Abstract: A computer-implemented method of predicting and providing an efficient driving route for an autonomous or semi-autonomous electric vehicle based upon battery health impact includes (i) generating one or more driving routes for the autonomous or semi-autonomous electric vehicle; (ii) predicting a projected battery health impact on a battery of the autonomous or semi-autonomous electric vehicle for each driving route of the one or more driving routes, wherein the projected battery health impact is based at least upon a predicted time of day for travel; (iii) determining one or more recommendations for the autonomous or semi-autonomous electric vehicle for a driving route of the one or more driving routes, the one or more recommendations based upon at least the projected battery health impact for each driving route and a preferred form of operation; and (iv) causing the autonomous or semi-autonomous electric vehicle to automatically drive along the recommended route.

Abstract: A server and non-transitory computer-readable storage medium storing instructions for monitoring one or more batteries of an electric vehicle (EV) comprising computing instructions for (i) receiving, from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV; (ii) determining a battery status of the one or more batteries based upon the telematics data; and (iii) mapping the battery status of the one more batteries to a digital record corresponding to the EV in a database.

Abstract: A matching computer system for electronically generating, matching, and providing online user profiles, and determining a sharing score between the online user profiles is provided. The matching computer system may be configured to generate online user profiles associated with users of the systems. The matching computer system may be also configured to calculate a base score based upon the generated online user profile. Each base score represents a level of trustworthiness of each respective user. The matching computer system may be further configured to determine a sharing score between users based upon the base scores. Each sharing score represents a level of matching between the online user profiles.

Abstract: A personalized insurance (“PI”) computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.

Abstract: A connected home analytics system may (1) ingest first operational information from a first connected device of a domicile and second operational information from a second connected device of the domicile; (2) identify, using at least one machine learning model, one or more conditions of the domicile based upon a combination of the first operational information and the second operational information; and (3) initiate one or more actions responsive to identifying the one or more conditions of the domicile.

Abstract: A connected home analytics system may (1) receive sensor data from one or more smart devices; (2) process the sensor data using a generative artificial intelligence (GAI) model trained to identify one or more current or future issues within the property, at least one of a location or a source of the one or more current or future issues, and one or more proposed solutions for addressing the one or more current or future issues using the sensor data; (3) generate, using the GAI model, a description; (4) generate a user interface including the description and one or more selectable options; (5) receive, via the user interface, a selection from the user of a first selectable option from among the one or more selectable options; and (6) initiate an action to implement a first proposed solution corresponding to the first selectable option.

Abstract: Systems and methods for monitoring the operational condition of target vehicles for predicting an inoperability period to activate system-based actions are disclosed.

Abstract: A computer-implemented method of monitoring one or more batteries of an electric vehicle (EV) includes (i) detecting an indication of a battery failure event for an electric vehicle; (ii) determining a response to the battery failure event based upon the indication; (iii) determining, based upon the battery failure event, an assistance location for the electric vehicle; (iv) generating a route from a location of an autonomous vehicle to the assistance location for the electric vehicle; and (v) transmitting a command to the autonomous vehicle to drive to the assistance location for the electric vehicle, wherein the command includes the response to the battery failure event and the route from the location of the autonomous vehicle to the assistance location.

Abstract: A computer-implemented method of predicting and providing an efficient driving route for an electric vehicle (EV) based upon battery health impact includes (i) receiving geographical telematics data; (ii) generating one or more driving routes for the electric vehicle based upon at least the geographical telematics data; (iii) predicting a projected battery health impact on a battery of the electric vehicle for each driving route of the one or more driving routes, wherein the projected battery health impact for each driving route is based at least upon geographical characteristics of the driving route; (iv) determining one or more battery-efficient driving route recommendations based upon at least the projected battery health impact for each driving route; and/or (v) providing the one or more battery-efficient driving route recommendations to a user.

Abstract: A server and non-transitory computer-readable storage medium storing instructions for monitoring one or more batteries of an electric vehicle (EV) comprising computing instructions for (i) receiving, from an electronic device associated with the EV, telematics data generated by one or more sensors associated with the electronic device that is indicative of operation of the EV; (ii) determining a battery status of the one or more batteries based upon the telematics data; and (iii) mapping the battery status of the one more batteries to a digital record corresponding to the EV in a database.

