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:
Jan. 02, 2018

Filed:

Jul. 20, 2016
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Janna S. Hamaker, Seattle, WA (US);

Sravan Babu Bodapati, Andhra Pradesh, IN;

John Hambacher, Redmond, WA (US);

Gururaj Narayanan, Karnataka, IN;

Sriraghavendra Ramaswamy, Chennai, IN;

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/27 (2006.01); G06F 17/24 (2006.01); G06N 99/00 (2010.01); G06N 7/00 (2006.01);
U.S. Cl.
CPC ...
G06F 17/273 (2013.01); G06F 17/241 (2013.01); G06F 17/274 (2013.01); G06F 17/275 (2013.01); G06F 17/2785 (2013.01); G06N 7/005 (2013.01); G06N 99/005 (2013.01);
Abstract

A machine learning engine may correlate contextual information associated with a misspelling in a publication with a likelihood that the misspelling is intentional in nature. Training data may be generated by analyzing one or more past publication to identify misspellings and labeling the misspellings as intentional. A contextual indicators application may analyze the context in which intentional misspellings have been previously included within publication to identify indicators of future misspellings being intentional. A machine learning engine may use the training data and indicators to generate an intentional linguistic deviation (ILD) prediction model to determine whether a new misspelling is an intentional misspelling. The machine learning engine may also determine weights for individual indicators that may calibrate the influence of the respective individual indicators. The ILD prediction model may be deployed to analyze a new publication to identify a likelihood of the new misspelling being intentional.


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