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:
Mar. 04, 2025

Filed:

Sep. 02, 2022
Applicant:

Lemon Inc., Grand Cayman, KY;

Inventors:

Shuo Cheng, Los Angeles, CA (US);

Wanchun Ma, Los Angeles, CA (US);

Linjie Luo, Los Angeles, CA (US);

Assignee:

LEMON INC., Grand Cayman, KY;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 3/0455 (2023.01); G06N 3/09 (2023.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 10/96 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); G06N 3/0455 (2023.01); G06N 3/09 (2023.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/776 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 10/96 (2022.01); G06V 40/171 (2022.01); G06V 40/174 (2022.01);
Abstract

Systems and methods for multi-task joint training of a neural network including an encoder module and a multi-headed attention mechanism are provided. In one aspect, the system includes a processor configured to receive input data including a first set of labels and a second set of labels. Using the encoder module, features are extracted from the input data. Using a multi-headed attention mechanism, training loss metrics are computed. A first training loss metric is computed using the extracted features and the first set of labels, and a second training loss metric is computed using the extracted features and the second set of labels. A first mask is applied to filter the first training loss metric, and a second mask is applied to filter the second training loss metric. A final training loss metric is computed based on the filtered first and second training loss metrics.


Find Patent Forward Citations

Loading…