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:
Feb. 22, 2022

Filed:

Dec. 15, 2017
Applicant:

Waveone Inc., Mountain View, CA (US);

Inventors:

Lubomir Bourdev, Mountain View, CA (US);

Carissa Lew, San Jose, CA (US);

Sanjay Nair, Fremont, CA (US);

Oren Rippel, Mountain View, CA (US);

Assignee:

WaveOne Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06N 20/00 (2019.01); G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06K 9/46 (2006.01); H04N 19/126 (2014.01); H04N 19/167 (2014.01); H04N 19/172 (2014.01); H04N 19/196 (2014.01); H04N 19/91 (2014.01); H04N 19/44 (2014.01); G06K 9/66 (2006.01); G06T 5/00 (2006.01); H04N 19/13 (2014.01); H04N 19/149 (2014.01); H04N 19/18 (2014.01); H04N 19/48 (2014.01); H04N 19/154 (2014.01); H04N 19/33 (2014.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06N 3/04 (2013.01); G06N 20/00 (2019.01); G06K 9/00288 (2013.01); G06K 9/00744 (2013.01); G06K 9/00771 (2013.01); G06K 9/4619 (2013.01); G06K 9/4628 (2013.01); G06K 9/6212 (2013.01); G06K 9/6232 (2013.01); G06K 9/6256 (2013.01); G06K 9/6263 (2013.01); G06K 9/6274 (2013.01); G06K 9/66 (2013.01); G06K 2209/01 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06T 5/002 (2013.01); H04N 19/126 (2014.11); H04N 19/13 (2014.11); H04N 19/149 (2014.11); H04N 19/154 (2014.11); H04N 19/167 (2014.11); H04N 19/172 (2014.11); H04N 19/18 (2014.11); H04N 19/197 (2014.11); H04N 19/33 (2014.11); H04N 19/44 (2014.11); H04N 19/48 (2014.11); H04N 19/91 (2014.11);
Abstract

A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.


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