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. 26, 2022

Filed:

Dec. 04, 2019
Applicant:

Facebook Technologies, Llc, Menlo Park, CA (US);

Inventors:

Ahmed Aly, Kenmore, WA (US);

Arun Babu, Mountain View, CA (US);

Armen Aghajanyan, Bellevue, WA (US);

Assignee:

Facebook Technologies, LLC., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 9/54 (2006.01); G06F 40/205 (2020.01); G06F 40/242 (2020.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); H04L 12/58 (2006.01); G06F 16/9536 (2019.01); G10L 15/18 (2013.01); G10L 15/22 (2006.01); G10L 15/30 (2013.01); G10L 15/32 (2013.01); G06F 40/253 (2020.01); G06K 9/00 (2006.01); H04L 29/08 (2006.01); G06N 20/00 (2019.01); G06F 3/01 (2006.01); G06K 9/32 (2006.01); G06Q 50/00 (2012.01); G06F 16/9032 (2019.01); H04L 51/52 (2022.01); H04L 51/00 (2022.01); H04L 67/75 (2022.01); G06F 9/48 (2006.01); G10L 15/08 (2006.01); H04N 7/14 (2006.01); H04L 67/306 (2022.01); G06F 3/16 (2006.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 3/011 (2013.01); G06F 3/013 (2013.01); G06F 9/485 (2013.01); G06F 9/4881 (2013.01); G06F 9/547 (2013.01); G06F 16/90332 (2019.01); G06F 16/9536 (2019.01); G06F 40/205 (2020.01); G06F 40/242 (2020.01); G06F 40/253 (2020.01); G06K 9/00302 (2013.01); G06K 9/00671 (2013.01); G06K 9/00677 (2013.01); G06K 9/00718 (2013.01); G06K 9/3241 (2013.01); G06N 3/0454 (2013.01); G06N 3/0472 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 50/01 (2013.01); G10L 15/08 (2013.01); G10L 15/1815 (2013.01); G10L 15/1822 (2013.01); G10L 15/22 (2013.01); G10L 15/30 (2013.01); G10L 15/32 (2013.01); H04L 51/12 (2013.01); H04L 51/32 (2013.01); H04L 67/306 (2013.01); H04L 67/36 (2013.01); H04N 7/147 (2013.01); G06F 3/017 (2013.01); G06F 3/167 (2013.01); G06K 2209/27 (2013.01); G10L 2015/088 (2013.01); G10L 2015/223 (2013.01); G10L 2015/227 (2013.01);
Abstract

In one embodiment, a method includes receiving a user input comprising one or more words at a client system, wherein each word comprises one or more characters, inputting the words to a convolutional neural network (CNN) model stored on the client system, accessing a plurality of character-embeddings for a plurality of characters, respectively, from a data store of the client system, generating one or more word-embeddings for the one or more words, respectively, based on the accessed character-embeddings by processing the accessed character-embeddings with one or more convolutional layers and one or more gated linear units of the CNN model, determining one or more tasks corresponding to the user input for execution based on an analysis of the one or more word-embeddings by the CNN model, and providing an output responsive to the user input based on the execution of the one or more tasks at the client system.


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