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:
Jan. 07, 2025

Filed:

Aug. 06, 2019
Applicant:

Sphere Fluidics Limited, Cambridgeshire, GB;

Inventors:

Nicholas Dayrell-Armes, Cambridgeshire, GB;

David Holmes, Cambridgeshire, GB;

Frank F Craig, Cambridgeshire, GB;

Marian Rehak, Cambridgeshire, GB;

Dimitris Josephides, Cambridgeshire, GB;

Robert Salter, Cambridgeshire, GB;

William Whitley, Cambridgeshire, GB;

Sinan Gokkaya, Cambridgeshire, GB;

Raphael Ruis, Cambridgeshire, GB;

Assignee:

SPHERE FLUIDICS LIMITED, Cambridge, GB;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); B01L 3/00 (2006.01); G01N 15/14 (2006.01); G01N 15/1433 (2024.01); G06F 18/2431 (2023.01); G06T 7/70 (2017.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 20/69 (2022.01); G01N 15/10 (2006.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); B01L 3/502784 (2013.01); G01N 15/1433 (2024.01); G01N 15/1459 (2013.01); G01N 15/1484 (2013.01); G06F 18/2431 (2023.01); G06T 7/70 (2017.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 20/698 (2022.01); B01L 2200/0652 (2013.01); B01L 2200/0673 (2013.01); B01L 2300/0864 (2013.01); G01N 2015/1006 (2013.01); G01N 2015/1486 (2013.01); G01N 2015/1488 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30242 (2013.01);
Abstract

A method of processing droplets in a microfluidic system. The method may comprise capturing a time sequence of images of a droplet as it passes through a channel in a microfluidic system. The method may further comprise processing each image of the sequence of images using a convolutional neural network to count a number of cells or other entities visible in each image the droplet. The method may further comprise processing the count of the number of cells or other entities visible in each image of the droplet to determine an estimated number of cells or other entities in the droplet. The method/system may further comprise controlling a microfluidic process performed on the droplet responsive to the estimated number of cells or other entities in the droplet. Implementations of the method use the changing orientation and disposition of droplet contents in combination with machine learning to improve monoclonality assurance.


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