The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 02, 2019

Filed:

Dec. 12, 2013
Applicant:

University of North Dakota, Grand Forks, ND (US);

Inventors:

Travis Desell, Grand Forks, ND (US);

James Higgins, Grand Forks, ND (US);

Sophine Clachar, Grand Forks, ND (US);

Assignee:

University of North Dakota, Grand Forks, ND (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 7/60 (2006.01); G06F 17/50 (2006.01); G06F 17/10 (2006.01); G06N 99/00 (2019.01); G06N 20/00 (2019.01); G01C 23/00 (2006.01); G06Q 10/04 (2012.01); G06Q 10/10 (2012.01); G06Q 50/30 (2012.01); G08G 5/00 (2006.01);
U.S. Cl.
CPC ...
G06F 17/5009 (2013.01); G01C 23/00 (2013.01); G06F 17/10 (2013.01); G06N 20/00 (2019.01); G06N 99/005 (2013.01); G06Q 10/04 (2013.01); G06Q 10/10 (2013.01); G06Q 50/30 (2013.01); G08G 5/0043 (2013.01);
Abstract

Various embodiments for analyzing flight data using predictive models are described herein. In various embodiments, a quadratic least squares model is applied to a matrix of time-series flight parameter data for a flight, thereby deriving a mathematical signature for each flight parameter of each flight in a set of data including a plurality of sensor readings corresponding to time-series flight parameters of a plurality of flights. The derived mathematical signatures are aggregated into a dataset. A similarity between each pair of flights within the plurality of flights is measured by calculating a distance metric between the mathematical signatures of each pair of flights within the dataset, and the measured similarities are combined with the dataset. A machine-learning algorithm is applied to the dataset, thereby identifying, without predefined thresholds, clusters of outliers within the dataset by using a unified distance matrix.


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