State Farm Mutual Automobile Insurance Company

Sripriya Sundararaman

Rajiv C Shah

Lucas Allen

Jeffrey David Hevrin

Michael Shawn Jacob

Jeffrey Wilson Stoiber

Sunish Shreenarayan Menon

Phillip Sangpil Moon

Erin Ann Olander

Jeffrey Kinsey

Ronald R Duehr

Taylor Griffin Smith

Jing Wan

Jeremy Cunningham

Brad Sliz

Shannon Morrison

James Dykeman

Ellakate LeFebre

Kenneth Jason Sanchez

Aaron Scott Chan

Utku Pamuksuz

Yue Liu

Aditya Subramanian

EllaKate LeFebre

Ben Blaisdell

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 computer-implemented method for identifying a property usage type based upon sensor data includes, with customer permission or affirmative consent, receiving data generated by various sensors; generating a report that includes a listing of events recorded by each sensor; analyzing data from the report to determine a property usage type score; receiving data regarding types and levels of insurance coverage associated with the property usage type score; receiving data derived from a homeowner's insurance policy; comparing the types and levels of insurance coverage associated with the property usage type score with the types and levels of insurance coverage from the homeowner's current insurance policy; and transmitting a message to the homeowner to update their insurance policy if there are differences between (i) the insurance coverage that the homeowner has, and (ii) the insurance coverage the homeowner should have based upon the property usage type score.

Identifying property usage type based upon smart sensor data

Systems and methods for assessing the movements of a driver while operating the vehicle are provided. According to certain aspects, an electronic device may receive and analyze image data from a 2-D image sensor and a 3-D image sensor, where the image data depicts an individual located within a vehicle. The electronic device may also access a library of baseline movements to which to compare the image data. Based on the comparing, the electronic device may identify and record a set of movements made by the driver.

Imaging analysis technology to assess movements of vehicle occupants

Growing community of inventors

Plano, TX, United States of America

Sripriya Sundararaman