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:
Feb. 22, 2022

Filed:

Sep. 28, 2017
Applicant:

National Institute of Information and Communications Technology, Tokyo, JP;

Inventors:

Canasai Kruengkrai, Tokyo, JP;

Chikara Hashimoto, Tokyo, JP;

Kentaro Torisawa, Tokyo, JP;

Julien Kloetzer, Tokyo, JP;

Jonghoon Oh, Tokyo, JP;

Masahiro Tanaka, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/95 (2019.01); G06F 16/2457 (2019.01); G06F 16/00 (2019.01); G06F 40/20 (2020.01); G06F 16/33 (2019.01); G06F 40/289 (2020.01); G06F 40/30 (2020.01); G06N 3/04 (2006.01); G06N 5/00 (2006.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/3344 (2019.01); G06F 40/20 (2020.01); G06F 40/289 (2020.01); G06F 40/30 (2020.01); G06N 3/04 (2013.01); G06N 5/003 (2013.01); G06N 5/022 (2013.01);
Abstract

A causality recognizing apparatus includes a candidate vector generating unit configured to receive a causality candidate for generating a candidate vector representing a word sequence forming the candidate; a context vector generating unit generating a context vector representing a context in which noun-phrases of cause and effect parts of the causality candidate appear; a binary pattern vector generating unit, an answer vector generating unit and a related passage vector generating unit, generating a word vector representing background knowledge for determining whether or not there is causality between the noun-phrase included in the cause part and the noun-phrase included in the effect part; and a multicolumn convolutional neural network learned in advance to receive these word vectors and to determine whether or not the causality candidate has causality.


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