Document Structure Detection Using Marker Sequence Eligibility
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Solution Overview
Problem
Existing methods for navigating and identifying the structure of legal documents, especially in digital form, are limited by reliance on formatting, language, and the need for pre-existing tables of contents, making it difficult to efficiently and universally access sections and subsections.
Innovation Solution
A computer-implemented method that identifies marker sequences in a document string to automatically detect sections, subsections, and numbered lists without relying on formatting or language-specific features, using eligibility calculations to generate output data that enhances document navigability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If document structure identification relies on formatting and styling information, then structure detection accuracy is improved, but applicability to diverse document formats deteriorates
Solution Approach 1:
The patent extracts and removes formatting and styling information from the document analysis process. By focusing solely on the textual content and sequential markers (such as numbered lists and section headings), the system achieves universal applicability across different document formats while maintaining structure detection accuracy through content-based patterns rather than format-dependent features.
Solution Approach 2:
The patent creates a universal document structure identification method that works across multiple document formats (plain text, HTML, PDF, Word) by using format-independent sequential marker detection. The system identifies sections, subsections, and lists through content patterns that are consistent across formats, making the solution broadly applicable without requiring format-specific processing.
2Ease of operation
If table of contents is added to improve document navigation, then accessibility to sections is improved, but document complexity and manual effort increase
Solution Approach 1:
The patent implements self-service document navigation by automatically generating tables of contents and section identifiers from the document's inherent sequential markers. The system detects section headings, numbered lists, and hierarchical structures within the content itself, then automatically creates navigation aids without requiring manual intervention or adding complex structural elements to the original document.
Solution Approach 2:
The patent performs preliminary analysis of document structure by identifying sequential markers and hierarchical relationships before generating navigation aids. This preliminary detection of sections, subsections, and lists enables the system to pre-generate accurate tables of contents and navigation structures that reflect the actual document organization without requiring post-processing or manual adjustments.
3Productivity
If known structure identification methods are used, then processing speed is improved, but language and format limitations increase
Solution Approach 1:
The patent segments the document analysis process into distinct stages: detection of sequential markers (numbers, letters, symbols), identification of hierarchical relationships, and generation of structure information. This segmentation allows the system to process different document types and languages through the same efficient framework, maintaining high processing speed while achieving broad adaptability by handling each segment in a format- and language-agnostic manner.
Data Source
AI summary
A computer implemented method for identifying one or more data elements in an input data stream formed from an input data structure using processing enabled by a processor-executable instruction can be stored on a non-transitory computerized storage medium. This stored method includes identifying a marker sequence set in the input document string, the marker sequence set including a marker sequence; assigning an eligibility value to each marker sequence, the eligibility value being a number calculated using an eligibility calculation, to obtain at least one eligible marker sequence; generating output data comprising data related to the at least one eligible marker sequence; and providing the output data, such as on a human-readable interface.