View source for 18508392. UTILIZING MACHINE LEARNING AND TRANSACTION DATA TO DETERMINE FUEL PRICES AT FUEL STATIONS simplified abstract (Capital One Services, LLC)

=UTILIZING MACHINE LEARNING AND TRANSACTION DATA TO DETERMINE FUEL PRICES AT FUEL STATIONS=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:James Zarakas of Centreville VA (US)|James Zarakas of Centreville VA (US)]][[Category:James Zarakas of Centreville VA (US)]]

[[:Category:Adam Vukich of Alexandria VA (US)|Adam Vukich of Alexandria VA (US)]][[Category:Adam Vukich of Alexandria VA (US)]]

[[:Category:Molly Johnson of Alexandria VA (US)|Molly Johnson of Alexandria VA (US)]][[Category:Molly Johnson of Alexandria VA (US)]]

==UTILIZING MACHINE LEARNING AND TRANSACTION DATA TO DETERMINE FUEL PRICES AT FUEL STATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18508392 titled 'UTILIZING MACHINE LEARNING AND TRANSACTION DATA TO DETERMINE FUEL PRICES AT FUEL STATIONS

==Simplified Explanation==

The patent application describes a fuel price determination system that uses transaction data, location data, and user history data to determine fuel prices at fuel stations.

* The system receives transaction data from purchases made with transaction cards and mobile transaction card applications.
* It also receives location data identifying locations associated with users of the client devices and transaction cards.
* User history data associated with prior fuel purchases at fuel stations is also collected.
* A machine learning model processes the transaction data, location data, and user history data to determine fuel prices at the fuel stations.
* The system then generates a ranked list of fuel stations in a geographical area based on fuel prices and populates a map with this information.

== Potential Applications ==
This technology could be applied in the fuel industry to optimize pricing strategies and attract customers based on competitive fuel prices.

== Problems Solved ==
This technology solves the problem of fluctuating fuel prices and helps users find the best fuel prices in their area.

== Benefits ==
The system provides users with real-time information on fuel prices, allowing them to make informed decisions on where to purchase fuel.

== Potential Commercial Applications ==
One potential commercial application of this technology is in fuel station locator apps, where users can find the nearest fuel stations with the best prices.

== Possible Prior Art ==
One possible prior art for this technology could be existing fuel price comparison websites or apps that provide users with information on fuel prices at different stations.

=== Unanswered Questions ===

=== How does the system ensure the accuracy of the fuel prices provided to users? ===
The system may use real-time data updates from fuel stations or rely on user feedback to verify the accuracy of the fuel prices displayed.

=== What measures are in place to protect user data privacy in the system? ===
The system may use encryption techniques to secure user data and comply with data protection regulations to ensure user privacy is maintained.


==Original Abstract Submitted==

A fuel price determination system may receive transaction data identifying purchases, made with transaction cards and mobile transaction card applications of client devices, of fuel at fuel stations. The fuel price determination system may receive location data identifying locations associated with users of the client devices and the transaction cards, and user history data associated with prior purchases of fuel at fuel stations by the users. The fuel price determination system may process the transaction data, location data, and user history data, with a machine learning model, to determine fuel prices at the fuel stations. The fuel price determination system may determine a ranked list of particular fuel stations in a geographical area based on the fuel prices and populate, based on the location data and the ranked list, a map to identify the particular fuel stations and the fuel prices at the particular fuel stations.



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