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. 03, 2020

Filed:

Sep. 28, 2018
Applicant:

Oracle International Corporation, Redwood Shores, CA (US);

Inventors:

Gautam Singaraju, Dublin, CA (US);

Jiarui Ding, Foster City, CA (US);

Vishal Vishnoi, Redwood City, CA (US);

Mark Joseph Sugg, River Forest, IL (US);

Edward E. Wong, Upland, CA (US);

Assignee:

Oracle International Corporation, Redwood Shores, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/10 (2019.01); G06N 20/00 (2019.01); G06F 16/28 (2019.01); G06F 16/22 (2019.01); H04L 12/58 (2006.01); G06N 5/00 (2006.01); G06F 16/9032 (2019.01); G06F 40/35 (2020.01); G06N 3/08 (2006.01); G10L 15/06 (2013.01); G10L 15/18 (2013.01); G06F 16/31 (2019.01); G06F 16/35 (2019.01); G06F 16/33 (2019.01); G06K 9/62 (2006.01); G10L 15/16 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/2246 (2019.01); G06F 16/285 (2019.01); G06F 16/322 (2019.01); G06F 16/3331 (2019.01); G06F 16/35 (2019.01); G06F 16/90332 (2019.01); G06F 40/35 (2020.01); G06K 9/627 (2013.01); G06K 9/6227 (2013.01); G06N 3/08 (2013.01); G06N 5/003 (2013.01); G06N 20/10 (2019.01); G10L 15/063 (2013.01); G10L 15/1815 (2013.01); H04L 51/02 (2013.01); H04L 51/04 (2013.01); G10L 15/16 (2013.01); G10L 2015/0638 (2013.01);
Abstract

Techniques for improving quality of classification models for differentiating different user intents by improving the quality of training samples used to train the classification models are described. Pairs of user intents that are difficult to differentiate by classification models trained using the given training samples are identified based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with a first intent and a training sample associated with a second intent in the pair of intents are ranked based upon a similarity score between the two training samples in each pair of training samples. The identified pairs of intents and the pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options or suggestions for improving the training samples.


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