Elastic Graph Document Tracking via Hierarchical Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document tracking systems face challenges in efficiently analyzing and comparing highly-sensitive documents due to complexities in processing sources, life cycles, and interdependencies, particularly in identifying and extracting critical information across different structures and contexts.
Innovation Solution
The method employs elastic graph-based hierarchical analysis, using natural language processing and customization libraries to identify markers, generate elastic graphs, and compare documents based on markers, weights, and context-specific metrics, allowing for the tracking of document contents and origins even across edited or rearranged text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional document analysis methods are used to track sensitive documents, then the processing of document sources and life cycles can be performed, but the complexity of processing interdependencies and extracting critical information across different structures and contexts increases significantly
Solution Approach 1:
The patent segments document analysis into hierarchical levels (full document, sections, paragraphs, sentences, phrases) and creates separate elastic graphs for each level. This segmentation allows the system to process complex documents by breaking them down into manageable components, reducing overall processing complexity while maintaining tracking accuracy through multi-level analysis.
Solution Approach 2:
The patent introduces elastic graphs as an intermediary representation between raw document text and analysis results. These graphs serve as a mediator that captures document structure, semantics, and relationships in a standardized format, simplifying the processing of interdependencies and enabling efficient comparison across different document structures and contexts.
2Adaptability or versatility
If document structure analysis is performed using natural language processing to identify markers, then the ability to compare documents with different structures improves, but the computational resources and processing time required increase
Solution Approach 1:
The patent performs preliminary action by pre-processing documents to identify and mark critical information elements before comparison. The system extracts and tags markers (entities, relationships, attributes) during the graph construction phase, so that when documents need to be compared, the analysis can proceed more quickly using the pre-identified markers rather than performing full NLP analysis at comparison time.
Solution Approach 2:
The patent implements dynamic marker identification where the system adapts marker selection based on document type, sector, and criticality level. The customization library allows markers to be dynamically adjusted according to document characteristics, enabling the system to focus computational resources on identifying the most relevant markers for each specific document rather than analyzing all possible elements uniformly.
3Measurement precision
If customization libraries are used to retrieve sector-specific analysis parameters, then the precision of document comparison increases, but the complexity of managing and retrieving customization libraries increases
Solution Approach 1:
The patent creates a universal customization library framework that serves multiple functions: storing sector-specific parameters, defining marker types, setting comparison thresholds, and managing document metadata schemas. This single multi-functional library system reduces the need for separate management mechanisms for each function, simplifying overall library management while maintaining high comparison precision through sector-specific customization.
4Loss of information
If elastic graphs are generated for multiple documents and compared to identify content origin and relationships, then the ability to track document provenance improves, but the computational complexity of generating and comparing graphs increases
Solution Approach 1:
The patent extracts only the essential elements needed for tracking and comparison when generating elastic graphs. Rather than processing entire documents, the system extracts markers, relationships, and structural elements that are critical for provenance tracking. This extraction approach preserves necessary information for tracking while reducing graph processing complexity by focusing only on relevant document components.
Data Source
AI summary
Systems and methods for document tracking using elastic graph-based hierarchical analysis are disclosed. According to one embodiment, a method may include at least one computer processor identifying a sector for the document; the at least one computer processor retrieving a customization library for the document based on the sector; the at least one computer processor analyzing a structure of the document using the customization library, whereby a plurality of markers in first the document are identified; and the at least one computer processor generating an elastic graph for the document based on the plurality of markers.