IP Library › Granted Patent US 12,268,418
Granted Patent B2
US 12,268,418 · App. 18/431,132 · Granted Apr 8, 2025

Egg preparation in an intelligent automated in vitro fertilization and intracytoplasmic sperm injection platform

Inventors: Adolfo Flores-Saiffe Farias (Zapopan, MX); Gerardo Mendizabal-Ruiz (Guadalajara, MX); Jacques Cohen (New York, NY); Alan Murray (Greenwich, CT); Alejandro Chavez-Badiola (Mexico City, MX); Cesar Millan (Zapopan, MX); Roberto Valencia-Murillo (Las Paz, MX); Vladimir C. Ocegueda Hernandez (Zapopan, MX); Nuno Costa-Borges (Barcelona, ES); Angel Alvarez (Guadalajara, MX); Johann Aguayo (Guadalajara, MX)
Assignee: Conceivable Life Sciences Inc.
A61B17/43A61B17/425A61B34/30C12M21/06C12M23/10C12M23/48C12M23/50C12M33/04C12M41/06C12M41/18C12M41/48C12N5/0604G01N33/5091G01N35/0099G01N35/10G01N35/1011G06T7/20G06T7/70G06V10/82G06V20/693A61B34/32G06T2207/20081G06T2207/30024G06V20/69G06V20/698G06V2201/03
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,268,418
App. No.
18/431,132
Granted
Apr 8, 2025
Kind
B2
Abstract

A method for automated, artificial-intelligence-based COC retrieval includes using an artificial intelligence/machine learning system (AI/ML system) to optically scan a follicular fluid sample to produce an image object using an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes comparing the image object to a predetermined threshold using an AI/ML system, wherein the predetermined threshold is at least in part an optical pattern with a probability of corresponding to a cumulus-oocyte-complex (COC). The method includes identifying a COC within the follicular fluid sample based at least in part on the image object satisfying the predetermined threshold. The method includes determining a COC location within the follicular fluid sample based at least in part on identifying a region of the image object corresponding to an optical pattern within the image object that satisfies the predetermined threshold.

Claims (28)

1. A method for automated, artificial-intelligence-based retrieval of a cumulus-oocyte-complex (COC) including an oocyte and cumulus cells, the method comprising:

placing a vessel containing a follicular fluid sample on a motorized stage of a microscope;

scanning the follicular fluid sample, robotically, using an imaging system and an artificial intelligence/machine learning system (AI/ML system) to create an image object, wherein:

the imaging system includes a microscopy system, a camera system, and a lighting system, and

the robotically scanning the follicular fluid sample includes controlling the motorized stage to move in a specified pattern and autonomously managing autofocusing and zooming to create the image object;

identifying the COC within the image object, and a location of the COC, based at least in part on comparing the image object to a predetermined threshold using an AI/ML system, wherein the predetermined threshold is an optical pattern with a probability of corresponding to a COC mass;

instructing a robotic pipettor to move to the location to be adjacent to the COC;

determining an intake volume based on outer physical limits of the COC detected in the image object;

once the robotic pipettor is adjacent to the COC, beginning aspiration, using the robotic pipettor, based at least in part by applying negative pressure to the COC and adjacent fluid to aspirate the intake volume;

removing the robotic pipettor from the vessel;

moving the motorized stage to present a wash dish containing handling medium in proximity to the robotic pipettor;

lowering the robotic pipettor into a wash area of the wash dish for removal of the cumulus cells from the COC; and

applying positive pressure to the robotic pipettor to expel a volume of the COC and adjacent fluid into the handling medium, wherein the expelled volume is controlled at least in part using the AI/ML system, so that an end of the positive pressure corresponds to a time at which the COC mass exits the robotic pipettor.

2. The method of claim 1 wherein the vessel and the wash dish are a single, common container.

3. The method of claim 1 wherein the microscope is an inverted microscope.

4. The method of claim 1 wherein the microscope is a movable microscope.

5. The method of claim 1 wherein the intake volume is determined at least in part by the AI/ML system.

6. The method of claim 1 wherein the COC is a plurality of COCs.

7. The method of claim 1 wherein the robotic pipettor is a plurality of robotic pipettes.

8. The method of claim 1 wherein scanning the follicular fluid sample is automatically performed using at least in part the AI/ML system and the imaging system.

9. The method of claim 1 wherein the aspiration continues until the AI/ML system and the imaging system verify completion.

10. The method of claim 9 wherein verifying the completion is performed using the AI/ML system and the imaging system to confirm that the COC mass is sufficiently isolated from other matter based at least in part on a second image taken during or following aspiration.

