IP Library › Granted Patent US 12,380,965
Granted Patent B2
US 12,380,965 · App. 17/499,068 · Granted Aug 5, 2025

Prediction of infection in plant products

Inventors: Cody Vild (Santa Barbara, CA); Savannah Braden (Goleta, CA); Matthew Kahlscheuer (Goleta, CA); Louis Perez (Santa Barbara, CA)
Assignee: Apeel Technology, Inc.
G16B40/00A01G22/00A01N1/00A01N3/00C12Q1/6895G06N5/04G06N20/00G06Q10/0832C12Q2600/13C12Q2600/158
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Quick Facts
Patent No.
US 12,380,965
App. No.
17/499,068
Granted
Aug 5, 2025
Kind
B2
Abstract

A method for predicting a likelihood of infection in a set of similarly sourced plant products is disclosed. A subset of plant products is selected from the set of plant products. For each plant product in the subset, a level of expression of one or more infection biomarkers, and optionally a level of expression of one more housekeeping biomarkers, are determined. A set of biomarker expression statistics for the subset of plant products is determined based on the determined levels of expression of the one or more infection biomarkers and optionally the levels of expression of the one or more housekeeping biomarkers for each plant product in the subset. A likelihood of infection in the set of plant products is then predicted based at least in part on the determined set of biomarker expression statistics for the subset of plant products.

Claims (72)

1. A method for identifying a latent infection in a plant product, the method comprising:

obtaining, by a processor, data describing a level of expression of one or more infection biomarkers of a plant product, the infection biomarkers indicating a likelihood of infection in the plant product, wherein the data identifies one or more variants that describe differences between a read sequence of the plant product and a reference genome of a healthy plant product;

encoding, by the processor, the obtained data into a data structure for input to a machine learning model;

providing, by the processor, the encoded data structure as input to the machine learning model for executing the machine learning model, wherein the machine learning model was previously trained, wherein the machine learning model comprises a function that represents a relationship between independent and dependent variables in a training data set such that a loss function is minimized, wherein the machine learning model was trained using a process comprising:

inputting, from the training data set, (i) a first set of training samples of infection biomarkers of one or more other plant products and (ii) known rates of infection for the first set of training samples into the machine learning model,

determining predicted likelihoods of infection in the one or more other plant products based on inputting the first set of training samples of infection biomarkers of the one or more of the other plant products (i) and (ii) into the machine learning model,

determining a difference between the predicted likelihoods of infection and the known rates of infection for the first set of training samples,

minimizing the loss function for the machine learning model based on the determined difference,

determining, by using gradient-based numerical optimization, a set of values for a set of parameters that minimize the loss function;

modifying the function of the machine learning model using the determined set of values for the set of parameters and providing the machine learning model for validating, and

validating, using the set of parameters, the machine learning model until the loss function for the machine learning model is within a predetermined threshold range, wherein validating the machine learning model comprises:

inputting a second set of training samples from the training data set into the machine learning model, and

outputting the validated machine learning model for runtime use based on determining that the loss function for the machine learning model is within the predetermined threshold range;

obtaining, by the processor and based on executing the output machine learning model based on the encoded data, generated output data indicating a likelihood that the plant product has a latent infection;

determining, by the processor and based on the generated output data, that the plant product has a latent infection; and

performing, by the processor, one or more operations to mitigate the latent infection in the plant product.

2. The method of claim 1 , wherein the machine learning model includes one or more of a binary logistic regression model, logistic model tree, random forest classifier, L2 regularization, partial least squares, Naive Bayes classifier, multivariate adaptive regression spines, convolutional neural networks (CNNs), and k-nearest neighbor classification.

3. The method of claim 1 , wherein the plant product does not include any visible signs of an infection.

4. The method of claim 1 , the method further comprising:

selecting, by the processor, a subset of m plant products from the set of plant products, wherein m is an integer greater than 1 and less than n/2; and

for each plant product of the subset, determining, by the processor, a level of expression of one or more infection biomarkers;

wherein obtaining, by the processor, data describing a level of expression of one or more biomarkers in a plant product comprises:

obtaining, for each plant product of the subset, data describing the determined level of expression of the one or more infection biomarkers; and

wherein encoding, by the processor, the obtained data into a data structure for input to a machine learning model comprises:

encoding the obtained data describing the determined level of expression of the one or more infection biomarkers into one or more data structures for input to the machine learning model.

