Machine Learning Error Detection in Technical Documentation Code

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Solution Overview

Problem

Current practices for reviewing code instance data for technical documentation creation involve manual, time-intensive processes prone to human error, failing to reliably detect a variety of potential errors such as extraneous text, inappropriate wording, hidden content, and missing plugins, which can lead to flawed documentation.

Innovation Solution

Utilizing a convolutional neural network and natural language processing techniques to generate potential error classifications, perform correlation analysis with historical data, and calculate scores for likelihood of errors, outputting notifications when scores exceed a threshold.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual review processes are used to check code instance data, then human oversight is provided, but the process is time-intensive and prone to human error

Engineering Contradiction:

Improveerror detection accuracyVSAvoidreview time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical review process with an automated machine learning system that uses natural language processing and convolutional neural networks to analyze code instance data, eliminating human time investment while maintaining or improving detection accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service error detection by automatically analyzing code instance data without requiring human reviewers, using trained machine learning models to identify potential errors independently

Inventive Principle:

Principle #25Self-service

2Reliability

If manual review is used to detect errors, then some errors are identified, but many potential errors such as extraneous text, inappropriate wording, hidden content, and missing plugins are not reliably detected

Engineering Contradiction:

Improveerror detection coverageVSAvoidreview process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The machine learning system performs multiple error detection functions simultaneously - identifying extraneous text, inappropriate wording, hidden content, missing plugins, and other error types through a single unified analysis process, providing comprehensive coverage without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The automated ML system replaces manual review mechanics with algorithmic analysis that systematically evaluates code instance data for multiple error types, achieving broader detection coverage through computational rather than human processes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If traditional error checking is performed, then basic errors are caught, but proactive identification of potential errors before publication is insufficient

Engineering Contradiction:

Improveproactive error identificationVSAvoidautomation level

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The system performs preliminary error identification by analyzing code instance data before publication occurs, using machine learning models to predict potential errors in advance, allowing corrective action to be taken before flawed content is released

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional reactive error checking with proactive automated analysis using machine learning, enabling the system to anticipate and identify potential errors before they manifest in published documentation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12405876B2Proactively identifying errors in technical documentation code

Publication Date: 2025.09.02 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

An embodiment for a method of using machine learning algorithms to proactively identify potential errors in code instance data for creating technical documentation. The embodiment may receive code instance data. The embodiment may generate error classifications for the received code instance data using a convolutional neural network and natural language processing techniques. The embodiment may perform a correlation analysis to derive correlations between the generated error classifications for the received code instance data and similarly occurring classifications in one or more historical code instances. The embodiment may calculate a score for each of the derived correlations, the calculated score corresponding to a likelihood that the similarly occurring classifications in the one or more other code instances represents a potential error. The embodiment may output notifications to a user for each of the derived correlations for which the calculated score exceeds a threshold value.