Abstract: A system for automated, artificial intelligence based oocyte identification and processing includes an imaging system, a set of stations, a set of robotic arms, and an artificial intelligence/machine learning system. The imaging system is positioned in proximity to a biological sample containing a candidate cumulus oocyte complex. The imaging system is configured to identify and locate the candidate cumulus oocyte complex within the biological sample. Each station of the set of stations is configured to receive the biological sample. The set of robotic arms is configured to move the biological sample between one or more stations of the set of stations and denude the candidate cumulus oocyte complex from the biological sample. The artificial intelligence/machine learning system is operatively coupled to at least one of: the imaging system, the set of robotic arms, or the set of stations.

Abstract: A method for automated ICSI includes receiving at least one droplet containing an egg in a dish placed on a stage. The method includes using an artificial intelligence/machine learning system (AI/ML system) and an imaging system to detect a zona pellucida. The imaging system includes a microscopy system, a camera system, and a lighting system. The method includes holding the egg using a robotic microtool and lowering a robotic pipettor into the droplet. The method includes using the AI/ML system and imaging system to determine an area at which to hold the egg and positioning the robotic microtool to that area. The method includes using the AI/ML system and imaging system to instruct the robotic microtool to apply negative pressure to hold the egg to the robotic pipettor. The method includes using the AI/ML system and imaging system to determine a target location where zona ablation should be performed.

Abstract: A system for removal of surrounding cells from a biological sample includes a pipette including a proximal section and a distal section with an opening configured to contact the biological sample. A suction channel, ending at the opening, is defined through the proximal and distal sections. A suction source is fluidly coupled with the pipette. The suction source is configured to create a negative pressure within the suction channel. A piezo actuator is mechanically coupled with the pipette. The piezo actuator is configured to drive piezo vibration of the pipette to remove ones of the surrounding cells.

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 denudation includes scanning, robotically, a dish containing a cumulus-oocyte-complex (COC) using an imaging system and an artificial intelligence/machine learning system (AI/ML system) to create an image object. The imaging system includes a microscopy system, a camera system, and a lighting system. The dish includes an enzyme to remove cumulus cells from an oocyte among the COC. The method includes identifying the COC within the image object, and a location, based on comparing the image object to a threshold using the AI/ML system. The threshold is an optical pattern with a probability of corresponding to a COC mass. The method includes instructing a robotic pipettor to collect the COC at the location. The method includes iteratively collecting the COC within the robotic pipettor and expelling the COC to return it to the dish until the AI/ML system confirms sufficient removal of cumulus and corona cells.

Abstract: A method for automated ICSI includes receiving at least one droplet containing an egg in a dish placed on a stage. The method includes using an artificial intelligence/machine learning system (AI/ML system) and an imaging system to detect a zona pellucida. The imaging system includes a microscopy system, a camera system, and a lighting system. The method includes holding the egg using a robotic microtool and lowering a robotic pipettor into the droplet. The method includes using the AI/ML system and imaging system to determine an area at which to hold the egg and positioning the robotic microtool to that area. The method includes using the AI/ML system and imaging system to instruct the robotic microtool to apply negative pressure to hold the egg to the robotic pipettor. The method includes using the AI/ML system and imaging system to determine a target location where zona ablation should be performed.

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: 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 IVF microtool control includes receiving a command at a controller associated with an artificial intelligence/machine learning system (AI/ML system) and an imaging system used at least in part to operate robotic components of in vitro fertilization (IVF) module. The method includes retrieving at least one IVF microtool assembly (MA) from a tool inventory location using a first robotic mechanism of the robotics system, based at least in part on the command. The method includes retrieving at least one IVF receptacle using a second robotic mechanism of the robotics system based at least in part on the command. The method includes placing the IVF receptacle on a stage within the IVF module based on the command. The method includes positioning the at least one MA in proximity to the IVF receptacle, using the first robotic mechanism, based at least in part on the command.

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 IVF microtool control includes receiving a command at a controller associated with an artificial intelligence/machine learning system (AI/ML system) and an imaging system used at least in part to operate robotic components of in vitro fertilization (IVF) module. The method includes retrieving at least one IVF microtool assembly (MA) from a tool inventory location using a first robotic mechanism of the robotics system, based at least in part on the command. The method includes retrieving at least one IVF receptacle using a second robotic mechanism of the robotics system based at least in part on the command. The method includes placing the IVF receptacle on a stage within the IVF module based on the command. The method includes positioning the at least one MA in proximity to the IVF receptacle, using the first robotic mechanism, based at least in part on the command.

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: A method for automated ICSI includes receiving at least one droplet containing an egg in a dish placed on a stage. The method includes using an artificial intelligence/machine learning system (AI/ML system) and an imaging system to detect a zona pellucida. The imaging system includes a microscopy system, a camera system, and a lighting system. The method includes holding the egg using a robotic microtool and lowering a robotic pipettor into the droplet. The method includes using the AI/ML system and imaging system to determine an area at which to hold the egg and positioning the robotic microtool to that area. The method includes using the AI/ML system and imaging system to instruct the robotic microtool to apply negative pressure to hold the egg to the robotic pipettor. The method includes using the AI/ML system and imaging system to determine a target location where zona ablation should be performed.

Abstract: A method for automated, artificial-intelligence-based denudation includes scanning, robotically, a dish containing a cumulus-oocyte-complex (COC) using an imaging system and an artificial intelligence/machine learning system (AI/ML system) to create an image object. The imaging system includes a microscopy system, a camera system, and a lighting system. The dish includes an enzyme to remove cumulus cells from an oocyte among the COC. The method includes identifying the COC within the image object, and a location, based on comparing the image object to a threshold using the AI/ML system. The threshold is an optical pattern with a probability of corresponding to a COC mass. The method includes instructing a robotic pipettor to collect the COC at the location. The method includes iteratively collecting the COC within the robotic pipettor and expelling the COC to return it to the dish until the AI/ML system confirms sufficient removal of cumulus and corona cells.

Abstract: The present invention is a method for monitoring equipment data including; identifying at least one group of equipment, compiling, at least one piece of equipment within the at least one group of equipment, wherein the at least one piece of equipment is based on the at least one group of equipment, assigning, at least one parameter for the at least one group of equipment, wherein the at least one parameter is applied to each of the at least one pieces of equipment within the at least one group of equipment, setting, a range of acceptable values for a first group of the at least one parameters, and receiving, at least one value associated with at least one piece of equipment of the at least one group of equipment.