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:
Jun. 01, 2021

Filed:

Jul. 09, 2019
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Puneet Agarwal, Noida, IN;

Prerna Khurana, Noida, IN;

Gautam Shroff, Gurgaon, IN;

Lovekesh Vig, Gurgaon, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 40/30 (2020.01); G06F 40/295 (2020.01); G06N 3/08 (2006.01); H04L 12/58 (2006.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/295 (2020.01); G06N 3/08 (2013.01); H04L 51/02 (2013.01);
Abstract

Conversational systems are required to be capable of handling more sophisticated interactions than providing factual answers only. Such interactions are handled by resolving abstract anaphoric references in conversational systems which includes antecedent fact references and posterior fact references. The present disclosure resolves abstract anaphoric references in conversational systems using hierarchically stacked neural networks. In the present disclosure, a deep hierarchical maxpool network based model is used to obtain a representation of each utterance received from users and a representation of one or more generated sequences of utterances. The obtained representations are further used to identify contextual dependencies with in the one or more generated sequences which helps in resolving abstract anaphoric references in conversational systems. Further, a response for an incoming sequence of utterances is retrieved based on classification of incoming sequence of utterances into one or more pre-created responses. The proposed model takes lesser time to retrain.


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