US 12,456,080 B2
Network digital twin of airline operations
Michael Arguello, Austin, TX (US); Charles P. Ephraim, Austin, TX (US); Jonathan Mark Dunsdon, Murphys, CA (US); Stephen Jonathan Davis, Eastleigh (GB); and Kristin M. Schanche, Fairfield, OH (US)
Assigned to GE Aviation Systems LLC, Grand Rapids, MI (US)
Filed by GE AVIATION SYSTEMS LLC, Grand Rapids, MI (US)
Filed on Oct. 19, 2021, as Appl. No. 17/504,732.
Prior Publication US 2023/0118644 A1, Apr. 20, 2023
Int. Cl. G06Q 10/00 (2023.01); G06N 20/00 (2019.01); G06Q 10/0631 (2023.01)
CPC G06Q 10/0631 (2013.01) [G06N 20/00 (2019.01)] 14 Claims
OG exemplary drawing
 
1. A method of identifying actions to improve a performance indicator related to airline operations in a system comprising a plurality of operational components, the method comprising:
receiving operations data corresponding to the plurality of operational components, wherein the operations data includes data corresponding to resources, and rules governing the usage of one or more of the resources;
training one or more machine learning models, using the received operations data, to generate custom behavioral characteristics corresponding to inputs or outcomes for each of the plurality of operational components, each custom behavioral characteristic corresponding to a variable used in simulating airline operations;
simulating operations of the airline using the generated one or more custom behavioral characteristics to determine values for the inputs of the plurality of operational components, wherein the simulated operations comprise a first scenario including one or more performance indicators corresponding to the first scenario;
generating a feedback loop where the one or more performance indicators are used as inputs to train the one or more machine learning models;
training the one or more machine learning models to identify interactions between the plurality of operational components based on the feedback loop;
refining the one or more performance indicators using the trained one or more machine learning models; and
displaying, using a user interface, the refined one or more performance indicators.