Template-Based Electronic Document Processing for DLP
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Solution Overview
Problem
Keyword-based searching for electronic documents is limited in retrieving relevant documents, as it may not account for synonyms or context, often resulting in irrelevant results.
Innovation Solution
A method and apparatus that analyze text content to match electronic documents with document templates, generating a template index for searching and applying Data Loss Prevention (DLP) policies based on these templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based searching is used to retrieve electronic documents, then the search process is simple and fast, but the retrieval accuracy and relevance of results deteriorate due to inability to account for synonyms and context
Solution Approach 1:
The system performs preliminary classification of electronic documents into categories using machine learning models before searching. Document templates are pre-defined for different categories (e.g., legal documents, financial records), and documents are automatically categorized during ingestion. This preliminary action enables more accurate searching by allowing users to search within specific document categories rather than across all documents, thereby improving retrieval accuracy without significantly increasing operational complexity.
Solution Approach 2:
The patent introduces document categories and templates as intermediary layers between keywords and documents. Instead of directly matching keywords to documents, the system uses categories as mediators: keywords are mapped to categories, and documents are retrieved based on category matching. This intermediary approach accounts for synonyms and contextual relevance, improving search accuracy while maintaining manageable system complexity through structured categorization.
2Reliability
If template-based processing is implemented to improve document retrieval accuracy, then the relevance of search results improves, but the system complexity and processing time increase
Solution Approach 1:
Document templates are pre-defined and configured before actual document processing begins. The system establishes category templates with expected document structures, keywords, and classification criteria in advance. During document ingestion, the machine learning model quickly matches documents against these pre-configured templates rather than creating templates on-the-fly, significantly reducing processing time while maintaining high search result relevance.
Solution Approach 2:
The machine learning model automatically classifies documents into categories and matches them with appropriate templates without requiring manual intervention for each document. The system self-adjusts and learns from document patterns, automatically refining category assignments. This automation reduces processing time by eliminating manual classification while maintaining high reliability through consistent template-based matching.
3Measurement precision
If manual classification of electronic documents is used, then the accuracy of document categorization improves, but the productivity and processing speed deteriorate
Solution Approach 1:
The system employs machine learning models that automatically classify documents into categories based on their content and structure. The model analyzes document features, identifies patterns, and assigns categories without human intervention. This self-service classification maintains high accuracy by learning from training data while dramatically increasing processing throughput compared to manual classification, as the automated system can process multiple documents simultaneously and consistently.
Solution Approach 2:
The patent replaces the mechanical process of manual document classification with an automated machine learning system. Instead of human reviewers manually examining and categorizing each document, the system uses computational algorithms to analyze document content, identify key features, and assign categories automatically. This substitution maintains categorization accuracy through trained models while increasing productivity by eliminating the time-consuming manual review process.
Data Source
AI summary
Techniques for processing electronic documents are disclosed. In one particular embodiment, the techniques may be realized as a method for processing electronic documents comprising obtaining an electronic document being sent over a network toward a destination, analyzing text content of the electronic documents to identify whether the electronic document matches any of a plurality of predefined document templates, wherein the electronic document conforms to a structure of at least one of the plurality of predefined document templates, and wherein the analyzing comprises executing at least one machine learning algorithm, the at least one machine learning algorithm trained using at least one sample electronic document having a predefined template, obtaining a document loss prevention (DLP) policy based on the at least one document template associated with the electronic document, and selectively allowing the electronic document to continue toward the destination based on the DLP policy.