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:
Jan. 31, 2023

Filed:

Aug. 25, 2020
Applicants:

Beijing Wodong Tianjun Information Technology Co., Ltd., Beijing, CN;

Jd.com American Technologies Corporation, Mountain View, CA (US);

Inventors:

Kevin Huang, Sunnyvale, CA (US);

Jing Huang, Mountain View, CA (US);

Guangtao Wang, Cupertino, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/258 (2020.01); G06F 40/253 (2020.01); G06F 40/289 (2020.01);
U.S. Cl.
CPC ...
G06F 40/258 (2020.01); G06F 40/253 (2020.01); G06F 40/289 (2020.01); G06F 40/30 (2020.01);
Abstract

System and method multitask prediction. The system include a computing device. The computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: provide a head entity and a document containing the head entity; process the head entity and the document by a language model to obtain head extraction corresponding to the head entity, tail extractions corresponding to tail entities in the document, and sentence extraction corresponding to sentences in the document; predict a head-tail relation between the head extraction and the tail extractions using a first bilinear layer; combine the sentence extraction and a relation vector corresponding to the predicted head-tail relation using a second bilinear layer to obtain a sentence-relation combination; and predict an evidence sentence supporting the head-tail relation using a third bilinear layer based on the sentence-relation combination and attention extracted from the language model.


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