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:
Oct. 01, 2024

Filed:

May. 14, 2018
Applicant:

Digital Reasoning Systems, Inc., Franklin, TN (US);

Inventors:

Cory Hughes, Panama City Beach, FL (US);

Timothy Estes, Nashville, TN (US);

John Liu, Nashville, TN (US);

Brandon Carl, Brooklyn, NY (US);

Uday Kamath, Ashburn, VA (US);

Assignee:

Digital Reasoning Systems, Inc., Franklin, TN (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 8/36 (2018.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06F 18/2113 (2023.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01); G06V 10/772 (2022.01); G06V 10/778 (2022.01);
U.S. Cl.
CPC ...
G06F 8/36 (2013.01); G06F 18/2113 (2023.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/285 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06V 10/772 (2022.01); G06V 10/7788 (2022.01); G06V 10/7796 (2022.01);
Abstract

In some aspects, systems and methods for rapidly building, managing, and sharing machine learning models are provided. Managing the lifecycle of machine learning models can include: receiving a set of unannotated data; requesting annotations of samples of the unannotated data to produce an annotated set of data; building a machine learning model based on the annotated set of data; deploying the machine learning model to a client system, wherein production annotations are generated; collecting the generated production annotations and generating a new machine learning model incorporating the production annotations; and selecting one of the machine learning model built based on the annotated set of data or the new machine learning model.


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