US 12,455,912 B1
Aircraft hardware component rotability classification using machine learning
Shivam Sharma, Memphis, TN (US); James E. Allman, Jr., Memphis, TN (US); Sean M. Lanagan, Concord, MA (US); John P. Herrman, Eads, TN (US); and Heather O. Levesque, New Brunswick (CA)
Assigned to CAMP Systems International, Inc., Merrimack, NH (US)
Filed by CAMP Systems International, Inc., Merrimack, NH (US)
Filed on Sep. 13, 2024, as Appl. No. 18/884,418.
Int. Cl. G06F 16/30 (2019.01); G06F 16/31 (2019.01); G06F 16/334 (2025.01); G06F 16/353 (2025.01); G06F 30/27 (2020.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06F 16/3347 (2019.01) [G06F 16/316 (2019.01); G06F 16/353 (2019.01); G06F 30/27 (2020.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
extracting a plurality of features of a hardware component of an aircraft;
inputting a first subset of features of the plurality of features into a first machine learning model;
receiving, as output from the first machine learning model, a first determination of whether the hardware component is rotable;
inputting a second subset of features of the plurality of features into a second machine learning model;
receiving, as output from the second machine learning model, a second determination of whether the hardware component is rotable;
determining, based on the first determination and the second determination, a final determination of whether the hardware component is rotable;
adding a data structure for the hardware component with the final determination in a searchable database;
receiving a query from a user that is associated with the hardware component;
searching the searchable database for a result matching the query; and
outputting the result matching the query, the result matching the query comprising the final determination of whether the hardware component is rotable.