5. The method of claim 1 , further comprising providing, by the processor, the encoded data structure as input to multiple machine learning models, wherein the multiple machine learning models were previously trained, using data that correlates other encoded data structures and determined one or more infection biomarkers of one or more other plant products, to predict likelihoods of infection in the one or more other plant products.

6. The method of claim 5 , wherein each of the multiple machine learning models was previously trained to predict likelihood of a particular type of infection in the one or more other plant products.

7. The method of claim 1 , further comprising training, by the processor, the machine learning model with the output data indicating the likelihood that the plant product has a latent infection that was generated by the machine learning model during runtime.

8. The method of claim 1 , wherein performing, by the processor, one or more operations to mitigate the latent infection in the plant product comprises identifying, based on the generated output data, a set of similarly sourced plant products that is at risk of developing the latent infection.

9. The method of claim 1 , wherein performing, by the processor, one or more operations to mitigate the latent infection in the plant product comprises determining a quantity of anti-microbial treatment to apply to the plant product.

10. A system for identifying a latent infection in a plant product comprising:

at least one processor; and

a memory device storing instructions that when executed by the at least one processor cause the at least one processor to perform operations comprising:

obtaining data describing a level of expression of one or more infection biomarkers of a plant product, the infection biomarkers indicating a likelihood of infection in the plant product, wherein the data identifies one or more variants that describe differences between a read sequence of the plant product and a reference genome of a healthy plant product;

encoding the obtained data into a data structure for input to a machine learning model;

providing, by the processor, the encoded data structure as input to the machine learning model for executing the machine learning model, wherein the machine learning model was previously trained, wherein the machine learning model comprises a function that represents a relationship between independent and dependent variables in a training data set such that a loss function is minimized, wherein the machine learning model was trained using a process comprising:

inputting, from the training data set, (i) a first set of training samples of infection biomarkers of one or more other plant products and (ii) known rates of infection for the first set of training samples into the machine learning model,

determining predicted likelihoods of infection in the one or more other plant products based on inputting the first set of training samples of infection biomarkers of the one or more of the other plant products into the machine learning model,

determining a difference between the predicted likelihoods of infection and the known rates of infection for the first set of training samples,

minimizing the loss function for the machine learning model based on the determined difference,

determining, by using gradient-based numerical optimization, a set of values for a set of parameters that minimize the loss function;

modifying the function of the machine learning model using the determined set of values for the set of parameters and providing the machine learning model for validating, and

validating, using the set of parameters, the machine learning model until the loss function for the machine learning model is within a predetermined threshold range, wherein validating the machine learning model comprises:

inputting a second set of training samples from the training data set into the machine learning model, and

outputting the validated machine learning model for runtime use based on determining that the loss function for the machine learning model is within the predetermined threshold range;

obtaining, by the processor and based on executing the output machine learning model based on the encoded data, generated output data indicating a likelihood that the plant product has a latent infection;

determining, by the processor and based on the generated output data, that the plant product has a latent infection; and

performing, by the processor, one or more operations to mitigate the latent infection in the plant product.

11. The system of claim 10 , wherein the machine learning model includes one or more of a binary logistic regression model, logistic model tree, random forest classifier, L2 regularization, partial least squares, Naive Bayes classifier, multivariate adaptive regression spines, convolutional neural networks (CNNs), and k-nearest neighbor classification.

12. The system of claim 10 , wherein the plant product does not include any visible signs of an infection.

13. The system of claim 10 , the operations further comprising providing the encoded data structure as input to multiple machine learning models, wherein the multiple machine learning models were previously trained, using data that correlates other encoded data structures and determined one or more infection biomarkers of one or more other plant products, to predict likelihoods of infection in the one or more other plant products.

