AI Document Analysis for Restricted Information Control
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Solution Overview
Problem
Existing computer systems are unable to effectively identify and control access to restricted information within documents, relying on inefficient keyword searches that consume significant memory and processing resources, limiting system throughput.
Innovation Solution
A document analysis system employing artificial intelligence and machine learning to analyze text, tag specific sentence types, train machine learning models, determine sentence similarities, and identify outlier sentences, thereby improving the accuracy and efficiency of information access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If keyword searches are used for controlling data access, then the system can identify restricted information, but the system consumes significant memory and processing resources, limiting throughput
Solution Approach 1:
The patent replaces the mechanical keyword search system with a machine learning-based natural language processing system. The ML model learns semantic representations of text and can identify restricted information through contextual understanding rather than exact keyword matching, significantly reducing processing resources while maintaining or improving detection accuracy
Solution Approach 2:
The patent transforms the search parameters from exact keyword matching to semantic similarity scoring. By changing the parameter space from discrete keywords to continuous semantic vectors, the system can efficiently identify restricted information with reduced computational overhead through optimized similarity searches in vector space
2Measurement precision
If a large set of keywords is used to describe a single term or concept, then the system can improve detection accuracy, but it consumes significant memory resources
Solution Approach 1:
The patent merges numerous individual keywords into a single unified machine learning model. The model learns to represent multiple related concepts and their variations through shared semantic vectors, eliminating the need to store and process separate keyword lists while maintaining comprehensive detection coverage
Solution Approach 2:
The patent uses learned semantic vector representations that capture the essence of multiple keywords and concepts in a compressed form. These vector embeddings serve as efficient copies that retain the semantic meaning of large keyword sets without requiring the original extensive keyword lists to be stored in memory
3Reliability
If keyword searches are used to search documents, then the system can identify restricted information, but it requires significant processing resources
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model on extensive corpora before deployment. The model learns semantic relationships and patterns in advance, enabling it to efficiently identify restricted information during runtime without requiring intensive processing resources for each document search
Data Source
AI summary
A document analysis device that includes a memory operable to store a machine learning model configured to receive a sentence as an input and to output a classification identifier that is associated with a sentence type for the received sentence. The device further includes an artificial intelligence (AI) processing engine configured to receive a document comprising text, to sentences within the document, and to classify the sentences using the machine learning model. The AI processing engine is further configured to identify tagging rules for the document and to annotate one or more sentences from the document with a sentence type that matches a sentence type that is identified by the tagging rules for the document.