Patent ID: 11880853
Assignee: CAPITAL ONE SERVICES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to:
receive transaction data identifying purchases of fuel at fuel stations,
wherein the purchases of fuel are made with:
mobile transaction card applications of client devices, and
transaction cards;

receive location data identifying locations associated with users of the client devices and the transaction cards;
receive user history data associated with prior purchases of fuel at fuel stations by the users;
process the transaction data, the location data, and the user history data, with a machine learning model, to determine fuel prices at the fuel stations,
wherein the one or more processors, to process the transaction data, the location data, and the user history data, with the machine learning model, are configured to:
determine a confidence score associated with a transaction amount, associated with the transaction data, that is associated with purchasing items other than fuel at a fuel station, of the fuel stations, based on determining whether a first transaction amount, of a single transaction, is associated with purchasing the items other than the fuel and determining whether a second transaction amount, of the single transaction, is associated with purchasing the fuel,
extrapolate a first fuel price, of the fuel prices, associated with a first grade of fuel, from a second fuel price, of the fuel prices, associated with a second grade of fuel,
 wherein the first grade of fuel differ from the second grade of fuel,
classify, using a clustering technique and based on the confidence score, the first transaction amount or the second transaction amount into a cluster, and
determine the fuel prices at the fuel stations based on classifying the first transaction amount or the second transaction amount into the cluster,
wherein the machine learning model is trained based on historical transaction data, historical location data, historical confidence scores, and

historical vehicle data to determine particular fuel prices, and
wherein the machine learning model employs cluster analysis to identify similarities between any two of the following types of data or within any of the following types of data: the historical transaction data, the historical location data, the historical confidence scores, the historical vehicle data and the particular fuel prices;

determine a ranked list of particular fuel stations, of the fuel stations, in a geographical area based on determining the fuel prices at the fuel stations;
populate, based on the location data and the ranked list of the particular fuel stations, a map to identify the particular fuel stations and the fuel prices at the particular fuel stations,
wherein the map is configured to selectively display determined fuel prices in a first category based on the confidence score failing to satisfy a threshold or a second category based on the confidence score satisfying the threshold,
wherein the first category includes an indication that a particular fuel price is estimated, and
wherein the second category does not include the indication that the particular fuel price is estimated;

determine, based on the location data and the ranked list of the particular fuel stations, information identifying the particular fuel stations and the fuel prices at the particular fuel stations; and
provide the information to one of the client devices.