Abstract: Aspects of the invention are directed to methods and systems for efficiently communicating data between an insurer and a non-referral repair shop, e.g., vehicle repair shops that are normally not preapproved by the insurer to perform the estimating and repair work. The methods and systems described herein are particularly useful for insurers utilizing non-referral repair shops for servicing vehicles involved in insurance claims. According to aspects of this invention, the insured may be able to select a non-referral repair shop, not delegated or preapproved by the insurer, thereby generally allowing the insured to select any available vehicle repair shop.

Abstract: Implementations include classifying vehicle trips as similar to previous trips based on location information of a vehicle received from a location device. Unique tile identifiers of the trip, each corresponding to a geographic area and the location information, may be determined and used to generate a fingerprint of the trip. The derived trip fingerprint of the trip information may be compared to stored fingerprints of one or more previously received trips to determine if the new trip is similar to one or more of the previous trips. In one instance, information or data of a new trip may be adjusted based on previous trip data. For example, aspects of the new trip or the previous trip may be updated with information or data of a previous trip fi the new trip and the previous trip are similar.

Abstract: Personalized insurance systems, including vehicle-based devices and modules, insurance system servers, personal mobile devices, and other computing devices, may operate individually or in combination to determine and implement personalized vehicle insurance policies. Such policies may be driver-specific, time-specific, vehicle-specific, and/or driving-trip specific. Various driver data, vehicle data, and driving trip data may be received and analyzed in order to determine usage-based insurance parameters for vehicle driving and non-driving activities. Personalized vehicle insurance policies may be presented to and accepted by drivers, after which the drivers and one or more associated vehicles may be monitored to detect and store vehicle usage data in accordance with the personalized insurance policy.

Abstract: System, methods, and apparatuses provide customizable product features by receiving, from a computing device associated with a user, an indication of user interest in a product, identifying, based on a first profile associated with the user, a first attribute, determining one or more second profiles that comprise the first attribute, determining one or more features, of the product, that are associated with the one or more second profiles, determining, for each of the one or more features, a utility value, simulating, based on strategic alternatives data and the utility value for each of the one or more features, demand for the one or more features, determining, based on the simulated demand, that a subset of the one or more features satisfies a need state associated with the user, and sending, to the computing device, a product package including the subset of the one or more features.

Abstract: Systems and apparatuses for using machine learning to generate a safety output are provided. In some examples, data may be received from a plurality of sources, may be analyzed and one or more machine learning datasets may be generated based on the analyzed data. In some arrangements, data may be received from one or more vehicles. The vehicles may be autonomous, semi-autonomous, or non-autonomous, and/or configured to operate in one or more of those modes. The data may be evaluated based on the one or more machine learning datasets to determine a safety output associated with the data. The safety output may then be used to classify the data and/or to generate one or more instructions for operation of an autonomous vehicle. The instruction(s) may be transmitted to the autonomous vehicle and may modify operation of the vehicle (e.g., to improve safety associated with the vehicle).

Abstract: Systems and methods are disclosed for using a hub to automate recovery.

Abstract: Aspects of the disclosure relate to using ultrasonic or other types of signals to detect a driver in a vehicle. A computing platform may receive ultrasonic sensing data associated with mobile devices in the vehicle from a signal transmitter. Unique identifiers of the mobile devices may be determined. Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined. The computing platform may use a machine learning classifier to determine that a particular occupant is a driver in the vehicle based on the relative distance.

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: 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: 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: 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: 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.