Machine Learning Code Origin Prediction via Image Embedding
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Solution Overview
Problem
The existing systems for tracking shipments using alphanumeric codes face challenges when incorrect information is provided, leading to failures in determining the origin of the code, which hampers the ability of consumers and payment service providers to track shipments accurately.
Innovation Solution
A machine learning-based code analysis system that embeds alphanumeric codes into images and uses these images as input for a machine learning model to predict the origin of the code, improving prediction accuracy by recognizing patterns in the image format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional code analysis methods are used to determine the origin of alphanumeric codes, then the system is simple and easy to implement, but the prediction accuracy is low when incorrect information is provided
Solution Approach 1:
The patent introduces an intermediary processing step that converts alphanumeric codes into image format before analysis. This intermediary representation allows machine learning models to recognize patterns more effectively, achieving over 95% prediction accuracy even when information is incorrect, while maintaining reasonable system complexity through the use of existing image processing and ML libraries.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based code analysis methods with a machine learning-based system. By substituting the analytical mechanism from deterministic rule-checking to probabilistic pattern recognition, the system achieves significantly higher accuracy in determining code origins, particularly when dealing with incorrect or noisy input data.
2Measurement precision
If machine learning models are trained with large amounts of data to improve prediction accuracy, then the prediction accuracy increases, but the training time and computational resources increase
Solution Approach 1:
The patent applies partial training by focusing on the most critical features and patterns for code origin determination. Instead of exhaustively training on all possible code variations, the system trains on representative samples that capture the essential characteristics needed for high-accuracy prediction, thereby reducing training time while maintaining over 95% accuracy in practical applications.
Data Source
AI summary
Methods and systems for using machine learning to determine an origin of an alphanumeric code are presented. The code is analyzed to determine a set of characteristics based on the values and the types of values within the code. The code and the set of characteristics may then be embedded into an image. To embed the code and the set of characteristics into an image, each of the code and the set of characteristics may be converted into a corresponding set of pixel values. The pixel values may then be combined to form the pixels in the image. The image is provided to a machine learning model as input. The machine learning model is configured to use the image to determine at least one entity that is associated with the code.