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:
Dec. 13, 2022

Filed:

Jun. 25, 2019
Applicant:

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

Inventors:

Nissim Halabi, Ramat Gan, IL;

Natali Arieli, Tel Aviv, IL;

Sagi Bernstein, Herzliya, IL;

Iftah Gamzu, Rishon Lezion, IL;

Marina Haikin, Tel Aviv, IL;

Yochai Zvik, Modiin, IL;

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/2453 (2019.01); G06F 16/2452 (2019.01); G06K 9/62 (2022.01); G10L 15/16 (2006.01); G06N 20/00 (2019.01); G10L 15/22 (2006.01); G06F 40/289 (2020.01);
U.S. Cl.
CPC ...
G06F 16/24534 (2019.01); G06F 16/24522 (2019.01); G06F 40/289 (2020.01); G06K 9/6256 (2013.01); G06N 20/00 (2019.01); G10L 15/16 (2013.01); G10L 15/22 (2013.01);
Abstract

Systems and methods are described for mitigating errors introduced during processing of user input such as voice input. A query may be derived from processed user input. A performance predictor analyzes the query and uses historical data to predict whether the query will return relevant results if executed. If the query's predicted performance is below a threshold, a query rewriter may identify potential alternatives to the query from a library of 'known good' queries. Different analyzers may be applied to identify different sets of alternatives, and machine learning models may be applied to rank the outputs of the analyzers. The best-matching alternatives from each analyzer may then be provided as inputs to a further machine learning model, which assesses the probability that each of the identified alternatives reflects the intent of the user. A most likely alternative may then be selected to execute in place of the original query.


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