The present development relates to applying topic discovery algorithms, such as a latent dirichlet allocation algorithm, to reduce the dimensionality of event data to develop segmentations based on user n behavior indicated by the event data.
With the advent of modern computing devices, the ways in which users use electronic devices to interact with various entities has dramatically increased. Each event a user performs, whether by making a small purchasing at a grocery store, logging into a web-site, checking a book out of a library, driving a car, making a phone call, or exercising at the gym, the digital foot print of the users interactions can be tracked. The quantity of event data collected for just one user can be immense. The enormity of the data may be compounded by the number of users connected and the increasing number of event types that are made possible through an increasing number of event sources and entities. To understand a particular user, one can provide large swaths of the user's event history, but there are several obstacles which make this difficult if not impossible. To ensure a full picture of the user is provided, all event data would need to be considered. As mentioned, this can include providing many megabytes, if not gigabytes of data, for one user. This may pose a problem in limited resource environments such as mobile computing or networked system. In the mobile world, mobile computing devices typically have constraints on the memory, power, and processing capabilities. To provide a lot of data to the device could drain these precious resources. In the networked systems, one concern is network utilization. Providing all of a user's event data for each event could increase the traffic flowing through the network and thus adversely impact the overall performance.
One solution could be to take a snapshot of a user's events. However, this method fails to consider longer term trends by making an arbitrary cutoff to the data. The cutoff may be based on date, event source, or other criteria to limit the event data that would be transmitted for a user. This can lead to inaccurate assumptions about the user based on the limited view of their historical events. Making sense of the collected event data and providing usable forms of the data.
Accordingly, improved systems, devices, and methods for compressing event data to reduce its dimensionality and then placing users into segments with similar behavior without losing descriptive details of the underlying set of event data set are desirable.
Various systems and methods are disclosed which include features relating to artificial intelligence directed compression of user event data based on complex analysis of user event data including latent feature detection and clustering. Further features are described for reducing the size of data transmitted during event processing data flows and devices such as card readers or point of sale systems. Machine learning features for dynamically determining an optimal compression as well as identifying targeted users and providing content to the targeted users based on the compressed data are also included.
The systems, methods, and devices of the disclosure each have several innovative aspects, no single one of which is solely responsible for the desirable attributes disclosed herein.
In one innovative aspect, a method of artificial intelligence guided segmentation of event data is provided. The method includes accessing, from a data store, a set of event records associated with respective users of a group of users. A first set of event records is associated with a first user is stored using a first quantity of storage. The method also includes accessing an event categories data structure indicating a set of event categories and, for each event category, attribute criteria usable to identify events associated with respective event categories. For the event records, the method includes identifying one or more attributes of the event record, comparing the identified one or more attributes of the event record to the attribute criteria of respective event categories, and based on said comparing, assigning, to the event record, an event category having attribute criteria matching the identified one or more attributes of the event record. The method also includes generating, for the first user, first compressed event data using the event records associated with the first user and a latent feature identification model such as a dirichlet allocation model. The latent feature identification model takes the event records for the first user and the event categories assigned thereto as inputs. The first compressed event data associated with the first user is stored using a second quantity of storage that is less than the first quantity of storage for storing the event records of the first user. The method also includes assigning each user to one of the data clusters included in a clustering model using respective first compressed event data for the user. The method further includes generating, for the first user, second compressed event data using a comparison between the first compressed event data for the first user and an average latent feature identification value for a latent feature included in the data cluster to which the first user has been assigned. The second compressed event data associated with the first user is stored using a third quantity of storage that is less than the second quantity of storage.
In some implementations of the method, assigning a user to one of the data clusters includes identifying center points for each data cluster included in the clustering model. In such implementations, the method may also include generating an association strength for each latent feature included in the first compressed event data for the users for each data cluster. The association strength may indicate a degree of association between the first compressed event data for a user and respective data cluster center points. The method may also include identifying the one of the data clusters as having the highest association strength for the user from amongst the data clusters included in the clustering model.
In some implementations, generating the association strength for a user includes comparing a latent feature identification value included in the first compressed event record for a latent feature for the user to the center point.
Generating the second compressed event data, may in some implementations, include calculating a secondary center point for a secondary data cluster using first compressed event data for each user assigned to the secondary data cluster. In such implementations, the method may include generating a secondary association strength for each latent feature included in the first compressed event data for a user assigned to the data cluster. The secondary association strength may indicate a secondary degree of association between the first compressed event data for the user assigned to the data cluster and the secondary center point of the secondary data cluster to which the user is not assigned. The second compressed event data may include an identifier for the secondary data cluster and the generated secondary association strengths.
In some implementations, the method may include accessing content data including a content identifier and an indication of a target data cluster of the data clusters. The method may also include identifying a group of users assigned to the target data cluster and selecting a target group of users having second compressed event data including generated association strengths indicating a threshold degree of association to the center point of the target data cluster. An electronic communication may be generated to provide to the target set of user profiles, the electronic communication including content indicated by the content identifier.
In some implementations, the method may include training the latent feature identification model through probabilistic analysis of a plurality of historical event records to identify a target number of topics. The method may also include training the clustering model using a desired compression level indicating a number of data clusters for the clustering model. Training the clustering model may include generating a center point for each data cluster using topically compressed historical event data.
In another innovative aspect, a method of compressing transaction data is provided. The method includes receiving a set of transaction records each identifying a transaction by one of a group of users. The method further includes assigning a category to each of the set of transaction records. The method also includes generating first compressed transaction records using a latent feature identification model. The method includes identifying a clustering compression model for the one of the group of users. The method further includes generating second compressed transaction records using the first compressed transaction records and the clustering compression model.
In some implementations, generating a first compressed transaction record for a user includes receiving association strengths for each topic identified by the latent feature identification model for a set of transactions for the user.
Some implementations of the method may include receiving a compression configuration indicating a target number of features to identify for an end user and training a latent dirichlet allocation model to identify the target number of features using the received set of transaction records. The latent feature identification model may include the latent dirichlet allocation model trained.
Each data cluster included in the clustering compression model may be associated with at least one latent feature identifiable by the latent feature identification model. In such implementations, generating the second compressed transaction records may include assigning each user to one of the data clusters using the first compressed transaction records. Generating the second compressed transaction records may also include generating the second compressed transaction records for each user using a comparison between the first compressed transaction data for a user and the center point for the cluster to which the user is assigned.
In some implementations, generating the second compressed transaction records may include calculating a secondary center point for a secondary data cluster using first compressed transaction data for each user assigned to the secondary data cluster, and generating a secondary association strength for each latent feature included in the first compressed transaction data for a user assigned to the data cluster. The secondary association strength may indicate a secondary degree of association between the first compressed transaction data for the user assigned to the data cluster and the secondary center point of the secondary data cluster to which the user is not assigned. The second compressed transaction data may include an identifier for the secondary data cluster and the generated secondary association strengths.
In some implementations, the method includes training a clustering model using the desired compression level and at least a portion of the set of transaction records.
In some implementations, the method includes receiving, from a transaction terminal, a pending transaction record for a user included in the group of users. The pending transaction record is not included in the set of transaction records. The method may also include retrieving a second compressed transaction record for the user using an identifier of the user included in the pending transaction record. The method may further include transmitting the second compressed transaction record to the transaction terminal.
A content element may be selected for presentation to the user during or after the current transaction using the second compressed transaction record.
The content element may be provided to a content delivery system configured to transmit the content element to the user.
In another innovative aspect, a transaction data compression system is provided. The system includes a data preparation module configured to access transaction data associated with a group of users. For a set of transactions in the transaction data, the data preparation module is configured to assign a transaction category based on one or more attributes of the transaction, and normalize a level of the transaction based on spend levels of individual users.
The system includes a compression module configured to generate, for each user, first compressed transaction data using the transaction categories assigned to the transaction records for a respective user and a latent feature identification model. The first compressed transaction data associated with the one of the respective users is stored using a second quantity of storage that is less than the first quantity of storage. The compression module is further configured to identify a clustering compression model for users included in the plurality of users. The compression module may be further configured to assign each of the users to one of a set of data clusters included in the respective clustering compression model using respective first compressed transaction data for the user, and generate, for each user, second compressed transaction data using a comparison between the first compressed transaction data for a user and an average for the data cluster to which the user has been assigned. The second compressed transaction data may be stored using a third quantity of storage that is less than the second quantity of storage.
In some implementations, the system may include a profile targeting module. The profile targeting module may be configured to access content data including a content identifier and an indication of a target data cluster of the data clusters. The profile targeting module may be further configured to identify a group of users assigned to the target data cluster. The profile targeting module may be further configured to select a target group of users having second compressed transaction data including generated association strengths indicating a threshold degree of association to the center point of the target data cluster. The profile targeting module may also be configured to generate an electronic communication to provide to the target set of user profiles, the electronic communication including content indicated by the content identifier.
A content generation module may be included in the system. The content generation module may be configured to access the target group of users and identify a target device for a user included in the target group of users. In some implementations, the content generation module may be configured to provide the electronic communication to the target device.
A card reader may be included in some implementations of the system. The card reader may include a payment information detector configured to detect payment information for a transaction for a user. The card reader may further include a targeted content generator configured to receive compressed transaction data during the transaction for the user, and identify content stored by the card reader using a comparison between a content selection rule and the compressed transaction data, the content for presentation via the card reader. The card reader may also include a display configured to present the content to the user.
A compression model generator may be included in the system. The compression model generator may be configured to generate at least one of the latent feature identification model and a clustering model identifying the set of data clusters for the set of transaction records.
Disclosed herein are system and methods of analyzing, processing, and manipulating large sets of event data of users in order to provide various visualizations, alerts, and other actionable intelligence to users, merchants, and others. The event data may include, for example, specific transactions on one or more credit cards of a user, such as the detailed transaction data that is available on credit card statements. Transaction data may include transaction-level debit information also, such as regarding debit card or checking account transactions. The transaction data may be obtained from various sources, such as from 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, merchant retailers, and/or any other source. Transaction data may also include non-financial exchanges. For example, login in activity, Internet search history, Internet browsing history, posts to a social media platform, or other interactions between communication devices. In some implementations, the users may be machines interacting with each other (e.g., machine to machine communications).
This disclosure describes several unique uses of such transaction data. In general, the features relate to compression of user transaction database on complex analysis of user transaction data including latent feature detection and clustering. Further features are described for including these features in transaction processing data flows and devices such as card readers or point of sale systems. Features for identifying targeted users and providing content to the targeted users based on the detected behavioral segmentation are also included. The identification may also be used for login authentication, fraud detection, or activity alerting. For example, the compressed user transaction data may provide a transaction “fingerprint” for a user. Using the fingerprint, a requested transaction may be analyzed to determine a likelihood that the transaction was initiated by the user. Where the transaction is a login in attempt, the authentication may include this likelihood in considering whether to authenticate the user. Where the transaction is an exchange, the authorization of the exchange may consider the likelihood in making the authorization decision.
Each of the processes described herein may be performed by a transaction analysis processing system (also referred to as simply “the system,” “the transaction analysis system,” or “the processing system” herein), such as the example transaction analysis system illustrated in
As noted above, in one embodiment the transaction analysis processing system accesses transaction data associated with a plurality of users in order to segment the users into groups. This transaction based segmentation provides advantages over other segmentation systems that make use of demographic information to find groups of “like” individuals, some of which are discussed below. Furthermore, it may be desirable to provide accurate information about a user during a transaction. Such “real-time” data allows the user to receive relevant information at a specific point in time.
To facilitate an understanding of the systems and methods discussed herein, a number of 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.
Transaction data (also referred to as event data) generally refers to data associated with any event, such as an interaction by a user device with a server, website, database, and/or other online data owned by or under control of a requesting entity, such as a server controlled by a third party, such as a merchant. Transaction data may include merchant name, merchant location, merchant category, transaction dollar amount, transaction date, transaction channel (e.g., physical point of sale, Internet, etc.) and/or an indicator as to whether or not the physical payment card (e.g., credit card or debit card) was present for a transaction. Transaction data structures may include, for example, specific transactions on one or more credit cards of a user, such as the detailed transaction data that is available on credit card statements. Transaction data may also include transaction-level debit information, such as regarding debit card or checking account transactions. The transaction data may be obtained from various sources, such as from 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, merchant retailers, and/or any other source. Transaction data may also include non-financial exchanges, such as login activity, Internet search history, Internet browsing history, posts to a social media platform, or other interactions between communication devices. In some implementations, the users may be machines interacting with each other (e.g., machine-to-machine communications). Transaction data may be presented in raw form. Raw transaction data generally refers to transaction data as received by the transaction processing system from a third party transaction data provider. Transaction data may be compressed. Compressed transaction data may refer to transaction data that may be stored and/or transmitted using fewer resources than when in raw form. Compressed transaction data need not be “uncompressible.” Compressed transaction data preferably retains certain identifying characteristics of the user associated with the transaction data such as behavior patterns (e.g., spend patterns), data cluster affinity, or the like.
A model generally refers to a machine learning construct which may be used by the segmentation system to automatically identify the latent topics in the transaction data and to generate segments for each user based on their transaction behavior as indicated by their transaction data. A model may be trained. Training a model generally refers to an automated machine learning process to generate the model. A model may be represented as a data structure that identifies, for a given value, one or more correlated values. For example, a topic identification data structure may include data indicating, for a candidate list of transactions, one or more topics.
A topic generally refers to a theme or common behavior exhibited in transaction data. Topics can be learned by examining the transaction behavior of users we are interested in analyzing. Each topic may be defined by a subset of merchant categories or other information included in the transaction data. The topics may be learned such that they closely reproduce the observed behaviors in the transaction data set. For example, a set of transaction may be analyzed to determine what transaction aspects (e.g., transaction category, merchant, amount, location, time and/or day of the week of the transaction) are represented across a significant number of transactions. These aspects may be included as topics describing, in general terms, what the set of transactions are directed to.
A segment (also referred to herein as a “data cluster”) generally refers to a group of users where each user is associated with one or more topics within a set of topics with different weights. A segment generally indicates a collection of users with similar topic distribution in their transaction behavior. For example, a segment identifying a lifestyle of “sports fan” may include a user having transactions identified in the topics of “athletic events,” “sporting goods,” “physical fitness,” and “sports bar.”
The term machine learning generally refers to automated processes by which received data is analyzed to generate and/or update one or more models. Machine learning may include artificial intelligence such as neural networks, genetic algorithms, clustering, or the like. Machine learning may be performed using a training set of data. The training data may be used to generate the model that best characterizes a feature of interest using the training data. In some implementations, the class of features may be identified before training. In such instances, the model may be trained to provide outputs most closely resembling the target class of features. In some implementations, no prior knowledge may be available for training the data. In such instances, the model may discover new relationships for the provided training data. Such relationships may include similarities between data elements such as transactions or transaction categories as will be described in further detail below.
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, a message may be composed, transmitted, stored, received, etc. in multiple parts.
The terms determine or determining encompass a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing, and the like.
The term selectively or selective may encompass a wide variety of actions. For example, a “selective” process may include determining one option from multiple options. A “selective” process may include one or more of: dynamically determined inputs, preconfigured inputs, or user-initiated inputs for making the determination. In some implementations, an n-input switch may be included to provide selective functionality where n is the number of inputs used to make the selection.
The terms provide or providing encompass a wide variety of actions. For example, “providing” may include storing a value in a location for subsequent retrieval, transmitting a value directly to a recipient, transmitting or storing a reference to a value, and the like. “Providing” may also include encoding, decoding, encrypting, decrypting, validating, verifying, and the like.
A user interface (also referred to as an interactive user interface, a graphical user interface or a UI) may refer to a web-based interface including data fields for receiving input signals or providing electronic information and/or for providing information to the user in response to any received input signals. A UI may be implemented in whole or in part using technologies such as HTML, Flash, Java, .net, web services, and RSS. In some implementations, a UI may be included in a stand-alone client (for example, thick client, fat client) configured to communicate (e.g., send or receive data) in accordance with one or more of the aspects described.
The transaction data may be received by a segmentation service 120. Although
The segmentation service 120 includes a data preparation module 130. The data preparation module is provided to ensure transaction data is uniformly organized prior to dimension reduction. A transaction data collection service 132 is included to receive the transaction data from the transaction data sources. The transaction data may be received via wire, wireless, or hybrid wired and wireless means. The transaction data collection service 132 may collect data by requesting transaction data from a data source. In some implementations, the collection service 132 may receive transaction data from a transaction data source such as according to a schedule.
The transaction data received from a transaction data source may be stored in a raw transaction data store 134. The raw transaction data store 134 may be a specialized data store device configured to handle large volumes of data.
The data preparation module 130 shown in
The instructions may further cause the device 136 to categorize or codify the transaction data. In some implementations, the set of all users is run through one or more models, such as a latent dirichlet allocation (LDA) algorithm, which was originally designed to discover topics in text documents. In this approach each user is treated as a document, and each transaction is converted to a “word” by codification. These “words,” which are analogous to the “categories” discussed herein, may be included in the raw transaction data. In some implementations, the categories may be added to the raw transaction data by the device 136. The category or “word” assigned to a particular transaction may be determined by the device 136 using the transaction data such as an item identifier, an item name, merchant name, a merchant code, or a merchant category code or merchant location, or transaction amount, or transaction time, or combinations of the above. For example, the segmentation service 120 may be segmenting the data for identifying users to whom the system 100 will be providing specific content, such as health and safety information. As such, it may be desirable to categorize the transactions in a variety of health and safety categories. The categories may be provided as a configuration to the segmentation service 120 such that the same raw transaction data may be compressed in different ways. The configuration may identify the available categories and transaction data that cause the categorization device 136 to assign the associated category or word.
The normalization and codification device 136 is in data communication with a codified transaction data store 138. The codified transaction data store 138 is a specially configured transaction data storage device capable of handling large volumes of data (e.g., hundreds of millions of records). Illustratively, the system 100 may include hundreds of millions, or billions of transaction records. Advantageously, the system 100 is able to process these records in a duration of a few hours, whereas if such processing were to be performed manually by humans, it could take days, weeks, months, or years, depending on the number of human resources applied to the processing task. In some implementations, the codified transaction data store 138 may be commonly implemented with the raw transaction data store 134. However, in some implementations, such as when the segmentation service 120 provides segmentation data for different end uses, it may be desirable maintain separate data stores to ensure the security of the categorized or codified data for each end user.
The segmentation service 120 includes a dimension reduction module 150. The dimension reduction module 150 is in data communication with the codified transaction data store 138. The dimension reduction module 150 may be configured to generate compressed transaction records. To reduce the dimensions of the normalized transaction records, the dimension reduction module 150 may include a latent topic compression unit 152. The latent topic compression unit 152 may be configured to analyze transaction data for a group of users and identify latent features, such as topics, included in the transaction data. One example of latent topic identification may include a latent dirichlet allocation (LDA) model. The topic identification information can be a compressed representation of the normalized transaction data for a user. The topic identification information may include, for each topic modeled by the latent feature model used by the latent topic compression unit 152, an association value. The association value may indicate how closely the user is associated with each of the topics modeled. The topic identification information may be stored in a topic compressed data store 182.
The dimension reduction module 150 may include a segment generator 154. The latent topic compression unit 152 may provide the topic compressed data to the segment generator 154. In some implementations, the latent topic compression unit 152 may transmit a message including an identifier for the topic compressed data. The segment generator 154 may use the identifier to obtain the topically compressed transaction records from the topic compressed data store 182. Once obtained, the segment generator 154 may assign each user to a data cluster or segment. The assignment information may be stored in a segment assignment data store 184. Although topic compressed data store 182 and the segment assignment data store 184 are shown as separate data storage devices, it will be understood that all or some portion of these may be commonly implemented on a single data storage device.
As discussed above, the topic and/or segmentation compression may be performed using models. The models may be generated by a compression model generator 200.
To train models for transaction data compression, the transaction analysis system may collect, for each entity (e.g., individual users), transaction data over a certain period of time (for example one year). Each user may be represented by a list of all of their transactions during the designated time period and each transaction may be converted to a “word” by means of categorical description of the type of transaction. For example, a particular card transaction may be associated with a category of “Restaurant,” or more specifically “Chinese Restaurant”
In some implementations, the set of all users is run through one or more models, such as a latent dirichlet allocation (LDA) algorithm, which was originally designed to discover topics in text documents. In this approach each user is treated as a document, and each transaction is converted to a “word” by codification. The transaction to word conversion can be based on merchant category code (MCC) or merchant name, or merchant location, or transaction amount, or transaction time, or combinations of the above. Transaction at restaurant, restaurant transaction with transaction amount larger than $100, restaurant transaction from 6 pm to 9 pm and with transaction amount larger than $100 are examples of valid conversions. Transaction codification or conversion is a crucial step in transaction data compression by using latent topic discovery algorithm such as LDA. Different codification will lead to different results and optimal codification is problem dependent. The optimal codification for fraud detection may not be optimal for extracting insights from transaction data for marketing optimization. After codification transactions are treated as words that make up the documents. Each document (a proxy for the user) may be represented by a collection of words. The words may be derived from transaction MCCs or merchant names or merchant location or transaction amount or transaction time or combination of the above for transactions performed by the user. The latent topic identification model may be configured to discover a set of statistical properties common in the dataset and creates topics which describe various archetypes of spend patterns. The number of spend patterns to be discovered can be set manually or discovered automatically. The output of this model may be a series of parameters describing the topics which may be referred to in this example as “Model A.”
In addition to creating “Model A”, each user may be assigned a likeness measure to each of the topics discovered by “Model A.” In one embodiment, this measure represents the weight of a particular topic in order to most accurately represent the user's spending behavior. The result of this step is a set of users which are each represented by a vector of the length of the number of topics which may be referred to in this example as “Vector A.”
“Vector A” can be used to assign each user to one or more segments in a variety of ways such as, for example, by assigning the user to the segment which represents the largest in the vector (e.g., strongest association) or assigning the user to all segments above a particular threshold. It is also possible to use “Vector A” as in input to additional algorithms which can be used to further classify a user. In addition, “Vector A” is itself a potential output of the system which describes the user's transactional behavior in a compressed manner.
“Vector A” may then be used as an input to a clustering algorithm, such as k-means clustering in order to produce clustering results, which will be referred to as “Model B.” In one embodiment, the clustering algorithm returns the location of the center of a preset number of clusters in the same space as “Vector A”. A segment can then be assigned to a user by measuring the distance from “Vector A” to each of the points described in “Model B.” This system may then, optionally, generate a second vector, “Vector B” which measures the distances of the given data point (user) to the center point of each cluster in the topic space. “Vector B” is of the same dimension as the number of clusters produced by “Model B” and can be used in a similar manner to “Vector A.”
In some embodiments, the topic compression model generator 208 may automatically determine an optimal number of topics to learn during the training phase. For example, the topic compression model generator 208 may divide the population into two pieces referred to as “Train and Test” groups. The topic compression model generator 208 may then execute the training algorithm described above multiple times with a variety number of topics in order to learn using the “Train” data. As a side-benefit of the training phase, a total probability of the dataset may be produced. This can be thought of as a goodness of fit measure. In general the more topics developed off of the training set the higher the total probability will be on the training data set. The “Test” data may then be run through the generated models (e.g., topic compression model). Finally, the topic compression model generator 208 may measure the total probability for the “Test” data for each model and select the model with the highest “Test” probability.
A network of card readers 302 may provide transaction data 304 for transactions processed via a reader. In this example, the transaction data may be obtained from a point of sale transaction processor, such as an entity that is the intermediary between the point-of-sale and the credit card issuer, which may be referred to herein as a transaction processor.
The transaction data 304 may include merchant name, merchant location, merchant category, transaction dollar amount, transaction date, and an indicator as to whether or not the card was present for the transaction.
At block 306, the number of dimensions of information represented by the transaction data may be reduced using latent topic detection techniques. Once the clustering is performed, at block 308, data clusters indicative of behavior segments may be generated. The clustering at block 308 may produce an interface, or underlying data for generating the interface, shown in
At block 308, the transaction data may be further processed, such as by the transaction analysis system using machine learning to a large set of transaction data associated with multiple users, to determine behavior segments associated with respective users. The behavior segments may be selected based on such transaction data and/or compressed transaction data to provide a different segmentation than is possible using traditional user information alone such as demographic data of users. Depending on the embodiment, a user can be associated of more than one segment.
Returning to
In one example, the clustering compression model generator 214 may compare the topically compressed user records to each other to identify clusters of compressed user records. The data clusters may be determined using an automated, machine-driven approach, without manually-driven rules. It may not be clear given the quantity of transactions and compressed transaction data records how users can be grouped to form data clusters. In one embodiment, the clustering compression model generator 214 may automatically group “like” users to indicate affinity groups. The grouping may be based on transaction data and/or topically compressed transaction data for the users. For example, data clusters may be identified using spend categories for a set of users. The clustering compression model generator 214 may process the transaction data and/or compressed transaction data to determine likely data clusters within the data set. It may be desirable to direct the comparison such that a predetermined number of clusters are identified. For example, some end users may wish to selectively provide content to ten different clusters of users. In such implementations, the identification of clusters may be guided such that the number of clusters identified matches the target number of clusters (e.g., in this example, ten clusters). In some embodiments, multiple techniques may be applied to identify clusters, such as combining traditional clustering algorithms with machine-learning based techniques, such as topic modeling.
