Abstract: Systems and methods are provided for providing recommendations of safe driving routes that are tailored to the driving habits of particular drivers. A machine learning model (e.g., an artificial neural network) may be trained using data indicative of insurance claim severity, road conditions, and/or vehicle telematics data associated with vehicle-related incidents, such as vehicle collisions. The machine learning model may be trained to identify road types and conditions that are predictive of claim frequency and severity. Any given driving route(s) may be provided to the trained machine learning model, and a risk value may be computed for the route(s). By further applying a personalized driver profile to the calculations of risk, personalized risk values may be computed for the route(s), and a safest route may be recommended to a driver.

Abstract: A user's credit score is determined based at least in part upon collected telematics data and user data associated with the user. A credit score prediction model is created using training datasets of historical data including demographics data, driver profile data, and established credit scores. The credit score prediction model is then used to predict credit scores for users without formal credit scores. A user's credit score is predicted based at least in part upon a driver profile built using telematics data, among other factors.

Abstract: A device for facilitating event processing is presented. The device includes a transceiver, one or more memories including an event processing model, and one or more processors. The processors retrieve a new event from an application programming interface (API). The processors then parse the new event to determine a parsed event and store the parsed event in the memories. After determining the presence or absence of an additional new event via the API, the processors transmit a notification for display to a user indicating the parsed event and prompting a user to process the parsed event, resulting in either a successful or a failure processing signal. Responsive to receiving a failure processing signal, the processors schedule a subsequent notification for transmission to the user device according to an event processing model by using an exponential back-off and a random jitter.

Abstract: A smart ring charging system comprises a charging source integrated into an object, the object configured to be held by a user wearing a smart ring. The smart ring may include a ring-shaped housing and a power source disposed within or at the ring-shaped housing and configured to receive energy from the charging source while the user is wearing the smart ring and holding the object. A controller may be disposed within or at the ring-shaped housing, or at the object with the charger and configured to estimate a charging rate at which the power source receives the energy from the charging source. The system may further include one or more indicators configured to indicate the charging rate.

Abstract: 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.

Abstract: Systems and methods of incentivizing the use of a safety device while operating a vehicle are provided. Data captured by sensors associated with a safety device may be analyzed to identify instances in which the safety device is used by an operator of a vehicle. Additionally, data captured by sensors associated with the vehicle may be analyzed to identify instances in which the vehicle is in motion. By comparing the instances in which the safety device is used by the operator of the vehicle to the instances in which the vehicle is in motion, instances in which the operator of the vehicle uses the safety device while operating the vehicle may be determined. Furthermore, a trend of the operator of the vehicle using the safety device while operating the vehicle may be identified. A notification related to the identified trend may be provided to the operator of the vehicle.

Abstract: Systems and methods for providing continuous safe-driver training safely are provided. A safe-driving challenge may be presented to an operator of a vehicle. Data captured by sensors associated with the vehicle may be analyzed to determine whether the operator of the vehicle has completed the safe-driving challenge. Based on a determination that the operator of the vehicle has completed the safe-driving challenge, a notification may be provided to the operator (e.g., indicating to the operator that he or she has successfully completed the challenge). A processor may randomly select whether a reward is to be provided to the operator of the vehicle based on the determination that the operator of the vehicle has completed the safe-driving challenge. Moreover, if a reward is to be provided to the operator of the vehicle, a processor may randomly select a type of reward to be provided to the operator of the vehicle.

Abstract: A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device, and the processor is programmed to: (i) receive labeled training data, the labeled training data including driving event records (a) each labeled as an actual distracted driving event or a passenger event and (b) including phone usage by a user that occurred within a time period of a driving event, (ii) identify common features of the actual distracted driving events and the passenger events by processing the training data using a supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the identified common features, (iv) process a new driving event, (v) assign the new driving event based at least in part upon features of the new driving event, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

Abstract: A system for one-click two-factor 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) receiving an access request from a user, the access request including a first authentication factor; (ii) generating a second authentication factor and a hyperlink that includes the second authentication factor; (iii) providing the hyperlink that includes the second authentication factor to a client device associated with the user; (iv) automatically receiving the second authentication factor in response to selection of the hyperlink by the user; and (v) verifying the first authentication factor and the second authentication factor to authenticate the identity of the user.

Abstract: A system for displaying information indicative of driving conditions, to a driver, using a smart ring are disclosed. An exemplary system includes a smart ring with a ring band having a plurality of surfaces including an inner surface, an outer surface, a first side surface, and a second side surface. The system further includes a processor, configured to obtain data from a communication module within the ring band, or from one or more sensors disposed within the ring band. The obtained data are representative of information indicative of one or more driving conditions to be displayed to the driver. The smart ring also includes a projector module display disposed on at least one of the plurality of surfaces, and configured to present information indicative of the one or more driving conditions.

