a 3rd party aggregator that queries the mobile network user data store in real-time (e.g., at the time of determining the identity).
- In an example embodiment, a user initiated text message from a user’s mobile device allows the system to obtain user attributes such as name, address, and/or ZIP code, from a service provider associated with the user device. For example, these user attributes may be automatically retrieved from a service provider, such as a mobile carrier, Internet service provider, social media provider, etc., based on the mobile device number from which the text message was received (e.g., caller ID information) and/or other device identifying information associated with the transmitted text message. Using these limited, but authoritative user attributes, the system may search a database which indexes the credit header file (CHF) of credit files for any users who match by name and zip code (or other combinations of user attributes in other embodiments). For example, if a user texts a specific term to a specified number associated with a content provisioning system, the system can receive the input information shown in Table 1 from the mobile carrier (such as via a data sharing arrangement that the content provisioning entity has with the mobile carrier).
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TABLE 1 Data Field Value First Name James Last Name McCoy Billing ZIP Code 92110 - In some implementations, the user may initiate the application of publication criteria, including an initial user authentication process, by transmitting a multimedia message (MMS) that includes media such as audio, an image, or video. In the case of audio input, the system may include audio processing (e.g., speech recognition) to convert the audio into text for further processing. In the case of image input, the system may include image processing such as object recognition to recognize an item shown in the image or facial recognition to identify the person shown in the image. When image data includes an item, the item information may be used to drive offers, terms, rewards, or other content relevant to not only the individual who transmitted the image, but to the image itself. For example, if the image includes a car, the system may identify the specific car shown, a license plate shown, or other vehicle identification information (e.g., vehicle identification number (VIN)) and tailor the offer terms based on the specific car and, in some instances, provide additional information about the car such as mileage, condition, location, model year, trim level, accessories, etc. In some implementations, the terms may be based on an existing financing option for the item shown (e.g., an existing mortgage on a home shown in an image, an existing lease or auto loan for a car shown in an image). In the case of video input, the audio and/or images included in the video input may be processed to obtain additional information about the user and/or item or service of interest. The image may include an identifier for the item such as a vehicle identification number, Universal Product Code, text, etc. that can be used as a key to identify additional details about the item.
- Media files may include metadata such as time recorded, author, person who recorded the media, location where the media was recorded, and the like. Such metadata may be extracted from an input media element and used to further distinguish the identity of the user and/or the offer, content, reward, etc. they are interested in. Such metadata may be used to determine a device fingerprint, which may then be matched against a database of device fingerprints associated with fraud to predict fraudulent intentions of the user and take appropriate action.
- While the implementations discussed thus far rely on SMS or MMS or other messaging, in some implementations, the user may initiate the process by scanning an identifier. For example, if the user is browsing a car dealership, a QR code or other scannable identifier (e.g., barcode, radio frequency identification tag, near field communication tag, beacon, etc.) may be scanned or otherwise detected using the user’s mobile device Upon detection, the mobile device may initiate a software application which thereby transmits information to the content provisioning system for application of publication criteria of one or more offer providers, such as a credit pre-qualification process that initially requires user authentication. For example, when a QR code is scanned by a mobile device, the QR code may include information to initiate the SMS feature of the mobile device. The decoded QR code information may include a “TO” number and a message to send. In some embodiments, the message is automatically sent by the user device to the content provisioning system, while in other embodiments the message may be automatically generated and the user only needs to send the pre-populated message (e.g., my pushing a “send” button on the user device). In implementations including beacons, a beacon signal may enable an automated (e.g., push) type notification to the mobile device. In the case of a beacon, the system may previously know some information about the user of the mobile device by virtue of the presence information (e.g., current location, previous location, duration at a given location, etc.) presented during the interaction between the beacon and the mobile device. In some implementations, the QR code may launch a web browser and direct it to a specific web address. In some implementations, the QR code may activate an application installed on the mobile device. The features described may be applied using SMS, web browser, installed application, operating system function/application, or other initiation mechanisms that allow the mobile device to transmit at least an identifier for a service accessed by the mobile device of a user thereof.
- Returning to the example shown in TABLE 1, using these three data fields, the system may query the database of indexed CHFs or other identity information data store to identify a candidate list of individual users matching the already known data attributes, as well as additional attributes about each candidate user. This query may be a “fuzzy” matching, which searches for alternatives of the user information, such as expanded to include fuzzy matches such as misspellings, phonetic variations, typographic variation (e.g., “rom” being a typographic variant for “tom” due to proximity of the letter “r” to “t” on a standard keyboard), stemming, nicknames, or the like. The level of fuzziness of this initial inquiry may be adjusted depending on the implementation. Table 2 shows an example of a response from such a query of the database based on the inputs from Table 1.
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TABLE 2 Record # First Name Last Name ZIP Code Date of Birth Last 4 SSN 1 Jay McCoy 92110 Jan. 1, 1980 1111 2 Jimmy McCoy 92110 Feb. 1, 1968 2222 3 James McKoy 92110 Oct. 1, 1972 2222 4 Jaime McCountinosh 92110 11/1/1951 8888 - In some implementations, the system may sacrifice specificity in the original query, such as by returning records based on a fuzzy, stem search that matches only a few leading characters from each field (e.g., First Name begins with “J”, Last Name begins with “McC”, ZIP Code begins with 921). Because the quick search does not require detailed analysis of the entire value for a given data field, the results may be obtained faster than if a full analysis were performed, and in those implementations, disambiguation applied to the returned candidate records (such as using the techniques described below) may be used to determine a confidence level in the user’s identity.
- In some implementations, the result set returned by the initial query (e.g., the records in Table 2) may be processed by the content provisioning system to determine whether there is a perfect match within the result set. For example, if the result set included another
Record # 5 for James McCoy in ZIP code 92110, the system may determine that record five is an exact match to the provided user information and further disambiguation (such as using the techniques described below) are not performed. Rather, with a single perfect match identified, the system may confirm identity of the user and provide the appropriate offer content to the user. In situations where a perfect match is not included in the candidate list or to or more perfect matches are included in the candidate list, further disambiguation may be performed. - The system may then evaluate the candidate list and eliminate candidate users having a probabilistic fuzzy match below a certain threshold. For example, “Jaime McCountinosh” may be eliminated because of the degree of difference between the user attributes received from the mobile carrier and attributes of that particular candidate from the credit header database (and/or other databases in other embodiments). For the remaining candidate users in the candidate list, the system may rank order the candidates based on the similarity to the user attributes from the carrier. In the example provided, there are 2 possible high quality matches in view of the significant match of CHF attributes of the three users with the user attributes from the mobile carrier (e.g., “Jay McCoy” and “Jimmy McCoy”). With this overlap in remaining candidate users, attributes shared by the candidate users may be used to interact with the user in a personalized way. For example, the system may present a greeting to the user such as “Hello, Mr. McCoy” but let the user know that they might need to do some additional validation.
- In the above example,
record numbers - When more than one possible candidate remains without a perfect match, the system may apply additional profiling to determine which disambiguation questions can be asked to arrive at a single candidate. Assuming the above example has three unique candidates, the system may determine if asking for the date of birth, month of birth, year of birth, or last four SSN or some combination thereof will uniquely identify only one of the three candidates. In the very rare case where the system cannot distinguish between two or more candidates, even with additional PII provided by the user, the process may provide an indication that the identity cannot be confirmed without further authentication procedures. In some embodiments, the further authentication procedures may be initiated at that time by the content provisioning system, such as by directing the user’s mobile device to an out of wallet authentication site, such as may be provided by a credit bureau, to ask the user for information regarding particular credit data items (e.g., balances of particular loans/mortgages, previous residence addresses, etc.). In some embodiments, further processing of publication criteria for offers requiring a higher level of user authentication, e.g., credit offers or insurance quotes, may be terminated when the identity of the user cannot be confirmed. This safeguards the system from making an offer to the wrong person.
- The various processes, hardware configurations, and variations of such, for identifying (or verifying an identity of) a specific user may be referred to herein as SmartPIN or Smart Lookup.
- In some embodiments, device-level fraud analysis may be integrated into the various user identification processes. For example, in some embodiments, script code (e.g., JavaScript) may be included on a landing page to which the user is directed after initiating an identity validation process (e.g., in response to the user completing a call to action, such as sending a specific text message to a particular telephone number to receive a prequalified offer). The script code may be configured to determine characteristics of the mobile device and/or user based on metadata associated with the URL request, and in some implementations by requesting further identification information from the mobile device without notifications to the user. For example, the script code may determine a “device fingerprint” that can be compared to one or more lists of device fingerprints associated with high risks of fraud. Thus, a likelihood of potential fraud by the particular user device may be determined and used to determine how to further interact with the user (e.g., to close the connection with the user device based on a high likelihood of fraud or continue the communication with the user device).
- The ability to uniquely identify a user (from a set of multiple possible users sharing a set of user attributes) based on minimal additional information requests from the user optimizes the user identification process. The determination as to whether a given candidate is a “match” presents a non-trivial problem for computer-implemented systems. To generate a value that can be compared to the threshold for matching, a scoring process may be implemented.
- In one example scoring algorithm, a minimum cutoff to qualify for consideration (e.g., to remain on a candidate user list) may be a 0.4 Jaccard index score (scaled from 0 to 1.0). Equation (1) below shows one expression of a Jaccard index for the similarity between two data sets A and B.
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- The system may use a bi-character Jaccard index for the fuzzy matching algorithm. This minimum cutoff is selected to be loose enough to allow for possible name variations. The system may implement further constraints to ensure validity. A nickname table may be included to enhance the candidate list and prevent the system from missing common name variations. The candidate list may be processed for relevant matches to the original input (e.g., from the service provider) as well as nicknames.
- Whether the input information to the process is received from a service provider by the user, the system may look for candidate records that have at least a Jaccard score of 0.4 or higher. If more than one candidate has a Jaccard score greater than or equal to 0.7, the system may initiate a disambiguation process. The disambiguation process may be skipped if a perfect match for the name is identified based on the Jaccard score and the candidate record has a perfect match to the provided address information.
- If the system has more than one candidate remaining and/or lacks the address information confirmation, the disambiguation process begins. In one implementation, the disambiguation may first parse the list of date of births from the remaining candidates and determine if each candidate in the list has a unique birthdate. If every remaining candidate has a unique date of birth (DOB), the system can ask the user for date of birth and, based on this information, one match can be identified. For the candidates shown in Table 2, DOB will provide a unique identification for any one of the candidates. If the DOB received by the system is not one of the candidate DOBs, the system may provide an indication that the identity cannot be confirmed and further processing terminated. The system also may also determine that other candidate information (e.g., Last 4 SSN) is not unique. In such instances, the system would not prompt the user to provide this information.
- The system may include two or more paths to the identification. For example, the identification inputs may be provided automatically via the service provider or directly from the user. The system may include features to prevent displaying any information that the user did not provide. For example, the system may require a candidate have at least 0.9 or higher Jaccard score before displaying a candidate’s information simply because it matches to the input name provided by the service provider. Table 3 shows some examples of the Jaccard score for various candidate comparisons.
-
TABLE 3 Candidate 1Candidate 2Jaccard score “James Mcoy” “James McCoy” 0.7273 “John Smith” “Jonh Smith” 0.5 “John Smith” “Johhn Smith” 0.9 “Bob Grey” “Bob Gery” 0.4 - Thus, even for a comparison having a 0.4 Jaccard score, the names may be visibly very similar. Thus, the disambiguation process may increase the clarity on distinguishing between facially related candidate by collecting information using follow-up questions that identify one and only one candidate. The system may be configured to prevent further activity until the user provides all the correct information that is required to positively identify the user.
- Based on automated and manual analysis of research data, user authentication based on only credit header file data may be able to get 96% unique identifications from the full name and zip code (such as may be provided by the mobile carrier through which the user sends an initial text message to initiate a prequalification process). Further, if the system also considers the date of birth and/or last four SSN, a unique identification can be identified more than 99.8% of the time. For the remaining 0.2% of the time or if the user is a no-hit (e.g., not found in the data stores accessible by the system), the system may simply default by not making any offer and terminating any further processing (e.g., decline to provide an offer, content, or other good/service).
- While the discussion has highlighted the features for identifying and providing authenticated user information based on a limited set of inputs in the context of credit offers, the identification process may be applicable in other fields. For example, when selling a home or other object, it is typical for the seller to place a sign advertising the availability of the item. This sign may include an active (e.g., user prompt; QR code; barcode) or passive (e.g., beacon, RFID, NFC) indicator. This indicator may cause a mobile device associated with a user interested in the item to send a text message or other initiation message to start the identification process. For example, a location indicator may detect presence of a user near a house that is for sale and initiate communications with the user regarding the house. In this example, the user may not provide any proactive request for the information, but rather the information is provided based on the user’s proximity to the house for sale. In some embodiments, other criteria may be assessed before initiating communication with the user, such as the user’s predicted interest in purchasing a house. In another example, the location indicator may detect presence of the user near the house that is for sale and directly communicate to the user device (e.g. via Bluetooth communication) information on how the user can initiate a prequalification process and/or receive further information on the house (and/or related houses for sale or other similar real estate information), such as by providing a short code that can be selected by the user to send a text message to the
content provisioning system 150. Thus, message content provided to the user may be associated with the specific item (e.g., the particular house for sale) such that the location and specific terms for the process (e.g., mortgage lending, credit lending, insurance rating, etc.) may be identified not just for the user but also for the specific item of interest. -
FIG. 1 is a block diagram showing an example environment for providing personalized content to a mobile device. As discussed, the resources available to amobile device 102 may be limited. The limitations may be physical, such as display size or input means for providing information. These limitations can present a barrier to viewing or otherwise interacting with content presented via themobile device 102. The limitation may be a computing resource such as power, processing speed, network bandwidth, network connectivity, or the like. It may be desirable to conserve these resources to ensure longer operational time for themobile device 102. Each message transmitted or received requires resources to process. Accordingly, the ability to accurately identify a user of themobile device 102 in a way that limits the number of resources can improve the overall speed at which the user can be identified but also improve the resource utilization for themobile device 102 during the identification or content provisioning process. - In the embodiment of
FIG. 1 , themobile device 102 is shown as a smart phone. Themobile device 102 may be implemented as a tablet computer, a wearable device (e.g., smartwatch, smart glasses), or other communications device configured to transmit and receive messages. Themobile device 102 may include one or more transceivers (not shown) to transmit and receive such messages. The messages may be transmitted to or receive from one ormore communication systems 110 such as asatellite 112, an access point 114 (e.g., cellular access point), or a wide area network router 116 (e.g., WI-FI®). Other examples ofcommunication systems 110 include BLUETOOTH® systems, ZIGBEE™ systems, Z-WAVE® systems, GIMBAL™ systems, near field communication systems, or other wired or wireless systems. The communications may provide data communication path to anetwork 120 such as the Internet, cellular networks, or combinations of networks. The communications may provide services such as location services. For example, a beacon may transmit location information that can be received by themobile device 102 and used to identify a location of themobile device 102. The geofencing information may include an identifier of the beacon which can then be used to look up a location associated with the beacon. In some implementations, the location services may include a global positioning service (GPS). The GPS signals provide a way for themobile device 102 to identify its current location. Location may also or alternatively be determined based on theaccess point 114 location or address information (e.g., IP address) of the widearea network router 116. - A personalized
content provisioning system 150 may be included in theenvironment 100. The personalizedcontent provisioning system 150 may include anidentity confirmation device 155, apublication manager 160, and acontent generator 170. Theidentity confirmation device 155 may be included to authenticate the identity of a user, such as using the various systems and methods discussed herein. In some implementations, authentication of a user may be referred to as Smart Lookup. The identity may be based on information provided by themobile device 102 or an application executing thereon. For example, themobile device 102 may include a device identifier in messages transmitted to the personalizedcontent provisioning system 150. Examples of a device identifier include an IP address, a telephone number, a tax identifier, a media access control (MAC) address, and a mobile equipment identifier (MEID). The information may be configuration information for themobile device 102 such as home address, username, account number, or the like. In some implementations, a software application such as an offer identification application, may be installed on themobile device 102 and used to interact with the content provisioning system to identify any offers for the user. The offer identification application, such as an application provided by a credit bureau or other entity that has access to regulated data of users, may receive user input values through a user interface such as name, mailing address, email address, birthdate, etc. One or more of the user input values may be included in the information transmitted to the personalizedcontent provisioning system 150. - In some implementations, the information provided to the
identity confirmation device 155 may not be sufficient to uniquely identify a user of themobile device 102. In such instances, theidentity confirmation device 155 may be further configured to cause presentation of a user interface to collect one or more additional elements of information to identify the user as described herein. - The
publication manager 160 may be configured to receive content and publication criteria that must be met for the content to be provided to a user. The content may include offer content or instructional content. The content may be associated with location criteria such that the content can be published to users at or associated with a specified location. In some implementations, the content may be associated with user behavior. The behavior may include historical locations, time between visits to one or more locations, mobile device behavior (e.g., logging into an application, executing a feature of an application), interactions with the personalizedcontent provisioning system 150, messages (e.g., text messages) received (e.g., specific message content or specific message destination), or the like. The content and criteria may be stored in a publicationinformation data store 184. - The
content generator 170 may be assessing the publication criteria received by the publication manager to determine, for specific behavior or requests, whether and when to respond to themobile device 102. Auser request processor 172 may be included to process user initiated requests for content. For example, a user may activate a control element on an application executing via his mobile device. The control element may transmit a request to view current content offerings. - In some implementations, the
content generator 170 may include identified information for a user to prefill content such as a form or other interactive interface. An identity data types may be mapped to specific fields and, if available for an identified user, included in the content. The prefilled content may be provided for display or printing such as via a kiosk. In some implementations, the prefill may be performed without presentation to the user. For example, consider an implementation where the user transmits a text message including “more info” to a predetermined destination. The user may be identified using information from the mobile carrier and, based on the identification information, additional user information such as address, gender, or the like may be received. This additional user information may be used to transmit a request to produce a physical mail item including the additional information. In this example, the user did not need to provide address or other information to complete the request. The additional user information may be provided via a machine interface such as an application programming interface configured to receive a device identifier or a limited set of user values (e.g., name and postal code) and return a set of additional user information if a user can be identified. The set of information may be accompanied by a confidence score indicating the likelihood that a given set of information is associated with input the user information. In some embodiments, third parties (e.g., entities that require consumers to provide PII to determine eligibility or enroll in a service) may leverage the prefill technology using an API that facilities direct communication with thecontent generator 170, such as to verify identity of a user based on a minimal set of user information (as discussed thoroughly herein) and then provide back more extensive user PII once the user’s identity is verified (such as from theidentity information database 188 and/or other internal or external databases of user information). - In some implementations, the content may include an authorization token such as an optical scancode (e.g., barcode, QR code). For example, if the user is identified and authorized for a specific line of credit, the mobile device may display an interface including the optical scancode to complete a transaction at a point of sale against the line of credit.
- A
behavior detector 174 may be included to generate and transmit content based on user behavior. In some implementations, this may be referred to as a content “push.” Thebehavior detector 174 may receive behavior information associated with themobile device 102. The information may be received from the mobile device 102 (e.g., included in a message transmitted from themobile device 102 to the personalizedcontent provisioning system 150. The information may be received from a service provider such as an application service provider or communication service provider (e.g., satellite, cellular, internet service provider). The behavior information may be stored in a userhistory data store 182. - The
behavior detector 174 may monitor the userhistory data store 182 for user records that include behaviors associated with content. Once a behavior condition is met, thebehavior detector 174 may initiate generation of content for themobile device 102 associated with the behavior. - Whether in response to a user request or a detected behavior, generating content may include identifying the user of the
mobile device 102. The identification may be performed using theidentity confirmation device 155. Generating the content may include customizing the content to include specific values for the user in the content. For example, it may be desirable to insert the user’s first and last name to personalize the message. The personal information may be retrieved from an identityinformation data store 188. The identityinformation data store 188 may be queried using at least a portion of the user information associated with a request for personalized content. One example of an identity information data store is a credit header database (and/or other databases in other embodiments). In some implementations, content may include variable features such as a prequalified line of credit. This value may be generated for the specific user based on their personal information. - For tracking purposes, it may be desirable, as part of generating the content, to maintain a record of the content provided. A content
history data store 186 may be included to store an identifier for a personalized content item provided to themobile device 102 and/or the authenticated user to which the content item was targeted. The contenthistory data store 186 may include an identifier of themobile device 102 and an expiration date for the content. In some implementations, one or more custom values generated for the content may be stored in association with the identifier for the personalized content item (e.g., prequalified line of credit, contact information of agent included for accepting or inquiring about the content, etc.). - An ID-device binding
data store 180 may also be included in theenvironment 100. The binding data may store an association of device identifiers and an identifier for a specific user. In some implementations, the identifier for a specific user may be a personal identifier that uniquely identifies the user within theenvironment 100 or at least in the context of the personalizedcontent provisioning system 150. -
FIG. 2 is a flow diagram showing an example method of behavior initiated content provisioning. Themethod 200 may be implemented in whole or in part by the personalizedcontent provisioning system 150. Themethod 200 illustrates an example method in which user behavior, such as location behavior, can be used to provide personalized content to a user. Consider the case where a user visits a car dealership on Monday. The system may detect a mobile device associated with the user at the dealership. Later in the week, the same user is detected at the dealership (or a related dealership). Based on an interaction with a sensor at the dealership or entering a geofenced area, which may or may not require any affirmative input from the user (as discussed in the various embodiments herein), an alert may be transmitted to the mobile device indicating that the user is prequalified for a loan to purchase a car at the dealership. Depending on the embodiment, the method ofFIG. 2 may include fewer or additional blocks and the blocks may be performed in an order that is different than illustrated. - At
block 202, a location of a device associated with a user may be detected such as by a presence system or a behavior detector. The detection may be based on information transmitted from the device. For example, a presence detection system may be implemented to identify where a mobile device is located, when it arrived at the location, and, in some implementations, how long it was at the location. The identification may be based on transmitting a beacon identifier from the mobile device to the presence detection system. In some implementations, the information may be transmitted by a service accessed by the device such as accessing the WI-FI® network at a location. - At block 204, the personalized
content provisioning system 150 may determine whether the location of the device is associated with content. The determination may include querying a publication information data store for content associated with the location. In some implementations, the association may be based on an area. For example, a content element may be associated with an entire city block. If the location of the device is within that city block, then the location may be deemed associated with the area. In some implementations, a threshold distance may be associated with an area. Thus, if the device location is determined to be within the threshold distance of a geographic area, the device location may be deemed associated with the area. - If the determination at block 204 is negative, the
method 200 may return to block 202 to detect another location for the device. If the determination at block 204 is affirmative, then content may be associated with the location. Atblock 206, the personalizedcontent provisioning system 150 may receive a publication rule for the content associated with the location. The publication rule may identify user attributes required for delivery of the content to the user. For example, the publication criteria may indicate that the user has visited a particular location two or more times in one week. - At
block 208, the personalizedcontent provisioning system 150 applies the remaining publication criteria (e.g., pre-validation criteria for a credit card offer) to user attributes (e.g., from third party data sources and/or directly Received from the user device) to determine if content (e.g., a credit card offer) should be provided to the user. The determination may include retrieving behavior data from a user history data store. - If the determination at
block 208 is negative (the user attributes identified by the system did not meet the publication criteria), themethod 200 may return to block 202 and continue as described above. If the determination atblock 208 is affirmative and the behavior meets a publication criterion, themethod 200 may continue to block 210. Atblock 210, information about the user associated with the device may be received by the personalizedcontent provisioning system 150. Receiving the information may include retrieving user information from the user history data store. Receiving information may include receiving information from the device or application executing thereon. In some implementations, it may be desirable to obtain consent of the user before requesting and receiving personal information. For example, information may be received from a service provider that provides a service to the user or the device. Before requesting such information, the user may be prompted such as via a graphical user interface to consent to data gathering. A response confirming the consent may be received to permit requesting and receipt of this information. - At
block 212, a message including the content for the user may be generated by the personalizedcontent provisioning system 150. Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor. For example, the metric may be a score based on prior transactions. The score may indicate how reliable the user performed one or more prior transactions. In some implementations, the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected. The content generation may include generating an identifier that uniquely identifies the personalized content provided to the user. For example, it may be desirable to maintain a record of the terms or conditions included in the content. The content may be associated with an expiration date. The expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof. - At block 214, the personalized
content provisioning system 150 may transmit the message to the device. The message including the personalized content may be transmitted via wired, wireless, or hybrid wired and wireless means such as via one or more of thecommunication systems 110 and/or thenetwork 120 shown inFIG. 1 . The message may cause the device to activate and/or initiate an application that is configured to present the content. - For example, in certain implementations, an alert indicating a product offer to a user may be automatically transmitted to the
mobile device 102 in response to determining that personalized content should be provide to the user (e.g., in block 214), such as in real-time as the user is at the location triggering the content analysis (e.g., at block 204). Such alert communications may be automatically transmitted to the user in one or more modes of communication, such as, for example, direct to a mobile application, electronic mail, text messaging (e.g., SMS, MMS, or other), to name a few. In certain modes of communication, the communication may be configured to automatically operate on the user’s electronic device. For example, upon receipt of an alert, a software application installed on the user’s mobile device may be automatically activated to deliver the communication to the user (e.g., a SMS viewer or application may automatically display information from the alert communication when received by the device or when the device is connected to the internet). Alternatively, the alert communication may activate a web browser and access a web site to present the alert communication to the entity. In another example, a communication may be transmitted to the user’s email account and, when received, automatically cause the user’s device, such as a computer, tablet, or the like, to display the transmitted communication or a link to take the entity to a webpage with additional information regarding the selected publication content (e.g., product or service offer). -
FIG. 2 describes a method of providing personalized content based on detected behaviors. In some implementations, it may be desirable to provide personalized content on demand such as in response to a user request. -
FIG. 3 is a flow diagram showing an example method of user requested content provisioning. Themethod 300 may be implemented in whole or in part by the personalizedcontent provisioning system 150. Themethod 300 illustrates an example method wherein a user can request that information regarding the user be obtained and a determination of eligibility for content be performed. Consider the case where a user visits a car dealership while walking home. The dealership may include a sign saying “Text ‘credit’ to 333333 to drive home today.” In such implementations, the act of submitting a text message with a specific keyword (e.g., credit) can be sufficient to identify the user and determine that the user is qualified to receive particular content, such as a credit prequalification offer that is tailored to the user. Depending on the embodiment, the method ofFIG. 3 may include fewer or additional blocks and the blocks may be performed in an order that is different than illustrated. - At block 302, a message requesting offer content may be received from a user device. The message may be received in response to a location alert (e.g., behavior based), such as a message sent to the user device as the user walks into the car dealership and requests a reply back from the user. The message may be received without direct solicitation via the user device such as by a user seeing a sign or hearing a radio advertisement, creating the message, and transmitting the message. In some implementations, the message may be generated via an application executing on the user device. For example, a bank may provide its users a custom application. The custom application may include a user interface element that, when activated, causes transmission of a message to the content provisioning system requesting identification of current credit offers for the use. It will be appreciated that the example method in
FIG. 3 discusses credit offers, but themethod 300 may be applicable for requesting and providing other types of personal content such as educational content, informational content, bank cards, mortgages, job applications, government forms, auto loans, retail loans, loyalty programs, pre-fillable content, timeshare or other eligibility based content, comparison service, or the like. - The message may include a keyword such as “credit.” In some implementations, the message may include an image such as an image of a car the user is thinking about purchasing. The message may be transmitted along with information about the user and/or the user device such as a device identifier, phone number, sender email address, IP address of the device, current location of the user device, or other identifying information for the user and/or user device.
