Markup Customization Engine for Readable Text Edits

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Solution Overview

Problem

Drafters and editors face challenges in manually generating and customizing markup for text changes, which can be time-intensive and prone to errors, as they often require manual specification and rewriting, failing to accommodate individual preferences for readability and accuracy.

Innovation Solution

A system and method for auto-generating readable markup by analyzing the difference between two text sets using Levenshtein distance and inflection analysis, allowing customization through a copy threshold value to control markup complexity, thereby providing a user-friendly and accurate representation of text edits.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual markup specification is used, then accuracy of individual changes is improved, but time consumption and complexity increase

Engineering Contradiction:

Improveaccuracy of markupVSAvoidtime consumption

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating markup through algorithmic analysis of text differences. The processor autonomously compares original and modified text, identifies changes, and generates appropriate markup without requiring manual specification, thereby eliminating time consumption while maintaining accuracy through systematic analysis.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of specifying and creating markup is replaced with an automated computational system. The processor executes algorithms to analyze text differences and generate markup automatically, substituting human manual operations with machine-based automated processing that is both faster and equally accurate.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If minimal technical markup is used, then accuracy of changes is improved, but readability decreases

Engineering Contradiction:

Improveaccuracy of changesVSAvoidreadability

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system dynamically adjusts markup presentation based on user preferences and context. It can switch between minimal technical markup for accuracy and more readable markup formats, allowing the markup style to be flexible and adaptive rather than fixed, thereby satisfying both accuracy and readability requirements in different scenarios.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

Different parts of the markup are assigned different qualities based on local needs. Technical accuracy is maintained in the core change identification, while readability is enhanced in the presentation layer through customizable formatting options, allowing each aspect to optimize for its specific function.

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If customizable markup display is implemented, then user preference accommodation is improved, but system complexity increases

Engineering Contradiction:

Improveuser preference accommodationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements a universal markup generation framework that can serve multiple user preferences through a single unified system. The processor executes the same core algorithms but applies different presentation rules based on user settings, allowing one system to fulfill multiple customization needs without requiring separate systems for each preference type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10891420B1Customization engine for the auto-generation of readable markup

Publication Date: 2021.01.12 ROWAN TELS CORP

AI summary

A system and method for controlling the display of markup on a textual interface measures the difference between edited and unedited text, and utilizes values for hidden-copy tokens to create an incremental scale of values to generate different complexity levels of marked-up text to display on a user interface.