Document Structure Identification via Visual Indicators
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Solution Overview
Problem
Existing content analysis applications neglect the visual layout and structure of documents, leading to inefficiencies in processing diverse document types, as they rely solely on textual content and fail to adapt to variations in section headings and keywords across different organizations.
Innovation Solution
A system and method that identifies and utilizes visual indicators, section types, and sub-section constructs to segment documents into sections and sub-sections, learning new keywords and constructs to process documents with similar types but different formatting, enabling adaptive processing across various document collections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple manually created modules are used to detect section types for different organizations, then processing accuracy for each organization is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent applies universality by creating a single processing module that can handle multiple document types and organizational formats through a unified section type detection mechanism. The system uses a database of section type keywords and visual indicator patterns that can be universally applied across different organizations, eliminating the need for separate manual modules for each organization while maintaining high detection accuracy.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting detection parameters based on document characteristics. The system modifies keyword databases, visual indicator weights, and section type classification criteria adaptively to match different organizational formats without requiring manual module creation. This allows the same core system to adapt to varying document structures through parameter adjustment rather than structural reconfiguration.
2Productivity
If text content analysis is performed without considering visual layout and structure, then processing speed is maintained, but analysis reliability and contextual accuracy deteriorate
Solution Approach 1:
The patent merges text content analysis with visual layout analysis by integrating multiple detection mechanisms into a unified processing pipeline. The system combines textual keyword matching, visual indicator recognition (such as font size, bolding, underlining), and structural pattern analysis to simultaneously extract both content and contextual information. This integration allows the system to maintain processing efficiency while significantly improving analysis reliability through multi-dimensional data utilization.
Solution Approach 2:
The patent applies dimensionality change by adding visual and structural dimensions to traditional text-based analysis. Instead of analyzing only textual content, the system incorporates visual characteristics (font properties, spacing, positioning) and document structure (section hierarchy, heading levels) as additional analysis dimensions. This multi-dimensional approach enriches the analysis without requiring complete reprocessing, thereby maintaining productivity while enhancing reliability.
3Stability of the object's composition
If section segmentation is performed without adaptive learning of new keywords and constructs, then processing consistency is maintained, but adaptability to new document formats decreases
Solution Approach 1:
The patent implements feedback mechanisms that allow the system to learn from processed documents and update its knowledge base. The system incorporates feedback loops where detected section types, keywords, and visual patterns are fed back into the database for refinement and expansion. This enables the system to adapt to new document formats while maintaining processing consistency, as the feedback-driven updates occur gradually and systematically rather than disrupting established processing workflows.
Solution Approach 2:
The patent applies dynamics by making the detection system adaptable and evolving rather than static. The system dynamically updates its keyword databases, visual indicator patterns, and section type classifications based on encountered document variations. This dynamic capability allows the system to maintain consistency in its core processing logic while adapting to new formats through continuous, incremental learning without requiring manual reconfiguration.
Data Source
AI summary
A system and method for processing documents by utilizing the textual content and layout of the documents, including visual indicators, to more efficiently and reliably process the documents across various document types. The system and method identifies visually distinguishable elements within the document, such as section and sub-section boundary indicators, to mark, divide and label the boundaries and content type such that the sections are more clearly identifiable and easily processed. The system and method uses known elements, including section heading types, keywords, section type classifiers, sub-section heading constructs, stop words, and the like to adaptively identify and process a broad range of document types. The system and method continually refines and updates these known elements and allows users to discover and define new elements for further refinement and updating.