- At block 304, the personalized
content provisioning system 150 may determine that the message received at block 302 is associated with content. The determination may include querying a publication information data store for content associated with the location of the user device or other attribute included in the request. For example, where the request includes a text message, the determination at block 304 may include identifying content associated with the destination number and specific message included. For example, a car dealership may be associated with a particular message destination code (e.g., a phone number or other short message code). The same dealership may have different promotions or content that can be served. In such implementations, each campaign may be associated with a different keyword. In some implementations, the code may be associated with date information indicating when the code is “active” for providing content. Table 1 below provides one example of how codes may be associated with different content (e.g., different campaigns, different credit levels (e.g., standard versus preferred), or different user types (e.g., individual versus business)). -
TABLE 1 Destination Number Code Content Identifier Description 444444 “credit” prequal-2017 2017 user credit prequalification campaign (standard) 444444 “accord rebate” accreb-39275 Mfg. Accord rebate program 39275 444444 “fleet fi” b2b-prequal-2017 2017 business credit prequalification campaign 888888 “credit” prequal-2017 2017 user credit prequalification campaign (preferred) - If the determination at block 304 is negative, the
method 300 may return to block 302 to receive another message from the user device. In the negative case, it may be that the destination number and/or code received at block 302 are no longer valid or are not yet active. If the determination at block 304 is affirmative, then content may be available for the user. - At
block 306, information about the user associated with the device may be received by the personalizedcontent provisioning system 150. Receiving the information may include retrieving user information from the user history data store. Receiving information may include receiving information from the device or application executing thereon. In some implementations, it may be desirable to obtain consent of the user before requesting and receiving personal information. For example, information may be received from a service provider that provides a service to the user or the device. Before requesting such information, the user may be prompted such as via a graphical user interface to consent to data gathering. A response confirming the consent may be received to permit requesting and receipt of this information. - At
block 308, the personalizedcontent provisioning system 150 may receive a publication rule for the available content. The publication rule may identify a user behavior to provide the content. For example, the user behavior may include visiting the location two or more times in one week. The publication rule may include consideration of user information. For example, demographic information (e.g., mailing address, age, gender, etc.) may be used to determine whether content is available for the user. - If the determination at
block 308 is negative, themethod 300 may return to block 302 and continue as described above. If the determination atblock 308 is affirmative and the user information or behavior meets a publication criterion, themethod 300 may continue to block 310. - At
block 310, a message including the content for the user may be generated by the personalizedcontent provisioning system 150. Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor. For example, the metric may be a score based on prior transactions. The score may indicate how reliable the user performed one or more prior transactions. In some implementations, the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected. The content generation may include generating an identifier that uniquely identifies the personalized content provided to the user. For example, it may be desirable to maintain a record of the terms or conditions included in the content. The content may be associated with an expiration date. The expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof. - At
block 312, the personalizedcontent provisioning system 150 may generate a message including offer content for the user and then at block 314 transmit the message to the device. The message including the personalized content may be transmitted via wired, wireless, or hybrid wired and wireless means such as via one or more of thecommunication systems 110 and/or thenetwork 120 shown inFIG. 1 . The message may cause the device to activate and/or initiate an application that is configured to present the content. -
FIG. 4 is a flow diagram showing an example method of identifying a user. Some implementations, such as the methods shown inFIGS. 2 and 3 , may include uniquely identifying a user based on limited information.FIG. 4 provides one way to efficiently identify a user of a device. Depending on the embodiment, the method ofFIG. 4 may include fewer or additional blocks and the blocks may be performed in an order that is different than illustrated. - The
method 400 may begin atblock 402 by receiving, e.g., at theidentity confirmation device 155 of the content provisioning system 150 (FIG. 1 ), first information identifying a user of a device from a service accessed by the device. For example, the first information may be user input to an application executing on the device. As another example, the first information may be a phone number or device identifier assigned by a service provider (e.g., mobile network operator) to the device, or even user information associated with the phone number provided by the service provider. If the first information is a phone number, a reverse lookup may be performed to identity user data associated with the phone number. The user data may include first name, last name, and mailing address information such as a ZIP code. In some implementations, the user data may include historical user data such as previous addresses or names (e.g., due to a change in marital or adoption status). - At
block 404, the system may query a user data source using at least a portion of the first information. For example, a credit data base including header files associated with different users may be queried using the first name, last name, and ZIP code identified by the first information. - A set of user data for different users corresponding to the first information may be received at
block 405. As discussed above, some users may have the same first name or last name. Such user records may correspond to the first information. In some implementations, the correspondence may be based on fuzzy logic or phonetic matching. Similarity may also be based on current user information or historical user information (e.g., prior address, name, etc.). Because there may be discrepancies between the received first information and the specific users identified from the user data source, themethod 400 may further process the received records to identify a record for a unique user included in the set of users. In some embodiments, if a “perfect match” is included in the candidate identity records returned from the query atblock 404, the system may forgo the remainder of the matching process by identifying that user as the authenticated user. In some embodiments, this short-circuiting of the matching process may only be performed if a single perfect match exists in the returned candidate identity records, where a perfect match may be defined as exact matching of all user provided data (e.g., exact match of first name, last name, ZIP code provided by the user), or in some embodiments could include variants of certain identity data (e.g., a perfect match could be identified for a candidate record for “Joe Jones” in response to the user providing the name of “Joseph Jones”), or as defined by the offer provider (e.g., based on the needed level of identity verification). - At
block 406, the system may generate a similarity metric identifying, for each of the candidate users, how closely the user data for a respective candidate user matches the first information. One example of a similarity metric is a Jaccard score. The degree of relatedness may be used to identify whether any candidate users may be identified as the user and to what level of confidence. In cases where historical information for a user is included, a weight may be applied to discount values occurring further from the current time. For example, if a user moved in the last six months, the prior address may be considered with a higher weight than a previous address for a person who moved thirteen years ago. The weighting may be dynamically specified for each data type (e.g., a first weight may be applied for names while a second weight may be used for ZIP code). The weighting applied for a data type may be identified in a configuration accessible by the system. - At
block 408, the system may determine whether at least one of the candidate users is associated with a metric that meets or exceeds a matching threshold. For example, it may be desirable to identify a “match” only when the Jaccard score exceeds a minimum threshold, such as to ensure that credit offers provided to a user are based on that same user’s unique credit data that qualifies the user for the credit offers. This may ensure a minimal degree of relatedness. If the determination atblock 408 is negative, the user data store may not include records with sufficient level of relatedness to identify the user. In such instances, themethod 400 may proceed to block 430 to identify a default user. Identifying a default user may include collecting generic user information that can be presented to any user. For example, in the context of credit offers, the user may be provided with generic invitations to apply for credit, rather than being provided with an offer of credit that may be provided only to qualifying authenticated users. This may include identifying no user information and providing a request to provide a more robust set of information than provided via the first information. - While the default user is a helpful fail-safe, it is envisioned that the determination at
block 410 will match at least one user. Based on experiments, it has been determined that many users may be uniquely identified on the basis of first name, last name, and ZIP code. Accordingly, the default identification atblock 410 may apply to a small percentage of the users submitted for identification. - In the case where at least one user is identified at
block 410, themethod 400 may continue to block 412 to determine whether only one user exceeds the threshold. The determination atblock 412 may be affirmative when only one user is identified with a similarity metric above the matching threshold. In such cases, themethod 400 may proceed to block 440 to positively identify the user as the candidate user associated with the metric. The positive identification may include providing additional information about the user retrieved from the user data source. One example of such additional information may include user information for processing credit inquiries. - Returning to block 412, if more than one candidate user is associated with a metric that exceeds the threshold, it may be that a unique user cannot be associated with the first information. In such instances, the
method 400 may compare the remaining candidate users’ information to identify a data type or field (e.g., first name, date of birth, last four of the Social Security Number, previous street address, etc.) that may be used to distinguish all candidate users. For example, consider the three example candidate user records shown in Table 4 below. -
TABLE 4 Candidate No. First Name ZIP code Street House No. 1 Pat 92212 Main St. 146 2 Pat 92212 Garden St. 735 3 Pat 93102 Lincoln Blvd. 33982 - In Table 4, the example candidates have the same first name, so the data type or field associated with first name cannot distinguish the candidate users. Similarly, the data type or field associated with ZIP code cannot distinguish all of the candidate users as two the three candidates are associated with the same ZIP code. The street address information can distinguish the candidate users as each user can be uniquely identified based on the house number. Accordingly, the data type or field associated with house number may be identified for the example shown in Table 4.
- At
block 414, the system may transmit a request to the user device requesting a value for the data type or field identified atblock 412. It will be appreciated that themethod 400 requires only this single value for distinguishing the candidate users. This ensures a limited collection and exchange of information between the system and the device to attain a unique identification. The request transmitted atblock 414 may cause the display of a data collection user interface to receive and transmit a value for the requested data type or field to the system. - At
block 416, the value may be received from the user device. The value may be compared to the candidate user values to ensure the received value is in fact associated with one of the candidate users. In the case where the received value is not associated with any of the candidate users, themethod 400 may proceed to block 440 as described. However, it is expected that the received value will match one of the candidate users thereby providing a unique identification of the user. - Once the user is uniquely identified, user information for the user may be provided for additional processing. For example, as discussed, personalized content may be provided based on the identity of a user. In some implementations, such personalization may require specific user information to identify or generate the content. One specific example is a prequalified credit offer. In some implementations, a model may be included. The model may accept a set of input values representing specific user information such as age, income, credit score, etc. These values may only be provided if the user is uniquely identified such as via the
method 400. Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor. For example, the metric may be a score based on prior transactions. The score may indicate how reliable the user performed one or more prior transactions. In some implementations, the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected. The content generation may include generating an identifier that uniquely identifies the personalized content provided to the user. For example, it may be desirable to maintain a record of the terms or conditions included in the content. The content may be associated with an expiration date. The expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof. -
FIG. 5 is a messaging diagram showing example communications for providing personalized content to a mobile device.FIG. 5 shows messages exchanged between a user device 502, ashort code provider 506, adecisioning service 508, and a service operator 510 (e.g., mobile carrier, internet service provider, application service provider, etc.). The messages inFIG. 5 are shown as being exchanged directly between entities. It will be appreciated that intermediaries such as security devices, routers, gateways, network switches, and the like may be included. These intermediaries have been omitted to focus the reader on certain features of providing personalized content. Additionally, in some embodiments functionality of two or more devices illustrated inFIG. 5 may be performed by a single entity and/or single device. - A user of the user device 502 may receive a call to action. For example, the user device 502 may transmit a message including device information, such as a location of the user device 502 or a signal detected by the user device 502 such as a beacon identifier. In response, the user device 502 may receive a message including a short code. The message may be a text message indicating that the short code may be used to request personalized content. The message may include instructions to adjust a graphical user interface of the user device 502 to display the short code for requesting personalized content. In some implementations, the message may include a URL that directs the user to a web portal (e.g., by launching a browser on the user’s mobile device) to provide further identity verification information and/or perform other actions to prequalify for an offer. In some embodiments, the
content provisioning system 150 includes a URL shortening logic that generates short URLs (e.g., <20 characters) that are redirected to the corresponding full URL (e.g., 80+ characters). Longer URLs may not work on certain mobile devices (e.g., may result in an error code on the user device) and/or are not user-friendly (e.g., may fill the entire mobile device display with irrelevant information to the user). Thus, providing of shortened URLs by shortening logic at the content provisioning system may reduce the occurrence of technological challenges and/or errors in interacting with users. The URL shortening logic may include code, e.g., python code that generates the shortened URLs and stores them in association with the full URL on a web services server (e.g., Amazon’s web services or AWS). In some embodiments, an external URL shortening service may be called to provide shortened URLs that may then be included in message content by thecontent provisioning system 150. - The user of the user device 502 may decide to request the content offered by activating a control element on the user device 502 to transmit a
short code message 524 to theshort code provider 506. In some implementations, theshort code message 524 may be manually input by the user such as based on information collected from a sign or other promotional material. Theshort code provider 506 may translate the short code received in theshort code message 524 to identify a process or service to respond to the request. Theshort code message 524 may include information identifying the user device 502 such as a phone number, mobile equipment identifier, username, or account number. This identifying information may be transmitted in amessage 526 to thedecisioning service 508. Thedecisioning service 508 may implement the personalized content selection and generation features described such as inFIGS. 2, 3, or 4 . - The
decisioning service 508 may request consent from the user of the user device 502 to retrieve additional information about the user such as performing a reverse lookup for personal information about the user based on the device identifier. In such implementations, aconsent request message 528 may be transmitted to the user device 502. Theconsent request message 528 may include a link that can be activated via the user device 502 to provide consent and continue the process. The link may include a token or other temporary credential to associate the consent with a particular session. In some implementations, theconsent request message 528 may request return of a password, personal access number, or other verification information. - As shown in
FIG. 5 , the consent may be provided in the form of ashort code message 530. Themessage 530 is received by theshort code provider 506 and provided in turn to thedecisioning service 508 viamessage 532. Themessage 532 may include consent information such as a password or token along with the device identifier. Thedecisioning service 508 may then associate the consent with a particular device and thus set of personal information. Thedecisioning service 508 may transmit amessage 534 to theservice provider 510 to retrieve additional user information for the user associated with the user device 502. For example, theservice provider 510 may be a cellular service provider for the user device 502. Using a phone number, additional information about the user account associated with the phone number may be retrieved viamessage 536. The user data collected, or a portion thereof, may be transmitted viamessage 538 to thedecisioning service 508. In some implementations, theservice provider 510 may be an application service provider, credit service provider, merchant, retailer, government, or other entity to whom users have identified themselves. In some implementations, more than oneservice provider 510 may be included and queried for user information. The user information received from respective providers may be different or may be the same, in which case, a comparison may be performed between values for a given data field to generate a confidence in the identification result. - In some implementations, the
service provider 510 may be a social media or other online media provider. For example, thecontent provisioning system 150 may request user PII from a social media server in response to the user indicating a desire to be prequalified for a particular offer (e.g., for an auto loan). Similar to embodiments discussed herein where a wireless service provider (e.g., Verizon, AT&T, Sprint, etc.) provide initial user identification information (e.g., name and ZIP Code), a social media server may provide similar and/or additional information regarding users that may then be used in a user verification process and determination of eligibility for the particular offer. - In some embodiments, a call to action may be presented via a social media service such as via a textual or image post. An interaction with the call to action (e.g., click, like, download, display, button press) may cause initiation of one or more of the identification and content provisioning features described. For example, a user may see content on their social media feed (on a mobile, portable, or desktop device) regarding prequalification for credit (which may be provided to the user based on analysis of the user’s profile and recent posts that indicate the user is searching for a new car, house, etc.) that, when selected, initiates any of the identity verification processes discussed herein.
- At this point, the
decisioning service 508 has at least two sources of information to help identify the user of the user device 502 namely the information provided by the user device 502 during the communication session and the information provided by theservice provider 510. Viamessaging 540, thedecisioning service 508 may identify the user based on the collected information. In some instances, the identification may include querying an additional user data source such as credit header files or other personal information data stores. In some instances, the collected data may not be sufficient to uniquely identify a specific user. In such instances, messaging 542 may be included to request and receive additional identity data to distinguish amongst multiple candidate users, such as discussed with reference to Table 4 above. - Once the user of the user device 520 is uniquely identified, via
message 544, the personalized content may be generated. Generating the content may include generating one or more values to include within the content. For example, a maximum line of credit may be identified based on the user information or a metric therefor. For example, the metric may be a score based on prior transactions. The score may indicate how reliable the user performed one or more prior transactions. In some implementations, the metric may be used to select a version of content for a user associated with the metric or other user data. For example, if the user is associated with a mailing address in California, a version of the content tailored to California residents may be selected. The content generation may include generating an identifier that uniquely identifies the personalized content provided to the user. For example, it may be desirable to maintain a record of the terms or conditions included in the content. The content may be associated with an expiration date. The expiration data may be selected based on the current date, a date when the offer was first presented, a promotion end date, or a combination thereof. Having identified and generated the personalize content, thedecisioning service 508 may transmit amessage 548 including the content or information that the user device 502 may use to access the content such as a network location (e.g., URL) of the content. -
FIG. 6A is a pictorial diagram showing user interface transitions for requesting and receiving personalized content on a mobile device. Awelcome interface 602 provides a welcome message. The welcome message may be generated by an application executing on the user device, such as one provided by a bank, a car dealership, a car manufacturer, or an online retailer or a general purpose application such as a web-browser, dynamic media streaming application, or other network content viewing application. Thewelcome interface 602 may include personalized content such as the user’s name “PAT SMITH.” Thewelcome interface 602 includes a textual control element (“Not me?”) that, when activated, may cause the user interface to collect user information for a different user. Thewelcome interface 602 includes a button control element (“Get Offer Content”) that, when activate, transmits a message to request content. The request may be transmitted to a personalized content provisioning system. - If the user submitting the request can be uniquely identified, such as via the
method 400 shown inFIG. 4 , the interface may receive information to for afirst transition 610 from thewelcome interface 602 to acontent presentation interface 608. Thecontent presentation interface 608 may include personalized content selected based on the user data received in the message as well as any user information identified from one or more service providers. Thecontent presentation interface 608 may include navigation arrows to allow viewing of different content for which the user is qualified to receive. The content shown on thecontent presentation interface 608 may include a personalized message. The personalized message may include name (“PAT SMITH”) or custom generated values (“$25,000”) based on the user data. Thecontent presentation interface 608 also shows an expiration date for the content and a unique identifier for the content. Thecontent presentation interface 608 also includes a button control element (“Begin Enrollment”) that, when activated, transmits a message to a network service to begin a process associated with the content. As shown inFIG. 6A , the process may be an enrollment process to accept the prequalified credit offer described in the received content. The message may include the offer identifier to facilitate expedited collection of the required information for the process. - Returning to the
welcome interface 602, if the user cannot be uniquely identified but a limited set of candidate users correspond to the user data provided, asecond transition 612 may present aclarification interface 604. Theclarification interface 608 may be generated to collect one or more elements of identity data that can uniquely distinguish the limited set of candidate users. As discussed, there may be one data type (e.g., month of birth, house number, street name, numeric day of birth, previous city of residence, etc.) that can distinguish all users in the limited set. In such implementations, a value for this data type may be the only requested information. As shown inFIG. 6A , theclarification interface 604 is requesting only the last four digits of the user’s social security number. - The
clarification interface 604 may include input control elements (shown as boxes) for receiving user input values such as respective digits of a social security number. Theclarification interface 604 also includes a control element (“submit” button) that, when activated, causes transmission of the input values to the system. If the provided information narrows the identity of the user to a unique user,transition 620 may provide the personalized content via thecontent presentation interface 608 as described above. If the provided information fails to uniquely identify a user,transition 622 may cause presentation of a userinformation input interface 606. - The user
information input interface 606 may include one or more input control elements to receive respective values for a set of data types such as first name, last name, last four digits of the social security number, home address ZIP code, and the like. An input control element may be prepopulated with known values, such as a first name common to the set of candidate users identified based on the initial request. The userinformation input interface 606 may include a control element such as a “submit” button that, when activated, causes transmission of the input values to the system. In some implementations, the interfaces shown inFIG. 6A may be provided within an application such as a web browser or custom built application. Upon submission of valid values,transition 624 may cause presentation of thecontent presentation interface 608 as described above. -
Transition 614 from thewelcome interface 602 to the userinformation input interface 606 may be initiated when the candidate list of users cannot be distinguished based on a single data type or field or no candidate users are found. In such implementations, the userinformation input interface 606 may prepopulate some fields with user values common to members of the candidate list and leave fields empty for those values which cannot be distinguished. - In some embodiments, the user identification and offer provision systems as discussed herein, such as with reference to
FIG. 6A , may be implemented in an online marketplace environment, wherein the user may be provided with multiple offers for which they are prequalified, rather than a single particular offer. For example, upon identification of a user (e.g., using some or all of the identity verification process ofFIG. 4 ), the user may be directed to a marketplace (e.g. via a shortened URL to the marketplace that is sent to the user’s mobile device as a text message or application-direct message) that displays multiple offers for exploration by the user (e.g. in a browser or standalone application provided by the content provisioning system 150). In the context of credit card offers, for example, the marketplace may identify multiple (two, three, four, five, or more) credit card offers for which the user is prequalified that are perhaps sorted to identify the prequalified offers with the most advantageous terms (e.g., lowest interest rates, best rewards programs, etc.) -or to display up all of the prequalified offers in some embodiments. The user may then be given the opportunity to filter the prequalified offers (e.g., by interest rate, rewards program features, etc.) and identify a particular offer for which the user would like to complete an application. Depending on the implementation, the offers may be selected from a plurality of offer providers, such that the user could potentially be provided with prequalified offers from multiple offer providers. In the example context of credit card offers, this means that are user could be provided with credit card offers from multiple financial institutions (e.g., a first credit card from big bank A, a second credit card from big bank B, etc.). As noted elsewhere, example implementations, such as in particular vertical markets (e.g., credit card offers) are provided for ease of explanation and do not limit the scope of any associated systems and methods to other vertical markets. -
FIG. 6B is a pictorial diagram showing another example user interface transitions for requesting and receiving personalized content on a mobile device. Aneligibility initiation interface 652 may be presented on a user device. Theeligibility initiation interface 652 may be presented in response to a message received from the personalizedcontent provisioning system 150. For example, the message may cause execution of an application on the user device to present theeligibility initiation interface 652. In some implementations, the application may be executed by the user via the user device. In such instances, theeligibility initiation interface 652 may be presented without remote communication (e.g., to the personalized content provisioning system 150). - The
eligibility initiation interface 652 may include input control elements (shown as boxes) for receiving user input values such as first name, last name, or postcode. In some implementations, the application presenting theeligibility initiation interface 652 may pre-populate the fields with a value for one or more of the input control elements. For example, if the user previously provided name or postcode information as a configuration for the application, these values may be retrieved from the configuration storage and displayed in respective input control elements. Theeligibility initiation interface 652 may include additional control elements such as a checkbox to indicate user’s consent to process his or her personal information. In some implementations, an additional control element may be provided that, when activated, shows additional details about the processing, what information may be collected, how the information may be used, etc. Theeligibility initiation interface 652 shown inFIG. 6B also includes a control element (e.g., button “check eligibility”) that, when activated, submits the user input values for further processing. For example, the user input values may be transmitted via a network for further processing such as to the personalizedcontent provisioning system 150. - After submission, a
transition 660 may cause presentation of astatus interface 654. Thestatus interface 654 may provide an indicator of the progress of the eligibility processing. The indicator shown in thestatus interface 654 comprises a progress bar. The progress bar may be updated based on one or more messages received from the system processing the eligibility request such as the personalizedcontent provisioning system 150. - The result of the processing may be that the user is identified and is eligible for particular content. In such instances, a
transition 662 may cause presentation of acontent offer interface 656. Thecontent offer interface 656 may include a personalized greeting (e.g., “Pat” is the name of the user and is used in the message shown). Thecontent offer interface 656 may include an estimate of a likelihood of obtaining the content referenced on thecontent offer interface 656. For example, in a credit prequalification context, the prequalification decision may not be a firm offer of credit, but rather an indicator of what credit terms might be offered to the user. An actual offer may be contingent on additional verification or information not available at the prequalification stage. - As shown in
FIG. 6B on thecontent offer interface 656, a percentage chance of acceptance is included. As discussed above, the content may be generated from a content template which includes a field for the likelihood information. The likelihood may be generated by thecontent generator 170. - The
content offer interface 656 may include additional text summarizing the terms of the offer. In some implementations, thecontent offer interface 656 may include an additional control element, such as hyperlinked text or a button, that, when activated, causes presentation of the full terms of the offer. The presentation may include retrieving the terms from a network location or displaying additional information received from the source system. Thecontent offer interface 656 may include a control element (e.g., button), that, when activated, begins the enrollment process. The control element may submit a session identifier to an enrollment system for continued processing. The session identifier may be used by the enrollment system to access user information collected by the prequalification process and/or the content offer identified. - In some implementations, once a user is approved for an offer, the approved offer information may be automatically provided to the offer provider, such as by pre-populating the offer provider’s online eligibility service with the user’s information. For example, the user may be provided with an option to perform a further approval process by selecting a link to the offer provider’s online eligibility service, which will be pre-populated (or auto-populated as the service is opened) with the user’s information. Additionally, the user information may be provided to other applications or entities. In the context of credit card offers, for example, once a user is approved for a particular credit card, the credit card information may be automatically (or after authorization by the user) provided to a mobile wallet on the user’s mobile device. Mobile wallets may be included as part of an operating system (e.g., Apple pay on iPhones) or third-party mobile payment applications (e.g., Google pay). Thus, a credit card could be applied for, approved, and available for use by a user within a significantly shortened time, and without the user ever receiving a physical credit card in some implementations.
- In some implementations, the user is presented with an option to receive a follow-up text or email (e.g., if they supply address) at some future time (e.g., 1 day, 1 week, or 1 month later - which may be determined automatically by the system or may be selectable by the user in some embodiments), that includes the link to the digital application process (e.g., either the same link as already provided or a modified link for identifying the user visit as responsive to a follow-up message).
- Another result of the processing may be that the user cannot be positively identified. In such instances, one or more additional interfaces may be presented to receive user input values to identify and distinguish the user from other candidate users. Examples of these interfaces are shown in
FIG. 6A , such as theinterfaces - In some instances, a content offer may not be identified. The failure to identify an offer may be because the user cannot be identified, because the user cannot be distinguished from other candidate users, because the user does not qualify for any available content offers, or because of a system failure (e.g., unavailable data source, system, etc.). In such instances, a
transition 664 may cause thestatus interface 654 to be replaced with anerror interface 658. Theerror interface 658 may include a description of the error identifying what caused the eligibility check to fail. - Design of computer user interfaces “that are useable and easily learned by humans is a non-trivial problem for software developers.” (Dillon, A. (2003) User Interface Design. MacMillan Encyclopedia of Cognitive Science, Vol. 4, London: MacMillan, 453-458.) The present disclosure describes various embodiments of interactive and dynamic user interfaces, such as the mobile user interfaces discussed above with reference to
FIGS. 6A-6B , that are the result of significant development. This non-trivial development has resulted in the user interfaces described herein which may provide significant cognitive and ergonomic efficiencies and advantages over previous systems. The interactive and dynamic user interfaces include improved human-computer interactions that may provide reduced mental workloads, improved decision-making, reduced work stress, and/or the like, for a user. For example, user interaction with the interactive user interface via the inputs described herein may provide an optimized display of, and interaction with, image data (including medical images) and may enable a user to more quickly and accurately access, navigate, assess, and digest the image data than previous systems. -
FIG. 7 is a block diagram showing example components of a location or behavior based contentprovisioning computing system 150. Thecomputing system 700 includes, for example, a personal computer that is IBM, Macintosh, or Linux/Unix compatible or a server or workstation. In one embodiment, thecomputing system 700 comprises a server, a laptop computer, a smart phone, a personal digital assistant, a kiosk, or a media player, for example. In one embodiment, theexemplary computing system 700 includes one or more central processing unit (“CPU”) 705, which may each include a conventional or proprietary microprocessor. Thecomputing system 700 further includes one ormore memory 732, such as random access memory (“RAM”) for temporary storage of information, one or more read only memory (“ROM”) for permanent storage of information, and one or moremass storage device 722, such as a hard drive, diskette, solid state drive, or optical media storage device. Typically, the components of thecomputing system 700 are connected to the computer using a standard basedbus system 790. In different embodiments, the standard based bus system could be implemented in Peripheral Component Interconnect (“PCI”), Microchannel, Small Computer System Interface (“SCSI”), Industrial Standard Architecture (“ISA”) and Extended ISA (“EISA”) architectures, for example. In addition, the functionality provided for in the components and modules ofcomputing system 700 may be combined into fewer components and modules or further separated into additional components and modules. - The
computing system 700 is generally controlled and coordinated by operating system software, such as Windows XP, Windows Vista,Windows 7,Windows 8, Windows Server, Unix, Linux, SunOS, Solaris, iOS, 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, thecomputing system 700 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, I/O services, and provide a user interface, such as a graphical user interface (“GUI”), among other things. - The
exemplary computing system 700 may include one or more commonly available input/output (I/O) devices and interfaces 712, such as a keyboard, mouse, touchpad, and printer. In one embodiment, the I/O devices and interfaces 712 include one or more display devices, 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. Thecomputing system 700 may also include one or more multimedia devices 742, such as speakers, video cards, graphics accelerators, and microphones, for example. - In the embodiment of
FIG. 7 , the I/O devices and interfaces 712 provide a communication interface to various external devices. In the embodiment ofFIG. 7 , thecomputing system 700 is electronically coupled to one or more networks, which comprise one or more of a LAN, WAN, and/or the Internet, for example, via a wired, wireless, or combination of wired and wireless, communication link. The networks communicate with various computing devices and/or other electronic devices via wired or wireless communication links, such as the credit bureau data source and financial information data sources. - In some embodiments, information may be provided to the
computing system 700 over a network from one or more data sources. The data sources may include one or more internal and/or external data sources that provide transaction data, such as credit issuers (e.g., financial institutions that issue credit cards), transaction processors (e.g., entities that process credit card swipes at points of sale), and/or transaction aggregators. The data sources may include internal and external data sources which store, for example, credit bureau data (for example, credit bureau data from File One(SM)) and/or other user data. In some embodiments, one or more of the databases or data sources 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, an object-oriented database, solr database, and/or a record-based database. - The
content data storage 708 may be included to support the identification of users and/or provisioning of content to a user. For example, the content data may include content templates, content models, content publication rules, and the like. - In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, Java, Lua, C or C++. A software module 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 software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices may be provided on a computer readable medium, such as a compact disc, digital video disc, flash drive, or any other tangible medium. Such software code may be stored, partially or fully, on a memory device of the executing computing device, such as the
computing system 700, for execution by the computing device. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules 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 described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage. - In the example of
FIG. 7 , themodules 710 may be configured for execution by theCPU 705 to perform any or all of the processes discussed above with reference toFIGS. 2-4 , the messaging shown inFIG. 5 , or to generate and/or present the interfaces shown inFIG. 6A orFIG. 6B . Depending on the embodiment, certain processes, or in the processes, or groups of processes discussed herein may be performed by multiple devices, such as multiple computing system similar tocomputing system 700. For example, depending on the embodiment, certain of the processes described herein may be performed by a computing system that identifies content, while other processes are performed an identification system that determines a user’s identity. - In the description, some implementations of the identity detection (e.g., Smart Lookup) features have been described with reference to providing personalized content. The identity detection features may be used in additional or alternative implementations where an efficient and accurate identification of a user based at least in part on a communication device associated with the user is desired. Such implementations may include test taking systems, personal account systems (e.g., banking, insurance, email, or loyalty programs), inquiry systems (e.g., credit inquiry, education record inquiry, government record inquiry), content prefilling services, monitoring services (e.g., credit monitoring), login/authentication services, or the like.
-
FIG. 8 is a block diagram showing example components of an identity confirmation device for identifying a user of a mobile device. Arequest processor 802 included in theidentity confirmation device 155 may receive an access request message from theuser device 102. The access request message may be received directly from theuser device 102 or from a service that is being accessed or used by theuser device 102. The access request message may include user information such as first name, last name, ZIP code or postcode, or an identifier that can be used to obtain the user information such from a service provider. Therequest processor 802 may present a machine interface such as an application programming interface or web service interface to receive messages from other services requesting identity confirmation and/or information. The interface may receive machine readable messages such as access request messages and return user information related thereto. - The
request processor 802 may retrieve digest data from a user data digestdata store 804. The digestdata store 804 may store a limited set of user information for user and an associated custom identifier (e.g., PIN) for the user. The digestdata store 804 may be a look up table indexed by the user information expected to be included in the access request. This provides a quick lookup of the custom identifier and other user information for the user. For example, the access request may include first name, last name, and postal code. The digestdata store 804 may include a record for a user with the same first name, last name, and postal code. The record may also include address information (e.g., street address), date of birth, and the custom identifier. - In some implementations, the digest
data store 804 may be generated based on information that does not include consideration of events that occurred since the last digest was generated. When dealing with hundreds of thousands of users who each may be associated with hundreds or thousands of events, it is not uncommon for a digest to lag behind current events by several days. Such events may impact identity decisioning or other downstream modeling based on user value inputs. - To mitigate the risk of stale or misidentification of a user, the digest data may be provided to a real-time
user data retrieval 806. The real-timeuser data retrieval 806 may receive all or a portion of the digest data retrieved for the access request. Using the digest data, the real-timeuser data retrieval 806 may initiate a separate request for user information from a real-timeuser data storage 808. The real-time nature of the real-timeuser data storage 808 indicates that the user information included in the real-timeuser data storage 808 is based on events as processed by the system rather than a digested snapshot from a past point in time. - The real-time data may include first name, last name, postal code, address information (e.g., street address), date of birth, and a custom identifier. An
identity reconciler 810 may compare the real-time data with the digest data to confirm the identity of the user. The confirmation may include comparing the custom identifiers (e.g., PINs) included in the real-time data and the digest data. - If the identifiers do not match or if there are multiple matches (e.g., several candidate users matching the digest data or several candidate users identified in the digest data), the
identity reconciler 810 may transmit a clarification request to aclarification collector 812. Theclarification collector 812 may be configured to identify what information is needed to identify a single user. For example, theclarification collector 812 may identify an identity data type that distinguishes all the candidate users. Theclarification collector 812 may then retrieve the clarification data. Retrieving the clarification data may include causing presentation of an interface on theuser device 102 to receive a user input including the clarification data. Retrieving the clarification data may include querying an additional service or data store for the clarification data. For example, the service that is to be accessed by theuser device 102 may store user profiles which may include additional information that can be used to clarify the identity of the user of theuser device 102. The received clarification data may be provided to theidentity reconciler 810 for further consideration and analysis to identify the user. - If these identifiers included in the real-time and digest data match or if the clarification data singles out one user, then the
identity reconciler 810 may confirm the identity of the user by transmitting identity data. The identity data may include transmitting additional user information such as address, date of birth, or other user information received from the real-timeuser data storage 808. In some implementations, the identity data may include a binary result indicating that the user is identified. This may be an efficient way to indicate to a service whether the user of the mobile device is authenticated and/or authorized to access the service. The identity information may then be used, in certain implementations, to automatically initiate additional processes based on the additional information regarding the now-authenticated user. For example, as discussed elsewhere herein, once the user’s identity is authenticated, a form fill process may be initiated using information pulled from one or more databases accessible by the content provisioning system, such as PII included in a credit report (e.g., full residence address, age, credit score, etc.) of the authenticated user. In this form fill example, the user information may be encoded and transmitted to the user device for auto population of a form (e.g., a house/apartment rental application, online account registration, car rental application, credit application, etc.), or in some embodiments may be transmitted directly to the offer provider. The user information may be included with the initial data indicating results of the identity verification process or may be included in a subsequent data transmission to the requesting entity. Advantageously, this identity validation process may be used by any third-party entity that desires identity determination and/or identity verification. - Each of the processes, methods, and algorithms described in the preceding sections may be embodied in, and fully or partially automated by, code modules executed by one or more computer systems or computer processors comprising computer hardware. The code modules may be stored on any type of non-transitory computer-readable medium or computer storage device, such as hard drives, solid state memory, optical disc, and/or the like. The systems and modules may also be transmitted as generated data signals (for example, as part of a carrier wave or other analog or digital propagated signal) on a variety of computer-readable transmission mediums, including wireless-based and wired/cable-based mediums, and may take a variety of forms (for example, as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames). The processes and algorithms may be implemented partially or wholly in application-specific circuitry. The results of the disclosed processes and process steps may be stored, persistently or otherwise, in any type of non-transitory computer storage such as, for example, volatile or non-volatile storage.
