Allstate Insurance Company

Arity International Limited

Hartford Fire Insurance Compnay

Connor Walsh

Jared S Snyder

Yasir Mukhtar

Varun Nagpal

Pratheek M Harish

Alexander Herrmann

Rebecca Harasymczuk

John P Kelsh

Christopher Paul Gutsell

Maureen King

Nandini Kannappan

Matthew Lamoureux

Matthew James Manzella

Kyle Patrick Schmitt

Benjamin Robertson Yeomans

Andrew L Carbery

Caleb Brian Slaughter Gore

Olivia Mahler-Haug

Cynthia Sandberg

Ryan Carmichael

Harideep Yeddula

Ross Kirk Eley

Matthew Cluck

Geoff Colgan

Caleb Brian Gore

Embodiments may monitor utilization of a machine. A risk relationship data store may contain electronic records that represent a plurality of risk relationships with an enterprise. A device located proximate to the machine includes a plurality of sensors, each sensor configured to monitor at least one parameter (e.g., acceleration, speed, mileage, run-time and location sensors, etc.). A back-end application computer server may calculate an operator behavior score and a machine operation score based on sensor signals. The computer server may also determine a machine characteristics score and an amount of utilization associated with the machine. A risk relationship identifier associated with the machine is identified, and the computer server may automatically and dynamically update a resource amount associated with the risk relationship identifier based on the operator behavior score, the machine operation score, the machine characteristics score, and the amount of utilization associated with the machine.

Systems and methods to remotely monitor machine usage

A turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Vehicle turn detection

Systems and methods are disclosed for using a hub to automate recovery. One method may include, receiving, a notification of a critical event, first vehicle information of a first vehicle associated with the critical event, and second vehicle information of a second vehicle associated with the critical event; receiving, first damage information related to the critical event; analyzing, the second vehicle information to identify the second vehicle; determining, a second user device associated with the second vehicle; establishing, communication with the second user device; transmitting to the second user device, a request for input wherein the input is second damage information related to the critical event; receiving, the second damage information; comparing the first damage information to the second damage information; generating, a response certainty score based on the comparison; determining, that the response certainty score satisfies a confidence value threshold; and determining, a fault assessment for the critical event.

Hub for automated recovery after a critical event in shared mobility services

Aspects of the disclosure relate to computing platforms that utilize improved natural language processing techniques for claims processing. A computing platform may receive an audio file and determine one or more utterance segments based on the audio file. The computing platform may generate and send one or more audio chunks based on the one or more utterance segments to an audio transcription system. The computing platform may receive one or more text segments in response and may generate an audio transcription file accordingly. The computing platform may assign a category to each word in the audio transcription file, and may send user interface information to a user device along with one or more commands directing the user device to generate a user interface based on the user interface information, which may cause the user device to display the user interface.

Natural language processing platform for automated event analysis, translation, and transcription verification

Systems, methods, and computer-readable media, are disclosed in which a variety of data describing the condition of an object can be obtained and probabilistic likelihoods of causes and/or value of damages to the object can be calculated. In a variety of embodiments, data obtained from third-party systems can be utilized in these calculations. Any of a number of machine classifiers can be utilized to generate the probabilistic likelihoods and confidence metrics in the calculated liabilities. A variety of user interfaces for efficiently obtaining and visualizing the object, the surrounding geographic conditions, and/or the probabilistic likelihoods can further be utilized as appropriate to the requirements of specific applications of embodiments of the invention.

Systems and methods for system generated damage analysis

An turn detection system is configured to determine headings or a course of a vehicle over a period of time and evaluate whether the vehicle has registered a turn based on these headings/course. In some arrangements, upon detecting a turn, sensor data may be collected to determine one or more characteristics or attributes of the turn. Such data may indicate a loss event associated with the turn and be used to calculate a probability or risk of loss given the various characteristics of the turn. These probabilities may further be applied to determine various costs and premiums.

Systems and methods are disclosed for using a hub to automate recovery after a critical event during shared mobility services. One method may include, receiving, by a computing device and from a user device, a notification of a critical event during a shared mobility service; receiving natural language input associated with a first aspect of the critical event; parsing the natural language input and identifying a first value corresponding to the first aspect; determining, based on the first value, a second aspect (e.g., severity of damage) of the critical event; requesting the user device to send images of the second aspect; after receiving the images, determining a second value corresponding to the second aspect; generating, by the computing device and via a user interface of the user device, a representation of the critical event; receiving, from the user device, an approval of the representation; and generating a damage assessment.

Systems and methods are disclosed for detecting vehicular accidents based on sensor data, such as data from a mobile device in a vehicle or data from the vehicle's sensors. If an accident is detected, the system may determine whether to collect sensor data from additional sources, such as additional vehicle sensors or additional mobile devices (e.g., a pedestrian's mobile device or a mobile device within a vehicle). The system may also determine whether to collect data from traffic devices or from contextual data sources. The data may be used by the system to determine which party is at fault in the accident. The data may also be used to generate a visualization of the accident.

Accident fault detection based on multiple sensor devices

One or more devices in a data analysis computing system may be configured to receive and analyze movement data and determine driving trips based on the received data. The driving trips may be used along with the movement data to authenticate drivers based on a determined driver profile.

Authenticating drivers

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

One or more mobile device movement detection computing devices and methods are disclosed herein based on acceleration data collected from an accelerometer of a mobile device found within an interior of a vehicle. The mobile device movement detection computing devices may identify a likely mobile device movement event based on a change of angle between two three-dimensional acceleration vectors. Where the mobile device movement detection computing devices detect a likely mobile device movement event, sensor data from various sensors of a mobile device are collected and aggregated for a window of time encompassing the mobile device movement event. Data from vehicle sensors and other external systems may also be used. The mobile device movement detection computing devices calculate a risk score based on the aggregates sensor data, and provide feedback to a mobile device or vehicle based on the calculated risk score.

Growing community of inventors

Lake Forest, IL, United States of America

Connor Walsh