Document Verification System for Omitted Content Detection
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Solution Overview
Problem
Users often inadvertently omit content such as hyperlinks, images, figures, tables, and citations in documents created using document preparation applications, which can go unnoticed until the final draft is published, leading to errors.
Innovation Solution
A content verification system utilizing natural language processing (NLP) to analyze documents and identify potential omissions by scanning for text strings that indicate intended content, providing notifications to users through a user interface element, allowing them to add missing content before saving or closing the document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually review documents for completeness, then accuracy can be improved, but time consumption increases significantly
Solution Approach 1:
The system enables the document to verify itself by automatically scanning its own content for referenced items. The computer program product autonomously identifies omissions without requiring manual user review, making the document self-checking and eliminating the need for time-consuming human verification.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computer-based system using natural language processing and pattern recognition algorithms. This substitution transforms manual document checking into an automated computational task, dramatically reducing time while maintaining accuracy.
2Measurement precision
If automated content verification is implemented, then detection precision improves, but device complexity increases
Solution Approach 1:
The computer program product is designed to perform multiple functions: parsing document text, identifying referenced content, verifying presence of referenced items, and generating notifications. This multi-functional approach consolidates what could be separate complex systems into a single integrated solution, managing complexity while maintaining high detection precision.
Solution Approach 2:
The system introduces an intermediary natural language processing layer that bridges the gap between raw document text and structured verification logic. This intermediary layer parses and interprets natural language references, transforming them into machine-checkable formats without requiring complex direct analysis of all document elements.
3Reliability
If comprehensive content scanning is performed, then completeness of verification improves, but processing speed decreases
Solution Approach 1:
The system extracts only the essential elements needed for verification: referenced content identifiers from the document text and corresponding actual content elements. By extracting and comparing only these critical pairs rather than analyzing the entire document comprehensively, the system maintains verification completeness while significantly improving processing speed.
Solution Approach 2:
The patent implements a targeted verification approach that focuses on specific referenced content items rather than performing exhaustive analysis of all document elements. This partial action approach verifies what is necessary for completeness (referenced items) without the overhead of comprehensive scanning, balancing reliability and speed.
Data Source
AI summary
A method for providing notification of content potentially omitted from within an active document in a document preparation application comprises defining a natural language model for a set of phrasal forms associating each phrasal form with a content type; parsing a textual content of the active document to generate one or more natural language tokens; accessing the natural language model to identify each of the one or more natural language tokens that matches with a phrasal form; generating a list of expected content items having an expected content item for each of the one or more natural language tokens that matches with a phrasal form; scanning the active document to attempt to locate each expected content item; and displaying a notification of each expected content item not located. Each expected content item is generated based upon the content type associated with the corresponding matching phrasal form in the natural language model.