- The various features and processes described above may be used independently of one another, or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. In addition, certain method or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states relating thereto can be performed in other sequences that are appropriate. For example, described blocks or states may be performed in an order other than that specifically disclosed, or multiple blocks or states may be combined in a single block or state. The example blocks or states may be performed in serial, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The example systems and components described herein may be configured differently than described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.
- Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
- As used herein, the terms “determine” or “determining” encompass a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, generating, obtaining, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like via a hardware element without user intervention. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like via a hardware element without user intervention. Also, “determining” may include resolving, selecting, choosing, establishing, and the like via a hardware element without user intervention.
- As used herein, the terms “provide” or “providing” encompass a wide variety of actions. For example, “providing” may include storing a value in a location of a storage device for subsequent retrieval, transmitting a value directly to the recipient via at least one wired or wireless communication medium, transmitting or storing a reference to a value, and the like. “Providing” may also include encoding, decoding, encrypting, decrypting, validating, verifying, and the like via a hardware element.
- As used herein, the term “message” encompasses a wide variety of formats for communicating (e.g., transmitting or receiving) information. A message may include a machine readable aggregation of information such as an XML document, fixed field message, comma separated message, or the like. A message may, in some implementations, include a signal utilized to transmit one or more representations of the information. While recited in the singular, it will be understood that a message may be composed, transmitted, stored, received, etc. in multiple parts.
- As used herein, “receive” or “receiving” may include specific algorithms for obtaining information. For example, receiving may include transmitting a request message for the information. The request message may be transmitted via a network as described above. The request message may be transmitted according to one or more well-defined, machine readable standards which are known in the art. The request message may be stateful in which case the requesting device and the device to which the request was transmitted maintain a state between requests. The request message may be a stateless request in which case the state information for the request is contained within the messages exchanged between the requesting device and the device serving the request. One example of such state information includes a unique token that can be generated by either the requesting or serving device and included in messages exchanged. For example, the response message may include the state information to indicate what request message caused the serving device to transmit the response message.
- As used herein, “generate” or “generating” may include specific algorithms for creating information based on or using other input information. Generating may include retrieving the input information such as from memory or as provided input parameters to the hardware performing the generating. Once obtained, the generating may include combining the input information. The combination may be performed through specific circuitry configured to provide an output indicating the result of the generating. The combination may be dynamically performed such as through dynamic selection of execution paths based on, for example, the input information, device operational characteristics (e.g., hardware resources available, power level, power source, memory levels, network connectivity, bandwidth, and the like). Generating may also include storing the generated information in a memory location. The memory location may be identified as part of the request message that initiates the generating. In some implementations, the generating may return location information identifying where the generated information can be accessed. The location information may include a memory location, network locate, file system location, or the like.
- As used herein, “activate” or “activating” may refer to causing or triggering a mechanical, electronic, or electro-mechanical state change to a device. Activation of a device may cause the device, or a feature associated therewith, to change from a first state to a second state. In some implementations, activation may include changing a characteristic from a first state to a second state such as, for example, changing the viewing state of a lens of stereoscopic viewing glasses. Activating may include generating a control message indicating the desired state change and providing the control message to the device to cause the device to change state.
- Any process descriptions, elements, or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those skilled in the art.
- All of the methods and processes described above may be embodied in, and partially or fully automated via, software code modules executed by one or more general purpose computers. For example, the methods described herein may be performed by the computing system and/or any other suitable computing device. The methods may be executed on the computing devices in response to execution of software instructions or other executable code read from a tangible computer-readable medium. A tangible computer-readable medium is a data storage device that can store data that is readable by a computer system. Examples of computer-readable mediums include read-only memory, random-access memory, other volatile or non-volatile memory devices, CD-ROMs, magnetic tape, flash drives, and optical data storage devices.
- It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure. The foregoing description details certain embodiments. It will be appreciated, however, that no matter how detailed the foregoing appears in text, the systems and methods can be practiced in many ways. As is also stated above, it should be noted that the use of particular terminology when describing certain features or aspects of the systems and methods should not be taken to imply that the terminology is being re-defined herein to be restricted to including any specific characteristics of the features or aspects of the systems and methods with which that terminology is associated.
- These and other embodiments relating to the systems and methods disclosed herein are detailed in the Appendices included in the priority applications, the entirety of which are bodily incorporated herein and the entirety of which is also incorporated herein by reference. The Appendices provides only examples of certain systems and methods that may be performed in accordance with the discussion above and does not limit the scope of alternative uses and implementations of such systems and methods.
- Acronyms that may referenced in this application or an incorporated document include:
- PPOI - Person places of interest
- ID - identifier
- HL - home location or home locator
- SSN - social security number
- DaaS - data as a service
- DL - data labs
- FN / LN - first name / last name
- KBA - knowledge based authentication
- API - application programming interface
- SDK - software development kit
- EXPN - Experian
- GPS - global positioning system
- WiFi - IEEE 802.11 family (e.g., c, d, e, n, g) of standards based wireless networking
- PIN - personal identification number
- FI - financial institution
- UX - user experience
Claims (21)
1. (canceled)
2. A computer-implemented method of identification of a user of a mobile device, the method comprising:
under control of one or more computing devices configured with specific computer-executable instructions,
receiving first information identifying a user of the mobile device from a service associated with the mobile device;
querying, based on a fuzzy matching of at least a portion of the first information, a database which indexes credit header files of a plurality of users for a set of candidate users having the largest quantity of identity data values that matches user information from the credit header files;
determining a degree of difference between the identity data values of candidate users of the set of candidate users and the first information;
determining a degree of relatedness between the identity data values and the user information;
removing users from the set of candidate users based on the degree of difference and based on the degree of relatedness;
for each user included in the set of candidate users, identifying an identity data type associated with different identity data values that distinguish between the candidate users;
receiving an input identity value from the user; and
identifying the user in the set of candidate users based on the received input identity value.
3. The computer-implemented method of claim 2 further comprising determining that the identified user qualifies for an offer for a line of credit.
4. The computer-implemented method of claim 2 further comprising transmitting instructions to be received at the mobile device to request the input identity value of the identified identity data type from the user.
5. The computer-implemented method of claim 2 , wherein the service comprises at least one of an internet service, an application service, a cellular service, or a merchant service.
6. The computer-implemented method of claim 2 , wherein generating the metric indicating the degree of relatedness between the identity data values and the user information comprises:
generating a count of identity data types included in identity data having corresponding identity data types included in the user information; and
for an identity data type having a corresponding identity data type included in the user information, generating a comparison value indicating how closely the identity data value matches a user information value for the corresponding identity data type included in the user information, wherein the metric is based at least in part on the count and the comparison value.
7. The computer-implemented method of claim 2 , wherein generating the metric indicating the degree of relatedness between the identity data values and the user information comprises:
identifying a historical weight for the identity data value based on a difference between a current time and a time when identity data was collected; and
generating a comparison value indicating how closely the identity data value matches a user information value for a corresponding identity data type included in the user information, wherein the comparison value is weighted based at least in part on the historical weight.
8. The computer-implemented method of claim 2 , wherein the first information is included in a request for personalized content, and wherein the computer-implemented method further comprises transmitting at least a portion of the user information and at least one of the identity data values of the one candidate user to a personalized content service.
9. The computer-implemented method of claim 2 further comprising:
receiving second information identifying a second user of a second mobile device from a second service associated with the second mobile device;
requesting candidate user information from the database using the second information; and
in response to receiving no candidate user information results, transmitting instructions to the second mobile device to request input values to identify the second user.
10. The computer-implemented method of claim 2 further comprising:
causing, in response to receiving the first information, a consent request to be received at the mobile device; and
receiving a consent response in response to causing the consent request, wherein said querying is based in part on the consent response.
11. A system comprising:
a computer-readable memory storing executable instructions; and
one or more computer processors in communication with the computer-readable memory, wherein the one or more computer processors are configured to execute the executable instructions to at least:
receive first information identifying a user of a device from a service provider, the first information based on a device identifier associated with the device;
query, based on a fuzzy matching of at least a portion of the first information, a database which indexes credit header files of a plurality of users for a set of candidate users having the largest quantity of identity data values that matches user information from the credit header files;
determine a degree of difference between the identity data values of candidate users of the set of candidate users and the first information;
determine a degree of relatedness between the identity data values and the user information;
remove users from the set of candidate users based on the degree of difference and based on the degree of relatedness;
for each user included in the set of candidate users, identify an identity data type associated with different identity data values that distinguish between the candidate users;
receive an input identity value from the user; and
identify the user in the set of candidate users based on the received input identity value.
12. The system of claim 11 , wherein the one or more computer processors are further configured to execute the executable instructions to determine that the identified user qualifies for an offer for a line of credit.
13. The system of claim 11 , wherein the one or more computer processors are further configured to execute the executable instructions to transmit instructions to be received at the mobile device to request the input identity value of the identified identity data type from the user.
14. The system of claim 11 , wherein the one or more computer processors are further configured to execute the executable instructions to:
identify the specific candidate and a second candidate having metrics corresponding to the threshold;
identify a differentiation data field, wherein a value for the differentiation data field for the specific candidate is different from a value for the differentiation data field for the second candidate;
receive input from the device for the differentiation data field; and determine the input corresponds to the value of the differentiation data field for the specific candidate.
15. The system of claim 11 , wherein the one or more computer processors are further configured to execute the executable instructions to transmit at least a portion of the device identifier and at least one of the values of the specific candidate to a personalized content service.
16. The system of claim 11 , wherein identifying the set of candidates comprises querying a database storing information regarding a plurality of users, wherein the set of candidates include one or more users having the largest quantity of identity data values matching the first information.
17. Non-transitory, computer-readable storage media storing computer-executable instructions that, when executed by a computer system that comprises one or more hardware processors, configure the computer system to perform operations comprising:
receiving first information identifying a user of the mobile device from a service associated with the mobile device;
querying, based on a fuzzy matching of at least a portion of the first information, a database which indexes credit header files of a plurality of users for a set of candidate users having the largest quantity of identity data values that matches user information from the credit header files;
determining a degree of difference between the identity data values of candidate users of the set of candidate users and the first information;
determining a degree of relatedness between the identity data values and the user information;
removing users from the set of candidate users based on the degree of difference and based on the degree of relatedness;
for each user included in the set of candidate users, identifying an identity data type associated with different identity data values that distinguish between the candidate users;
receiving an input identity value from the user; and
identifying the user in the set of candidate users based on the received input identity value.
18. The non-transitory, computer-readable storage media of claim 17 , wherein the computer system is further configured to determine that the identified user qualifies for an offer for a line of credit.
19. The non-transitory, computer-readable storage media of claim 17 , wherein the computer system is further configured to transmit instructions to be received at the mobile device to request the input identity value of the identified identity data type from the user.
20. The non-transitory, computer-readable storage media of claim 17 , wherein the computer system is further configured to generate an individual metric for each of the candidates included in the set of candidates, wherein the individual metric indicates the degree of relatedness.
21. The non-transitory, computer-readable storage media of claim 17 , wherein the computer system is further configured to:
determine the set of candidates includes no candidate users;
in response to receiving no candidate user information results, cause instructions to be received at the mobile device to request input values;
generate a custom candidate user based on received input values; and
add the custom candidate user to the set of candidates.
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Families Citing this family (54)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8346593B2 (en) | 2004-06-30 | 2013-01-01 | Experian Marketing Solutions, Inc. | System, method, and software for prediction of attitudinal and message responsiveness |
WO2014176688A1 (en) * | 2013-04-29 | 2014-11-06 | Think Cards Intellectual Property Corp. | Systems and methods for onsite or remote dispensing of credit instruments |
US10102536B1 (en) | 2013-11-15 | 2018-10-16 | Experian Information Solutions, Inc. | Micro-geographic aggregation system |
US9576030B1 (en) | 2014-05-07 | 2017-02-21 | Consumerinfo.Com, Inc. | Keeping up with the joneses |
US11257117B1 (en) | 2014-06-25 | 2022-02-22 | Experian Information Solutions, Inc. | Mobile device sighting location analytics and profiling system |
US10242019B1 (en) | 2014-12-19 | 2019-03-26 | Experian Information Solutions, Inc. | User behavior segmentation using latent topic detection |
US9767309B1 (en) | 2015-11-23 | 2017-09-19 | Experian Information Solutions, Inc. | Access control system for implementing access restrictions of regulated database records while identifying and providing indicators of regulated database records matching validation criteria |
US10949850B1 (en) * | 2015-12-30 | 2021-03-16 | Wells Fargo Bank, N.A. | Systems and methods for using location services to detect fraud |
WO2018039377A1 (en) | 2016-08-24 | 2018-03-01 | Experian Information Solutions, Inc. | Disambiguation and authentication of device users |
US10484382B2 (en) | 2016-08-31 | 2019-11-19 | Oracle International Corporation | Data management for a multi-tenant identity cloud service |
US10594684B2 (en) * | 2016-09-14 | 2020-03-17 | Oracle International Corporation | Generating derived credentials for a multi-tenant identity cloud service |
US11423475B2 (en) * | 2016-09-27 | 2022-08-23 | Visa International Service Association | Distributed electronic record and transaction history |
US11159633B2 (en) * | 2016-09-30 | 2021-10-26 | International Business Machines Corporation | Validating push communications |
WO2018126177A1 (en) * | 2016-12-29 | 2018-07-05 | Wohlken Jay | Trusted mobile biometric enrollment |
JP6822484B2 (en) * | 2017-01-13 | 2021-01-27 | 日本電気株式会社 | Information processing equipment, information processing methods and programs |
JP6621776B2 (en) * | 2017-03-22 | 2019-12-18 | 株式会社東芝 | Verification system, verification method, and program |
US11625774B2 (en) * | 2017-08-07 | 2023-04-11 | Bread Financial Payments, Inc | Using position location information to pre-populate and verify information on a credit application |
US10831789B2 (en) | 2017-09-27 | 2020-11-10 | Oracle International Corporation | Reference attribute query processing for a multi-tenant cloud service |
US10992593B2 (en) * | 2017-10-06 | 2021-04-27 | Bank Of America Corporation | Persistent integration platform for multi-channel resource transfers |
US10803139B2 (en) * | 2017-10-27 | 2020-10-13 | Intuit Inc. | Instrument disambiguation to facilitate electronic data consolidation |
US11348116B2 (en) * | 2017-11-07 | 2022-05-31 | Mastercard International Incorporated | Systems and methods for enhancing online user authentication using a personal cloud platform |
US10757123B2 (en) | 2018-01-25 | 2020-08-25 | Bank Of America Corporation | Dynamic record identification and analysis computer system with event monitoring components |
US10715564B2 (en) | 2018-01-29 | 2020-07-14 | Oracle International Corporation | Dynamic client registration for an identity cloud service |
US11087237B2 (en) * | 2018-01-30 | 2021-08-10 | Walmart Apollo, Llc | Machine learning techniques for transmitting push notifications |
CN110619253B (en) * | 2018-06-19 | 2022-06-07 | 北京京东尚科信息技术有限公司 | Identity recognition method and device |
US20200027158A1 (en) * | 2018-07-17 | 2020-01-23 | Credit One Bank, N.A. | Distributed consumer authentication and credit offer location |
US11907993B1 (en) * | 2018-08-20 | 2024-02-20 | United Services Automobile Association (Usaa) | Value metric and comparison interface for payment cards |
US11468486B1 (en) * | 2018-09-25 | 2022-10-11 | Wells Fargo Bank, N.A. | Location based vehicle transactions |
US11792226B2 (en) | 2019-02-25 | 2023-10-17 | Oracle International Corporation | Automatic api document generation from scim metadata |
US11423111B2 (en) | 2019-02-25 | 2022-08-23 | Oracle International Corporation | Client API for rest based endpoints for a multi-tenant identify cloud service |
US11238105B2 (en) * | 2019-03-29 | 2022-02-01 | Salesforce.Com, Inc. | Correlating user device attribute groups |
CN110191460B (en) * | 2019-05-29 | 2021-11-19 | 中国联合网络通信集团有限公司 | New network access user monitoring method and platform |
US11694201B2 (en) * | 2019-06-10 | 2023-07-04 | Jpmorgan Chase Bank, N.A. | ATM intercommunication system and method for fraudulent and forced transactions |
CN110278329B (en) * | 2019-06-19 | 2021-12-10 | 维沃移动通信有限公司 | Notification message management method and mobile terminal |
US11176275B2 (en) * | 2019-07-08 | 2021-11-16 | International Business Machines Corporation | De-identifying source entity data |
US10963828B2 (en) * | 2019-07-19 | 2021-03-30 | Capital One Services, Llc | Identifying and managing enterprise product availability |
US10810528B1 (en) | 2019-07-19 | 2020-10-20 | Capital One Services, Llc | Identifying and utilizing the availability of enterprise resources |
US20210042398A1 (en) * | 2019-08-08 | 2021-02-11 | Pulsepoint, Inc. | Validation of Properties of a User Device in a Network |
US11403649B2 (en) | 2019-09-11 | 2022-08-02 | Toast, Inc. | Multichannel system for patron identification and dynamic ordering experience enhancement |
US11687378B2 (en) | 2019-09-13 | 2023-06-27 | Oracle International Corporation | Multi-tenant identity cloud service with on-premise authentication integration and bridge high availability |
US11870770B2 (en) | 2019-09-13 | 2024-01-09 | Oracle International Corporation | Multi-tenant identity cloud service with on-premise authentication integration |
CN112579984B (en) * | 2019-09-30 | 2024-03-15 | 广州艾美网络科技有限公司 | Multimedia information authentication method, system, computer device and storage medium |
US20210204116A1 (en) * | 2019-12-31 | 2021-07-01 | Payfone, Inc. | Identity verification platform |
US11682041B1 (en) | 2020-01-13 | 2023-06-20 | Experian Marketing Solutions, Llc | Systems and methods of a tracking analytics platform |
US11539752B2 (en) * | 2020-04-28 | 2022-12-27 | Bank Of America Corporation | Selective security regulation for network communication |
US20220035939A1 (en) * | 2020-08-03 | 2022-02-03 | Jpmorgan Chase Bank, N.A. | Method and system for dynamic data masking |
US11544343B1 (en) * | 2020-10-16 | 2023-01-03 | Splunk Inc. | Codeless anchor generation for detectable features in an environment |
US20220248168A1 (en) * | 2021-02-01 | 2022-08-04 | Incognia Tecnologia da Informação Ltda. | Systems and methods for using non-identifiable sensor information to validate user information |
US20220318425A1 (en) * | 2021-04-01 | 2022-10-06 | Ford Global Technologies, Llc | Occupant feature recognition to ensure privacy consent |
US11645419B2 (en) * | 2021-06-14 | 2023-05-09 | Volvo Car Corporation | Dynamic anonymization for automotive subscriptions |
US11861521B2 (en) * | 2021-12-21 | 2024-01-02 | PolyAI Limited | System and method for identification and verification |
US20230247097A1 (en) * | 2022-02-03 | 2023-08-03 | At&T Intellectual Property I, L.P. | Split input and output (io) for managing interactive sessions between users |
US20230260069A1 (en) * | 2022-02-14 | 2023-08-17 | Evernorth Strategic Development, Inc. | Methods and systems for verifying an individual's identity |
US20230359588A1 (en) * | 2022-05-04 | 2023-11-09 | Protiviti Inc. | Systems and methods for automated audit artifact reconciliation |
Family Cites Families (1144)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US3316395A (en) | 1963-05-23 | 1967-04-25 | Credit Corp Comp | Credit risk computer |
US4774664A (en) | 1985-07-01 | 1988-09-27 | Chrysler First Information Technologies Inc. | Financial data processing system and method |
US4775935A (en) | 1986-09-22 | 1988-10-04 | Westinghouse Electric Corp. | Video merchandising system with variable and adoptive product sequence presentation order |
US4827508A (en) | 1986-10-14 | 1989-05-02 | Personal Library Software, Inc. | Database usage metering and protection system and method |
US4935870A (en) | 1986-12-15 | 1990-06-19 | Keycom Electronic Publishing | Apparatus for downloading macro programs and executing a downloaded macro program responding to activation of a single key |
US4872113A (en) | 1987-08-27 | 1989-10-03 | Jbs Associates, Inc. | Credit check scanner data analysis system |
US4868570A (en) | 1988-01-15 | 1989-09-19 | Arthur D. Little, Inc. | Method and system for storing and retrieving compressed data |
CA1337132C (en) | 1988-07-15 | 1995-09-26 | Robert Filepp | Reception system for an interactive computer network and method of operation |
US5247575A (en) | 1988-08-16 | 1993-09-21 | Sprague Peter J | Information distribution system |
US4982346A (en) | 1988-12-16 | 1991-01-01 | Expertel Communications Incorporated | Mall promotion network apparatus and method |
US5201010A (en) | 1989-05-01 | 1993-04-06 | Credit Verification Corporation | Method and system for building a database and performing marketing based upon prior shopping history |
US5649114A (en) | 1989-05-01 | 1997-07-15 | Credit Verification Corporation | Method and system for selective incentive point-of-sale marketing in response to customer shopping histories |
US5560008A (en) | 1989-05-15 | 1996-09-24 | International Business Machines Corporation | Remote authentication and authorization in a distributed data processing system |
US5056019A (en) | 1989-08-29 | 1991-10-08 | Citicorp Pos Information Servies, Inc. | Automated purchase reward accounting system and method |
US5202986A (en) | 1989-09-28 | 1993-04-13 | Bull Hn Information Systems Inc. | Prefix search tree partial key branching |
US5276868A (en) | 1990-05-23 | 1994-01-04 | Digital Equipment Corp. | Method and apparatus for pointer compression in structured databases |
US5555409A (en) | 1990-12-04 | 1996-09-10 | Applied Technical Sysytem, Inc. | Data management systems and methods including creation of composite views of data |
US5274547A (en) | 1991-01-03 | 1993-12-28 | Credco Of Washington, Inc. | System for generating and transmitting credit reports |
US5325509A (en) | 1991-03-05 | 1994-06-28 | Zitel Corporation | Method of operating a cache memory including determining desirability of cache ahead or cache behind based on a number of available I/O operations |
DE9108341U1 (en) | 1991-07-04 | 1991-10-17 | Groenda, Juergen, O-2794 Schwerin, De | |
US6009415A (en) | 1991-12-16 | 1999-12-28 | The Harrison Company, Llc | Data processing technique for scoring bank customer relationships and awarding incentive rewards |
US5903454A (en) | 1991-12-23 | 1999-05-11 | Hoffberg; Linda Irene | Human-factored interface corporating adaptive pattern recognition based controller apparatus |
US5875108A (en) | 1991-12-23 | 1999-02-23 | Hoffberg; Steven M. | Ergonomic man-machine interface incorporating adaptive pattern recognition based control system |
US5283731A (en) | 1992-01-19 | 1994-02-01 | Ec Corporation | Computer-based classified ad system and method |
JPH05346915A (en) | 1992-01-30 | 1993-12-27 | Ricoh Co Ltd | Learning machine and neural network, and device and method for data analysis |
GB9204450D0 (en) | 1992-03-02 | 1992-04-15 | Ibm | Concurrent access to indexed data files |
US5305195A (en) | 1992-03-25 | 1994-04-19 | Gerald Singer | Interactive advertising system for on-line terminals |
EP0564669A1 (en) | 1992-04-04 | 1993-10-13 | Alcatel SEL Aktiengesellschaft | Network of voice and/or fax storage systems |
US5563783A (en) | 1992-05-13 | 1996-10-08 | The Trustees Of Columbia University In The City Of New York | Method and system for securities pool allocation |
US5446885A (en) | 1992-05-15 | 1995-08-29 | International Business Machines Corporation | Event driven management information system with rule-based applications structure stored in a relational database |
US5737732A (en) | 1992-07-06 | 1998-04-07 | 1St Desk Systems, Inc. | Enhanced metatree data structure for storage indexing and retrieval of information |
US5819226A (en) | 1992-09-08 | 1998-10-06 | Hnc Software Inc. | Fraud detection using predictive modeling |
US5341429A (en) | 1992-12-04 | 1994-08-23 | Testdrive Corporation | Transformation of ephemeral material |
US5982868A (en) | 1993-02-22 | 1999-11-09 | Murex Securities, Ltd. | Automatic routing and information system for telephonic services |
AU674189B2 (en) | 1993-02-23 | 1996-12-12 | Moore North America, Inc. | A method and system for gathering and analyzing customer and purchasing information |
