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:
Jun. 27, 2023

Filed:

Oct. 30, 2020
Applicant:

Guerbet, Villepinte, FR;

Inventors:

Thomas Binder, Paris, FR;

Giovanni John Jacques Palma, Chaville, FR;

Assignee:

Guerbet, Villepinte, FR;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 50/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01); G06F 16/55 (2019.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); G06N 20/00 (2019.01); G06F 18/21 (2023.01); G06F 18/2431 (2023.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); A61B 6/032 (2013.01); A61B 6/50 (2013.01); A61B 6/5211 (2013.01); G06F 16/55 (2019.01); G06F 18/217 (2023.01); G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01);
Abstract

A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.


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