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: Method and system for generating a hybrid epoch score for a user. In some examples, a computer-implemented method includes: receiving, from at least one of a mobile device associated with the user and a server, prior telematics data indicative of the operation of a vehicle by the user during one or more prior trips in a prior epoch; receiving, from the mobile device, recent telematics data indicative of the operation of the vehicle by the user during one or more recent trips in a current epoch; generating a prior epoch score based at least in part upon the prior telematics data; generating a partial current epoch score based at least in part upon the recent telematics data; and generating a hybrid current epoch score based at least in part upon the prior epoch score and the partial current epoch score.

Abstract: Method and system for validating whether a user of a mobile device is a vehicle driver. For example, the method includes receiving telematics data and device interaction data generated by the mobile device during a vehicle trip, analyzing the telematics data and the device interaction data to determine driving instances in which the user interacts with the mobile device during the vehicle trip, determining whether the user of the mobile device is the driver of the vehicle during the vehicle trip based on the driving instances by using a classification technique, receiving positioning data associated with the mobile device within the vehicle during the vehicle trip, and validating the classification technique based on the positioning data and whether the user of the mobile device is determined to be the driver of the vehicle.

Abstract: Method and system for updating a character profile of a virtual character of a telematics-based game. In some examples, a computer-implemented method includes: generating a virtual trip including one or more virtual occurrences with the associated one or more outcomes; presenting a trip success prediction, a predicted change in vehicle condition, a first user-selectable command, and a second user-selectable command to the user; upon receiving the user's selection of the first user-selectable command, updating the character profile by at least initiating the virtual trip with the virtual character; upon receiving the user's selection of the second user-selectable command: updating the one or more outcomes according to a predetermined adjustment; and updating the character profile by at least initiating the virtual trip with the virtual character based on the updated one or more outcomes.

Abstract: A computing system for generating a list of items predicted to be associated with a candidate user may be provided. The computing system may include a processor in communication with a memory device, and the processor may be configured to generate a predictive possession model, receive personal data associated with the candidate user, predict a first set of items owned by the candidate user, assign a value and a range of predesignated values for each item included in the first set of items, cause the first set of items and their corresponding values to be displayed on a user device of the candidate user, and prompt the candidate user to input confirmation data including one of a confirmation that the first set of items accurately describes a set of actual items possessed by the candidate user and a confirmation that the values corresponding to the first set of items are satisfactory.

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 smart ring includes a body including flexible material, a first part, a second part removably connected to the first part, and at least one pair of break-away portions disposed within the body separate from the first part and the second part. One or more of a battery, a charging unit, a processor unit, a user input unit, a communication unit, a memory unit, at least one sensor unit, an output unit or a user input unit is disposed in or on one of the first part and the second part. Each of the break-away portions of the pair of break-away portions is removable from the other break-away portion upon movement of one break-away portion in a direction away from the other break-away portion.

Abstract: A system and method captures various gestures or movements made by a driver of a vehicle while driving. Based on the captured gestures or movements, the system and method determines driver gesture behaviors which relate to the driver's driving behaviors. The system and method then performs risk assessment by correlating the driver gesture behaviors with potential recognizable losses involving the driver and/or vehicle. Based on the risk assessment, the system and method assigns ratings for the driver which may be used for insurance rating purposes.

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: 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 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: 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: Method and system for updating a character profile of a virtual character of a telematics-based game. In some examples, a computer-implemented method includes: generating a virtual trip including one or more virtual occurrences with the associated one or more outcomes; presenting a trip success prediction, a predicted change in vehicle condition, a first user-selectable command, and a second user-selectable command to the user; upon receiving the user's selection of the first user-selectable command, updating the character profile by at least initiating the virtual trip with the virtual character; upon receiving the user's selection of the second user-selectable command: updating the one or more outcomes according to a predetermined adjustment; and updating the character profile by at least initiating the virtual trip with the virtual character based on the updated one or more outcomes.

Abstract: A smart ring may be configured with a removable power source and an internal power source. The internal power source may enable continued operation of the smart ring disposed at a finger of a user, while the removable power source may be detached from the smart ring for charging or to be replaced with another charged removable power source. The removable power source may be disposed as a platform at an outer surface of a band-shaped housing of the smart ring, for example, via an adapter.

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: Method and system for generating virtual experiences. For example, the method includes receiving first real-world telematics data associated with a first real-world user, determining first real-world driving characteristics based upon the first real-world telematics data, generating first virtual experiences based upon the first real-world driving characteristics, presenting the first virtual experiences in a virtual game, receiving second real-world telematics data associated with a second real-world user, determining second real-world driving characteristics based upon the second real-world telematics data, generating second virtual experiences based upon the second real-world driving characteristics, and presenting the second virtual experiences in the virtual game, where the first virtual experiences are different from the second virtual experiences.

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 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: Method and system for detecting potential fraudulent activities in vehicle insurance claims. For example, the method includes receiving, from a mobile computing device, a claim request comprising claims data, receiving, from the mobile computing device, a digital file comprising first images of a vehicle involved in an accident supporting the claim request, extracting metadata from the first images, comparing the metadata with the claims data, generating an assessment of the claim request based at least in part upon the comparing, and displaying the assessment of the claim request via a user interface.

Abstract: Method and system for generating virtual characters. For example, the method includes receiving first real-world telematics data associated with a first real-world user, determining first real-world driving characteristics based upon the first real-world telematics data, determining first virtual characteristics based upon the first real-world driving characteristics, generating and presenting the first virtual character in a virtual game, receiving second real-world telematics data associated with a second real-world user, determining second real-world driving characteristics based upon the second real-world telematics data, determining second virtual characteristics based upon the second real-world driving characteristics, and generating and presenting the second virtual character in the virtual game, where the first and second virtual characters are generated with different virtual characteristics.