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. 20, 2026

Filed:

Oct. 12, 2023
Applicant:

Oracle International Corporation, Redwood Shores, CA (US);

Inventors:

Duy Vu, Melbourne, AU;

Poorya Zaremoodi, Melbourne, AU;

Nagaraj N. Bhat, Bengaluru, IN;

Srijon Sarkar, Bengaluru, IN;

Varsha Kuppur Rajendra, Bellevue, WA (US);

Thanh Long Duong, Melbourne, AU;

Mark Edward Johnson, Sydney, AU;

Pramir Sarkar, Bengaluru, IN;

Shahid Reza, Bengaluru, IN;

Assignee:

Oracle International Corporation, Redwood Shores, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/58 (2020.01); G06F 40/20 (2020.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 40/58 (2020.01); G06F 40/20 (2020.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01);
Abstract

A computer-implemented method includes: accessing a plurality of datasets, where each dataset of the plurality of datasets includes training examples; selecting datasets that include the training examples in a source language and a target language; and sampling, based on a sampling weight that is determined for each of the selected datasets, the training examples from the selected datasets to generate the training batches; training an ML model for performing at least a first task using the training examples of the training batches, by interleavingly inputting the training batches to the ML model; and outputting the trained ML model configured to perform the at least the first task on input utterances provided in at least one among the source language and the target language. The sampling weight is determined for each of the selected datasets based on one or more attributes common to the training examples of the selected dataset.


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