Document Section Identification via Metadata and Bullet Points
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Solution Overview
Problem
Existing methods for identifying and processing complex digital documents with hierarchical structures and varied language are inefficient, relying on manual review or processor-intensive machine learning techniques that struggle with dynamic document formats and require extensive annotated training data.
Innovation Solution
A computer system that converts documents into a searchable hierarchical data structure by generating metadata from text alignment, position, spacing, and fonts, using bullet points to identify sections and subsections, and employing fuzzification to handle minor indentation discrepancies, thereby reducing processing time and improving adaptability across different document templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review is used to identify document sections, then precision of section identification is improved, but processing time increases significantly and errors may occur
Solution Approach 1:
The system performs self-service by automatically identifying document sections through metadata analysis without requiring manual review. The computer system extracts positional metadata, identifies bullet point strings, and generates hierarchical structures autonomously, eliminating the need for human reviewers while maintaining high precision through algorithmic analysis of document structure patterns.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computerized system that uses metadata extraction and pattern recognition algorithms. Instead of human eyes and brains analyzing document structure, the system uses computational methods to identify bullet point strings, determine hierarchical relationships, and generate searchable structures automatically.
2Productivity
If basic computerized analysis with sliding window approach is used, then processing speed is improved, but adaptability to diverse document formats deteriorates
Solution Approach 1:
The system achieves universality by designing a metadata-based analysis approach that works across diverse document formats and templates. Instead of relying on format-specific rules, the system extracts general positional metadata (coordinates, indentation levels, spacing) that are common to various document types, enabling the same algorithm to adaptively identify sections in different document structures without requiring format-specific customization.
Solution Approach 2:
The patent uses parameter changes by analyzing positional metadata parameters (coordinates, indentation levels, spacing distances) rather than fixed structural patterns. The system identifies bullet point strings by evaluating these dynamic parameters, allowing it to adapt to different document templates where section positions and formatting may vary. The fuzzy logic component adjusts parameter thresholds based on document characteristics.
3Adaptability or versatility
If advanced machine learning techniques are used, then dynamic analysis capability is improved, but processing complexity and computation time increase
Solution Approach 1:
The system extracts only the essential positional metadata from documents (coordinates, indentation levels, spacing) rather than processing entire document contents through complex machine learning models. By taking out and analyzing only the structural metadata elements that define section boundaries, the system achieves dynamic adaptability without the computational overhead of training and running large neural networks on full document texts.
Solution Approach 2:
The patent employs simpler, more efficient algorithms that are computationally inexpensive compared to advanced machine learning techniques. Instead of using processor-intensive neural networks, the system uses straightforward metadata extraction, pattern matching for bullet point strings, and fuzzy logic comparisons that require minimal computational resources, making the approach more practical for routine document processing.
4Adaptability or versatility
If advanced machine learning techniques are used, then dynamic analysis capability is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary action by pre-extracting and organizing positional metadata from documents before conducting section identification. The metadata (coordinates, indentation levels, spacing) is extracted and structured in advance, creating a ready-to-analyze framework that enables rapid section boundary detection without requiring time-consuming processing of the entire document content during the analysis phase.
5Measurement precision
If machine learning with annotated training data is used, then analysis accuracy is improved, but data preparation complexity and cost increase
Solution Approach 1:
The system achieves self-service by using metadata that is automatically generated during document conversion and extraction, eliminating the need for manual annotation of training data. The positional metadata (coordinates, indentation, spacing) is inherently present in digital document formats and can be extracted programmatically, providing the system with the information it needs to identify sections accurately without requiring human-labeled training datasets.
Data Source
AI summary
Methods and apparatuses are described for automatically identifying text sections of a document to generate a searchable hierarchical data structure. A computing device receives a document comprising text entities and converts the document from a first format to a second format, including generating metadata associated with text alignment, text position, text spacing, or fonts. The computing device extracts the text blocks, including determining coordinates associated with each text block using the metadata. The computing device determines document sections using the document metadata by identifying strings in the extracted text blocks that indicate a presence of a bullet point in the document, assigns a hierarchical category to each identified document section, and inserts text of each document section into a hierarchical data structure based upon the assigned hierarchical category. The computing device traverses the hierarchical data structure using search request data to identify document sections relating to the search request data.