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. 17, 2023

Filed:

Jan. 11, 2022
Applicant:

Enlitic, Inc., San Francisco, CA (US);

Inventors:

Kevin Lyman, Fords, NJ (US);

Li Yao, San Francisco, CA (US);

Eric C. Poblenz, Palo Alto, CA (US);

Jordan Prosky, San Francisco, CA (US);

Ben Covington, Berkeley, CA (US);

Anthony Upton, Malvern, AU;

Lionel Lints, Oakland, CA (US);

Assignee:

Enlitic, Inc., Fort Collins, CO (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 50/20 (2018.01); G06Q 10/0631 (2023.01); G16H 10/60 (2018.01); G16H 30/40 (2018.01); G16H 15/00 (2018.01); G06T 5/00 (2006.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06N 5/04 (2023.01); G16H 30/20 (2018.01); G06N 20/00 (2019.01); G06F 9/54 (2006.01); G06T 7/187 (2017.01); G06T 7/11 (2017.01); G06F 3/0482 (2013.01); G06T 3/40 (2006.01); A61B 5/00 (2006.01); G06F 21/62 (2013.01); G06Q 20/14 (2012.01); G16H 40/20 (2018.01); G06F 3/0484 (2022.01); G16H 10/20 (2018.01); G06N 5/045 (2023.01); G06T 7/10 (2017.01); G06T 11/20 (2006.01); G06F 16/245 (2019.01); G06T 7/44 (2017.01); G06N 20/20 (2019.01); H04L 67/12 (2022.01); H04L 67/01 (2022.01); G06V 10/82 (2022.01); G06F 18/40 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06F 18/2115 (2023.01); G06F 18/2415 (2023.01); G06V 10/25 (2022.01); G06V 30/19 (2022.01); G06V 10/764 (2022.01); G06V 40/16 (2022.01); G06V 10/22 (2022.01); G16H 50/70 (2018.01); G06T 7/70 (2017.01); G16H 50/30 (2018.01); A61B 5/055 (2006.01); A61B 6/03 (2006.01); A61B 8/00 (2006.01); A61B 6/00 (2006.01); G06Q 50/22 (2018.01); G06F 40/295 (2020.01); G06F 18/24 (2023.01); G06F 18/2111 (2023.01); G06V 30/194 (2022.01);
U.S. Cl.
CPC ...
G06Q 10/06315 (2013.01); A61B 5/7264 (2013.01); G06F 3/0482 (2013.01); G06F 3/0484 (2013.01); G06F 9/542 (2013.01); G06F 16/245 (2019.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/2115 (2023.01); G06F 18/2415 (2023.01); G06F 18/41 (2023.01); G06F 21/6254 (2013.01); G06N 5/04 (2013.01); G06N 5/045 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 20/14 (2013.01); G06T 3/40 (2013.01); G06T 5/002 (2013.01); G06T 5/008 (2013.01); G06T 5/50 (2013.01); G06T 7/0012 (2013.01); G06T 7/0014 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/187 (2017.01); G06T 7/44 (2017.01); G06T 7/97 (2017.01); G06T 11/001 (2013.01); G06T 11/006 (2013.01); G06T 11/206 (2013.01); G06V 10/225 (2022.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 30/19173 (2022.01); G06V 40/171 (2022.01); G16H 10/20 (2018.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01); H04L 67/01 (2022.05); H04L 67/12 (2013.01); A61B 5/055 (2013.01); A61B 6/032 (2013.01); A61B 6/5217 (2013.01); A61B 8/4416 (2013.01); G06F 18/2111 (2023.01); G06F 18/24 (2023.01); G06F 40/295 (2020.01); G06Q 50/22 (2013.01); G06T 7/70 (2017.01); G06T 2200/24 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30008 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30061 (2013.01); G06V 30/194 (2022.01); G06V 2201/03 (2022.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01);
Abstract

A model-assisted annotating system is operable to receive a first set of annotation data, corresponding to a broad type of annotation data output. A first training step is performed to train a computer vision model using the first set of annotation data. A second set of annotation data corresponding to the broad type of annotation data output is generated performing an inference function utilizing the computer vision model on medical scans. Additional annotation data further specifies the broad type of annotation data output is received. A second training step is performed to generate an updated computer vision model using set of additional annotation data. A third set of annotation data corresponding to the specified type of annotation data output is generated by performing an updated inference function utilizing the updated computer vision model on medical scans.


Find Patent Forward Citations

Loading…