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. 26, 2024

Filed:

Aug. 18, 2020
Applicant:

Nantcell, Inc., Culver City, CA (US);

Inventors:

Bing Song, La Canada, CA (US);

Nicholas James Witchey, Laguna Hills, CA (US);

Albert Wu, Calabasas, CA (US);

Krsto Sbutega, Redondo Beach, CA (US);

Patrick Soon-Shiong, Los Angeles, CA (US);

Assignee:

NantCell, Inc., Culver City, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/0455 (2023.01); G06F 18/21 (2023.01); G06F 18/211 (2023.01); G06F 18/241 (2023.01); G06N 3/084 (2023.01); G06N 5/046 (2023.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/73 (2017.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); G06F 18/211 (2023.01); G06F 18/2163 (2023.01); G06F 18/241 (2023.01); G06N 3/0455 (2023.01); G06N 3/084 (2013.01); G06N 5/046 (2013.01); G06T 7/10 (2017.01); G06T 7/74 (2017.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06T 2207/20081 (2013.01); G06T 2210/22 (2013.01);
Abstract

An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.


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