The topic compression model 210 and the clustering compression model 216 may be provided to the compression module 150 and used to compress subsequently received transaction data as described above and in further detail below.
The models generated by the compression model generator 200 shown in
The behavior segmentation systems and methods described herein may group like sets of users into overlapping groups based on their transactional behavior. This differs from other traditional transaction based segmentation in a variety of ways. One way is that the described features may include a latent topic identification model, such as a model generated via a latent dirichlet allocation (LDA) algorithm, to uncover spending patterns among the population automatically. Unlike other methods, LDA does not require grouping merchants either manually using ad hoc rules or statistically by counting co-occurrence as in content vector approach. It operates directly on the collection of transactions over many users and allows users to belong to multiple groups. This makes particular sense when thinking about transactional behavior as each transaction may be driven by different characteristics about the user. For instance, some spend may be driven by necessity, e.g., grocery shopping, whereas other types of spend may be hobby driven, e.g., photography or entertainment.
As noted above, the behavior segments may be determined using an automated, machine-driven approach, without manually-driven rules. In one embodiment, clustering algorithms automatically group “like” individuals by their spend. In some embodiments, multiple techniques may be applied to develop more optimized data clusters, such as combining clustering algorithms with machine-learning based techniques, such as topic modeling. In some embodiments, a clustering output maps distance of users to the developed segment centers. Thus, a user may be assigned to a segment they are closest to along with distance measurements that show the user's proximity to other (possibly all) segments. This creates opportunities to consider multiple types of transaction behavior of the user in assessing how their behavior (such as spending patterns) is unique from other users in the population and target content accordingly.
In some embodiments, the segmentation methodology is data driven with a few parameters that can be tuned to produce different model outputs.
The compression models may be used to compress transaction data. Transaction data for users may be initially prepared in the same manner as described in
The latent topic compression 152 may be configured to generate topic compressed transaction data 412 using the user level normalized transaction data 406 and the topic compression model 410. The topic compressed transaction data 412 may include, for each user, an indication of how closely the transaction data for the user is associated with the topics identified by the model. As noted above, the topic compressed transaction data 412 may be stored using a quantity of storage resources that are less than the storage resources used to store the user level normalized transaction data 406.
The compression segment generator 156 included in the compression module 150 may be similarly configured to obtain the clustering compression model 416. Using the topic compressed transaction data 1012 and the clustering compression model 416, the compression segment generator 156 may generate cluster compressed transaction data 418 for the users. As noted above, the cluster compressed transaction data 418 may be stored using a quantity of storage resources that are less than the storage resources used to store the topic compressed transaction data 412.
Returning to
In implementations where the profile target service 110 generates an electronic communication to provide to the target set of user profiles, the electronic communication may be implemented as or included within a message transmitted via a communication channel such as a wireless communication channel to a wireless device of a targeted user. The message may cause the wireless device to activate and/or initiate an application that is configured to acquire content for the user based on the segment identified for the user. In some implementations, the application may be initiated on the user device and, upon receipt of the message, the interface of the application may be adjusted using the received message. For example, a card issuer may provide an interactive application for managing a user account. As a user device operates the interactive application, a message including segmentation information may be received. This message may cause the interactive application to adjust one or more functions using the segmentation information. For example, the segmentation information may indicate the user has a disability. In such instances, a prompt may be presented via the interactive application, asking whether the user would like to switch to a high contrast mode. Content may also be selected for presentation using data provided in the message as selection criteria. Because the application may be initiated or adjusted upon receipt of the message, additional attributes may be identified at or near the same time the message is provided. These additional attributes may include location of the wireless device, power mode of the wireless device, connectivity of the wireless device (e.g., WiFi, cellular, USB tether), other applications executing on the wireless device (e.g., music, photos, movies, Internet browsing), or the like. These attributes may also be used in conjunction with the segmentation data to provide a contextually relevant interactive application adjustments and/or content to the user.
To support these features of the profile target service 110, a targeting rules data store 111 may be provided. The targeting rules data store 111 may include the targeting goals to be achieved by the profile target service 110. A targeting rule may identify an assigned segment, association strengths to the assigned segment, association strengths to a non-assigned segment, or other transaction data that can be used to determine which users should receive the content.
A segment selector 112 may be included to compare the segment assignment data with one or more targeting rule to select a portion of the users which are associated with a desired target segment. A profile selection engine 114 may then narrow the users identified by the segment selector 112 to focus on a particular set of the users to target. For example, the profile selection engine 114 may identify users having a certain distance to the center point of the assigned cluster. The distance may be a short distance, which would indicate a group of users who are strongly identified with the cluster. Such strong affinity can be useful in providing specific content of interest to those within the cluster. The distance may be larger distance, which would indicate a group of users who are identified, but not as strongly as others, with the cluster. Such loose affinity can be useful in providing specific content to increase a user's affinity with the assigned segment.
As noted above, the profile selection engine 114 may also consider relationships between the users assigned to the target cluster and another data cluster to which the users have not been assigned. Such relationships may indicate that while a user is strongly affiliated with the assigned cluster, there may be some interest in another cluster. Such relationships may indicate that a user has a very strong distaste for a very distant cluster. These valuable insights may be determined using the smaller compressed records for the users quickly and with efficient use of system resources.
Similar to the profile selection engine 114, a profile exclusion engine 116 may be included to filter out selected target users. Using targeting rules, the automatically generated target set of users can be further processed to ensure accurate and timely selection. For example, it may be desirable to exclude targeting of a user who has transaction data indicating a recent illness or death (e.g., transactions at a hospital, funeral home, or pharmacy). As another example, it may be desirable to avoid targeting of a user for an end user who is already a loyal customer of a merchant identified in a user's transaction data. For instance, a new user incentive need not be provided to a long time user of a service.
The profile target service 110 may store information for the identified target users in a selected profiles data store 118. The selected profiles data store 118 may be access by the content generation service 170 to generate and deliver the content to each of the identified target users. To generate the content, a targeted content generator 172 may be included in the content generation service 170. The targeted content generator 172 may be configured to format the targeted content element for each user. For example, different targeted users may use different devices to receive content. In such instance, the targeted content generator 172 may adjust, reformat, convert, or otherwise change the targeted content so that a registered user device for a targeted user can receive the content. The targeted content generator 172 may also dynamically adjust content to include targeted user specific information in the content such as the user's name, home town, or other user or transaction information available to the content generation service. The targeted content generator 172 may also prepare printable materials for mailing to the user.
Once the targeted content is prepared, a communication service 174 is included to communicate the generated content to the targeted users. As shown in
At block 502, raw transaction data is received from a transaction data source such as a financial institution or credit data repositories. As discussed above, the data may be received in batch mode. The data may be pushed from the source to the transaction data processing system. The data may be requested by the system 100 from a source. In some implementations, some data may be pushed and some may be requested.
At block 504, raw transaction data may be categorized or codified using one or more transaction attributes. The transaction attributes that may be used to categorize transactions include merchant name, merchant category code, transaction amount, transaction channel (online versus off-line), or the like. The categorization may utilize match rules which identify transaction data attribute and values therefor that match a given category.
At block 506, the transaction data may be normalized. For example, the normalization may be performed to ensure transaction data from different sources are represented in a consistent format.
At block 508, latent topics for the codified transaction data are identified. The latent topics may be identified using latent topic detection which may include, in some implementations, an LDA model. The topics may be identified by training an LDA model using previous transaction data. For example, the training may reveal that a set of transactions are each related to a transaction topic such as traveling.
At block 510, a compressed record of the user is generated. The compressed record may be generated using the latent topics identified at block 508. To generate the compressed record, for an identified latent topic for a given user's transaction data, a value may be generated. The value may indicate how closely a specific user's transactions match an identified topic. In implementations where multiple topics are identified, values for each topic may be generated. The values may be expressed as an ordered vector of match values. Each match value may indicate how closely the user matches the associated topic. The match value may be expressed as an integer number or decimal number depending on the target compression level. For example, in some implementations, it may be desirable to provide a binary indication as to whether a topic applied to a given user. In such implementations, the values may be 1 or 0. In some implementations, it may be desirable to provide a decimal value where 0 is no match and 1 is a perfect match. The decimal values between 0 and 1 identify the degree of matching for a given value.
Having generated compressed transaction data using topics, it may be desirable perform a segmentation based on the topically compressed user records. The segmentation provides further summarization the behavior of a user. This can be useful in implementations where users with similar behavior patterns will be analyzed or provided content in similar fashions. This summary may also be useful to reduce the amount of data transmitted for a user such as in environments where resources are limited for exchanging, storing, and processing transaction data such as via mobile devices.
At block 602, the topically compressed user records are received. The records may be received via wired, wireless, or hybrid wired-wireless means. In some implementations, the segment generator 154 may receive the records.
At block 604, a cluster compression model for the compressed user records is received. The cluster compression model may be received along with the user records. In some implementations, the cluster compression model may be received from a model storage device such as in response to a query. The query may include information to identify the model of interest. For example, the query may indicate a target entity for which the compression is being performed, such as a bank or credit card issuing company.
At block 606, statistical information (e.g., an association strength) for each topic (e.g., latent feature) included in the compressed transaction data for the users is generated. The cluster compression model may include one or more center points for each cluster included in the model. Where the compressed transaction data is used for clustering, the center point of a cluster may identify a point within the data cluster that is most centrally located to represent an average topic match value for each topic included in the data cluster.
The statistical information generated at block 606 may indicate how well the compressed user record matches to the respective cluster. In some embodiments, a clustering output maps distance of users to the modeled segment centers. The association strength may indicate a degree of association between a topic in the topically compressed transaction data for a user and the center point of a data cluster included in the cluster compression model.
In
Returning to
Thus, a user may be assigned to a data cluster they are closest to along with distance measurements that show the customer's proximity to other (possibly all) data cluster. This creates opportunities to consider multiple types of behavior of the user in assessing how their behavior is unique from other users in the population and target products, offers or messaging accordingly.
At block 802, content to provide to users associated with a predetermined cluster is received. The content may be received via wired, wireless, or hybrid wired and wireless means. The content may be received in a machine readable message. The content may include audio, video, text, graphic, olfactory, or haptic content.
At block 804, content access information for a set of users for content associated with a data cluster is received. The content access information may indicate which users within a data cluster should have access to the content. The content access information may include compression values for a given user record such as topic association strengths. For example, a content element may be accessed if, for a given data cluster, the association strength for a first topic is greater than a first threshold and the association strength for a second topic is less than a second threshold. In some implementations, the compression information for association strength with non-assigned data clusters may be specified in the content access information. The content access information may be received via wired, wireless, or hybrid wired and wireless means. The content access information may also include time information indicating when access to the content should be granted. For example, the time may be specified as a date range during which a particular video should be available for access. The content access information may be received in a machine readable message. The content access information may be received together with the content or in a separate message.
At block 806, a compressed user record for each user included in the set of users is received. The compressed user records may be received via wired, wireless, or hybrid wired and wireless means. The compressed user records may be received in a machine readable message. The compressed user records may include topically compressed transaction data and/or clustered compressed data. The compressed user records may be received from a transaction processing system.
At block 808, the compressed user records are filtered. The filtering may be applied using the content access information in comparison to the compressed user records. For example, a record may be filtered out of consideration if the compression information for the record includes an association strength with a non-assigned data cluster at or above a threshold value. Accordingly, the filtering may use statistical information for the compressed user record indicating a relative match level for another identified cluster to which the compressed user record has not been assigned.
At block 810, a subset of the filtered user records are selected as candidates to receive the content based on the statistical information for the compressed user record indicating a relative match level for the predetermined data cluster. In some implementations, it may be desirable to target only a portion of the filtered user records to receive the content. The selection may be based on a comparison of the content access information with the compressed user record. For example, a comparison of topic match level to a threshold may be performed to determine whether a user record should be included in the subset of the filtered user records.
At block 812, a content message is generated and provided to at least a portion of the candidates. The content message includes the content. The content message may include a dynamic portion of content generated based on compressed user records for each candidate. This provides a second level of tailoring and access control for the content. The content message may be provide via wired or wireless means to a device of the user. In some implementations, the content may be provided to a fulfillment device such as a bulk mail generator for printing and shipping to one or more of the candidate users. In such implementations, the content may be provided with shipping information or an identifier that can be used to obtain the shipping information for the content.
In certain implementations, one or more of the content messages are operable to automatically activate a user communication service program on the user device 190 or a client service system (not shown). The activated user communication service program automatically generates one or more communications directed to the user for whom at least one of the content messages was transmitted. Generation of the user communications can be informed by informational included in the content message. The user communications are then automatically transmitted to the user in one or more modes of communication, such as, for example, electronic mail, text messaging, and regular postal mail, to name a few. In certain modes of communication to the user, the user communication may be configured to automatically operate on the user device 190. For example, a user's mobile device may, upon receipt of the transmitted user communication, activate a software application installed on the user's mobile device to deliver the user communication to the user. Alternatively, the user communication may activate a web browser and access a web site to present the user communication to the user. In another example, a user communication may be transmitted to a 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 user communication. In another example, the user may receive from the client service system a coupon/discount offer in various manners, such as in a billing statement delivered via postal or other delivery service, in a text message to the user's mobile device, and in an email message sent to one or more of the user's email accounts. When a content message is transmitted to the client in response to a transaction, such offers may be effective because they are provided at or near a time that the product or service may be purchased by the user.
A topic identification model 914 may be used in conjunction with the set of user transaction data 910 to generate first compressed user transaction data 916. One example topic identification model 914 is a latent feature model such as an LDA model. The latent feature model may be used to discover the behavior topic and to reduce dimensionality of the transaction data. The discovery via the latent feature model is probabilistic based on identified connections between transaction data elements.
The first compressed user transaction data 916 may include a list of values indicating how closely the transaction data matches a given topic. The values are generated by the topic identification model. As shown in
Transactional data for one user or a group of users may then be provided to a cluster identifier 920. This allows the cluster identifier to group the transaction data for the users based on the intrinsic similarities of their transaction data in the topic space.
The cluster identifier 920 may obtain a set of data clusters 930 for the transaction data 902. The set of data cluster 930 may provide as a segmentation model (e.g., “Model B”). The cluster identifier 920 may generate association strength values for transaction data for a user with one or more data clusters included in the data clusters 930. The association strength values for a given user may represent the distance of the user to a center point of a data cluster, such as the center point shown in
The message flow 1100 shown in
The message flow 1100 may begin with a card swipe detection by the card reader 1102 based on a message 1120. The message 1120 may include the payment information read from the card such as from a magnetic strip, an embedded memory chip, a near-field communication element, or other information source included on the card. Via message 1122, the card information may be transmitted from the card reader 1102 to the point of sale system 1104. The point of sale system 1104 may determine that the card is a credit card and identify the acquisition server 1112 as a source for determining whether the payment is authorized.
The point of sale system 1104 may transmit a message 1124 to the acquisition server 1112 including the card information and merchant information. The merchant information may include a merchant identifier, merchant transaction information (e.g., desired payment amount), or other information available to the merchant for the transaction. The acquisition service 1112 may identify the card issuer based on the card information received and transmit a message 1126 to the card issuer 1114. The message 1126 may include the card information and merchant information received via message 1124. The message 1126 may also include information about the acquisition server 1112. The card issuer 1114 may then authorize the requested payment amount via message 1128. The authorization of payment is known in the field of payment processing. The authorization determines whether or not the requested payment for the transaction is to be honored. The card issuer 1114 also knows one of their users is at a payment terminal trying to make a purchase. This can be a desirable moment to interact with the customer to provide additional content to the customer, such as content selected by the card issuer or by the merchant. To select content however, an accurate record of the user may be needed to provide relevant content for the specific user involved in the transaction. As such, via message 1130, one or more compressed transaction records may be generated by the card issuer 1114.
It will be understood that the compression may be performed by a third-party system, such as the system 100 shown in
Via message 1132, the authorization decision and compressed user data may be transmitted back to the merchant system 1110 via the acquisition server 1112. Because the compressed user data may be represented using a relatively small quantity of resources, this data may be easily included in the current messaging used to authorize transactions. The point of sale system 1104 may use the authorization information to determine whether or not to allow the transaction to proceed. If the authorization is negative, then the point of sale system 1104 may request alternate payment from the user. As shown in
In some implementations, the user data may be transmitted via a message 1144 to a card holder device 1116. The card holder device 1116 is an electronic communication device associated with a user who has been issued a payment card or otherwise accesses the system to perform a transaction. As shown in
The merchant system 1110 may transmit a batch of transaction data for multiple transactions with multiple users via message 1220. The batch of transaction data from a single merchant may include hundreds, thousands, or millions of individual transaction records. The merchant system 1110 may transmit the message 1220 to the acquisition server 1112 to initiate payment for the transactions. The acquisition server 1112, via message 1222, may transmit those transactions from a specific card network to the card network server 120 to request payment from the specific network. Because a single card network may have multiple card issuers (e.g., banks, credit unions, etc.), the card network server 1202 splits the batch of transactions by card issuer. The transactions for a specific issuer are transmitted via message 1224 to, as shown in
The new transaction data may also indicate that the models used to compress the transaction data should be retrained. For example, if the new transactions represent a significant percentage of the overall transaction data stored by the system 100, the retraining of the models may be desirable to ensure an accurate and current view of the users. The retraining may also be needed to account for new transactions for new users who were previously not included in the training process.
Via message 1230, the card issuer 1114 may initiate a transfer of funds to settle one or more of the transactions included in the transaction data batch. As shown in
The card issuer 1114 may interface with the card holder. For example, the card issuer 1114 may provide an interactive application for reporting transaction information to the card holder such as via the card holder device. Using the compressed transaction records, users may be identified to receive particular content. In some implementations, the profile target service 110 shown in
The card issuer 1114 may interface via mail such as by printing and mailing content to the card holder. In such implementations, the content included in the message 1240 may be provided to a fulfillment server responsible for printing and mailing the content to the card holder.
The point-of-sale card reader 1300 includes a keypad 16, which interfaces with the point-of-sale transaction circuitry to provide input signals indicative of transaction or event events at or near the point-of-sale card reader 1300. The point-of-sale card reader 1300 also includes a magnetic card reader 18 and a smart card reader 20, which may be adapted to receive a smart card 22.
The point-of-sale card reader 1300 also includes a display 24 and a printer 26 configured to provide output information prior to, during, or after a transaction. The content may include single media or multimedia content. The content may be static (e.g., a movie, a text, an image, and/or audio) or dynamically generated. For example, using the compressed transaction data, the card swiped may be identified with a data cluster for sports fans. In such an implementation, the content may be adapted to include sports-centric information such as inserting a team logo into the presented content.
The card reader 900 shown in
The targeted content generator 60 may be configured to obtain content and compressed transaction data. Using the compressed transaction data, the targeted content generator 60 may identify one or more elements of obtained content for presentation via one or more of the outputs of the card reader. For example, the display 24 may be used to show content to a user who presented a card at the card reader. During the transaction, such as part of the authorization process, a compressed transactional record for the user may be received by the card reader 1300 and processed by the targeted content generator 60. By comparing at least a portion of the compressed data to selection criteria associated with the obtained content, the targeted content generator 60 may identify a relevant content element for presentation and cause it to be presented.
Typically, the components of the processing system 1500 are connected using a standards-based bus system 1590. In different embodiments, the standards-based bus system 1590 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 of processing system 1500 may be combined into fewer components and modules or further separated into additional components and modules.
The processing system 1500 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, Android, or other compatible operating systems. In Macintosh systems, the operating system may be any available operating system, such as MAC OS X. In other embodiments, the processing system 1500 may be controlled by a proprietary operating system. The operating system is configured to 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 processing system 1500 may include one or more commonly available input/output (I/O) devices and interfaces 112, such as a keyboard, mouse, touchpad, and printer. In one embodiment, the I/O devices and interfaces 1512 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 processing system 1500 may also include one or more multimedia devices 1542, such as speakers, video cards, graphics accelerators, and microphones, for example.
In the embodiment of
In some embodiments, information may be provided to the processing system 1500 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 OneSM) 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, and object-oriented database, and/or a record-based database.
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 processing system 1500, 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
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.
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 a processing system, card reader, point of sale device, acquisition server, card issuer server, 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, compact disk read-only memories (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, 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.
This application is a continuation application and claims the benefit under 35 U.S.C. § 120 of U.S. application Ser. No. 14/975,654, filed on Dec. 18, 2015, entitled “USER BEHAVIOR SEGMENTATION USING LATENT TOPIC DETECTION,” which claims priority benefit under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/094,819, filed on Dec. 19, 2014, entitled “SYSTEMS AND INTERACTIVE USER INTERFACES FOR DATABASE ACCESS AND APPLICATION OF RULES TO DETERMINE RECOMMENDATIONS FOR USER ACTIONS,” the disclosure of which is hereby incorporated herein by reference in its entirety. 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. This application is also related to U.S. application Ser. No. 14/975,536 filed on Dec. 18, 2015, entitled “ENTITY RECOMMENDATION SYSTEMS AND METHODS,” the disclosure of which is hereby incorporated herein by reference in its entirety. This application is also related to U.S. application Ser. No. 14/975,440 filed on Dec. 18, 2015, entitled “SYSTEMS AND METHODS FOR DYNAMIC REPORT GENERATION BASED ON AUTOMATIC MODELING OF COMPLEX DATA STRUCTURES,” the disclosure of which is hereby incorporated herein by reference in its entirety.