Abstract: Provided herein is a computer system for creating driving challenges for drivers. The computer system may include a processor in communication with a memory device, and the processor may be programmed to: (i) receive driving data associated with a driver, (ii) generate a first model that models the driving data associated with the driver, (iii) calculate a predicted driving score for the driver based at least in part upon the first model, (iv) generate a second model that predicts a confidence of the predicted driving score, (v) calculate a confidence value of the predicted driving score, wherein the confidence value is a squared error of the predicted driving score, and (vi) generate at least one driving challenge for the driver based at least in part upon the predicted driving score and the confidence value for that predicted driving score.

Abstract: Systems and methods for generating calm or quiet routes are provided. When a user requests navigation directions and indicates a preference for a calm route, a plurality of routes between the origin location and the destination location requested by the user may be identified. Historical sensor data (e.g., including heart rate data, exterior vehicle noise level data, accelerometer data) and traffic data associated with route segments of each of the routes may be analyzed to identify indications of calmness associated with each route segment. Current traffic data associated with the route segments may be analyzed to assign a traffic score representing a level of congestion along the segment and corresponding to a level of insurance risk associated with traversing the segment. Based at least in part upon the indications of calmness and the traffic score associated with each route segment, a route may be selected and presented to the user.

Abstract: A computer-implemented method for improving compression of predictive models includes generating an unlabeled simulated data set by expanding an initial data set, and generating a labeled data set by predicting the unlabeled, simulated data set using a complex model to output a plurality of labels. The method also includes training a relatively simple neural network using the labeled data set.

Abstract: A system for displaying contextually-sensitive braking information on a surface of a vehicle is presented. The system may include a transceiver, one or more memories, an electronic display disposed on the surface, and one or more processors. The one or more processors may be configured to detect a braking event of the vehicle, wherein the braking event has an associated braking force. The one or more processors may compare the braking force to a predetermined threshold braking force to determine whether the braking force exceeds the threshold braking force. The one or more processors may further cause the electronic display to display a braking indication having an intensity that is proportional to the braking force, wherein the braking indication may include a braking rationale corresponding to the braking event in response to determining that the braking force exceeds the threshold braking force.

Abstract: A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device programmed to: (i) receive driving event records including phone usage by a user that occurred within a time period of a driving event, (ii) divide the driving event records into at least two clusters based at least in part upon common features of one or more driving event records of the plurality of driving event records by processing the driving event records using an unsupervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters including cluster labels, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the at least two clusters using the trained model, and (vi) based at least in part upon the cluster labels for the assigned cluster, determine whether the new driving event is an actual distracted driving event or a passenger event.

Abstract: 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.

Abstract: Systems and methods of automatically controlling a user's data footprint are provided. Data associated with a user may be analyzed to determine an action the user is preparing to take. Based on the analysis, a potential risk associated with the action the user is preparing to take may be identified. The potential risk associated with the action the user is preparing to take may be, for example, a data security risk, a data privacy risk, a physical risk, a risk of damage to property, and/or a financial risk. A notification indicating the potential risk associated with the action the user is preparing to take may be provided to the user. The notification may include one or more suggestions for mitigating the potential risk associated with the action the user is preparing to take.

Abstract: Method and system for verifying a reliability of sensor data received from a mobile device of a user are disclosed. For example, the method includes receiving first sensor data collected and/or generated by one or more sensors of the mobile device from an application installed on a mobile device of a user at a first time, receiving second sensor data collected and/or generated by the one or more sensors of the mobile device from the application at a second time, determining whether the mobile device is in a vehicle that the user is driving during a time interval based at least upon the first sensor data and the second sensor data, and in response to the mobile device not being in the vehicle that the user is driving during the time interval, transmitting a notification to the mobile device indicating that the application does not work properly.

Abstract: Method and system for determining a status of a mobile device of a user are disclosed. For example, the method includes receiving first sensor data at a first time from an application installed on a mobile device of a user, determining a first location of the mobile device, immediately subsequent to receiving the first sensor data at the first time, receiving second sensor data at a second time from the application, the second time following the first time by a time interval, determining a second location of the mobile device, determining a distance between the first location and the second location, determining whether the distance corresponding to the time interval exceeds a predetermined threshold, and determining whether a trip log indicative of at least one trip during the time interval is received from the application.

Abstract: 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.