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:
Jan. 06, 2015

Filed:

Apr. 29, 2014
Applicant:

GM Global Technology Operations Llc, Detroit, MI (US);

Inventors:

Jeffrey A Abell, Rochester Hills, MI (US);

John Patrick Spicer, Plymouth, MI (US);

Michael Anthony Wincek, Rochester, MI (US);

Hui Wang, Highland, MI (US);

Debejyo Chakraborty, Sterling Heights, MI (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B23K 1/06 (2006.01); B23K 5/20 (2006.01); B23K 20/00 (2006.01); B23K 20/10 (2006.01); G05B 11/00 (2006.01); G05B 11/06 (2006.01); G06N 99/00 (2010.01); G05B 23/02 (2006.01); B23K 20/12 (2006.01); G05B 1/00 (2006.01); G05B 1/01 (2006.01); G05B 1/04 (2006.01); G05B 11/01 (2006.01); G05B 19/00 (2006.01); G05B 19/02 (2006.01); G05B 19/04 (2006.01); G05B 19/18 (2006.01);
U.S. Cl.
CPC ...
G06N 99/005 (2013.01); G05B 23/0221 (2013.01); B32K 20/10 (2013.01); B23K 20/106 (2013.01); B23K 20/12 (2013.01); G05B 1/00 (2013.01); G05B 1/01 (2013.01); G05B 1/04 (2013.01); G05B 11/00 (2013.01); G05B 11/01 (2013.01); G05B 11/011 (2013.01); G05B 19/00 (2013.01); G05B 19/02 (2013.01); G05B 19/04 (2013.01); G05B 19/18 (2013.01);
Abstract

A system includes host and learning machines. Each machine has a processor in electrical communication with at least one sensor. Instructions for predicting a binary quality status of an item of interest during a repeatable process are recorded in memory. The binary quality status includes passing and failing binary classes. The learning machine receives signals from the at least one sensor and identifies candidate features. Features are extracted from the candidate features, each more predictive of the binary quality status. The extracted features are mapped to a dimensional space having a number of dimensions proportional to the number of extracted features. The dimensional space includes most of the passing class and excludes at least 90 percent of the failing class. Received signals are compared to the boundaries of the recorded dimensional space to predict, in real time, the binary quality status of a subsequent item of interest.


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