# US 12,178,475 B1 · Robotic sperm preparation in an intelligent automated in vitro fertilization and intracytoplasmic sperm injection platform

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- Number: US 12,178,475 B1
- Document ID: US12178475B1
- Status: Granted
- Kind: B1
- Application number: 18/431,032
- 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: Cesar Millan; Adolfo Flores-Saiffe Farias; Gerardo Mendizabal-Ruiz; Giuseppe Silvestri; Victor Manuel Medina Perez; David Martínez; Jacques Cohen; Alejandro Chaves-Badiola; Alan Murray
- Web page: https://patentgrep.com/patent/US12178475B1

## 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 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.

## Claims

### Claim 1

A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation, the method comprising:
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;
confirming a position of the vessel, using the AI/ML system;
directing a robotic pipettor to place a pipette tip within the semen sample at a position specified by the AI/ML system;
aspirating and mixing the semen sample using the robotic pipettor;
receiving an image object from an imaging system that includes a microscopy system, a camera system, and a lighting system;
determining a semen liquefaction state of the semen sample based at least in part on the image object, using the AI/ML system;
comparing the determined semen liquefaction state to a predetermined liquefaction threshold using the AI/ML system to confirm the adequacy of the determined semen liquefaction state for spermatozoa collection;
using the robotic pipettor to mix the semen sample in response to the determined semen liquefaction state assessment; and
using the robotic pipettor to obtain at least one sperm from the semen sample at a position specified by the AI/ML system.

### Claim 2 (depends on 1)

The method of claim 1 wherein the specified orientation is a degree of tilt.

### Claim 3 (depends on 1)

The method of claim 1 wherein the specified orientation is a degree of rotation.

### Claim 4 (depends on 1)

The method of claim 1 wherein the specified orientation is a speed of movement.

### Claim 5 (depends on 1)

The method of claim 1 wherein the specified orientation is determined algorithmically by the AI/ML system.

### Claim 6 (depends on 1)

The method of claim 1 wherein the image object is a still image.

### Claim 7 (depends on 1)

The method of claim 1 wherein the image object is a time-lapse image.

### Claim 8 (depends on 1)

The method of claim 1 wherein the image object is a video.

### Claim 9 (depends on 1)

The method of claim 1 wherein aspirating and mixing the semen is performed iteratively using the AI/ML system and the imaging system to produce a plurality of image objects until the predetermined liquefaction threshold is met by at least one image object among the plurality of image objects.

### Claim 10 (depends on 1)

The method of claim 1 wherein the mixing includes an addition of culture media including liquefaction-reducing agents.

### Claim 11 (depends on 1)

The method of claim 1 wherein the mixing does not include the addition of culture media including liquefaction-reducing agents.

### Claim 12 (depends on 1)

The method of claim 1, wherein the semen sample is placed within layered media within the vessel.

### Claim 13

A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation, comprising:
positioning a first vessel containing a semen sample within a staging mechanism;
receiving an image object depicting sperm within the semen sample from an imaging system that includes a microscopy system, a camera system, and a lighting system;
ranking a plurality of sperm within the image object according to a degree of correspondence of each sperm of the plurality to a sperm quality criterion, using an artificial intelligence system/machine learning (AI/ML system);
selecting a high-ranking sperm from the ranked plurality of sperm, using the AI/ML system;
collecting and isolating the high-ranking sperm in a second vessel, using a robotic pipettor;
moving the staging mechanism to place the second vessel in a specified position for viewing by the imaging system, using the AI/ML system;
instructing an immobilization device to interrupt a tail function of the high-ranking sperm, using the AI/ML system, wherein the interruption of the tail function reduces the mobility of the high-ranking sperm;
directing a robotic pipettor to place a pipette tip within the second vessel at a position specified by the AI/ML system to collect the high-ranking sperm; and
using the robotic pipettor to obtain the high-ranking sperm from the second vessel at a position specified by the AI/ML system.

### Claim 14 (depends on 13)

The method of claim 13 wherein the first image object is at least one of a single image, a time-lapse image, or a video.

### Claim 15 (depends on 13)

The method of claim 13 wherein a reduction in the mobility of the high-ranking sperm is automatically measured by the AI/ML system.

### Claim 16 (depends on 15)

The method of claim 15 wherein the automatic measurement of the reduction in mobility is compared against a predetermined threshold to determine sufficient immobilization, using the AI/ML system.

### Claim 17 (depends on 13)

The method of claim 13 wherein the high-ranking sperm is a plurality of sperm.

### Claim 18 (depends on 13)

The method of claim 13 wherein the immobilization device is a laser.

### Claim 19 (depends on 13)

The method of claim 13 wherein the immobilization device is a pipette.

### Claim 20 (depends on 13)

The method of claim 13 wherein the immobilization device is a piezo actuator.