Machine Learning Module for Fuel Transaction Error Correction
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Solution Overview
Problem
Current systems fail to accurately analyze fuel transaction data due to miscodings, which hinder the identification of cost-saving opportunities for companies with fleets, as errors in fuel type or grade codes lead to incorrect analyses.
Innovation Solution
A machine learning module is integrated into the transaction processing system to detect and correct miscodings by training on datasets with known labels, using variables such as transaction history, unit price, and fuel consumption patterns to classify transactions accurately and correct errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional transaction processing systems are used to analyze fuel purchases, then basic transaction data is collected, but miscodings in fuel type or grade codes lead to incorrect analyses and prevent accurate identification of cost-saving opportunities
Solution Approach 1:
A machine learning module is introduced as an intermediary between the transaction processing system and the analysis system. This module receives transaction data including fuel codes, and outputs corrected fuel type and grade classifications. The machine learning model acts as a mediator that transforms unreliable coded data into reliable classification data, resolving the contradiction between measurement precision and reliability.
Solution Approach 2:
The patent replaces the traditional mechanical/coded classification system with a machine learning-based classification system. Instead of relying on fixed code mappings that are prone to errors, the system uses trained machine learning models to dynamically classify fuel types and grades based on multiple transaction variables, thereby improving both measurement precision and reliability.
2Measurement precision
If machine learning module is integrated to detect and correct miscodings, then accurate classification of fuel types and grades is achieved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: the original transaction processing system, the machine learning module for correction, and the analysis system. This segmentation allows the complex machine learning functionality to be added as a separate component rather than integrating it throughout the entire system, thereby managing complexity while maintaining improved classification accuracy.
Solution Approach 2:
The machine learning module serves as an intermediary layer that handles the complexity of machine learning operations independently. It receives simple transaction data and returns corrected classifications, shielding the rest of the system from machine learning complexity while still benefiting from improved accuracy.
Data Source
AI summary
A computer implemented method comprises storing a transaction variable-set corresponding to a fuel transaction for a vehicle, wherein the transaction variable-set includes a fuel consumption history for the vehicle, a net sale, a number of purchased fuel units, and a diff value. The method further comprises deriving, by a machine learning module and based on the transaction variable-set, a plurality of characteristics of the purchased fuel, wherein the plurality of characteristics includes a fuel type of the purchased fuel and a fuel grade of the purchased fuel.