Machine-Learned Document Compliance Classification System
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Solution Overview
Problem
The increasing number of documents requiring compliance review leads to inefficiencies, as compliance associates spend excessive time reviewing documents, identifying non-compliant content, and making edits, often requiring multiple rounds of revisions.
Innovation Solution
A computer system utilizing a machine learning model trained on historical documents labeled as compliant or non-compliant, which automates document compliance processing by classifying documents based on compliance scores and providing feedback for revisions, thereby streamlining the review process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual compliance review process is used, then compliance accuracy is maintained, but review time and processing duration increase significantly
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the document and compliance reviewers. The model pre-analyzes documents, identifies potential compliance issues, and generates suggestions before human reviewers examine the documents. This intermediary system filters and prioritizes content, allowing reviewers to focus on critical issues rather than performing exhaustive manual checks, thereby reducing review time while maintaining compliance accuracy.
Solution Approach 2:
The system performs preliminary compliance analysis using the machine learning model before documents reach human reviewers. The model pre-identifies non-compliant content, generates correction suggestions, and ranks documents by compliance risk. This preliminary action prepares documents in advance, reducing the time required for manual review while ensuring that compliance issues are not missed.
2Quantity of substance
If more documents are reviewed manually, then compliance coverage increases, but resource consumption and processing time increase
Solution Approach 1:
The machine learning model enables documents to undergo self-service compliance screening before reaching human reviewers. The model automatically analyzes document content, identifies compliance issues, and generates correction suggestions without requiring immediate human intervention. This self-service capability allows the system to handle a larger volume of documents efficiently, improving productivity while maintaining comprehensive compliance coverage.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated machine learning-based analysis system. The model processes documents using computational algorithms rather than human cognitive processes, enabling faster processing of larger document volumes. This substitution maintains compliance coverage while significantly improving processing efficiency and reducing resource consumption.
3Reliability
If multiple rounds of revisions are required, then compliance quality is ensured, but time loss and processing duration increase
Solution Approach 1:
The machine learning model provides immediate feedback to document creators by identifying specific compliance issues and generating actionable correction suggestions. The system analyzes document content, pinpoints non-compliant elements, and offers targeted recommendations for improvement. This feedback mechanism enables document creators to make precise revisions in a single pass, reducing the need for multiple revision rounds while ensuring compliance quality is maintained.
4Productivity
If automated processing is implemented, then processing speed and productivity improve, but system complexity increases
Solution Approach 1:
The patent implements a universal machine learning model that performs multiple compliance analysis functions within a single system. The model can identify various types of compliance issues, generate different kinds of corrections, and adapt to different document types and compliance requirements. This multi-functionality consolidates what would otherwise require multiple separate systems, managing complexity while delivering comprehensive automated processing at high speed.
Data Source
AI summary
A computer system includes memory configured to store a document database and a machine learning model. The document database includes multiple historical documents each having at least one version labeled as compliant and at least one version labeled as non-compliant. The system includes a creator user interface, a compliance user interface, an automated distribution module, and a model building module configured to train the machine learning model to classify a document according to a compliance score indicating a likelihood of document compliance with one or more compliance criteria. The system also includes an orchestrator module configured to receive the compliance score for the submitted document from the machine learning model, determine whether the compliance score is greater than or equal to a compliance score threshold, and supply the submitted document to the compliance user interface for transmission to the compliance team device when the compliance score is above a threshold.