Number | Name | Date | Kind |
---|---|---|---|
3316395 | Lavin et al. | Apr 1967 | A |
4305059 | Benton | Dec 1981 | A |
4371739 | Lewis et al. | Feb 1983 | A |
4398055 | Ijaz et al. | Aug 1983 | A |
4578530 | Zeidler | Mar 1986 | A |
4617195 | Mental | Oct 1986 | A |
4672149 | Yoshikawa et al. | Jun 1987 | A |
4736294 | Gill | Apr 1988 | A |
4754544 | Hanak | Jul 1988 | A |
4774664 | Campbell et al. | Sep 1988 | A |
4775935 | Yourick | Oct 1988 | A |
4827508 | Shear | May 1989 | A |
4868570 | Davis | Sep 1989 | A |
4872113 | Dinerstein | Oct 1989 | A |
4876592 | Von Kohorn | Oct 1989 | A |
4895518 | Arnold | Jan 1990 | A |
4935870 | Burk, Jr. et al. | Jun 1990 | A |
4947028 | Gorog | Aug 1990 | A |
5025138 | Cuervo | Jun 1991 | A |
5025373 | Keyser, Jr. et al. | Jun 1991 | A |
5034807 | Von Kohorn | Jul 1991 | A |
5056019 | Schultz et al. | Oct 1991 | A |
5060153 | Nakagawa | Oct 1991 | A |
5148365 | Dembo | Sep 1992 | A |
5201010 | Deaton et al. | Apr 1993 | A |
5220501 | Lawlor et al. | Jun 1993 | A |
5247575 | Sprague et al. | Sep 1993 | A |
5259766 | Sack | Nov 1993 | A |
5262941 | Saladin | Nov 1993 | A |
5274547 | Zoffel et al. | Dec 1993 | A |
5297031 | Gutterman et al. | Mar 1994 | A |
5325509 | Lautzenheiser | Jun 1994 | A |
5336870 | Hughes et al. | Aug 1994 | A |
5341429 | Stringer et al. | Aug 1994 | A |
5454030 | de Oliveira et al. | Sep 1995 | A |
5468988 | Glatfelter et al. | Nov 1995 | A |
5504675 | Cragun et al. | Apr 1996 | A |
5528701 | Aref | Jun 1996 | A |
5555409 | Leenstra, Sr. et al. | Sep 1996 | A |
5563783 | Stolfo et al. | Oct 1996 | A |
5583763 | Atcheson et al. | Dec 1996 | A |
5590038 | Pitroda | Dec 1996 | A |
5592560 | Deaton et al. | Jan 1997 | A |
5611052 | Dykstra et al. | Mar 1997 | A |
5615408 | Johnson | Mar 1997 | A |
5621201 | Langhans et al. | Apr 1997 | A |
5629982 | Micali | May 1997 | A |
5630127 | Moore et al. | May 1997 | A |
5640551 | Chu et al. | Jun 1997 | A |
5640577 | Scharmer | Jun 1997 | A |
5655129 | Ito | Aug 1997 | A |
5659731 | Gustafson | Aug 1997 | A |
5666528 | Thai | Sep 1997 | A |
5679176 | Tsuzuki et al. | Oct 1997 | A |
5689651 | Lozman | Nov 1997 | A |
5696907 | Tom | Dec 1997 | A |
5704029 | Wright, Jr. | Dec 1997 | A |
5732400 | Mandler | Mar 1998 | A |
5737732 | Gibson et al. | Apr 1998 | A |
5739512 | Tognazzini | Apr 1998 | A |
5745654 | Titan | Apr 1998 | A |
5748098 | Grace | May 1998 | A |
5754938 | Herz et al. | May 1998 | A |
5768423 | Aref et al. | Jun 1998 | A |
5771562 | Harvey et al. | Jun 1998 | A |
5774692 | Boyer et al. | Jun 1998 | A |
5774868 | Cragun et al. | Jun 1998 | A |
5774883 | Andersen | Jun 1998 | A |
5778405 | Ogawa | Jul 1998 | A |
5793972 | Shane | Aug 1998 | A |
5797136 | Boyer et al. | Aug 1998 | A |
5802142 | Browne | Sep 1998 | A |
5812840 | Shwartz | Sep 1998 | A |
5819226 | Gopinathan et al. | Oct 1998 | A |
5822410 | McCausland et al. | Oct 1998 | A |
5822750 | Jou et al. | Oct 1998 | A |
5822751 | Gray et al. | Oct 1998 | A |
5825884 | Zdepski et al. | Oct 1998 | A |
5828833 | Belville et al. | Oct 1998 | A |
5835915 | Carr et al. | Nov 1998 | A |
5844218 | Kawan et al. | Dec 1998 | A |
5848396 | Gerace | Dec 1998 | A |
5864830 | Armetta et al. | Feb 1999 | A |
5870721 | Norris | Feb 1999 | A |
5875108 | Hoffberg et al. | Feb 1999 | A |
5875236 | Jankowitz | Feb 1999 | A |
5878403 | DeFrancesco | Mar 1999 | A |
5881131 | Farris et al. | Mar 1999 | A |
5884287 | Edesess | Mar 1999 | A |
5884289 | Anderson et al. | Mar 1999 | A |
5893090 | Friedman et al. | Apr 1999 | A |
5905985 | Malloy et al. | May 1999 | A |
5912839 | Ovshinsky et al. | Jun 1999 | A |
5915243 | Smolen | Jun 1999 | A |
5924082 | Silverman et al. | Jul 1999 | A |
5926800 | Baronowski et al. | Jul 1999 | A |
5930764 | Melchione et al. | Jul 1999 | A |
5930774 | Chennault | Jul 1999 | A |
5930776 | Dykstra et al. | Jul 1999 | A |
5940812 | Tengel et al. | Aug 1999 | A |
5950172 | Klingman | Sep 1999 | A |
5950179 | Buchanan et al. | Sep 1999 | A |
5956693 | Geerlings | Sep 1999 | A |
5963932 | Jakobsson et al. | Oct 1999 | A |
5966695 | Melchione et al. | Oct 1999 | A |
5974396 | Anderson et al. | Oct 1999 | A |
5978780 | Watson | Nov 1999 | A |
5995947 | Fraser et al. | Nov 1999 | A |
6009415 | Shurling et al. | Dec 1999 | A |
6014688 | Venkatraman et al. | Jan 2000 | A |
6018723 | Siegel et al. | Jan 2000 | A |
6021362 | Maggard et al. | Feb 2000 | A |
6026368 | Brown et al. | Feb 2000 | A |
6029139 | Cunningham et al. | Feb 2000 | A |
6029149 | Dykstra et al. | Feb 2000 | A |
6029154 | Pettitt | Feb 2000 | A |
6038551 | Barlow et al. | Mar 2000 | A |
6044357 | Garg | Mar 2000 | A |
6058375 | Park | May 2000 | A |
6061658 | Chou et al. | May 2000 | A |
6061691 | Fox | May 2000 | A |
6064973 | Smith et al. | May 2000 | A |
6064987 | Walker | May 2000 | A |
6064990 | Goldsmith | May 2000 | A |
6070141 | Houvener | May 2000 | A |
6070142 | McDonough et al. | May 2000 | A |
6073140 | Morgan et al. | Jun 2000 | A |
6073241 | Rosenberg et al. | Jun 2000 | A |
6088686 | Walker et al. | Jul 2000 | A |
6094643 | Anderson et al. | Jul 2000 | A |
6098052 | Kosiba et al. | Aug 2000 | A |
6105007 | Norris | Aug 2000 | A |
6115690 | Wong | Sep 2000 | A |
6115693 | McDonough et al. | Sep 2000 | A |
6119103 | Basch et al. | Sep 2000 | A |
6121901 | Welch et al. | Sep 2000 | A |
6128599 | Walker | Oct 2000 | A |
6128602 | Northington et al. | Oct 2000 | A |
6128603 | Dent | Oct 2000 | A |
6128624 | Papierniak et al. | Oct 2000 | A |
6134548 | Gottsman et al. | Oct 2000 | A |
6144957 | Cohen et al. | Nov 2000 | A |
6151601 | Papierniak et al. | Nov 2000 | A |
6154729 | Cannon et al. | Nov 2000 | A |
6178442 | Yamazaki | Jan 2001 | B1 |
6182060 | Hedgcock et al. | Jan 2001 | B1 |
6198217 | Suzuki et al. | Mar 2001 | B1 |
6202053 | Christiansen et al. | Mar 2001 | B1 |
6208979 | Sinclair | Mar 2001 | B1 |
6223171 | Chaudhuri et al. | Apr 2001 | B1 |
6226408 | Sirosh | May 2001 | B1 |
6233566 | Levine et al. | May 2001 | B1 |
6236977 | Verba et al. | May 2001 | B1 |
6239352 | Luch | May 2001 | B1 |
6249770 | Erwin et al. | Jun 2001 | B1 |
6254000 | Degen et al. | Jul 2001 | B1 |
6256630 | Gilai et al. | Jul 2001 | B1 |
6263334 | Fayyad et al. | Jul 2001 | B1 |
6263337 | Fayyad et al. | Jul 2001 | B1 |
6266649 | Linden et al. | Jul 2001 | B1 |
6278055 | Forrest et al. | Aug 2001 | B1 |
6285983 | Jenkins | Sep 2001 | B1 |
6285987 | Roth et al. | Sep 2001 | B1 |
6289252 | Wilson et al. | Sep 2001 | B1 |
6304860 | Martin et al. | Oct 2001 | B1 |
6304869 | Moore et al. | Oct 2001 | B1 |
6307958 | Deaton et al. | Oct 2001 | B1 |
6311169 | Duhon | Oct 2001 | B2 |
6324524 | Lent et al. | Nov 2001 | B1 |
6330546 | Gopinathan et al. | Dec 2001 | B1 |
6330575 | Moore et al. | Dec 2001 | B1 |
6334110 | Walter et al. | Dec 2001 | B1 |
6339769 | Cochrane et al. | Jan 2002 | B1 |
6345300 | Bakshi et al. | Feb 2002 | B1 |
6366903 | Agrawal et al. | Apr 2002 | B1 |
6385594 | Lebda et al. | May 2002 | B1 |
6393406 | Eder | May 2002 | B1 |
6397197 | Gindlesperger | May 2002 | B1 |
6405173 | Honarvar | Jun 2002 | B1 |
6405181 | Lent et al. | Jun 2002 | B2 |
6412012 | Bieganski et al. | Jun 2002 | B1 |
6418436 | Degen et al. | Jul 2002 | B1 |
6424956 | Werbos | Jul 2002 | B1 |
6430539 | Lazarus et al. | Aug 2002 | B1 |
6442577 | Britton et al. | Aug 2002 | B1 |
6456979 | Flagg | Sep 2002 | B1 |
6457012 | Jatkowski | Sep 2002 | B1 |
6460036 | Herz | Oct 2002 | B1 |
6496819 | Bello et al. | Dec 2002 | B1 |
5870721 | Norris | Jan 2003 | C1 |
6505176 | DeFrancesco, Jr. et al. | Jan 2003 | B2 |
6513018 | Culhane | Jan 2003 | B1 |
6523022 | Hobbs | Feb 2003 | B1 |
6523041 | Morgan et al. | Feb 2003 | B1 |
6532450 | Brown et al. | Mar 2003 | B1 |
6542894 | Lee et al. | Apr 2003 | B1 |
6543683 | Hoffman | Apr 2003 | B2 |
6549919 | Lambert et al. | Apr 2003 | B2 |
6567791 | Lent et al. | May 2003 | B2 |
6574623 | Laung et al. | Jun 2003 | B1 |
6597775 | Lawyer et al. | Jul 2003 | B2 |
6598030 | Siegel et al. | Jul 2003 | B1 |
6601234 | Bowman-Amuah | Jul 2003 | B1 |
6611816 | Lebda et al. | Aug 2003 | B2 |
6615193 | Kingdon et al. | Sep 2003 | B1 |
6615247 | Murphy | Sep 2003 | B1 |
6622266 | Goddard et al. | Sep 2003 | B1 |
6623529 | Lakritz | Sep 2003 | B1 |
6631496 | Li et al. | Oct 2003 | B1 |
6640215 | Galperin et al. | Oct 2003 | B1 |
6651220 | Penteroudakis et al. | Nov 2003 | B1 |
6654727 | Tilton | Nov 2003 | B2 |
6658393 | Basch et al. | Dec 2003 | B1 |
6665715 | Houri | Dec 2003 | B1 |
6687713 | Mattson et al. | Feb 2004 | B2 |
6708166 | Dysart et al. | Mar 2004 | B1 |
6714918 | Hillmer et al. | Mar 2004 | B2 |
6735572 | Landesmann | May 2004 | B2 |
6748426 | Shaffer et al. | Jun 2004 | B1 |
6757740 | Parekh et al. | Jun 2004 | B1 |
6766327 | Morgan, Jr. et al. | Jul 2004 | B2 |
6801909 | Delgado et al. | Oct 2004 | B2 |
6804346 | Mewhinney | Oct 2004 | B1 |
6804701 | Muret et al. | Oct 2004 | B2 |
6807533 | Land et al. | Oct 2004 | B1 |
6823319 | Lynch et al. | Nov 2004 | B1 |
6836764 | Hucal | Dec 2004 | B1 |
6839682 | Blume et al. | Jan 2005 | B1 |
6839690 | Foth et al. | Jan 2005 | B1 |
6850606 | Lawyer et al. | Feb 2005 | B2 |
6859785 | Case | Feb 2005 | B2 |
6865566 | Serrano-Morales et al. | Mar 2005 | B2 |
6873972 | Marcial et al. | Mar 2005 | B1 |
6873979 | Fishman et al. | Mar 2005 | B2 |
6901406 | Nabe et al. | May 2005 | B2 |
6910624 | Natsuno | Jun 2005 | B1 |
6915269 | Shapiro et al. | Jul 2005 | B1 |
6925441 | Jones, III et al. | Aug 2005 | B1 |
6925442 | Shapira et al. | Aug 2005 | B1 |
6959281 | Freeling et al. | Oct 2005 | B1 |
6965889 | Serrano-Morales et al. | Nov 2005 | B2 |
6968328 | Kintzer et al. | Nov 2005 | B1 |
6970830 | Samra et al. | Nov 2005 | B1 |
6983379 | Spalink et al. | Jan 2006 | B1 |
6983478 | Grauch et al. | Jan 2006 | B1 |
6985882 | Del Sesto | Jan 2006 | B1 |
6985887 | Sunstein et al. | Jan 2006 | B1 |
6991159 | Zenou | Jan 2006 | B2 |
6993493 | Galperin et al. | Jan 2006 | B1 |
6993514 | Majoor | Jan 2006 | B2 |
6999941 | Agarwal | Feb 2006 | B1 |
7000199 | Steele et al. | Feb 2006 | B2 |
7003504 | Angus et al. | Feb 2006 | B1 |
7003792 | Yuen | Feb 2006 | B1 |
7028052 | Chapman et al. | Apr 2006 | B2 |
7031945 | Donner | Apr 2006 | B1 |
7039176 | Borodow et al. | May 2006 | B2 |
7039607 | Watarai et al. | May 2006 | B2 |
7047251 | Reed et al. | May 2006 | B2 |
7050982 | Sheinson et al. | May 2006 | B2 |
7050986 | Vance et al. | May 2006 | B1 |
7050989 | Hurt et al. | May 2006 | B1 |
7054828 | Heching et al. | May 2006 | B2 |
7069240 | Spero et al. | Jun 2006 | B2 |
7072963 | Anderson et al. | Jul 2006 | B2 |
7076462 | Nelson et al. | Jul 2006 | B1 |
7076475 | Honarvar et al. | Jul 2006 | B2 |
7082435 | Guzman et al. | Jul 2006 | B1 |
7092898 | Mattick et al. | Aug 2006 | B1 |
7117172 | Black | Oct 2006 | B1 |
7130853 | Roller et al. | Oct 2006 | B2 |
7133935 | Hedy | Nov 2006 | B2 |
7136448 | Venkataperumal et al. | Nov 2006 | B1 |
7139734 | Nathans et al. | Nov 2006 | B2 |
7143063 | Lent | Nov 2006 | B2 |
7152053 | Serrano-Morales et al. | Dec 2006 | B2 |
7165036 | Kruk et al. | Jan 2007 | B2 |
7165037 | Lazarus et al. | Jan 2007 | B2 |
7184974 | Shishido | Feb 2007 | B2 |
7185016 | Rasmussen | Feb 2007 | B1 |
7191144 | White | Mar 2007 | B2 |
7191150 | Shao et al. | Mar 2007 | B1 |
7200602 | Jonas | Apr 2007 | B2 |
7206768 | deGroeve et al. | Apr 2007 | B1 |
7212995 | Schulkins | May 2007 | B2 |
7222101 | Bishop et al. | May 2007 | B2 |
7234156 | French et al. | Jun 2007 | B2 |
7236950 | Savage et al. | Jun 2007 | B2 |
7240059 | Bayliss et al. | Jul 2007 | B2 |
7249048 | O'Flaherty | Jul 2007 | B1 |
7249114 | Burchetta et al. | Jul 2007 | B2 |
7263506 | Lee et al. | Aug 2007 | B2 |
7275083 | Seibel et al. | Sep 2007 | B1 |
7277869 | Starkman | Oct 2007 | B2 |
7277875 | Serrano-Morales et al. | Oct 2007 | B2 |
7277900 | Ganesh et al. | Oct 2007 | B1 |
7283974 | Katz et al. | Oct 2007 | B2 |
7296734 | Pliha | Nov 2007 | B2 |
7308418 | Malek et al. | Dec 2007 | B2 |
7313538 | Wilmes et al. | Dec 2007 | B2 |
7313618 | Braemer et al. | Dec 2007 | B2 |
7314166 | Anderson et al. | Jan 2008 | B2 |
7314167 | Kiliccote | Jan 2008 | B1 |
7324962 | Valliani et al. | Jan 2008 | B1 |
7328169 | Temares et al. | Feb 2008 | B2 |
7337133 | Bezos et al. | Feb 2008 | B1 |
7343149 | Benco | Mar 2008 | B2 |
7346551 | Pe Jimenez et al. | Mar 2008 | B2 |
7346573 | Cobrinik et al. | Mar 2008 | B1 |
7356503 | Johnson et al. | Apr 2008 | B1 |
7360251 | Spalink et al. | Apr 2008 | B2 |
7366694 | Lazerson | Apr 2008 | B2 |
7367011 | Ramsey et al. | Apr 2008 | B2 |
7370044 | Mulhern et al. | May 2008 | B2 |
7373324 | Engin et al. | May 2008 | B1 |
7376603 | Mayr et al. | May 2008 | B1 |
7376618 | Anderson et al. | May 2008 | B1 |
7376714 | Gerken | May 2008 | B1 |
7379880 | Pathria et al. | May 2008 | B1 |
7383215 | Navarro et al. | Jun 2008 | B1 |
7383227 | Weinflash et al. | Jun 2008 | B2 |
7386528 | Maloche et al. | Jun 2008 | B2 |
7386786 | Davis et al. | Jun 2008 | B2 |
7392203 | Edison et al. | Jun 2008 | B2 |
7392216 | Palmgren et al. | Jun 2008 | B1 |
7395273 | Khan et al. | Jul 2008 | B2 |
7398225 | Voltmer et al. | Jul 2008 | B2 |
7398226 | Haines et al. | Jul 2008 | B2 |
7403923 | Elliott et al. | Jul 2008 | B2 |
7403942 | Bayliss | Jul 2008 | B1 |
7409362 | Calabria | Aug 2008 | B2 |
7418431 | Nies et al. | Aug 2008 | B1 |
7421322 | Silversmith et al. | Sep 2008 | B1 |
7424439 | Fayyad et al. | Sep 2008 | B1 |
7428498 | Voltmer et al. | Sep 2008 | B2 |
7428509 | Klebanoff | Sep 2008 | B2 |
7428519 | Minsky et al. | Sep 2008 | B2 |
7428526 | Miller et al. | Sep 2008 | B2 |
7433855 | Gavan et al. | Oct 2008 | B2 |
7444302 | Hu et al. | Oct 2008 | B2 |
7458508 | Shao et al. | Dec 2008 | B1 |
7467096 | Antonucci et al. | Dec 2008 | B2 |
7467127 | Baccash et al. | Dec 2008 | B1 |
7467401 | Cicchitto | Dec 2008 | B2 |
7472088 | Taylor et al. | Dec 2008 | B2 |
7496524 | Voltmer et al. | Feb 2009 | B2 |
7499868 | Galperin et al. | Mar 2009 | B2 |
7505938 | Lang et al. | Mar 2009 | B2 |
7509117 | Yum | Mar 2009 | B2 |
7512221 | Toms | Mar 2009 | B2 |
7516149 | Motwani et al. | Apr 2009 | B2 |
7529698 | Joao | May 2009 | B2 |
7536329 | Goldberg et al. | May 2009 | B2 |
7536346 | Aliffi et al. | May 2009 | B2 |
7536348 | Shao et al. | May 2009 | B2 |
7542993 | Satterfield et al. | Jun 2009 | B2 |
7546266 | Beirne et al. | Jun 2009 | B2 |
7548886 | Kirkland et al. | Jun 2009 | B2 |
7552089 | Bruer et al. | Jun 2009 | B2 |
7556192 | Wokaty, Jr. | Jul 2009 | B2 |
7562184 | Henmi et al. | Jul 2009 | B2 |
7571139 | Giordano et al. | Aug 2009 | B1 |
7575157 | Barnhardt et al. | Aug 2009 | B2 |
7580856 | Pliha | Aug 2009 | B1 |
7580884 | Cook | Aug 2009 | B2 |
7581112 | Brown et al. | Aug 2009 | B2 |
7584126 | White | Sep 2009 | B1 |
7584146 | Duhon | Sep 2009 | B1 |
7584149 | Bishop et al. | Sep 2009 | B1 |
7590589 | Hoffberg | Sep 2009 | B2 |
7593893 | Ladd et al. | Sep 2009 | B1 |
7596512 | Raines et al. | Sep 2009 | B1 |
7596716 | Frost et al. | Sep 2009 | B2 |
7606778 | Dewar | Oct 2009 | B2 |
7610216 | May et al. | Oct 2009 | B1 |
7610243 | Haggerty et al. | Oct 2009 | B2 |
7610257 | Abrahams | Oct 2009 | B1 |
7610261 | Maloche et al. | Oct 2009 | B2 |
7613628 | Ariff et al. | Nov 2009 | B2 |
7613629 | Antonucci et al. | Nov 2009 | B2 |
7613671 | Serrano-Morales et al. | Nov 2009 | B2 |
7620592 | O'Mara et al. | Nov 2009 | B2 |
7620596 | Knudson et al. | Nov 2009 | B2 |
7623844 | Herrmann et al. | Nov 2009 | B2 |
7624068 | Heasley et al. | Nov 2009 | B1 |
7653592 | Flaxman et al. | Jan 2010 | B1 |
7653593 | Zarikian et al. | Jan 2010 | B2 |
7657471 | Sankaran et al. | Feb 2010 | B1 |
7657540 | Bayliss | Feb 2010 | B1 |
7668769 | Baker et al. | Feb 2010 | B2 |
7668840 | Bayliss et al. | Feb 2010 | B2 |
7672865 | Kumar et al. | Mar 2010 | B2 |
7672870 | Haines et al. | Mar 2010 | B2 |
7676410 | Petralia | Mar 2010 | B2 |
7676418 | Chung et al. | Mar 2010 | B1 |
7676751 | Allen et al. | Mar 2010 | B2 |
7676756 | Vedula et al. | Mar 2010 | B2 |
7686214 | Shao et al. | Mar 2010 | B1 |
7689494 | Torre et al. | Mar 2010 | B2 |
7689504 | Warren et al. | Mar 2010 | B2 |
7689505 | Kasower | Mar 2010 | B2 |
7689506 | Fei et al. | Mar 2010 | B2 |
7690032 | Peirce | Mar 2010 | B1 |
7693787 | Provinse | Apr 2010 | B2 |
7698163 | Reed et al. | Apr 2010 | B2 |
7702550 | Perg et al. | Apr 2010 | B2 |
7702576 | Fahner et al. | Apr 2010 | B2 |
7707059 | Reed et al. | Apr 2010 | B2 |
7707102 | Rothstein | Apr 2010 | B2 |
7708190 | Brandt et al. | May 2010 | B2 |
7711635 | Steele et al. | May 2010 | B2 |
7711636 | Robida et al. | May 2010 | B2 |
7720846 | Bayliss | May 2010 | B1 |
7725300 | Pinto et al. | May 2010 | B2 |
7734522 | Johnson et al. | Jun 2010 | B2 |
7734523 | Cui et al. | Jun 2010 | B1 |
7734539 | Ghosh et al. | Jun 2010 | B2 |
7739223 | Vaschillo et al. | Jun 2010 | B2 |
7742982 | Chaudhuri et al. | Jun 2010 | B2 |
7747480 | Agresta et al. | Jun 2010 | B1 |
7747559 | Leitner et al. | Jun 2010 | B2 |
7761379 | Zoldi et al. | Jul 2010 | B2 |
7761384 | Madhogarhia | Jul 2010 | B2 |
7769998 | Lynch et al. | Aug 2010 | B2 |
7774272 | Fahner et al. | Aug 2010 | B2 |
7778885 | Semprevivo et al. | Aug 2010 | B1 |
7783515 | Kumar et al. | Aug 2010 | B1 |
7783562 | Ellis | Aug 2010 | B1 |
7788147 | Haggerty et al. | Aug 2010 | B2 |
7788152 | Haggerty et al. | Aug 2010 | B2 |
7792732 | Haggerty et al. | Sep 2010 | B2 |
7792864 | Rice et al. | Sep 2010 | B1 |
7793835 | Coggeshall et al. | Sep 2010 | B1 |
7797252 | Rosskamm et al. | Sep 2010 | B2 |
7801811 | Merrell et al. | Sep 2010 | B1 |
7801828 | Candella et al. | Sep 2010 | B2 |
7802104 | Dickinson | Sep 2010 | B2 |
7805345 | Abrahams et al. | Sep 2010 | B2 |
7805362 | Merrell et al. | Sep 2010 | B1 |
7813955 | Ariff et al. | Oct 2010 | B2 |
7813981 | Fahner et al. | Oct 2010 | B2 |
7814004 | Haggerty et al. | Oct 2010 | B2 |
7818231 | Rajan | Oct 2010 | B2 |
7822665 | Haggerty et al. | Oct 2010 | B2 |
7827115 | Weller et al. | Nov 2010 | B2 |
7831526 | Crawford et al. | Nov 2010 | B1 |
7835932 | Minsky et al. | Nov 2010 | B2 |
7835983 | Lefner et al. | Nov 2010 | B2 |
7836111 | Shan | Nov 2010 | B1 |
7840484 | Haggerty et al. | Nov 2010 | B2 |
7844534 | Haggerty et al. | Nov 2010 | B2 |
7848972 | Sharma | Dec 2010 | B1 |
7848978 | Imrey et al. | Dec 2010 | B2 |
7848987 | Haig | Dec 2010 | B2 |
7849004 | Choudhuri et al. | Dec 2010 | B2 |
7853518 | Cagan | Dec 2010 | B2 |
7853541 | Kapadia et al. | Dec 2010 | B1 |
7853998 | Blaisdell et al. | Dec 2010 | B2 |
7856386 | Hazlehurst et al. | Dec 2010 | B2 |
7856397 | Whipple et al. | Dec 2010 | B2 |
7856494 | Kulkarni | Dec 2010 | B2 |
7860782 | Cash et al. | Dec 2010 | B2 |
7860786 | Blackburn et al. | Dec 2010 | B2 |
