Automated Q-Code Generation from NOTAM Text
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Solution Overview
Problem
Current flight planning systems rely on human-generated Q-Codes from NOTAMs, which often contain errors, leading to suboptimal or incorrect flight plans, with up to 30% of Q-Codes containing at least one error, affecting fuel consumption and flight safety.
Innovation Solution
A system for automatically generating Q-Codes from text descriptions in NOTAMs using a learned classifier model, involving pre-processing, tokenization, one-hot encoding, and mapping to generate accurate Q-Codes, which can verify or confirm human-generated codes, reducing errors in flight planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-generated Q-Codes are used from NOTAMs, then flight planning can be performed with existing manual processes, but errors in Q-Codes occur frequently (up to 30% error rate), leading to suboptimal flight plans
Solution Approach 1:
The system enables automated Q-Code generation that self-corrects errors by using machine learning models to predict accurate Q-Codes from NOTAM text descriptions, eliminating reliance on manual human generation and achieving high accuracy without proportionally increasing system complexity
Solution Approach 2:
The patent replaces the manual mechanical process of Q-Code generation with an automated electronic system using natural language processing and machine learning algorithms, substituting human cognitive work with computational processes that achieve higher consistency and accuracy
2Measurement precision
If automated Q-Code generation is implemented using machine learning models, then Q-Code accuracy increases significantly, but system complexity and processing requirements increase
Solution Approach 1:
The system introduces natural language processing and machine learning models as intermediaries between NOTAM text descriptions and Q-Code generation, enabling accurate automated code creation without requiring direct complex rule-based systems, thereby achieving high precision with manageable computational complexity
Solution Approach 2:
The patent transforms the Q-Code generation problem into a parameter optimization problem by training machine learning models on historical data, allowing the system to learn optimal Q-Code assignments from patterns in the data rather than relying on explicit complex rules, achieving high accuracy through statistical parameter optimization
3Reliability
If manual verification of Q-Codes is performed, then some errors can be detected, but the process remains time-consuming and error-prone
Solution Approach 1:
The system performs preliminary automated verification of Q-Codes during the generation process itself, using machine learning models to predict and validate codes before final flight plan creation, thereby ensuring high reliability without adding separate time-consuming verification steps to the workflow
Data Source
AI summary
A system for automatic prediction and generation of a Q-Code based on a text description provided in a NOTAM is provided. The present system may be utilized at a top level to generate a Q-Code from a text description or at a mid-level in the flight planning process to verify and/or confirm a human-generated Q-Code based on the text description in a NOTAM. Further, the present disclosure may allow for higher accuracy in the generation of Q-Codes thereby reducing the number of incorrect suboptimal and/or rejected flight plans produced by automatic flight planning systems.