14. The system of claim 13 , wherein each of the multiple machine learning models was previously trained to predict likelihood of a particular type of infection in the one or more other plant products.

15. The system of claim 10 , the operations further comprising training the machine learning model with the output data indicating the likelihood that the plant product has a latent infection that was generated by the machine learning model during runtime.

16. A non-transitory computer-readable medium storing instructions stored thereon that, when executed by at least one processor of a computing device, cause the at least one processor of the computing device to perform operations comprising:

obtaining data describing a level of expression of one or more infection biomarkers of a plant product, the infection biomarkers indicating a likelihood of infection in the plant product, wherein the data identifies one or more variants that describe differences between a read sequence of the plant product and a reference genome of a healthy plant product;

encoding the obtained data into a data structure for input to a machine learning model;

providing, by the processor, the encoded data structure as input to the machine learning model for executing the machine learning model, wherein the machine learning model was previously trained, wherein the machine learning model comprises a function that represents a relationship between independent and dependent variables in a training data set such that a loss function is minimized, wherein the machine learning model was trained using a process comprising:

inputting, from the training data set, (i) a first set of training samples of infection biomarkers of one or more other plant products and (ii) known rates of infection for the first set of training samples into the machine learning model,

determining predicted likelihoods of infection in the one or more other plant products based on inputting the first set of training samples of infection biomarkers of the one or more of the other plant products into the machine learning model,

determining a difference between the predicted likelihoods of infection and the known rates of infection for the first set of training samples,

minimizing the loss function for the machine learning model base on the determined difference,

determining, by using gradient-based numerical optimization, a set of values for a set of parameters that minimize the loss function;

modifying the function of the machine learning model using the determined set of values for the set of parameters and providing the machine learning model for validating, and

validating, using the set of parameters, the machine learning model until the loss function for the machine learning model is within a predetermined threshold range, wherein validating the machine learning model comprises:

inputting a second set of training samples from the training data set into the machine learning model, and

outputting the validated machine learning model for runtime use based on determining that the loss function for the machine learning model is within the predetermined threshold range;

obtaining, by the processor and based on executing the output machine learning model based on the encoded data, generated output data indicating a likelihood that the plant product has a latent infection;

determining, by the processor and based on the generated output data, that the plant product has a latent infection; and

performing, by the processor, one or more operations to mitigate the latent infection in the plant product.

17. The non-transitory computer-readable medium of claim 16 , the operations further comprising providing the encoded data structure as input to multiple machine learning models, wherein the multiple machine learning models were previously trained, using data that correlates other encoded data structures and determined one or more infection biomarkers of one or more other plant products, to predict likelihoods of infection in the one or more other plant products.

18. The non-transitory computer-readable medium of claim 17 , wherein each of the multiple machine learning models was previously trained to predict likelihood of a particular type of infection in the one or more other plant products.

19. The non-transitory computer-readable medium of claim 16 , the operations further comprising training the machine learning model with the output data indicating the likelihood that the plant product has a latent infection that was generated by the machine learning model during runtime.

Assignments (3)
TERMINATION AND RELEASE OF INTELLECTUAL PROPERTY SECURITY AGREEMENT AT REEL/FRAME NO. 60562/0503 Recorded Dec 15, 2023
From: SLR INVESTMENT CORP., AS AGENT
To: APEEL TECHNOLOGY, INC.
Reel/Frame 066045/0001 →
SECURITY INTEREST Recorded Jul 1, 2022
From: APEEL TECHNOLOGY, INC.
To: SLR INVESTMENT CORP., AS COLLATERAL AGENT
Reel/Frame 060562/0503 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 12, 2021
From: VILD, CODY; BRADEN, SAVANNAH; KAHLSCHEUER, MATTHEW; PEREZ, LOUIS
To: APEEL TECHNOLOGY, INC.
Reel/Frame 058101/0816 →
Continuity (3)
Continuation 17090834 · Nov 5, 2020
Provisional Application 62930999 · Nov 5, 2019
Related Publication 20220028496A1 · Jan 27, 2022
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