Allstate Insurance Company

Arity International Limited

Other

Venu Madhav Tammali

Kyle Patrick Schmitt

Pratheek M Harish

Dana Ferguson

Larry Layne

Benjamin Robertson Yeomans

Jared S Snyder

Alexander Herrmann

Clayton Jeschke

Jeremy Deram

Melanie Hanna

Samantha Mait

Eric Campbell

Ross Reichardt

Ronald Lettofsky

Systems and methods are disclosed for determining whether or not a crash involving a vehicle has occurred. A computing device may receive acceleration measurement(s) measured by one or more accelerometers during a time window. The computing device may determine, for one or more acceleration measurements, a corresponding acceleration magnitude. Based on the corresponding acceleration magnitude(s), the computing device may identify, from the acceleration measurement(s), an acceleration measurement and/or may determine whether the acceleration magnitude exceeds a threshold acceleration magnitude. The computing device may corroborate whether a vehicle associated with the mobile computing device was involved in a crash. Data associated with the acceleration magnitude and/or an event, such as a crash event, may be transmitted to a server.

Automatic crash detection

Systems and methods in accordance with embodiments of the invention can obtain and use a variety of telematics and other data to predict potential claim or accident outcomes arising from a variety of incidents, such as automobile accidents. The claim or accident outcomes, such as parts of the vehicle damaged, accident category, expected liabilities, and many others, can be predicted based on the telematics and other data, such as accelerometer data, heading data, location/GPS, barometer, gyroscope, magnetometer data, and the like. The machine learning classifiers that can be generated are trained on historical data, consisting of claims, telematics, and/or other relevant data. In a variety of embodiments, the telematics data can be captured using a telematics device installed in the vehicle and/or via a mobile device associated with the vehicle.

Applying machine learning to telematics data to predict accident outcomes

Methods, computer-readable media, software, and apparatuses may determine, based upon edge-computing operations, that a vehicular trip has been initiated and cause one or more sensors to collect vehicle data. One or more trip segments for at least a portion of the vehicular trip may be determined. In some aspects, for each trip segment, a first plurality of time features and a second plurality of frequency features may be determined, and may be concatenated with a third plurality of GPS features to form a feature vector. An accuracy measure may be determined based on the feature vector, and a mode for the vehicle may be predicted.

Vehicle mode determination based on edge-computing

Systems and methods are disclosed for receiving and transmitting accelerometer data and/or usage data, and analyzing the data to detect movement or usage of the device within a vehicle. A device, such as a mobile device, may detect a device movement event or a device usage event associated with the device. Based on the detection of the device movement event or the device usage event, a time associated with the event may be stored. The device may determine whether another event associated with the device occurs within a threshold amount of time from the time associated with the event. Based on a determination of whether the other event occurs within the threshold amount of time, the device may determine an event session associated with the device. The event session may comprise an instantaneous event or a continuous event. Data indicative of the event session may be transmitted to a server.

Detecting device movement and/or device usage within a vehicle

Aspects of the disclosure relate to computing platforms that utilize machine learning to reduce false positive/negative collision output generation. A computing platform may apply machine learning algorithms on received data to generate a collision output. In response to generating the collision output indicating a collision, the computing platform may identify a data collection location. If the data collection location is within a predetermined radius of a false positive collection location, the computing platform may modify the collision output to indicate a non-collision. If the data collection location is not within the predetermined radius, the computing platform may compute a score using telematics data and compare the score to a predetermined threshold. If the score does not exceed the predetermined threshold, the computing platform may modify the collision output to indicate a non-collision. If the score exceeds the predetermined threshold, the computing platform may affirm the collision output indicating a collision.

Collision analysis platform using machine learning to reduce generation of false collision outputs

Aspects of the disclosure relate to processing remotely captured sensor data. A computing platform having at least one processor, a communication interface, and memory may receive, via the communication interface, from a user computing device, sensor data captured by the user computing device using one or more sensors built into the user computing device. Subsequently, the computing platform may analyze the sensor data received from the user computing device by executing one or more data processing modules. Then, the computing platform may generate trip record data based on analyzing the sensor data received from the user computing device and may store the trip record data in a trip record database. In addition, the computing platform may generate user record data based on analyzing the sensor data received from the user computing device and may store the user record data in a user record database.

Distributed data processing systems for processing remotely captured sensor data

Growing community of inventors

Chicago, IL, United States of America

Venu Madhav Tammali