Abstract: A computer system for creating driving tournaments for users including a processor in communication with a memory device is provided. The processor may be programmed to: i) receive user data associated with a plurality of users, ii) generate, based at least in part upon the received user data, a driving profile including driving characteristics of each user, iii) match users based at least in part upon the driving profiles of the users, wherein a match occurs when driving characteristics of the driving profiles are substantially similar, iv) create a driving tournament for the matched one or more users, v) receive current vehicle telematics data and current mobile device data associated with the matched one or more users, and vi) determine, after the predetermined time period has ended, a winner of the driving tournament.

Abstract: Systems and methods for reducing vehicle collisions based on driver risk groups are provided. A plurality of vehicle operators may be classified into a driver risk group based on one or more attributes (e.g., location, workplace, school, demographic, hobby, interest, etc.) shared by the plurality of vehicle operators. Vehicle sensor data (e.g., speed data, acceleration data, braking data, cornering data, following distance data, turn signal data, seatbelt use data, etc.) associated with each of the plurality of vehicle operators of the driver risk group may be analyzed. Based on the analysis of the vehicle sensor data, one or more indicia of safe driving behavior associated with the driver risk group may be identified. In response to a third-party query regarding a vehicle operator in the driver risk group, an indication of the safe driving behavior associated with the driver risk group may be provided to the third party.

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: Systems, methods, and techniques for assessing driver safety based on noise levels or sounds associated with the interior of a vehicle are provided. Noise levels or sounds associated with the interior of a vehicle over a first interval of time may be compared to indications of vehicle motion over the first interval of time to identify noise levels or sounds at times when the indications of vehicle motion indicate unsafe driving. The identified noise levels or sounds may be analyzed to determine noise levels, sources, and/or characteristics of sounds associated with unsafe driving. Noise levels or sounds associated with the interior of the vehicle over a second interval of time may be analyzed to determine instances in which the noise levels, sources, and/or characteristics of sounds associated with unsafe driving occur over the second interval of time. Alerts may be generated for vehicle operators based on the determined instances.

Abstract: Methods and systems for improving vehicular safety by utilizing uplift modeling techniques to improve a driving tip treatment generation model are provided. According to embodiments, a tip server can analyze telematics data associated with operation of one or more vehicles to determine that a driving tip should be provided to a driver of a vehicle. The tip server then utilize a treatment generation model to determine a treatment for how to provide the driving tip in a manner optimized for the particular driver. The tip server can analyze additional telematics data to determine an effectiveness of the driving tip and to update the treatment generation model in accordance with uplift modeling techniques.

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 an electronic ink (e-ink) 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: 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: The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed impairment patterns. A smart ring, wearable on a user's finger, continuously monitors impairment levels. This impairment data, representing impairment patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed impairment patterns. The user can be warned of this risk to prevent them from driving or to encourage them to delay driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while the user is in a state of impairment induced by substance intoxication.

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 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.

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 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, each driving event record including phone usage by a user, wherein a driving event record is labeled as an actual distracted driving event or a passenger event, (ii) divide the driving event records into at least two clusters based at least in part upon common features and the labels of each driving event record by processing the plurality of driving event records with a semi-supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the clusters using the trained model, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

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 generating virtual encounters.

Abstract: Method and system for validating a plurality of policies. For example, a computer-implemented method includes receiving a plurality of policies, each policy of the plurality of policies associated with a policy lifecycle including one or more validation actions, each validation action of the one or more validation actions being associated with an action time; mapping the one or more validation actions associated with each policy of the plurality of policies on a common timeline of a shared clock; and parallelly validating the plurality of policies by at least: moving the shared clock forward in time through a plurality of validation times; and executing the one or more validation actions associated with the plurality of policies at the plurality of validation times.

Abstract: A computer-implemented method for detecting medical emergency events may include, via one or more processors, data sensors, and/or transceivers: (1) obtaining sensor data indicative of kinetic actions of a user; (2) analyzing the sensor data to associate the sensor data with a one or more kinetic actions of the user; (3) comparing the one or more kinetic actions of the user with a model of kinetic actions to determine whether the one or more kinetic actions correspond with the model of kinetic actions, with the model being indicative of a medical emergency event; and (4) upon determining that the one or more kinetic actions correspond with the model, contacting medical emergency responders to request medical emergency services for the user. As such, medical emergency events being experienced by a user can be detected, and medical emergency responders may be quickly contacted to provide essential emergency medical services to the individual.

Abstract: A computer-implemented method for detecting medical emergency events may include, via one or more processors, data sensors, and/or transceivers: (1) obtaining sensor data indicative of kinetic actions of a user; (2) analyzing the sensor data to associate the sensor data with a one or more kinetic actions of the user; (3) comparing the one or more kinetic actions of the user with a model of kinetic actions to determine whether the one or more kinetic actions correspond with the model of kinetic actions, with the model being indicative of a medical emergency event; and (4) upon determining that the one or more kinetic actions correspond with the model, contacting medical emergency responders to request medical emergency services for the user. As such, medical emergency events being experienced by a user can be detected, and medical emergency responders may be quickly contacted to provide essential emergency medical services to the individual.

Abstract: A computer system for determining personalized loss event valuations is provided. The computer system includes one processor in communication with at least one memory device, and the at least one processor is programmed to: (i) receive user data from the user computer device, wherein the user data include data related to a user's personal information and personal property, (ii) utilize a trained machine learning model to determine a first loss valuation associated with a first loss event based at least in part upon the user data, (iii) utilize the trained machine learning model to determine a second loss valuation associated with a second loss event based at least in part upon the user data, wherein the first loss event and the second loss event are different loss events, and/or (iv) generate a loss event policy that includes the first loss valuation and the second loss valuation.

Abstract: The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed UVB exposure patterns, where the driver's UVB exposure levels may serve as a proxy for vitamin D levels in that driver's body. A smart ring, wearable on a user's finger, continuously monitors user's exposure to UVB light. This UVB exposure data, representing UVB exposure patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed UVB exposure patterns. The user can be warned of this risk to prevent them from driving or to encourage them to get more sunlight exposure before driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while exposed to high risk due to deteriorated psychological or physiological conditions stemming from insufficient UVB exposure.

Abstract: A computing system including a processor in communication with a memory device for generating a predicted one or more values of personal property items associated with a candidate user enrolling in an insurance policy may be provided.