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:
Apr. 14, 2025

Filed:

Aug. 08, 2019
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Andrew Edelsten, Morgan Hill, CA (US);

Jen-Hsun Huang, Los Altos Hills, CA (US);

Bojan Skaljak, San Jose, CA (US);

Tony Tamasi, Portola Valley, CA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2022.12); G06F 8/30 (2017.12); G06F 8/71 (2017.12); G06F 9/54 (2005.12); G06F 18/214 (2022.12); G06N 3/04 (2022.12); G06N 3/082 (2022.12); G06N 3/10 (2005.12); G06T 5/70 (2023.12); G06V 10/774 (2021.12); G06V 10/82 (2021.12); H04L 67/01 (2021.12); G06F 8/65 (2017.12); G06F 8/70 (2017.12);
U.S. Cl.
CPC ...
G06F 8/30 (2012.12); G06F 8/71 (2012.12); G06F 9/541 (2012.12); G06F 18/214 (2022.12); G06N 3/04 (2012.12); G06N 3/08 (2012.12); G06N 3/082 (2012.12); G06N 3/10 (2012.12); G06T 5/70 (2023.12); G06V 10/774 (2021.12); G06V 10/82 (2021.12); G06F 8/65 (2012.12); G06F 8/70 (2012.12); H04L 67/01 (2022.04);
Abstract

Traditionally, a software application is developed, tested, and then published for use by end users. Any subsequent update made to the software application is generally in the form of a human programmed modification made to the code in the software application itself, and further only becomes usable once tested, published, and installed by end users having the previous version of the software application. This typical software application lifecycle causes delays in not only generating improvements to software applications, but also to those improvements being made accessible to end users. To help avoid these delays and improve performance of software applications, deep learning models may be made accessible to the software applications for use in providing inferenced data to the software applications, which the software applications may then use as desired. These deep learning models can furthermore be improved independently of the software applications using manual and/or automated processes.


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