Blueowl, LLC

Quanata, LLC

Bennett Smith

Kenneth Jason Sanchez

Theobolt N Leung

Mary Piontkowski

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: building, using a machine learning algorithm, a transit model based at least in part upon telematics data associated with a user; generating, based at least in part upon the transit model, a dynamic transit route for the user; calculating, based at least in part upon the telematics data associated with the user, a potential benefit comprising an amount of fuel cost savings for the user, reduced travel time for the user, insurance savings, or environmental pollution reduction for the user; generating a notification comprising the dynamic transit route and the potential benefit; and transmitting the notification to a mobile device of the user. Other embodiments are described.

Systems and methods for generating dynamic transit routes

A system for analyzing wireless telematics data to determine that a vehicle is engaged in an extended period of travel and prompt additional devices within the vehicle regarding the extended period of travel. The system may comprise a telematics server and a plurality of mobile applications to be implemented on a plurality of telematics devices belonging to a plurality of users. The plurality of telematics devices transmit telematics data to the telematics server. The data is analyzed and the telematics server transmits an indication to the telematics devices, causing the mobile applications to execute in a driver application mode or passenger application mode. Messages containing notifications to change drivers or pull over are sent to the telematics devices.

Systems and methods for analyzing wireless telematics data of a vehicle engaged in an extended period of travel

A computer system for generating a public transportation schedule for users is provided. The computer system includes a processor in communication with a memory device, and the processor may be programmed to: (i) receive telematics data associated with a user, wherein the telematics data include location data associated with the user, and wherein the telematics data are received from at least one of a mobile device associated with the user, a vehicle associated with the user, and a public transportation vehicle utilized by the user, (ii) generate, based at least in part upon the telematics data, one or more travel patterns associated with the user, (iii) retrieve one or more public transportation routes from the memory device, wherein the one or more public transportation routes are associated with a location associated with the user, (iv) compare the one or more travel patterns and the one or more public transportation routes, and/or (v) generate a public transportation schedule for the user based at least in part upon the compared one or more travel patterns and the one or more public transportation routes.

Systems and methods for creating personalized public transit guides

A user computing device for identifying a driver of a vehicle on a trip is provided. The user computing device is associated with a first vehicle occupant, and is programmed to: (i) detect a second user computing device associated with a second vehicle occupant, (ii) initiate a ping exchange process including emitting a set of non-audible sonic ping signals and detecting a set of signals from the second user computing device over a duration of the trip, (iii) generate a relative positioning map of the user computing device with respect to the second user computing device, (iv) determine that the first vehicle occupant is one of a driver and a passenger of the vehicle, and (v) transmit, to a driver identification ('DI') server, a trip report including the determination and the generated relative positioning map.

Systems and methods for determining a vehicle driver using at least peer-to-peer network signals

Systems and methods are provided for determining a health indicator, such as a life expectancy, of a target person using a proxy model comprising an artificial neural network, in lieu of more complex, costly, and/or invasive health assessment processes. The artificial neural network may be constructed from labeled training data comprising a plurality of activity data training sets, each set including personal activity metrics associated with activities performed by a respective training person as derived from sensor data obtained via one or more mobile electronic devices (e.g., a smartphone or a wearable device) of the respective training person. The trained artificial neural network may then obtain and process an activity data set associated with the target person to determine a health indicator of the target person.

Proxy model using mobile device data to provide health indicators

The present embodiments may relate to generating dynamic transit routes based at least in part upon telematics data of users. For instance, a transit analysis (TA) computing device may be configured to: (1) receive telematics data from the plurality of user mobile devices, each user mobile device of the plurality of user mobile devices corresponding to one user identifier of a plurality of user identifiers; (2) build, for each user identifier of the plurality of user identifiers, a transit model; (3) generate, for each user identifier of the plurality of user identifiers, based at least in part upon the transit model associated with the user identifier, one or more transit predictions; and/or (4) generate, based at least in part upon the one or more transit predictions, for each user identifier of the plurality of user identifiers, a dynamic transit route.

Technology for identifying potentially fraudulent user behavior related to the capture and transmission of vehicle trip telemetry data is provided. Automatic and periodic attempts may be made to wake up (e.g., launch, or change to an active mode) an application installed on a mobile device configured to transmit mobile device data, including captured sensor data, to a remote computer device. Based on the sensor data, the remote computer device may determine a number, frequency, and/or duration of vehicle trips associated with the user of the mobile device. When one or more types of expected mobile device data are not received by the remote computer device after a particular attempt to wake the application, data indicating a failure to receive expected mobile device data may be recorded in a log. Based on the log, an alert indicating potentially fraudulent behavior associated with the user of the mobile device may be generated.

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Inver Grove Heights, MN, United States of America

Bennett Smith