Abstract: An on-demand insurance (ODI) server for generating dynamic user offerings may be provided. The ODI server may include at least one processor in communication with a memory device. The at least one processor may be programmed to (i) receive, from a user computing device associated with a user, an insurance policy request for a trip from a start location to an end location, (ii) determine at least one transportation mode available for the trip, (iii) access contextual data associated with the trip, (iv) assess a risk associated with the at least one transportation mode, (v) calculate a risk score associated with the at least one transportation mode based upon at least the contextual data, (vi) generate an insurance offering associated with the at least one transportation mode, and/or (vii) transmit the insurance offering in real time to the user computing device for purchase by the user.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by a controller operatively coupled to a network of electric vehicles. The methods and systems include the controller (i) receiving a notification that an electric vehicle is stranded without sufficient power to operate; (ii) receiving information regarding the stranded electric vehicle; (iii) detecting one or more other electric vehicles in a vicinity of the stranded electric vehicle; (iv) receiving information regarding the detected one or more other electric vehicles; and/or (v) determining, based upon the received information, which of the detected one or more other electric vehicles to send a power source request. Alternatively, the notification may indicate that an electric vehicle has a low state of charge (SOC), or is otherwise has a battery in need of being recharged to facilitate the electric vehicle traveling to a destination.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by a controller operatively coupled to a network of electric vehicles. The methods and systems include the controller (i) receiving a notification that an electric vehicle is stranded without sufficient power to operate; (ii) receiving information regarding the stranded electric vehicle; (iii) detecting one or more other electric vehicles in a vicinity of the stranded electric vehicle; (iv) receiving information regarding the detected one or more other electric vehicles; and/or (v) determining, based upon the received information, which of the detected one or more other electric vehicles to send a power source request. Alternatively, the notification may indicate that an electric vehicle has a low state of charge (SOC), or is otherwise has a battery in need of being recharged to facilitate the electric vehicle traveling to a destination.

Abstract: A system for ultrasound interrogation of a lung including a memory, an electromagnetic (EM) board, an extended working channel (EWC), an EM sensor, a US transducer, and a processor. The memory stores a three dimensional (3D) model and pathway plan for navigating a luminal network. The EM board generates an EM field. The EWC is configured to navigate the luminal network toward a target following the pathway plan. The EM sensor extends distally from a distal end of the EWC and is configured to sense the EM field. The US transducer extends distally from a distal end of the EWC, generates US waves, and receives US waves reflected from the luminal network. The processor processes the sensed EM field to synchronize a location of the EM sensor in the 3D model, to process the reflected US waves to generate images, or to integrate the generated images with the 3D model.

Abstract: A system for ultrasound interrogation of a lung includes a memory, an electromagnetic (EM) board, an extended working channel (EWC), an EM sensor, a US transducer, and a processor. The memory stores a three dimensional (3D) model, a pathway plan for navigating a luminal network. An EM board generates an EM field. The EWC is configured to navigate the luminal network of a patient toward a target following the pathway plan and the EM sensor extends distally from the EWC and senses the EM field. The US transducer extends distally from a distal end of the EWC and generates US waves and receives US waves reflected from the luminal network and the processor processes the sensed EM field to synchronize a location of the EM sensor in the 3D model, to process the reflected US waves to generate images, or to integrate the generated images with the 3D model.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by a controller operatively coupled to a network of electric vehicles. The methods and systems include the controller (i) receiving a notification that an electric vehicle is stranded without sufficient power to operate; (ii) receiving information regarding the stranded electric vehicle; (iii) detecting one or more other electric vehicles in a vicinity of the stranded electric vehicle; (iv) receiving information regarding the detected one or more other electric vehicles; and/or (v) determining, based upon the received information, which of the detected one or more other electric vehicles to send a power source request. Alternatively, the notification may indicate that an electric vehicle has a low state of charge (SOC), or is otherwise has a battery in need of being recharged to facilitate the electric vehicle traveling to a destination.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by an autonomous recharging vehicle (ARV) as well as one or more local or remote processors, transceivers, servers, and/or sensors.

