Hierarchical Document Parsing for Context-Preserving AI Extraction
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Solution Overview
Problem
Traditional document parsers fail to consider the hierarchical structure of documents, leading to loss of context and inaccurate data extraction and interpretation by generative AI models.
Innovation Solution
A system that analyzes documents to identify formatting attributes, calculates statistical measures, and establishes hierarchical relationships between elements, parsing the document into chunks while preserving context, and storing the representation as a vector for machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document parsers are used to extract data from documents, then the parsing process is simple and fast, but the hierarchical structure and context of the document are lost, leading to inaccurate data extraction and interpretation
Solution Approach 1:
The patent segments the document into a hierarchical structure with multiple levels (e.g., document level, section level, paragraph level, sentence level). Each level is processed separately to extract relevant features while maintaining the hierarchical relationships. This segmentation allows the system to capture both the global structure and local details, improving data extraction accuracy without overwhelming complexity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the document parsing process by organizing extracted elements into multiple nested levels. Instead of treating all text as a flat sequence, the system creates a multi-dimensional structure where parent-child relationships are preserved. This dimensional transformation enables the model to understand context at different granularities simultaneously.
2Loss of information
If hierarchical structure and formatting attributes are analyzed in detail, then context preservation is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant formatting attributes and hierarchical features needed for context preservation, rather than processing all possible document properties. By selectively extracting key features such as heading levels, paragraph structures, and semantic relationships, the system maintains context while reducing unnecessary computational overhead and processing time.
Solution Approach 2:
The patent performs preliminary analysis of the document structure before main processing, identifying and tagging hierarchical elements in advance. This preliminary action organizes the document into a structured format that facilitates faster subsequent processing, as the hierarchical relationships are already established and can be referenced efficiently during the main extraction and interpretation phases.
3Measurement precision
If statistical measures of formatting attributes are calculated to identify element types, then the accuracy of hierarchical relationship identification is improved, but the computational complexity increases
Solution Approach 1:
The patent applies different statistical measurement strategies to different document elements based on their local characteristics. For example, heading elements may be analyzed using font size and boldness statistics, while paragraph elements use spacing and indentation statistics. This localized approach improves element type identification accuracy by tailoring the analysis to each element's specific formatting properties, while avoiding the need to compute all possible statistical measures for every element.
Data Source
AI summary
A document is analyzed to identify attributes of the document. A set of statistical measures are calculated for each of the formatting attributes. A correlation between these statistical measures and elements of the document are identified. A hierarchical relationship is determined between the elements of the document. Data from the document is split into chunks using the hierarchical relationship. A representation of the document that includes the hierarchical structure is then generated. The representation is stored in a data store as a vector usable by a machine learning model to perform a query on the document according to the hierarchical structure.