Abstract: A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation includes positioning a vessel containing a semen sample within a staging mechanism. The method includes using a robotic pipettor to make at least two droplets within a dish on the staging mechanism. The method includes using the robotic pipettor to connect the at least two droplets with a medium channel according to a programmable design commanded by an artificial intelligence/machine learning system (AI/ML system). The method includes using the robotic pipettor to deposit a quantity of sperm from the semen sample into one of the droplets. The method includes using the AI/ML system to optically scan the quantity of sperm to produce first and second image objects using an imaging system that includes a microscopy system, a camera system, and a lighting system. Creation of the image objects is separated by a specified time duration.

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.

Abstract: A method for automated, artificial-intelligence-based egg identification using optical coherence tomography (OCT) includes positioning an OCT imaging system head in proximity to a biological sample containing an oocyte, wherein the OCT is operatively coupled to an artificial intelligence/machine learning system (AI/ML system) and an imaging system, wherein the imaging system includes a camera system, and a lighting system. The method includes creating at least one three-dimensional image of the oocyte using the OCT, AI/ML system, and imaging system. The method includes using the AI/ML system to analyze the three-dimensional image, wherein an analysis includes detection of a polar body's presence or absence based at least in part on planar views of the oocyte.

Abstract: Disclosed is a method of artificial intelligence-based robotic pipetting for cell preparation. The method includes training an artificial intelligence/machine learning system (AI/ML system) to classify images of biological specimens. The method includes storing the classified images. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system configured to receive imaging from the microscopy system, and a lighting system configured to illuminate a biological material. The method includes processing the image object, using the AJ/ML system to determine a presence of a retrievable target cell. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target cell. The method includes confirming the robotic pipettor's location at the target physical orientation, using AI/ML system.

Abstract: A method of artificial-intelligence-based robotic vitrification includes placing at least one oocyte in a buffer or CPA solution. The method includes processing an image object produced by an imaging system, using an AI/ML system, to determine a location of a retrievable target oocyte. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target oocyte. The method includes confirming the robotic pipettor's location at the target physical orientation, using the AI/ML system. The method includes instructing the robotic pipettor to initiate contact with the retrievable target oocyte and apply negative pressure to secure the oocyte to the robotic pipettor. The method includes using a robotic microtool holder to lower a vitrification assembly or cryo-device of any other make into a buffer or CPA solution dish to a position in proximity to the target physical orientation.

Abstract: A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation includes positioning a vessel containing a semen sample on a staging mechanism, wherein the staging mechanism includes at least one motor to position the vessel at a specified orientation, using an artificial intelligence/machine learning (AI/ML system) system. The method includes confirming a position of the vessel, using the AI/ML system. The method includes directing a robotic pipettor to place a pipette tip within the semen sample at a position specified by the AI/ML system. The method includes aspirating and mixing the semen sample using the robotic pipettor. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes determining a semen liquefaction state of the semen sample based at least in part on the image object, using the AI/ML system.

Abstract: A method for automated, artificial-intelligence-based egg identification using optical coherence tomography (OCT) includes positioning an OCT imaging system head in proximity to a biological sample containing an oocyte, wherein the OCT is operatively coupled to an artificial intelligence/machine learning system (AI/ML system) and an imaging system, wherein the imaging system includes a camera system, and a lighting system. The method includes creating at least one three-dimensional image of the oocyte using the OCT, AI/ML system, and imaging system. The method includes using the AI/ML system to analyze the three-dimensional image, wherein an analysis includes detection of a polar body's presence or absence based at least in part on planar views of the oocyte.

Abstract: A method of artificial-intelligence-based robotic vitrification includes placing at least one oocyte in a buffer or CPA solution. The method includes processing an image object produced by an imaging system, using an AI/ML system, to determine a location of a retrievable target oocyte. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target oocyte. The method includes confirming the robotic pipettor's location at the target physical orientation, using the AI/ML system. The method includes instructing the robotic pipettor to initiate contact with the retrievable target oocyte and apply negative pressure to secure the oocyte to the robotic pipettor. The method includes using a robotic microtool holder to lower a vitrification assembly or cryo-device of any other make into a buffer or CPA solution dish to a position in proximity to the target physical orientation.

Abstract: A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation includes positioning a vessel containing a semen sample on a staging mechanism, wherein the staging mechanism includes at least one motor to position the vessel at a specified orientation, using an artificial intelligence/machine learning (AI/ML system) system. The method includes confirming a position of the vessel, using the AI/ML system. The method includes directing a robotic pipettor to place a pipette tip within the semen sample at a position specified by the AI/ML system. The method includes aspirating and mixing the semen sample using the robotic pipettor. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes determining a semen liquefaction state of the semen sample based at least in part on the image object, using the AI/ML system.

Abstract: A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation includes positioning a vessel containing a semen sample within a staging mechanism. The method includes using a robotic pipettor to make at least two droplets within a dish on the staging mechanism. The method includes using the robotic pipettor to connect the at least two droplets with a medium channel according to a programmable design commanded by an artificial intelligence/machine learning system (AI/ML system). The method includes using the robotic pipettor to deposit a quantity of sperm from the semen sample into one of the droplets. The method includes using the AI/ML system to optically scan the quantity of sperm to produce first and second image objects using an imaging system that includes a microscopy system, a camera system, and a lighting system. Creation of the image objects is separated by a specified time duration.

Abstract: Disclosed is a method of artificial intelligence-based robotic pipetting for cell preparation. The method includes training an artificial intelligence/machine learning system (AI/ML system) to classify images of biological specimens. The method includes storing the classified images. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system configured to receive imaging from the microscopy system, and a lighting system configured to illuminate a biological material. The method includes processing the image object, using the AJ/ML system to determine a presence of a retrievable target cell. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target cell. The method includes confirming the robotic pipettor's location at the target physical orientation, using AI/ML system.

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.

Abstract: The present invention is directed to a dog training collar that is comprised of at least three collars, where at least one of the collars is a slip collar, and a plurality of perpendicular elements running perpendicular to the collars, which are also connected and link to the collars.

Abstract: The present invention is directed to a dog collar that is comprised of at least three collars, where at least one of the collars is a slip collar, and a plurality of perpendicular elements running perpendicular to the collars, which are also connected and link to the collars.