Systems and methods for determining fuel information of a vehicle
First Claim
1. A computer-implemented method for improving an automatic determination of a fueling event of a vehicle using a mobile computing device onboard the vehicle, comprising:
- (a) tracking the vehicle along a route using sensors on the mobile computing device;
(b) detecting multiple stop events when tracking the vehicle using the sensors on the mobile computing device, wherein the sensors include an accelerometer and a Global Positioning System;
(c) for each of the multiple stop events, automatically determining each of a duration and a geographic location based on sensor information generated from the sensors on the mobile computing device;
(d) using one or more programmed computer processors to execute a machine learning algorithm to automatically determine that a given stop event of the multiple stop events is a fueling event based at least in part on a duration and geographic location of the given stop event;
(e) providing information on the fueling event to a user on a graphical user interface (GUI) of the mobile computing device; and
(f) in response to providing the information on the GUI, receiving through the GUI feedback from the user, wherein the machine learning algorithm uses the feedback from the user to improve the automatic determination of the fueling event.
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Accused Products
Abstract
The present disclosure provides systems and methods for determining or providing fuel information of a vehicle, such as predicting fuel intake and usage for the vehicle. A method for predicting a fueling event of a vehicle comprises using a mobile computing device carried in the vehicle to track the vehicle along a route. Multiple stop events may be detected when tracking the vehicle, and for each of the multiple stop events, each of duration and a geographic location may be determined. Next, a given stop event of the multiple stop events may be determined to be a fueling event based at least in part on a duration and geographic location of the given stop event.
113 Citations
28 Claims
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1. A computer-implemented method for improving an automatic determination of a fueling event of a vehicle using a mobile computing device onboard the vehicle, comprising:
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(a) tracking the vehicle along a route using sensors on the mobile computing device; (b) detecting multiple stop events when tracking the vehicle using the sensors on the mobile computing device, wherein the sensors include an accelerometer and a Global Positioning System; (c) for each of the multiple stop events, automatically determining each of a duration and a geographic location based on sensor information generated from the sensors on the mobile computing device; (d) using one or more programmed computer processors to execute a machine learning algorithm to automatically determine that a given stop event of the multiple stop events is a fueling event based at least in part on a duration and geographic location of the given stop event; (e) providing information on the fueling event to a user on a graphical user interface (GUI) of the mobile computing device; and (f) in response to providing the information on the GUI, receiving through the GUI feedback from the user, wherein the machine learning algorithm uses the feedback from the user to improve the automatic determination of the fueling event. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A computer-implemented method for improving an automatic calculation of a fuel level of a vehicle using a mobile computing device comprising sensors onboard the vehicle, comprising:
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recording, in computer memory, sensor device data from the sensors on the mobile computing device along a route traveled by the vehicle, the sensors including an accelerometer and a Global Positioning System, wherein the mobile computing device is in the vehicle, and wherein the sensor device data includes position information and accelerometer data; determining, from the sensor device data, one or more roadway conditions and/or events along the route; using one or more programmed computer processors to execute a machine learning algorithm to automatically calculate the fuel level of the vehicle at a given instance by (i) determining, from the position information, a distance traveled by the vehicle since a most recent fuel stop, (ii) calculating a fuel consumption rate based on (A) a fuel consumption rate for a vehicle type of the vehicle, and (B) the one or more roadway conditions and/or events; providing the fuel level of the vehicle to a user on a graphical user interface (GUI) of the mobile computing device; and in response to providing the fuel level on the GUI, receiving through the GUI feedback from the user, wherein the machine learning algorithm uses the feedback from the user to improve the automatic calculation of the fuel level of the vehicle. - View Dependent Claims (18, 19, 20, 21, 22, 23)
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24. A computer-implemented method for improving an automatic prediction of a fuel level of a vehicle using a mobile computing device comprising sensors onboard the vehicle, comprising:
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recording, in computer memory, sensor device data from the sensors on the mobile computing device along a route traveled by the vehicle, the sensors including an accelerometer and a Global Positioning System, wherein the mobile computer device is in the vehicle, and wherein the sensor device data includes position information and accelerometer data; determining, from the sensor device data, at least one of a velocity and acceleration of the vehicle at multiple instances along the route; determining, from the sensor device data, one or more roadway conditions and/or events along the route; using one or more programmed computer processors to execute a machine learning algorithm to automatically calculate a fuel consumption rate of the vehicle based at least in part on (A) an approximate fuel consumption rate for a vehicle type of the vehicle, (B) the at least one of velocity and acceleration of the vehicle at the multiple instances along the route, and (C) the one or more roadway conditions and/or events, which fuel consumption rate is used to automatically predict a fuel level of the vehicle; upon determining that the fuel level of the vehicle is at or below a predetermined fuel threshold, selecting a fuel station for the vehicle based at least in part on a refueling cost; providing information on the fuel station and the fuel level to a user on a graphical user interface (GUI) of the mobile computing device; and in response to providing the fuel level on the GUI, receiving through the GUI feedback from the user, wherein the machine learning algorithm uses the feedback from the user to improve the automatic prediction of the fuel level of the vehicle. - View Dependent Claims (25, 26, 27, 28)
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Specification