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. 04, 2023

Filed:

Mar. 24, 2022
Applicant:

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

Inventors:

Kevin Lyman, Fords, NJ (US);

Anthony Upton, Malvern, AU;

Ben Covington, Berkeley, CA (US);

Li Yao, San Francisco, CA (US);

Keith Lui, San Francisco, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
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); G16H 50/20 (2018.01); 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 method includes generating a longitudinal lesion model by performing a training step on a plurality of sets of longitudinal data. Dates of medical scans of different ones of the plurality of sets of longitudinal data have relative time differences corresponding to different time spans, and each set of the plurality of sets of longitudinal data corresponds to one of a plurality of different patients. The longitudinal lesion model is utilized to perform an inference step on a received medical scan to generate, for a lesion detected in the received medical scan, a plurality of lesion change prediction data for a corresponding plurality of different projected time spans ending after the current date. At least one of the plurality of lesion change prediction data is transmitted for display.


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