# US 12,180,441 B1 · Robotic command and microtool control within an intelligent automated in vitro fertilization and intracytoplasmic sperm injection platform

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- Number: US 12,180,441 B1
- Document ID: US12180441B1
- Status: Granted
- Kind: B1
- Application number: 18/431,340
- Filed: 2024-02-02
- Published: 2024-12-26
- Granted: 2024-12-31
- Earliest claimed: 2023-06-26
- Assignees: Conceivable Life Sciences Inc.
- Applicants: Conceivable Life Sciences Inc.
- Inventors: Alan Murray; Gerardo Mendizabal-Ruiz; Alejandro Chavez-Badiola; Jacques Cohen
- Web page: https://patentgrep.com/patent/US12180441B1

## CPC

- A61B 17/43: for artificial insemination
- G16H 30/40: for processing medical images, e.g. editing
- A61B 17/425: for reproduction or fertilisation
- A61B 34/30: Surgical robots
- C12M 21/06: {for in vitro fertilization}
- C12M 23/10: {Petri dish (crystallising dishes B01L3/06)}
- C12M 23/48: {Holding appliances; Racks; Supports (holding devices for laboratory apparatus B01L9/00)}
- C12M 23/50: {Means for positioning or orientating the apparatus (C12M41/08 takes precedence)}
- C12M 33/04: {by injection or suction, e.g. using pipettes, syringes, needles (pipettes in general B01L3/02)}
- C12M 33/07: {Dosage or metering devices therefore}
- C12M 41/06: {of illumination}
- C12M 41/18: {Heat exchange systems, e.g. heat jackets or outer envelopes}
- C12M 41/36: {of biomass, e.g. colony counters or by turbidity measurements (electrooptical investigation of individual particles G01N15/14, flow cytometers G01N15/1404)}
- C12M 41/48: {Automatic or computerized control (automatic analysis G01N35/00)}
- C12M 47/04: {Cell isolation or sorting (purging biological preparations of unwanted cells C12N5/0081, determining the presence or kind of microorganism C12Q1/04)}
- C12N 5/0604: {Whole embryos; Culture medium therefor}
- G01N 15/01: specially adapted for biological cells, e.g. blood cells (investigating sedimentation of particle suspensions in blood G01N15/05)
- G01N 15/1433: using image recognition
- G01N 15/1468: {with spatial resolution of the texture or inner structure of the particle}
- G01N 33/5091: {for testing the pathological state of an organism}
- G01N 35/0099: {comprising robots or similar manipulators (robots per se B25J)}
- G01N 35/10: Devices for transferring samples {or any liquids} to, in, or from, the analysis apparatus, e.g. suction devices, injection devices {(G01N35/0099 takes precedence)}
- G01N 35/1011: {Control of the position or alignment of the transfer device}
- G06N 3/08: Learning methods
- G06N 20/00: Machine learning
- G06T 7/0012: {Biomedical image inspection}
- G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51)
- G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80)
- G06V 10/82: using neural networks
- G06V 20/693: {Acquisition}
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 34/32: operating autonomously
- G01N 2015/1006: {for cytology}
- G01N 2015/1493: {Particle size}
- G01N 2015/1497: {Particle shape}
- G01N 2035/1058: {for mixing}
- G06T 2207/10056: Microscopic image
- G06T 2207/10101: Optical tomography; Optical coherence tomography [OCT]
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06T 2207/30044: Fetus; Embryo
- G06V 20/69: Microscopic objects, e.g. biological cells or cellular parts
- G06V 20/698: {Matching; Classification}
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G16H 50/50: for simulation or modelling of medical disorders
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- C12M: APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; {APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS}
- C12N: MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA (microbiological testing media C12Q1/00)
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## 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.

## Claims

### Claim 1

A method for automated, artificial-intelligence-based IVF microtool control, comprising:
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 a robotics system of an in vitro fertilization (IVF) module;
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;
retrieving an IVF receptacle using a second robotic mechanism of the robotics system based at least in part on the command;
placing the IVF receptacle on a stage within the IVF module based at least in part on the command;
automatically 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, wherein:
the positioning is in accordance with a planned IVF procedure,
the positioning includes moving the at least one MA from the tool inventory location to a location in proximity to the IVF receptacle, and
the first robotic mechanism is controlled at least in part by the AI/ML system and the imaging system to locate the at least one MA in position with respect to the IVF receptacle;
confirming a correct positioning of the at least one MA with respect to the IVF receptacle in the IVF module; and
sending an alert to confirm the correct positioning.

### Claim 2 (depends on 1)

The method of claim 1 wherein the first and second robotic mechanisms are one robotic mechanism.

### Claim 3 (depends on 1)

The method of claim 1 wherein the first robotic mechanism is a plurality of robotic mechanisms.

### Claim 4 (depends on 1)

The method of claim 1 wherein the second robotic mechanism is a plurality of robotic mechanisms.

### Claim 5 (depends on 1)

The method of claim 1 wherein retrieval of the IVF receptacle is from a conveyance used to hold or move the IVF receptacle.

### Claim 6 (depends on 5)

The method of claim 5 wherein the conveyance is a belt conveyor.

### Claim 7 (depends on 5)

The method of claim 5 wherein the conveyance is a rail-based conveyor.

### Claim 8 (depends on 5)

The method of claim 5 wherein the conveyance includes at least one form capable of holding the IVF receptacle.

### Claim 9 (depends on 5)

The method of claim 5 wherein:
the conveyance includes a temperature-controlled plate in proximity to the IVF receptacle,
the temperature-controlled plate is configured to maintain a predetermined temperature of a biological sample, and
the temperature-controlled plate is controlled by the AI/ML system.

### Claim 10 (depends on 1)

The method of claim 1 wherein the imaging system includes a microscopy system, a camera system, and a lighting system.

### Claim 11 (depends on 1)

The method of claim 1 wherein the tool inventory location is a plurality of locations.

### Claim 12 (depends on 1)

The method of claim 1 wherein the IVF receptacle is a dish.

### Claim 13 (depends on 1)

The method of claim 1 wherein the IVF receptacle is a tube.

### Claim 14 (depends on 1)

The method of claim 1 wherein the IVF receptacle is a microtool.

### Claim 15 (depends on 1)

The method of claim 1 wherein the IVF receptacle is an ICSI needle.

### Claim 16 (depends on 1)

The method of claim 1 wherein the first robotic mechanism is controlled at least in part by the AI/ML system and the imaging system to locate the at least one MA in a specified position with respect to the IVF receptacle along x and y axes.

### Claim 17 (depends on 16)

The method of claim 16 wherein the first robotic mechanism includes an X-Y positioning system.

### Claim 18 (depends on 1)

The method of claim 1 wherein the first robotic mechanism is controlled at least in part by the AI/ML system and the imaging system to control an orientation of the at least one MA.

### Claim 19 (depends on 1)

The method of claim 1 wherein the confirming the correct positioning is performed by the AI/ML system.