US5640551A (en) | 1993-04-14 | 1997-06-17 | Apple Computer, Inc. | Efficient high speed trie search process |
US5560007A (en) | 1993-06-30 | 1996-09-24 | Borland International, Inc. | B-tree key-range bit map index optimization of database queries |
US5794207A (en) | 1996-09-04 | 1998-08-11 | Walker Asset Management Limited Partnership | Method and apparatus for a cryptographically assisted commercial network system designed to facilitate buyer-driven conditional purchase offers |
US5751915A (en) | 1993-07-13 | 1998-05-12 | Werbos; Paul J. | Elastic fuzzy logic system |
ATE202864T1 (en) | 1993-08-27 | 2001-07-15 | Affinity Technology Inc | CLOSED LOOP FINANCIAL TRANSACTION METHOD AND DEVICE |
US5930776A (en) | 1993-11-01 | 1999-07-27 | The Golden 1 Credit Union | Lender direct credit evaluation and loan processing system |
US5436965A (en) | 1993-11-16 | 1995-07-25 | Automated Systems And Programming, Inc. | Method and system for optimization of telephone contact campaigns |
US5881131A (en) | 1993-11-16 | 1999-03-09 | Bell Atlantic Network Services, Inc. | Analysis and validation system for provisioning network related facilities |
EP0734556B1 (en) | 1993-12-16 | 2002-09-04 | Open Market, Inc. | Network based payment system and method for using such system |
US5627973A (en) | 1994-03-14 | 1997-05-06 | Moore Business Forms, Inc. | Method and apparatus for facilitating evaluation of business opportunities for supplying goods and/or services to potential customers |
US5584024A (en) | 1994-03-24 | 1996-12-10 | Software Ag | Interactive database query system and method for prohibiting the selection of semantically incorrect query parameters |
US6513018B1 (en) | 1994-05-05 | 2003-01-28 | Fair, Isaac And Company, Inc. | Method and apparatus for scoring the likelihood of a desired performance result |
JP2683870B2 (en) | 1994-05-23 | 1997-12-03 | 日本アイ・ビー・エム株式会社 | Character string search system and method |
US5528701A (en) | 1994-09-02 | 1996-06-18 | Panasonic Technologies, Inc. | Trie based method for indexing handwritten databases |
ATE210856T1 (en) | 1994-06-06 | 2001-12-15 | Nokia Networks Oy | METHOD FOR STORING AND FINDING DATA AND A STORAGE ARRANGEMENT |
US5873068A (en) | 1994-06-14 | 1999-02-16 | New North Media Inc. | Display based marketing message control system and method |
US5459306A (en) | 1994-06-15 | 1995-10-17 | Blockbuster Entertainment Corporation | Method and system for delivering on demand, individually targeted promotions |
WO1996000945A1 (en) | 1994-06-30 | 1996-01-11 | International Business Machines Corp. | Variable length data sequence matching method and apparatus |
US5577239A (en) | 1994-08-10 | 1996-11-19 | Moore; Jeffrey | Chemical structure storage, searching and retrieval system |
GB9416673D0 (en) | 1994-08-17 | 1994-10-12 | Reuters Ltd | Data exchange filtering system |
JP2776301B2 (en) | 1994-08-30 | 1998-07-16 | 日本電気株式会社 | Line reservation apparatus and method, line reservation receiving apparatus and method |
US5768423A (en) | 1994-09-02 | 1998-06-16 | Panasonic Technologies Inc. | Trie structure based method and apparatus for indexing and searching handwritten databases with dynamic search sequencing |
US5515098A (en) | 1994-09-08 | 1996-05-07 | Carles; John B. | System and method for selectively distributing commercial messages over a communications network |
WO1996010795A1 (en) | 1994-10-03 | 1996-04-11 | Helfgott & Karas, P.C. | A database accessing system |
US5717923A (en) | 1994-11-03 | 1998-02-10 | Intel Corporation | Method and apparatus for dynamically customizing electronic information to individual end users |
US5724521A (en) | 1994-11-03 | 1998-03-03 | Intel Corporation | Method and apparatus for providing electronic advertisements to end users in a consumer best-fit pricing manner |
JPH10508964A (en) | 1994-11-08 | 1998-09-02 | バーミア、テクノロジーズ、インコーポレーテッド | Online service development tool with pricing function |
US6460036B1 (en) | 1994-11-29 | 2002-10-01 | Pinpoint Incorporated | System and method for providing customized electronic newspapers and target advertisements |
US5758257A (en) | 1994-11-29 | 1998-05-26 | Herz; Frederick | System and method for scheduling broadcast of and access to video programs and other data using customer profiles |
US5504675A (en) | 1994-12-22 | 1996-04-02 | International Business Machines Corporation | Method and apparatus for automatic selection and presentation of sales promotion programs |
US5774868A (en) | 1994-12-23 | 1998-06-30 | International Business And Machines Corporation | Automatic sales promotion selection system and method |
US5835915A (en) | 1995-01-24 | 1998-11-10 | Tandem Computer | Remote duplicate database facility with improved throughput and fault tolerance |
US6209033B1 (en) | 1995-02-01 | 2001-03-27 | Cabletron Systems, Inc. | Apparatus and method for network capacity evaluation and planning |
US6058378A (en) | 1995-02-22 | 2000-05-02 | Citibank, N.A. | Electronic delivery system and method for integrating global financial services |
US5696907A (en) | 1995-02-27 | 1997-12-09 | General Electric Company | System and method for performing risk and credit analysis of financial service applications |
US5553145A (en) | 1995-03-21 | 1996-09-03 | Micali; Silvia | Simultaneous electronic transactions with visible trusted parties |
AU5538196A (en) | 1995-04-13 | 1996-10-30 | Helfgott & Karas, P.C. | Sales promotion data processor system and interactive change able display particularly useful therein |
US6601048B1 (en) | 1997-09-12 | 2003-07-29 | Mci Communications Corporation | System and method for detecting and managing fraud |
US5926800A (en) | 1995-04-24 | 1999-07-20 | Minerva, L.P. | System and method for providing a line of credit secured by an assignment of a life insurance policy |
US5696898A (en) | 1995-06-06 | 1997-12-09 | Lucent Technologies Inc. | System and method for database access control |
AU694367B2 (en) | 1995-06-07 | 1998-07-16 | Soverain Software Llc | Internet server access control and monitoring systems |
US5740549A (en) | 1995-06-12 | 1998-04-14 | Pointcast, Inc. | Information and advertising distribution system and method |
US5659731A (en) | 1995-06-19 | 1997-08-19 | Dun & Bradstreet, Inc. | Method for rating a match for a given entity found in a list of entities |
US5689565A (en) | 1995-06-29 | 1997-11-18 | Microsoft Corporation | Cryptography system and method for providing cryptographic services for a computer application |
US5583380A (en) | 1995-07-11 | 1996-12-10 | Atmel Corporation | Integrated circuit contacts with secured stringers |
US6026368A (en) | 1995-07-17 | 2000-02-15 | 24/7 Media, Inc. | On-line interactive system and method for providing content and advertising information to a targeted set of viewers |
US5857175A (en) | 1995-08-11 | 1999-01-05 | Micro Enhancement International | System and method for offering targeted discounts to customers |
US5774692A (en) | 1995-10-05 | 1998-06-30 | International Business Machines Corporation | Outer quantifiers in object-oriented queries and views of database systems |
US5797136A (en) | 1995-10-05 | 1998-08-18 | International Business Machines Corporation | Optional quantifiers in relational and object-oriented views of database systems |
US5966695A (en) | 1995-10-17 | 1999-10-12 | Citibank, N.A. | Sales and marketing support system using a graphical query prospect database |
EP0770967A3 (en) | 1995-10-26 | 1998-12-30 | Koninklijke Philips Electronics N.V. | Decision support system for the management of an agile supply chain |
JP3152871B2 (en) | 1995-11-10 | 2001-04-03 | 富士通株式会社 | Dictionary search apparatus and method for performing a search using a lattice as a key |
US5774870A (en) | 1995-12-14 | 1998-06-30 | Netcentives, Inc. | Fully integrated, on-line interactive frequency and award redemption program |
US5970469A (en) | 1995-12-26 | 1999-10-19 | Supermarkets Online, Inc. | System and method for providing shopping aids and incentives to customers through a computer network |
US5822410A (en) | 1996-01-11 | 1998-10-13 | Gte Telecom Services Inc | Churn amelioration system and method therefor |
US5907830A (en) | 1996-02-13 | 1999-05-25 | Engel; Peter | Electronic coupon distribution |
US5745654A (en) | 1996-02-13 | 1998-04-28 | Hnc Software, Inc. | Fast explanations of scored observations |
US5884287A (en) | 1996-04-12 | 1999-03-16 | Lfg, Inc. | System and method for generating and displaying risk and return in an investment portfolio |
US5828837A (en) | 1996-04-15 | 1998-10-27 | Digilog As | Computer network system and method for efficient information transfer |
US6014645A (en) | 1996-04-19 | 2000-01-11 | Block Financial Corporation | Real-time financial card application system |
US5848396A (en) | 1996-04-26 | 1998-12-08 | Freedom Of Information, Inc. | Method and apparatus for determining behavioral profile of a computer user |
US5995922A (en) | 1996-05-02 | 1999-11-30 | Microsoft Corporation | Identifying information related to an input word in an electronic dictionary |
US5739512A (en) | 1996-05-30 | 1998-04-14 | Sun Microsystems, Inc. | Digital delivery of receipts |
US5987434A (en) | 1996-06-10 | 1999-11-16 | Libman; Richard Marc | Apparatus and method for transacting marketing and sales of financial products |
US7774230B2 (en) | 1996-06-10 | 2010-08-10 | Phoenix Licensing, Llc | System, method, and computer program product for selecting and presenting financial products and services |
US6999938B1 (en) | 1996-06-10 | 2006-02-14 | Libman Richard M | Automated reply generation direct marketing system |
US5864822A (en) | 1996-06-25 | 1999-01-26 | Baker, Iii; Bernard R. | Benefits tracking and correlation system for use with third-party enabling organization |
US5825884A (en) | 1996-07-01 | 1998-10-20 | Thomson Consumer Electronics | Method and apparatus for operating a transactional server in a proprietary database environment |
US7146327B1 (en) | 1996-07-01 | 2006-12-05 | Electronic Data Systems Corporation | Electronic publication distribution method and system |
US6070147A (en) | 1996-07-02 | 2000-05-30 | Tecmark Services, Inc. | Customer identification and marketing analysis systems |
US5944790A (en) | 1996-07-19 | 1999-08-31 | Lucent Technologies Inc. | Method and apparatus for providing a web site having a home page that automatically adapts to user language and customs |
US5956693A (en) | 1996-07-19 | 1999-09-21 | Geerlings; Huib | Computer system for merchant communication to customers |
US5861827A (en) | 1996-07-24 | 1999-01-19 | Unisys Corporation | Data compression and decompression system with immediate dictionary updating interleaved with string search |
US5933811A (en) | 1996-08-20 | 1999-08-03 | Paul D. Angles | System and method for delivering customized advertisements within interactive communication systems |
US6073241A (en) | 1996-08-29 | 2000-06-06 | C/Net, Inc. | Apparatus and method for tracking world wide web browser requests across distinct domains using persistent client-side state |
US5915243A (en) | 1996-08-29 | 1999-06-22 | Smolen; Daniel T. | Method and apparatus for delivering consumer promotions |
US5974572A (en) | 1996-10-15 | 1999-10-26 | Mercury Interactive Corporation | Software system and methods for generating a load test using a server access log |
US5948061A (en) | 1996-10-29 | 1999-09-07 | Double Click, Inc. | Method of delivery, targeting, and measuring advertising over networks |
US8225003B2 (en) | 1996-11-29 | 2012-07-17 | Ellis Iii Frampton E | Computers and microchips with a portion protected by an internal hardware firewall |
US5950179A (en) | 1996-12-03 | 1999-09-07 | Providian Financial Corporation | Method and system for issuing a secured credit card |
US5822751A (en) | 1996-12-16 | 1998-10-13 | Microsoft Corporation | Efficient multidimensional data aggregation operator implementation |
JPH10261009A (en) | 1996-12-20 | 1998-09-29 | Ryoichi Ino | Method and device for processing assessment of used car |
US5889958A (en) | 1996-12-20 | 1999-03-30 | Livingston Enterprises, Inc. | Network access control system and process |
US6983478B1 (en) | 2000-02-01 | 2006-01-03 | Bellsouth Intellectual Property Corporation | Method and system for tracking network use |
US8640160B2 (en) | 1997-01-06 | 2014-01-28 | At&T Intellectual Property I, L.P. | Method and system for providing targeted advertisements |
EP0965192B1 (en) | 1997-01-06 | 2007-02-28 | Bellsouth Intellectual Property Corporation | Method and system for tracking network use |
JPH10222559A (en) | 1997-02-10 | 1998-08-21 | Ryoichi Ino | Used car trade-in estimation assessment processing method and used car trade-in estimation assessment processor |
JPH10293732A (en) | 1997-02-20 | 1998-11-04 | Just Syst Corp | Electronic mail server device, electronic mail system, electronic mail opening confirming method, and computer readable recording medium recorded with program for executing the method by computer |
US6178442B1 (en) | 1997-02-20 | 2001-01-23 | Justsystem Corp. | Electronic mail system and electronic mail access acknowledging method |
US5903888A (en) | 1997-02-28 | 1999-05-11 | Oracle Corporation | Method and apparatus for using incompatible types of indexes to process a single query |
FI102426B1 (en) | 1997-03-14 | 1998-11-30 | Nokia Telecommunications Oy | Method for implementing memory |
FI102424B (en) | 1997-03-14 | 1998-11-30 | Nokia Telecommunications Oy | Method for implementing memory |
FI102425B (en) | 1997-03-14 | 1998-11-30 | Nokia Telecommunications Oy | Procedure for memory formation |
US5987606A (en) | 1997-03-19 | 1999-11-16 | Bascom Global Internet Services, Inc. | Method and system for content filtering information retrieved from an internet computer network |
US6182060B1 (en) | 1997-04-15 | 2001-01-30 | Robert Hedgcock | Method and apparatus for storing, retrieving, and processing multi-dimensional customer-oriented data sets |
US6014688A (en) | 1997-04-25 | 2000-01-11 | Postx Corporation | E-mail program capable of transmitting, opening and presenting a container having digital content using embedded executable software |
US5963932A (en) | 1997-04-29 | 1999-10-05 | Oracle Corporation | Method and apparatus for transforming queries |
US7917259B2 (en) | 1997-05-16 | 2011-03-29 | Snap-On Technologies, Inc. | Distributed vehicle service method and system |
US6018723A (en) | 1997-05-27 | 2000-01-25 | Visa International Service Association | Method and apparatus for pattern generation |
US6119103A (en) | 1997-05-27 | 2000-09-12 | Visa International Service Association | Financial risk prediction systems and methods therefor |
US6523022B1 (en) | 1997-06-09 | 2003-02-18 | Allen Hobbs | Method and apparatus for selectively augmenting retrieved information from a network resource |
US5991740A (en) | 1997-06-10 | 1999-11-23 | Messer; Stephen Dale | Data processing system for integrated tracking and management of commerce related activities on a public access network |
US20030040962A1 (en) | 1997-06-12 | 2003-02-27 | Lewis William H. | System and data management and on-demand rental and purchase of digital data products |
US6144948A (en) | 1997-06-23 | 2000-11-07 | Walker Digital, Llc | Instant credit card marketing system for reservations for future services |
US5905985A (en) | 1997-06-30 | 1999-05-18 | International Business Machines Corporation | Relational database modifications based on multi-dimensional database modifications |
US5822750A (en) | 1997-06-30 | 1998-10-13 | International Business Machines Corporation | Optimization of correlated SQL queries in a relational database management system |
EP0998712A4 (en) | 1997-07-16 | 2005-04-20 | Paul Michael O'connor | Method and system for compiling demographic data |
US7403922B1 (en) | 1997-07-28 | 2008-07-22 | Cybersource Corporation | Method and apparatus for evaluating fraud risk in an electronic commerce transaction |
US6766327B2 (en) | 1997-07-29 | 2004-07-20 | Acxiom Corporation | Data linking system and method using encoded links |
US6523041B1 (en) | 1997-07-29 | 2003-02-18 | Acxiom Corporation | Data linking system and method using tokens |
US6073140A (en) | 1997-07-29 | 2000-06-06 | Acxiom Corporation | Method and system for the creation, enhancement and update of remote data using persistent keys |
US7376603B1 (en) | 1997-08-19 | 2008-05-20 | Fair Isaac Corporation | Method and system for evaluating customers of a financial institution using customer relationship value tags |
JP3003640B2 (en) | 1997-08-20 | 2000-01-31 | 村田機械株式会社 | Communication terminal device with e-mail function and recording medium |
DE19743266C1 (en) | 1997-09-30 | 1999-03-11 | Siemens Ag | Address management method in binary search tree |
DE19743267C1 (en) | 1997-09-30 | 1998-12-03 | Siemens Ag | Address localization in partially occupied, unbalanced binary tree |
US6304860B1 (en) | 1997-10-03 | 2001-10-16 | Joseph B. Martin, Jr. | Automated debt payment system and method using ATM network |
US6421653B1 (en) | 1997-10-14 | 2002-07-16 | Blackbird Holdings, Inc. | Systems, methods and computer program products for electronic trading of financial instruments |
US6128602A (en) | 1997-10-27 | 2000-10-03 | Bank Of America Corporation | Open-architecture system for real-time consolidation of information from multiple financial systems |
US6925441B1 (en) | 1997-10-27 | 2005-08-02 | Marketswitch Corp. | System and method of targeted marketing |
US6128624A (en) | 1997-11-12 | 2000-10-03 | Ncr Corporation | Collection and integration of internet and electronic commerce data in a database during web browsing |
US6151601A (en) | 1997-11-12 | 2000-11-21 | Ncr Corporation | Computer architecture and method for collecting, analyzing and/or transforming internet and/or electronic commerce data for storage into a data storage area |
US5978780A (en) | 1997-11-21 | 1999-11-02 | Craig Michael Watson | Integrated bill consolidation, payment aggregation, and settlement system |
GB9725347D0 (en) | 1997-11-28 | 1998-01-28 | Ncr Int Inc | Database relationship analysis and strategy implementation tool |
US20020169664A1 (en) | 1997-12-01 | 2002-11-14 | Walker Jay S. | System for providing offers using a billing statement |
US20010014868A1 (en) | 1997-12-05 | 2001-08-16 | Frederick Herz | System for the automatic determination of customized prices and promotions |
AU1711399A (en) | 1997-12-19 | 1999-07-12 | Branddirect Marketing, Inc. | Method and apparatus for targeting offers to consumers |
AU1907899A (en) | 1997-12-22 | 1999-07-12 | Accepted Marketing, Inc. | E-mail filter and method thereof |
US6192165B1 (en) | 1997-12-30 | 2001-02-20 | Imagetag, Inc. | Apparatus and method for digital filing |
US5999932A (en) | 1998-01-13 | 1999-12-07 | Bright Light Technologies, Inc. | System and method for filtering unsolicited electronic mail messages using data matching and heuristic processing |
TR200002119T2 (en) | 1998-01-22 | 2000-12-21 | Ori Software Development Ltd. | Database device. |
US6202053B1 (en) | 1998-01-23 | 2001-03-13 | First Usa Bank, Na | Method and apparatus for generating segmentation scorecards for evaluating credit risk of bank card applicants |
US6029139A (en) | 1998-01-28 | 2000-02-22 | Ncr Corporation | Method and apparatus for optimizing promotional sale of products based upon historical data |
US6098052A (en) | 1998-02-10 | 2000-08-01 | First Usa Bank, N.A. | Credit card collection strategy model |
US6623529B1 (en) | 1998-02-23 | 2003-09-23 | David Lakritz | Multilingual electronic document translation, management, and delivery system |
US7185355B1 (en) | 1998-03-04 | 2007-02-27 | United Video Properties, Inc. | Program guide system with preference profiles |
US6055513A (en) | 1998-03-11 | 2000-04-25 | Telebuyer, Llc | Methods and apparatus for intelligent selection of goods and services in telephonic and electronic commerce |
US20020055906A1 (en) | 1998-03-11 | 2002-05-09 | Katz Ronald A. | Methods and apparatus for intelligent selection of goods and services in telephonic and electronic commerce |
US6263337B1 (en) | 1998-03-17 | 2001-07-17 | Microsoft Corporation | Scalable system for expectation maximization clustering of large databases |
US6289318B1 (en) | 1998-03-24 | 2001-09-11 | Timothy P. Barber | Method and architecture for multi-level commissioned advertising on a computer network |
US6064990A (en) | 1998-03-31 | 2000-05-16 | International Business Machines Corporation | System for electronic notification of account activity |
US6078892A (en) | 1998-04-09 | 2000-06-20 | International Business Machines Corporation | Method for customer lead selection and optimization |
US6064973A (en) | 1998-04-17 | 2000-05-16 | Andersen Consulting Llp | Context manager and method for a virtual sales and service center |
US6070142A (en) | 1998-04-17 | 2000-05-30 | Andersen Consulting Llp | Virtual customer sales and service center and method |
US6115693A (en) | 1998-04-17 | 2000-09-05 | Andersen Consulting Llp | Quality center and method for a virtual sales and service center |
US6101486A (en) | 1998-04-20 | 2000-08-08 | Nortel Networks Corporation | System and method for retrieving customer information at a transaction center |
US7580856B1 (en) | 1998-04-27 | 2009-08-25 | Robert K. Pliha | Systems and methods for distributing targeted incentives to financial institution customers |
US6044357A (en) | 1998-05-05 | 2000-03-28 | International Business Machines Corporation | Modeling a multifunctional firm operating in a competitive market with multiple brands |
FI981028A (en) | 1998-05-08 | 1999-11-09 | Ericsson Telefon Ab L M | Procedure and apparatus for access to service providers |
US6385594B1 (en) | 1998-05-08 | 2002-05-07 | Lendingtree, Inc. | Method and computer network for co-ordinating a loan over the internet |
US6061658A (en) | 1998-05-14 | 2000-05-09 | International Business Machines Corporation | Prospective customer selection using customer and market reference data |
US6212522B1 (en) | 1998-05-15 | 2001-04-03 | International Business Machines Corporation | Searching and conditionally serving bookmark sets based on keywords |
US6505176B2 (en) | 1998-06-12 | 2003-01-07 | First American Credit Management Solutions, Inc. | Workflow management system for an automated credit application system |
US6698020B1 (en) | 1998-06-15 | 2004-02-24 | Webtv Networks, Inc. | Techniques for intelligent video ad insertion |
US6154729A (en) | 1998-06-19 | 2000-11-28 | First Data Corporation | Method of reporting merchant information to banks |
US6161130A (en) | 1998-06-23 | 2000-12-12 | Microsoft Corporation | Technique which utilizes a probabilistic classifier to detect "junk" e-mail by automatically updating a training and re-training the classifier based on the updated training set |
US5912839A (en) | 1998-06-23 | 1999-06-15 | Energy Conversion Devices, Inc. | Universal memory element and method of programming same |
US6731612B1 (en) | 1998-06-29 | 2004-05-04 | Microsoft Corporation | Location-based web browsing |
US6144958A (en) | 1998-07-15 | 2000-11-07 | Amazon.Com, Inc. | System and method for correcting spelling errors in search queries |
EP0977128A1 (en) | 1998-07-28 | 2000-02-02 | Matsushita Electric Industrial Co., Ltd. | Method and system for storage and retrieval of multimedia objects by decomposing a tree-structure into a directed graph |
US6223171B1 (en) | 1998-08-25 | 2001-04-24 | Microsoft Corporation | What-if index analysis utility for database systems |
AU5898099A (en) | 1998-08-25 | 2000-03-14 | Accompany Inc. | On-line marketing system and method |
US6397197B1 (en) | 1998-08-26 | 2002-05-28 | E-Lynxx Corporation | Apparatus and method for obtaining lowest bid from information product vendors |
WO2000013122A1 (en) | 1998-08-27 | 2000-03-09 | Upshot Corporation | A method and apparatus for network-based sales force management |
US6253187B1 (en) | 1998-08-31 | 2001-06-26 | Maxagrid International, Inc. | Integrated inventory management system |
GB2343763B (en) | 1998-09-04 | 2003-05-21 | Shell Services Internat Ltd | Data processing system |
US6339769B1 (en) | 1998-09-14 | 2002-01-15 | International Business Machines Corporation | Query optimization by transparently altering properties of relational tables using materialized views |
US6266649B1 (en) | 1998-09-18 | 2001-07-24 | Amazon.Com, Inc. | Collaborative recommendations using item-to-item similarity mappings |
US7236950B2 (en) | 1998-10-29 | 2007-06-26 | Universal Card Services Corp. | Method and system of combined billing of multiple accounts on a single statement |
US6442577B1 (en) | 1998-11-03 | 2002-08-27 | Front Porch, Inc. | Method and apparatus for dynamically forming customized web pages for web sites |
US6405181B2 (en) | 1998-11-03 | 2002-06-11 | Nextcard, Inc. | Method and apparatus for real time on line credit approval |
AU1612500A (en) | 1998-11-09 | 2000-05-29 | E-Fin, Llc | Computer-driven information management system for selectively matching credit applicants with money lenders through a global communications network |
US20010014878A1 (en) | 1998-11-09 | 2001-08-16 | Nilotpal Mitra | Transaction method and apparatus |
US6263334B1 (en) | 1998-11-11 | 2001-07-17 | Microsoft Corporation | Density-based indexing method for efficient execution of high dimensional nearest-neighbor queries on large databases |
BR9916143A (en) | 1998-11-30 | 2001-11-06 | Index Systems Inc | Intelligent agent based on habits, statistical inference and psychodemographic profile |
US7185353B2 (en) | 2000-08-31 | 2007-02-27 | Prime Research Alliance E., Inc. | System and method for delivering statistically scheduled advertisements |
US7150030B1 (en) | 1998-12-03 | 2006-12-12 | Prime Research Alliance, Inc. | Subscriber characterization system |
US7260823B2 (en) | 2001-01-11 | 2007-08-21 | Prime Research Alliance E., Inc. | Profiling and identification of television viewers |
US20020123928A1 (en) | 2001-01-11 | 2002-09-05 | Eldering Charles A. | Targeting ads to subscribers based on privacy-protected subscriber profiles |
US20020083445A1 (en) | 2000-08-31 | 2002-06-27 | Flickinger Gregory C. | Delivering targeted advertisements to the set-top-box |
US6317752B1 (en) | 1998-12-09 | 2001-11-13 | Unica Technologies, Inc. | Version testing in database mining |
US6532450B1 (en) | 1998-12-09 | 2003-03-11 | American Management Systems, Inc. | Financial management system including an offset payment process |
US6308210B1 (en) | 1998-12-10 | 2001-10-23 | International Business Machines Corporation | Method and apparatus for traffic control and balancing for an internet site |
US6412012B1 (en) | 1998-12-23 | 2002-06-25 | Net Perceptions, Inc. | System, method, and article of manufacture for making a compatibility-aware recommendations to a user |
US6496819B1 (en) | 1998-12-28 | 2002-12-17 | Oracle Corporation | Rewriting a query in terms of a summary based on functional dependencies and join backs, and based on join derivability |
US6055573A (en) | 1998-12-30 | 2000-04-25 | Supermarkets Online, Inc. | Communicating with a computer based on an updated purchase behavior classification of a particular consumer |
US6233566B1 (en) | 1998-12-31 | 2001-05-15 | Ultraprise Corporation | System, method and computer program product for online financial products trading |
IL127889A0 (en) | 1998-12-31 | 1999-10-28 | Almondnet Ltd | A method for transacting an advertisement transfer |
US6236977B1 (en) | 1999-01-04 | 2001-05-22 | Realty One, Inc. | Computer implemented marketing system |
US6985882B1 (en) | 1999-02-05 | 2006-01-10 | Directrep, Llc | Method and system for selling and purchasing media advertising over a distributed communication network |
EP1028401A3 (en) | 1999-02-12 | 2003-06-25 | Citibank, N.A. | Method and system for performing a bankcard transaction |
US7571139B1 (en) | 1999-02-19 | 2009-08-04 | Giordano Joseph A | System and method for processing financial transactions |
US6334110B1 (en) | 1999-03-10 | 2001-12-25 | Ncr Corporation | System and method for analyzing customer transactions and interactions |
US7117172B1 (en) | 1999-03-11 | 2006-10-03 | Corecard Software, Inc. | Methods and systems for managing financial accounts |
CA2403245A1 (en) | 1999-03-15 | 2000-09-21 | Marketswitch Corporation | Integral criterion for model training and method of application to targeted marketing optimization |
CA2403249A1 (en) | 1999-03-15 | 2000-09-21 | Marketswitch Corporation | Gradient criterion method for neural networks and application to targeted marketing |
US6631356B1 (en) | 1999-03-15 | 2003-10-07 | Vulcan Portals, Inc. | Demand aggregation through online buying groups |
WO2000055778A1 (en) | 1999-03-16 | 2000-09-21 | Rafael Carey A De | Digital-timeshare-exchange |
US6631496B1 (en) | 1999-03-22 | 2003-10-07 | Nec Corporation | System for personalizing, organizing and managing web information |
US20050192008A1 (en) | 1999-03-31 | 2005-09-01 | Nimesh Desai | System and method for selective information exchange |
US6578078B1 (en) | 1999-04-02 | 2003-06-10 | Microsoft Corporation | Method for preserving referential integrity within web sites |
US7685311B2 (en) | 1999-05-03 | 2010-03-23 | Digital Envoy, Inc. | Geo-intelligent traffic reporter |
US6757740B1 (en) | 1999-05-03 | 2004-06-29 | Digital Envoy, Inc. | Systems and methods for determining collecting and using geographic locations of internet users |
US6430539B1 (en) | 1999-05-06 | 2002-08-06 | Hnc Software | Predictive modeling of consumer financial behavior |
IL142473A0 (en) | 1999-05-06 | 2002-03-10 | Sharinga Networks Inc | A communications network access method and system |
US6901383B1 (en) | 1999-05-20 | 2005-05-31 | Ameritrade Holding Corporation | Stock purchase indices |
JP2003500751A (en) | 1999-05-21 | 2003-01-07 | マーケットソフト ソフトウェア コーポレイション | Customer lead management system |
US8620740B2 (en) | 1999-05-21 | 2013-12-31 | International Business Machines Corporation | Offer delivery system |
US8533038B2 (en) | 1999-05-21 | 2013-09-10 | International Business Machines Corporation | Offer delivery system |
JP3313084B2 (en) | 1999-05-24 | 2002-08-12 | 和男 原田 | USED CAR INFORMATION MANAGEMENT DEVICE, USED VEHICLE ASSESSED PRICE CALCULATING METHOD, AND COMPUTER-READABLE RECORDING MEDIUM CONTAINING CONTROL PROGRAM FOR USED VEHICLE INFORMATION MANAGEMENT |
US6721713B1 (en) | 1999-05-27 | 2004-04-13 | Andersen Consulting Llp | Business alliance identification in a web architecture framework |
US6748369B2 (en) | 1999-06-21 | 2004-06-08 | General Electric Company | Method and system for automated property valuation |
US20040153509A1 (en) | 1999-06-30 | 2004-08-05 | Alcorn Robert L. | Internet-based education support system, method and medium with modular text-editing component for use in a web-based application |
US6615247B1 (en) | 1999-07-01 | 2003-09-02 | Micron Technology, Inc. | System and method for customizing requested web page based on information such as previous location visited by customer and search term used by customer |
