Machine Learning Fuel Price Mapping from Transaction Data
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Solution Overview
Problem
Current fuel price applications rely on human input, which is often inaccurate, time-consuming, and prone to misrepresentation, leading to inefficient use of computing resources and user time when users discover inaccuracies upon arrival at fuel stations.
Innovation Solution
A fuel price determination system utilizing machine learning to process transaction data, location data, and user history data to determine accurate fuel prices at fuel stations, providing a ranked list and mapping functionality to assist users in selecting the most cost-effective stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If human input is used to collect fuel price information, then the system can obtain fuel price data, but the accuracy and reliability of the price information deteriorates due to human error and misrepresentation
Solution Approach 1:
The patent replaces the manual mechanical process of human price entry with an automated electronic system that captures fuel price data directly from point-of-sale terminals and transaction systems at fuel stations. This substitution eliminates human error and intentional misrepresentation, ensuring accurate and reliable price information is collected and transmitted to the application.
2Loss of information
If human manual entry of fuel prices is used, then the system can collect price data, but the time consumption and operational efficiency worsen
Solution Approach 1:
The system implements self-service automation where fuel station point-of-sale terminals and transaction systems automatically generate and transmit price data without requiring manual human intervention. The automated data collection process continuously updates fuel prices in real-time, eliminating the time-consuming manual entry process while ensuring current and accurate information is available to users.
3Measurement precision
If automated machine learning processing is used to determine fuel prices, then the accuracy of fuel price information improves, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary automated processing layer that collects raw data from multiple fuel station sources, applies machine learning algorithms to analyze and validate the information, and generates reliable fuel price determinations. This intermediary system manages the complexity internally while presenting simplified, accurate price information to users through the application interface.
Data Source
AI summary
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.