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:
Dec. 08, 2020

Filed:

Dec. 19, 2018
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Keith A. Ellis, Carlow, IE;

Giovani Estrada, Dublin, IE;

David Coates, Leixlip, IE;

Michael Nolan, Maynooth, IE;

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/08 (2006.01); H04W 4/38 (2018.01); G06F 13/16 (2006.01); G06K 9/62 (2006.01); G06N 20/00 (2019.01); H04L 12/24 (2006.01);
U.S. Cl.
CPC ...
H04W 4/38 (2018.02); G06F 13/1668 (2013.01); G06K 9/626 (2013.01); G06N 20/00 (2019.01); H04L 41/12 (2013.01); H04L 41/145 (2013.01); H04L 67/10 (2013.01); H04L 67/12 (2013.01); H04L 67/125 (2013.01); H04L 67/16 (2013.01);
Abstract

Methods, apparatus, systems, and articles of manufacture for conditional classifier chaining in a constrained machine learning environment are disclosed. An example apparatus includes a classification controller to select a first model to be utilized to classify a first feature identified from sensor data. A memory controller is to copy the first model to a memory. A machine learning processor is to apply the first model to the first feature to create a first classification output, the first classification output indicating an identified class. The classification controller is to, in response to a determination that the first classification output identifies a second model to be used for classification, instruct the memory controller to load the second model into the memory. The machine learning processor is to apply the second model to the second feature to create a second classification output.


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