7870078 | Clark et al. | Jan 2011 | B2 |
7877304 | Coulter | Jan 2011 | B1 |
7877320 | Downey | Jan 2011 | B1 |
7877322 | Nathans et al. | Jan 2011 | B2 |
7890367 | Senghore et al. | Feb 2011 | B2 |
7890420 | Haggerty et al. | Feb 2011 | B2 |
7899750 | Klieman et al. | Mar 2011 | B1 |
7904366 | Pogust | Mar 2011 | B2 |
7908242 | Achanta | Mar 2011 | B1 |
7912770 | Haggerty et al. | Mar 2011 | B2 |
7912842 | Bayliss et al. | Mar 2011 | B1 |
7912865 | Akerman et al. | Mar 2011 | B2 |
7925549 | Looney et al. | Apr 2011 | B2 |
7925582 | Kornegay et al. | Apr 2011 | B1 |
7925917 | Roy | Apr 2011 | B1 |
7930196 | Fung et al. | Apr 2011 | B2 |
7930242 | Morris et al. | Apr 2011 | B2 |
7930285 | Abraham et al. | Apr 2011 | B2 |
7937335 | Crawford et al. | May 2011 | B2 |
7941363 | Tanaka et al. | May 2011 | B2 |
7945516 | Bishop et al. | May 2011 | B2 |
7949597 | Zadoorian et al. | May 2011 | B2 |
7953695 | Roller et al. | May 2011 | B2 |
7954698 | Pliha | Jun 2011 | B1 |
7958126 | Schachter | Jun 2011 | B2 |
7962404 | Metzger, II et al. | Jun 2011 | B1 |
7966255 | Wong et al. | Jun 2011 | B2 |
7970676 | Feinstein | Jun 2011 | B2 |
7970698 | Gupta et al. | Jun 2011 | B2 |
7970701 | Lewis et al. | Jun 2011 | B2 |
7974860 | Travis | Jul 2011 | B1 |
7983976 | Nafeh et al. | Jul 2011 | B2 |
7991666 | Haggerty et al. | Aug 2011 | B2 |
7991677 | Haggerty et al. | Aug 2011 | B2 |
7991688 | Phelan et al. | Aug 2011 | B2 |
7991689 | Brunzell et al. | Aug 2011 | B1 |
7996320 | Bishop et al. | Aug 2011 | B2 |
7996521 | Chamberlain et al. | Aug 2011 | B2 |
8001042 | Brunzell et al. | Aug 2011 | B1 |
8005712 | von Davier et al. | Aug 2011 | B2 |
8005759 | Hirtenstein et al. | Aug 2011 | B2 |
8006261 | Haberman et al. | Aug 2011 | B1 |
8015045 | Galperin et al. | Sep 2011 | B2 |
8019828 | Cash et al. | Sep 2011 | B2 |
8019843 | Cash et al. | Sep 2011 | B2 |
8024220 | Ariff et al. | Sep 2011 | B2 |
8024245 | Haggerty et al. | Sep 2011 | B2 |
8024263 | Zarikian et al. | Sep 2011 | B2 |
8024264 | Chaudhuri et al. | Sep 2011 | B2 |
8024778 | Cash et al. | Sep 2011 | B2 |
8025220 | Zoldi et al. | Sep 2011 | B2 |
8027894 | Feinstein et al. | Sep 2011 | B2 |
8036979 | Torrez et al. | Oct 2011 | B1 |
8046271 | Jimenez et al. | Oct 2011 | B2 |
8050968 | Antonucci et al. | Nov 2011 | B2 |
8060424 | Kasower | Nov 2011 | B2 |
8064586 | Shaffer et al. | Nov 2011 | B2 |
8065182 | Voltmer et al. | Nov 2011 | B2 |
8065233 | Lee et al. | Nov 2011 | B2 |
8065234 | Liao et al. | Nov 2011 | B2 |
8073752 | Haggerty et al. | Dec 2011 | B2 |
8073768 | Haggerty et al. | Dec 2011 | B2 |
8073785 | Candella et al. | Dec 2011 | B1 |
8078453 | Shaw | Dec 2011 | B2 |
8078524 | Crawford et al. | Dec 2011 | B2 |
8078528 | Vicente et al. | Dec 2011 | B1 |
8082202 | Weiss | Dec 2011 | B2 |
8086509 | Haggerty et al. | Dec 2011 | B2 |
8086524 | Craig et al. | Dec 2011 | B1 |
8086525 | Atwood et al. | Dec 2011 | B2 |
8090734 | Maloche et al. | Jan 2012 | B2 |
8095443 | DeBie | Jan 2012 | B2 |
8099356 | Feinstein et al. | Jan 2012 | B2 |
8099376 | Serrano-Morales et al. | Jan 2012 | B2 |
8103530 | Quiring et al. | Jan 2012 | B2 |
8104671 | Besecker et al. | Jan 2012 | B2 |
8104679 | Brown | Jan 2012 | B2 |
8108301 | Gupta et al. | Jan 2012 | B2 |
8121918 | Haggerty et al. | Feb 2012 | B2 |
8126805 | Sulkowski et al. | Feb 2012 | B2 |
8127982 | Casey et al. | Mar 2012 | B1 |
8131614 | Haggerty et al. | Mar 2012 | B2 |
8131639 | Haggerty et al. | Mar 2012 | B2 |
8135642 | Krause | Mar 2012 | B1 |
8145754 | Chamberlain et al. | Mar 2012 | B2 |
8150744 | Zoldi et al. | Apr 2012 | B2 |
8155999 | de Boer et al. | Apr 2012 | B2 |
8160960 | Fei et al. | Apr 2012 | B1 |
8161104 | Tomkow | Apr 2012 | B2 |
8170938 | Haggerty et al. | May 2012 | B2 |
8170958 | Gremett et al. | May 2012 | B1 |
8175945 | Haggerty et al. | May 2012 | B2 |
8180654 | Berkman et al. | May 2012 | B2 |
8185408 | Baldwin, Jr. et al. | May 2012 | B2 |
RE43474 | Majoor | Jun 2012 | E |
8195550 | Haggerty et al. | Jun 2012 | B2 |
8200595 | De Zilwa et al. | Jun 2012 | B1 |
8200609 | Crawford et al. | Jun 2012 | B2 |
8200693 | Steele et al. | Jun 2012 | B2 |
8204774 | Chwast et al. | Jun 2012 | B2 |
8209250 | Bradway et al. | Jun 2012 | B2 |
8214238 | Fairfield et al. | Jul 2012 | B1 |
8214262 | Semprevivo et al. | Jul 2012 | B1 |
8219464 | Inghelbrecht et al. | Jul 2012 | B2 |
8219535 | Kobori et al. | Jul 2012 | B1 |
8234209 | Zadoorian et al. | Jul 2012 | B2 |
8234498 | Britti et al. | Jul 2012 | B2 |
8237716 | Kolipaka et al. | Aug 2012 | B2 |
8239130 | Upstill et al. | Aug 2012 | B1 |
8255423 | Ralph et al. | Aug 2012 | B2 |
8266090 | Crawford et al. | Sep 2012 | B2 |
8271378 | Chaudhuri et al. | Sep 2012 | B2 |
8271935 | Lewis | Sep 2012 | B2 |
8280805 | Abrahams et al. | Oct 2012 | B1 |
8280836 | Kumar | Oct 2012 | B2 |
8281180 | Roy | Oct 2012 | B1 |
8285577 | Galperin et al. | Oct 2012 | B1 |
8285656 | Chang et al. | Oct 2012 | B1 |
8290840 | Kasower | Oct 2012 | B2 |
8296205 | Zoldi | Oct 2012 | B2 |
8296213 | Haggerty et al. | Oct 2012 | B2 |
8296229 | Yellin et al. | Oct 2012 | B1 |
8301574 | Kilger et al. | Oct 2012 | B2 |
8306890 | Haggerty et al. | Nov 2012 | B2 |
8312389 | Crawford et al. | Nov 2012 | B2 |
8315895 | Kilat et al. | Nov 2012 | B1 |
8315933 | Haggerty et al. | Nov 2012 | B2 |
8315942 | Haggerty et al. | Nov 2012 | B2 |
8315943 | Torrez et al. | Nov 2012 | B2 |
8321335 | Bramlage et al. | Nov 2012 | B1 |
8326671 | Haggerty et al. | Dec 2012 | B2 |
8326672 | Haggerty et al. | Dec 2012 | B2 |
8326760 | Ma et al. | Dec 2012 | B2 |
8340685 | Cochran et al. | Dec 2012 | B2 |
8341073 | Bramlage et al. | Dec 2012 | B1 |
8352343 | Haggerty et al. | Jan 2013 | B2 |
8364518 | Blake et al. | Jan 2013 | B1 |
8364582 | Haggerty et al. | Jan 2013 | B2 |
8364588 | Celka et al. | Jan 2013 | B2 |
8365212 | Orlowski | Jan 2013 | B1 |
8386377 | Xiong et al. | Feb 2013 | B1 |
8392334 | Hirtenstein et al. | Mar 2013 | B2 |
8401889 | Chwast et al. | Mar 2013 | B2 |
8401946 | Zoldi et al. | Mar 2013 | B2 |
8401950 | Lyons et al. | Mar 2013 | B2 |
8407137 | Thomas | Mar 2013 | B2 |
8417587 | Jimenez et al. | Apr 2013 | B2 |
8417612 | Chatterji et al. | Apr 2013 | B2 |
8418254 | Britti et al. | Apr 2013 | B2 |
8423488 | Surpi | Apr 2013 | B2 |
8433512 | Lopatenko et al. | Apr 2013 | B1 |
8438105 | Haggerty et al. | May 2013 | B2 |
8458026 | Voltmer et al. | Jun 2013 | B2 |
8458052 | Libman | Jun 2013 | B2 |
8458074 | Showalter | Jun 2013 | B2 |
8463595 | Rehling et al. | Jun 2013 | B1 |
8468198 | Tomkow | Jun 2013 | B2 |
8473354 | Psota et al. | Jun 2013 | B2 |
8473380 | Thomas et al. | Jun 2013 | B2 |
8478673 | Haggerty et al. | Jul 2013 | B2 |
8489482 | Haggerty et al. | Jul 2013 | B2 |
8494855 | Khosla et al. | Jul 2013 | B1 |
8504470 | Chirehdast | Aug 2013 | B1 |
8510184 | Imrev et al. | Aug 2013 | B2 |
8510189 | Imrey et al. | Aug 2013 | B2 |
8515828 | Wolf et al. | Aug 2013 | B1 |
8515862 | Zhang et al. | Aug 2013 | B2 |
8527596 | Long et al. | Sep 2013 | B2 |
8533322 | Chamberlain et al. | Sep 2013 | B2 |
8560434 | Morris et al. | Oct 2013 | B2 |
8566029 | Lopatenko et al. | Oct 2013 | B1 |
8566167 | Munjal | Oct 2013 | B2 |
8589069 | Lehman | Nov 2013 | B1 |
8589208 | Kruger et al. | Nov 2013 | B2 |
8595101 | Daukas et al. | Nov 2013 | B1 |
8595219 | Thompson | Nov 2013 | B1 |
8600854 | Mayr et al. | Dec 2013 | B2 |
8600870 | Milana | Dec 2013 | B2 |
8606626 | DeSoto et al. | Dec 2013 | B1 |
8606632 | Libman | Dec 2013 | B2 |
8606666 | Courbage et al. | Dec 2013 | B1 |
8620579 | Upstill et al. | Dec 2013 | B1 |
8626560 | Anderson | Jan 2014 | B1 |
8626582 | Ariff et al. | Jan 2014 | B2 |
8626618 | Psota et al. | Jan 2014 | B2 |
8626646 | Torrez et al. | Jan 2014 | B2 |
8630929 | Haggerty et al. | Jan 2014 | B2 |
8639568 | de Boer et al. | Jan 2014 | B2 |
8639920 | Stack et al. | Jan 2014 | B2 |
8660943 | Chirehdast | Feb 2014 | B1 |
8666885 | Bramlage et al. | Mar 2014 | B1 |
8682762 | Fahner | Mar 2014 | B2 |
8682770 | Haggerty et al. | Mar 2014 | B2 |
8694390 | Imrey et al. | Apr 2014 | B2 |
8694403 | Haggerty et al. | Apr 2014 | B2 |
8700597 | Gupta et al. | Apr 2014 | B2 |
8706545 | Narayanaswamy et al. | Apr 2014 | B2 |
8706596 | Cohen et al. | Apr 2014 | B2 |
8706615 | Merkle | Apr 2014 | B2 |
8719114 | Libman | May 2014 | B2 |
8730241 | Chhaparwal et al. | May 2014 | B2 |
8732004 | Ramos et al. | May 2014 | B1 |
8732013 | Senghore et al. | May 2014 | B2 |
8732073 | Thomas | May 2014 | B2 |
8738435 | Libman | May 2014 | B2 |
8738515 | Chaudhuri et al. | May 2014 | B2 |
8738532 | Ariff et al. | May 2014 | B2 |
8744944 | Haggerty et al. | Jun 2014 | B2 |
8751378 | Dornhelm et al. | Jun 2014 | B2 |
8751461 | Abraham | Jun 2014 | B2 |
8762053 | Lehman | Jun 2014 | B1 |
8768826 | Imrey et al. | Jul 2014 | B2 |
8775290 | Haggerty et al. | Jul 2014 | B2 |
8775291 | Mellman et al. | Jul 2014 | B1 |
8775299 | Achanta et al. | Jul 2014 | B2 |
8775301 | Haggerty et al. | Jul 2014 | B2 |
8781877 | Kruger et al. | Jul 2014 | B2 |
8781933 | Haggerty et al. | Jul 2014 | B2 |
8781951 | Lewis et al. | Jul 2014 | B2 |
8781953 | Kasower | Jul 2014 | B2 |
8781975 | Bennett et al. | Jul 2014 | B2 |
8788388 | Chatterji et al. | Jul 2014 | B2 |
8805805 | Kobori et al. | Aug 2014 | B1 |
8825544 | Imrey et al. | Sep 2014 | B2 |
8843780 | Roy | Sep 2014 | B1 |
8930251 | DeBie | Jan 2015 | B2 |
8938432 | Rossmark et al. | Jan 2015 | B2 |
8966649 | Stack et al. | Feb 2015 | B2 |
8984022 | Crawford et al. | Mar 2015 | B1 |
9026088 | Groenjes | May 2015 | B1 |
9043930 | Britti et al. | May 2015 | B2 |
9057616 | Lopatenko et al. | Jun 2015 | B1 |
9057617 | Lopatenko et al. | Jun 2015 | B1 |
9058340 | Chamberlain et al. | Jun 2015 | B1 |
9063226 | Zheng et al. | Jun 2015 | B2 |
9087335 | Rane et al. | Jul 2015 | B2 |
9123056 | Singh et al. | Sep 2015 | B2 |
9143541 | Szamonek et al. | Sep 2015 | B1 |
9147042 | Haller et al. | Sep 2015 | B1 |
9147152 | Nack et al. | Sep 2015 | B2 |
9152727 | Balducci et al. | Oct 2015 | B1 |
9213646 | LaPanse et al. | Dec 2015 | B1 |
9251541 | Celka et al. | Feb 2016 | B2 |
9256866 | Pontious | Feb 2016 | B2 |
9292581 | Thompson | Mar 2016 | B2 |
9292860 | Singh et al. | Mar 2016 | B2 |
9318105 | Khosla | Apr 2016 | B1 |
9329715 | Schwarz et al. | May 2016 | B2 |
9378500 | Jimenez et al. | Jun 2016 | B2 |
9483236 | Yershov et al. | Nov 2016 | B2 |
9483606 | Dean et al. | Nov 2016 | B1 |
9483727 | Zhao et al. | Nov 2016 | B2 |
9489497 | MaGill et al. | Nov 2016 | B2 |
9489614 | Nack et al. | Nov 2016 | B2 |
9508092 | De Soto et al. | Nov 2016 | B1 |
9509711 | Keanini | Nov 2016 | B1 |
9553936 | Dijk et al. | Jan 2017 | B2 |
9563916 | Torrez et al. | Feb 2017 | B1 |
9576030 | Kapczynski et al. | Feb 2017 | B1 |
9595051 | Stack et al. | Mar 2017 | B2 |
9619579 | Courbage et al. | Apr 2017 | B1 |
9632847 | Raghavan et al. | Apr 2017 | B2 |
9652802 | Kasower | May 2017 | B1 |
9660869 | Ripley et al. | May 2017 | B2 |
9690575 | Prismon et al. | Jun 2017 | B2 |
9704192 | Ainsworth et al. | Jul 2017 | B2 |
9710663 | Britti et al. | Jul 2017 | B2 |
9710841 | Ainsworth, III et al. | Jul 2017 | B2 |
9721267 | Fahner et al. | Aug 2017 | B2 |
9779187 | Gao et al. | Oct 2017 | B1 |
9842345 | Ariff et al. | Dec 2017 | B2 |
9870589 | Arnold et al. | Jan 2018 | B1 |
9916596 | DeSoto et al. | Mar 2018 | B1 |
9916621 | Wasser et al. | Mar 2018 | B1 |
9990270 | Ballal | Jun 2018 | B2 |
10019508 | Kapczynski | Jul 2018 | B1 |
10078868 | Courbage et al. | Sep 2018 | B1 |
10083263 | Gao et al. | Sep 2018 | B2 |
10102536 | Hickman et al. | Oct 2018 | B1 |
10121194 | Torrez et al. | Nov 2018 | B1 |
10133562 | Yershov et al. | Nov 2018 | B2 |
10133980 | Turner et al. | Nov 2018 | B2 |
10140193 | Roy | Nov 2018 | B1 |
10162630 | Bouley et al. | Dec 2018 | B2 |
10178111 | Wilson et al. | Jan 2019 | B1 |
10242019 | Shan et al. | Mar 2019 | B1 |
10262362 | Hu et al. | Apr 2019 | B1 |
10311466 | DeSoto et al. | Jun 2019 | B1 |
10366342 | Zhao et al. | Jul 2019 | B2 |
10380508 | Prismon et al. | Aug 2019 | B2 |
10380619 | Pontious | Aug 2019 | B2 |
10380654 | Hirtenstein et al. | Aug 2019 | B2 |
10402901 | Courbage et al. | Sep 2019 | B2 |
10423976 | Walz | Sep 2019 | B2 |
10445152 | Zhang et al. | Oct 2019 | B1 |
10460335 | West | Oct 2019 | B2 |
10474566 | Indurthivenkata et al. | Nov 2019 | B2 |
10482531 | Drotos et al. | Nov 2019 | B2 |
10515412 | Rocklitz | Dec 2019 | B2 |
10521735 | Ballal | Dec 2019 | B2 |
10535009 | Turner et al. | Jan 2020 | B2 |
10558913 | Turner et al. | Feb 2020 | B1 |
10565178 | Rajagopal | Feb 2020 | B1 |
10572891 | Walz | Feb 2020 | B2 |
10580025 | Hickman et al. | Mar 2020 | B2 |
10620944 | Prismon et al. | Apr 2020 | B2 |
10643154 | Litherland et al. | May 2020 | B2 |
10650449 | Courbage et al. | May 2020 | B2 |
10671812 | Bondugula et al. | Jun 2020 | B2 |
10678894 | Yin et al. | Jun 2020 | B2 |
10692105 | DeSoto et al. | Jun 2020 | B1 |
10713140 | Gupta et al. | Jul 2020 | B2 |
10713596 | Cozine et al. | Jul 2020 | B2 |
10726440 | Bradford | Jul 2020 | B1 |
10789422 | Banaszak et al. | Sep 2020 | B2 |
10810463 | Min et al. | Oct 2020 | B2 |
20010013011 | Day et al. | Aug 2001 | A1 |
20010014868 | Herz et al. | Aug 2001 | A1 |
20010014878 | Mitra et al. | Aug 2001 | A1 |
20010016833 | Everling et al. | Aug 2001 | A1 |
20010027413 | Bhutta | Oct 2001 | A1 |
20010029470 | Schultz et al. | Oct 2001 | A1 |
20010034631 | Kiselik | Oct 2001 | A1 |
20010037332 | Miller et al. | Nov 2001 | A1 |
20010039523 | Iwamoto | Nov 2001 | A1 |
20010049620 | Blasko | Dec 2001 | A1 |
20020019804 | Sutton | Feb 2002 | A1 |
20020023051 | Kunzle et al. | Feb 2002 | A1 |
20020023143 | Stephenson et al. | Feb 2002 | A1 |
20020026411 | Nathans et al. | Feb 2002 | A1 |
20020029162 | Mascarenhas | Mar 2002 | A1 |
20020035511 | Haji et al. | Mar 2002 | A1 |
20020046096 | Srinivasan et al. | Apr 2002 | A1 |
20020049626 | Mathis et al. | Apr 2002 | A1 |
20020049701 | Nabe et al. | Apr 2002 | A1 |
20020049738 | Epstein | Apr 2002 | A1 |
20020052836 | Galperin et al. | May 2002 | A1 |
20020052841 | Guthrie et al. | May 2002 | A1 |
20020055869 | Hegg | May 2002 | A1 |
20020069122 | Yun et al. | Jun 2002 | A1 |
20020072927 | Phelan et al. | Jun 2002 | A1 |
20020077964 | Brody et al. | Jun 2002 | A1 |
20020082892 | Raffel et al. | Jun 2002 | A1 |
20020087460 | Hornung | Jul 2002 | A1 |
20020091706 | Anderson et al. | Jul 2002 | A1 |
20020095360 | Joao | Jul 2002 | A1 |
20020099628 | Takaoka et al. | Jul 2002 | A1 |
20020099641 | Mills et al. | Jul 2002 | A1 |
20020099649 | Lee et al. | Jul 2002 | A1 |
20020099824 | Bender et al. | Jul 2002 | A1 |
20020099936 | Kou et al. | Jul 2002 | A1 |
20020111845 | Chong | Aug 2002 | A1 |
20020119824 | Allen | Aug 2002 | A1 |
20020120504 | Gould et al. | Aug 2002 | A1 |
20020123928 | Eldering et al. | Sep 2002 | A1 |
20020128960 | Lambiotte et al. | Sep 2002 | A1 |
20020128962 | Kasower | Sep 2002 | A1 |
20020129368 | Schlack et al. | Sep 2002 | A1 |
20020133444 | Sankaran et al. | Sep 2002 | A1 |
20020138297 | Lee | Sep 2002 | A1 |
20020138331 | Hosea et al. | Sep 2002 | A1 |
20020138333 | DeCotiis et al. | Sep 2002 | A1 |
20020138334 | DeCotiis et al. | Sep 2002 | A1 |
20020138417 | Lawrence | Sep 2002 | A1 |
20020143661 | Tumulty et al. | Oct 2002 | A1 |
20020147623 | Rifaat | Oct 2002 | A1 |
20020147669 | Taylor et al. | Oct 2002 | A1 |
20020147695 | Khedkar et al. | Oct 2002 | A1 |
20020156676 | Ahrens et al. | Oct 2002 | A1 |
20020161496 | Yamaki | Oct 2002 | A1 |
20020161664 | Shaya et al. | Oct 2002 | A1 |
20020161711 | Sartor et al. | Oct 2002 | A1 |
20020165757 | Lisser | Nov 2002 | A1 |
20020169747 | Chapman et al. | Nov 2002 | A1 |
20020173984 | Robertson et al. | Nov 2002 | A1 |
20020173994 | Ferguson, III | Nov 2002 | A1 |
20020184255 | Edd et al. | Dec 2002 | A1 |
20020188544 | Wizon et al. | Dec 2002 | A1 |
20020194099 | Weiss | Dec 2002 | A1 |
20020194103 | Nabe | Dec 2002 | A1 |
20020194140 | Makuck | Dec 2002 | A1 |
20020198824 | Cook | Dec 2002 | A1 |
20030000568 | Gonsiorawski | Jan 2003 | A1 |
20030002639 | Huie | Jan 2003 | A1 |
20030004787 | Tripp et al. | Jan 2003 | A1 |
20030004855 | Dutta et al. | Jan 2003 | A1 |
20030004865 | Kinoshita | Jan 2003 | A1 |
20030009368 | Kitts | Jan 2003 | A1 |
20030009393 | Norris et al. | Jan 2003 | A1 |
20030009418 | Green et al. | Jan 2003 | A1 |
20030009426 | Ruiz-Sanchez | Jan 2003 | A1 |
20030018549 | Fei et al. | Jan 2003 | A1 |
20030018578 | Schultz | Jan 2003 | A1 |
20030018769 | Foulger et al. | Jan 2003 | A1 |
20030023489 | McGuire et al. | Jan 2003 | A1 |
20030033242 | Lynch et al. | Feb 2003 | A1 |
20030033261 | Knegendorf | Feb 2003 | A1 |
20030036996 | Lazerson | Feb 2003 | A1 |
20030041031 | Hedy | Feb 2003 | A1 |
20030046222 | Bard et al. | Mar 2003 | A1 |
20030060284 | Hamalainen et al. | Mar 2003 | A1 |
20030061132 | Yu et al. | Mar 2003 | A1 |
20030061163 | Durfield | Mar 2003 | A1 |
20030061233 | Manasse et al. | Mar 2003 | A1 |
20030065563 | Elliott et al. | Apr 2003 | A1 |
20030069839 | Whittington et al. | Apr 2003 | A1 |
20030078877 | Beirne et al. | Apr 2003 | A1 |
20030093289 | Thornley et al. | May 2003 | A1 |
20030093311 | Knowlson | May 2003 | A1 |
20030093366 | Halper et al. | May 2003 | A1 |
20030097320 | Gordon | May 2003 | A1 |
20030097342 | Whittingtom | May 2003 | A1 |
20030097380 | Mulhern et al. | May 2003 | A1 |
20030101111 | Dang et al. | May 2003 | A1 |
20030105696 | Kalotay et al. | Jun 2003 | A1 |
20030105728 | Yano et al. | Jun 2003 | A1 |
20030110111 | Nalebuff et al. | Jun 2003 | A1 |
20030110293 | Friedman et al. | Jun 2003 | A1 |
20030113727 | Girn et al. | Jun 2003 | A1 |
20030115080 | Kasravi et al. | Jun 2003 | A1 |
20030115133 | Bian | Jun 2003 | A1 |
20030120591 | Birkhead et al. | Jun 2003 | A1 |
20030135451 | O'Brien et al. | Jul 2003 | A1 |
20030139986 | Roberts | Jul 2003 | A1 |
20030144950 | O'Brien et al. | Jul 2003 | A1 |
20030149610 | Rowan et al. | Aug 2003 | A1 |
20030158751 | Suresh et al. | Aug 2003 | A1 |
20030158776 | Landesmann | Aug 2003 | A1 |
20030163708 | Tang | Aug 2003 | A1 |
20030164497 | Carcia et al. | Sep 2003 | A1 |
20030167218 | Field et al. | Sep 2003 | A1 |
20030167226 | Britton et al. | Sep 2003 | A1 |
20030171942 | Gaito | Sep 2003 | A1 |
20030182214 | Taylor | Sep 2003 | A1 |
20030195830 | Merkoulovitch et al. | Oct 2003 | A1 |
20030195859 | Lawrence | Oct 2003 | A1 |
20030200151 | Ellenson et al. | Oct 2003 | A1 |
20030205845 | Pichler et al. | Nov 2003 | A1 |
20030208362 | Enthoven et al. | Nov 2003 | A1 |
20030208428 | Raynes et al. | Nov 2003 | A1 |
20030212618 | Keyes et al. | Nov 2003 | A1 |
20030212654 | Harper et al. | Nov 2003 | A1 |
