How AI Helps Pilots Talk to Flight Management Systems

A system that uses artificial intelligence to understand pilot requests and automatically trigger the right flight data or software services.

What it covers

This patent describes a way to make flight management systems (FMS) smarter by using reinforcement learning—a type of AI that learns by trial and error. When a pilot or crew member sends a query, the system uses one AI model to figure out the intent, context, and even the emotion behind the request. A second AI model then decides which specific software tools, databases, or third-party services are needed to answer that request. For example, if a pilot asks about a specific maintenance issue, the system identifies the intent, pulls data from aircraft maintenance databases, and presents the relevant information back to the pilot.

What it doesn't cover

• —Does not cover basic voice-to-text systems that lack reinforcement learning-based intent analysis.
• —Does not cover systems that rely solely on hard-coded rules or decision trees rather than machine learning.
• —Does not cover the physical hardware of the flight management system itself.
• —Does not cover general-purpose AI assistants like Siri or Alexa that are not integrated with aviation-specific FMS data.

The clever bit

The system uses reinforcement learning to treat pilot queries as a 'partially observable' problem, meaning it can make smart guesses about what a pilot needs even when the request is vague or incomplete.

Why it matters

Aviation cockpits are increasingly complex, and pilots are often overwhelmed by data. This technology aims to reduce pilot workload by creating a conversational, intelligent interface for flight management, potentially making flight operations safer and more efficient.

Real-world examples

• 1.Honeywell's connected aircraft platforms
• 2.Smart cockpit voice assistants
• 3.Automated flight planning software