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:
Sep. 20, 2022

Filed:

Jul. 20, 2020
Applicant:

Digital Surgery Limited, London, GB;

Inventors:

Andre Chow, London, GB;

Danail Stoyanov, London, GB;

Imanol Luengo Muntion, London, GB;

Petros Giataganas, London, GB;

Jean Nehme, London, GB;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 34/10 (2016.01); G06K 9/62 (2022.01); G06N 20/00 (2019.01); G05B 19/4155 (2006.01); A61F 5/00 (2006.01); A61B 17/32 (2006.01); A61B 17/068 (2006.01); G06V 20/40 (2022.01); A61B 17/00 (2006.01);
U.S. Cl.
CPC ...
A61B 34/10 (2016.02); A61B 17/068 (2013.01); A61B 17/320016 (2013.01); A61B 17/320092 (2013.01); A61F 5/0076 (2013.01); G05B 19/4155 (2013.01); G06K 9/6262 (2013.01); G06N 20/00 (2019.01); G06V 20/41 (2022.01); A61B 2017/00061 (2013.01); A61B 2017/00119 (2013.01); A61B 2017/00199 (2013.01); A61B 2017/00212 (2013.01); A61B 2017/320082 (2017.08); A61B 2017/320095 (2017.08); A61B 2034/107 (2016.02); G05B 2219/36414 (2013.01); G06V 2201/034 (2022.01);
Abstract

The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.


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