ML Document Processing System for Compliance Analysis
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Solution Overview
Problem
Current systems lack a consistent solution for managing document changes and assessing their impact across an enterprise, particularly in analyzing regulations, which are often embedded in dense documents, leading to duplicative and overlapping requirements, making compliance assessments time-consuming and inefficient.
Innovation Solution
A document processing system utilizing machine learning to analyze, classify, and map text within documents, including processes for deconstructing documents, identifying relationships, and generating custom machine learning models to classify sentences and identify commonality, thereby reducing the complexity of compliance assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing manual techniques are used to deconstruct regulations into line level requirements, then comprehensive analysis can be achieved, but the process takes over a year to complete
Solution Approach 1:
The patent replaces manual mechanical analysis with machine learning-based automated text processing. The system uses natural language processing and machine learning models to automatically deconstruct regulations into line-level requirements, mapping them to enterprise policies and controls without human intervention, thereby reducing processing time from over a year to a fraction of that time while maintaining comprehensive analysis capability
Solution Approach 2:
The patent transforms the analysis process by changing the operational parameters from manual line-by-line review to automated machine learning processing. The system processes entire documents at scale simultaneously, using trained models to identify requirements, map them to policies, and generate compliance assessments automatically, fundamentally altering the speed and efficiency parameters of the analysis process
2Reliability
If regulations are analyzed in dense documents containing thousands of regulations, then complete regulatory coverage is achieved, but identifying particular regulations becomes technically challenging
Solution Approach 1:
The patent replaces manual regulation identification with machine learning-based automated detection. The system uses natural language processing to scan dense documents, automatically identify individual regulations within them, extract requirements, and map them to enterprise policies. This automated approach overcomes the technical challenge of locating particular regulations in documents containing thousands of regulations while ensuring complete regulatory coverage
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the dense regulatory documents and the enterprise policy mapping system. These models act as intelligent mediators that parse, understand, and structure the unstructured text, identifying regulations and their requirements automatically, thereby simplifying the complex task of regulation detection in dense documents
3Measurement precision
If each regulation is interpreted to understand how it applies to policies and controls, then accurate compliance assessment is achieved, but the complexity of the process increases
Solution Approach 1:
The patent creates a universal machine learning-based platform that handles multiple functions: interpreting regulations, mapping them to enterprise policies, identifying controls, and generating compliance assessments. This multi-functional system replaces multiple separate manual processes with a single integrated automated platform, reducing overall process complexity while maintaining accurate compliance assessment across all regulations
Solution Approach 2:
The patent replaces complex manual interpretation processes with automated machine learning models. The system uses natural language processing to understand regulation meaning, automatically map requirements to relevant policies and controls, and generate compliance assessments without human intervention, thereby reducing process complexity while maintaining or improving assessment accuracy
4Reliability
If duplicative and overlapping regulations are identified and analyzed, then comprehensive compliance coverage is ensured, but the time required for compliance assessments increases
Solution Approach 1:
The patent replaces manual analysis of duplicative regulations with automated machine learning processing. The system simultaneously processes all regulations, identifies duplicative or overlapping requirements through pattern recognition, and maps them to enterprise policies in parallel. This automated approach maintains comprehensive compliance coverage by catching all requirements while dramatically reducing the time needed compared to sequential manual analysis
Data Source
AI summary
A device that includes an enterprise data indexing engine (EDIE) configured to obtain a set of sentences from a document and to compare the words from each of the sentences to a set of keywords. The EDIE is further configured to identify one or more sentences that do not contain any of the keywords and to associate the identified sentences with a first classification type. The EDIE is further configured to identify a sentence that contains one or more keywords and to associate the sentence with a second classification type. The EDIE is further configured to link together the sentence that is associated with the second classification type and the sentences that are associated with the first classification type. The EDIE is further configured to obtain a classification description and a token and to link the classification description and its token with the classified sentences.