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. 23, 2021

Filed:

Nov. 15, 2018
Applicant:

Snap Inc., Santa Monica, CA (US);

Inventors:

Xiaoyu Wang, Playa Vista, CA (US);

Ning Xu, Irvine, CA (US);

Ning Zhang, Los Angeles, CA (US);

Vitor R. Carvalho, San Diego, CA (US);

Jia Li, Marina Del Rey, CA (US);

Assignee:

Snap Inc., Santa Monica, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06N 3/08 (2006.01); G06T 1/00 (2006.01); G06F 16/58 (2019.01);
U.S. Cl.
CPC ...
G06K 9/6267 (2013.01); G06F 16/5866 (2019.01); G06N 3/08 (2013.01); G06T 1/0007 (2013.01);
Abstract

Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.


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