20030216965 | Libman | Nov 2003 | A1 |
20030219709 | Olenick et al. | Nov 2003 | A1 |
20030225656 | Aberman et al. | Dec 2003 | A1 |
20030225692 | Bosch et al. | Dec 2003 | A1 |
20030225742 | Tenner et al. | Dec 2003 | A1 |
20030229507 | Perge | Dec 2003 | A1 |
20030229892 | Sardera | Dec 2003 | A1 |
20030233278 | Marshall | Dec 2003 | A1 |
20030233323 | Bilski et al. | Dec 2003 | A1 |
20030233370 | Barabas et al. | Dec 2003 | A1 |
20030233655 | Gutta et al. | Dec 2003 | A1 |
20030236738 | Lange et al. | Dec 2003 | A1 |
20040002916 | Timmerman et al. | Jan 2004 | A1 |
20040006536 | Kawashima et al. | Jan 2004 | A1 |
20040010443 | May et al. | Jan 2004 | A1 |
20040019518 | Abraham et al. | Jan 2004 | A1 |
20040023637 | Johnson et al. | Feb 2004 | A1 |
20040024692 | Turbeville et al. | Feb 2004 | A1 |
20040029311 | Snyder et al. | Feb 2004 | A1 |
20040030649 | Nelson et al. | Feb 2004 | A1 |
20040030667 | Xu et al. | Feb 2004 | A1 |
20040033375 | Mori | Feb 2004 | A1 |
20040034570 | Davis et al. | Feb 2004 | A1 |
20040039681 | Cullen et al. | Feb 2004 | A1 |
20040039688 | Sulkowski et al. | Feb 2004 | A1 |
20040044615 | Xue et al. | Mar 2004 | A1 |
20040044617 | Lu | Mar 2004 | A1 |
20040046497 | Shaepkens et al. | Mar 2004 | A1 |
20040049452 | Blagg | Mar 2004 | A1 |
20040054619 | Watson et al. | Mar 2004 | A1 |
20040059626 | Smallwood | Mar 2004 | A1 |
20040059653 | Verkuylen et al. | Mar 2004 | A1 |
20040062213 | Koss | Apr 2004 | A1 |
20040078248 | Altschuler | Apr 2004 | A1 |
20040078324 | Lonnberg et al. | Apr 2004 | A1 |
20040083215 | de Jong | Apr 2004 | A1 |
20040088221 | Katz et al. | May 2004 | A1 |
20040093278 | Burchetta et al. | May 2004 | A1 |
20040098625 | Lagadec et al. | May 2004 | A1 |
20040102197 | Dietz | May 2004 | A1 |
20040103147 | Flesher et al. | May 2004 | A1 |
20040107123 | Haffner et al. | Jun 2004 | A1 |
20040107125 | Guheen et al. | Jun 2004 | A1 |
20040111305 | Gavan et al. | Jun 2004 | A1 |
20040111358 | Lange et al. | Jun 2004 | A1 |
20040111363 | Trench et al. | Jun 2004 | A1 |
20040117235 | Shacham | Jun 2004 | A1 |
20040117358 | Von Kaenel et al. | Jun 2004 | A1 |
20040122730 | Tucciarone et al. | Jun 2004 | A1 |
20040122735 | Meshkin | Jun 2004 | A1 |
20040128150 | Lundegren | Jul 2004 | A1 |
20040128227 | Whipple et al. | Jul 2004 | A1 |
20040128230 | Oppenheimer et al. | Jul 2004 | A1 |
20040128232 | Descloux | Jul 2004 | A1 |
20040128236 | Brown et al. | Jul 2004 | A1 |
20040139035 | Wang | Jul 2004 | A1 |
20040143526 | Monasterio et al. | Jul 2004 | A1 |
20040143546 | Wood et al. | Jul 2004 | A1 |
20040153330 | Miller et al. | Aug 2004 | A1 |
20040153448 | Cheng et al. | Aug 2004 | A1 |
20040158520 | Noh | Aug 2004 | A1 |
20040158523 | Dort | Aug 2004 | A1 |
20040163101 | Swix | Aug 2004 | A1 |
20040167793 | Masuoka et al. | Aug 2004 | A1 |
20040176995 | Fusz | Sep 2004 | A1 |
20040177046 | Ogram | Sep 2004 | A1 |
20040186807 | Nathans et al. | Sep 2004 | A1 |
20040193535 | Barazesh | Sep 2004 | A1 |
20040193538 | Raines | Sep 2004 | A1 |
20040199456 | Flint et al. | Oct 2004 | A1 |
20040199458 | Ho | Oct 2004 | A1 |
20040199462 | Starrs | Oct 2004 | A1 |
20040199789 | Shaw et al. | Oct 2004 | A1 |
20040205157 | Bibelnieks et al. | Oct 2004 | A1 |
20040212299 | Ishikawa et al. | Oct 2004 | A1 |
20040220896 | Finlay et al. | Nov 2004 | A1 |
20040225545 | Turner et al. | Nov 2004 | A1 |
20040225586 | Woods et al. | Nov 2004 | A1 |
20040225594 | Nolan, III et al. | Nov 2004 | A1 |
20040225596 | Kemper et al. | Nov 2004 | A1 |
20040230448 | Schaich | Nov 2004 | A1 |
20040230459 | Dordick et al. | Nov 2004 | A1 |
20040230527 | Hansen et al. | Nov 2004 | A1 |
20040230534 | McGough | Nov 2004 | A1 |
20040230820 | Hui Hsu et al. | Nov 2004 | A1 |
20040243450 | Bernard, Jr. et al. | Dec 2004 | A1 |
20040243518 | Clifton et al. | Dec 2004 | A1 |
20040243588 | Tanner et al. | Dec 2004 | A1 |
20040261116 | Mckeown et al. | Dec 2004 | A1 |
20050004805 | Srinivasan | Jan 2005 | A1 |
20050015330 | Beery et al. | Jan 2005 | A1 |
20050021397 | Cui et al. | Jan 2005 | A1 |
20050021476 | Candella et al. | Jan 2005 | A1 |
20050027633 | Fortuna et al. | Feb 2005 | A1 |
20050027983 | Klawon | Feb 2005 | A1 |
20050033734 | Chess et al. | Feb 2005 | A1 |
20050038726 | Salomon et al. | Feb 2005 | A1 |
20050050027 | Yeh et al. | Mar 2005 | A1 |
20050058262 | Timmins et al. | Mar 2005 | A1 |
20050065874 | Lefner et al. | Mar 2005 | A1 |
20050086261 | Mammone | Apr 2005 | A1 |
20050091164 | Varble | Apr 2005 | A1 |
20050097039 | Kulcsar et al. | May 2005 | A1 |
20050102206 | Savasoglu et al. | May 2005 | A1 |
20050102225 | Oppenheimer et al. | May 2005 | A1 |
20050102226 | Oppenheimer et al. | May 2005 | A1 |
20050113991 | Rogers et al. | May 2005 | A1 |
20050120249 | Shuster | Jun 2005 | A1 |
20050125350 | Tidwell et al. | Jun 2005 | A1 |
20050130704 | McParland et al. | Jun 2005 | A1 |
20050137899 | Davies et al. | Jun 2005 | A1 |
20050137963 | Ricketts et al. | Jun 2005 | A1 |
20050144452 | Lynch et al. | Jun 2005 | A1 |
20050144641 | Lewis | Jun 2005 | A1 |
20050154664 | Guy et al. | Jul 2005 | A1 |
20050154665 | Kerr | Jul 2005 | A1 |
20050154769 | Eckart et al. | Jul 2005 | A1 |
20050159996 | Lazaraus et al. | Jul 2005 | A1 |
20050177489 | Neff et al. | Aug 2005 | A1 |
20050189414 | Fano et al. | Sep 2005 | A1 |
20050192008 | Desai et al. | Sep 2005 | A1 |
20050197953 | Broadbent et al. | Sep 2005 | A1 |
20050197954 | Maitland et al. | Sep 2005 | A1 |
20050201272 | Wang et al. | Sep 2005 | A1 |
20050209892 | Miller | Sep 2005 | A1 |
20050209922 | Hofmeister | Sep 2005 | A1 |
20050222900 | Fuloria et al. | Oct 2005 | A1 |
20050228692 | Hodgon | Oct 2005 | A1 |
20050246256 | Gastineau et al. | Nov 2005 | A1 |
20050251408 | Swaminathan et al. | Nov 2005 | A1 |
20050251474 | Shinn et al. | Nov 2005 | A1 |
20050251820 | Stefanik et al. | Nov 2005 | A1 |
20050256780 | Eldred | Nov 2005 | A1 |
20050256809 | Sadri | Nov 2005 | A1 |
20050257250 | Mitchell et al. | Nov 2005 | A1 |
20050262014 | Fickes | Nov 2005 | A1 |
20050262158 | Sauermann | Nov 2005 | A1 |
20050267774 | Merritt et al. | Dec 2005 | A1 |
20050273442 | Bennett et al. | Dec 2005 | A1 |
20050273849 | Araujo et al. | Dec 2005 | A1 |
20050278246 | Friedman et al. | Dec 2005 | A1 |
20050278542 | Pierson et al. | Dec 2005 | A1 |
20050279824 | Anderson et al. | Dec 2005 | A1 |
20050279827 | Mascavage et al. | Dec 2005 | A1 |
20050288954 | McCarthy et al. | Dec 2005 | A1 |
20050288998 | Verma et al. | Dec 2005 | A1 |
20050289003 | Thompson et al. | Dec 2005 | A1 |
20060004626 | Holmen et al. | Jan 2006 | A1 |
20060004731 | Seibel et al. | Jan 2006 | A1 |
20060004753 | Coifman et al. | Jan 2006 | A1 |
20060010055 | Morita et al. | Jan 2006 | A1 |
20060014129 | Coleman et al. | Jan 2006 | A1 |
20060015425 | Brooks | Jan 2006 | A1 |
20060020611 | Gilbert et al. | Jan 2006 | A1 |
20060031158 | Orman | Feb 2006 | A1 |
20060031747 | Wada et al. | Feb 2006 | A1 |
20060032909 | Seegar | Feb 2006 | A1 |
20060041443 | Horvath | Feb 2006 | A1 |
20060041464 | Powers et al. | Feb 2006 | A1 |
20060041840 | Blair | Feb 2006 | A1 |
20060059073 | Walzak | Mar 2006 | A1 |
20060059110 | Madhok et al. | Mar 2006 | A1 |
20060074986 | Mallalieu et al. | Apr 2006 | A1 |
20060080126 | Greer et al. | Apr 2006 | A1 |
20060080230 | Freiberg | Apr 2006 | A1 |
20060080233 | Mendelovich et al. | Apr 2006 | A1 |
20060080251 | Fried et al. | Apr 2006 | A1 |
20060080263 | Willis et al. | Apr 2006 | A1 |
20060085334 | Murphy | Apr 2006 | A1 |
20060089842 | Medawar | Apr 2006 | A1 |
20060095363 | May | May 2006 | A1 |
20060095923 | Novack et al. | May 2006 | A1 |
20060100954 | Schoen | May 2006 | A1 |
20060122921 | Comerford et al. | Jun 2006 | A1 |
20060129428 | Wennberg | Jun 2006 | A1 |
20060129481 | Bhatt et al. | Jun 2006 | A1 |
20060131390 | Kim | Jun 2006 | A1 |
20060136330 | DeRoy et al. | Jun 2006 | A1 |
20060144927 | Love et al. | Jul 2006 | A1 |
20060149674 | Cook et al. | Jul 2006 | A1 |
20060155624 | Schwartz | Jul 2006 | A1 |
20060155639 | Lynch et al. | Jul 2006 | A1 |
20060161435 | Atef et al. | Jul 2006 | A1 |
20060173726 | Hall et al. | Aug 2006 | A1 |
20060173772 | Hayes et al. | Aug 2006 | A1 |
20060173776 | Shalley et al. | Aug 2006 | A1 |
20060177226 | Ellis, III | Aug 2006 | A1 |
20060178189 | Walker et al. | Aug 2006 | A1 |
20060178957 | LeClaire | Aug 2006 | A1 |
20060178971 | Owen et al. | Aug 2006 | A1 |
20060178983 | Nice et al. | Aug 2006 | A1 |
20060184440 | Britti et al. | Aug 2006 | A1 |
20060195390 | Rusk et al. | Aug 2006 | A1 |
20060202012 | Grano et al. | Sep 2006 | A1 |
20060204051 | Holland, IV | Sep 2006 | A1 |
20060206416 | Farias | Sep 2006 | A1 |
20060212350 | Ellis et al. | Sep 2006 | A1 |
20060218069 | Aberman et al. | Sep 2006 | A1 |
20060218079 | Goldblatt et al. | Sep 2006 | A1 |
20060229943 | Mathias et al. | Oct 2006 | A1 |
20060229961 | Lyftogt et al. | Oct 2006 | A1 |
20060229996 | Keithley et al. | Oct 2006 | A1 |
20060235743 | Long et al. | Oct 2006 | A1 |
20060239512 | Petrillo | Oct 2006 | A1 |
20060241923 | Xu et al. | Oct 2006 | A1 |
20060242046 | Haggerty et al. | Oct 2006 | A1 |
20060242047 | Haggerty et al. | Oct 2006 | A1 |
20060242048 | Haggerty et al. | Oct 2006 | A1 |
20060242050 | Haggerty et al. | Oct 2006 | A1 |
20060253328 | Kohli et al. | Nov 2006 | A1 |
20060253358 | Delgrosso et al. | Nov 2006 | A1 |
20060259364 | Strock et al. | Nov 2006 | A1 |
20060262929 | Vatanen et al. | Nov 2006 | A1 |
20060265243 | Racho et al. | Nov 2006 | A1 |
20060265323 | Winter et al. | Nov 2006 | A1 |
20060267999 | Cash et al. | Nov 2006 | A1 |
20060271456 | Romain et al. | Nov 2006 | A1 |
20060271457 | Romain et al. | Nov 2006 | A1 |
20060271552 | McChesney et al. | Nov 2006 | A1 |
20060276171 | Pousti | Dec 2006 | A1 |
20060277102 | Agliozzo | Dec 2006 | A1 |
20060277141 | Palmer | Dec 2006 | A1 |
20060282328 | Gerace et al. | Dec 2006 | A1 |
20060282359 | Nobili et al. | Dec 2006 | A1 |
20060293921 | McCarthy et al. | Dec 2006 | A1 |
20060293932 | Cash et al. | Dec 2006 | A1 |
20060293979 | Cash et al. | Dec 2006 | A1 |
20060294199 | Bertholf | Dec 2006 | A1 |
20070005508 | Chiang | Jan 2007 | A1 |
20070011026 | Higgins et al. | Jan 2007 | A1 |
20070011039 | Oddo | Jan 2007 | A1 |
20070011083 | Bird et al. | Jan 2007 | A1 |
20070011099 | Sheehan | Jan 2007 | A1 |
20070016500 | Chatterji et al. | Jan 2007 | A1 |
20070016501 | Chatterji et al. | Jan 2007 | A1 |
20070016518 | Atkinson et al. | Jan 2007 | A1 |
20070016522 | Wang | Jan 2007 | A1 |
20070022141 | Singleton et al. | Jan 2007 | A1 |
20070022297 | Britti et al. | Jan 2007 | A1 |
20070027778 | Schellhammer et al. | Feb 2007 | A1 |
20070027791 | Young et al. | Feb 2007 | A1 |
20070030282 | Cash et al. | Feb 2007 | A1 |
20070033227 | Gaito et al. | Feb 2007 | A1 |
20070038483 | Wood | Feb 2007 | A1 |
20070038497 | Britti et al. | Feb 2007 | A1 |
20070043654 | Libman | Feb 2007 | A1 |
20070055598 | Arnott et al. | Mar 2007 | A1 |
20070055599 | Arnott | Mar 2007 | A1 |
20070055618 | Pogust | Mar 2007 | A1 |
20070055621 | Tischler et al. | Mar 2007 | A1 |
20070061195 | Liu et al. | Mar 2007 | A1 |
20070061243 | Ramer et al. | Mar 2007 | A1 |
20070067207 | Haggerty et al. | Mar 2007 | A1 |
20070067208 | Haggerty et al. | Mar 2007 | A1 |
20070067209 | Haggerty et al. | Mar 2007 | A1 |
20070067235 | Nathans et al. | Mar 2007 | A1 |
20070067285 | Blume et al. | Mar 2007 | A1 |
20070067297 | Kublickis | Mar 2007 | A1 |
20070067437 | Sindambiwe | Mar 2007 | A1 |
20070072190 | Aggarwal | Mar 2007 | A1 |
20070078741 | Haggerty et al. | Apr 2007 | A1 |
20070078985 | Shao et al. | Apr 2007 | A1 |
20070083460 | Bachenheimer | Apr 2007 | A1 |
20070093234 | Willis et al. | Apr 2007 | A1 |
20070094137 | Phillips et al. | Apr 2007 | A1 |
20070106582 | Baker et al. | May 2007 | A1 |
20070112579 | Ratnakaran et al. | May 2007 | A1 |
20070112667 | Rucker | May 2007 | A1 |
20070112668 | Celano et al. | May 2007 | A1 |
20070118393 | Rosen et al. | May 2007 | A1 |
20070121843 | Atazky et al. | May 2007 | A1 |
20070124235 | Chakraborty et al. | May 2007 | A1 |
20070127702 | Shaffer et al. | Jun 2007 | A1 |
20070130026 | O'Pray et al. | Jun 2007 | A1 |
20070156515 | Hasselback et al. | Jul 2007 | A1 |
20070156589 | Zimler et al. | Jul 2007 | A1 |
20070156718 | Hossfeld et al. | Jul 2007 | A1 |
20070168246 | Haggerty et al. | Jul 2007 | A1 |
20070168267 | Zimmerman et al. | Jul 2007 | A1 |
20070179860 | Romero | Aug 2007 | A1 |
20070192165 | Haggerty et al. | Aug 2007 | A1 |
20070192248 | West | Aug 2007 | A1 |
20070192347 | Rossmark et al. | Aug 2007 | A1 |
20070205266 | Carr et al. | Sep 2007 | A1 |
20070208653 | Murphy | Sep 2007 | A1 |
20070208729 | Martino | Sep 2007 | A1 |
20070220611 | Socolow et al. | Sep 2007 | A1 |
20070226093 | Chan et al. | Sep 2007 | A1 |
20070226114 | Haggerty et al. | Sep 2007 | A1 |
20070244732 | Chatterji et al. | Oct 2007 | A1 |
20070244807 | Andringa et al. | Oct 2007 | A1 |
20070250327 | Hedy | Oct 2007 | A1 |
20070271178 | Davis et al. | Nov 2007 | A1 |
20070282684 | Prosser et al. | Dec 2007 | A1 |
20070282730 | Carpenter et al. | Dec 2007 | A1 |
20070282736 | Conlin et al. | Dec 2007 | A1 |
20070288271 | Klinkhammer | Dec 2007 | A1 |
20070288355 | Roland et al. | Dec 2007 | A1 |
20070288360 | Seeklus | Dec 2007 | A1 |
20070288559 | Parsadayan | Dec 2007 | A1 |
20070294163 | Harmon et al. | Dec 2007 | A1 |
20070299759 | Kelly | Dec 2007 | A1 |
20070299771 | Brody | Dec 2007 | A1 |
20080004957 | Hildreth et al. | Jan 2008 | A1 |
20080005313 | Flake et al. | Jan 2008 | A1 |
20080010687 | Gonen et al. | Jan 2008 | A1 |
20080015887 | Drabek et al. | Jan 2008 | A1 |
20080015938 | Haddad et al. | Jan 2008 | A1 |
20080016099 | Ikeda | Jan 2008 | A1 |
20080021804 | Deckoff | Jan 2008 | A1 |
20080027859 | Nathans et al. | Jan 2008 | A1 |
20080028067 | Berkhin et al. | Jan 2008 | A1 |
20080033852 | Megdal et al. | Feb 2008 | A1 |
20080040475 | Bosworth et al. | Feb 2008 | A1 |
20080052182 | Marshall | Feb 2008 | A1 |
20080059224 | Schechter | Mar 2008 | A1 |
20080059317 | Chandran et al. | Mar 2008 | A1 |
20080059364 | Tidwell et al. | Mar 2008 | A1 |
20080059449 | Webster et al. | Mar 2008 | A1 |
20080065774 | Keeler | Mar 2008 | A1 |
20080066188 | Kwak | Mar 2008 | A1 |
20080071882 | Hering et al. | Mar 2008 | A1 |
20080077526 | Arumugam | Mar 2008 | A1 |
20080086368 | Bauman et al. | Apr 2008 | A1 |
20080091463 | Shakamuri | Apr 2008 | A1 |
20080094230 | Mock et al. | Apr 2008 | A1 |
20080097768 | Godshalk | Apr 2008 | A1 |
20080097928 | Paulson | Apr 2008 | A1 |
20080103800 | Domenikos et al. | May 2008 | A1 |
20080103972 | Lanc | May 2008 | A1 |
20080110973 | Nathans et al. | May 2008 | A1 |
20080120155 | Pliha | May 2008 | A1 |
20080120569 | Mann et al. | May 2008 | A1 |
20080126233 | Hogan | May 2008 | A1 |
20080133273 | Marshall | Jun 2008 | A1 |
20080133322 | Kalia et al. | Jun 2008 | A1 |
20080133325 | De et al. | Jun 2008 | A1 |
20080133531 | Baskerville et al. | Jun 2008 | A1 |
20080134042 | Jankovich | Jun 2008 | A1 |
20080140476 | Anand et al. | Jun 2008 | A1 |
20080140507 | Hamlisch et al. | Jun 2008 | A1 |
20080140549 | Eder | Jun 2008 | A1 |
20080140576 | Lewis et al. | Jun 2008 | A1 |
20080140694 | Mangla | Jun 2008 | A1 |
20080147454 | Walker et al. | Jun 2008 | A1 |
20080147523 | Mulry et al. | Jun 2008 | A1 |
20080154766 | Lewis et al. | Jun 2008 | A1 |
20080167883 | Thavildar Khazaneh | Jul 2008 | A1 |
20080167936 | Kapoor | Jul 2008 | A1 |
20080167956 | Keithley | Jul 2008 | A1 |
20080172324 | Johnson | Jul 2008 | A1 |
20080175360 | Schwarz et al. | Jul 2008 | A1 |
20080177655 | Zalik | Jul 2008 | A1 |
20080177836 | Bennett | Jul 2008 | A1 |
20080183564 | Tien et al. | Jul 2008 | A1 |
20080195425 | Haggerty et al. | Aug 2008 | A1 |
20080195600 | Deakter | Aug 2008 | A1 |
20080208548 | Metzger et al. | Aug 2008 | A1 |
20080208610 | Thomas et al. | Aug 2008 | A1 |
20080208631 | Morita et al. | Aug 2008 | A1 |
20080208788 | Merugu et al. | Aug 2008 | A1 |
20080215470 | Sengupta et al. | Sep 2008 | A1 |
20080221934 | Megdal et al. | Sep 2008 | A1 |
20080221947 | Megdal et al. | Sep 2008 | A1 |
20080221970 | Megdal et al. | Sep 2008 | A1 |
20080221971 | Megdal et al. | Sep 2008 | A1 |
20080221972 | Megdal et al. | Sep 2008 | A1 |
20080221973 | Megdal et al. | Sep 2008 | A1 |
20080221990 | Megdal et al. | Sep 2008 | A1 |
20080222016 | Megdal et al. | Sep 2008 | A1 |
20080222027 | Megdal et al. | Sep 2008 | A1 |
20080228538 | Megdal et al. | Sep 2008 | A1 |
20080228539 | Megdal et al. | Sep 2008 | A1 |
20080228540 | Megdal et al. | Sep 2008 | A1 |
20080228541 | Megdal et al. | Sep 2008 | A1 |
20080228556 | Megdal et al. | Sep 2008 | A1 |
20080228606 | Megdal et al. | Sep 2008 | A1 |
20080228635 | Megdal et al. | Sep 2008 | A1 |
20080243680 | Megdal et al. | Oct 2008 | A1 |
20080244008 | Wilkinson et al. | Oct 2008 | A1 |
20080255897 | Megdal et al. | Oct 2008 | A1 |
20080255992 | Lin | Oct 2008 | A1 |
20080262925 | Kim et al. | Oct 2008 | A1 |
20080263638 | McMurtry et al. | Oct 2008 | A1 |
20080270245 | Boukadoum et al. | Oct 2008 | A1 |
20080281737 | Fajardo | Nov 2008 | A1 |
20080288382 | Smith et al. | Nov 2008 | A1 |
20080294540 | Celka et al. | Nov 2008 | A1 |
20080294546 | Flannery | Nov 2008 | A1 |
20080300977 | Gerakos et al. | Dec 2008 | A1 |
20080301016 | Durvasula et al. | Dec 2008 | A1 |
20080301188 | O'Hara | Dec 2008 | A1 |
20080312963 | Reiner | Dec 2008 | A1 |
20080312969 | Raines et al. | Dec 2008 | A1 |
20090006185 | Stinson | Jan 2009 | A1 |
20090006475 | Udezue et al. | Jan 2009 | A1 |
20090012889 | Finch | Jan 2009 | A1 |
20090018996 | Hunt et al. | Jan 2009 | A1 |
20090019027 | Ju et al. | Jan 2009 | A1 |
20090024505 | Patel et al. | Jan 2009 | A1 |
20090030776 | Walker et al. | Jan 2009 | A1 |
20090037247 | Quinn | Feb 2009 | A1 |
20090037323 | Feinstein et al. | Feb 2009 | A1 |
20090043637 | Eder | Feb 2009 | A1 |
20090044279 | Crawford et al. | Feb 2009 | A1 |
20090048877 | Binns et al. | Feb 2009 | A1 |
20090048957 | Celano | Feb 2009 | A1 |
20090076883 | Kilger et al. | Mar 2009 | A1 |
20090089190 | Girulat | Apr 2009 | A1 |
20090089205 | Bayne | Apr 2009 | A1 |
20090094675 | Powers | Apr 2009 | A1 |
20090099914 | Lang et al. | Apr 2009 | A1 |
20090106150 | Pelegero et al. | Apr 2009 | A1 |
20090112650 | Iwane | Apr 2009 | A1 |
20090113532 | Lapidous | Apr 2009 | A1 |
20090119169 | Chandratillake et al. | May 2009 | A1 |
20090119199 | Salahi | May 2009 | A1 |
20090125369 | Kloostra et al. | May 2009 | A1 |
20090132347 | Anderson et al. | May 2009 | A1 |
20090132559 | Chamberlain et al. | May 2009 | A1 |
20090144102 | Lopez | Jun 2009 | A1 |
