ML Classifier for Document Section Mapping
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Solution Overview
Problem
It is challenging to track and map individual sections in policy documents, such as medical clinical trial criteria, as they evolve over time due to changes in language and content, making it difficult to determine which criteria from an updated document correspond to previous versions.
Innovation Solution
A machine learning classifier is trained using sections from an initial document as both classifications and training data, allowing it to map sections from a new document to the corresponding sections in the initial document based on similarity thresholds, enabling accurate detection and tracking of changes across document versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document comparison methods are used, then document changes can be tracked, but the accuracy of mapping individual sections between document versions deteriorates when language and content change significantly
Solution Approach 1:
The patent transforms the document comparison problem from exact text matching to semantic similarity assessment by changing the parameter from literal string comparison to machine learning-based classification scores, enabling accurate section mapping even when language and content evolve between document versions
Solution Approach 2:
The patent introduces a machine learning classifier as an intermediary between document sections to assess semantic similarity. This classifier acts as a mediator that translates text sections into comparable feature representations, allowing accurate mapping despite linguistic variations between document versions
2Loss of information
If manual tracking of document sections is performed, then detailed metadata can be maintained, but the time and resources required increase significantly
Solution Approach 1:
The patent implements self-service by training the machine learning classifier to autonomously perform section mapping and metadata tracking without manual intervention. The system automatically compares document sections, generates classification scores, and maintains version tracking metadata, eliminating the need for manual document analysis while preserving complete tracking information
Solution Approach 2:
The patent replaces manual mechanical document analysis with an automated machine learning system. The classifier automatically processes document sections, generates similarity scores, and performs mapping, substituting human effort with computational processes that maintain complete metadata tracking while dramatically reducing processing time
3Difficulty of detecting and measuring
If section-by-section document comparison is performed, then detailed change detection is achieved, but the processing complexity and resource requirements increase
Solution Approach 1:
The patent applies segmentation by dividing documents into individual sections and training separate classification models for each section type. This modular approach enables detailed change detection at the section level while managing complexity through organized, reusable classification components that can be independently developed and maintained
Data Source
AI summary
A present invention embodiment detects changes between documents. A machine learning classifier is trained with one or more sections within an initial document to learn classifications for the sections. The one or more sections of the initial document serve as the classifications. One or more second sections of a second document are applied to the machine learning classifier to classify the one or more second sections. The one or more second sections are mapped to the one or more sections of the initial document based on the classification of the one or more second sections.