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:
May. 13, 2025

Filed:

Oct. 12, 2022
Applicant:

Sparkcognition, Inc., Austin, TX (US);

Inventors:

Alexandru Ardel, Austin, TX (US);

Elad Liebman, Austin, TX (US);

Mrinal Sen, Austin, TX (US);

Georgios Alexandros Dimakis, Austin, TX (US);

Yash Gandhi, Austin, TX (US);

Sriram Ravula, Plano, TX (US);

Dimitri Voytan, Austin, TX (US);

Assignee:

SPARKCOGNITION, INC., Austin, TX (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 5/50 (2006.01); G06N 20/00 (2019.01); G06T 5/00 (2006.01); G06T 5/70 (2024.01); G06T 5/77 (2024.01); G06T 11/00 (2006.01); G06V 10/25 (2022.01); G06V 10/75 (2022.01);
U.S. Cl.
CPC ...
G06T 5/70 (2024.01); G06N 20/00 (2019.01); G06T 5/00 (2013.01); G06T 5/50 (2013.01); G06T 5/77 (2024.01); G06T 11/006 (2013.01); G06V 10/25 (2022.01); G06V 10/751 (2022.01); G06V 10/758 (2022.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20216 (2013.01);
Abstract

A method includes using a machine-learning model to determine multiple sets of image data, each representing an estimated solution to an inverse problem associated with multiple waveform return measurements. First image data are based on a first set of waveform return measurements and first model parameters of the machine-learning model, and second image data are based on a second set of waveform return measurements and a second model parameters of the machine-learning model. The method also includes determining, based on the multiple sets of image data, a representative image. The method further includes generating output data that identifies a first area of the representative image as less reliable than a second area of the representative image based on a statistical evaluation of two or more sets of image data of the multiple sets of image data.


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