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. 06, 2026

Filed:

Sep. 29, 2022
Applicant:

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

Inventors:

Ahmed Ataallah Ataallah Abobakr, Geelong, AU;

Mark Edward Johnson, Sydney, AU;

Thanh Long Duong, Seabrook, AU;

Vladislav Blinov, Melbourne, AU;

Yu-Heng Hong, Carlton, AU;

Cong Duy Vu Hoang, Wantirna South, AU;

Duy Vu, Melbourne, AU;

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 16/3329 (2025.01); G06F 16/35 (2025.01); G06F 40/205 (2020.01); G06F 40/263 (2020.01); G06F 40/30 (2020.01); H04L 51/02 (2022.01);
U.S. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 16/3329 (2019.01); G06F 16/35 (2019.01); G06F 40/205 (2020.01); G06F 40/263 (2020.01); G06F 40/30 (2020.01); H04L 51/02 (2013.01);
Abstract

Techniques disclosed herein relate generally to text classification and include techniques for fusing word embeddings with word scores for text classification. In one particular aspect, a method for text classification is provided that includes obtaining an embedding vector for a textual unit, based on a plurality of word embedding vectors and a plurality of word scores. The plurality of word embedding vectors includes a corresponding word embedding vector for each of a plurality of words of the textual unit, and the plurality of word scores includes a corresponding word score for each of the plurality of words of the textual unit. The method also includes passing the embedding vector for the textual unit through at least one feed-forward layer to obtain a final layer output, and performing a classification on the final layer output.


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