Abstract: Systems and methods are provided for automatically changing or updating insurance coverage using a computer device. An insurance analyzer may receive data associated with an insurance user. The insurance analyzer may determine types of insurance coverage available and coverage amounts associated with the received user data. Based on the received data the insurance analyzer may automatically activate insurance coverage based on determined types of insurance available and coverage amounts. The insurance analyzer may also deactivate insurance coverage or change insurance coverage amounts based on determined user activity.

Abstract: Methods, computer-readable media, software, and apparatuses include receiving sensor data from a sensor system associated with a vehicle during operation of the vehicle over a plurality of modes of operation, computing, based on the sensor data, a vehicle fingerprint comprising one or more vehicle characteristics over the plurality of modes of operation, monitoring additional received sensor data from the sensor system during further operation of the vehicle, determining whether an anomaly exists based on comparing the additional received sensor data to the vehicle fingerprint, and based upon determining that an anomaly exists, providing an alert to a communication interface associated with the vehicle.

Abstract: Methods, computer-readable media, software, and apparatuses may collect, in real-time and via an edge-computing device located in a vehicle, vehicle driving event data including data indicative of driving characteristics associated with an operation of the vehicle. The edge-computing device may analyze, based on a machine learning model, characteristics of the vehicle driving event data. The edge-computing device may, based on the machine learning model, determine at least one of: a driving behavior, a driver rating, occurrence of a collision, and vehicle diagnostics, and the information may be displayed via a graphical user interface to a user in the vehicle.

Abstract: Aspects of the disclosure relate to enhanced telematics processing systems with improved third party source data integration features and enhanced customized driving output determinations. A computing platform may receive telematics data and third party source data. The computing platform may enrich the telematics data using the third party source data. After generating the enriched telematics data, the computing platform may use machine learning algorithms and datasets to validate the enriched telematics data. The computing platform may ingest, via a batch ingestion process, the enriched telematics data. For example, the computing platform may store the enriched telematics data and generate additional enriched telematics data until expiration of a predetermined period of time. The computing platform may ingest the enriched telematics data associated with each trip. Once the enriched telematics data has been ingested, the computing platform may generate a standardized common trip format output for each trip.

Abstract: Methods, computer-readable media, software, systems and apparatuses may receive, from a user device, notification of a user enrolling in a privacy incident protection application, receive, from the user device, user account information associated with one or more user accounts of the user, where the user account information includes a plurality of contextual settings, determine a risk footprint associated with the user based on the user account information, monitor the one or more user accounts, receive an indication of an incident based on monitoring the one or more user accounts and based on the risk footprint, and transmit an incident notification to a data server provider associated with the incident. The incident notification may include instructions to perform a mitigation action associated with the incident.

Abstract: One or more devices in an accident detection and recovery computing system may be configured to determine that vehicle accidents have occurred, collect and analyze accident characteristics and other related data, and providing customized accident recovery services. Mobile computing devices, alone or in combination with vehicle-based systems and external devices, may detect accidents or receive accident indication data. After determining that an accident has occurred, mobile computing devices and/or vehicle-based systems may be configured to determine accident characteristics, retrieve vehicle data and vehicle occupant data from one or external servers, determine the damages or potential damages resulting from the accident, and determine one or more accident recovery options or recommendations based on the accident damages.

Abstract: Apparatuses, systems, methods, and computer-readable media are provided for the preemptive logical configuration of vehicle control systems. A vehicle control computer may determine a location of a vehicle. The vehicle control computer may query a historical data source server for historical incident data corresponding to a first vicinity around the first location of the vehicle. Based on the historical incident data, the vehicle control computer may identify one or more driving danger areas in the first vicinity around the first location of the vehicle, wherein each of the one or more driving danger areas are associated with one or more driving hazards. The vehicle control computer may generate a configuration for vehicle operation in the first vicinity around the first location of the vehicle based on the one or more driving danger areas and may update driving logic of the vehicle with the configuration.

Abstract: Systems and methods are provided for obtaining data annotations from a crowdsourced group of individuals. The individuals can be provided with a set of data describing damage to an item and a variety of annotations can be applied to the data. In a variety of embodiments, multiple individuals can review the same claim and a final claim outcome can be determined based on the multiple reviews. In many embodiments, machine classifiers can process the set of data to identify particular features within the data. Scoring data can be generated, based on annotations provided by other individuals and/or machine classifiers that reflects the adjuster's skill at identifying features within the data and annotating the data. Claims can be assigned to individuals based on the score assigned to the individual.

Abstract: A multi-stop route selection system may include a telematics device associated with a vehicle having one or more sensors arranged therein, a mobile device, and a server computer. The server computer may receive driving data of a driver of the vehicle and a vehicle location from the telematics device, determine one or more driving behaviors of the driver based on the driving data, receive data regarding a calendar of the driver from the mobile device, identify a plurality of appointments in the calendar, determine a route comprising multiple destinations for the driver based on the vehicle location, the one or more driving behaviors, and the plurality of appointments, transmit the route to the mobile device, receive a request to add a new destination to the route from the mobile device, generate a modified route comprising the new destination, and transmit the modified route for the driver to the mobile device.

Abstract: A system may receive information indicating a service area of a shared mobility service, calculate information indicating one or more of risk or revenue associated with the provision of shared mobility services in the service area, determine an adjusted service area, wherein the adjusted service area is associated with one or more of a reduction in risk or an increase in revenue, and transmit information indicating the adjusted service area to a device associated with the shared mobility service.