WO2001010090A1 (en) | 1999-07-28 | 2001-02-08 | Tomkow Terrance A | System and method for verifying delivery and integrity of electronic messages |
US6993493B1 (en) | 1999-08-06 | 2006-01-31 | Marketswitch Corporation | Method for optimizing net present value of a cross-selling marketing campaign |
EP1212717A2 (en) | 1999-08-06 | 2002-06-12 | Marketswitch Corporation | Method for optimizing net present value of a cross-selling marketing campaign |
US6505168B1 (en) | 1999-08-16 | 2003-01-07 | First Usa Bank, Na | System and method for gathering and standardizing customer purchase information for target marketing |
EP1077419A3 (en) | 1999-08-17 | 2004-04-14 | Citibank, N.A. | System and method for use of distributed electronic wallets |
US7092898B1 (en) | 1999-09-01 | 2006-08-15 | Environmental Performance Research Institute, L.L.C. | Communication system and method for sustaining the environment by using the internet |
US7185016B1 (en) | 2000-09-01 | 2007-02-27 | Cognos Incorporated | Methods and transformations for transforming metadata model |
US7424439B1 (en) | 1999-09-22 | 2008-09-09 | Microsoft Corporation | Data mining for managing marketing resources |
US7949722B1 (en) | 1999-09-29 | 2011-05-24 | Actv Inc. | Enhanced video programming system and method utilizing user-profile information |
US20020138297A1 (en) | 2001-03-21 | 2002-09-26 | Lee Eugene M. | Apparatus for and method of analyzing intellectual property information |
US20040102197A1 (en) | 1999-09-30 | 2004-05-27 | Dietz Timothy Alan | Dynamic web page construction based on determination of client device location |
US6792458B1 (en) | 1999-10-04 | 2004-09-14 | Urchin Software Corporation | System and method for monitoring and analyzing internet traffic |
US6988085B2 (en) | 1999-10-19 | 2006-01-17 | Shad Hedy | System and method for real-time electronic inquiry, delivery, and reporting of credit information |
WO2001031543A1 (en) | 1999-10-26 | 2001-05-03 | Fusz Eugene A | Method and apparatus for anonymous data profiling |
US7630986B1 (en) | 1999-10-27 | 2009-12-08 | Pinpoint, Incorporated | Secure data interchange |
US6606744B1 (en) | 1999-11-22 | 2003-08-12 | Accenture, Llp | Providing collaborative installation management in a network-based supply chain environment |
US6671818B1 (en) | 1999-11-22 | 2003-12-30 | Accenture Llp | Problem isolation through translating and filtering events into a standard object format in a network based supply chain |
US7130807B1 (en) | 1999-11-22 | 2006-10-31 | Accenture Llp | Technology sharing during demand and supply planning in a network-based supply chain environment |
US8032409B1 (en) | 1999-11-22 | 2011-10-04 | Accenture Global Services Limited | Enhanced visibility during installation management in a network-based supply chain environment |
US20030065563A1 (en) | 1999-12-01 | 2003-04-03 | Efunds Corporation | Method and apparatus for atm-based cross-selling of products and services |
US6445975B1 (en) | 1999-12-03 | 2002-09-03 | R.R. Donnelly & Sons Company | Carrier route optimization system |
US6959281B1 (en) | 1999-12-06 | 2005-10-25 | Freeling Kenneth A | Digital computer system and methods for conducting a poll to produce a demographic profile corresponding to an accumulation of response data from encrypted identities |
US7720750B2 (en) | 1999-12-15 | 2010-05-18 | Equifax, Inc. | Systems and methods for providing consumers anonymous pre-approved offers from a consumer-selected group of merchants |
US6915269B1 (en) | 1999-12-23 | 2005-07-05 | Decisionsorter Llc | System and method for facilitating bilateral and multilateral decision-making |
US6970830B1 (en) | 1999-12-29 | 2005-11-29 | General Electric Capital Corporation | Methods and systems for analyzing marketing campaigns |
US7277869B2 (en) | 1999-12-29 | 2007-10-02 | General Electric Capital Corporation | Delinquency-moving matrices for visualizing loan collections |
US6901406B2 (en) | 1999-12-29 | 2005-05-31 | General Electric Capital Corporation | Methods and systems for accessing multi-dimensional customer data |
US7082435B1 (en) | 2000-01-03 | 2006-07-25 | Oracle International Corporation | Method and mechanism for implementing and accessing virtual database table structures |
US6477509B1 (en) | 2000-01-06 | 2002-11-05 | Efunz.Com | Internet marketing method and system |
US20020055869A1 (en) | 2000-01-13 | 2002-05-09 | David Hegg | Housing market analysis method |
US20010034631A1 (en) | 2000-01-21 | 2001-10-25 | Kiselik Daniel R. | Method and apparatus for the automatic selection of parties to an arrangement between a requestor and a satisfier of selected requirements |
US20030097342A1 (en) | 2000-01-24 | 2003-05-22 | Whittingtom Barry R. | Method for verifying employment data |
US20030069839A1 (en) | 2000-01-24 | 2003-04-10 | Whittington Barry R. | Method for confirming and reporting financial data |
US6546257B1 (en) | 2000-01-31 | 2003-04-08 | Kavin K. Stewart | Providing promotional material based on repeated travel patterns |
US7191150B1 (en) | 2000-02-01 | 2007-03-13 | Fair Isaac Corporation | Enhancing delinquent debt collection using statistical models of debt historical information and account events |
US20010029470A1 (en) | 2000-02-03 | 2001-10-11 | R. Steven Schultz | Electronic transaction receipt system and method |
US20030018578A1 (en) | 2000-02-03 | 2003-01-23 | Schultz Roger Stephen | Product registration using an electronically read serial number |
JP2001216403A (en) | 2000-02-04 | 2001-08-10 | Hiroshi Shirakawa | Auction system and auction method |
US7734570B2 (en) | 2001-02-16 | 2010-06-08 | Christopher J. Sole | Collaborative linking system with bi-directed variable granularity search engine |
US7310618B2 (en) | 2000-02-22 | 2007-12-18 | Lehman Brothers Inc. | Automated loan evaluation system |
US7472072B2 (en) | 2000-02-24 | 2008-12-30 | Twenty-Ten, Inc. | Systems and methods for targeting consumers attitudinally aligned with determined attitudinal segment definitions |
US6873979B2 (en) | 2000-02-29 | 2005-03-29 | Marketswitch Corporation | Method of building predictive models on transactional data |
WO2001065453A1 (en) | 2000-02-29 | 2001-09-07 | Expanse Networks, Inc. | Privacy-protected targeting system |
US6904412B1 (en) | 2000-03-14 | 2005-06-07 | Everbank | Method and apparatus for a mortgage loan originator compliance engine |
US7050989B1 (en) | 2000-03-16 | 2006-05-23 | Coremetrics, Inc. | Electronic commerce personalized content delivery system and method of operation |
KR20000036498A (en) | 2000-03-17 | 2000-07-05 | 조항민 | Electronic Commece for Trading a Used Car and Equipment |
FI20000637A0 (en) | 2000-03-17 | 2000-03-17 | Codeonline Oy | Procedure and system for presenting questions and receiving answers |
KR20000036594A (en) | 2000-03-22 | 2000-07-05 | 이기원 | used-car price & estimate method |
US7013285B1 (en) | 2000-03-29 | 2006-03-14 | Shopzilla, Inc. | System and method for data collection, evaluation, information generation, and presentation |
US6757242B1 (en) | 2000-03-30 | 2004-06-29 | Intel Corporation | System and multi-thread method to manage a fault tolerant computer switching cluster using a spanning tree |
US20020023051A1 (en) | 2000-03-31 | 2002-02-21 | Kunzle Adrian E. | System and method for recommending financial products to a customer based on customer needs and preferences |
US6549919B2 (en) | 2000-04-03 | 2003-04-15 | Lucent Technologies Inc. | Method and apparatus for updating records in a database system based on an improved model of time-dependent behavior |
US6684250B2 (en) | 2000-04-03 | 2004-01-27 | Quova, Inc. | Method and apparatus for estimating a geographic location of a networked entity |
US6665715B1 (en) | 2000-04-03 | 2003-12-16 | Infosplit Inc | Method and systems for locating geographical locations of online users |
US7848972B1 (en) | 2000-04-06 | 2010-12-07 | Metavante Corporation | Electronic bill presentment and payment systems and processes |
US7263506B2 (en) | 2000-04-06 | 2007-08-28 | Fair Isaac Corporation | Identification and management of fraudulent credit/debit card purchases at merchant ecommerce sites |
US8006261B1 (en) | 2000-04-07 | 2011-08-23 | Visible World, Inc. | System and method for personalized message creation and delivery |
US6847934B1 (en) | 2000-04-11 | 2005-01-25 | Center For Adaptive Systems Applications | Marketing selection optimization process |
AU2001293359A1 (en) | 2000-04-14 | 2001-10-30 | Mathias Client Management Software Company | Method and system for interfacing clients with relationship management (rm) accounts and for permissioning marketing |
JP2001297141A (en) | 2000-04-14 | 2001-10-26 | Toyota Motor Corp | Device and method for calculating vehicle price |
US6366903B1 (en) | 2000-04-20 | 2002-04-02 | Microsoft Corporation | Index and materialized view selection for a given workload |
AU6108901A (en) | 2000-04-27 | 2001-11-07 | Webfeat Inc | Method and system for retrieving search results from multiple disparate databases |
JP2001312586A (en) | 2000-04-28 | 2001-11-09 | Tokio Marine & Fire Insurance Co Ltd | Support system for providing of ranking-related service and support method therefor |
US6807533B1 (en) | 2000-05-02 | 2004-10-19 | General Electric Canada Equipment Finance G.P. | Web-based method and system for managing account receivables |
US6862610B2 (en) * | 2000-05-08 | 2005-03-01 | Ideaflood, Inc. | Method and apparatus for verifying the identity of individuals |
US20030167222A1 (en) | 2000-05-08 | 2003-09-04 | Sunil Mehrotra | Method and apparatus for marketing within a complex product space |
US7617184B2 (en) | 2000-05-18 | 2009-11-10 | Endeca Technologies, Inc. | Scalable hierarchical data-driven navigation system and method for information retrieval |
GB0012211D0 (en) | 2000-05-19 | 2000-07-12 | Gemstar Dev Limited | A targeted advertising system |
US7401131B2 (en) | 2000-05-22 | 2008-07-15 | Verizon Business Global Llc | Method and system for implementing improved containers in a global ecosystem of interrelated services |
US7003517B1 (en) | 2000-05-24 | 2006-02-21 | Inetprofit, Inc. | Web-based system and method for archiving and searching participant-based internet text sources for customer lead data |
US7673329B2 (en) | 2000-05-26 | 2010-03-02 | Symantec Corporation | Method and apparatus for encrypted communications to a secure server |
JP2001344463A (en) | 2000-05-30 | 2001-12-14 | System Location Co Ltd | Vehicle resale price analysis system |
US20030105728A1 (en) | 2000-05-30 | 2003-06-05 | Seiichi Yano | Vehicle resale price analysis system |
US20060155639A1 (en) | 2000-06-03 | 2006-07-13 | Joan Lynch | System and method for automated process of deal structuring |
US6901384B2 (en) | 2000-06-03 | 2005-05-31 | American Home Credit, Inc. | System and method for automated process of deal structuring |
US6622266B1 (en) | 2000-06-09 | 2003-09-16 | International Business Machines Corporation | Method for specifying printer alert processing |
CA2349914C (en) | 2000-06-09 | 2013-07-30 | Invidi Technologies Corp. | Advertising delivery method |
KR100398020B1 (en) | 2000-06-12 | 2003-09-19 | 지철수 | system and method for selling and estimating price of used automobile on network |
WO2001097083A1 (en) | 2000-06-12 | 2001-12-20 | Epredix.Com | Computer-implemented system for human resources management |
US7593893B1 (en) | 2000-06-13 | 2009-09-22 | Fannie Mae | Computerized systems and methods for facilitating the flow of capital through the housing finance industry |
JP2001357256A (en) | 2000-06-15 | 2001-12-26 | Ryoichi Ino | Supply system for used-car assessed value information |
US6748426B1 (en) | 2000-06-15 | 2004-06-08 | Murex Securities, Ltd. | System and method for linking information in a global computer network |
KR100348153B1 (en) | 2000-06-16 | 2002-08-09 | 최승환 | Method for trade a used car through the internet |
US7024386B1 (en) | 2000-06-23 | 2006-04-04 | Ebs Group Limited | Credit handling in an anonymous trading system |
US7162432B2 (en) | 2000-06-30 | 2007-01-09 | Protigen, Inc. | System and method for using psychological significance pattern information for matching with target information |
US6983379B1 (en) | 2000-06-30 | 2006-01-03 | Hitwise Pty. Ltd. | Method and system for monitoring online behavior at a remote site and creating online behavior profiles |
US7249048B1 (en) | 2000-06-30 | 2007-07-24 | Ncr Corporation | Incorporating predicrive models within interactive business analysis processes |
US6999941B1 (en) | 2000-07-11 | 2006-02-14 | Amazon.Com, Inc. | Providing gift clustering functionality to assist a user in ordering multiple items for a recipient |
AU2001277071A1 (en) | 2000-07-21 | 2002-02-13 | Triplehop Technologies, Inc. | System and method for obtaining user preferences and providing user recommendations for unseen physical and information goods and services |
US7031945B1 (en) | 2000-07-24 | 2006-04-18 | Donner Irah H | System and method for reallocating and/or upgrading and/or rewarding tickets, other event admittance means, goods and/or services |
AU2001278004A1 (en) | 2000-07-25 | 2002-02-05 | Energy E-Comm.Com, Inc. | Internet information retrieval method and apparatus |
US20030018769A1 (en) | 2000-07-26 | 2003-01-23 | Davis Foulger | Method of backtracing network performance |
US20020120504A1 (en) | 2000-07-31 | 2002-08-29 | Intermedia Advertising Group | Computerized system and method for increasing the effectiveness of advertising |
US6873972B1 (en) | 2000-08-01 | 2005-03-29 | General Electric Company | Systems and methods for credit line monitoring |
US20040199456A1 (en) | 2000-08-01 | 2004-10-07 | Andrew Flint | Method and apparatus for explaining credit scores |
AUPQ924000A0 (en) | 2000-08-07 | 2000-08-31 | Sharinga Networks Inc. | An access system for use with lans |
JP2002055997A (en) | 2000-08-08 | 2002-02-20 | Tsubasa System Co Ltd | Device and method for retrieving used-car information |
US20020026411A1 (en) | 2000-08-11 | 2002-02-28 | Nathans Michael G. | National housing credit repository protocols |
US7206768B1 (en) | 2000-08-14 | 2007-04-17 | Jpmorgan Chase Bank, N.A. | Electronic multiparty accounts receivable and accounts payable system |
US6574623B1 (en) | 2000-08-15 | 2003-06-03 | International Business Machines Corporation | Query transformation and simplification for group by queries with rollup/grouping sets in relational database management systems |
US20050154664A1 (en) | 2000-08-22 | 2005-07-14 | Guy Keith A. | Credit and financial information and management system |
CA2423113A1 (en) | 2000-08-25 | 2002-02-28 | American Express Travel Related Services Company, Inc. | System and method for account reconciliation |
US7155508B2 (en) | 2000-09-01 | 2006-12-26 | Yodlee.Com, Inc. | Target information generation and ad server |
US7599851B2 (en) | 2000-09-05 | 2009-10-06 | Renee Frengut | Method for providing customized user interface and targeted marketing forum |
US20020091706A1 (en) | 2000-09-06 | 2002-07-11 | Johnson Controls Technology Company | Vehicle history and personalization information management system and method |
JP2002163498A (en) | 2000-09-14 | 2002-06-07 | Jidosha Ryutsu Hosho:Kk | System and method for assessing buying price of used car |
US7392216B1 (en) | 2000-09-27 | 2008-06-24 | Ge Capital Mortgage Corporation | Methods and apparatus for utilizing a proportional hazards model to evaluate loan risk |
US6631374B1 (en) | 2000-09-29 | 2003-10-07 | Oracle Corp. | System and method for providing fine-grained temporal database access |
US7243075B1 (en) | 2000-10-03 | 2007-07-10 | Shaffer James D | Real-time process for defining, processing and delivering a highly customized contact list over a network |
US7043531B1 (en) | 2000-10-04 | 2006-05-09 | Inetprofit, Inc. | Web-based customer lead generator system with pre-emptive profiling |
WO2002029691A1 (en) | 2000-10-06 | 2002-04-11 | Argus Information & Advisory Services, Llc | System and method for revolving credit product offer customization |
US7983976B2 (en) | 2000-10-17 | 2011-07-19 | Hedgestreet, Inc. | Methods and apparatus for formulation, initial public or private offering, and secondary market trading of risk management contracts |
US7809601B2 (en) | 2000-10-18 | 2010-10-05 | Johnson & Johnson Consumer Companies | Intelligent performance-based product recommendation system |
US6904408B1 (en) | 2000-10-19 | 2005-06-07 | Mccarthy John | Bionet method, system and personalized web content manager responsive to browser viewers' psychological preferences, behavioral responses and physiological stress indicators |
US7930252B2 (en) | 2000-10-24 | 2011-04-19 | Google, Inc. | Method and system for sharing anonymous user information |
US6456979B1 (en) | 2000-10-24 | 2002-09-24 | The Insuranceadvisor Technologies, Inc. | Method of evaluating a permanent life insurance policy |
US20020052841A1 (en) | 2000-10-27 | 2002-05-02 | Guthrie Paul D. | Electronic payment system |
US20030158776A1 (en) | 2000-10-30 | 2003-08-21 | Mark Landesmann | Buyer-driven targeting of purchasing entities |
US20100299251A1 (en) | 2000-11-06 | 2010-11-25 | Consumer And Merchant Awareness Foundation | Pay yourself first with revenue generation |
US8473380B2 (en) | 2000-11-06 | 2013-06-25 | Propulsion Remote Holdings, Llc | Pay yourself first budgeting |
US7398225B2 (en) | 2001-03-29 | 2008-07-08 | American Express Travel Related Services Company, Inc. | System and method for networked loyalty program |
US7398226B2 (en) | 2000-11-06 | 2008-07-08 | American Express Travel Related Services Company, Inc. | System and method for networked loyalty program |
US7313622B2 (en) | 2000-11-08 | 2007-12-25 | [X+1] Solutions, Inc. | Online system and method for dynamic segmentation and content presentation |
US7194420B2 (en) | 2000-11-13 | 2007-03-20 | Ricoh Company, Ltd. | Method and system for planning supply of commodities |
US7991688B2 (en) | 2000-11-14 | 2011-08-02 | Knowledge Works Inc. | Methods and apparatus for automatically exchanging credit information |
US7392201B1 (en) | 2000-11-15 | 2008-06-24 | Trurisk, Llc | Insurance claim forecasting system |
JP2002149778A (en) | 2000-11-16 | 2002-05-24 | Susumu Fujii | Method and system for calculating buying assessed price |
US20030233278A1 (en) | 2000-11-27 | 2003-12-18 | Marshall T. Thaddeus | Method and system for tracking and providing incentives for tasks and activities and other behavioral influences related to money, individuals, technology and other assets |
US6980977B2 (en) | 2000-11-30 | 2005-12-27 | Yokogawa Electric Corporation | System for acquiring and analyzing personal profile data and providing the service of delivering various information |
US20020065716A1 (en) | 2000-11-30 | 2002-05-30 | Kuschill James E. | Methods and system for processing loyalty transactions |
CA2327078C (en) | 2000-11-30 | 2005-01-11 | Ibm Canada Limited-Ibm Canada Limitee | Secure session management and authentication for web sites |
KR20010016349A (en) | 2000-12-05 | 2001-03-05 | 강욱성 | floating vehicles(cars,heavy machinery)database business model using on-line and off-line network |
US20020073138A1 (en) | 2000-12-08 | 2002-06-13 | Gilbert Eric S. | De-identification and linkage of data records |
US20020077890A1 (en) | 2000-12-14 | 2002-06-20 | Lapointe Patrick L. | Methods and systems for interactive collection, exchange and redemption of points |
US7054828B2 (en) | 2000-12-20 | 2006-05-30 | International Business Machines Corporation | Computer method for using sample data to predict future population and domain behaviors |
US7363308B2 (en) | 2000-12-28 | 2008-04-22 | Fair Isaac Corporation | System and method for obtaining keyword descriptions of records from a large database |
US20020128960A1 (en) | 2000-12-29 | 2002-09-12 | Lambiotte Kenneth G. | Systems and methods for managing accounts |
US20040122730A1 (en) | 2001-01-02 | 2004-06-24 | Tucciarone Joel D. | Electronic messaging system and method thereof |
US7343294B1 (en) | 2001-01-05 | 2008-03-11 | Fair Isaac Corporation | Multi-channel marketing database development methodology |
US7472088B2 (en) | 2001-01-19 | 2008-12-30 | Jpmorgan Chase Bank N.A. | System and method for offering a financial product |
JP2002297934A (en) | 2001-01-23 | 2002-10-11 | Mazda Motor Corp | Estimated value providing device, estimated value providing system, estimated value providing method, computer program and computer readable storage medium |
US7672897B2 (en) | 2001-01-24 | 2010-03-02 | Scott Chung | Method of community purchasing through the internet |
US7346492B2 (en) | 2001-01-24 | 2008-03-18 | Shaw Stroz Llc | System and method for computerized psychological content analysis of computer and media generated communications to produce communications management support, indications, and warnings of dangerous behavior, assessment of media images, and personnel selection support |
US20020138331A1 (en) | 2001-02-05 | 2002-09-26 | Hosea Devin F. | Method and system for web page personalization |
US20060014129A1 (en) | 2001-02-09 | 2006-01-19 | Grow.Net, Inc. | System and method for processing test reports |
US6543683B2 (en) | 2001-02-12 | 2003-04-08 | Ncr Corporation | System and method for providing consumer access to a stored digital receipt generated as a result of a purchase transaction and to business/consumer applications related to the stored digital receipt |
US7313538B2 (en) | 2001-02-15 | 2007-12-25 | American Express Travel Related Services Company, Inc. | Transaction tax settlement in personal communication devices |
US20020116253A1 (en) | 2001-02-20 | 2002-08-22 | Coyne Kevin P. | Systems and methods for making a prediction utilizing admissions-based information |
US20020123904A1 (en) | 2001-02-22 | 2002-09-05 | Juan Amengual | Internet shopping assistance technology and e-mail place |
US8078524B2 (en) | 2001-02-22 | 2011-12-13 | Fair Isaac Corporation | Method and apparatus for explaining credit scores |
US7711635B2 (en) | 2001-02-22 | 2010-05-04 | Fair Isaac Corporation | System and method for helping consumers understand and interpret credit scores |
US7584149B1 (en) | 2001-02-26 | 2009-09-01 | American Express Travel Related Services Company, Inc. | System and method for securing data through a PDA portal |
US7222101B2 (en) | 2001-02-26 | 2007-05-22 | American Express Travel Related Services Company, Inc. | System and method for securing data through a PDA portal |
CA2340562A1 (en) | 2001-02-28 | 2002-08-28 | Midway Amusement Games, Llc | Tournament network for linking amusement games |
JP2002259753A (en) | 2001-03-01 | 2002-09-13 | Nissan Motor Co Ltd | Evaluation method and evaluation system for used vehicle |
US20020133404A1 (en) | 2001-03-19 | 2002-09-19 | Pedersen Brad D. | Internet advertisements having personalized context |
US20020138417A1 (en) | 2001-03-20 | 2002-09-26 | David Lawrence | Risk management clearinghouse |
US20020138333A1 (en) | 2001-03-22 | 2002-09-26 | Decotiis Allen R. | System, method and article of manufacture for a weighted model to conduct propensity studies |
US20020138334A1 (en) | 2001-03-22 | 2002-09-26 | Decotiis Allen R. | System, method and article of manufacture for propensity-based scoring of individuals |
US7467096B2 (en) | 2001-03-29 | 2008-12-16 | American Express Travel Related Services Company, Inc. | System and method for the real-time transfer of loyalty points between accounts |
US7305364B2 (en) | 2001-04-06 | 2007-12-04 | General Electric Capital Corporation | Methods and systems for supplying customer leads to dealers |
US7216102B2 (en) | 2001-04-06 | 2007-05-08 | General Electric Capital Corporation | Methods and systems for auctioning of pre-selected customer lists |
US7040987B2 (en) | 2001-04-11 | 2006-05-09 | Walker Digital, Llc | Method and apparatus for remotely customizing a gaming device |
US20030041050A1 (en) | 2001-04-16 | 2003-02-27 | Greg Smith | System and method for web-based marketing and campaign management |
US20020156676A1 (en) | 2001-04-17 | 2002-10-24 | Ahrens John C. | System, method, and apparatus for creating and securely managing accounts holding cash equivalents |
JP2002329055A (en) | 2001-04-26 | 2002-11-15 | Dentsu Tec Inc | Customer's property value-evaluating system |
JP2002323409A (en) | 2001-04-26 | 2002-11-08 | Fuji Heavy Ind Ltd | Vehicle control system |
GB0110893D0 (en) | 2001-05-03 | 2001-06-27 | Gems Dev Organisation The Ltd | Transaction management systems |
WO2002091186A1 (en) | 2001-05-08 | 2002-11-14 | Ipool Corporation | Privacy protection system and method |
US20020169655A1 (en) | 2001-05-10 | 2002-11-14 | Beyer Dirk M. | Global campaign optimization with promotion-specific customer segmentation |
US7028052B2 (en) | 2001-05-10 | 2006-04-11 | Equifax, Inc. | Systems and methods for notifying a consumer of changes made to a credit report |
WO2002093436A1 (en) | 2001-05-11 | 2002-11-21 | Swisscom Mobile Ag | Method for transmitting an anonymous request from a consumer to a content or service provider through a telecommunication network |
US20080021802A1 (en) | 2001-05-14 | 2008-01-24 | Pendleton Mark R | Method for providing credit offering and credit management information services |
AU2002303719A1 (en) | 2001-05-21 | 2002-12-03 | Pe Corporation (Ny) | Isolated human secreted proteins, nucleic acid molecules encoding human secreted proteins, and uses thereof |
US20020173994A1 (en) | 2001-05-21 | 2002-11-21 | Ferguson Joseph M. | Method and apparatus for insuring an insured from identity theft peril and identity reclamation and credit restoration |
US7428526B2 (en) | 2001-05-29 | 2008-09-23 | Claritas, Inc. | Household level segmentation method and system |
US7325193B2 (en) | 2001-06-01 | 2008-01-29 | International Business Machines Corporation | Automated management of internet and/or web site content |
US7689506B2 (en) | 2001-06-07 | 2010-03-30 | Jpmorgan Chase Bank, N.A. | System and method for rapid updating of credit information |
US7730509B2 (en) | 2001-06-08 | 2010-06-01 | Invidi Technologies Corporation | Asset delivery reporting in a broadcast network |
US20040006536A1 (en) | 2001-06-11 | 2004-01-08 | Takashi Kawashima | Electronic money system |
AU2002320087A1 (en) | 2001-06-14 | 2003-01-02 | Dizpersion Group, L.L.C. | Method and system for providing network based target advertising |
US8407136B2 (en) | 2001-06-15 | 2013-03-26 | Capital One Financial Corporation | System and methods for providing starter credit card accounts |
WO2003005195A2 (en) | 2001-07-03 | 2003-01-16 | Imagine Broadband Limited | Broadband communications |
US7085734B2 (en) | 2001-07-06 | 2006-08-01 | Grant D Graeme | Price decision support |
US7715546B2 (en) | 2001-07-09 | 2010-05-11 | Austin Logistics Incorporated | System and method for updating contact records |
US20030229507A1 (en) | 2001-07-13 | 2003-12-11 | Damir Perge | System and method for matching donors and charities |
US8560666B2 (en) | 2001-07-23 | 2013-10-15 | Hitwise Pty Ltd. | Link usage |
US20030093289A1 (en) | 2001-07-31 | 2003-05-15 | Thornley Robert D. | Reporting and collecting rent payment history |
WO2003017044A2 (en) | 2001-08-13 | 2003-02-27 | Gresham Financial Services, Inc. | Loan securitization pool having pre-defined requirements |
US7366694B2 (en) | 2001-08-16 | 2008-04-29 | Mortgage Grader, Inc. | Credit/financing process |
US7203343B2 (en) * | 2001-09-21 | 2007-04-10 | Hewlett-Packard Development Company, L.P. | System and method for determining likely identity in a biometric database |
US8332291B2 (en) | 2001-10-05 | 2012-12-11 | Argus Information and Advisory Services, Inc. | System and method for monitoring managing and valuing credit accounts |
US7403923B2 (en) | 2001-10-12 | 2008-07-22 | Accenture Global Services Gmbh | Debt collection practices |
US7546266B2 (en) | 2001-10-18 | 2009-06-09 | General Electric Company | Method, system, and storage medium for pre-screening customers for credit card approval at a point of sale |
US7536346B2 (en) | 2001-10-29 | 2009-05-19 | Equifax, Inc. | System and method for facilitating reciprocative small business financial information exchanges |
US20030093311A1 (en) | 2001-11-05 | 2003-05-15 | Kenneth Knowlson | Targeted advertising |
US7370044B2 (en) | 2001-11-19 | 2008-05-06 | Equifax, Inc. | System and method for managing and updating information relating to economic entities |
US7783562B1 (en) | 2001-11-21 | 2010-08-24 | Clayton Fixed Income Services Inc. | Credit risk managing loan pools |
US7895097B2 (en) | 2001-11-26 | 2011-02-22 | Hewlett-Packard Development Company, L.P. | Intelligent apparatus, system and method for financial data computation, report remittance and funds transfer over an interactive communications network |
US6959420B1 (en) | 2001-11-30 | 2005-10-25 | Microsoft Corporation | Method and system for protecting internet users' privacy by evaluating web site platform for privacy preferences policy |
US20030110111A1 (en) | 2001-12-07 | 2003-06-12 | Nalebuff Barry J. | Home equity insurance financial product |
US7212979B1 (en) | 2001-12-14 | 2007-05-01 | Bellsouth Intellectuall Property Corporation | System and method for identifying desirable subscribers |
GB2383505B (en) | 2001-12-21 | 2004-03-31 | Searchspace Ltd | System and method for monitoring usage patterns |
US20030120591A1 (en) | 2001-12-21 | 2003-06-26 | Mark Birkhead | Systems and methods for facilitating responses to credit requests |
US7428509B2 (en) | 2002-01-10 | 2008-09-23 | Mastercard International Incorporated | Method and system for detecting payment account fraud |
US20030212654A1 (en) | 2002-01-25 | 2003-11-13 | Harper Jonathan E. | Data integration system and method for presenting 360° customer views |
US20040205157A1 (en) | 2002-01-31 | 2004-10-14 | Eric Bibelnieks | System, method, and computer program product for realtime profiling of web site visitors |
US20050222906A1 (en) | 2002-02-06 | 2005-10-06 | Chen Timothy T | System and method of targeted marketing |
WO2003073286A1 (en) | 2002-02-27 | 2003-09-04 | James Tang | Eliminating fraud using secret gesture and identifier |
US20030171942A1 (en) | 2002-03-06 | 2003-09-11 | I-Centrix Llc | Contact relationship management system and method |
US8095589B2 (en) | 2002-03-07 | 2012-01-10 | Compete, Inc. | Clickstream analysis methods and systems |
JP4080769B2 (en) | 2002-03-15 | 2008-04-23 | ソフトバンクモバイル株式会社 | Used car sales brokerage system |
US7107285B2 (en) | 2002-03-16 | 2006-09-12 | Questerra Corporation | Method, system, and program for an improved enterprise spatial system |
US20040122764A1 (en) | 2002-03-27 | 2004-06-24 | Bernie Bilski | Capped bill systems, methods and products |
US7680796B2 (en) | 2003-09-03 | 2010-03-16 | Google, Inc. | Determining and/or using location information in an ad system |
US20050021397A1 (en) | 2003-07-22 | 2005-01-27 | Cui Yingwei Claire | Content-targeted advertising using collected user behavior data |
US20030200135A1 (en) | 2002-04-19 | 2003-10-23 | Wright Christine Ellen | System and method for predicting and preventing customer churn |
