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:
Sep. 15, 2020

Filed:

Jun. 29, 2018
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Yen-Kuang Chen, Palo Alto, CA (US);

Shao-Wen Yang, San Jose, CA (US);

Ibrahima J. Ndiour, Portland, OR (US);

Yiting Liao, Sunnyvale, CA (US);

Vallabhajosyula S. Somayazulu, Portland, OR (US);

Omesh Tickoo, Portland, OR (US);

Srenivas Varadarajan, Bangalore, IN;

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 9/06 (2006.01); G06F 21/64 (2013.01); G06F 21/53 (2013.01); G06N 5/02 (2006.01); G06K 9/00 (2006.01); G06N 3/04 (2006.01); H04L 29/08 (2006.01); G06F 21/45 (2013.01); H04L 9/32 (2006.01); H04W 4/70 (2018.01); G06F 21/44 (2013.01); G06K 9/46 (2006.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01); H04N 19/80 (2014.01); G06F 16/951 (2019.01); G06K 9/36 (2006.01); H04N 19/46 (2014.01); G06T 7/70 (2017.01); G06K 9/64 (2006.01); G06K 9/72 (2006.01); H04W 12/02 (2009.01); H04N 19/42 (2014.01); H04N 19/625 (2014.01); H04N 19/63 (2014.01); G06T 7/223 (2017.01);
U.S. Cl.
CPC ...
H04L 9/0643 (2013.01); G06F 16/951 (2019.01); G06F 21/44 (2013.01); G06F 21/45 (2013.01); G06F 21/53 (2013.01); G06F 21/64 (2013.01); G06K 9/00624 (2013.01); G06K 9/00979 (2013.01); G06K 9/36 (2013.01); G06K 9/46 (2013.01); G06K 9/4628 (2013.01); G06K 9/6215 (2013.01); G06K 9/6217 (2013.01); G06K 9/6232 (2013.01); G06K 9/6261 (2013.01); G06K 9/6267 (2013.01); G06K 9/6274 (2013.01); G06K 9/64 (2013.01); G06K 9/72 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06N 5/022 (2013.01); G06T 7/70 (2017.01); H04L 9/3239 (2013.01); H04L 67/12 (2013.01); H04L 67/16 (2013.01); H04N 19/46 (2014.11); H04N 19/80 (2014.11); H04W 4/70 (2018.02); G06F 2221/2117 (2013.01); G06K 2209/27 (2013.01); G06T 7/223 (2017.01); G06T 2207/20024 (2013.01); G06T 2207/20052 (2013.01); G06T 2207/20056 (2013.01); G06T 2207/20064 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30242 (2013.01); H04L 67/10 (2013.01); H04L 2209/38 (2013.01); H04N 19/42 (2014.11); H04N 19/625 (2014.11); H04N 19/63 (2014.11); H04W 12/02 (2013.01);
Abstract

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.


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