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.
Patent No.:
Date of Patent:
Jan. 07, 2020
Filed:
Dec. 04, 2017
Applicants:
Gabriel Fine, Salt Lake City, UT (US);
Nathan Silberman, Brooklyn, NY (US);
Inventors:
Gabriel Fine, Salt Lake City, UT (US);
Nathan Silberman, Brooklyn, NY (US);
Assignee:
Other;
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/20 (2017.01); G06T 7/70 (2017.01); G06T 7/00 (2017.01); G06K 9/66 (2006.01); A61B 34/10 (2016.01); A61B 34/20 (2016.01); G16H 30/20 (2018.01); G06K 9/62 (2006.01); G06T 7/73 (2017.01); G16H 50/50 (2018.01); G06N 3/04 (2006.01); G16H 40/63 (2018.01); G16H 30/40 (2018.01); G06N 3/08 (2006.01); A61F 2/01 (2006.01); G06N 20/00 (2019.01); A61B 90/00 (2016.01);
U.S. Cl.
CPC ...
G06T 7/70 (2017.01); A61B 34/10 (2016.02); A61B 34/20 (2016.02); G06K 9/6273 (2013.01); G06K 9/66 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G06T 7/75 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/50 (2018.01); A61B 2034/102 (2016.02); A61B 2034/2051 (2016.02); A61B 2034/2065 (2016.02); A61B 2090/367 (2016.02); A61B 2090/376 (2016.02); A61F 2/01 (2013.01); G06N 20/00 (2019.01); G06T 2207/10072 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10121 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30021 (2013.01);
Abstract
A method of determining the position and orientation of an invasive medical device is described using altered devices and a machine learning algorithm for detecting the position and orientation of such devices from imagery. Such predictions can subsequently be displayed to an operator to improve the speed and accuracy by which they perform procedures.