20090144160 | Haggerty et al. | Jun 2009 | A1 |
20090144201 | Gierkink et al. | Jun 2009 | A1 |
20090172815 | Gu et al. | Jul 2009 | A1 |
20090182653 | Zimiles | Jul 2009 | A1 |
20090182872 | Hong | Jul 2009 | A1 |
20090198557 | Wang et al. | Aug 2009 | A1 |
20090198602 | Wang et al. | Aug 2009 | A1 |
20090198612 | Meimes et al. | Aug 2009 | A1 |
20090199264 | Lang | Aug 2009 | A1 |
20090210886 | Bhojwani et al. | Aug 2009 | A1 |
20090215479 | Karmarkar | Aug 2009 | A1 |
20090216591 | Buerger et al. | Aug 2009 | A1 |
20090222308 | Zoldi et al. | Sep 2009 | A1 |
20090222373 | Choudhuri et al. | Sep 2009 | A1 |
20090222374 | Choudhuri et al. | Sep 2009 | A1 |
20090222375 | Choudhuri et al. | Sep 2009 | A1 |
20090222376 | Choudhuri et al. | Sep 2009 | A1 |
20090222377 | Choudhuri et al. | Sep 2009 | A1 |
20090222378 | Choudhuri et al. | Sep 2009 | A1 |
20090222379 | Choudhuri et al. | Sep 2009 | A1 |
20090222380 | Choudhuri et al. | Sep 2009 | A1 |
20090228918 | Rolff et al. | Sep 2009 | A1 |
20090234665 | Conkel | Sep 2009 | A1 |
20090234775 | Whitney et al. | Sep 2009 | A1 |
20090240609 | Cho et al. | Sep 2009 | A1 |
20090248567 | Haggerty et al. | Oct 2009 | A1 |
20090248568 | Haggerty et al. | Oct 2009 | A1 |
20090248569 | Haggerty et al. | Oct 2009 | A1 |
20090248570 | Haggerty et al. | Oct 2009 | A1 |
20090248571 | Haggerty et al. | Oct 2009 | A1 |
20090248572 | Haggerty et al. | Oct 2009 | A1 |
20090248573 | Haggerty et al. | Oct 2009 | A1 |
20090249440 | Platt et al. | Oct 2009 | A1 |
20090254476 | Sharma et al. | Oct 2009 | A1 |
20090254971 | Herz et al. | Oct 2009 | A1 |
20090265326 | Lehrman et al. | Oct 2009 | A1 |
20090271248 | Sherman et al. | Oct 2009 | A1 |
20090271265 | Lay et al. | Oct 2009 | A1 |
20090276233 | Brimhall et al. | Nov 2009 | A1 |
20090276368 | Martin et al. | Nov 2009 | A1 |
20090300066 | Guo et al. | Dec 2009 | A1 |
20090313163 | Wang et al. | Dec 2009 | A1 |
20090319648 | Dutta et al. | Dec 2009 | A1 |
20090327120 | Eze et al. | Dec 2009 | A1 |
20100009320 | Wilkelis | Jan 2010 | A1 |
20100010935 | Shelton | Jan 2010 | A1 |
20100030649 | Ubelhor | Feb 2010 | A1 |
20100043055 | Baumgart | Feb 2010 | A1 |
20100049651 | Lang et al. | Feb 2010 | A1 |
20100082384 | Bohrer et al. | Apr 2010 | A1 |
20100094704 | Subramanian et al. | Apr 2010 | A1 |
20100094758 | Chamberlain et al. | Apr 2010 | A1 |
20100094768 | Miltonberger | Apr 2010 | A1 |
20100094774 | Jackowitz et al. | Apr 2010 | A1 |
20100100945 | Ozzie et al. | Apr 2010 | A1 |
20100107225 | Spencer et al. | Apr 2010 | A1 |
20100114646 | McIlwain et al. | May 2010 | A1 |
20100114724 | Ghosh et al. | May 2010 | A1 |
20100114744 | Gonen | May 2010 | A1 |
20100121767 | Coulter et al. | May 2010 | A1 |
20100130172 | Vendrow et al. | May 2010 | A1 |
20100142698 | Spottiswoode et al. | Jun 2010 | A1 |
20100145836 | Baker et al. | Jun 2010 | A1 |
20100145847 | Zarikian et al. | Jun 2010 | A1 |
20100169159 | Rose et al. | Jul 2010 | A1 |
20100169264 | O'Sullivan | Jul 2010 | A1 |
20100185453 | Satyavolu et al. | Jul 2010 | A1 |
20100198629 | Wesileder et al. | Aug 2010 | A1 |
20100205662 | Ibrahim et al. | Aug 2010 | A1 |
20100211445 | Bodington | Aug 2010 | A1 |
20100217837 | Ansari et al. | Aug 2010 | A1 |
20100223168 | Haggerty et al. | Sep 2010 | A1 |
20100228657 | Kagarlis | Sep 2010 | A1 |
20100229245 | Singhal | Sep 2010 | A1 |
20100248681 | Phills | Sep 2010 | A1 |
20100250364 | Song et al. | Sep 2010 | A1 |
20100250434 | Megdal et al. | Sep 2010 | A1 |
20100250469 | Megdal et al. | Sep 2010 | A1 |
20100268557 | Faith et al. | Oct 2010 | A1 |
20100274739 | Haggerty et al. | Oct 2010 | A1 |
20100293114 | Khan et al. | Nov 2010 | A1 |
20100312717 | Haggerty et al. | Dec 2010 | A1 |
20100312769 | Bailey et al. | Dec 2010 | A1 |
20100332292 | Anderson | Dec 2010 | A1 |
20110004498 | Readshaw | Jan 2011 | A1 |
20110016042 | Cho et al. | Jan 2011 | A1 |
20110023115 | Wright | Jan 2011 | A1 |
20110029388 | Kendall et al. | Feb 2011 | A1 |
20110035333 | Haggerty et al. | Feb 2011 | A1 |
20110047071 | Choudhuri et al. | Feb 2011 | A1 |
20110054981 | Faith et al. | Mar 2011 | A1 |
20110066495 | Ayloo et al. | Mar 2011 | A1 |
20110071950 | Ivanovic | Mar 2011 | A1 |
20110076663 | Krallman et al. | Mar 2011 | A1 |
20110078073 | Annappindi et al. | Mar 2011 | A1 |
20110093383 | Haggerty et al. | Apr 2011 | A1 |
20110112958 | Haggerty et al. | May 2011 | A1 |
20110125595 | Neal et al. | May 2011 | A1 |
20110126275 | Anderson et al. | May 2011 | A1 |
20110131131 | Griffin et al. | Jun 2011 | A1 |
20110137789 | Kortina et al. | Jun 2011 | A1 |
20110145122 | Haggerty et al. | Jun 2011 | A1 |
20110161323 | Hagiwara | Jun 2011 | A1 |
20110164746 | Nice et al. | Jul 2011 | A1 |
20110173116 | Yan et al. | Jul 2011 | A1 |
20110178922 | Imrey et al. | Jul 2011 | A1 |
20110184838 | Winters et al. | Jul 2011 | A1 |
20110184851 | Megdal et al. | Jul 2011 | A1 |
20110196791 | Dominguez | Aug 2011 | A1 |
20110211445 | Chen | Sep 2011 | A1 |
20110213641 | Metzger, II et al. | Sep 2011 | A1 |
20110218826 | Birtel et al. | Sep 2011 | A1 |
20110219421 | Ullman et al. | Sep 2011 | A1 |
20110238566 | Santos | Sep 2011 | A1 |
20110251946 | Haggerty et al. | Oct 2011 | A1 |
20110258050 | Chan et al. | Oct 2011 | A1 |
20110258142 | Haggerty et al. | Oct 2011 | A1 |
20110264581 | Clyne | Oct 2011 | A1 |
20110270779 | Showalter | Nov 2011 | A1 |
20110276396 | Rathod | Nov 2011 | A1 |
20110282779 | Megdal et al. | Nov 2011 | A1 |
20110307397 | Benmbarek | Dec 2011 | A1 |
20110320307 | Mehta et al. | Dec 2011 | A1 |
20120005070 | McFall et al. | Jan 2012 | A1 |
20120011056 | Ward et al. | Jan 2012 | A1 |
20120011158 | Avner et al. | Jan 2012 | A1 |
20120016948 | Sinha | Jan 2012 | A1 |
20120029956 | Ghosh et al. | Feb 2012 | A1 |
20120029996 | Lang et al. | Feb 2012 | A1 |
20120035980 | Haggerty et al. | Feb 2012 | A1 |
20120047219 | Feng et al. | Feb 2012 | A1 |
20120054592 | Jaffe et al. | Mar 2012 | A1 |
20120066065 | Switzer | Mar 2012 | A1 |
20120066106 | Papadimitriou | Mar 2012 | A1 |
20120084230 | Megdal et al. | Apr 2012 | A1 |
20120089605 | Bangalore | Apr 2012 | A1 |
20120101938 | Kasower | Apr 2012 | A1 |
20120101939 | Kasower | Apr 2012 | A1 |
20120106801 | Jackson | May 2012 | A1 |
20120110677 | Abendroth et al. | May 2012 | A1 |
20120116807 | Hane et al. | May 2012 | A1 |
20120123968 | Megdal et al. | May 2012 | A1 |
20120124498 | Santoro et al. | May 2012 | A1 |
20120136763 | Megdal et al. | May 2012 | A1 |
20120143637 | Paradis et al. | Jun 2012 | A1 |
20120143921 | Wilson | Jun 2012 | A1 |
20120150587 | Kruger et al. | Jun 2012 | A1 |
20120158460 | Kruger et al. | Jun 2012 | A1 |
20120158574 | Brunzell et al. | Jun 2012 | A1 |
20120158654 | Behren et al. | Jun 2012 | A1 |
20120173339 | Flynt et al. | Jul 2012 | A1 |
20120179536 | Kalb et al. | Jul 2012 | A1 |
20120191479 | Gupta et al. | Jul 2012 | A1 |
20120209586 | Mieritz et al. | Aug 2012 | A1 |
20120215682 | Lent et al. | Aug 2012 | A1 |
20120216125 | Pierce | Aug 2012 | A1 |
20120232958 | Silbert | Sep 2012 | A1 |
20120239497 | Nuzzi | Sep 2012 | A1 |
20120239515 | Batra et al. | Sep 2012 | A1 |
20120265661 | Megdal et al. | Oct 2012 | A1 |
20120284118 | Mamich, Jr. et al. | Nov 2012 | A1 |
20120290660 | Rao et al. | Nov 2012 | A1 |
20120317016 | Hughes | Dec 2012 | A1 |
20120323954 | Bonalle et al. | Dec 2012 | A1 |
20130080242 | Alhadeff et al. | Mar 2013 | A1 |
20130080467 | Carson et al. | Mar 2013 | A1 |
20130085804 | Leff et al. | Apr 2013 | A1 |
20130085902 | Chew | Apr 2013 | A1 |
20130103571 | Chung et al. | Apr 2013 | A1 |
20130117832 | Gandhi | May 2013 | A1 |
20130124263 | Amaro et al. | May 2013 | A1 |
20130132151 | Stibel et al. | May 2013 | A1 |
20130137464 | Kramer et al. | May 2013 | A1 |
20130151388 | Falkenborg et al. | Jun 2013 | A1 |
20130159168 | Evans | Jun 2013 | A1 |
20130159411 | Bowen | Jun 2013 | A1 |
20130173359 | Megdal et al. | Jul 2013 | A1 |
20130173450 | Celka et al. | Jul 2013 | A1 |
20130173481 | Hirtenstein et al. | Jul 2013 | A1 |
20130218638 | Kilger et al. | Aug 2013 | A1 |
20130226787 | Haggerty et al. | Aug 2013 | A1 |
20130226820 | Sedota, Jr. et al. | Aug 2013 | A1 |
20130238413 | Carlson et al. | Sep 2013 | A1 |
20130268324 | Megdal et al. | Oct 2013 | A1 |
20130275331 | Megdal et al. | Oct 2013 | A1 |
20130293363 | Plymouth | Nov 2013 | A1 |
20130347059 | Fong et al. | Dec 2013 | A1 |
20140006523 | Hofman | Jan 2014 | A1 |
20140012633 | Megdal et al. | Jan 2014 | A1 |
20140019331 | Megdal et al. | Jan 2014 | A1 |
20140032265 | Paprocki et al. | Jan 2014 | A1 |
20140032384 | Megdal et al. | Jan 2014 | A1 |
20140046887 | Lessin | Feb 2014 | A1 |
20140095251 | Huovilainen | Apr 2014 | A1 |
20140096249 | Dupont et al. | Apr 2014 | A1 |
20140149179 | Haggerty et al. | May 2014 | A1 |
20140156501 | Howe | Jun 2014 | A1 |
20140164112 | Kala | Jun 2014 | A1 |
20140164398 | Smith et al. | Jun 2014 | A1 |
20140172686 | Haggerty et al. | Jun 2014 | A1 |
20140181285 | Stevens et al. | Jun 2014 | A1 |
20140244353 | Winters | Aug 2014 | A1 |
20140278774 | Cai et al. | Sep 2014 | A1 |
20140279197 | Ainsworth, III et al. | Sep 2014 | A1 |
20140310157 | Haggerty et al. | Oct 2014 | A1 |
20140316852 | Chatterji et al. | Oct 2014 | A1 |
20140316855 | Haggerty et al. | Oct 2014 | A1 |
20140316969 | Imrey | Oct 2014 | A1 |
20140317022 | Haggerty et al. | Oct 2014 | A1 |
20140324538 | Haggerty et al. | Oct 2014 | A1 |
20140344069 | Haggerty et al. | Nov 2014 | A1 |
20140365357 | Bohrer et al. | Dec 2014 | A1 |
20150026039 | Annappindi | Jan 2015 | A1 |
20150051948 | Aizono | Feb 2015 | A1 |
20150066772 | Griffin et al. | Mar 2015 | A1 |
20150095184 | Ainsworth et al. | Apr 2015 | A1 |
20150095187 | Ainsworth et al. | Apr 2015 | A1 |
20150106192 | Guo et al. | Apr 2015 | A1 |
20150108227 | Nack et al. | Apr 2015 | A1 |
20150120391 | Jodice et al. | Apr 2015 | A1 |
20150120755 | Burger et al. | Apr 2015 | A1 |
20150235230 | Ainsworth, III et al. | Aug 2015 | A1 |
20150248661 | Pontious | Sep 2015 | A1 |
20150248665 | Walz | Sep 2015 | A1 |
20150248691 | Pontious | Sep 2015 | A1 |
20150248716 | Ainsworth, III et al. | Sep 2015 | A1 |
20150262109 | Ainsworth, III et al. | Sep 2015 | A1 |
20150262246 | Stack et al. | Sep 2015 | A1 |
20150262291 | West et al. | Sep 2015 | A1 |
20150278225 | Weiss et al. | Oct 2015 | A1 |
20150286747 | Anastasakos | Oct 2015 | A1 |
20150295906 | Ufford et al. | Oct 2015 | A1 |
20150310543 | DeBie | Oct 2015 | A1 |
20150332414 | Unser | Nov 2015 | A1 |
20150363328 | Candelaria | Dec 2015 | A1 |
20160005114 | Donovan et al. | Jan 2016 | A1 |
20160055487 | Votaw et al. | Feb 2016 | A1 |
20160071175 | Reuss et al. | Mar 2016 | A1 |
20160086190 | Bohrer et al. | Mar 2016 | A1 |
20160092997 | Shen et al. | Mar 2016 | A1 |
20160098775 | Ainsworth, III et al. | Apr 2016 | A1 |
20160098776 | Ainsworth, III et al. | Apr 2016 | A1 |
20160098784 | Ainsworth, III et al. | Apr 2016 | A1 |
20160110694 | Walz et al. | Apr 2016 | A1 |
20160110707 | Nack et al. | Apr 2016 | A1 |
20160140639 | Ainsworth, III et al. | May 2016 | A1 |
20160155160 | Walz et al. | Jun 2016 | A1 |
20160155191 | Walz et al. | Jun 2016 | A1 |
20160171542 | Fanous et al. | Jun 2016 | A1 |
20160180258 | Walz | Jun 2016 | A1 |
20160180349 | Korra et al. | Jun 2016 | A1 |
20160183051 | Nack et al. | Jun 2016 | A1 |
20160189152 | Walz | Jun 2016 | A1 |
20160189192 | Walz | Jun 2016 | A1 |
20160210224 | Cohen | Jul 2016 | A1 |
20160246581 | Jimenez et al. | Aug 2016 | A1 |
20160267485 | Walz et al. | Sep 2016 | A1 |
20160267508 | West | Sep 2016 | A1 |
20160267513 | Walz et al. | Sep 2016 | A1 |
20160267514 | Walz et al. | Sep 2016 | A1 |
20160267515 | Walz et al. | Sep 2016 | A1 |
20160267516 | Walz et al. | Sep 2016 | A1 |
20160350851 | Ainsworth, III et al. | Dec 2016 | A1 |
20170039588 | Koltnow et al. | Feb 2017 | A1 |
20170039616 | Korra et al. | Feb 2017 | A1 |
20170061511 | Korra et al. | Mar 2017 | A1 |
20170061532 | Koltnow et al. | Mar 2017 | A1 |
20170161780 | Michalek | Jun 2017 | A1 |
20170186297 | Brenner | Jun 2017 | A1 |
20170193315 | El-Khamy et al. | Jul 2017 | A1 |
20170200222 | Barber et al. | Jul 2017 | A1 |
20170278182 | Kasower | Sep 2017 | A1 |
20180025273 | Jordan et al. | Jan 2018 | A1 |
20180053172 | Nack et al. | Feb 2018 | A1 |
20180053252 | Koltnow et al. | Feb 2018 | A1 |
20180060546 | Yin | Mar 2018 | A1 |
20180101889 | Nack et al. | Apr 2018 | A1 |
20180189871 | Lennert | Jul 2018 | A1 |
20180308151 | Ainsworth, III et al. | Oct 2018 | A1 |
20180330383 | Pontious et al. | Nov 2018 | A1 |
20180330415 | Billman et al. | Nov 2018 | A1 |
20190005498 | Roca et al. | Jan 2019 | A1 |
20190026354 | Kapczynski | Jan 2019 | A1 |
20190042947 | Turner et al. | Feb 2019 | A1 |
20190043126 | Billman et al. | Feb 2019 | A1 |
20190095939 | Hickman et al. | Mar 2019 | A1 |
20190311427 | Quinn et al. | Oct 2019 | A1 |
20190318255 | Ripley et al. | Oct 2019 | A1 |
20190340526 | Turner et al. | Nov 2019 | A1 |
20190347092 | Bouley et al. | Nov 2019 | A1 |
20190354613 | Zoldi et al. | Nov 2019 | A1 |
20190354853 | Zoldi et al. | Nov 2019 | A1 |
20200026642 | Indurthivenkata et al. | Jan 2020 | A1 |
20200034419 | Bondugula et al. | Jan 2020 | A1 |
20200042887 | Marcé et al. | Feb 2020 | A1 |
20200043103 | Sheptunov | Feb 2020 | A1 |
20200082302 | Zoldi et al. | Mar 2020 | A1 |
20200090080 | Ballal | Mar 2020 | A1 |
20200097591 | Basant et al. | Mar 2020 | A1 |
20200097881 | Krone et al. | Mar 2020 | A1 |
20200098041 | Lawrence et al. | Mar 2020 | A1 |
20200104734 | Turner et al. | Apr 2020 | A1 |
20200134387 | Liu et al. | Apr 2020 | A1 |
20200134439 | Turner et al. | Apr 2020 | A1 |
20200134474 | Marcé et al. | Apr 2020 | A1 |
20200134500 | Marcé et al. | Apr 2020 | A1 |
20200159989 | Banaszak et al. | May 2020 | A1 |
20200202425 | Taylor-Shoff et al. | Jun 2020 | A1 |
20200218629 | Chen et al. | Jul 2020 | A1 |
20200242216 | Zoldi et al. | Jul 2020 | A1 |
20200250185 | Anderson | Aug 2020 | A1 |
20200250556 | Nourian et al. | Aug 2020 | A1 |
20200250716 | Laura | Aug 2020 | A1 |
20200265059 | Patel et al. | Aug 2020 | A1 |
20200265513 | Drotos et al. | Aug 2020 | A1 |
20200272853 | Zoldi et al. | Aug 2020 | A1 |
20200293557 | Farrell et al. | Sep 2020 | A1 |
20200293912 | Williams et al. | Sep 2020 | A1 |
20200334748 | Courbage et al. | Oct 2020 | A1 |
20200342556 | Zoldi et al. | Oct 2020 | A1 |
20200349240 | Yin et al. | Nov 2020 | A1 |
Number | Date | Country |
---|---|---|
2019250275 | May 2020 | AU |
2 865 348 | Mar 2015 | CA |
2 895 452 | Jan 2016 | CA |
2 901 057 | Apr 2016 | CA |
2 909 392 | Jun 2016 | CA |
2 915 375 | Jun 2016 | CA |
2 923 334 | Sep 2016 | CA |
3 059 314 | Mar 2020 | CA |
91 08 341 | Oct 1991 | DE |
0 350 907 | Jan 1990 | EP |
0 419 889 | Apr 1991 | EP |
0 458 698 | Nov 1991 | EP |
0 468 440 | Jan 1992 | EP |
0 554 083 | Aug 1993 | EP |
0 566 736 | Aug 1993 | EP |
0 559 358 | Sep 1993 | EP |
0 869 652 | Oct 1998 | EP |
0 913 789 | May 1999 | EP |
0 919 942 | Jun 1999 | EP |
0 977 128 | Feb 2000 | EP |
1 028 401 | Aug 2000 | EP |
1 077 419 | Feb 2001 | EP |
0 772 836 | Dec 2001 | EP |
2 088 743 | Aug 2009 | EP |
2 151 793 | Feb 2010 | EP |
3 572 985 | Nov 2019 | EP |
3 573 009 | Nov 2019 | EP |
3 690 762 | Aug 2020 | EP |
3 699 827 | Aug 2020 | EP |
3 719 710 | Oct 2020 | EP |
2 392 748 | Mar 2004 | GB |
2 579 139 | Jun 2020 | GB |
10-222559 | Aug 1998 | JP |
10-261009 | Sep 1998 | JP |
10-293732 | Nov 1998 | JP |
2000-331068 | Nov 2000 | JP |
2001-282957 | Oct 2001 | JP |
2001-297141 | Oct 2001 | JP |
2001-344463 | Dec 2001 | JP |
2001-357256 | Dec 2001 | JP |
2002-149778 | May 2002 | JP |
2002-163449 | Jun 2002 | JP |
2002-163498 | Jun 2002 | JP |
2002-259753 | Sep 2002 | JP |
2003-271851 | Sep 2003 | JP |
2003-316881 | Nov 2003 | JP |
2003-316950 | Nov 2003 | JP |
10-2000-0036594 | Jul 2000 | KR |
10-2000-0063995 | Nov 2000 | KR |
10-2001-0016349 | Mar 2001 | KR |
10-2001-0035145 | May 2001 | KR |
10-2002-0007132 | Jan 2002 | KR |
10-2013-0107394 | Oct 2013 | KR |
256569 | Jun 2006 | TW |
WO 94006103 | Mar 1994 | WO |
WO 95034155 | Dec 1995 | WO |
WO 96000945 | Jan 1996 | WO |
WO 97023838 | Jul 1997 | WO |
WO 98041931 | Sep 1998 | WO |
WO 98041932 | Sep 1998 | WO |
WO 98041933 | Sep 1998 | WO |
WO 98049643 | Nov 1998 | WO |
WO 99008218 | Feb 1999 | WO |
WO 99017225 | Apr 1999 | WO |
WO 99017226 | Apr 1999 | WO |
WO 99022328 | May 1999 | WO |
WO 99038094 | Jul 1999 | WO |
WO 00004465 | Jan 2000 | WO |
WO 00028441 | May 2000 | WO |
WO 00055778 | Sep 2000 | WO |
WO 00055789 | Sep 2000 | WO |
WO 00055790 | Sep 2000 | WO |
WO 01010090 | Feb 2001 | WO |
WO 01011522 | Feb 2001 | WO |
WO 01016896 | Mar 2001 | WO |
WO 01039090 | May 2001 | WO |
WO 01039589 | Jun 2001 | WO |
WO 01041083 | Jun 2001 | WO |
WO 01057720 | Aug 2001 | WO |
WO 01080053 | Oct 2001 | WO |
WO 01084281 | Nov 2001 | WO |
WO 02001462 | Jan 2002 | WO |
WO 02027610 | Apr 2002 | WO |
WO 03071388 | Aug 2003 | WO |
WO 03101123 | Dec 2003 | WO |
WO 2004046882 | Jun 2004 | WO |
WO 2004051436 | Jun 2004 | WO |
WO 2004061563 | Jul 2004 | WO |
WO 2004114160 | Dec 2004 | WO |
WO 2005059781 | Jun 2005 | WO |
WO 2005124619 | Dec 2005 | WO |
WO 2007004158 | Jan 2007 | WO |
WO 2007014271 | Feb 2007 | WO |
WO 2007149941 | Dec 2007 | WO |
WO 2008022289 | Feb 2008 | WO |
WO 2008054403 | May 2008 | WO |
WO 2008076343 | Jun 2008 | WO |
WO 2008127288 | Oct 2008 | WO |
WO 2008147918 | Dec 2008 | WO |
WO 2008148819 | Dec 2008 | WO |
WO 2009061342 | May 2009 | WO |
WO 2009076555 | Jun 2009 | WO |
WO 2009117518 | Sep 2009 | WO |
WO 2009132114 | Oct 2009 | WO |
WO 2010045160 | Apr 2010 | WO |
WO 2010062537 | Jun 2010 | WO |
WO 2010132492 | Nov 2010 | WO |
WO 2010150251 | Dec 2010 | WO |
WO 2011005876 | Jan 2011 | WO |
WO 2014018900 | Jan 2014 | WO |
WO 2015162681 | Oct 2015 | WO |
WO-2015162681 | Oct 2015 | WO |
WO 2016160539 | Oct 2016 | WO |
WO 2018039377 | Mar 2018 | WO |
WO 2018057701 | Mar 2018 | WO |
WO 2018084867 | May 2018 | WO |
WO 2018128866 | Jul 2018 | WO |
WO 2019035809 | Feb 2019 | WO |
WO 2019067497 | Apr 2019 | WO |
WO 2019088972 | May 2019 | WO |
WO 2019089990 | May 2019 | WO |
WO 2019094910 | May 2019 | WO |
WO 2019104088 | May 2019 | WO |
WO 2019104089 | May 2019 | WO |
WO 2019217876 | Nov 2019 | WO |
WO 2020055904 | Mar 2020 | WO |
WO 2020132026 | Jun 2020 | WO |
WO 2020142417 | Jul 2020 | WO |
WO 20200219839 | Oct 2020 | WO |
Entry |
---|
International Preliminary Report on Patentability in Application No. PCT/US2017/048265, dated Mar. 7, 2019. |
International Preliminary Report on Patentability in Application No. PCT/US2017/068340, dated Jul. 18, 2019. |
International Search Report and Written Opinion in PCT Application No. PCT/US07/76152, dated Mar. 20, 2009. |