11. The method of claim 1 wherein instructing the robotic pipettor to move to the location includes:

instructing the robotic pipettor to enter the follicular fluid sample at a distance from the location; and

after entering the follicular fluid sample, causing the robotic pipettor to approach the COC.

12. The method of claim 11 wherein:

instructing the robotic pipettor to enter the follicular fluid sample includes instructing the robotic pipettor to translate vertically into the follicular fluid sample; and

causing the robotic pipettor to approach the COC includes controlling the motorized stage.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 3, 2024
From: CHAVEZ-BADIOLA, ALEJANDRO; MURRAY, ALAN; COHEN, JACQUES; ABRAM, JOSHUA; MENDIZABAL-RUIZ, GERARDO; FLORES-SAIFFE FARIAS, ADOLFO; MILLAN, CESAR; VALENCIA-MURILLO, ROBERTO; OCEGUEDA HERNANDEZ, VLADIMIR C.; SILVESTRI, GIUSEPPE; COSTA-BORGES, NUNO; HERNANDEZ, ESTEFANIA; ALVAREZ, ANGEL; GREGORIO ESPINOZA FIGUEROA, JOSÉ; MANUEL RICO BOTERO, VICTOR; MANUEL MEDINA PEREZ, VICTOR; AGUAYO, JOHANN; GARBARINI, WILLIAM NICHOLAS, JR.; MARTÍNEZ, DAVID; PIZANO PARRA, ALEJANDRA; MURILLO, ANAIS LEROY; MAURICIO LÓPEZ GÓMEZ, CARLOS; ZAMARRIPA GONZALEZ, VALENTIN; ACOSTA GOMEZ, FATIMA J.; REYES LLAMAS, MARIO; KUKU, STEPHANIE
To: CONCEIVABLE LIFE SCIENCES INC.
Reel/Frame 068470/0904 →
Continuity (3)
Continuation PCTUS2024013428 · Jan 30, 2024
Provisional Application 63523258 · Jun 26, 2023
Related Publication 20240428601A1 · Dec 26, 2024
References Cited (21)
US 11494578B1 · Chian · 2022 [cited by applicant]
US 20130337487A1 · Loewke · 2013 [cited by applicant]
US 20140297199A1 · Osten · 2014 [cited by applicant]
US 20150252328A1 · Woodruff · 2015 [cited by applicant]
US 20170140535A1 · Hamamah · 2017 [cited by applicant]
US 20200305967A1 · Getman · 2020 [cited by applicant]
US 20220189640A1 · Wessels Wells · 2022 [cited by applicant]
US 20220358655A1 · Wessels Wells · 2022 [cited by applicant]
US 20230093989A1 · Mahajan · 2023 [cited by applicant]
US 20230303959A1 · Blanchard · 2023 [cited by applicant]
CN 112401994A · 2021 [cited by applicant]
WO 0004929A1 · 2000 [cited by applicant]
WO 2016001754A2 · 2016 [cited by applicant]
Zhu et al. “Study of Robotic System for Automated Oocyte Manipulation” (2017), IEEE, 2017 Intern'l Conf on Manipulation, Automation, and Robotics at Small Scales (MARSS). (Year: 2017). [cited by examiner]
Abdullah et al. “Automation in Art: Paving the Way for the Future of Infertility Treatment” (published online Aug. 2022), Reproductive Sciences, vol. 30: 1006-1016. (Year: 2023). [cited by examiner]
Zhu et al. “Study of Robotic System for Automated Oocyte Manipulation” 2017 International Conference on Manipulation, Automation, and Robotics at Small Scales (MARSS), 2017, pp. 1-6, doi 1109/MARSS.2017.8001898. (Year: … [cited by examiner]
Casciani et al. “Are we approaching automated assisted reproductive technology? Sperm analysis, oocyte manipulation, and insemination” (2021), Fertil Steril, vol. 2, No. 3: 189-203. (Year: 2021). [cited by examiner]
Trottmann et al. “Ex vivo investigations on the potential of optical coherence tomography (OCT) as a diagnostic tool for reproductive medicine in a bovine model” (2016) vol. 9, No. 1-2: 129-137. (Year: 2016). [cited by examiner]
Fan et al. “Optimized Optical Coherence Tomography Imaging with Hough Transform-Based Fixed-Pattern Noise Reduction” (2018) IEEE Access, vol. 6, 32087-32096. (Year: 2018). [cited by examiner]
Zhai, et al., “Automated Denudation of Oocytes,” Micromachines, 2022. [cited by applicant]
Targosz, et al., “Semantic segmentation of human oocyte images using deep neural networks,” BioMedical Engineering, 20:40, 2021. [cited by applicant]