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. 02, 2026

Filed:

Jun. 22, 2023
Applicant:

Smart Eye International Inc., Boston, MA (US);

Inventors:

Ajjen Das Joshi, Arlington, MA (US);

Sandipan Banerjee, Boston, MA (US);

Panu James Turcot, Pacifica, CA (US);

Assignee:

Smart Eye International Inc., Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/774 (2022.01); G06N 3/0455 (2023.01); G06N 3/0895 (2023.01); G06T 11/00 (2026.01); G06V 10/77 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/59 (2022.01); G06V 40/16 (2022.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); G06N 3/0455 (2023.01); G06N 3/0895 (2023.01); G06T 11/00 (2013.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06V 40/171 (2022.01); G06V 10/776 (2022.01); G06V 20/597 (2022.01);
Abstract

Machine learning is used for a neural network multi-attribute facial encoder and decoder. A facial image is obtained for processing on a neural network and is encoded into two or more orthogonal feature subspaces. The encoding is performed by a single, trained encoder. The encoder is a downsampling encoder, orthogonality of the feature subspaces is established using metrics, and orthogonality enables separability of the feature subspaces. Embeddings are generated for two or more attributes of the facial image, wherein the embeddings are generated using one or more copies of the single, trained encoder. The embeddings comprise a vector representation of the two or more attributes of the facial image. A neural network is trained for a multi-task objective, wherein the training is based on the embeddings. The embeddings replace and augment training images. The multi-task objective provides identification of the two or more attributes of the facial image.


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