Text Readability Analysis System for Concise Editing
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Solution Overview
Problem
Current automated grammar checkers are inadequate in providing precise and effective editing suggestions for improving writing conciseness, as they often suggest inaccurate or undesirable edits and are cumbersome to use.
Innovation Solution
A text analysis system that utilizes a database of rules to analyze authored documents, identifying and suggesting edits to remove unnecessary words, passive voice, and other writing issues, with a user interface for accepting or rejecting suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated grammar checkers are used to analyze text, then writing problems can be identified, but the suggestions provided are often inaccurate or undesirable
Solution Approach 1:
The system segments the text analysis process into distinct rule-based modules, each targeting specific writing problems (passive voice, wordiness, clarity issues). By dividing the complex task of text improvement into manageable, specialized components, the system achieves more accurate and reliable suggestions for each type of writing issue.
Solution Approach 2:
The system changes the operational parameters of text analysis by using configurable rules with adjustable sensitivity and specificity thresholds. This allows the system to adapt its analysis depth and suggestion accuracy based on the specific text being analyzed, improving overall reliability while maintaining precision.
2Manufacturing precision
If comprehensive text analysis rules are applied to improve writing conciseness, then readability improves, but the system complexity increases
Solution Approach 1:
The system divides comprehensive text analysis into separate, modular rule sets that can be independently applied. Each rule module focuses on specific aspects of writing quality (conciseness, active voice, sentence structure), allowing the system to achieve comprehensive improvement without requiring a monolithic complex system.
Solution Approach 2:
The system applies preliminary filtering and prioritization rules that identify the most impactful writing issues first. By addressing high-priority problems before lower-priority ones, the system achieves significant readability improvement with fewer rules, reducing overall system complexity while maintaining effectiveness.
3Productivity
If automated editing suggestions are provided, then writing efficiency improves, but user control over edits is reduced
Solution Approach 1:
The system implements feedback mechanisms where user acceptance or rejection of editing suggestions is tracked and used to refine future suggestions. This creates a collaborative loop that maintains high writing efficiency through automation while preserving and enhancing user control over the editing process.
Solution Approach 2:
The system dynamically adjusts its level of automation based on user preferences and interaction patterns. Users can configure the degree of automated application versus manual review of suggestions, allowing the system to adapt between high efficiency (more automation) and high user control (less automation) based on specific needs.
Data Source
AI summary
Computer-based processes are disclosed for analyzing and improving document readability. Document readability is improved by using rules and associated logic to automatically detect various types of writing problems and to make and/or suggest edits for eliminating such problems. Many of the rules seek to generate more concise formulations of the analyzed sentences, such as by eliminating unnecessary words, rearranging words and phrases, and making various other types of edits. Proposed edits can be conveyed, e.g., through a word processing platform, by changing the visual appearance of text to indicate how the text would appear with (or with and without) the edit.