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:
Aug. 03, 2021

Filed:

Dec. 12, 2017
Applicant:

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

Inventors:

Chengwei Su, Belmont, MA (US);

Sankaranarayanan Ananthakrishnan, Belmont, MA (US);

Spyridon Matsoukas, Hopkinton, MA (US);

Shirin Saleem, Belmont, MA (US);

Rahul Gupta, Cambridge, MA (US);

Kavya Ravikumar, Mercer Island, WA (US);

John Will Crimmins, Seattle, WA (US);

Kelly James Vanee, Shoreline, WA (US);

John Pelak, Harvard, MA (US);

Melanie Chie Bomke Gens, Seattle, WA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/18 (2013.01); G10L 15/22 (2006.01); G10L 15/06 (2013.01); G10L 15/183 (2013.01); H04L 29/08 (2006.01); G10L 15/32 (2013.01); G06K 9/00 (2006.01); H04W 4/02 (2018.01); G10L 15/26 (2006.01); G06F 16/31 (2019.01); G06F 40/295 (2020.01);
U.S. Cl.
CPC ...
G10L 15/063 (2013.01); G06F 16/313 (2019.01); G06F 40/295 (2020.01); G06K 9/00456 (2013.01); G10L 15/183 (2013.01); G10L 15/1815 (2013.01); G10L 15/22 (2013.01); G10L 15/26 (2013.01); G10L 15/32 (2013.01); H04L 67/306 (2013.01); H04W 4/02 (2013.01);
Abstract

A natural language understanding system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing as well as context data describing the context surrounding the utterance corresponding to the natural language hypotheses. The individual component scores may be input into a feature vector at a location corresponding to a type of a device captured by the utterance. Other locations in the feature vector corresponding to other device types may be populated with zero values. The feature vector may also be populated with other values represent other context data. The feature vector may then be multiplied by a weight vector comprising trained weights corresponding to the feature vector positions to determine a new overall score for each hypothesis, where the overall score incorporates the impact of the context data. Natural language hypotheses can be ranked using their respective new overall scores.


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