Description: With the advent of Natural Language Processing (NLP) and Artificial Intelligence (AI) applications growing in the last decade, many aspects of NLP and AI are ready to be applied to new problems in new domains. This SBIR effort specifically merges NLP and AI technologies in a system that is designed to ensure aviation systems safety. A combination of speech analytics, voice-to-text conversion, intent inference, and anomaly detection are implemented to form a real-time monitoring of system safety.

Benefits: This effort addresses In-Time System-Wide Safety Assurance (ISSA) objectives of NASA’s Airspace Operations and Safety Program (AOSP) System Wide Safety (SWS) Project: monitor the ATM system continuously and to extract and fuse information from diverse data sources (voice, ADS-B, weather, map, etc.) to identify emergent anomalous behaviors add new intent models as new rules are introduced in the NAS (for instance, with respect to new UAM/AAM concepts and vehicles researched by NASA)

Airline dispatcher positions will benefit from this technology by providing real-time monitoring of pilot-controller dialog and conformance to the controller directives. Non-conformance can be immediately notified to airline dispatchers as a safety net.