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.

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 determining the plurality of validation times based at least in part upon the action time associated with each validation action of the one or more validation actions by at least one of: minimizing the amount of validation times of the plurality of validation times; and maximizing the amount of validation actions contained within each validation time of the plurality of validation times on average across the plurality of validation threads.

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 light emitting diode (LED) 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 method and system is provided for identifying local businesses visited by a user. Location and movement data for a user is analyzed to identify points of interest (POIs) visited by a user, and POIs which were not visited while the user travelled to the visited POIs. The POIs are categorized as corresponding to local businesses or national/global businesses, and local business metrics are generated according to frequencies in which the user visits local businesses and/or passes over national/global businesses in favor of local businesses. Then the local business metrics are presented on the user's client computing device along with a heat map indicating frequencies in which the user visited businesses within a geographic area, and/or a cool map indicating frequencies in which the user passed by businesses within the geographic area.

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: Telematics systems and methods are described for generating interactive animated guided user interfaces (GUIs). A telematics cloud platform is configured to receive vehicular telematics data from a telematics device onboard a vehicle. A GUI value compression component determines, based on the vehicular telematics data, a plurality of GUI position values and a plurality of corresponding GUI time values. A geospatial animation app receives the plurality of GUI position values and the plurality of corresponding GUI time values. The geospatial animation app implements an interactive animated GUI that renders a plurality of geospatial graphics or graphical routes on a geographic area map via a display device. The geospatial graphics or graphical routes are rendered to have different visual forms based on differences between respective GUI position values and corresponding GUI time values.

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 generating scenic routes and safe parking locations near scenic points are provided. Based at least in part upon a driver indication of a preference for a scenic route from an origin location to a destination location, a mobile device application may generate a scenic route by selecting route segments between the origin location and the destination location associated with a large number of scenic points. Scenic points may be identified based at least in part upon historical driver telemetry data and POI data. For instance, if historical telemetry data indicates that a large number of drivers stop briefly at a location not known to be near a POI, that location may be a scenic location (e.g., a location where drivers frequently leave their vehicles to take photos). Additionally, safe parking locations near each scenic point may be identified based at least in part upon historical vehicle damage data, and navigation directions to safe parking locations may be provided.

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.