Abstract: Aspects of the disclosure relate to computing platforms that utilize improved machine learning techniques for dynamic device quality evaluation. A computing platform may receive driving data from a mobile device. Using the driving data, the computing platform may compute a plurality of driving metrics, which may include: a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, and a distance between consecutive trips score. By applying a machine learning model to the plurality of driving metrics, the computing platform may compute a device evaluation score, indicating a quality of the driving data received from the mobile device. Based on the device evaluation score, the computing platform may set flags, which may be accessible by a driver score generation platform, causing the driver score generation platform to perform an action with regard to the mobile device.

Abstract: Systems, methods, apparatuses and computer-readable media for receiving data from one or more sensors associated with one or more home devices, such as appliances, home systems, etc. are provided. The data may be used to determine whether the home device is operating within an expected range or, if it is outside of an expected range or threshold, whether a home event has occurred. Upon occurrence of the home event, an insurance claim to cover any damage caused by the home event may be initiated and/or processed. In some examples, repair of any damage may also be coordinated by the system. In some arrangements, the initiation and/or processing of the claim, and/or coordination of the repair may be performed without any additional input from the user associated with the policy, home device, etc. or with limited additional input from the user.

Abstract: Aspects of the present disclosure relate to using vehicle telematics to detect, evaluate, and respond to driving behaviors observed during a trip in a vehicle. The vehicle telematics information may characterize the observed driving behaviors, the segments traveled in the vehicle, and type of use of the vehicle. A rate to charge a driver may be based on the observed driving behaviors and a range of rates associated with the driver. The rate may fluctuate across a series of observation periods based on the driving behaviors observed during those observation periods. The rate may be presented to the driver in real-time while the driver operates a vehicle during a trip. The rate to charge the driver may further be based on use of the vehicle for personal use, ride shares, or vehicle shares.

Abstract: Implementations claimed and described herein provide systems and methods for determining fuel efficiency based on sensor data from a mobile device. In one implementation, sensor data from a mobile device is collected. The sensor data includes a dataset that reflects a last trip on a vehicle by the mobile device, wherein the sensor data is collected from at least one of global position system (GPS) data and micro-electro-mechanical system (MEMS) sensor data of the mobile device. Driving events comprising at least one of one or more braking events, one or more speeding events, and one or more acceleration events are determined based on the sensor data. A fuel consumption prediction is predicted via a trained prediction model based on the driving events.

Abstract: Apparatuses, systems, and methods are provided for the utilization of vehicle control systems to cause a vehicle to take preventative action responsive to the detection of a near short term adverse driving scenario. A vehicle control system may receive information corresponding to vehicle operation data and ancillary data. Based on the received vehicle operation data and the received ancillary data, a multi-dimension risk score module may calculate risk scores associated with the received vehicle operation data and the received ancillary data. Subsequently, the vehicle control systems may cause the vehicle to perform at least one of a close call detection action and a close call detection alert to lessen the risk associated with the received vehicle operation data and the received ancillary data.

Abstract: A driving analysis server may be configured to receive vehicle operation data from vehicle sensors and telematics devices of a first vehicle, and may use the data to identify a potentially high-risk or unsafe driving behavior by the first vehicle. The driving analysis server also may retrieve corresponding vehicle operation data from one or more other vehicles, and may compare the potentially high-risk or unsafe driving behavior of the first vehicle to corresponding driving behaviors in the other vehicles. A driver score for the first vehicle may be calculated or adjusted based on the comparison of the driving behavior in the first vehicle to the corresponding driving behaviors in the other vehicles.

Abstract: One or more driving analysis computing devices in a driving analysis system may be configured to analyze driving data, determine driving behaviors, and calculate driver scores based on driving data transmitted using vehicle-to-vehicle (V2V) communications. Driving data from multiple vehicles may be collected by vehicle sensors or other vehicle-based systems, transmitted using V2V communications, and then analyzed and compared to determine various driving behaviors by the drivers of the vehicles. Driver scores may be calculated or adjusted based on the determined driving behaviors of vehicle drivers, and also may be calculated or adjusted based on other the driver scores of nearby vehicles.

Abstract: Systems and methods are disclosed for providing roadside service through a synchronized interactive voice response (IVR) system and graphical user interface (GUI). One method may include: receiving, based on an incoming phone call from a vehicle user device, a request for roadside service for a disabled vehicle of a vehicle user; sending a text to a phone number of the vehicle user device, wherein the text includes a link to a mobile application for roadside service requests; receiving, from the vehicle user device and via the mobile application, information associated with the request for roadside service; determining, based on a location sensor of the vehicle user device, a location of the disabled vehicle; matching a roadside service provider with the disabled vehicle based on the request for roadside service; and enabling the vehicle user to track the service status of the roadside service provider through the mobile application.

Abstract: System, apparatuses, computer-implemented methods, and computer-readable media executable by insurance system servers and user computing devices for receiving requests for insurance products are provided. In order to determine one or more factors of the insurance product or policy, the system may use body characteristics of the customer or potential customer, such as height, weight, body mass index, and the like. In some examples, this information may be determined from one or more images provided by the user. For instance, one or more images of the customer or potential customer may be captured and transmitted to the system for processing. Based on the received images, the system may determine various body characteristics of the user and may use that information to determine one or more policy factors for the insurance product or policy, such as premium, coverage, term, type of policy, or the like.

Abstract: A provider computing system includes a claims database storing claim statements for a plurality of claims, the claim statements including a plurality of claim variable, an orchestration circuit storing computer-executable instructions embodying one or more machine learning models, at least one processor, and at least one memory, the at least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations. The operations include receive a first claim statement corresponding to a new claim; cause, by the orchestration circuit, the one or more machine learning models to parse the first claim statement; determine a first claim variable from the first claim statement; generate, by the orchestration circuit, a response including a request for a second claim statement; and transmit the request for a second claim statement.