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:
Apr. 20, 2021

Filed:

Jun. 19, 2017
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Mu Qiao, Belmont, CA (US);

Yuya J. Ong, Tenafly, NJ (US);

Ramani Routray, San Jose, CA (US);

Roger C. Raphael, San Jose, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06F 21/62 (2013.01); G06N 3/04 (2006.01); G06F 40/20 (2020.01); G06F 40/295 (2020.01); G06N 5/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 21/6245 (2013.01); G06F 40/20 (2020.01); G06F 40/295 (2020.01); G06N 3/0427 (2013.01); G06N 3/0445 (2013.01); G06N 3/0481 (2013.01); G06N 5/046 (2013.01);
Abstract

A method loads training samples and forms training data set from the training samples. The method uses the bidirectional LSTM recurrent neural network that includes one or more input cells and one or more output cells and trains it with the training data set. The method determines a sensitive information and confidence values based on analyzing a text with the trained neural network. The method selects predicted samples from the text, where the sensitive information confidence value corresponding to a one or more predicted samples is above a threshold value, based on determining that a sensitive information accuracy has improved. The method forms a new training data set, where the new training data set comprises the samples and the verified one or more predicted samples based on the verified one or more predicted samples, and trains the previously trained neural network with the new training data set.


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