# US 12,602,785 B2 · Training a machine learning model to assess embryo characteristics from video image data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,785 B2 - Document ID: US12602785B2 - Status: Granted - Kind: B2 - Application number: 19/032,338 - Filed: 2025-01-20 - Published: 2025-05-22 - Granted: 2026-04-14 - Earliest claimed: 2020-08-03 - Assignees: Emgenisys, Inc. - Applicants: EmGenisys, Inc. - Inventors: Cara Wells; Russell Killingsworth - Web page: https://patentgrep.com/patent/US12602785B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 7/62: of area, perimeter, diameter or volume - C12N 5/0604: {Whole embryos; Culture medium therefor} - G01N 33/5088: {of vertebrates} - G01N 33/5091: {for testing the pathological state of an organism} - G06N 3/02: Neural networks - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G06T 7/0014: {using an image reference approach} - G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - H04N 5/77: between a recording apparatus and a television camera - G06T 2207/10016: Video; Image sequence - G06T 2207/10056: Microscopic image - G06T 2207/20076: Probabilistic image processing - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30044: Fetus; Embryo - G06V 10/82: using neural networks - G06V 2201/03: Recognition of patterns in medical or anatomical images - Y02A 90/10: Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - 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 - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE ## Abstract A method for training a computer-based machine learning model to assess a characteristics-of-interest in embryos such as viability, sex or genetic superiority/inferiority by processing video of each of a plurality of embryos and wherein a subset of the embryos each embodies the characteristic-of-interest. The method includes obtaining (taking or otherwise procuring) a plurality of training videos, each video being of a target embryo and each video having a real-time frame speed (specifically not time-lapse frame speed) over a continuous recording duration of ten minutes or less. Each video includes image data representing micro-movement of the target embryo. The image data is processed thereby generating a computer-based machine learning model for assessing the characteristic-of-interest in future embryos from image data of those embryos. ## Claims ### Claim 1 A method for training a computer-based machine learning model to assess an embryo characteristic-of-interest by processing video of each of a plurality of embryos and wherein a subset of said embryos each embodies the characteristic-of-interest, the method comprising: obtaining a plurality of training videos, each video being of a target embryo and each video having a real-time frame speed over a continuous recording duration of ten minutes or less and wherein each video comprises image data representing micro-movement of the target embryo; pairing said image data to known outcome data of the characteristic-of-interest; and analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning to thereby generate a computer-based machine learning model. ### Claim 2 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that established a pregnancy. ### Claim 3 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that produced a livebirth offspring. ### Claim 4 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that established a specific-sex pregnancy. ### Claim 5 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that produced a specific-sex livebirth offspring. ### Claim 6 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that established a male-sex pregnancy. ### Claim 7 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that produced a male-sex livebirth offspring. ### Claim 8 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that established a female-sex pregnancy. ### Claim 9 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that produced a female-sex livebirth offspring. ### Claim 10 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing embryo outcome data that represents embryo transfers into recipients that produced a genetically superior offspring. ### Claim 11 (depends on 1) The method of claim 1, wherein analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning includes analyzing correlated embryo outcome data that represents embryo transfers into recipients that produced a genetically inferior offspring. ### Claim 12 (depends on 1) The method of claim 1, wherein said video has a continuous recording duration of thirty seconds or less. ### Claim 13 (depends on 1) The method of claim 1, wherein said video has a continuous recording duration greater than thirty seconds. ### Claim 14 (depends on 1) The method of claim 1, wherein said video has a frame speed at least as fast as ten frames per second. ### Claim 15 (depends on 1) The method of claim 1, wherein said video has a frame speed at least as fast as two frames per second. ### Claim 16 (depends on 1) The method of claim 1, wherein micro-movement of the target embryo comprises morphokinetic movement of the target embryo. ### Claim 17 (depends on 1) The method of claim 1, wherein said real-time frame speed video is time-lapse free video. ### Claim 18 (depends on 1) The method of claim 1, wherein said real-time frame speed video is prerecorded at a time before the image data is processed. ### Claim 19 A system comprising: one or more processors; and a computer-readable medium comprising instructions stored therein, which when executed by the one or more processors, cause the one or more processors to: execute training of a computer-based machine learning model that assesses an embryo characteristic-of-interest by processing video of each of a plurality of embryos and wherein a subset of said embryos each embodies the characteristic-of-interest, said training comprising: obtaining a plurality of training videos, each video being of a target embryo and each video having a real-time frame speed over a continuous recording duration of ten minutes or less and wherein each video comprises image data representing micro-movement of the target embryo; pairing said image data to known outcome data of the characteristic-of-interest; and analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning to thereby generate a computer-based machine learning model. ### Claim 20 A non-transitory computer-readable storage medium comprising computer-readable instructions, which when executed by a computing system, execute training of a computer-based machine learning model that assesses an embryo characteristic-of-interest by processing video of each of a plurality of embryos and wherein a subset of said embryos each embodies the characteristic-of-interest, said training comprising: obtaining a plurality of training videos, each video being of a target embryo and each video having a real-time frame speed over a continuous recording duration of ten minutes or less and wherein each video comprises image data representing micro-movement of the target embryo; pairing said image data to known outcome data of the characteristic-of-interest; and analyzing the paired data by determining characteristics in said image data that are indicative of the characteristic-of-interest using machine learning to thereby generate a computer-based machine learning model.