Methods and systems for tracking biological material

Patent metadata

Field	Value
Publication number	US-12530910-B2
Application number	US-202318218666-A
Country	US
Kind code	B2
Filing date	Jul 6, 2023
Priority date	Apr 3, 2023
Publication date	Jan 20, 2026
Grant date	Jan 20, 2026

Abstract

The present disclosure relates to a method performed by one or more computers for tracking a biological material of a subject during an in-vitro fertilization process. The method includes receiving, from a camera, an image of a dish having a visual characteristic and a drop disposed on the dish, the dish holding the biological material at a drop location. The method then includes processing the image of the dish, using a drop identification model, to identify the drop according to the visual characteristic. Further, the method includes assigning an identifier to the drop associated with the drop location, and recording the identifier of the drop associated with the drop location.

First claim

What is claimed is:

1 . A method performed by one or more computers for tracking a biological material of a subject during an in-vitro fertilization process, the method comprising:
receiving, from a camera, an image of a dish having a visual characteristic and a drop disposed on the dish, the dish holding the biological material at a drop location; processing the image of the dish, using a drop identification model, to identify the drop according to the visual characteristic; assigning an identifier to the drop associated with the drop location; recording the identifier of the drop associated with the drop location; and processing the image of the dish having a drop pattern, using a drop pattern identification model, to classify a type of dish associated with the drop pattern.

2 . The method of claim 1 , wherein the receiving comprises receiving a partial or entire layout image of the dish using a microscope camera.

3 . The method of claim 1 , wherein the receiving comprises receiving an entire layout of the dish using a wide-view camera.

4 . The method of claim 1 , comprising identifying a first status or condition of a pipette at the drop location, wherein the pipette receives the biological material at the drop location; and
recording in a memory the first status or condition of the pipette holding the biological material.

5 . The method of claim 4 , wherein the identifying the first status or condition comprises determining that the pipette enters a first drop holding the biological material.

6 . The method of claim 4 , comprising identifying a second status or condition of the pipette holding the biological material at a second location.

7 . The method of claim 6 , comprising analyzing the second status or condition of the pipette.

8 . The method of claim 7 , comprising determining, before the biological material is delivered to the second location, that the second location for depositing the biological material correlates with a standard operating protocols stored in a database of the memory.

9 . The method of claim 8 , comprising signaling an error message after determining that the second location does not correlate with standard operating protocols.

10 . The method of claim 8 , comprising signaling a correct message after determining that the second location correlates with standard operating protocols.

11 . The method of claim 6 , comprising recording a delivery status of the biological material from the pipette to the second location, wherein the second location is a tube having a unique identity.

12 . The method of claim 6 , comprising recording a delivery status of the biological material from the pipette to the second location, wherein the second location is a drop of washing solution.

13 . The method of claim 6 , comprising recording a delivery status of the biological material from the pipette to the second location, wherein the second location is a drop on a second dish.

14 . The method of claim 6 , comprising identifying a third status or condition of the pipette holding the biological material at a third location.

15 . The method of claim 1 , comprising assigning the biological material a unique identity, wherein the unique identity of the biological material is maintained as the biological material moves.

16 . The method of claim 15 , wherein the identifying the biological material comprises identifying that the biological material is an embryo associated with the drop location.

17 . The method of claim 15 , wherein the identifying the biological material comprises identifying that the biological material is a biopsy of an embryo associated with the drop location.

18 . The method of claim 1 , comprising processing the image of the dish, using a subject identification model, to classify a subject identification associated with the dish, and recording in the memory the subject identification associated with the dish.

19 . The method of claim 1 , comprising:
obtaining, from a database, a pattern of drops on the dish; and processing a model input that comprises the pattern of drops on the dish using a machine learning model, having a set of machine learning model parameters, to generate a model output that characterizes a likelihood that the pattern of drops on the dish is associated with a type of dish; classifying, based on the model output of the machine learning model, whether the pattern of drops is associated with the type of dish.

20 . The method of claim 19 , comprising training the machine learning model, by a machine learning training technique, to determine trained values of the set of machine learning model parameters.

21 . The method of claim 20 , wherein training the machine learning model by the machine learning training technique comprises:
obtaining a set of training examples, wherein each training example comprises:
(i) a training input comprising a pattern of drops on a dish, and (ii) a target output based on whether the pattern of drops designates the type of dish; and
training the machine learning model on the set of training examples.

22 . The method of claim 21 , wherein training the machine learning model on the set of training examples comprises training the machine learning model to, for each training example, process the training input of the training example to generate a model output that matches the target output of the training example.

23 . One or more non-transitory computer storage media storing:
instructions that when executed by one or more computers cause the one or more computers to perform operations for tracking a biological material in an in-vitro fertilization (IVF) process, the operations comprising:
receiving an image of a dish, wherein the dish comprises a visual characteristic, one or more drops, and the biological material at a drop location;
processing the image of the dish, using a drop identification model, to identify a drop according to the visual characteristic;
assigning an identifier to the drop based on the visual characteristic;
recording the identifier of the drop associated with the drop location;
receiving an image of a second dish having a visual characteristic and one or more drops; processing the image of the second dish, using a drop identification model, to identify a drop associated with a drop location of the second dish according to the visual characteristic; assigning an identifier to the drop based on the visual characteristic; and recording the identifier of the drop associated with the drop location of the second dish.

24 . The non-transitory computer storage media of claim 23 , comprising a database containing information related to a plurality of dish types and a plurality of drop patterns for each of the plurality of the types of dishes; wherein the operations comprise:
receiving an image of a dish having a drop pattern; comparing the drop pattern to the plurality of drop patterns associated with the plurality of types of dishes stored in the database; and identifying a dish type of the dish according to the drop pattern.

25 . The non-transitory computer storage media of claim 23 , wherein the operations comprise:
receiving an image of a pipette adjacent to or in the drop; and identifying a status or condition of the pipette as receiving the biological material associated with the drop.

26 . The non-transitory computer st

Assignees

• Coopersurgical Inc

Inventors

• Paradise Charles
• Gerbush Michael
• Ivosevic Milan
• Costello Brian
• Dicesare Paul
• Ferreira Danial
• Green Ronald

Classifications

• G06T2207/30024

  Cell structures in vitro; Tissue sections in vitro · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/10056

  Microscopic image · CPC title

• G06T7/40

  Analysis of texture (depth or shape recovery from texture G06T7/529) · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title