Enhanced Aircraft Crew Operational State Monitoring System with Advanced Analytics

Publication ID: 24-11857324_0001_PTD
Published: November 07, 2025
Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “Enhanced Aircraft Crew Operational State Monitoring System with Advanced Analytics,” Published Technical Disclosure No. 24-11857324_0001_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857324_0001_PTD
This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,324.

Summary of the Inventive Concept

The present disclosure relates to an enhanced system for monitoring the operational state of an aircraft crew, incorporating advanced analytics and machine learning techniques to improve the accuracy and reliability of pilot state determination.

Background and Problem Solved

The original patent disclosed a system for monitoring the operational state of an aircraft crew, comprising a first interface for receiving high-design-assurance-level crew monitoring data and a second interface for receiving lower-design-assurance-level data. However, this system has limitations in terms of data analysis and pilot state determination accuracy. The new inventive concept addresses these limitations by introducing advanced analytics and machine learning techniques to enhance the system's performance and reliability.

Detailed Description of the Inventive Concept

The enhanced system comprises a first interface configured to receive real-time crew monitoring data, which is then analyzed using machine learning-based algorithms to determine at least one pilot state. The system also integrates data from multiple sensors to provide a comprehensive view of the pilot's operational state. Additionally, the system utilizes a cloud-based analytics platform to analyze the received data and provide real-time insights into the pilot's operational state, thereby improving situational awareness. Furthermore, the system can receive biometric data from the pilot and analyze it to detect early signs of pilot fatigue or stress.

Novelty and Inventive Step

The new claims introduce advanced analytics and machine learning techniques, which are not present in the original patent. The use of real-time data, integration of data from multiple sensors, and cloud-based analytics platform are novel and non-obvious improvements over the original system. The incorporation of biometric data analysis is also a new and inventive aspect of the enhanced system.

Alternative Embodiments and Variations

Alternative embodiments of the enhanced system could include the use of different machine learning algorithms, such as deep learning or reinforcement learning, to analyze the crew monitoring data. Another variation could be the integration of additional data sources, such as weather or air traffic control data, to further enhance the system's performance.

Potential Commercial Applications and Market

The enhanced aircraft crew operational state monitoring system has significant commercial potential in the aviation industry, particularly in the areas of safety, efficiency, and passenger experience. The system could be marketed to airlines, aircraft manufacturers, and aviation regulatory bodies, with potential applications in commercial and military aviation.

Field of Art

Aerospace systems engineering, specifically aircraft crew monitoring and operational state assessment technologies, with expertise in sensor integration, data analysis, and human factors engineering

Person of Ordinary Skill (PHOSITA) Profile

A professional with advanced engineering degree, 5-7 years experience in aerospace systems design, familiar with sensor technologies, machine learning applications in safety-critical systems, and aviation human performance monitoring

Obviousness Rationale

A PHOSITA would recognize that enhancing the source patent's crew monitoring system with machine learning, multi-sensor integration, and advanced analytics represents a predictable technological progression. The fundamental architecture of crew state determination remains consistent, with the PTD introducing incremental improvements in data processing and analysis techniques. These variations would be considered obvious extensions of the existing technological framework, leveraging known machine learning and data integration strategies to enhance the original system's capabilities.

Obvious Combinations & Variations

Source Patent Element
Finite-state machine for pilot state determination with high and low design assurance level interfaces
PTD Variation
Machine learning-based neural network for analyzing crew monitoring data across multiple sensor inputs
Obviousness Reasoning
Substituting deterministic finite-state machines with machine learning algorithms is a known technique for improving pattern recognition and state assessment in complex systems, representing a predictable technological evolution
Source Patent Element
Interfaces for receiving crew monitoring data with different design assurance levels
PTD Variation
Cloud-based analytics platform for real-time data processing and comprehensive operational state insights
Obviousness Reasoning
Extending data processing capabilities through cloud computing is a standard engineering approach for enhancing system scalability and analytical capabilities, representing an obvious design optimization
Source Patent Element
System for determining pilot operational state based on monitoring data
PTD Variation
Integration of biometric data analysis to detect pilot fatigue and stress indicators
Obviousness Reasoning
Incorporating additional physiological data sources is a foreseeable enhancement for improving operational state assessment, utilizing known techniques in human factors engineering
Source Patent Element
Crew monitoring system with deterministic state assessment algorithms
PTD Variation
Advanced analytics using multiple machine learning techniques like deep learning and reinforcement learning
Obviousness Reasoning
Exploring alternative machine learning approaches represents a routine design variation for improving predictive capabilities in safety-critical systems
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857324, a person having ordinary skill in the art would find the variations disclosed in the present technical publication to be obvious extensions of the prior art, rendering obvious any patent claims directed to machine learning-enhanced crew monitoring systems with multi-sensor data integration and advanced analytics capabilities.

Original Patent Information

Patent NumberUS 11,857,324
TitleSystem for monitoring the operational status of an aircraft crew, and associated method
Assignee(s)DASSAULT AVIATION