Unsupervised ML for Form Structure Clustering and Adaptation
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Solution Overview
Problem
Existing software applications face challenges in automatically generating structured, machine-readable representations of forms, especially when dealing with multiple jurisdictions and regulatory changes, as rule-based approaches are not scalable and require frequent updates.
Innovation Solution
A method and system that utilize unsupervised machine learning models to derive features from geometric attributes of document elements, cluster form components into structured representations, and update these representations in a repository when changes occur, facilitating the automatic adaptation of forms to different jurisdictions and regulatory updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based approaches are used to process forms, then processing accuracy can be maintained, but scalability and adaptability to different jurisdictions deteriorate
Solution Approach 1:
The patent replaces rule-based mechanical processing systems with machine learning models that automatically learn form structures and components. The ML models analyze geometric attributes and visual patterns to identify form elements, eliminating the need for manual rule creation and adaptation when processing forms across different jurisdictions.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically adapt to new form types and jurisdictions without human intervention. The model continuously learns from new form data, automatically updating its understanding of form structures, components, and relationships across different regulatory environments.
2Ease of operation
If rule-based approaches are used to process forms, then processing logic can be controlled, but maintenance burden and scalability worsen
Solution Approach 1:
The patent replaces manual rule-based processing logic with automated machine learning-based processing. The ML model automatically learns processing logic from form data patterns, eliminating the need for manual rule maintenance and enabling scalable processing across large volumes of forms in multiple jurisdictions.
3Reliability
If manual updates are performed for regulatory changes, then accuracy can be maintained, but time consumption and productivity deteriorate
Solution Approach 1:
The system automatically detects and adapts to regulatory changes through continuous machine learning from new form data. The ML model self-updates its understanding of form structures and requirements, eliminating the need for manual updates while maintaining high accuracy in processing forms across changing regulatory environments.
Solution Approach 2:
The patent implements continuous learning and adaptation through ongoing processing of form data. The machine learning model continuously improves its understanding of form structures and regulatory requirements through uninterrupted processing, ensuring up-to-date accuracy without discrete manual update interventions.
4Adaptability or versatility
If extensive rule sets are created for different jurisdictions, then coverage can be improved, but system complexity and ease of operation worsen
Solution Approach 1:
The patent replaces complex manual rule sets with a unified machine learning system that automatically adapts to different jurisdictions. The ML model learns jurisdiction-specific form patterns directly from data, providing comprehensive coverage across multiple jurisdictions through a single, manageable system rather than numerous separate rule sets.
Data Source
AI summary
A method may include acquiring, from an initial document having a document type, initial document elements and initial attributes, deriving initial features for the initial document elements using the initial attributes, detecting initial form components using the initial features, clustering the initial form components into initial line objects of an initial structured representation by applying an unsupervised machine learning model to the geometric attributes of the initial document elements, acquiring, from a next document having the document type, next document elements and next attributes describing the next document elements, deriving next features for the next document elements using the next attributes, detecting next form components using the next features, determining that the initial form components and the next form components are different, clustering the next form components into next line objects of a next structured representation, and replacing the initial structured representation with the next structured representation.