Term Freshness Detection in Documentation via Deep Learning
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Solution Overview
Problem
The IT industry faces challenges in maintaining the freshness of terms in product documentation, as manual updates are time-consuming and error-prone, leading to outdated information that can frustrate users and hinder effective communication.
Innovation Solution
A method using a self-supervised deep learning model to compute the freshness of terms by analyzing their active year distribution space and co-occurrence patterns, allowing for automatic identification and updating of terms, either by retrieving the latest term from a database or predicting the most likely latest term.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates are used to maintain term freshness in documentation, then accuracy of terminology can be controlled, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables self-service by automatically detecting term freshness status and initiating updates without human intervention. The deep learning model autonomously analyzes documentation, identifies outdated terms, retrieves latest terms from databases, and performs replacements, allowing the system to maintain itself without continuous manual input.
Solution Approach 2:
The patent replaces the mechanical manual update process with an automated computational system. A self-supervised deep learning model processes documentation text, determines term freshness, and executes updates programmatically, substituting human labor with AI-based automation that operates continuously without fatigue.
2Reliability
If manual term updates are performed, then terminology accuracy can be maintained, but productivity of technical writers decreases
Solution Approach 1:
The system performs self-service by autonomously managing term freshness detection and updates. Technical writers no longer need to manually review and update terms, as the AI system handles these tasks independently, freeing writers to focus on higher-value content creation activities.
Solution Approach 2:
The system performs preliminary action by proactively identifying outdated terms before they cause issues. The freshness detection model continuously monitors documentation and preemptively updates terms based on predicted obsolescence, preventing terminology decay before it affects user experience.
3Loss of time
If automated freshness detection is implemented, then time for updates is reduced, but system complexity increases
Solution Approach 1:
The patent replaces complex manual review processes with a streamlined automated system. The self-supervised deep learning model processes documentation through neural networks that automatically determine term freshness and execute updates, simplifying the overall workflow despite the computational complexity of the AI models themselves.
Data Source
AI summary
The freshness of one or more terms in a documentation, indicative of a currency of the one or more terms is computed. Each term includes one or more constituent words, and the terms are visually marked as current or out-of-date based on the computed freshness. Upon marking a term as out-of-date, a latest term for the out-of-date term is retrieved or a most possible latest term for the out-of-date term is predicted.