Abstract: A user analytics computing device for processing vehicle-based telematics data and generating user offerings responsive to the vehicle-based telematics data includes at least one processor in communication with a memory device. The processor is programmed to: (i) receive, from a vehicle computing device, telematics data associated with a plurality of trips taken by a first driver using a first vehicle, (ii) generate a driver profile of the first driver based at least in part upon the telematics data, (iii) access, from the memory device, contextual data associated with an environment in which the first vehicle travelled during the plurality of trips, (iv) synthesize the driver profile and the contextual data to generate a contextualized driver profile, (v) based upon the contextualized driver profile, generate a user offering to influence the driver profile, and (vi) transmit the user offering to a user computing device associated with the first driver.

Abstract: A computing device for activating on-demand products based upon real-time geolocation data is configured to store at least one product definition associated with a respective location-specific product. Each product definition includes a respective geofence within which the location-specific product is available for activation. The computing device is also configured to receive real-time telematics data from a user computing device, and periodically query a memory device with the real-time telematics data. In response to a first successful query, the computing device is configured to receive a first product definition associated with a first location-specific product available for activation within a corresponding first geofence.

Abstract: Systems and methods are described for performing analysis of parametric events. The method may include: (1) receiving weather data from a weather oracle network; (2) calculating, using a first trained machine learning algorithm, a likelihood of a trigger activation for a parametric event (or a trigger event) for a user based at least upon the weather data; (3) calculating, using a second trained machine learning algorithm, an estimated loss for the user based at least upon the likelihood of the trigger activation; (4) determining an initial coverage for the user; and (5) determining whether to offer the user updated coverage for the parametric event based at least upon a comparison of the initial coverage and the estimated loss for the user.

Abstract: A user analytics computing device for processing vehicle-based telematics data and generating user offerings responsive to the vehicle-based telematics data includes at least one processor in communication with a memory device. The processor is programmed to: (i) receive, from a vehicle computing device, telematics data associated with a plurality of trips taken by a first driver using a first vehicle, (ii) generate a driver profile of the first driver based at least in part upon the telematics data, (iii) access, from the memory device, contextual data associated with an environment in which the first vehicle travelled during the plurality of trips, (iv) synthesize the driver profile and the contextual data to generate a contextualized driver profile, (v) based upon the contextualized driver profile, generate a user offering to influence the driver profile, and (vi) transmit the user offering to a user computing device associated with the first driver.

Abstract: A personalized insurance (“PI”) computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.

Abstract: A computing device for generating on-demand products based upon real-time geolocation data is configured to receive and store at least one product definition associated with a respective product. Each product definition defines an availability of the product and includes a product availability location. The computing device is also configured to receive real-time location data from a user computing device, and periodically query a memory device with the real-time location data. The computing device detects that the user computing device has entered a first location by retrieving, in response to a successful query of the memory device, a first product definition associated with a first product having a product availability location including the first location. The computing device is also configured to generate a product identification message including at least a portion of the first product definition, and transmit the product identification message to the user computing device.

Abstract: Described herein are methods and systems for generating and applying vehicle maintenance models to vehicle telematics data. A vehicle telematics analytics (VTA) computing device is configured to receive vehicle telematics data associated with operation of a subject vehicle, and apply the received vehicle telematics data to a vehicle maintenance model to determine whether the operation of the subject vehicle is better or worse than standard operation of like vehicles. The VTA computing device may adjust a maintenance cost and/or determine if a user of the vehicle is eligible for a reward based upon this determination. The VTA computing device is configured to transmit an itemized report to the user including the adjusted maintenance cost and/or the reward as well as one or more other vehicle costs.

Abstract: A personalized insurance (“PI”) computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.

Abstract: A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

Abstract: Described herein are methods and systems for generating and applying vehicle maintenance models to vehicle telematics data. A vehicle telematics analytics (VTA) computing device is configured to receive vehicle telematics data associated with operation of a subject vehicle, and apply the received vehicle telematics data to a vehicle maintenance model to determine whether the operation of the subject vehicle is better or worse than standard operation of like vehicles. The VTA computing device may adjust a maintenance cost and/or determine if a user of the vehicle is eligible for a reward based upon this determination. The VTA computing device is configured to transmit an itemized report to the user including the adjusted maintenance cost and/or the reward as well as one or more other vehicle costs.