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:
Nov. 26, 2019

Filed:

Jan. 02, 2018
Applicant:

Facebook, Inc., Menlo Park, CA (US);

Inventors:

Xian Li, San Francisco, CA (US);

Irina-Elena Veliche, Los Altos, CA (US);

Debnil Sur, Menlo Park, CA (US);

Shaomei Wu, Mountain View, CA (US);

Amit Bahl, Pleasanton, CA (US);

Juan Miguel Pino, San Francisco, CA (US);

Assignee:

Facebook, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/27 (2006.01); G06F 17/24 (2006.01); G06N 20/00 (2019.01); G06F 3/0481 (2013.01);
U.S. Cl.
CPC ...
G06F 17/273 (2013.01); G06F 17/24 (2013.01); G06F 17/274 (2013.01); G06N 20/00 (2019.01); G06F 3/0481 (2013.01);
Abstract

In one embodiment, a method includes identifying a plurality of dyslexic users on an online social network. The plurality of dyslexic users may be identified based on content objects posted by these users over a particular time period, where the content objects may include one or more of word-level errors or sentence-level errors. A machine-learning model may be trained for text correction using a corpus of social network data, which may include at least the content objects with one or more of word-level errors or sentence-level errors, and a corresponding set of corrected content objects. A text string including one or more errors may be received from a client system associated with a first user. The text string may be transformed into a vector representation using an encoder of the machine-learning model. A corrected text string may be generated from the vector representation using a decoder of the machine-learning model.


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