The Boeing Company (20240304095). METHOD AND SYSTEM FOR PREDICTING A DELAY FOR A FLIGHT OF AN AIRCRAFT simplified abstract
Contents
METHOD AND SYSTEM FOR PREDICTING A DELAY FOR A FLIGHT OF AN AIRCRAFT
Organization Name
Inventor(s)
Chaitanya Pavan Kumar Aripirala of Bangalore (IN)
Veeresh Kumar Masaru Narasimhulu of Bangalore (IN)
Satyendra Yadav of Lone Tree CO (US)
METHOD AND SYSTEM FOR PREDICTING A DELAY FOR A FLIGHT OF AN AIRCRAFT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240304095 titled 'METHOD AND SYSTEM FOR PREDICTING A DELAY FOR A FLIGHT OF AN AIRCRAFT
- Simplified Explanation:**
The patent application describes a method and system for predicting aircraft delays using a centralized machine learning model that is trained with both public and proprietary data from airlines and other entities.
- Key Features and Innovation:**
- Centralized machine learning model built and trained with public data.
- Version of the model sent to edge computing devices for input of proprietary data.
- Edge devices train the model with proprietary data to improve predictions.
- Neural network gain determined based on weighted average of edge gains.
- Gain used to further train the centralized model for more accurate predictions.
- Potential Applications:**
This technology can be used in the aviation industry to predict flight delays, optimize flight schedules, and improve overall operational efficiency.
- Problems Solved:**
The technology addresses the challenge of accurately predicting aircraft delays by incorporating proprietary data from airlines and other entities into a centralized machine learning model.
- Benefits:**
- More accurate predictions of flight delays.
- Improved operational efficiency for airlines.
- Better flight scheduling and resource allocation.
- Commercial Applications:**
The technology can be commercially applied by airlines, airports, and aviation service providers to enhance their operational efficiency, reduce costs, and improve customer satisfaction.
- Prior Art:**
Prior art related to this technology may include existing systems for predicting flight delays using machine learning models and data from various sources in the aviation industry.
- Frequently Updated Research:**
Researchers in the field of aviation and machine learning may be conducting studies on improving the accuracy of flight delay predictions using advanced algorithms and real-time data.
- Questions about Aircraft Delay Prediction:**
1. How does this technology compare to traditional methods of predicting flight delays? 2. What are the potential limitations of using proprietary data from airlines in training the machine learning model?
Original Abstract Submitted
a method and system for predicting a delay of an aircraft is disclosed. first, a centralized machine learning model is built and trained using available public data. a version of the centralized model is then sent to one or more edge computing devices controlled by entities that own aircraft, and they input their proprietary data into the version of the centralized model they receive. the received version of the centralized model is trained using the proprietary data and a neural network gain for the centralized machine learning model is determined based on a weighted average of edge neural network gains from the edge computing devices. this neural network gain is then used to further train the centralized model to give a more accurate prediction of the flight delays without the centralized machine learning model actually ever receiving and being trained with the proprietary data of the airlines and other entities.
