Experian Information Solutions, Inc.

Kevin Chen

Zhixuan Wang

Hua Li

Shanji Xiong

Mason Carpenter

Eric Haller

Yi He

Alan Tsang

Christer DiChiara

Honghao Shan

Mark Hirn

Julian Yarkony

Chi Zhang

Derek Toub

Steven Yin

David Kerry

Sophie Liu

Olcay Boz

Jack Yu

Christopher Wheeler

Sreeram Upendran

Juncheng C Liu

Steve Yin

Shumin Guo

The present disclosure recites systems and methods for generating record clusters. For example, a computer-implemented method comprises receiving a plurality of records from data sources and providing at least a subset of the records to a scoring model that determines scores for various pairings of the records, a score for a given pair of the records representing a probability that the given pair of records contain data elements about the same entity. The method further comprises generating a graph data structure that includes a plurality of nodes, individual nodes representing a different record from the records. The method also comprises assigning a different unique identifier to individual clusters of the final clusters and responding to a request for data regarding a given entity by providing aggregated data elements from those records of the records associated with a cluster of the final clusters having an identifier that represents the given entity.

Single identifier platform for storing entity data

The features relate to artificial intelligence directed detection of user behavior based on complex analysis of user event data including language modeling to generate distributed representations of user behavior. Further features are described for reducing the amount of data needed to represent relationships between events such as transaction events received from card readers or point of sale systems. Machine learning features for dynamically determining an optimal set of attributes to use as the language model as well as for comparing current event data to historical event data are also included.

Behavior analysis using distributed representations of event data

Systems and methods are described for generating record clusters. The methods comprise receiving a plurality of records from data sources and providing at least a subset of the records to a scoring model that determines scores for various pairings of the records, a score for a given pair of the records representing a probability that the given pair of records contain data elements about the same entity. The method further comprises generating a graph data structure that includes a plurality of nodes, individual nodes representing a different record from the records. The method also comprises assigning a different unique identifier to individual clusters of the final clusters and responding to a request for data regarding a given entity by providing aggregated data elements from those records of the records associated with a cluster of the final clusters having an identifier that represents the given entity.

Systems and methods for data aggregation and processing are provided in manner that is decentralized and preserves privacy. A data aggregation and computation system may include an interface, a controller, and one or more clusters of computation nodes. The interface may receive an inquiry from a requesting entity for computing information regarding an individual based on pieces of information held by a plurality of entities. The controller may communicate an identifier for the individual to a processor system associated with each of the entities based on the inquiry. The clusters of computation nodes may each receive encrypted data fragments from each of the processor systems, the data fragments comprising unrecognizable fragments that no individual processor system can re-assemble to recover the information, perform secure, multi-party computations based on the data fragments, and generate a result based on the secure, multi-party computations for the individual.

Systems and methods for secure data aggregation and computation

Features are described for efficiently and accurately identifying a user of an electronic device with limited user interaction. The features include receiving a mobile device identifier from the mobile device. The features include transmitting the mobile device identifier to a service provider associated with the mobile device. The features include receiving information identifying the user from the service provider. The features include identifying a set of candidates associated with at least a portion of the information. The features include generating a metric for the candidates included in the set of candidates. An individual metric indicates a degree of relatedness between a value for the user for the at least one data field and a value for a candidate for the at least one data field. The features include identifying the user as a specific candidate included in the set of candidates based on the metric corresponding to a threshold.

Disambiguation and authentication of device users

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San Diego, CA, United States of America

Kevin Chen