Abstract: A computer-implemented method includes generating one or more virtual occurrences to be encountered by the virtual character. The computer implemented method further includes determining one or more virtual outcomes associated with the one or more virtual occurrences. The computer implemented method further includes generating a virtual trip comprising the one or more virtual occurrences with the one or more virtual outcomes associated with the one or more virtual occurrences. The computer implemented method further includes determining a predicted change in a vehicle condition of a virtual vehicle. The computer implemented method further includes updating the character profile of the virtual character. The computer implemented method further includes transmitting for display, the character profile, as updated, and the vehicle condition, as updated, to an electronic device of a user. Other embodiments are disclosed.

Abstract: Systems and methods for using collecting and analyzing device sensor data to determine whether an individual is an operator or a passenger of a vehicle are disclosed. According to certain aspects, an electronic device associated with the individual may collect or access sensor data that is indicative of or associated with an operation of the vehicle. The electronic device may transmit pertinent portion(s) of the sensor data to a backend server, which may input the portion(s) into a neural network for analysis. The neural network may output a probability metric(s) indicative of whether the individual is a passenger or an operator of the vehicle.

Abstract: Method and system for training a virtual character of a telematics-based game. In some examples, a computer-implemented method includes: initiating a virtual trip including virtual occurrences to be encountered by the virtual character; receiving, in real-time, telematics data associated with the real trip; determining, based on the telematics data, one or more real obstacles encountered by the user during the real trip; determining, based on the telematics data, one or more performances indicative of how proficient the user operated the real vehicle upon encountering the one or more real obstacles; determining, based on the one or more performances, one or more skill points associated with a plurality of real skills; training the virtual character by at least updating, based on the plurality of skill points, the plurality of virtual ratings; updating, based on the updated plurality of virtual ratings, the one or more outcomes.

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: 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: 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: 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 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: 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 ring can include a housing. The housing can include a first power source configured to be charged by a second power source removably positioned adjacent to the housing. The housing also can include a component. The component can be configured to draw energy from the first power source. The component also can sense a physical phenomenon external to the housing. The component further can send communication signals to a communication device. Other embodiments are disclosed.

Abstract: A method can include detecting an amount of a force applied to a brake of a vehicle. The method can also include, after detecting the force applied to the brake of the vehicle, illuminating one or more light emitting devices of the vehicle in a manner indicative of the amount of the force detected to have been applied to the brake of the vehicle. Other embodiments are described.

Abstract: Method, system, device, and non-transitory computer-readable medium for adjusting a dynamic discount. In one aspect, a computer-implemented method includes: receiving an enrollment request from a user associated with a vehicle for a vehicle insurance policy; issuing an initial discount to the user for the vehicle insurance policy at a first time; collecting trip data associated with a set of vehicular trips operated by the user with the vehicle, the trip data including telematics data and travel condition data; and dynamically updating the initial discount based at least in part upon the trip data as trip data are collected.

Abstract: Method and system for determining which mobile device among multiple mobile devices is used by a vehicle driver. For example, the method includes receiving first telematics data and first device interaction data generated by a first mobile device, receiving second telematics data and second device interaction data generated by a second mobile device, analyzing the first telematics data and the first device interaction data to determine whether a first user is interacting with the first mobile device, analyzing the second telematics data and the second device interaction data to determine whether a second user is interacting with the second mobile device, determining whether the first user or the second user is the vehicle driver by using a classification technique, and calibrating the classification technique based on whether the first user or the second user has been determined to be the vehicle driver.

Abstract: Techniques are disclosed to calculate a “life script.” The life script may include various behaviors and/or monetary allocations required for a user to meet one or more user-defined objectives. These objectives may indicate various lifestyle habits desired by the user such as where to live, a maximum commuting time to work, how often to travel, etc. Because each of these objectives requires the user to allocate his funds in a certain way and to live in accordance with a particular set of restraints, user information may be collected and aggregated from several sources to generate a user profile indicating the user's financial information, demographics, psychographics, and lifestyle information. The user profile may then be analyzed to calculate a constraint framework that guides the generation of a life script to allow the user to achieve the desired objectives.

Abstract: A method can comprise receiving location data and movement data of the user. The method can further comprise identifying one or more points of interest (POIs) comprising one or more local businesses. The method can also comprise generating one or more local business metrics for the one or more local businesses. The method can further comprise generating a map overlayed with one or more indicators identifying one or more local business frequencies for the one or more local businesses visited. Additionally, the method can comprise transmitting, for display on an electronic device of the user, the one or more local business metrics and the map with the one or more indicators. Other embodiments are described.

Abstract: A computer-implemented method includes: receiving a set of unlabeled telematics data; identifying a first set of baseline operation features; identifying a second set of baseline operation features; identifying a set of representative operation features associated with the unlabeled vehicular trip; generating a first set of baseline operation feature vectors, a second set of baseline operation feature vectors, and a set of representative operation feature vectors; classifying the unlabeled vehicular trip as a work trip when the set of representative operation feature vectors deviate from the first set of baseline operation feature vectors by less than a first deviation threshold; and classifying the unlabeled vehicular trip as a personal trip when the set of representative operation feature vectors deviates from the second set of baseline operation feature vectors by less than a second deviation threshold. Other embodiments are described.

Abstract: A method for implementing a machine learning model to predict risk exposure can include acquiring, via a sleep detecting device associated with a user, a set of user data. The method for implementing the machine learning model can also include predicting, by at least a trained ML model, a level of risk exposure for an other activity for the user. The trained ML model can be trained utilizing data indicative of one or more sleep patterns and data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity. The method can further include generating a notification to alert the user of the level of risk exposure, as predicted, for the other activity. Other embodiments are disclosed.

Abstract: A computer-implemented method for training a virtual character, the computer-implemented method: receiving telematics data associated with one or more real trips; determining, based at least in part upon the telematics data, skill points associated with real skills detected during the one or more real trips; generating, based at least in part upon a virtual character profile for the virtual character, one or more virtual occurrences to be encountered by the virtual character; determining, based at least in part upon virtual ratings associated with the skill points and the virtual character profile, one or more virtual outcomes associated with the one or more virtual occurrences; updating the virtual character profile based at least in part upon the one or more virtual outcomes; and providing a reward based upon the one or more virtual outcomes. Other descriptions are included.

Abstract: A computer-implemented method for identifying occupants of a vehicle, the computer-implemented method including: receiving, using a sensor associated with a first user device of a first vehicle occupant in the vehicle, an echo signal associated with a first ping signal emitted by the first user device; generating a first relative positioning map based upon the echo signal; and determining that the first vehicle occupant is a driver or a passenger of the vehicle based at least in part upon the first relative positioning map.

Abstract: Method and system for transforming telematics data for agnostic use. For example, a computer-implemented method includes: receiving, from a first telematics system of a first type, telematics data in a first form, the telematics data being associated with a vehicle; and transforming, via a prediction model, the telematics data into a first prediction in a second form, the second form being: different from the first form, and the form which data collected, processed, stored, or transmitted by a second telematics system of a second type would be, the second type being different from the first type.