This defensive publication discloses a comprehensive Artificial Intelligence-Based Universal Communications (UNICOM) Advisory System designed for deployment at non-towered airports. The system combines fuzzy-logic inference and machine-learning (ML) models within a hybrid architecture to generate real-time, context-sensitive traffic advisories for pilots operating in uncontrolled airspace. Unlike deterministic rule-based systems that produce binary outputs (alert or no alert) based on fixed thresholds, the disclosed system generates graded, confidence-weighted advisories that adapt continuously to the full spectrum of operational conditions — including traffic density, conflict geometry, weather degradation, runway occupancy ambiguity, and non-standard traffic patterns. The system ingests multiple data streams — including Automatic Dependent Surveillance-Broadcast (ADS-B) position reports, Common Traffic Advisory Frequency (CTAF) audio processed through speech-to-text, weather sensor data (AWOS/ASOS or on-site instrumentation), and Notice to Air Missions (NOTAM) / Temporary Flight Restriction (TFR) feeds — and fuses them into a unified traffic and environmental picture. A fuzzy-logic subsystem applies approximately 150 expert-derived rules across linguistic variables representing traffic density, conflict proximity, convergence rate, weather severity, runway occupancy state, and pattern position conflict. Concurrently, machine-learning subsystems — including Long Short-Term Memory (LSTM) networks for trajectory prediction, gradient-boosted classifiers for conflict detection, isolation-forest anomaly detectors, and reinforcement-learning agents for advisory timing optimization — provide continuously valued inputs to the fuzzy inference engine. The system produces natural-language advisories using FAA-standard phraseology, delivered via VHF radio text-to-speech synthesis, digital datalink (FIS-B), or Electronic Flight Bag (EFB) application integration. Critically, the system is advisory-only: it does not issue air traffic control clearances or instructions, and it is designed to complement — not replace — existing CTAF/UNICOM self-announce procedures as described in FAA Advisory Circular 90-66C. Seven distinct physical embodiments are disclosed to maximize the scope of prior art established by this publication. This disclosure covers all novel aspects of fuzzy-ML hybrid inference for aviation traffic advisories, including specific membership function definitions, rule base structures, ML model architectures, integration patterns, and operational workflows.Version 2 (April 24th, 2026) adds a companion addendum document extending the original prior art disclosure with four new sections. Section 14 introduces a computer vision subsystem using multi-modal camera arrays (visible-spectrum, infrared, NIR, and PTZ) for aircraft detection, classification, and NORDO identification independent of ADS-B or radio communication. Section 15 extends the system to accommodate eVTOL, UAS/drone, and Advanced Air Mobility (AAM) vehicles, including Remote ID integration and a UTM interface. Section 16 defines a modular Communications Interface Abstraction Layer (CIAL) that decouples the advisory engine from any specific transport protocol, enabling future machine-to-machine communication for fully autonomous aircraft operations. Section 17 describes a progressive scalability path from single non-towered airport deployment through multi-airport networks, terminal area management, towered airport augmentation, en-route airspace advisory, and ultimately autonomous system-wide air traffic management. Twelve additional claims of novelty are disclosed.
Jeffrey David Booker (Fri,) studied this question.