US 12,437,312 B2
Utilizing machine learning and transaction data to determine fuel prices at fuel stations
James Zarakas, Centreville, VA (US); Adam Vukich, Alexandria, VA (US); and Molly Johnson, Alexandria, VA (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Nov. 14, 2023, as Appl. No. 18/508,392.
Application 18/508,392 is a continuation of application No. 17/088,211, filed on Nov. 3, 2020, granted, now 11,880,853.
Prior Publication US 2024/0086954 A1, Mar. 14, 2024
Int. Cl. G06Q 30/0201 (2023.01); G06F 18/214 (2023.01); G06F 18/23 (2023.01); G06N 20/00 (2019.01); G06Q 30/0203 (2023.01); G06Q 30/0204 (2023.01); G06Q 50/06 (2024.01)
CPC G06Q 30/0206 (2013.01) [G06F 18/214 (2023.01); G06F 18/23 (2023.01); G06N 20/00 (2019.01); G06Q 30/0205 (2013.01); G06Q 50/06 (2013.01); G06Q 30/0203 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
determining, by a device and based on sensor information associated with a vehicle, a first fuel amount and a second fuel amount;
processing, by the device, transaction data, location data, user history data, the first fuel amount, and the second fuel amount with a machine learning model, to determine a predicted fuel price for a predicted grade of fuel,
wherein the machine learning model is trained based on historical transaction data, historical location data, and historical user history data to determine the predicted fuel price, and
wherein processing the transaction data, the location data, the user history data, the first fuel amount, and the second fuel amount with the machine learning model comprises:
using machine learning to extrapolate the predicted fuel price from a fuel price for another grade of fuel when insufficient information exists for the predicted grade of fuel;
determining, by the device, a confidence score associated with the predicted fuel price based on the transaction data, the location data, or the user history data;
populating, by the device and based on the location data, a map to identify the predicted fuel price, an associated fuel station, and an indicator of the confidence score;
generating, by the device, a list of fuel stations based on the map;
automatically selecting, by the device, a fuel station from the generated list of fuel stations;
deploying, by the device and based on generating directions to the selected fuel station, an autonomous vehicle to the selected fuel station;
causing, by the device and based on deploying the autonomous vehicle to the selected fuel station, the autonomous vehicle to capture one or more images associated with the selected fuel station; and
retraining, by the device, the machine learning model based on the one or more images.