US20030200151A1 (en) | 2002-04-22 | 2003-10-23 | John Ellenson | System and method for facilitating the real-time pricing, sale and appraisal of used vehicles |
JP2003316881A (en) | 2002-04-26 | 2003-11-07 | Tsubasa System Co Ltd | Used car price retrieval method |
US20040010443A1 (en) | 2002-05-03 | 2004-01-15 | May Andrew W. | Method and financial product for estimating geographic mortgage risk |
US20030208362A1 (en) | 2002-05-03 | 2003-11-06 | Resident Data, Inc. | Integrated screening system and method for tenants and rental applicants |
AU2003245253A1 (en) | 2002-05-06 | 2003-11-11 | Zoot Enterprises, Inc. | System and method of application processing |
US7383227B2 (en) | 2002-05-14 | 2008-06-03 | Early Warning Services, Llc | Database for check risk decisions populated with check activity data from banks of first deposit |
US20030219709A1 (en) | 2002-05-24 | 2003-11-27 | Mollee Olenick | System and method for educating, managing, and evaluating clients of professionals |
EP1574081A4 (en) | 2002-05-28 | 2006-03-22 | Voxtime Inc | Dynamic pricing and yield management in mobile communications |
US7200619B2 (en) | 2002-05-31 | 2007-04-03 | International Business Machines Corporation | Method and process to optimize correlation of replicated with extracted data from disparate data sources |
US7444655B2 (en) | 2002-06-11 | 2008-10-28 | Microsoft Corporation | Anonymous aggregated data collection |
US7444302B2 (en) | 2002-06-14 | 2008-10-28 | Ellie Mae, Inc. | Online system for fulfilling loan applications from loan originators |
US20030233655A1 (en) | 2002-06-18 | 2003-12-18 | Koninklijke Philips Electronics N.V. | Method and apparatus for an adaptive stereotypical profile for recommending items representing a user's interests |
US7505938B2 (en) | 2002-06-20 | 2009-03-17 | Alliance Data Systems Corporation | Interactive voice response quick credit system and associated methods |
US20070192409A1 (en) | 2002-07-23 | 2007-08-16 | Amir Kleinstern | Advertising based on location behavior |
US20040030667A1 (en) | 2002-08-02 | 2004-02-12 | Capital One Financial Corporation | Automated systems and methods for generating statistical models |
US20040128193A1 (en) | 2002-08-06 | 2004-07-01 | Sabre Inc. | Methods and systems for providing an integrated merchandising and shopping environment |
US7050982B2 (en) | 2002-08-14 | 2006-05-23 | Veretech, Llc | Lead generation system using buyer criteria |
US6810356B1 (en) | 2002-08-30 | 2004-10-26 | Advertising.Com | Traffic estimation |
US20040049729A1 (en) | 2002-09-10 | 2004-03-11 | University Of Florida | Computer-based statistical analysis method and system |
US7380280B2 (en) | 2002-09-13 | 2008-05-27 | Sun Microsystems, Inc. | Rights locker for digital content access control |
US8255263B2 (en) | 2002-09-23 | 2012-08-28 | General Motors Llc | Bayesian product recommendation engine |
US20040193487A1 (en) | 2002-10-08 | 2004-09-30 | Coolsavings, Inc. | Secure promotions |
US20040122735A1 (en) | 2002-10-09 | 2004-06-24 | Bang Technologies, Llc | System, method and apparatus for an integrated marketing vehicle platform |
US20040122736A1 (en) | 2002-10-11 | 2004-06-24 | Bank One, Delaware, N.A. | System and method for granting promotional rewards to credit account holders |
US20040078324A1 (en) | 2002-10-16 | 2004-04-22 | Carl Lonnberg | Systems and methods for authenticating a financial account at activation |
CN103714481A (en) | 2002-10-21 | 2014-04-09 | 瑞菲尔·斯贝茹 | System and method for capture, storage and processing of receipts and related data |
US7451095B1 (en) | 2002-10-30 | 2008-11-11 | Freddie Mac | Systems and methods for income scoring |
US7395273B2 (en) | 2002-10-31 | 2008-07-01 | General Electric Company | System providing receipt inspection reporting |
US20060004626A1 (en) | 2002-10-31 | 2006-01-05 | Eric Holmen | Targeted marketing for subscriptions |
US20040143546A1 (en) | 2002-11-01 | 2004-07-22 | Wood Jeff A. | Easy user activation of electronic commerce services |
US7469416B2 (en) | 2002-11-05 | 2008-12-23 | International Business Machines Corporation | Method for automatically managing information privacy |
US7240059B2 (en) | 2002-11-14 | 2007-07-03 | Seisint, Inc. | System and method for configuring a parallel-processing database system |
US7136448B1 (en) | 2002-11-18 | 2006-11-14 | Siebel Systems, Inc. | Managing received communications based on assessments of the senders |
US7698163B2 (en) | 2002-11-22 | 2010-04-13 | Accenture Global Services Gmbh | Multi-dimensional segmentation for use in a customer interaction |
US7047251B2 (en) | 2002-11-22 | 2006-05-16 | Accenture Global Services, Gmbh | Standardized customer application and record for inputting customer data into analytic models |
US7707059B2 (en) | 2002-11-22 | 2010-04-27 | Accenture Global Services Gmbh | Adaptive marketing using insight driven customer interaction |
US8290840B2 (en) | 2002-11-27 | 2012-10-16 | Consumerinfo.Com, Inc. | Method for determining insurance benefits and premiums from dynamic credit information |
US7370057B2 (en) | 2002-12-03 | 2008-05-06 | Lockheed Martin Corporation | Framework for evaluating data cleansing applications |
US7139734B2 (en) | 2002-12-04 | 2006-11-21 | Nathans Michael G | Preferred credit information data collection method |
US7023980B2 (en) | 2002-12-04 | 2006-04-04 | Avaya Technology Corp. | Outbound dialing decision criteria based |
US20040186807A1 (en) | 2003-03-21 | 2004-09-23 | Nathans Michael G. | Credit data collection method and apparatus |
US20080027859A1 (en) | 2002-12-04 | 2008-01-31 | Pay Rent, Build Credit, Inc. | Preferred credit information data collection method |
US20040117235A1 (en) | 2002-12-13 | 2004-06-17 | Nachum Shacham | Automated method and system to recommend one or more supplier-side responses to a transaction request |
US8538840B2 (en) | 2002-12-20 | 2013-09-17 | Siebel Systems, Inc. | Financial services data model |
WO2004061735A1 (en) | 2002-12-30 | 2004-07-22 | Fannie Mae | System and method for creating financial assets |
US20040128236A1 (en) | 2002-12-30 | 2004-07-01 | Brown Ron T. | Methods and apparatus for evaluating and using profitability of a credit card account |
WO2004061561A2 (en) | 2002-12-30 | 2004-07-22 | Fannie Mae | System and method for facilitating delivery of a loan to a secondary mortgage market purchaser |
US20040128230A1 (en) | 2002-12-30 | 2004-07-01 | Fannie Mae | System and method for modifying attribute data pertaining to financial assets in a data processing system |
US20040199789A1 (en) | 2002-12-30 | 2004-10-07 | Shaw Terry D. | Anonymizer data collection device |
US20040128150A1 (en) | 2002-12-31 | 2004-07-01 | Lundegren Mark Edward | Methods and structure for collaborative customer account management |
US20040138932A1 (en) | 2003-01-09 | 2004-07-15 | Johnson Christopher D. | Generating business analysis results in advance of a request for the results |
WO2004066102A2 (en) | 2003-01-17 | 2004-08-05 | Barra, Inc. | Method and apparatus for an incomplete information model of credit risk |
US7565153B2 (en) | 2003-01-22 | 2009-07-21 | Cml Emergency Services Inc. | Method and system for delivery of location specific information |
US7386786B2 (en) | 2003-01-24 | 2008-06-10 | The Cobalt Group, Inc. | Method and apparatus for processing a dynamic webpage |
CA2418163A1 (en) | 2003-01-31 | 2004-07-31 | Ibm Canada Limited - Ibm Canada Limitee | Method of query transformation using window aggregation |
US7403942B1 (en) | 2003-02-04 | 2008-07-22 | Seisint, Inc. | Method and system for processing data records |
US7617160B1 (en) | 2003-02-05 | 2009-11-10 | Michael I. Grove | Choice-based relationship system (CRS) |
US8566190B2 (en) | 2003-02-06 | 2013-10-22 | Goldman, Sachs & Co. | Method and apparatus for evaluating and monitoring collateralized debt obligations |
US20040158523A1 (en) | 2003-02-06 | 2004-08-12 | Dort David Bogart | Method providing contingency access to valuable accounts or information |
US7200602B2 (en) | 2003-02-07 | 2007-04-03 | International Business Machines Corporation | Data set comparison and net change processing |
US20040199584A1 (en) | 2003-03-05 | 2004-10-07 | Evan Kirshenbaum | Method and system for customized configuration of an appearance of a website for a user |
US8082202B2 (en) | 2003-03-07 | 2011-12-20 | Market Shield Capital, Llc | Market-indexed mortgage system and method |
CA2520117A1 (en) | 2003-03-25 | 2004-10-14 | Sedna Patent Services, Llc | Generating audience analytics |
US20040193538A1 (en) | 2003-03-31 | 2004-09-30 | Raines Walter L. | Receipt processing system and method |
US20040199462A1 (en) | 2003-04-02 | 2004-10-07 | Ed Starrs | Fraud control method and system for network transactions |
US7376714B1 (en) | 2003-04-02 | 2008-05-20 | Gerken David A | System and method for selectively acquiring and targeting online advertising based on user IP address |
US20040199458A1 (en) | 2003-04-07 | 2004-10-07 | Thinh Ho | System and method for on-line mortgage services |
US7080027B2 (en) | 2003-04-17 | 2006-07-18 | Targetrx, Inc. | Method and system for analyzing the effectiveness of marketing strategies |
CA2427209A1 (en) | 2003-04-30 | 2004-10-30 | Ibm Canada Limited - Ibm Canada Limitee | Optimization of queries on views defined by conditional expressions having mutually exclusive conditions |
US20040225545A1 (en) | 2003-05-08 | 2004-11-11 | Turner James E. | System and method for offering unsecured consumer credit transactions |
US8386377B1 (en) | 2003-05-12 | 2013-02-26 | Id Analytics, Inc. | System and method for credit scoring using an identity network connectivity |
US20040230534A1 (en) | 2003-05-12 | 2004-11-18 | Digital Matrix Systems, Inc. | Encrypted credit application processing system and method |
US7458508B1 (en) | 2003-05-12 | 2008-12-02 | Id Analytics, Inc. | System and method for identity-based fraud detection |
US7562814B1 (en) | 2003-05-12 | 2009-07-21 | Id Analytics, Inc. | System and method for identity-based fraud detection through graph anomaly detection |
US7686214B1 (en) | 2003-05-12 | 2010-03-30 | Id Analytics, Inc. | System and method for identity-based fraud detection using a plurality of historical identity records |
US20040243588A1 (en) | 2003-05-29 | 2004-12-02 | Thomas Tanner | Systems and methods for administering a global information database |
US20050004805A1 (en) | 2003-06-10 | 2005-01-06 | Venkataraman Srinivasan | System and method of suggestive analysis of customer data |
EP1636747A4 (en) | 2003-06-10 | 2007-01-03 | Citibank Na | System and method for analyzing marketing efforts |
CA2527281C (en) | 2003-06-13 | 2013-09-17 | Equifax, Inc. | Systems and processes for automated criteria and attribute generation, searching, auditing and reporting of data |
US8296229B1 (en) | 2003-06-17 | 2012-10-23 | Citicorp Credit Services, Inc. | Method and system for associating consumers with purchase transactions |
US20050027633A1 (en) | 2003-06-25 | 2005-02-03 | Joey Fortuna | Application and processes for the review and adjustment of the full lifecycle of consumer finances |
WO2005003907A2 (en) | 2003-06-26 | 2005-01-13 | Ebay Inc. | Method and apparatus to authenticate and authorize user access to a system |
US7617136B1 (en) | 2003-07-15 | 2009-11-10 | Teradata Us, Inc. | System and method for capturing, storing and analyzing revenue management information for the travel and transportation industries |
US7392203B2 (en) | 2003-07-18 | 2008-06-24 | Fortrex Technologies, Inc. | Vendor security management system |
US20050065809A1 (en) | 2003-07-29 | 2005-03-24 | Blackbaud, Inc. | System and methods for maximizing donations and identifying planned giving targets |
US20050038726A1 (en) | 2003-08-12 | 2005-02-17 | Ewt, Llc | On-demand defined securitization methods and systems |
US20090132347A1 (en) | 2003-08-12 | 2009-05-21 | Russell Wayne Anderson | Systems And Methods For Aggregating And Utilizing Retail Transaction Records At The Customer Level |
US7584126B1 (en) | 2003-08-18 | 2009-09-01 | Capital One Financial Corporation | System and method for managing dedicated use of a credit account |
US20110076663A1 (en) | 2003-08-18 | 2011-03-31 | Retail Optimization International | Systems and methods for selecting survey questions and available responses |
US7853469B2 (en) | 2003-08-22 | 2010-12-14 | Mastercard International | Methods and systems for predicting business behavior from profiling consumer card transactions |
US7769626B2 (en) | 2003-08-25 | 2010-08-03 | Tom Reynolds | Determining strategies for increasing loyalty of a population to an entity |
US20050234969A1 (en) | 2003-08-27 | 2005-10-20 | Ascential Software Corporation | Services oriented architecture for handling metadata in a data integration platform |
US7783534B2 (en) | 2003-09-12 | 2010-08-24 | International Business Machines Corporation | Optimal method, system, and storage medium for resolving demand and supply imbalances |
US7191144B2 (en) | 2003-09-17 | 2007-03-13 | Mentor Marketing, Llc | Method for estimating respondent rank order of a set stimuli |
BRPI0414607A (en) | 2003-09-22 | 2006-12-26 | Citicorp Credit Services Inc | Method and system for classification based on purchasing procedures |
US20060085334A1 (en) | 2004-10-14 | 2006-04-20 | Murphy Kevin M | Dynamic financial liability management |
US7707102B2 (en) | 2003-10-23 | 2010-04-27 | Rothstein Robert E | Method and apparatus for monitoring the collateral risk analysis commodity lenders |
US20050097039A1 (en) | 2003-11-05 | 2005-05-05 | Laszlo Kulcsar | Multiple credit card management system |
US20050102206A1 (en) | 2003-11-07 | 2005-05-12 | Serkan Savasoglu | Systems and methods for contingent obligation retainable deduction securities |
US20050120045A1 (en) | 2003-11-20 | 2005-06-02 | Kevin Klawon | Process for determining recording, and utilizing characteristics of website users |
US7596512B1 (en) | 2003-11-26 | 2009-09-29 | Carfax, Inc. | System and method for determining vehicle price adjustment values |
US8423451B1 (en) | 2003-12-01 | 2013-04-16 | Fannie Mai | System and method for processing a loan |
TWM256569U (en) | 2003-12-09 | 2005-02-01 | Optimum Care Int Tech Inc | Memory module device |
US7543739B2 (en) | 2003-12-17 | 2009-06-09 | Qsecure, Inc. | Automated payment card fraud detection and location |
US7756778B1 (en) | 2003-12-18 | 2010-07-13 | Fannie Mae | System and method for tracking and facilitating analysis of variance and recourse transactions |
US20050144067A1 (en) | 2003-12-19 | 2005-06-30 | Palo Alto Research Center Incorporated | Identifying and reporting unexpected behavior in targeted advertising environment |
US8036907B2 (en) | 2003-12-23 | 2011-10-11 | The Dun & Bradstreet Corporation | Method and system for linking business entities using unique identifiers |
US20050251474A1 (en) | 2003-12-31 | 2005-11-10 | Michael Shinn | Method of financing home ownership for sub prime prospective home buyers |
JP4069078B2 (en) | 2004-01-07 | 2008-03-26 | 松下電器産業株式会社 | DRAM control device and DRAM control method |
US20050177442A1 (en) | 2004-01-09 | 2005-08-11 | Sullivan James B. | Method and system for performing a retail transaction using a wireless device |
US20050177489A1 (en) | 2004-02-09 | 2005-08-11 | National Title Loans, Inc. | Method for licensed lender disbursement of loan proceeds to an out-of-state borrower to establish with greater certainty that the law of the state in which the licensed lender is located is the state law which governs the loan transaction |
US20090313163A1 (en) | 2004-02-13 | 2009-12-17 | Wang ming-huan | Credit line optimization |
US8650079B2 (en) | 2004-02-27 | 2014-02-11 | Accenture Global Services Limited | Promotion planning system |
US7467127B1 (en) | 2004-02-27 | 2008-12-16 | Hyperion Solutions Corporation | View selection for a multidimensional database |
US8949899B2 (en) | 2005-03-04 | 2015-02-03 | Sharp Laboratories Of America, Inc. | Collaborative recommendation system |
US20050204381A1 (en) | 2004-03-10 | 2005-09-15 | Microsoft Corporation | Targeted advertising based on consumer purchasing data |
US7636941B2 (en) | 2004-03-10 | 2009-12-22 | Microsoft Corporation | Cross-domain authentication |
US20050273849A1 (en) | 2004-03-11 | 2005-12-08 | Aep Networks | Network access using secure tunnel |
WO2005086878A2 (en) | 2004-03-11 | 2005-09-22 | Atrana Solutions, Inc. | System for processing stored value instrument |
US20050209922A1 (en) | 2004-03-19 | 2005-09-22 | Hofmeister Kurt J | Credit based product marketing method |
US20050209892A1 (en) | 2004-03-19 | 2005-09-22 | Miller Jonathan K | [Automated system and method for providing accurate, non-invasive insurance status verification] |
US20050222900A1 (en) | 2004-03-30 | 2005-10-06 | Prashant Fuloria | Selectively delivering advertisements based at least in part on trademark issues |
US7367011B2 (en) | 2004-04-13 | 2008-04-29 | International Business Machines Corporation | Method, system and program product for developing a data model in a data mining system |
US20050233742A1 (en) | 2004-04-16 | 2005-10-20 | Jeyhan Karaoguz | Location based directories Via a broadband access gateway |
US7725300B2 (en) | 2004-04-16 | 2010-05-25 | Fortelligent, Inc. | Target profiling in predictive modeling |
US20050251408A1 (en) | 2004-04-23 | 2005-11-10 | Swaminathan S | System and method for conducting intelligent multimedia marketing operations |
US20050279827A1 (en) | 2004-04-28 | 2005-12-22 | First Data Corporation | Methods and systems for providing guaranteed merchant transactions |
US7761351B2 (en) | 2004-04-29 | 2010-07-20 | Ford Motor Company | Method and system for assessing the risk of a vehicle dealership defaulting on a financial obligation |
US20070067297A1 (en) | 2004-04-30 | 2007-03-22 | Kublickis Peter J | System and methods for a micropayment-enabled marketplace with permission-based, self-service, precision-targeted delivery of advertising, entertainment and informational content and relationship marketing to anonymous internet users |
US7421322B1 (en) | 2004-04-30 | 2008-09-02 | Carfax, Inc. | System and method for automatic identification of vehicle identification number |
US20050256780A1 (en) | 2004-05-14 | 2005-11-17 | Wired Logic, Inc. | Electronically implemented vehicle marketing services |
US7739142B2 (en) | 2004-05-17 | 2010-06-15 | Yahoo! Inc. | System and method for providing automobile marketing research information |
US20050261959A1 (en) | 2004-05-20 | 2005-11-24 | Moyer Michael D | System and method for targeted marketing through intermediate resellers |
US7308418B2 (en) | 2004-05-24 | 2007-12-11 | Affinova, Inc. | Determining design preferences of a group |
US20090043637A1 (en) | 2004-06-01 | 2009-02-12 | Eder Jeffrey Scott | Extended value and risk management system |
US20050267774A1 (en) | 2004-06-01 | 2005-12-01 | David Merritt | Method and apparatus for obtaining and using vehicle sales price data in performing vehicle valuations |
US7954698B1 (en) | 2004-06-02 | 2011-06-07 | Pliha Robert K | System and method for matching customers to financial products, services, and incentives based on bank account transaction activity |
US7296734B2 (en) | 2004-06-02 | 2007-11-20 | Robert Kenneth Pliha | Systems and methods for scoring bank customers direct deposit account transaction activity to match financial behavior to specific acquisition, performance and risk events defined by the bank using a decision tree and stochastic process |
WO2005122733A2 (en) | 2004-06-09 | 2005-12-29 | James Bergin | Systems and methods for management of contact information |
US20050278246A1 (en) | 2004-06-14 | 2005-12-15 | Mark Friedman | Software solution management of problem loans |
US7314166B2 (en) | 2004-06-16 | 2008-01-01 | American Express Travel Related Services Company, Inc. | System and method for calculating recommended charge limits |
US7467106B1 (en) | 2004-06-18 | 2008-12-16 | Jpmorgan Chase Bank, N.A. | System and method for offer management |
WO2005124619A1 (en) | 2004-06-18 | 2005-12-29 | George Walter Owen | Credit management system |
US20060004753A1 (en) | 2004-06-23 | 2006-01-05 | Coifman Ronald R | System and method for document analysis, processing and information extraction |
US8346593B2 (en) | 2004-06-30 | 2013-01-01 | Experian Marketing Solutions, Inc. | System, method, and software for prediction of attitudinal and message responsiveness |
US7689528B2 (en) | 2004-07-09 | 2010-03-30 | Fair Isaac Corporation | Method and apparatus for a scalable algorithm for decision optimization |
US7672889B2 (en) | 2004-07-15 | 2010-03-02 | Brooks Kent F | System and method for providing customizable investment tools |
US7596716B2 (en) | 2004-07-29 | 2009-09-29 | Sobha Renaissance Information Technology | Method and system for managing networks |
US8407137B2 (en) | 2004-08-02 | 2013-03-26 | Propulsion Remote Holdings, Llc | Pay yourself first with user guidance |
US20080172324A1 (en) | 2004-08-03 | 2008-07-17 | Tom Johnson | System and method for modifying criteria used with decision engines |
US20080126476A1 (en) | 2004-08-04 | 2008-05-29 | Nicholas Frank C | Method and System for the Creating, Managing, and Delivery of Enhanced Feed Formatted Content |
GB2417345A (en) | 2004-08-13 | 2006-02-22 | Ebs Group Ltd | Automated trading system |
US7970690B2 (en) | 2004-08-19 | 2011-06-28 | Leadpoint, Inc. | System for implementing automated open market auctioning of leads |
US20060041443A1 (en) | 2004-08-23 | 2006-02-23 | Horvath Charles W Jr | Variable data business system and method therefor |
US20060053047A1 (en) | 2004-08-30 | 2006-03-09 | Garcia Rita M | System and method for selecting targets for sales and marketing campaigns |
US20060089914A1 (en) | 2004-08-30 | 2006-04-27 | John Shiel | Apparatus, systems and methods for compensating broadcast sources |
US7970672B2 (en) | 2004-09-01 | 2011-06-28 | Metareward, Inc. | Real-time marketing of credit-based goods or services |
US7590589B2 (en) | 2004-09-10 | 2009-09-15 | Hoffberg Steven M | Game theoretic prioritization scheme for mobile ad hoc networks permitting hierarchal deference |
US20060059073A1 (en) | 2004-09-15 | 2006-03-16 | Walzak Rebecca B | System and method for analyzing financial risk |
WO2006029681A2 (en) | 2004-09-17 | 2006-03-23 | Accenture Global Services Gmbh | Personalized marketing architecture |
US8732004B1 (en) | 2004-09-22 | 2014-05-20 | Experian Information Solutions, Inc. | Automated analysis of data to generate prospect notifications based on trigger events |
US20060080251A1 (en) | 2004-09-22 | 2006-04-13 | Fried Steven M | Systems and methods for offering credit line products |
US8494855B1 (en) | 2004-10-06 | 2013-07-23 | West Interactive Corporation Ii | Method, system, and computer readable medium for comparing phonetic similarity of return words to resolve ambiguities during voice recognition |
TWI256569B (en) | 2004-10-14 | 2006-06-11 | Uniminer Inc | System and method of credit scoring by applying data mining method |
US7848978B2 (en) | 2004-10-19 | 2010-12-07 | Apollo Enterprise Solutions, Inc. | Enhanced transaction resolution techniques |
US20060089842A1 (en) | 2004-10-22 | 2006-04-27 | Medawar Cherif R | System and method for finding, analyzing, controlling, timing and strategizing real estate investing online |
US8326671B2 (en) | 2004-10-29 | 2012-12-04 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet to analyze vendors in online marketplaces |
US7822665B2 (en) | 2004-10-29 | 2010-10-26 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet in private equity investments |
US20060242050A1 (en) | 2004-10-29 | 2006-10-26 | American Express Travel Related Services Company, Inc. | Method and apparatus for targeting best customers based on spend capacity |
US20070244732A1 (en) | 2004-10-29 | 2007-10-18 | American Express Travel Related Services Co., Inc., A New York Corporation | Using commercial share of wallet to manage vendors |
US8326672B2 (en) | 2004-10-29 | 2012-12-04 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet in financial databases |
US20070016501A1 (en) | 2004-10-29 | 2007-01-18 | American Express Travel Related Services Co., Inc., A New York Corporation | Using commercial share of wallet to rate business prospects |
US7912770B2 (en) | 2004-10-29 | 2011-03-22 | American Express Travel Related Services Company, Inc. | Method and apparatus for consumer interaction based on spend capacity |
US20060242048A1 (en) | 2004-10-29 | 2006-10-26 | American Express Travel Related Services Company, Inc. | Method and apparatus for determining credit characteristics of a consumer |
US20070016500A1 (en) | 2004-10-29 | 2007-01-18 | American Express Travel Related Services Co., Inc. A New York Corporation | Using commercial share of wallet to determine insurance risk |
US7610243B2 (en) | 2004-10-29 | 2009-10-27 | American Express Travel Related Services Company, Inc. | Method and apparatus for rating asset-backed securities |
US7788147B2 (en) | 2004-10-29 | 2010-08-31 | American Express Travel Related Services Company, Inc. | Method and apparatus for estimating the spend capacity of consumers |
US7840484B2 (en) | 2004-10-29 | 2010-11-23 | American Express Travel Related Services Company, Inc. | Credit score and scorecard development |
US8086509B2 (en) | 2004-10-29 | 2011-12-27 | American Express Travel Related Services Company, Inc. | Determining commercial share of wallet |
US7792732B2 (en) | 2004-10-29 | 2010-09-07 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet to rate investments |
US8204774B2 (en) | 2004-10-29 | 2012-06-19 | American Express Travel Related Services Company, Inc. | Estimating the spend capacity of consumer households |
US8543499B2 (en) | 2004-10-29 | 2013-09-24 | American Express Travel Related Services Company, Inc. | Reducing risks related to check verification |
US7814004B2 (en) | 2004-10-29 | 2010-10-12 | American Express Travel Related Services Company, Inc. | Method and apparatus for development and use of a credit score based on spend capacity |
US8630929B2 (en) | 2004-10-29 | 2014-01-14 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet to make lending decisions |
US20060202012A1 (en) | 2004-11-12 | 2006-09-14 | David Grano | Secure data processing system, such as a system for detecting fraud and expediting note processing |
AU2005307823B2 (en) | 2004-11-16 | 2012-03-08 | Health Dialog Services Corporation | Systems and methods for predicting healthcare related risk events and financial risk |
US8775253B2 (en) | 2004-12-06 | 2014-07-08 | Capital One Financial Corporation | Systems, methods and computer readable medium for wireless solicitations |
EP1672571A1 (en) | 2004-12-15 | 2006-06-21 | Sap Ag | Method and computer system for e-mail processing |
WO2006069199A2 (en) | 2004-12-20 | 2006-06-29 | Armorpoint, Inc. | Personal credit management and monitoring system and method |
US7566002B2 (en) * | 2005-01-06 | 2009-07-28 | Early Warning Services, Llc | Identity verification systems and methods |
US20060293954A1 (en) | 2005-01-12 | 2006-12-28 | Anderson Bruce J | Voting and headend insertion model for targeting content in a broadcast network |
US20090094640A1 (en) | 2007-09-26 | 2009-04-09 | Anderson Bruce J | Targeted advertising in unicast, multicast and hybrid distribution system contexts |
US20060184440A1 (en) | 2005-01-26 | 2006-08-17 | Britti Michael A | Risk-based pricing for rental property |
US20060173772A1 (en) | 2005-02-02 | 2006-08-03 | Hayes John B | Systems and methods for automated processing, handling, and facilitating a trade credit transaction |
US20060178983A1 (en) | 2005-02-07 | 2006-08-10 | Robert Nice | Mortgage broker system allowing broker to match mortgagor with multiple lenders and method therefor |
US20060218079A1 (en) | 2005-02-08 | 2006-09-28 | Goldblatt Joel N | Web-based consumer loan database with automated controls for preventing predatory lending practices |
US7703114B2 (en) | 2005-02-25 | 2010-04-20 | Microsoft Corporation | Television system targeted advertising |
US7734523B1 (en) | 2005-03-03 | 2010-06-08 | Federal Home Loan Mortgage Corporation (Freddie Mac) | Method, system, and computer program product for grading a collateralized mortgage obligation or other asset-backed security |
US8768766B2 (en) | 2005-03-07 | 2014-07-01 | Turn Inc. | Enhanced online advertising system |
US7822681B2 (en) | 2005-03-11 | 2010-10-26 | Farias David G | Financial collaboration networks |
US20060206379A1 (en) | 2005-03-14 | 2006-09-14 | Outland Research, Llc | Methods and apparatus for improving the matching of relevant advertisements with particular users over the internet |
US20060253323A1 (en) | 2005-03-15 | 2006-11-09 | Optical Entertainment Network, Inc. | System and method for online trading of television advertising space |
WO2006099583A2 (en) | 2005-03-16 | 2006-09-21 | 121 Media, Inc. | Targeted advertising system and method |
US20060230415A1 (en) | 2005-03-30 | 2006-10-12 | Cyriac Roeding | Electronic device and methods for reproducing mass media content |
US20060224696A1 (en) | 2005-04-01 | 2006-10-05 | Blair King | Targeted advertorial and multimedia delivery system and method |
US20060229961A1 (en) | 2005-04-08 | 2006-10-12 | Efunds Corporation | Risk evaluation method and system using ACH data |
US8756099B2 (en) | 2005-04-11 | 2014-06-17 | Bill Me Later, Inc. | Consumer processing system and method |
US20100057556A1 (en) | 2005-04-12 | 2010-03-04 | Armand Rousso | Apparatuses, Methods And Systems To Identify, Generate, And Aggregate Qualified Sales and Marketing Leads For Distribution Via an Online Competitive Bidding System |
US7802723B2 (en) | 2005-04-19 | 2010-09-28 | American Exrpess Travel Related Services Company, Inc. | System and method for nameless biometric authentication and non-repudiation validation |
US20060247991A1 (en) | 2005-04-29 | 2006-11-02 | American Express Marketing & Development Corp. | System, method, and computer program product for searching credit agencies using partial identification numbers |
US7853518B2 (en) | 2005-05-24 | 2010-12-14 | Corelogic Information Solutions, Inc. | Method and apparatus for advanced mortgage diagnostic analytics |
US20060271457A1 (en) | 2005-05-26 | 2006-11-30 | Romain Martin R | Identity theft monitoring and prevention |
US20060287919A1 (en) | 2005-06-02 | 2006-12-21 | Blue Mustard Llc | Advertising search system and method |
US20060277102A1 (en) | 2005-06-06 | 2006-12-07 | Better, Inc. | System and Method for Generating Effective Advertisements in Electronic Commerce |
CA2610216A1 (en) | 2005-06-06 | 2006-12-14 | Sms.Ac, Inc. | Billing system and method for micro-transactions |
US7904520B2 (en) | 2005-06-09 | 2011-03-08 | Trueffect, Inc. | First party advertisement serving |
US10510043B2 (en) | 2005-06-13 | 2019-12-17 | Skyword Inc. | Computer method and apparatus for targeting advertising |
US20060294199A1 (en) | 2005-06-24 | 2006-12-28 | The Zeppo Network, Inc. | Systems and Methods for Providing A Foundational Web Platform |
WO2007002702A2 (en) | 2005-06-24 | 2007-01-04 | Fair Isaac Corporation | Mass compromise / point of compromise analytic detection and compromised card portfolio management system |