International Search Report for Application No. PCT/US2005/041814, dated Aug. 29, 2007. |
Reinartz et al., “On the Profitability of Long-Life Customers in a Noncontractual Setting: An Empirical Investigation and Implications for Marketing” Journal of Marketing, Oct. 2000, vol. 64, pp. 17-35. |
U.S. Appl. No. 14/975,536, Systems and Methods for Generating Entity Recommendation Data, filed Dec. 18, 2015. |
U.S. Appl. No. 14/975,654, U.S. Pat. No. 10,242,019, User Behavior Segmentation Using Latent Topic Detection, filed Dec. 18, 2015. |
U.S. Appl. No. 14/975,440, Systems and Methods for Dynamic Report Generation Based on Automatic Modeling of Complex Data Structures, filed Dec. 18, 2015. |
U.S. Appl. No. 12/705,489, filed Feb. 12, 2010, Bargoli et al. |
“A Google Health update,” Google Official Blog, Sep. 15, 2010 in 4 pages, http://googleblog.blogspot.com/2010/09/google-health-update.html. |
“A New Approach to Fraud Solutions”, BasePoint Science Solving Fraud, pp. 8, 2006. |
“Aggregate and Analyze Social Media Content: Gain Faster and Broader Insight to Market Sentiment,” SAP Partner, Mantis Technology Group, Apr. 2011, pp. 4. |
Akl, Selim G., “Digital Signatures: A Tutorial Survey,” Computer, Feb. 1983, pp. 15-24. |
“Auto Market StatisticsSM:Drive Response with Aggregated Motor Vehicle Information”, Experian, Apr. 2007, http://www.experian.com/assets/marketing-services/product-sheets/auto-market-statistics.pdf, pp. 2. |
Adzilla, Press Release, “Zillacasting Technology Approved and Patent Pending,” http://www.adzilla.com/newsroom/pdf/patent_051605.pdf, May 16, 2005, pp. 2. |
AISG's National Underwriting Database, A-Plus, is Now the Largest in the Industry, Business Wire, Aug. 7, 1997. |
Alexander, Walter, “What's the Score”, ABA Banking Journal, vol. 81, 1989. [Journal Article Excerpt]. |
Amo, Tina, “How to Find Out Who Has Lived inYour House Before You”, https://web.archive.org/web/20130327090532/http://homeguides.sfgate.com/out-lived-house-before-50576.html as archived Mar. 27, 2013, pp. 2. |
Announcing TrueProfiler, http://web.archive.org/web/20021201123646/http://www.truecredit.com/index.asp, dated Dec. 1, 2002, 2 pages. |
Applied Geographic Solutions, “What is Mosaic™”, as captured Feb. 15, 2004 from http://web.archive.org/web/20040215224329/http://www.appliedgeographic.com/mosaic.html in 2 pages. |
“AT&T Expected to Turn Up Heat in Card Wars”, American Banker, May 27, 1993, vol. 158, No. 101, pp. 3. |
Babcock, Gwen, “Aggregation Without Aggravation: Determining Spatial Contiguity and Joining Geographic Areas Using Hashing”, SAS Global Forum 2010, Reporting and Information Visualization, Paper 223-2010, pp. 17. |
BackupBox, http://mybackupbox.com printed Feb. 8, 2013 in 2 pages. |
“Balance Transfers Offer Opportunities”, Risk Credit Risk Management Report, Jan. 29, 1996, vol. 6, No. 2, pp. 2. |
“Bank of America Direct Web-Based Network Adds Core Functionality to Meet Day-To-Day Treasury Needs”, Business Wire, Oct. 25, 1999, pp. 2. |
“Bank of America Launches Total Security Protection™; Features Address Cardholders' Financial Safety Concerns; Supported by $26 Million National Advertising Campaign; Free Educational Materials”, PR Newswire, Oct. 9, 2002, pp. 2. |
BBC Green Home, “My Action Plan”, as printed from the Wayback Machine at http://web.archive.org/web/20080513014731/http://www.bbcgreen.com/actionplan, May 13, 2008, pp. 50. |
BERR: Department for Business Enterprise & Regulatory Reform, “Regional Energy Consumption Statistics”, Jun. 10, 2008, http://webarchive.nationalarchives.gov.uk/20080610182444/http://www.berr.gov.uk/energy/statistics/regional/index.html. |
“Beverly Hills Man Convicted of Operating ‘Bust-Out’ Schemes that Caused More than $8 Million in Losses”, Department of Justice, Jul. 25, 2006, 2 Pgs. |
Bitran et al., “Mailing Decisions in Catalog Sales Industry”, Management Science (JSTOR), vol. 42, No. 9, pp. 1364-1381, Sep. 1996. |
Brown et al., “ALCOD IDSS:Assisting the Australian Stock Market Surveillance Team's Review Process,” Applied Artificial Intelligence Journal, Dec. 1, 1996, pp. 625-641. |
Bult et al., “Optimal Selection for Direct Mail,” Marketing Science, 1995, vol. 14, No. 4, pp. 378-394. |
Burr Ph.D., et al., “Utility Payments as Alternative Credit Data: A Reality Check”, Asset Builders of America, Inc., Oct. 5, 2006, pp. 1-18, Washington, D.C. |
Burr Ph.D., et al., “Payment Aggregation and Information Dissemination (Paid): Annotated Literature Search”, Asset Builders of America, Inc., Sep. 2005. |
“Bust-Out Schemes”, Visual Analytics Inc. Technical Product Support, Newsletter vol. 4, Issue 1, Jan. 2005, pp. 7. |
Caliendo, et al., “Some Practical Guidance for the Implementation of Propensity Score Matching”, IZA:Discussion Paper Series, No. 1588, Germany, May 2005, pp. 32. |
Cantor, R. and Packer, F., “The Credit Rating Industry,” FRBNY Quarterly Review, Summer-Fall, 1994, pp. 1-24. |
“Carbon Calculator—Calculation Explanation,” Warwick University Carbon Footprint Project Group, 2005, pp. 5, http://www.carboncalculator.co.uk/explanation.php. |
Chandler et al., “The Benefit to Consumers from Generic Scoring Models Based on Credit Reports”, The MDS Group Atlanta, Georgia, Jul. 1, 1991, Abstract. |
ChannelWave.com, PRM Central—About PRM, http://web.archive.org/web/20000510214859/http://www.channelwave.com as printed on Jun. 21, 2006, May 2000 Archive. |
“Chase Gets Positive,” Bank Technology News, May 6, 2000, vol. 14, No. 5, p. 33. |
Chatterjee et al., “Expenditure Patterns and Aggregate Consumer Behavior, Some Experiments with Australian and New Zealand Data”, The Economic Record, vol. 70, No. 210, Sep. 1994, pp. 278-291. |
Chen, et al., “Modeling Credit Card ‘Share of Wallet’: Solving the Incomplete Information Problem”, New York University: Kauffman Management Center, http://www.rhsmith.umd.edu/marketing/pdfs_docs/seminarsspr05/abstract%20-%20chen.pdf , Spring 2005, 48 pages. |
“Cole Taylor Bank Chooses Integrated E-Banking/E-Payments/Reconciliation Solution From Fundtech”, Business Wire, Oct. 21, 1999, pp. 2. |
“Consumer Reports Finds American-Made Vehicles Close Reliability Gap with European-Made Vehicle—As Japanese Continue to Set New Benchmarks for the Industry”, Consumer Reports: Consumers Union, Yonkers, NY, Apr. 2003, pp. 2. |
Corepoint Health, “The Continuity of Care Document—Changing the Landscape of Healthcare Information Exchange,” Jan. 2009, pp. 9. |
CreditAnalyst, Digital Matrix Systems, as printed out Mar. 4, 2008, pp. 2. |
CreditKarma, http://www.creditkarma.com printed Feb. 8, 2013 in 2 pages. |
CreditSesame, http://www.creditsesame.com/how-it-works/our-technology/ printed Feb. 5, 2013 in 2 pages. |
CreditXpert, http://www.creditxpert.com/Products/individuals.asp printed Oct. 12, 2012 in 1 page. |
ComScore Networks Launches Business Unit to Help Credit Card Marketers Master Online and Multi-Channel Strategies—Solutions Provide Unprecedented Insight Into Customer Acquisition and Usage Opportunities, Reston, VA, Oct. 11, 2001, 2 pages. |
Cowie, Norman, “Warning Bells & ‘The Bust-Out’”, Business Credit, Jul. 1, 2000, pp. 5. |
Credit Card Management, “Neural Nets Shoot for Jackpot,” Dec. 1995, pp. 1-6. |
Credit Risk Management Report, Potomac, Mar. 9, 1998, vol. 8, No. 4. |
CreditXpert Inc., CreditXpert 3-Bureau Comparison™, 2002, pp. 5, http://web.archive.org/web/20030608171018/http://creditxpert.com/CreditXpert%203-Bureau%20Comparison(TM)%20sample.pdf. |
CreditXpert Inc., CreditXpert Credit Score & Analysis™, Jan. 11, 2000, pp. 6, http://web.archive.org/web/20030611070058/http://www.creditxpert.com/CreditXpert%20Score%20&%20Analysis%20and%20Credit%20Wizard%20sample.pdf. |
CreditXpert Inc., CreditXpert Essentials™, Advisor View—Experian on Jul. 7, 2003, http://www.creditxpert.com/cx_ess_app.pdf. |
CreditXpert Inc., CreditXpert Essentials™, Advisor View—TransUnion on Oct. 10, 1999, pp. 6, http://web.archive.org/web/20041211052543/http://creditxpert.com/cx_ess_app.pdf. |
CreditXpert Inc., CreditXpert Essentials™, Applicant View—TransUnion on Oct. 10, 1999, pp. 6, http://www.creditxpert.com/cx_ess_app.pdf. |
CreditXpert Inc., CreditXpert What-If Simulator™, 2002, pp. 8, http://web.archive.org/web/20030630132914/http://creditxpert.com/CreditXpert%20What-If%20Simulator(TM)%20sample.pdf. |
Dankar et al., “Efficient Private Information Retrieval for Geographical Aggregation”, Procedia Computer Science, 2014, vol. 37, pp. 497-502. |
Dataman Group, “Summarized Credit Statistics,” Aug. 22, 2001, http://web.archive.org/web/20010822113446/http://www.datamangroup.com/summarized_credit.asp. |
David, Alexander, “Controlling Information Premia by Repackaging Asset-Backed Securities,” The Journal of Risk and Insurance, Dec. 1997, 26 pages. |
Davies, Donald W., “Applying the RSA Digital Signature to Electronic Mail,” Computer, Feb. 1983, pp. 55-62. |
Dé, Andy, “Will mHealth Apps and Devices Empower ePatients for Wellness and Disease Management? A Case Study,” Jan. 10, 2011 in 6 pages, http://www.healthsciencestrategy.com/2011/04/will-mhealth-apps-and-devices-empower-epatients-for-wellness-and-disease-management-a-case-study-2/. |
DeGruchy, et al., “Geodemographic Profiling Benefits Stop-Smoking Service;” The British Journal of Healthcare Computing & Information Management; Feb. 2007; 24, 7; pp. 29-31. |
Dillon et al., “Good Science”, Marketing Research: A Magazine of Management & Applications™, Winter 1997, vol. 9, No. 4; pp. 11. |
Downey, Sarah A., “Smile, you're on Spokeo.com! Concerned? (here's what to do)”, https://www.abine.com/blog/2011/how-to-remove-yourself-from-spokeo/, as posted Jan. 13, 2011 in 7 pages. |
EFunds Corporation, “Data & Decisioning: Debit Report” printed Apr. 1, 2007, http://www.efunds.com/web/industry-solutions/financial-services/frm-debit-report/htm in 1 page. |
Egol, Len; “What's New in Database Marketing Software,” Direct, Aug. 1994, vol. 6, No. 8, pp. 39. |
Elmasri et al., “Fundamentals of Database Systems, Third Edition (Excerpts)”, Jun. 2000, pp. 253, 261, 268-270, 278-280, 585, 595. |
Energy Saving Trust™, “HEED Online User Manual (1.7)”, Jul. 24, 2008, pp. 18, www.energysavingtrust.org.uk, Jul. 24, 2008. |
Equifax; “White Paper: Driving Safe Growth in a Fluid Economy”, http://www.equifax.com/assets/USCIS/efx_safeGrowth_wp.pdf, Oct. 2012 in 14 pages. |
Equifax; “True In-Market Propensity Scores™”, http://www.equifax.com/assets/USCIS/efx-00174-11-13_efx_tips.pdf, Nov. 2013 in 1 page. |
Ettorre, “Paul Kahn on Exceptional Marketing,” Management Review, vol. 83, No. 11, Nov. 1994, pp. 48-51. |
“Equifax and FICO Serve Consumers”, Mortgage Servicing News, Mar. 2001, vol. 5, No. 3, p. 19. |
Experian Announces PLUS Score; Experian Press Release dated Oct. 16, 2003; Experian Global Press Office. |
Experian and AGS Select SRC to Deliver Complete Marketing Solutions; Partnership First to Marketplace with Census2000 Data. PR Newswire. New York: Mar. 21, 2001. p. 1. |
“Experian Helps Verify the Identity of Patients and Provide Secure Enrollment to Healthcare Portals by Integrating with Major Electronic Medical Records Platform,” http://press.experian.com/United-States/Press-Release/experian-helps-verify-the-identity-of-patients-and-provide-secure-enrollment-to-healthcare.aspx?&p=1, Dec. 19, 2013, pp. 2. |
“Experian Launches Portfolio Monitor—Owner Notices℠”, News Release, Feb. 2003, Costa Mesa, CA. |
Experian—Scorex Announces New Credit Simulation Tool, PR Newswire, Costa Mesa, CA, Jun. 13, 2005. |
Experian; “Case study: SC Telco Federal Credit Union”, http://annualcreditreport.experian.com/assets/consumer-information/case-studies/sc-telco-case-study.pdf, Jun. 2011 in 2 pages. |
Experian; “In the Market Models℠”, http://www.experian.com/assets/consumer-information/product-sheets/in-the-market-models.pdf, Sep. 2013 in 2 pages. |
Experian Information Solutions, Inc., Credit Trends: Access Credit Trending Information Instantly, http://kewaneecreditbureau.com/Credit.Trends.pdf, Aug. 2000, pp. 4. |
Experian: Improve Outcomes Through Applied Customer Insight, Brochure, Nov. 2009, pp. 20. |
Experian: Mosaic Geodemographic Lifestyle Segmentation on ConsumerView [Data Card], as printed from http://datacards.experian.com/market?p.=research/datacard_print&prin, Apr. 6, 2012, pp. 4. |
Experian: Mosaic Public Sector 2009 Launch, 2009, pp. 164. |
Experian: Mosaic United Kingdom, Brochure, Jun. 2009, pp. 24. |
Experian: Mosaic UK—Optimise the Value of Your Customers and Locations, Now and in the Future, Brochure, 2010, pp. 24. |
Experian: Mosaic UK—Unique Consumer Classification Based on In-Depth Demographic Data, as printed from http://www.experian.co.uk/business-strategies/mosaic-uk.html, Jul. 30, 2012, pp. 2. |
Experian: Mosaic USA, Brochure, May 2009, pp. 14. |
Experian: Mosaic USA—Consumer Lifestyle Segmentation [Data Card], Dec. 2009, pp. 2. |
Experian: Public Sector, as printed form http://publicsector.experian.co.uk/Products/Mosaicpublicsector.aspx, 2012, pp. 2. |
Fair Isaac Announces Integrated, End-to-End Collection and Recovery Solution, Business Wire, New York, Sep. 2, 2004, p. 1. |
“Fair Isaac Introduces Falcon One System to Combat Fraud at Every Customer Interaction”, Business Wire, May 5, 2005, pp. 3. |
“Fair Isaac Offers New Fraud Tool”, National Mortgage News & Source Media, Inc., Jun. 13, 2005, pp. 2. |
Fanelli, Marc, “Building a Holistic Customer View”, MultiChannel Merchant, Jun. 26, 2006, pp. 2. |
Fickenscher, Lisa, “Merchant American Express Seeks to Mine its Data on Cardholder Spending Patterns,” American Banker, vol. 162, Issue 56, Mar. 24, 1997, pp. 1-2. |
“Fighting the New Face of Fraud”, FinanceTech, http://www.financetech.com/showArticle.jhtml?articleID=167100405, Aug. 2, 2005. |
Findermind, “PeopleFinders Review”, as archived Jun. 1, 2012 in 4 pages. http://web.archive.org/web/20120601010134/http://www.findermind.com/tag/peoplefinders-review/. |
“FinExtra, Basepoint Analytics Introduces Predictive Technology for Mortgage Fraud”, Oct. 5, 2005, pp. 3. |
Fisher, Joseph, “Access to Fair Credit Reports: Current Practices and Proposed Legislation,” American Business Law Journal, Fall 1981, vol. 19, No. 3, p. 319. |
Frontporch, “Ad Networks—Partner with Front Porch!,” www.frontporch.com printed Apr. 2008 in 2 pages. |
Frontporch, “New Free Revenue for Broadband ISPs!”, http://www.frontporch.com/html/bt/FPBroadbandISPs.pdf printed May 28, 2008 in 2 pages. |
“FTC Testifies: Identity Theft on the Rise”, FTC News Release, Mar. 7, 2000, pp. 3. |
Gao-03-661, Best Practices: Improved Knowledge of DOD Service Contracts Could Reveal Significant Savings, Gao, Jun. 2003. |
Gao et al., “Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks”, RecSys'13, Oct. 12-16, 2013, Hong Kong, China, pp. 93-100. |
Garcia-Molina et al., “Database Systems: The Complete Book”, Prentice Hall, Inc., Ch. 15, 2002, pp. 713-715. |
“Geographic Aggregation Tool SAS Beta Version 4.1”, Environmental Health Surveillance Section, New York State Dept. in Health, Troy, NY, Mar. 24, 2015, pp. 10. |
Gilje, Shelby, “Keeping Tabs on Businesses That Keep Tabs on Us”, NewsRoom, The Seattle Times, Section: Scene, Apr. 19, 1995, pp. 4. |
Glenn, Brandon, “Multi-provider patient portals get big boost with ONC ruling”, Feb. 25, 2013, http://medicaleconomics.modernmedicine.com/medical-economics/news/user-defined-tags/meaningful-use/multi-provider-patient-portals-get-big-boost in 2 pages. |
Gonul, et al., “Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models”, 14 pages, Management Science, vol. 44, No. 9, Sep. 1998. |
Hampton et al., “Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking”, American Journal of Epidemiology, Sep. 3, 2010, vol. 172, No. 9, pp. 8. |
Haughton et al., “Direct Marketing Modeling with CART and CHAID”, Journal of Direct Marketing, Fall 1997, vol. 11, No. 4, pp. 42-52. |
Healow.com, Various screenshots from page titled “Health and Online Wellness,” https://healow.com/apps/jsp/webview/index.jsp printed Aug. 19, 2013 in 4 pages. |
Healthspek.com, “How Good Are We?” http://healthspek.com/how-good-are-we/ printed Jan. 21, 2014 in 2 pages. |
“Healthspek Users Can Now Import Their Doctors' Records into Their Personal Health Record,” PRWeb, Nashville, TN, Jan. 14, 2014, pp. 1 http://www.prweb.com/releases/2014/01/prweb11485346.htm. |
HealthVault, “Share Health Information,” https://account.healthvault.com/sharerecord.aspx, printed Feb. 20, 2013 in 2 pages. |
HealthVault, “What Can you do with HealthVault?” https://www.healthvault.com/us/en/overview, http://www.eweek.com/mobile/diversinet-launches-mobihealth-wallet-for-patient-data-sharing/, printed Feb. 20, 2013 in 2 pages. |
Hill, Kerry, “Identity Theft Your Social Security Number Provides Avenue for Thieves”, NewsRoom, Wisconsin State Journal, Sep. 13, 1998, pp. 4. |
Hojoki, http://hojoki.com printed Feb. 8, 2013 in 5 pages. |
Horowitz, Brian T., “Diversinet Launches MobiHealth Wallet for Patient Data Sharing,” eWeek, Dec. 4, 2012, http://www.eweek.com/mobile/diversinet-launches-mobihealth-wallet-for-patient-data-sharing/. |
“ID Thieves These Days Want Your Number, Not Your Name”, The Columbus Dispatch, Columbus, Ohio, http://www.dispatch.com/content/stories/business/2014/08/03/id-thieves-these-days-want-your-number-not-your-name.html, Aug. 3, 2014 in 2 pages. |
Ideon, Credit-Card Registry that Bellyflopped this Year, Is Drawing some Bottom-Fishers, The Wall Street Journal, Aug. 21, 1995, pp. C2. |
IFTTT, “About IFTTT,” http://ifttt.com/wtf printed Feb. 18, 2013 in 4 pages. |
igiHealth.com, “Orbit® PHR: Personal Health Record (PHR),” http://www.igihealth.com/consumers/orbit_phr.html, printed Jan. 21, 2014 in 2 pages. |
“Impac Funding Introduces Enhanced Website for Static Pool Tracking of MBS Transactions,” Waltham, MA; Webpage printed out from http://www.lewtan.com/press/1208044_Impac-Lewtan.htm on Mar. 20, 2008. |
“Industry News, New Technology Identifies Mortgage Fraud: Basepoint Analytics Launches FraudMark”, Inman News, American Land Title Association, Oct. 5, 2005, pp. 1. |
InsightsOne.com, “Healthcare,” http://insightsone.com/healthcare-predictive-analytics/ printed Mar. 6, 2014 in 5 pages. |
Instant Access to Credit Reports Now Available Online with DMS' CreditBrowser-based system also Simplifies Credit Decisioning and Offers a Central Point of Control, Business Wire, Dallas, May 23, 2000, p. 0264. |
“Intelligent Miner Applications Guide”, IBM Corp., Apr. 2, 1999, Chapters 4-7, pp. 33-132. |
Internal Revenue Service Data Book 2000, Issued Aug. 2001, Revised May 2003. |
Jacob et al., A Case Study of Checking Account Inquiries and Closures in Chicago, The Center for Financial Services Innovation, Nov. 2006. |