Abstract: Provided herein is a computing system including a processor in communication with at least one memory. The processor is configured to (i) build a model using historical vehicle telematics data and historical vehicle data, the model configured to: (a) predict an operating cost of a vehicle based upon a vehicle type and user data and (b) output operational parameters for operating the vehicle, (ii) receive a vehicle acquisition request for a selected vehicle including vehicle data for the selected vehicle and selected user data for the user of the selected vehicle, (iii) input the vehicle acquisition request data into the model, and (iv) output from the model an acquisition cost and operational parameters for the selected vehicle and the selected user including a procurement cost for the selected vehicle and an operating cost for operating the selected vehicle within the operational parameters for a predetermined time period.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by one or more local or remote processors, transceivers, servers, and/or sensors. The methods and systems include the one or more processors (i) determining an electric vehicle has a state of charge (SOC) below a predetermined threshold; (ii) if the SOC is determined to be below the predetermined threshold, determining homes capable of charging electric vehicles within a vicinity of, or a predetermined distance of, the low SOC vehicle's GPS location; (iii) from among the homes within the predetermined distance of the low SOC vehicle, ranking the homes based upon various factors; and/or (iv) scheduling a rendezvous or appointment for the low SOC vehicle with the highest ranked home for recharging the low SOC vehicle, the highest ranked home being equipped to recharge the low SOC vehicle and is available to charge the low SOC vehicle.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by one or more local or remote processors, transceivers, servers, and/or sensors. The methods and systems include the one or more processors (i) determining an electric vehicle has a state of charge (SOC) below a predetermined threshold; (ii) if the SOC is determined to be below the predetermined threshold, determining homes capable of charging electric vehicles within a vicinity of, or a predetermined distance of, the low SOC vehicle's GPS location; (iii) from among the homes within the predetermined distance of the low SOC vehicle, ranking the homes based upon various factors; and/or (iv) scheduling a rendezvous or appointment for the low SOC vehicle with the highest ranked home for recharging the low SOC vehicle, the highest ranked home being equipped to recharge the low SOC vehicle and is available to charge the low SOC vehicle.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by one or more processors associated with an autonomous recharging vehicle (ARV). The methods and systems include: forming a list of electric vehicles waiting in queue for charging by the ARV; performing an optimization algorithm to rearrange the order of the electric vehicles and determine whether the ARV needs to be recharged between charging the electric vehicles on the list and the amount of time needed to finish recharging the ARV; scheduling rendezvous points for the ARV to recharge the electric vehicles based upon the rearranged order of the electric vehicles on the list; and automatically routing the ARV to the rendezvous points based upon the scheduling.

Abstract: Provided herein is a computing system including a processor in communication with at least one memory. The processor is configured to (i) build a model using historical vehicle telematics data and historical vehicle data, the model configured to: (a) predict an operating cost of a vehicle based upon a vehicle type and user data and (b) output operational parameters for operating the vehicle, (ii) receive a vehicle acquisition request for a selected vehicle including vehicle data for the selected vehicle and selected user data for the user of the selected vehicle, (iii) input the vehicle acquisition request data into the model, and (iv) output from the model an acquisition cost and operational parameters for the selected vehicle and the selected user including a procurement cost for the selected vehicle and an operating cost for operating the selected vehicle within the operational parameters for a predetermined time period.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by an autonomous recharging vehicle (ARV) as well as one or more local or remote processors, transceivers, servers, and/or sensors.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by a controller operatively coupled to a network of electric vehicles. The methods and systems include the controller (i) receiving a notification that an electric vehicle is stranded without sufficient power to operate; (ii) receiving information regarding the stranded electric vehicle; (iii) detecting one or more other electric vehicles in a vicinity of the stranded electric vehicle; (iv) receiving information regarding the detected one or more other electric vehicles; and/or (v) determining, based upon the received information, which of the detected one or more other electric vehicles to send a power source request. Alternatively, the notification may indicate that an electric vehicle has a low state of charge (SOC), or is otherwise has a battery in need of being recharged to facilitate the electric vehicle traveling to a destination.

Abstract: Computer-implemented methods and computer systems are disclosed herein as implemented by an autonomous recharging vehicle (ARV) as well as one or more local or remote processors, transceivers, servers, and/or sensors.