US20070011020A1 (en) | 2005-07-05 | 2007-01-11 | Martin Anthony G | Categorization of locations and documents in a computer network |
WO2007004158A2 (en) | 2005-07-05 | 2007-01-11 | Kreditinform (Pty) Limited | Debtor management system and method |
WO2007008860A2 (en) * | 2005-07-11 | 2007-01-18 | Conrad Sheehan | Secure electronic transactions between a mobile device and other mobile, fixed or virtual devices |
US20070016518A1 (en) | 2005-07-12 | 2007-01-18 | Paul Atkinson | System and process for providing loans or other financing instruments |
CA2615659A1 (en) | 2005-07-22 | 2007-05-10 | Yogesh Chunilal Rathod | Universal knowledge management and desktop search system |
US8234498B2 (en) | 2005-07-25 | 2012-07-31 | Britti Michael A | Screening using a personal identification code |
US8418254B2 (en) | 2005-07-25 | 2013-04-09 | Transunion Rental Screening Solutions, Inc. | Applicant screening |
US20070027791A1 (en) | 2005-07-28 | 2007-02-01 | Zopa Limited | Method of and apparatus for matching lenders of money with borrowers of money |
US7556192B2 (en) | 2005-08-04 | 2009-07-07 | Capital One Financial Corp. | Systems and methods for decisioning or approving a financial credit account based on a customer's check-writing behavior |
US20070033227A1 (en) | 2005-08-08 | 2007-02-08 | Gaito Robert G | System and method for rescoring names in mailing list |
US20070038516A1 (en) | 2005-08-13 | 2007-02-15 | Jeff Apple | Systems, methods, and computer program products for enabling an advertiser to measure user viewing of and response to an advertisement |
US8126772B1 (en) | 2005-08-15 | 2012-02-28 | Dale LeFebvre | Rebate cross-sell network and systems and methods implementing the same |
US7805345B2 (en) | 2005-08-26 | 2010-09-28 | Sas Institute Inc. | Computer-implemented lending analysis systems and methods |
US20070112579A1 (en) | 2005-09-01 | 2007-05-17 | Ads Alliance Data Systems, Inc. | Market management system |
US20070055621A1 (en) | 2005-09-01 | 2007-03-08 | First Advantage Corporation | Automated method and system for predicting and/or verifying income |
US8560385B2 (en) | 2005-09-02 | 2013-10-15 | Bees & Pollen Ltd. | Advertising and incentives over a social network |
US7904366B2 (en) | 2005-09-02 | 2011-03-08 | General Electric Capital Corporation | Method and system to determine resident qualifications |
US20070061195A1 (en) | 2005-09-13 | 2007-03-15 | Yahoo! Inc. | Framework for selecting and delivering advertisements over a network based on combined short-term and long-term user behavioral interests |
US8229914B2 (en) | 2005-09-14 | 2012-07-24 | Jumptap, Inc. | Mobile content spidering and compatibility determination |
US20080134042A1 (en) | 2005-09-14 | 2008-06-05 | Magiq Technologies, Dac , A Corporation | Qkd System Wth Ambiguous Control |
US8090818B2 (en) | 2005-09-19 | 2012-01-03 | Sap Ag | Generation of customized client proxies |
US9166823B2 (en) | 2005-09-21 | 2015-10-20 | U Owe Me, Inc. | Generation of a context-enriched message including a message component and a contextual attribute |
US20070078835A1 (en) | 2005-09-30 | 2007-04-05 | Boloto Group, Inc. | Computer system, method and software for creating and providing an individualized web-based browser interface for wrappering search results and presenting advertising to a user based upon at least one profile or user attribute |
US20070220553A1 (en) | 2005-09-30 | 2007-09-20 | Bellsouth Intellectual Property Corporation | Methods, systems, and computer program products for providing customized content |
US20070208619A1 (en) | 2005-09-30 | 2007-09-06 | Bellsouth Intellectual Property Corporation | Methods, systems, and computer program products for providing targeted advertising to communications devices |
WO2007041709A1 (en) | 2005-10-04 | 2007-04-12 | Basepoint Analytics Llc | System and method of detecting fraud |
US8396747B2 (en) | 2005-10-07 | 2013-03-12 | Kemesa Inc. | Identity theft and fraud protection system and method |
US7672865B2 (en) | 2005-10-21 | 2010-03-02 | Fair Isaac Corporation | Method and apparatus for retail data mining using pair-wise co-occurrence consistency |
US8768743B2 (en) | 2005-10-21 | 2014-07-01 | Fair Isaac Corporation | Product space browser |
US20080228635A1 (en) | 2005-10-24 | 2008-09-18 | Megdal Myles G | Reducing risks related to check verification |
US20080221970A1 (en) | 2005-10-24 | 2008-09-11 | Megdal Myles G | Method and apparatus for targeting best customers based on spend capacity |
US20080228540A1 (en) | 2005-10-24 | 2008-09-18 | Megdal Myles G | Using commercial share of wallet to compile marketing company lists |
US20080228606A1 (en) | 2005-10-24 | 2008-09-18 | Megdal Myles G | Determining commercial share of wallet |
US20080222027A1 (en) | 2005-10-24 | 2008-09-11 | Megdal Myles G | Credit score and scorecard development |
US20080221990A1 (en) | 2005-10-24 | 2008-09-11 | Megdal Myles G | Estimating the spend capacity of consumer households |
US20080033852A1 (en) | 2005-10-24 | 2008-02-07 | Megdal Myles G | Computer-based modeling of spending behaviors of entities |
US20080243680A1 (en) | 2005-10-24 | 2008-10-02 | Megdal Myles G | Method and apparatus for rating asset-backed securities |
US20080255897A1 (en) | 2005-10-24 | 2008-10-16 | Megdal Myles G | Using commercial share of wallet in financial databases |
US20080221971A1 (en) | 2005-10-24 | 2008-09-11 | Megdal Myles G | Using commercial share of wallet to rate business prospects |
US20080228556A1 (en) | 2005-10-24 | 2008-09-18 | Megdal Myles G | Method and apparatus for consumer interaction based on spend capacity |
US20080221972A1 (en) | 2005-10-24 | 2008-09-11 | Megdal Myles G | Method and apparatus for determining credit characteristics of a consumer |
US8346638B2 (en) | 2005-10-26 | 2013-01-01 | Capital One Financial Corporation | Systems and methods for processing transaction data to perform a merchant chargeback |
CA2527538A1 (en) | 2005-11-12 | 2007-05-14 | Matt Celano | Method and apparatus for a consumer interactive credit report analysis and score reconciliation adaptive education and counseling system |
US20070124235A1 (en) | 2005-11-29 | 2007-05-31 | Anindya Chakraborty | Method and system for income estimation |
US20070129993A1 (en) | 2005-12-02 | 2007-06-07 | Robert Alvin | System and method for automated lead nurturing |
US20070156515A1 (en) | 2005-12-29 | 2007-07-05 | Kimberly-Clark Worldwide, Inc. | Method for integrating attitudinal and behavioral data for marketing consumer products |
US20070156589A1 (en) | 2005-12-30 | 2007-07-05 | Randy Zimler | Integrating personalized listings of media content into an electronic program guide |
US20070157105A1 (en) | 2006-01-04 | 2007-07-05 | Stephen Owens | Network user database for a sidebar |
US8280805B1 (en) | 2006-01-10 | 2012-10-02 | Sas Institute Inc. | Computer-implemented risk evaluation systems and methods |
US7610257B1 (en) | 2006-01-10 | 2009-10-27 | Sas Institute Inc. | Computer-implemented risk evaluation systems and methods |
US7930736B2 (en) | 2006-01-13 | 2011-04-19 | Google, Inc. | Providing selective access to a web site |
US20070294126A1 (en) | 2006-01-24 | 2007-12-20 | Maggio Frank S | Method and system for characterizing audiences, including as venue and system targeted (VAST) ratings |
US20070179860A1 (en) | 2006-01-30 | 2007-08-02 | Romero Craig D | Motor vehicle remarketing service |
US9105039B2 (en) | 2006-01-30 | 2015-08-11 | Groupon, Inc. | System and method for providing mobile alerts to members of a social network |
US8135642B1 (en) | 2006-02-07 | 2012-03-13 | Sprint Communications Company L.P. | Resilient messaging system and method |
US8219535B1 (en) | 2006-02-15 | 2012-07-10 | Allstate Insurance Company | Retail deployment model |
CA2541763A1 (en) | 2006-02-15 | 2007-08-15 | Sharon Rossmark | Retail deployment model |
US20070220611A1 (en) | 2006-02-17 | 2007-09-20 | Ari Socolow | Methods and systems for sharing or presenting member information |
US7788358B2 (en) | 2006-03-06 | 2010-08-31 | Aggregate Knowledge | Using cross-site relationships to generate recommendations |
US7711636B2 (en) | 2006-03-10 | 2010-05-04 | Experian Information Solutions, Inc. | Systems and methods for analyzing data |
WO2007106787A2 (en) | 2006-03-10 | 2007-09-20 | Vantagescore Solutions, Llc | Methods and systems for characteristic leveling |
US7689494B2 (en) | 2006-03-23 | 2010-03-30 | Advisor Software Inc. | Simulation of portfolios and risk budget analysis |
US7966256B2 (en) | 2006-09-22 | 2011-06-21 | Corelogic Information Solutions, Inc. | Methods and systems of predicting mortgage payment risk |
US7970699B1 (en) | 2006-03-27 | 2011-06-28 | Loan Insights, Inc. | Customized consumer loan search and optimized loan pricing |
US8438170B2 (en) | 2006-03-29 | 2013-05-07 | Yahoo! Inc. | Behavioral targeting system that generates user profiles for target objectives |
WO2007123760A2 (en) | 2006-03-30 | 2007-11-01 | Nebuad, Inc. | Network device for monitoring and modifying network traffic between an end user and a content provider |
US7747480B1 (en) | 2006-03-31 | 2010-06-29 | Sas Institute Inc. | Asset repository hub |
US8005712B2 (en) | 2006-04-06 | 2011-08-23 | Educational Testing Service | System and method for large scale survey analysis |
US20070250327A1 (en) | 2006-04-24 | 2007-10-25 | Shad Hedy | System and method for used vehicle valuation based on actual transaction data provided by industry participants |
US20090133058A1 (en) | 2007-11-21 | 2009-05-21 | Michael Kouritzin | Method and apparatus to perform real-time audience estimation and commercial selection suitable for targeted advertising |
WO2007131069A2 (en) | 2006-05-02 | 2007-11-15 | Inividi Technologies Corporation | Fuzzy logic based viewer identification |
US20070288360A1 (en) | 2006-05-03 | 2007-12-13 | Joseph Guy Seeklus | Systems and methods for determining whether candidates are qualified for desired situations based on credit scores |
US20070282684A1 (en) | 2006-05-12 | 2007-12-06 | Prosser Steven H | System and Method for Determining Affinity Profiles for Research, Marketing, and Recommendation Systems |
WO2007134310A2 (en) | 2006-05-12 | 2007-11-22 | Monster (California), Inc. | System and method for advertisement generation |
US7676410B2 (en) | 2006-05-12 | 2010-03-09 | CompuCredit Intellectual Property Holdings, Corp. II | Combined debt consolidation and settlement program |
US20070271178A1 (en) | 2006-05-22 | 2007-11-22 | Davis Richard S | Loan program and process for transacting the same |
US20080133325A1 (en) | 2006-05-30 | 2008-06-05 | Sruba De | Systems And Methods For Segment-Based Payment Card Solutions |
US7962368B2 (en) | 2006-06-02 | 2011-06-14 | Fair Isaac Corporation | Purchase sequence browser |
US20100138290A1 (en) | 2006-06-12 | 2010-06-03 | Invidi Technologies Corporation | System and Method for Auctioning Avails |
CA2654866C (en) | 2006-06-12 | 2016-09-27 | Invidi Technologies Corporation | System and method for auctioning avails |
EP2039154A4 (en) | 2006-06-12 | 2011-05-04 | Invidi Tech Corp | System and method for inserting media based on keyword search |
US20070288271A1 (en) | 2006-06-13 | 2007-12-13 | Kirk William Klinkhammer | Sub-prime automobile sale and finance system |
US20070294303A1 (en) | 2006-06-20 | 2007-12-20 | Harmon Richard L | System and method for acquiring mortgage customers |
US20070294163A1 (en) | 2006-06-20 | 2007-12-20 | Harmon Richard L | System and method for retaining mortgage customers |
US20080005313A1 (en) | 2006-06-29 | 2008-01-03 | Microsoft Corporation | Using offline activity to enhance online searching |
US20080004957A1 (en) | 2006-06-29 | 2008-01-03 | Microsoft Corporation | Targeted advertising for portable devices |
MX2009000355A (en) | 2006-07-11 | 2009-04-08 | Welcome Real Time Pte Ltd | A promotions system and method. |
US20110276377A1 (en) | 2006-07-17 | 2011-11-10 | Next Jump, Inc. | Communication system and method for narrowcasting |
US8676961B2 (en) | 2006-07-27 | 2014-03-18 | Yahoo! Inc. | System and method for web destination profiling |
US8458062B2 (en) | 2006-08-11 | 2013-06-04 | Capital One Financial Corporation | Real-time product matching |
EP2074572A4 (en) | 2006-08-17 | 2011-02-23 | Experian Inf Solutions Inc | System and method for providing a score for a used vehicle |
US8321342B2 (en) | 2006-08-28 | 2012-11-27 | Choicepay, Inc. | Method and system to accept and settle transaction payments for an unbanked consumer |
US20080110973A1 (en) | 2006-08-30 | 2008-05-15 | Nathans Michael G | System and method of credit data collection and verification |
US20080059224A1 (en) | 2006-08-31 | 2008-03-06 | Schechter Alan M | Systems and methods for developing a comprehensive patient health profile |
US8027888B2 (en) | 2006-08-31 | 2011-09-27 | Experian Interactive Innovation Center, Llc | Online credit card prescreen systems and methods |
US8799148B2 (en) | 2006-08-31 | 2014-08-05 | Rohan K. K. Chandran | Systems and methods of ranking a plurality of credit card offers |
US20080059364A1 (en) | 2006-09-01 | 2008-03-06 | Tidwell Lisa C | Systems and methods for performing a financial trustworthiness assessment |
US7848987B2 (en) | 2006-09-01 | 2010-12-07 | Cabot Research, Llc | Determining portfolio performance measures by weight-based action detection |
US7606752B2 (en) | 2006-09-07 | 2009-10-20 | Yodlee Inc. | Host exchange in bill paying services |
US9529854B2 (en) | 2006-09-12 | 2016-12-27 | Wayport, Inc. | Providing location-based services in a distributed environment without direct control over the point of access |
WO2008039860A1 (en) | 2006-09-26 | 2008-04-03 | Experian Information Solutions, Inc. | System and method for linking mutliple entities in a business database |
WO2008042853A2 (en) | 2006-10-02 | 2008-04-10 | Russel Robert Ii Heiser | Personalized consumer advertising placement |
US8036979B1 (en) | 2006-10-05 | 2011-10-11 | Experian Information Solutions, Inc. | System and method for generating a finance attribute from tradeline data |
US20080086368A1 (en) | 2006-10-05 | 2008-04-10 | Google Inc. | Location Based, Content Targeted Online Advertising |
US20080294546A1 (en) | 2006-10-11 | 2008-11-27 | Matthew Flannery | System and method for peer-to-peer financing |
US20080097768A1 (en) | 2006-10-12 | 2008-04-24 | Godshalk Edward L | Visualization of future value predictions and supporting factors for real estate by block |
US7860786B2 (en) | 2006-10-17 | 2010-12-28 | Canopy Acquisition, Llc | Predictive score for lending |
US8892756B2 (en) | 2006-10-19 | 2014-11-18 | Ebay Inc. | Method and system of publishing campaign data |
US7797252B2 (en) | 2006-10-20 | 2010-09-14 | Target Brands, Inc. | Service plan product and associated system |
GB0621189D0 (en) | 2006-10-25 | 2006-12-06 | Payfont Ltd | Secure authentication and payment system |
US20100205179A1 (en) | 2006-10-26 | 2010-08-12 | Carson Anthony R | Social networking system and method |
US7899750B1 (en) | 2006-10-31 | 2011-03-01 | Intuit Inc. | Goal orientated computing system implemented financial management using projected balances |
US8027871B2 (en) | 2006-11-03 | 2011-09-27 | Experian Marketing Solutions, Inc. | Systems and methods for scoring sales leads |
US7752236B2 (en) | 2006-11-03 | 2010-07-06 | Experian Marketing Solutions, Inc. | Systems and methods of enhancing leads |
WO2008057853A2 (en) | 2006-11-03 | 2008-05-15 | Experian Marketing Solutions, Inc | Systems and methods of enhancing leads |
US8135607B2 (en) | 2006-11-03 | 2012-03-13 | Experian Marketing Solutions, Inc. | System and method of enhancing leads by determining contactability scores |
US7657569B1 (en) | 2006-11-28 | 2010-02-02 | Lower My Bills, Inc. | System and method of removing duplicate leads |
US20080133273A1 (en) | 2006-12-04 | 2008-06-05 | Philip Marshall | System and method for sharing medical information |
US20080140476A1 (en) | 2006-12-12 | 2008-06-12 | Shubhasheesh Anand | Smart advertisement generating system |
WO2008076343A2 (en) | 2006-12-15 | 2008-06-26 | American Express Travel Related Service Company, Inc. | Identifying and managing strategic partner relationships |
US20080147425A1 (en) | 2006-12-15 | 2008-06-19 | American Express Travel Related Services Company, Inc. | Strategic Partner Recognition |
US8694361B2 (en) | 2006-12-15 | 2014-04-08 | American Express Travel Related Services Company, Inc. | Identifying and managing strategic partner relationships |
US8706575B2 (en) | 2006-12-18 | 2014-04-22 | Mastercard International Incorporated | Method and apparatus for transaction management |
US8781951B2 (en) | 2006-12-22 | 2014-07-15 | Ccip Corp. | Method and system for providing financing |
US8010403B2 (en) | 2006-12-29 | 2011-08-30 | American Express Travel Related Services Company, Inc. | System and method for targeting transaction account product holders to receive upgraded transaction account products |
US20080168001A1 (en) | 2007-01-05 | 2008-07-10 | Kagarlis Marios A | Price Indexing |
US8554669B2 (en) | 2007-01-09 | 2013-10-08 | Bill Me Later, Inc. | Method and system for offering a credit product by a credit issuer to a consumer at a point-of sale |
US20080177836A1 (en) | 2007-01-23 | 2008-07-24 | Organizational Wellness & Learning Systems, Inc. | Method and system for managing health and wellness programs |
US20080177655A1 (en) | 2007-01-23 | 2008-07-24 | David Zalik | Systems and methods of underwriting business credit |
US20080228578A1 (en) | 2007-01-25 | 2008-09-18 | Governing Dynamics, Llc | Digital rights management and data license management |
WO2008092147A2 (en) | 2007-01-26 | 2008-07-31 | Information Resources, Inc. | Analytic platform |
WO2008094960A2 (en) | 2007-01-30 | 2008-08-07 | Invidi Technologies Corporation | Asset targeting system for limited resource environments |
US20080183564A1 (en) | 2007-01-30 | 2008-07-31 | Microsoft Corporation | Untethered Interaction With Aggregated Metrics |
US8606626B1 (en) | 2007-01-31 | 2013-12-10 | Experian Information Solutions, Inc. | Systems and methods for providing a direct marketing campaign planning environment |
US9396475B2 (en) | 2007-02-01 | 2016-07-19 | Invidi Technologies Corporation | Intelligent targeting of tags in a broadcast network |
US8146126B2 (en) | 2007-02-01 | 2012-03-27 | Invidi Technologies Corporation | Request for information related to broadcast network content |
US8850473B2 (en) | 2007-02-01 | 2014-09-30 | Invidi Technologies Corporation | Targeting content based on location |
US8190470B2 (en) | 2007-02-16 | 2012-05-29 | American Express Travel Related Services Company | Non pre-approved channel filtering for card acquisition |
US20080208548A1 (en) | 2007-02-27 | 2008-08-28 | Transunion Interactive, Inc., A Delaware Corporation | Credit Report-Based Predictive Models |
US20080208610A1 (en) | 2007-02-28 | 2008-08-28 | Nicholas Arthur Thomas | Methods and Systems for Script Operations Management |
US20080215470A1 (en) | 2007-03-02 | 2008-09-04 | Sabyaschi Sengupta | Methods and apparatus for use in association with payment card accounts |
US7853998B2 (en) | 2007-03-22 | 2010-12-14 | Mocana Corporation | Firewall propagation |
US8285656B1 (en) | 2007-03-30 | 2012-10-09 | Consumerinfo.Com, Inc. | Systems and methods for data verification |
US8955122B2 (en) | 2007-04-04 | 2015-02-10 | Sri International | Method and apparatus for detecting malware infection |
WO2008127288A1 (en) | 2007-04-12 | 2008-10-23 | Experian Information Solutions, Inc. | Systems and methods for determining thin-file records and determining thin-file risk levels |
US20080312969A1 (en) | 2007-04-20 | 2008-12-18 | Richard Raines | System and method for insurance underwriting and rating |
US7979896B2 (en) | 2007-04-20 | 2011-07-12 | Microsoft Corporation | Authorization for access to web service resources |
US20080294540A1 (en) | 2007-05-25 | 2008-11-27 | Celka Christopher J | System and method for automated detection of never-pay data sets |
US20080301016A1 (en) | 2007-05-30 | 2008-12-04 | American Express Travel Related Services Company, Inc. General Counsel's Office | Method, System, and Computer Program Product for Customer Linking and Identification Capability for Institutions |
US20080300977A1 (en) | 2007-05-31 | 2008-12-04 | Ads Alliance Data Systems, Inc. | Method and System for Fractionally Allocating Transactions to Marketing Events |
US20080301188A1 (en) | 2007-06-04 | 2008-12-04 | O'hara Daniel P | Method and system to track accomplishments and to compute relative rankings |
US8655677B2 (en) | 2007-06-12 | 2014-02-18 | Bruce Reiner | Productivity workflow index |
CN101681399A (en) | 2007-06-12 | 2010-03-24 | 卡塔里纳销售公司 | Store solutions |
WO2009006448A1 (en) | 2007-06-28 | 2009-01-08 | Cashedge, Inc. | Global risk administration method and system |
US8046306B2 (en) | 2007-06-29 | 2011-10-25 | Zaio Corporation | System, method, and apparatus for property appraisals |
US8037046B2 (en) | 2007-06-29 | 2011-10-11 | Microsoft Corporation | Collecting and presenting temporal-based action information |
US7958050B2 (en) | 2007-07-02 | 2011-06-07 | Early Warning Services, Llc | Payment account monitoring system and method |
US7949651B2 (en) | 2007-07-09 | 2011-05-24 | Microsoft Corporaiton | Disambiguating residential listing search results |
US20090024462A1 (en) | 2007-07-16 | 2009-01-22 | Credit Karma, Inc. | Method and system for providing targeted offers based on a credit attribute |
US7970676B2 (en) | 2007-08-01 | 2011-06-28 | Fair Isaac Corporation | Method and system for modeling future action impact in credit scoring |
US20090064326A1 (en) | 2007-09-05 | 2009-03-05 | Gtb Technologies | Method and a system for advanced content security in computer networks |
US8086524B1 (en) | 2007-09-10 | 2011-12-27 | Patrick James Craig | Systems and methods for transaction processing and balance transfer processing |
US8301574B2 (en) | 2007-09-17 | 2012-10-30 | Experian Marketing Solutions, Inc. | Multimedia engagement study |
US20090089190A1 (en) | 2007-09-27 | 2009-04-02 | Girulat Jr Rollin M | Systems and methods for monitoring financial activities of consumers |
US20090089205A1 (en) | 2007-09-29 | 2009-04-02 | Anthony Jeremiah Bayne | Automated qualifying of a customer to receive a cash loan at an automated teller machine |
US20090119169A1 (en) | 2007-10-02 | 2009-05-07 | Blinkx Uk Ltd | Various methods and apparatuses for an engine that pairs advertisements with video files |
US20090099914A1 (en) | 2007-10-16 | 2009-04-16 | Alliance Data Systems Corporation | Automated transactional credit system and method for electronic transactions |
US9846884B2 (en) | 2007-10-19 | 2017-12-19 | Fair Isaac Corporation | Click conversion score |
US8191117B2 (en) | 2007-10-25 | 2012-05-29 | Anchorfree, Inc. | Location-targeted online services |
US9547870B1 (en) | 2007-11-02 | 2017-01-17 | Fair Isaac Corporation | System and methods for selective advertising |
US8799068B2 (en) | 2007-11-05 | 2014-08-05 | Facebook, Inc. | Social advertisements and other informational messages on a social networking website, and advertising model for same |
US7962404B1 (en) | 2007-11-07 | 2011-06-14 | Experian Information Solutions, Inc. | Systems and methods for determining loan opportunities |
US20090119199A1 (en) | 2007-11-07 | 2009-05-07 | Nameyourloan | Loan determination method and apparatus |
US7653593B2 (en) | 2007-11-08 | 2010-01-26 | Equifax, Inc. | Macroeconomic-adjusted credit risk score systems and methods |
CA2742395C (en) | 2007-11-14 | 2019-01-08 | Panjiva, Inc. | Evaluating public records of supply transactions |
JP4861965B2 (en) | 2007-11-14 | 2012-01-25 | 株式会社日立製作所 | Information distribution system |
US8626618B2 (en) | 2007-11-14 | 2014-01-07 | Panjiva, Inc. | Using non-public shipper records to facilitate rating an entity based on public records of supply transactions |
FR2923972B1 (en) | 2007-11-15 | 2010-02-26 | Radiotelephone Sfr | METHOD AND SYSTEM FOR MANAGING COMMUNICATIONS |
US20090132559A1 (en) | 2007-11-19 | 2009-05-21 | Simon Chamberlain | Behavioral segmentation using isp-collected behavioral data |
US7996521B2 (en) | 2007-11-19 | 2011-08-09 | Experian Marketing Solutions, Inc. | Service for mapping IP addresses to user segments |
WO2009070573A1 (en) | 2007-11-30 | 2009-06-04 | Data Logix, Inc. | Targeting messages |
US8041592B2 (en) | 2007-11-30 | 2011-10-18 | Bank Of America Corporation | Collection and analysis of multiple data sources |
US20090164293A1 (en) | 2007-12-21 | 2009-06-25 | Keep In Touch Systemstm, Inc. | System and method for time sensitive scheduling data grid flow management |
US20090171755A1 (en) | 2007-12-28 | 2009-07-02 | Kane Francis J | Behavior-based generation of site-to-site referrals |
US20090172035A1 (en) | 2007-12-31 | 2009-07-02 | Pieter Lessing | System and method for capturing and storing casino information in a relational database system |
US20090177480A1 (en) | 2008-01-07 | 2009-07-09 | American Express Travel Related Services Company, Inc. | System And Method For Identifying Targeted Consumers Using Partial Social Security Numbers |
US8744908B2 (en) | 2008-01-17 | 2014-06-03 | Analog Analytics, Inc. | System and method for management and optimization of off-line advertising campaigns with a consumer call to action |
US8165940B2 (en) | 2008-01-31 | 2012-04-24 | Visa U.S.A. Inc. | Non-credit account credit rating |
US20090198602A1 (en) | 2008-01-31 | 2009-08-06 | Intuit Inc. | Ranking commercial offers based on user financial data |
US20090198557A1 (en) | 2008-01-31 | 2009-08-06 | Intuit Inc. | Timing commercial offers based on long-term user data |
EP2088743B1 (en) | 2008-02-11 | 2013-07-03 | Accenture Global Services Limited | Digital file locker |
US20090210886A1 (en) | 2008-02-19 | 2009-08-20 | Bhojwani Sandeep M | Method and system for defining financial transaction notification preferences |
US8554652B1 (en) | 2008-02-21 | 2013-10-08 | Jpmorgan Chase Bank, N.A. | System and method for providing borrowing schemes |
US7849004B2 (en) | 2008-02-29 | 2010-12-07 | American Express Travel Related Services Company, Inc. | Total structural risk model |
US20090222373A1 (en) | 2008-02-29 | 2009-09-03 | American Express Travel Related Services Company, Inc. | Total structural risk model |
US8458083B2 (en) | 2008-02-29 | 2013-06-04 | American Express Travel Related Services Company, Inc. | Total structural risk model |
US20090222380A1 (en) | 2008-02-29 | 2009-09-03 | American Express Travel Related Services Company, Inc | Total structural risk model |
US20090222376A1 (en) | 2008-02-29 | 2009-09-03 | American Express Travel Related Services Company, Inc. | Total structural risk model |
US20090222378A1 (en) | 2008-02-29 | 2009-09-03 | American Express Travel Related Services Company, Inc. | Total structural risk model |
US7814008B2 (en) | 2008-02-29 | 2010-10-12 | American Express Travel Related Services Company, Inc. | Total structural risk model |
US7853520B2 (en) | 2008-02-29 | 2010-12-14 | American Express Travel Related Services Company, Inc. | Total structural risk model |
US20090222308A1 (en) | 2008-03-03 | 2009-09-03 | Zoldi Scott M | Detecting first party fraud abuse |
US20090228918A1 (en) | 2008-03-05 | 2009-09-10 | Changingworlds Ltd. | Content recommender |
US20090234665A1 (en) | 2008-03-11 | 2009-09-17 | Electronic Data Systems Corporation | System and method for customer feedback |
US20090234775A1 (en) | 2008-03-12 | 2009-09-17 | Jason Whitney | Real estate appraisal system and method |
US8239256B2 (en) | 2008-03-17 | 2012-08-07 | Segmint Inc. | Method and system for targeted content placement |
US8234159B2 (en) | 2008-03-17 | 2012-07-31 | Segmint Inc. | Method and system for targeted content placement |
US8825520B2 (en) | 2008-03-17 | 2014-09-02 | Segmint Inc. | Targeted marketing to on-hold customer |
US11669866B2 (en) | 2008-03-17 | 2023-06-06 | Segmint Inc. | System and method for delivering a financial application to a prospective customer |
US20110016042A1 (en) | 2008-03-19 | 2011-01-20 | Experian Information Solutions, Inc. | System and method for tracking and analyzing loans involved in asset-backed securities |
US20090271248A1 (en) | 2008-03-27 | 2009-10-29 | Experian Information Solutions, Inc. | Precalculation of trending attributes |
US7844544B2 (en) | 2008-03-28 | 2010-11-30 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US7877323B2 (en) | 2008-03-28 | 2011-01-25 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US7882027B2 (en) | 2008-03-28 | 2011-02-01 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US20090248573A1 (en) | 2008-03-28 | 2009-10-01 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US7805363B2 (en) | 2008-03-28 | 2010-09-28 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US20090248569A1 (en) | 2008-03-28 | 2009-10-01 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US20090248572A1 (en) | 2008-03-28 | 2009-10-01 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US8990911B2 (en) | 2008-03-30 | 2015-03-24 | Emc Corporation | System and method for single sign-on to resources across a network |
US7925917B1 (en) | 2008-04-03 | 2011-04-12 | United Services Automobile Association (Usaa) | Systems and methods for enabling failover support with multiple backup data storage structures |
US20090265326A1 (en) | 2008-04-17 | 2009-10-22 | Thomas Dudley Lehrman | Dynamic personal privacy system for internet-connected social networks |
WO2009132114A2 (en) | 2008-04-23 | 2009-10-29 | Visa U.S.A. Inc. | Payment portfolio optimization |
US8417559B2 (en) | 2008-04-25 | 2013-04-09 | Fair Isaac Corporation | Assortment planning based on demand transfer between products |
US20090271265A1 (en) | 2008-04-28 | 2009-10-29 | Cyndigo, Corp. | Electronic receipt system and method |
US20090276368A1 (en) | 2008-04-28 | 2009-11-05 | Strands, Inc. | Systems and methods for providing personalized recommendations of products and services based on explicit and implicit user data and feedback |
US20090276233A1 (en) | 2008-05-05 | 2009-11-05 | Brimhall Jeffrey L | Computerized credibility scoring |
US7974860B1 (en) | 2008-05-09 | 2011-07-05 | ExperienceLab, Inc. | Consumer directed health plan (CDHP) and high deductible health plan (HDHP) counseling and information distribution |
US8515862B2 (en) | 2008-05-29 | 2013-08-20 | Sas Institute Inc. | Computer-implemented systems and methods for integrated model validation for compliance and credit risk |
US8037097B2 (en) | 2008-05-30 | 2011-10-11 | Yahoo! Inc. | Universal device identifier for globally identifying and binding disparate device identifiers to the same mobile device |
US8744946B2 (en) | 2008-06-09 | 2014-06-03 | Quest Growth Partners, Llc | Systems and methods for credit worthiness scoring and loan facilitation |
US8095443B2 (en) | 2008-06-18 | 2012-01-10 | Consumerinfo.Com, Inc. | Debt trending systems and methods |
WO2009158361A1 (en) | 2008-06-24 | 2009-12-30 | Mobile Tribe Llc | Branded advertising based dynamic experience generator |
