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:
Jul. 19, 2022

Filed:

Sep. 30, 2020
Applicant:

Shanghai United Imaging Intelligence Co., Ltd., Shanghai, CN;

Inventors:

Shanhui Sun, Lexington, MA (US);

Hanchao Yu, Champaign, IL (US);

Xiao Chen, Lexington, MA (US);

Zhang Chen, Brookline, MA (US);

Terrence Chen, Lexington, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06K 9/62 (2022.01); G06N 3/04 (2006.01); G16H 50/50 (2018.01); G16H 50/30 (2018.01); G16H 30/40 (2018.01); G06F 3/0485 (2022.01); G06T 11/20 (2006.01); G06T 13/80 (2011.01); G06T 19/00 (2011.01); G06T 7/55 (2017.01); G06T 7/73 (2017.01); G06T 7/246 (2017.01); A61B 5/00 (2006.01); A61B 5/11 (2006.01); G06T 3/00 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/0044 (2013.01); A61B 5/1128 (2013.01); A61B 5/7264 (2013.01); G06F 3/0485 (2013.01); G06K 9/6267 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06T 3/0093 (2013.01); G06T 7/0014 (2013.01); G06T 7/11 (2017.01); G06T 7/248 (2017.01); G06T 7/55 (2017.01); G06T 7/73 (2017.01); G06T 11/206 (2013.01); G06T 13/80 (2013.01); G06T 19/00 (2013.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G06T 2200/24 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01); G06T 2210/41 (2013.01);
Abstract

Described herein are neural network-based systems, methods and instrumentalities associated with imagery data processing. The neural networks may be pre-trained to learn parameters or models for processing the imagery data and upon deployment the neural networks may automatically perform further optimization of the learned parameters or models based on a small set of online data samples. The online optimization may be facilitated via offline meta-learning so that the optimization may be accomplished quickly in a few optimization steps.


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