State Farm Mutual Automobile Insurance Company

Taylor Griffin Smith

Tim G Sanidas

Jason Matthew White

Joseph David Albright

Rajiv C Shah

Ronald R Duehr

Jeffrey David Hevrin

Lucas Allen

Sripriya Sundararaman

Jing Wan

Jeremy Cunningham

Shannon Morrison

Brad Sliz

Chenchao Shou

Systems and methods are described for generating efficient iterative electronic recommendation structures. In various aspects, one or more processors aggregate a plurality of ratings vectors, where each ratings vector is associated with a vector type and contains one or more content ranking metrics associated with one or more users. The one or more processors generate similarity pairing values from the plurality of ratings vectors, where each similarity pairing value is based on a similarity mapping between a first ratings vector and a second ratings vector. The one or more processors further generate an electronic recommendation structure based on the similarity pairing values, where the electronic recommendation structure includes a bi-directional look-up interface that is configured to return a bi-directional recommendation value after receiving a lookup request for either the vector type of the first ratings vector or the vector type of the second ratings vector.

Systems and methods for generating efficient iterative recommendation structures

Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.

Systems and methods for providing user offers based on efficient iterative recommendation structures

A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Detection of anomalous computer behavior

A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

A method of identifying a home to facilitate a real estate transaction includes generating a set of neural network objects wherein each object includes a neural network model, an address, and an image, training the neural network model in each object to emit a confidence score by a neural network analyzing the image to identify key features, receiving an example address and example image, selecting a set of trained neural network objects wherein each object in the set of trained neural network objects includes a trained neural network model, landmark address, and landmark image, generating a set of confidence scores by applying the example image to the trained neural network model in each object, generating based on the set of confidence scores a result set, transmitting the result set to a user, and displaying the result in the user device.

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Addison, TX, United States of America

Taylor Griffin Smith