US20100191598A1 (en) | 2008-06-25 | 2010-07-29 | Roger Leon Toennis | Automated Multimedia Gateway For Consumer-Controlled Specification, Filtering And Delivery Of Personalized Product/Service Offers |
US20100030649A1 (en) | 2008-06-27 | 2010-02-04 | Trans Union Llc | Method and system for batch execution of variable input data |
US20090327120A1 (en) | 2008-06-27 | 2009-12-31 | Eze Ike O | Tagged Credit Profile System for Credit Applicants |
US8793183B2 (en) | 2008-07-15 | 2014-07-29 | Loaninsights, Llc | Reverse customized consumer loan search |
US7991689B1 (en) | 2008-07-23 | 2011-08-02 | Experian Information Solutions, Inc. | Systems and methods for detecting bust out fraud using credit data |
ZA200903243B (en) | 2008-07-25 | 2010-03-31 | Transunion Decision Support Se | A method of processing a credit application |
CA2733199C (en) | 2008-08-06 | 2018-01-09 | Invidi Technologies Corporation | Third party data matching for targeted advertising |
NL2001879C2 (en) | 2008-08-07 | 2010-02-09 | Stroeve Beheer B V A | Method for creating a series of weighted areas of interest of a user of multiple social computer networks, and system for that. |
US8805110B2 (en) | 2008-08-19 | 2014-08-12 | Digimarc Corporation | Methods and systems for content processing |
US20100049651A1 (en) | 2008-08-25 | 2010-02-25 | Alliance Data Systems Corporation | Loyalty-Based Credit Prescreening System |
US9129325B2 (en) | 2008-09-09 | 2015-09-08 | Truecar, Inc. | System and method for aggregation, analysis, presentation and monetization of pricing data for vehicles and other commodities |
US20110178899A1 (en) | 2008-09-25 | 2011-07-21 | Maria Huszar | Borrowing and lending platform and method |
US8412593B1 (en) | 2008-10-07 | 2013-04-02 | LowerMyBills.com, Inc. | Credit card matching |
US20100094758A1 (en) | 2008-10-13 | 2010-04-15 | Experian Marketing Solutions, Inc. | Systems and methods for providing real time anonymized marketing information |
CN102203816A (en) | 2008-10-15 | 2011-09-28 | 康塔网络公司 | Method and system for displaying internet ad media using etags |
US8606678B2 (en) | 2008-10-15 | 2013-12-10 | Bank Of America Corporation | Interactive and collaborative financial customer experience application |
WO2010047854A2 (en) | 2008-10-24 | 2010-04-29 | Cardlytics, Inc. | System and methods for delivering targeted marketing offers to consumers via an online portal |
US8682785B2 (en) | 2008-10-30 | 2014-03-25 | Bank Of America Corporation | Bank card authorization with balance indicator |
US20100114646A1 (en) | 2008-10-30 | 2010-05-06 | Alliance Data Systems, Inc. | Method and System for Segmenting Customers for Marketing and Other Projects |
US8775230B2 (en) | 2008-11-03 | 2014-07-08 | Oracle International Corporation | Hybrid prediction model for a sales prospector |
WO2010062537A2 (en) | 2008-11-26 | 2010-06-03 | Motorola, Inc. | Method and apparatus for providing an advertisement to a user based on an action of a friend |
US8634542B2 (en) | 2008-12-09 | 2014-01-21 | Satmap International Holdings Limited | Separate pattern matching algorithms and computer models based on available caller data |
US8862519B2 (en) | 2008-12-29 | 2014-10-14 | International Business Machines Corporation | Predicting email response patterns |
US20100169159A1 (en) | 2008-12-30 | 2010-07-01 | Nicholas Rose | Media for Service and Marketing |
US8127982B1 (en) | 2009-01-09 | 2012-03-06 | Apple Inc. | Parental controls |
US9063226B2 (en) | 2009-01-14 | 2015-06-23 | Microsoft Technology Licensing, Llc | Detecting spatial outliers in a location entity dataset |
US20100211445A1 (en) | 2009-01-15 | 2010-08-19 | Shaun Bodington | Incentives associated with linked financial accounts |
US20100185489A1 (en) | 2009-01-21 | 2010-07-22 | Satyavolu Ramakrishna V | Method for determining a personalized true cost of service offerings |
US8170958B1 (en) | 2009-01-29 | 2012-05-01 | Intuit Inc. | Internet reputation manager |
US20100198629A1 (en) | 2009-02-02 | 2010-08-05 | Vuenu Media, LLC | Motor vehicle valuation system and method with data filtering, analysis, and reporting capabilities |
US7783515B1 (en) | 2009-03-27 | 2010-08-24 | Bank Of America Corporation | Itemized receipt tracking system |
US20100268660A1 (en) | 2009-04-15 | 2010-10-21 | Jared Ekdahl | Systems and methods for verifying and rating mortgage financial companies |
US20100268557A1 (en) | 2009-04-17 | 2010-10-21 | Patrick Faith | Enrollment server |
US8214238B1 (en) | 2009-04-21 | 2012-07-03 | Accenture Global Services Limited | Consumer goods and services high performance capability assessment |
WO2010132492A2 (en) | 2009-05-11 | 2010-11-18 | Experian Marketing Solutions, Inc. | Systems and methods for providing anonymized user profile data |
US20100293114A1 (en) | 2009-05-15 | 2010-11-18 | Mohammed Salahuddin Khan | Real estate investment method for purchasing a plurality of distressed properties from a single institution at formula-derived prices |
US20120101970A1 (en) | 2009-06-22 | 2012-04-26 | United Parents Online Ltd. | Method and system of monitoring a network based communication among users |
US20100332292A1 (en) | 2009-06-30 | 2010-12-30 | Experian Information Solutions, Inc. | System and method for evaluating vehicle purchase loyalty |
WO2011000417A1 (en) | 2009-06-30 | 2011-01-06 | Nokia Siemens Networks Oy | System for protecting personal data |
RU2012103456A (en) | 2009-07-07 | 2013-08-20 | Логикс Фьюзион Инк. | METHOD FOR DISTRIBUTION OF INFORMATION AND POSITIVE FEEDBACKS ABOUT PRODUCTS |
US8364518B1 (en) | 2009-07-08 | 2013-01-29 | Experian Ltd. | Systems and methods for forecasting household economics |
US8607340B2 (en) | 2009-07-21 | 2013-12-10 | Sophos Limited | Host intrusion prevention system using software and user behavior analysis |
US20110047072A1 (en) | 2009-08-07 | 2011-02-24 | Visa U.S.A. Inc. | Systems and Methods for Propensity Analysis and Validation |
US8850328B2 (en) | 2009-08-20 | 2014-09-30 | Genesismedia Llc | Networked profiling and multimedia content targeting system |
US20110054981A1 (en) | 2009-08-27 | 2011-03-03 | Faith Patrick L | Analyzing Local Non-Transactional Data with Transactional Data in Predictive Models |
US20110066495A1 (en) | 2009-09-11 | 2011-03-17 | Yahoo! Inc. | System and method for customizing ads in web and mobile applications |
US20110071950A1 (en) | 2009-09-23 | 2011-03-24 | Webcom, Inc. | Customer-oriented customer relationship management process and system |
US8799150B2 (en) | 2009-09-30 | 2014-08-05 | Scorelogix Llc | System and method for predicting consumer credit risk using income risk based credit score |
US8315895B1 (en) | 2009-10-05 | 2012-11-20 | Intuit Inc. | Method and system for obtaining review updates within a review and rating system |
US8595058B2 (en) | 2009-10-15 | 2013-11-26 | Visa U.S.A. | Systems and methods to match identifiers |
US20120137367A1 (en) | 2009-11-06 | 2012-05-31 | Cataphora, Inc. | Continuous anomaly detection based on behavior modeling and heterogeneous information analysis |
US8589069B1 (en) | 2009-11-12 | 2013-11-19 | Google Inc. | Enhanced identification of interesting points-of-interest |
US8239130B1 (en) | 2009-11-12 | 2012-08-07 | Google Inc. | Enhanced identification of interesting points-of-interest |
US8566029B1 (en) | 2009-11-12 | 2013-10-22 | Google Inc. | Enhanced identification of interesting points-of-interest |
US8433512B1 (en) | 2009-11-12 | 2013-04-30 | Google Inc. | Enhanced identification of interesting points-of-interest |
US20110137789A1 (en) | 2009-12-03 | 2011-06-09 | Venmo Inc. | Trust Based Transaction System |
JP2011138197A (en) | 2009-12-25 | 2011-07-14 | Sony Corp | Information processing apparatus, method of evaluating degree of association, and program |
US8520842B2 (en) | 2010-01-07 | 2013-08-27 | Microsoft Corporation | Maintaining privacy during user profiling |
US8489499B2 (en) | 2010-01-13 | 2013-07-16 | Corelogic Solutions, Llc | System and method of detecting and assessing multiple types of risks related to mortgage lending |
US8571919B2 (en) | 2010-01-20 | 2013-10-29 | American Express Travel Related Services Company, Inc. | System and method for identifying attributes of a population using spend level data |
US20110178855A1 (en) | 2010-01-20 | 2011-07-21 | American Express Travel Related Services Company, | System and method for increasing marketing performance using spend level data |
US8255268B2 (en) | 2010-01-20 | 2012-08-28 | American Express Travel Related Services Company, Inc. | System and method for matching merchants based on consumer spend behavior |
US20110178848A1 (en) | 2010-01-20 | 2011-07-21 | American Express Travel Related Services Company, Inc. | System and method for matching consumers based on spend behavior |
US20110178845A1 (en) | 2010-01-20 | 2011-07-21 | American Express Travel Related Services Company, Inc. | System and method for matching merchants to a population of consumers |
US20110178844A1 (en) | 2010-01-20 | 2011-07-21 | American Express Travel Related Services Company, Inc. | System and method for using spend behavior to identify a population of merchants |
US20110178847A1 (en) | 2010-01-20 | 2011-07-21 | American Express Travel Related Services Company, Inc. | System and method for identifying a selected demographic's preferences using spend level data |
US20110178843A1 (en) | 2010-01-20 | 2011-07-21 | American Express Travel Related Services Company, Inc. | System and method for using spend behavior to identify a population of consumers that meet a specified criteria |
US20110178846A1 (en) | 2010-01-20 | 2011-07-21 | American Express Travel Related Services Company, Inc. | System and method for using spend level data to match a population of consumers to merchants |
US8600855B2 (en) | 2010-01-26 | 2013-12-03 | Visa International Service Association | Transaction data repository for risk analysis |
US20140058818A1 (en) | 2010-01-27 | 2014-02-27 | Envizio, Inc. | Offer redemption of an offer at a retailer interface that identifies a retail transaction and line items used by offer validation |
US20130339143A1 (en) | 2010-01-27 | 2013-12-19 | Envizio, Inc. | Campaign reward system with targeting of users for offers |
US10089683B2 (en) | 2010-02-08 | 2018-10-02 | Visa International Service Association | Fraud reduction system for transactions |
US20110238566A1 (en) | 2010-02-16 | 2011-09-29 | Digital Risk, Llc | System and methods for determining and reporting risk associated with financial instruments |
WO2011103429A2 (en) | 2010-02-18 | 2011-08-25 | Finshphere Corporation | System and method for improving internet search results using telecommunications data |
US20110218826A1 (en) | 2010-02-19 | 2011-09-08 | Lighthouse Group International, Llc | System and method of assigning residential home price volatility |
US8885459B2 (en) | 2010-02-26 | 2014-11-11 | Futurewei Technologies, Inc. | System and method for computing a backup ingress of a point-to-multipoint label switched path |
US8738418B2 (en) | 2010-03-19 | 2014-05-27 | Visa U.S.A. Inc. | Systems and methods to enhance search data with transaction based data |
US9613139B2 (en) | 2010-03-24 | 2017-04-04 | Taykey Ltd. | System and methods thereof for real-time monitoring of a sentiment trend with respect of a desired phrase |
US20110258050A1 (en) | 2010-04-16 | 2011-10-20 | Bread Labs Inc. A Delaware Corporation | Social advertising platform |
US20110264581A1 (en) | 2010-04-23 | 2011-10-27 | Visa U.S.A. Inc. | Systems and Methods to Provide Market Analyses and Alerts |
US20110270618A1 (en) | 2010-04-30 | 2011-11-03 | Bank Of America Corporation | Mobile commerce system |
US8458074B2 (en) | 2010-04-30 | 2013-06-04 | Corelogic Solutions, Llc. | Data analytics models for loan treatment |
US20110282739A1 (en) | 2010-05-11 | 2011-11-17 | Alex Mashinsky | Method and System for Optimizing Advertising Conversion |
US8655938B1 (en) | 2010-05-19 | 2014-02-18 | Adobe Systems Incorporated | Social media contributor weight |
US20110307397A1 (en) | 2010-06-09 | 2011-12-15 | Akram Benmbarek | Systems and methods for applying social influence |
US8396759B2 (en) | 2010-06-18 | 2013-03-12 | Google Inc. | Context-influenced application recommendations |
US20120011056A1 (en) | 2010-07-07 | 2012-01-12 | Roderick Ward | System and method for processing commerical loan information |
US20120011068A1 (en) | 2010-07-09 | 2012-01-12 | United States Postal Service | System and method of electronic and physical mail categorization and targeted delivery |
US20120016733A1 (en) | 2010-07-13 | 2012-01-19 | Visible Brands, Inc. | System and method for correlating electronic advertisements presented to consumers on computing devices with consumer visits to retail environments |
US20120016948A1 (en) | 2010-07-15 | 2012-01-19 | Avaya Inc. | Social network activity monitoring and automated reaction |
US20120029996A1 (en) | 2010-07-30 | 2012-02-02 | Alliance Data Systems Corporation | Loyalty-Based Credit Prescreening System |
US20120029956A1 (en) | 2010-07-30 | 2012-02-02 | Bank Of America Corporation | Comprehensive exposure analysis system and method |
US9262517B2 (en) | 2010-08-18 | 2016-02-16 | At&T Intellectual Property I, L.P. | Systems and methods for social media data mining |
US9152727B1 (en) | 2010-08-23 | 2015-10-06 | Experian Marketing Solutions, Inc. | Systems and methods for processing consumer information for targeted marketing applications |
US8340685B2 (en) | 2010-08-25 | 2012-12-25 | The Nielsen Company (Us), Llc | Methods, systems and apparatus to generate market segmentation data with anonymous location data |
US8560935B2 (en) | 2010-08-31 | 2013-10-15 | American Sterling Dental Plan, Llc | Segmenting forms for multiple user completion |
US8474018B2 (en) | 2010-09-03 | 2013-06-25 | Ebay Inc. | Role-based attribute based access control (RABAC) |
US20120066065A1 (en) | 2010-09-14 | 2012-03-15 | Visa International Service Association | Systems and Methods to Segment Customers |
US20120101938A1 (en) | 2010-10-25 | 2012-04-26 | Sheldon Kasower | Method and system for secure online payments |
US8589208B2 (en) | 2010-11-19 | 2013-11-19 | Information Resources, Inc. | Data integration and analysis |
US9147042B1 (en) | 2010-11-22 | 2015-09-29 | Experian Information Solutions, Inc. | Systems and methods for data verification |
US8892605B2 (en) | 2010-12-03 | 2014-11-18 | Relationship Capital Technologies, Inc. | Systems and methods for managing social networks based upon predetermined objectives |
US20120158654A1 (en) | 2010-12-17 | 2012-06-21 | Google Inc. | Receipt storage in a digital wallet |
US8365212B1 (en) | 2010-12-29 | 2013-01-29 | Robert Alan Orlowski | System and method for analyzing human interaction with electronic devices that access a computer system through a network |
US20120203639A1 (en) | 2011-02-08 | 2012-08-09 | Cbs Interactive, Inc. | Targeting offers to users of a web site |
US20120209586A1 (en) | 2011-02-16 | 2012-08-16 | Salesforce.Com, Inc. | Contextual Demonstration of Applications Hosted on Multi-Tenant Database Systems |
US9003297B2 (en) | 2011-02-17 | 2015-04-07 | Mworks Worldwide, Inc. | Integrated enterprise software and social network system user interfaces utilizing cloud computing infrastructures and single secure portal access |
BR112013022510A2 (en) * | 2011-03-04 | 2017-01-17 | Foursquare Labs Inc | system and method for managing and redeeming offers with a location-based service |
US20120232958A1 (en) | 2011-03-11 | 2012-09-13 | Bar & Club Statistics, Inc. | Systems and methods for dynamic venue demographics and marketing |
US20120239497A1 (en) | 2011-03-17 | 2012-09-20 | Ebay Inc. | Method and process of using a social network to retarget a personal advertisement |
US20120239515A1 (en) | 2011-03-18 | 2012-09-20 | International Business Machines Corporation | Systems and methods for dynamic product and service bundling |
US20120284118A1 (en) | 2011-05-04 | 2012-11-08 | Microsoft Corporation | Using collective data for targeting of advertisements |
US9317404B1 (en) | 2011-05-08 | 2016-04-19 | Panaya Ltd. | Generating test scenario templates from test runs collected from different organizations |
WO2012170838A1 (en) | 2011-06-09 | 2012-12-13 | My Interest Broker, LLC | System and method for trading debt instruments |
US8676992B2 (en) | 2011-06-14 | 2014-03-18 | American Express Travel Related Services Company, Inc. | Systems and methods for cooperative data exchange |
US8606695B1 (en) | 2011-07-01 | 2013-12-10 | Biz2credit Inc. | Decision making engine and business analysis tools for small business credit product offerings |
US9483606B1 (en) | 2011-07-08 | 2016-11-01 | Consumerinfo.Com, Inc. | Lifescore |
US8392230B2 (en) | 2011-07-15 | 2013-03-05 | Credibility Corp. | Automated omnipresent real-time credibility management system and methods |
US8566167B2 (en) | 2011-07-27 | 2013-10-22 | Sears Brands, L.L.C. | System and method for using data points collected from a customer to provide customer specific offerings |
US8818839B2 (en) | 2011-10-04 | 2014-08-26 | Reach Pros, Inc. | Online marketing, monitoring and control for merchants |
US8639596B2 (en) | 2011-10-04 | 2014-01-28 | Galisteo Consulting Group, Inc. | Automated account reconciliation method |
US20130252638A1 (en) | 2011-10-21 | 2013-09-26 | Alohar Mobile Inc. | Real-Time Determination of User Stays of a Mobile Device |
US20130159411A1 (en) | 2011-11-02 | 2013-06-20 | Barbara Bowen | Data sharing and content delivery system |
US8966602B2 (en) | 2011-11-07 | 2015-02-24 | Facebook, Inc. | Identity verification and authentication |
US20130124263A1 (en) | 2011-11-14 | 2013-05-16 | Visa International Service Association | Systems and Methods to Summarize Transaction data |
US9143541B1 (en) | 2011-11-17 | 2015-09-22 | Google Inc. | Systems, computer-implemented methods, and computer-readable media to target internet-based services on a geographic location |
US20130151388A1 (en) | 2011-12-12 | 2013-06-13 | Visa International Service Association | Systems and methods to identify affluence levels of accounts |
US20130226656A1 (en) | 2012-02-16 | 2013-08-29 | Bazaarvoice, Inc. | Determining influence of a person based on user generated content |
US8943060B2 (en) | 2012-02-28 | 2015-01-27 | CQuotient, Inc. | Systems, methods and apparatus for identifying links among interactional digital data |
US8463595B1 (en) | 2012-03-06 | 2013-06-11 | Reputation.Com, Inc. | Detailed sentiment analysis |
US10672018B2 (en) | 2012-03-07 | 2020-06-02 | Visa International Service Association | Systems and methods to process offers via mobile devices |
KR101337447B1 (en) | 2012-03-22 | 2013-12-05 | (주)네오위즈게임즈 | Method and server for authenticatiing user in onlie game |
CA2868933C (en) | 2012-03-31 | 2021-06-01 | Trans Union Llc | Systems and methods for targeted internet marketing based on offline, online, and credit-related data |
US9953326B2 (en) | 2012-05-02 | 2018-04-24 | Jpmorgan Chase Bank, N.A. | Alert optimization system and method |
US8515828B1 (en) | 2012-05-29 | 2013-08-20 | Google Inc. | Providing product recommendations through keyword extraction from negative reviews |
US9026088B1 (en) | 2012-05-29 | 2015-05-05 | West Corporation | Controlling a crowd of multiple mobile station devices |
US9621554B2 (en) | 2012-06-26 | 2017-04-11 | Cisco Technology, Inc. | Method for propagating access policies |
AU2013295603A1 (en) | 2012-07-26 | 2015-02-05 | Experian Marketing Solutions, Inc. | Systems and methods of aggregating consumer information |
US8938411B2 (en) | 2012-08-08 | 2015-01-20 | Facebook, Inc. | Inferring user family connections from social information |
US9785890B2 (en) | 2012-08-10 | 2017-10-10 | Fair Isaac Corporation | Data-driven product grouping |
US20200043103A1 (en) | 2012-09-12 | 2020-02-06 | Experian Information Solutions, Inc. | Proactive offers |
US20150193821A1 (en) | 2012-09-28 | 2015-07-09 | Rakuten, Inc. | Information processing apparatus, information processing method, and information processing program |
US20140095251A1 (en) | 2012-10-03 | 2014-04-03 | Citicorp Credit Services, Inc. | Methods and Systems for Optimizing Marketing Strategy to Customers or Prospective Customers of a Financial Institution |
US9736271B2 (en) | 2012-12-21 | 2017-08-15 | Akamai Technologies, Inc. | Scalable content delivery network request handling mechanism with usage-based billing |
US20140222908A1 (en) | 2013-02-01 | 2014-08-07 | Nextdoor.Com, Inc. | Methods and systems for a location-based online social network |
US8799053B1 (en) | 2013-03-13 | 2014-08-05 | Paul R. Goldberg | Secure consumer data exchange method, apparatus, and system therfor |
US20140278774A1 (en) | 2013-03-13 | 2014-09-18 | Experian Information Solutions, Inc. | In the market model systems and methods |
US9553936B2 (en) | 2013-03-15 | 2017-01-24 | Google Inc. | Targeting of digital content to geographic regions |
US20140279420A1 (en) | 2013-03-15 | 2014-09-18 | Michael D. Okerlund | System and method for facilitating financial transactions utilizing a plurality of networked databases |
US20140279197A1 (en) | 2013-03-15 | 2014-09-18 | Alliance Data Systems Corporation | Enhancing revenue of a retailer by making a recommendation to a customer |
CA2910281A1 (en) | 2013-05-07 | 2014-11-13 | Equifax, Inc. | Increasing reliability of information available to parties in market transactions |
US9807049B2 (en) | 2013-05-24 | 2017-10-31 | erodr, Inc. | Systems and methods to present messages in location-based social networking communities |
US9213646B1 (en) * | 2013-06-20 | 2015-12-15 | Seagate Technology Llc | Cache data value tracking |
US9710841B2 (en) | 2013-09-30 | 2017-07-18 | Comenity Llc | Method and medium for recommending a personalized ensemble |
CA2865348C (en) | 2013-09-30 | 2020-12-01 | Alliance Data Systems Corporation | Recommending a personalized ensemble |
US9704192B2 (en) | 2013-09-30 | 2017-07-11 | Comenity Llc | Method for displaying items on a 3-D shape |
WO2015057538A1 (en) | 2013-10-14 | 2015-04-23 | Equifax Inc. | Providing identification information to mobile commerce applications |
US10373270B2 (en) | 2013-10-14 | 2019-08-06 | Facebook, Inc. | Identifying posts in a social networking system for presentation to one or more user demographic groups |
US9147152B2 (en) | 2013-10-18 | 2015-09-29 | Comenity Llc | Displaying an animated digital watermark |
US9471944B2 (en) | 2013-10-25 | 2016-10-18 | The Mitre Corporation | Decoders for predicting author age, gender, location from short texts |
US20150120391A1 (en) | 2013-10-25 | 2015-04-30 | Cellco Partnership (D/B/A Verizon Wireless) | Enhanced weighing and attributes for marketing reports |
WO2015066511A1 (en) | 2013-11-01 | 2015-05-07 | Ncluud Corporation | Determining identity of individuals using authenticators |
CA2929269C (en) | 2013-11-01 | 2019-06-04 | Anonos Inc. | Dynamic de-identification and anonymity |
US10102536B1 (en) | 2013-11-15 | 2018-10-16 | Experian Information Solutions, Inc. | Micro-geographic aggregation system |
US20160055487A1 (en) * | 2014-02-07 | 2016-02-25 | Bank Of America Corporation | Determining user authentication based on user patterns within application |
US20150235230A1 (en) | 2014-02-17 | 2015-08-20 | Comenity Llc | Prioritizing customer service |
US20170039616A1 (en) | 2014-02-17 | 2017-02-09 | Comenity Llc | Customer queue prioritization through location detection |
US20150248665A1 (en) | 2014-03-03 | 2015-09-03 | Comenity Llc | Providing dynamic results from a static barcode |
US10380619B2 (en) | 2014-03-03 | 2019-08-13 | Comenity Llc | Drivers license parser |
US9256866B2 (en) | 2014-03-03 | 2016-02-09 | Comenity Llc | Drivers license look-up |
US20150248716A1 (en) | 2014-03-03 | 2015-09-03 | Comenity Llc | Collaborative jewelry builder |
CA2942328C (en) | 2014-03-11 | 2020-04-07 | Trans Union Llc | Digital prescreen targeted marketing system and method |
US20150262109A1 (en) | 2014-03-17 | 2015-09-17 | Comenity Llc | Gamification based performance tracking |
US20150262291A1 (en) | 2014-03-17 | 2015-09-17 | Comenity Llc | Apply and buy with a co-branded virtual card |
US9608982B2 (en) * | 2014-04-14 | 2017-03-28 | Trulioo Information Services, Inc. | Identity validation system and associated methods |
US10222988B2 (en) | 2014-04-22 | 2019-03-05 | Hitachi, Ltd. | Efficient management storage system via defining of several size units in advance |
US10319206B2 (en) * | 2014-04-25 | 2019-06-11 | Tyco Safety Products Canada Ltd. | Identifying persons of interest using mobile device information |
US9576030B1 (en) | 2014-05-07 | 2017-02-21 | Consumerinfo.Com, Inc. | Keeping up with the joneses |
US20150332414A1 (en) | 2014-05-13 | 2015-11-19 | Mastercard International Incorporated | System and method for predicting items purchased based on transaction data |
US20150348200A1 (en) | 2014-06-03 | 2015-12-03 | Christopher T. Fair | Systems and methods for facilitating communication and investment |
CA2895452A1 (en) | 2014-07-02 | 2016-01-02 | Comenity Llc | Seamless progression of credit related processes on a mobile device |
US20160005114A1 (en) | 2014-07-02 | 2016-01-07 | Comenity Llc | Seamless progression of credit related processes on a mobile device |
US9811848B2 (en) * | 2014-09-08 | 2017-11-07 | Facebook, Inc. | Verifying purchasers of restricted gifts |
US9329715B2 (en) * | 2014-09-11 | 2016-05-03 | Qeexo, Co. | Method and apparatus for differentiating touch screen users based on touch event analysis |
US11042946B2 (en) * | 2014-09-30 | 2021-06-22 | Walmart Apollo, Llc | Identity mapping between commerce customers and social media users |
CA2901057A1 (en) | 2014-10-07 | 2016-04-07 | Comenity Llc | Determining preference of an ensemble of items |
US10354311B2 (en) | 2014-10-07 | 2019-07-16 | Comenity Llc | Determining preferences of an ensemble of items |
US9953357B2 (en) | 2014-10-07 | 2018-04-24 | Comenity Llc | Sharing an ensemble of items |
US20160098784A1 (en) | 2014-10-07 | 2016-04-07 | Comenity Llc | Generating a user dashboard associated with ensembles of retail items |
US10990937B2 (en) | 2014-10-16 | 2021-04-27 | Comenity Llc | Retail card application |
US10984404B2 (en) | 2014-10-16 | 2021-04-20 | Comenity Llc | Retail card application |
US10664759B2 (en) | 2014-10-23 | 2020-05-26 | Fair Isaac Corporation | Dynamic business rule creation using scored sentiments |
US10204368B2 (en) | 2014-11-13 | 2019-02-12 | Comenity Llc | Displaying an electronic product page responsive to scanning a retail item |
CA2909392C (en) | 2014-12-01 | 2023-03-07 | Comenity Llc | Applying for a credit card account on a mobile device |
US20160155191A1 (en) | 2014-12-01 | 2016-06-02 | Comenity Llc | Applying for a credit card account on a mobile device |
US11037212B2 (en) | 2014-12-01 | 2021-06-15 | Comenity Llc | Pre-populating a credit card number field |
US20160171531A1 (en) | 2014-12-11 | 2016-06-16 | Connectivity, Inc. | Systems and Methods for Generating Advertising Targeting Data Using Customer Profiles Generated from Customer Data Aggregated from Multiple Information Sources |
US10726425B2 (en) | 2014-12-18 | 2020-07-28 | Comenity Llc | Custom communication generator |
US10242019B1 (en) | 2014-12-19 | 2019-03-26 | Experian Information Solutions, Inc. | User behavior segmentation using latent topic detection |
US10572891B2 (en) | 2014-12-23 | 2020-02-25 | Comenity Llc | Punchcard check-in system |
US10292008B2 (en) | 2014-12-23 | 2019-05-14 | Comenity Llc | Geofencing messaging system |
US10423976B2 (en) | 2014-12-29 | 2019-09-24 | Comenity Llc | Collecting and analyzing data for targeted offers |
US10157397B2 (en) | 2014-12-29 | 2018-12-18 | Comenity Llc | Collecting and analyzing data from a mobile device |
CA2915375A1 (en) | 2014-12-30 | 2016-06-30 | Comenity Llc | Retail card application and method |
US10460335B2 (en) | 2015-03-10 | 2019-10-29 | Comenity Llc | Geo-filtering consumers |
CA2923334A1 (en) | 2015-03-11 | 2016-09-11 | Comenity Llc | Providing mobile loyalty services via a native mobile application |
US10783542B2 (en) | 2015-03-11 | 2020-09-22 | Comenity, LLC | Providing biometric security for mobile loyalty services via a native mobile application |
MX2017013850A (en) | 2015-04-28 | 2018-02-21 | Trans Union Llc | System and method for automated communications session routing in a communications handling system. |
US20160350851A1 (en) | 2015-05-26 | 2016-12-01 | Comenity Llc | Clienteling credit suggestion confidence |
US10169775B2 (en) | 2015-08-03 | 2019-01-01 | Comenity Llc | Mobile credit acquisition |
US10929924B2 (en) | 2015-08-25 | 2021-02-23 | Comenity Llc | Mobile number credit prescreen |
US20170061511A1 (en) | 2015-08-31 | 2017-03-02 | Comenity Llc | Mobile device initiated concierge experience |
US9767309B1 (en) | 2015-11-23 | 2017-09-19 | Experian Information Solutions, Inc. | Access control system for implementing access restrictions of regulated database records while identifying and providing indicators of regulated database records matching validation criteria |
US20170161780A1 (en) | 2015-12-04 | 2017-06-08 | Comenity Llc | Using micropatterns to motivate a customer |
US9928435B2 (en) * | 2015-12-30 | 2018-03-27 | Samsung Electronics Co., Ltd | System and method for providing an on-chip context aware contact list |
US20180053252A1 (en) | 2016-08-16 | 2018-02-22 | Comenity Llc | Mobile credit acquisition with form population |
US20180053172A1 (en) | 2016-08-18 | 2018-02-22 | Comenity Llc | Seamless integration of financial information within a mobile retail application framework |
WO2018039377A1 (en) | 2016-08-24 | 2018-03-01 | Experian Information Solutions, Inc. | Disambiguation and authentication of device users |
US11645697B2 (en) | 2016-10-06 | 2023-05-09 | Bread Financial Payments, Inc. | Simple checkout |
US20180330383A1 (en) | 2017-05-12 | 2018-11-15 | Comenity Llc | Limited use temporary credit account |
US11288721B2 (en) | 2017-05-12 | 2022-03-29 | Comenity Llc | System and method for a delayed purchase based on input from another |
US20190005498A1 (en) | 2017-06-29 | 2019-01-03 | Comenity Llc | Private label account number protection |
US11625774B2 (en) | 2017-08-07 | 2023-04-11 | Bread Financial Payments, Inc | Using position location information to pre-populate and verify information on a credit application |
US10657229B2 (en) | 2017-11-21 | 2020-05-19 | Fair Isaac Corporation | Building resilient models to address dynamic customer data use rights |
US20190180327A1 (en) | 2017-12-08 | 2019-06-13 | Arun BALAGOPALAN | Systems and methods of topic modeling for large scale web page classification |
US20190244237A1 (en) | 2018-02-02 | 2019-08-08 | Comenity Llc | Intermediary to manage a point exchange across a plurality of different reward programs |
US20190311427A1 (en) | 2018-04-04 | 2019-10-10 | Fair Isaac Corporation | Score Change Analyzer |
US11853966B2 (en) | 2018-10-18 | 2023-12-26 | Bread Financial Payments, Inc. | Internet-based management of displayed printed media |
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