“Japan's JAAI System Appraises Used Cars Over Internet”, Asia Pulse, Mar. 3, 2000, p. 1. |
Jost, Allen; Neural Networks, Credit World, Mar./Apr. 1993, vol. 81, No. 4, pp. 26-33. |
Jowit, Juliette, “Ever wondered how big your own carbon footprint might be?”, Nov. 4, 2007, pp. 4, http://www.guardian.co.uk/money/2007/nov/04/cash.carbonfootprints/print. |
“JPMorgan Worldwide Securities Services to Acquire Paloma's Middle and Back Office Operations,” Webpage printed from http://www.jpmorgan.com on Apr. 1, 2009. |
Karlan et al., “Observing Unobservables:Identifying Information Asymmetries with a Consumer Credit Field Experiment”, Jun. 17, 2006, pp. 58, http://aida.econ.yale.edu/karlan/papers/ObservingUnobservables.KarlanZinman.pdf. |
Kessler, Josh “How to Reach the Growing ‘Thin File’ Market: Huge Immigration Market and Other Groups with Little or No Credit History May Be Creditworthy. There are Several Ways to Tap This Well of Business”, ABA Banking Journal, vol. 97, 2005. |
King et al., Local and Regional CO2 Emissions Estimates for 2004 for the UK, AEA Energy & Environment, Report for Department for Environment, Food and Rural Affairs, Nov. 2006, London, UK, pp. 73. |
Klein, et al., “A Constant-Utility Index of the Cost of Living”, The Review of Economic Studies, pp. 84-87, vol. XV-XVI, Kraus Reprint Corporation, New York, 1960. |
Klein, et al., “An Econometric Model of the United States: 1929-1952”, North-Holland Publishing Company, Amsterdam, 1955, pp. 4-41. |
Klein, Lawrence R., “The Keynesian Revolution”, New York, The MacMillan Company, 1947, pp. 56-189. |
Kohavi, Ron, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection”, International Joint Conference on Artificial Intelligence, 1995,pp. 7. |
Kwan et al., “Protection of Geoprivacy and Accuracy of Spatial Information: How Effective Are Geographical Masks?”, Carographica, Summer 2004, vol. 39, No. 2, pp. 15-27. |
Lamons, Bob, “Be Smart: Offer Inquiry Qualification Services,” Marketing News, ABI/Inform Global, Nov. 6, 1995, vol. 29, No. 23, pp. 13. |
Lee, W.A.; “Experian, on Deal Hunt, Nets Identity Theft Insurer”, American Banker: The Financial Services Daily, Jun. 4, 2003, New York, NY, 1 page. |
Leskovec, Jure, “Social Media Analytics: Tracking, Modeling and Predicting the Flow of Information through Networks”, WWW 2011—Tutorial, Mar. 28-Apr. 1, 2011, Hyderabad, India, pp. 277-278. |
LifeLock, http://web.archive.org/web/20110724011010/http://www.lifelock.com/? as archived Jul. 24, 2011 in 1 page. |
LifeLock, “How LifeLock Works,” http://www.lifelock.com/lifelock-for-people printed Mar. 14, 2008 in 1 page. |
LifeLock, “LifeLock Launches First ID Theft Prevention Program for the Protection of Children,” Press Release, Oct. 14, 2005, http://www.lifelock.com/about-us/press-room/2005-press-releases/lifelock-protection-for-children. |
LifeLock, Various Pages, www.lifelock.com/, 2007. |
Longo, Tracey, “Managing Money: Your Family Finances”, Kiplinger's Personal Finance Magazine, Jun. 1, 1995, vol. 49, No. 6, pp. 4. |
Lovelace, Robin, “IPFinR: An Implementation of Spatial Microsimulation in R”, RL's Powerstar, Jun. 12, 2013, pp. 9, https://robinlovelace.wordpress.com/2013/06/12/ipfinr-an-implementation-of-spatial-microsimulation-in-r/. |
Maciejewski et al., “Understanding Syndromic Hotspots—A Visual Analytics Approach”, Conference Paper, IEEE Symposium on Visual Analytics Science and Technology, Oct. 21-23, 2017, pp. 35-42. |
McManus et al.; “Street Wiser,” American Demographics; ABI/Inform Global; Jul./Aug. 2003; 25, 6; pp. 32-35. |
McNamara, Paul, “Start-up's pitch: The Envelope, please,” Network World, Apr. 28, 1997, vol. 14, No. 17, p. 33. |
MergePower, Inc., “Attribute Pro”, http://web.archive.org/web/20060520135324/http://www.mergepower.com/attribute_pro.html, dated May 20, 2006 in 1 page. |
MergePower, Inc., “Attribute Pro”, http://web.archive.org/web/20080708204709/http:/www.mergepower.com/APInfo.aspx, dated Jul. 8, 2008 in 2 pages. |
MergePower, Inc., “Attribute Pro®—Credit Bureau Attributes”, http://web.archive.org/web/20120307000028/http:/www.mergepower.com/APInfo.aspx, dated Mar. 7, 2012 in 2 pages. |
MergePower, Inc., “MergePower, Inc”, http://web.archive.org/web/20060513003556/http:/www.mergepower.com/, dated May 13, 2006 in 1 page. |
MergePower, Inc., “MergePower, Inc”, http://web.archive.org/web/20070208144622/http:/www.mergepower.com/, dated Feb. 8, 2007 in 1 page. |
MergePower, Inc., “MergePower, Inc”, http://web.archive.org/web/20070914144019/http:/www.mergepower.com/, dated Sep. 14, 2007 in 1 page. |
MergePower, Inc., “MergePower, Inc”, http://web.archive.org/web/20110828073054/http:/www.mergepower.com/, dated Aug. 28, 2011 in 2 pages. |
MERit Credit Engine™, Diagram, http://creditengine.net/diagram.htm, copyright 1997, pp. 1. |
Merugu, et al.; “A New Multi-View Regression Method with an Application to Customer Wallet Estimation,” The 12th International Conference on Knowledge Discovery and Data Mining, Aug. 20-23, 2006, Philadelphia, PA. |
Miller, Joe, “NADA Used-Car Prices Go Online”, Automotive News, Jun. 14, 1999, p. 36. |
Mint.com, http://www.mint.com/how-it-works/ printed Feb. 5, 2013 in 2 pages. |
“Mosaic” (geodemography), available from http://en.wikipedia.org/wiki/Mosaic_(geodemography), as last modified Jul. 13, 2012. pp. 4. |
Mover, “One API for the Cloud,” http://mover.io printed Feb. 6, 2013 in 3 pages. |
Muus, et al., “A Decision Theoretic Framework for Profit Maximization in Direct Marketing”, Sep. 1996, pp. 20. |
MyReceipts, http://www.myreceipts.com/, printed Oct. 16, 2012 in 1 page. |
MyReceipts—How it Works, http://www.myreceipts.com/howItWorks.do, printed Oct. 16, 2012 in 1 page. |
NebuAd, “Venture Capital: What's New—The Latest on Technology Deals From Dow Jones VentureWire”, Press Release, http://www.nebuad.com/company/media_coverage/media_10_22_07.php, Oct. 22, 2007, pp. 2. |
“New FICO score extends lenders' reach to credit-underserved millions”, Viewpoints: News, Ideas and Solutions from Fair Isaac, Sep./Oct. 2004 as downloaded from http://www.fairisaac.com/NR/exeres/F178D009-B47A-444F-BD11-8B4D7D8B3532,frame . . . in 6 pages. |
“New Privista Product Provides Early Warning System to Combat Identity Theft”, PR Newswire, Oct. 24, 2000, PR Newswire Association, Inc., New York. |
Occasional CF Newsletter; http://www.halhelms.com/index.cfm?fuseaction=newsletters.oct1999; Oct. 1999. |
Office of Integrated Analysis and Forecasting, DOE/EIA-M065(2004), Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, EIA, Washington DC, Feb. 2004. |
Organizing Maniac's Blog—Online Receipts Provided by MyQuickReceipts.com, http://organizingmaniacs.wordpress.com/2011/01/12/online-receipts-provided-by-myquickreceipts.com/ dated Jan. 12, 2011 printed Oct. 16, 2012 in 3 pages. |
Otixo, “Your Dashboard for the Cloud,” http://Otixo.com/product printed Feb. 6, 2013 in 3 pages. |
Otter, et al., “Direct Mail Selection by Joint Modeling of the Probability and Quantity of Response”, Jun. 1997, pp. 14. |
Padgett et al., “A Comparison of Carbon Calculators”, Environmental Impact Assessment Review 28, pp. 106-115, Jun. 7, 2007. |
Pagano, et al., “Information Sharing in Credit Markets,” Dec. 1993, The Journal of Finance, vol. 48, No. 5, pp. 1693-1718. |
“Parse”, Definition from PC Magazine Encyclopedia, http://www/pcmag.com/encyclopedia_term_0,2542,t=parse&i=48862,00.asp as downloaded Mar. 5, 2012. |
Partnoy, Frank, Rethinking Regulation of Credit Rating Agencies: An Institutional Investor Perspective, Council of Institutional Investors, Apr. 2009, pp. 21. |
Perlich et al., “High Quantile Modeling for Customer Wallet Estimation with Other Applications,” The 13th International Conference on Knowledge Discovery and Data Mining, Aug. 12-15, 2007, San Jose, CA. |
Perry et al., “Integrating Waste and Renewable Energy to Reduce the Carbon Footprint of Locally Integrated Energy Sectors”, Energy 33, Feb. 15, 2008, pp. 1489-1497. |
Phorm, “BT PLC TalkTalk and Virgin Media Inc. confirm exclusive agreements with Phorm”, Press Release, http://www.phorm.com/about/launch_agreement.php, Feb. 14, 2008, pp. 2. |
Phorm, “The Open Internet Exchange, ‘Introducing the OIX’”, http://www.phorm.com/oix/ printed May 29, 2008 in 1 page. |
Pipes, http://pipes.yahoo.com/pipes printed Feb. 18, 2013 in 1 page. |
Planet Receipt—Home, http://www.planetreceipt.com/home printed Oct. 16, 2012 in 1 page. |
Planet Receipt—Solutions & Features, http://www.planetreceipt.com/solutions-features printed Oct. 16, 2012 in 2 pages. |
Planwise, http://planwise.com printed Feb. 8, 2013 in 5 pages. |
Polatoglu et al., “Theory and Methodology, Probability Distributions of Cost, Revenue and Profit over a Warranty Cycle”, European Journal of Operational Research, Jul. 1998, vol. 108, Issue 1, pp. 170-183. |
“PostX to Present at Internet Showcase”, PR Newswire, Apr. 28, 1997, pp. 2. |
PostX, “PostX® Envelope and ActiveView”, http://web.archive.org/web/19970714203719/http://www.postx.com/priducts_fm.html, Jul. 14, 1997 (retrieved Nov. 7, 2013) in 2 pages. |
Powerforms: Declarative Client-Side for Field Validation, ISSN 1386-145x, Dec. 2000. |
“PremierGuide Announces Release 3.0 of Local Search Platform”, Business Wire, Mar. 4, 2004, Palo Alto, CA, p. 5574. |
Primadesk, http://primadesk.com printed Feb. 8, 2013 in 1 page. |
PrivacyGuard, http://web.archive.org/web/20110728114049/http://www.privacyguard.com/ as archived Jul. 28, 2011 in 1 page. |
RapUP, Attribute Management & Report Systems:Absolute Advantage!, Magnum Communications Brochure, Copyright 2004, pp. 5. |
Rodgers, Zachary, “ISPs Collect User Data for Behavioral Ad Targeting”, ClickZ, www.clickz.com/showPage.html?page=clickz, Jan. 3, 2008, pp. 3. |
Rosset et al., “Wallet Estimation Models”, IBM TJ Watson Research Center, 2005, Yorktown Heights, NY, pp. 12. |
Sakia, R.M., “The Box-Cox Transformation Technique: a Review”, The Statistician, 41, 1992, pp. 169-178. |
SalesLogix.net, SalesLogix Sales Tour, Apr. 11, 2001, http:///www.saleslogix.com, pp. 19. |
Saunders, A., “Data Goldmine,” Management Today, London: Mar. 1, 2004, 6 pages. |
Sawyers, Arlena, “NADA to Offer Residual Guide”, Automotive News, May 22, 2000, p. 1. |
Schmittlein et al., “Customer Base Analysis: An Industrial Purchase Process Application”, Marketing Science, vol. 13, No. 1, Winter 1994, pp. 41-67. |
“ScoreNet® Network”, FairIsaac, web.archive.org/web/20071009014242/http://www.fairisaac.com/NR/rdonlyres/AC4C2F79-4160-4E44-B0CB-5C899004879A/0/ScoreNetnetworkBR.pdf, May 2006, pp. 6. |
ServiceObjects, “DOTS Web Services—Product Directory”, http://www.serviceobjects.com/products/directory_of_web_services.asp printed Aug. 17, 2006 in 4 pages. |
ShoeBoxed, https://www.shoeboxed.com/sbx-home/ printed Oct. 16, 2012 in 4 pages. |
Shvachko et al., “The Hadoop Distributed File System”, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), May 3, 2010, pp. 1-10. |
Singletary, Michelle, “Score One for Open Credit Ratings”, The Washington Post, Washington DC, Jun. 18, 2000, 3 pages. |
Smith, Richard M., “The Web Bug FAQ”, Nov. 11, 1999, Version 1.0, pp. 4. |
Smith, Wendell R., “Product Differentiation and Market Segmentation as Alternative Marketing Strategies”, The Journal of Marketing, The American Marketing Association, Brattleboro, Vermont, Jul. 1956, vol. XXI, pp. 3-8. |
“STAGG Variables Sum Up Credit Attributes for Automated Decisions”, PRWeb, May 11, 2011, pp. 2. http://www.prweb.com/releases/2011/5/prweb8404324.htm. |
Stanton, T.H., “Credit Scoring and Loan Scoring as Tools for Improved Management of Federal Credit Programs”, Financier, Philadelphia, Summer 1999, vol. 6, 36 pages. |
Stein, Benchmarking Default Prediction Models: Pitfalls and Remedies in Model Validation, Moody's KMV, Revised Jun. 13, 2002, Technical Report #020305; New York. |
Stone, “Linear Expenditure Systems and Demand Analysis: An Application to the Pattern of British Demand”, The Economic Journal: The Journal of the Royal Economic Society, Sep. 1954, pp. 511-527, vol. LXIV, Macmillan & Co., London. |
Storage Made Easy(SME), http://storagemadeeasy.com printed Feb. 6, 2013 in 1 page. |
Sumner, Anthony, “Tackling the Issue of Bust-Out Fraud”, Retail Banker International, Jul. 24, 2007, pp. 4. |
Sumner, Anthony, “Tackling the Issue of Bust-Out Fraud”, Experian: Decision Analytics, Dec. 18, 2007, pp. 24. |
Sumner, Anthony, “Tackling the Issue of Bust-Out Fraud”, e-News, Experian: Decision Analytics, pp. 4, [Originally Published in Retail Banker International Magazine Jul. 24, 2007]. |
Sweat, Jeff; “Know Your Customers,” Information Week, Nov. 30, 1998, pp. 20. |
Tao, Lixin, “Shifting Paradigms with the Application Service Provider Model”; Concordia University, IEEE, Oct. 2001, Canada. |
Tennant, Don, “How a Health Insurance Provider Uses Big Data to Predict Patient Needs,” http://www.itbusinessedge.com/blogs/from-under-the-rug/how-a-health-insurance-provider-uses-big-data-to-predict-patient-needs.html, printed Mar. 6, 2014 in 2 pages. |
Thoemmes, Felix, “Propensity Score Matching in SPSS”, Center for Educational Science and Psychology, University of Tübingen, Jan. 2012. |
Truston, “Checking if your Child is an ID Theft Victim can be Stressful,” as posted by Michelle Pastor on Jan. 22, 2007 at http://www.mytruston.com/blog/credit/checking_if_your_child_is_an_id_theft_vi.html. |
Van Collie, Shimon, “The Road to Better Credit-Card Marketing,” Bank Technology News, Sep. 1995, pp. 4. |
Verstraeten, Geert, Ph.D.; Issues in predictive modeling of individual customer behavior: Applications in targeted marketing and consumer credit scoring; Universiteit Gent (Belgium) 2005. |
“WashingtonPost.com and Cars.com Launch Comprehensive Automotive Web Site for the Washington Area”, PR Newswire, Oct. 22, 1998. pp. 2. |
Watts, Craig, “Consumers Now Can Know What Loan Rate Offers to Expect Based on Their FICO Credit Score at MyFICO.com,” San Rafael, CA, Mar. 6, 2002, pp. 2, http://www.myfico.com/PressRoom/PressReleases/2002_03_06.aspx. |
Watts, Craig, “Fair, Isaac and Equifax Give Consumers New Score Power Tools Offering Greater Insights for Managing Their Credit Health,” May 21, 2002, pp. 3, http://www.myfico.com/PressRoom/PressReleases/2002_05_21.aspx. |
Webber, Richard, “The Relative Power of Geodemographics vis a vis Person and Household Level Demographic Variables as Discriminators of Consumer Behavior,” CASA:Working Paper Series, http://www.casa.ucl.ac.uk/working_papers/paper84.pdf, Oct. 2004, pp. 17. |
Webpage printed out from http://www.jpmorgan.com/cm/ContentServer?c=TS_Content&pagename=jpmorgan%2Fts%2FTS_Content%2FGeneral&cid=1139403950394 on Mar. 20, 2008, Feb. 13, 2006, New York, NY. |
White, Ron, “How Computers Work”, Millennium Edition, Que Corporation, Indianapolis, IN, Sep. 1999, pp. 284. |
Wiedmann, et al., “Report No. 2: The use of input-output analysis in REAP to allocate Ecological Footprints and material flows to final consumption categories”, Resources and Energy Analysis Programme, Stockholm Environment Institute—York, Feb. 2005, York, UK, pp. 33. |
Wilson, Andrea, “Escaping the Alcatraz of Collections and Charge-Offs”, http://www.transactionworld.net/articles/2003/october/riskMgmt1.asp, Oct. 2003. |
Working, Holbrook, “Statistical Laws of Family Expenditure”, Journal of the American Statistical Association, pp. 43-56, vol. 38, American Statistical Association, Washington, D.C., Mar. 1943. |
Wyatt, Craig, “Usage Models just for Merchants,” Credit Card Management, Sep. 1995, vol. 8, No. 6, pp. 4. |
Yuan et al., “Time-Aware Point-of-Interest Recommendation”, SIGIR'13, Jul. 28-Aug. 1, 2013, Dublin, Ireland, pp. 363-372. |
Yücesan et al., “Distributed Web-Based Simulation Experiments for Optimization”, Simulation Practice and Theory 9, 2001, pp. 73-90. |
Zandbergen, Paul A., “Ensuring Confidentiality of Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-Level Data”, Review Article, Hindawi Publishing Corporation, Advances in Medicine, VI. 2014, pp. 14. |
Zapier, “Integrate Your Web Services,” http://www.Zapier.com printed Feb. 18, 2013 in 3 pages. |
Zimmerman et al., “A Web-Based Platform for Experimental Investigation of Electric Power Auctions,” Decision Support Systems, 1999, vol. 24, pp. 193-205. |
Declaration of Paul Clark, DSc. for Inter Partes Review of U.S. Pat. No. 8,504,628 (Symantec Corporation, Petitioner), dated Jan. 15, 2014 in 76 pages. |
Exhibit D to Joint Claim Construction Statement, filed in Epsilon Data Management, LLC, No. 2:12-cv-00511-JRG (E.D. Tex.) (combined for pretrial purposes with RPost Holdings. Inc., et al. v. Experian Marketing Solutions. Inc., No. 2:12-cv-00513-JRG (E.D. Tex.)) Filed Jan. 14, 2014 in 9 pages. |
First Amended Complaint in Civil Action No. 2:12-cv-511-JRG (Rpost Holdings, Inc. and Rpost Communications Limited V. Constant Contact, Inc.; et al.) filed Feb. 11, 2013 in 14 pages. |
First Amended Complaint in Civil Action No. 2:12-cv-511-JRG (Rpost Holdings, Inc. and Rpost Communications Limited V. Epsilon Data Management, LLC.) filed Sep. 13, 2013 in 9 pages. |
First Amended Complaint in Civil Action No. 2:12-cv-513-JRG (Rpost Holdings, Inc. and Rpost Communications Limited V. Experian Marketing Solutions, Inc.) filed Aug. 30, 2013 in 9 pages. |
Petition for Covered Business Method Patent Review in U.S. Pat. No. 8,161,104 (Experian Marketing Solutions, Inc., Epsilon Data Management, LLC, and Constant Contact, Inc., v. Rpost Communications Limited) dated Jan. 29, 2014 in 90 pages. |
Source Code Appendix attached to U.S. Appl. No. 08/845,722 by Venkatraman et al., Exhibit A, Part 1 & 2, pp. 32. |
Official Communication in Canadian Patent Application No. 2,381,349, dated Jul. 31, 2014. |
International Search Report and Written Opinion for Application No. PCT/US2007/06070, dated Nov. 10, 2008. |
International Search Report and Written Opinion for Application No. PCT/US2008/064594, dated Oct. 30, 2008. |
International Preliminary Report and Written Opinion in PCT/US2008/064594, dated Dec. 10, 2009. |
International Search Report and Written Opinion for Application No. PCT/US09/37565, dated May 12, 2009. |
International Search Report and Written Opinion for Application No. PCT/US2013/052342, dated Nov. 21, 2013. |
International Preliminary Report on Patentability for Application No. PCT/US2013/052342, dated Feb. 5, 2015. |
International Search Report and Written Opinion for Application No. PCT/US2017/048265, dated Dec. 5, 2017. |
International Search Report and Written Opinion for Application No. PCT/US2017/068340, dated Feb. 26, 2018. |
Number | Date | Country | |
---|---|---|---|
62094819 | Dec 2014 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 14975654 | Dec 2015 | US |
Child | 16357106 | US |