Blueowl, LLC

Quanata, LLC

Vinay Kumar

Kenneth Jason Sanchez

Theobolt N Leung

Holger Struppek

Scott Howard

John Minichiello

Systems and methods for using collecting and analyzing device sensor data to determine whether an individual is an operator or a passenger of a vehicle are disclosed. According to certain aspects, an electronic device associated with the individual may collect or access sensor data that is indicative of or associated with an operation of the vehicle. The electronic device may transmit pertinent portion(s) of the sensor data to a backend server, which may input the portion(s) into a neural network for analysis. The neural network may output a probability metric(s) indicative of whether the individual is a passenger or an operator of the vehicle.

Technology for analyzing sensor data to detect configurations of vehicle operation

A video analytics based image verification system for obtaining initial vehicle profiles is presented. The system may include an external processing server that receives a location of a vehicle and proximate traffic information to determine whether it is safe for a user to obtain an initial vehicle profile. The external processing server may further determine first and second profile features from video data indicative of the vehicle. The external processing server may compare the second profile feature to an image verification indicator to generate an image verification score. A provider server may receive the first profile feature and the image verification score from the external processing server, and update a risk evaluation to include the initial vehicle profile if the image verification score is above an image verification threshold.

Systems and methods for fraud prevention based on video analytics

A telematics analysis (TA) computing device including a processor in communication with a memory device for monitoring driving behavior of a driver of a vehicle may be provided. The processor may be configured to: (i) aggregate historical location data and historical telematics data from a plurality of users, (ii) generate mapping data based at least in part upon the historical location data and the historical telematics data, (iii) identify one or more stop locations based at least in part upon the mapping data, (iv) store the one or more stop locations, (v) receive current location data and current telematics data after each trip taken by the driver, (vi) compare the current location data and the current telematics data to the one or more stop locations, (vii) generate stop data associated with the driver for each trip taken by the driver, wherein the stop data includes whether the driver stopped at the one or more stop locations during each trip, and (viii) determine one or more driving behaviors of the driver based at least in part upon the stop data.

Systems and methods for detecting full-stops to reduce vehicle accidents

Systems and methods for analyzing documentation for assessing potential fraudulent user submissions are provided. According to certain aspects, a server computer may receive an initial set of documentation descriptive of damage to a property asset, and may analyze the initial set of documentation to determine whether additional documentation is needed. The server computer may initiate a communication channel with a user device via which the additional documentation may be submitted, and the server computer may similarly analyze the additional documentation to determine a likelihood of fraud. The server computer may process the user submission accordingly.

Systems and methods for obtaining incident information to reduce fraud

A gate-based vehicle image capture system for analyzing vehicle image data is presented. The system may include a portable imaging gate apparatus configured to capture vehicle image data of a vehicle. The portable imaging gate apparatus may include a plurality of imaging assemblies positioned at a plurality of viewing angles. The system may further include an external processing server configured to receive the vehicle image data from the portable imaging gate apparatus. The external processing server may also analyze the vehicle image data to identify a plurality of vehicle features, and determine a first vehicle feature from the plurality of vehicle features. The first vehicle feature may be related to a vehicle incident. The system may also include a provider server configured to receive the first vehicle feature from the external processing server, and update an aspect of a risk evaluation based on the first vehicle feature.

Systems and methods for gate-based vehicle image capture

A computer system for assessing sound to analyze a user's sleep includes a processor configured to perform operations including: (i) storing sample sound data associated with a plurality of sample sleep events, the sample sound data including a plurality of sample characteristics each respectively associated with at least one sample sleep event of the plurality of sample sleep events; (ii) receiving, from a client device, subject sound data collected during a sleep interval; (iii) analyzing, using a machine learning algorithm, the subject sound data collected during the sleep interval; (iv) identifying, based upon the analyzing, a subject characteristic associated with the subject sound data; (v) comparing the subject characteristic with the plurality of sample characteristics; and (vi) determining, based upon the comparing, whether the subject characteristic substantially matches at least one sample characteristic to identify one or more subject sleep events occurring during the sleep interval.

Audio assessment for analyzing sleep trends using machine learning techniques

A computer system for assessing sound within a vehicle to alert a driver of the vehicle to an approaching emergency services vehicle includes a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (i) analyzing, based upon a machine learning algorithm, sound data associated with an emergency services vehicle siren; (ii) identifying, based upon the analyzing, a first characteristic associated with the emergency services vehicle siren; (iii) receiving, from a client device disposed within the vehicle, sound data collected within the vehicle; (iv) analyzing the sound data collected within the vehicle; (v) identifying, based upon the analyzing, a second characteristic associated with the sound data collected within the vehicle; (vi) comparing the first characteristic to the second characteristic; and (vii) determining, based upon the comparing, whether the first characteristic substantially matches the second characteristic to determine whether the emergency services vehicle is approaching the vehicle.

Systems and methods for assessing sound within a vehicle using machine learning techniques

A computer system may include at least one processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the at least one processor, cause the processor to perform operations including: (i) receiving, from a client device associated with a user within a user environment, sound data collected from the user environment; (ii) analyzing the sound data collected from the user environment; (iii) identifying, based upon the analyzing, a noise characteristic associated with the sound data and a duration of the sound data; (iv) comparing the noise characteristic and the duration to a predetermined noise threshold; (v) determining whether the sound data represents unsafe noise conditions based upon the comparison; and/or (vi) notifying the client device when the sound data is determined to represent unsafe noise conditions. The client device may generate an audible, haptic, and/or visible alert in response.

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Fremont, CA, United States of America

Vinay Kumar