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. 17, 2023

Filed:

Jun. 15, 2020
Applicant:

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

Inventors:

Subhajit Chaudhury, Kawasaki, JP;

Daiki Kimura, Tokyo, JP;

Michiaki Tatsubori, Oiso, JP;

Asim Munawar, Ichikawa, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/00 (2020.01); G06N 3/08 (2006.01); G06F 40/20 (2020.01); G06F 40/16 (2020.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 40/16 (2020.01); G06F 40/20 (2020.01);
Abstract

In an approach, a processor trains a model, via a reinforcement learning process, to produce a first action function for relating states of a natural language based response environment to actions applicable to the natural language based response environment. A processor retrains the model, via the reinforcement learning process, to produce a second action function, including iterations of: applying the first action function to a current state representation of the natural language based response environment to obtain a ground-truth action representation, emphasizing a word of the current state representation based on relevancy to the ground-truth action representation to obtain a modified state representation, applying a model to the modified state representation to obtain an untrained action representation, and submitting the untrained action representation to a natural language based response environment to obtain a subsequent state representation, where the subsequent state representation becomes the current state representation for a subsequent iteration.


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