AI Model Predicts Protein Shapes from Infrared Light Data
This patent describes a computer method that uses an artificial intelligence model to predict the detailed 3D shapes of proteins within a food ingredient by analyzing how the ingredient absorbs infrared light.
Original patent title: “Protein secondary structure prediction”
This patent describes a computer method that uses an artificial intelligence model to predict the detailed 3D shapes of proteins within a food ingredient by analyzing how the ingredient absorbs infrared light. Granted to Notco Delaware in 2021 with 24 claims and 14 forward citations, and it is expected to expire in 2040.
Coverage
What does this patent actually cover?
The patent outlines a computer-based method for predicting the secondary structure of a specific protein in an ingredient. First, it collects two types of digital data: Fourier Transform Infrared Spectroscopy (FTIR) spectra from many ingredients and their known protein secondary structures (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). The FTIR data is then transformed into a 'quantized' format. This prepared data trains an artificial intelligence (AI) model. Once trained, the AI model receives the FTIR spectrum of a new, specific ingredient and predicts its protein secondary structure. For example, a food company could use this to quickly determine if a new plant-based protein ingredient has the desired alpha-helix or beta-sheet structures for a specific texture or function in a product.
The gap
What does this patent NOT cover?
- Does not cover predicting protein secondary structure without using FTIR spectra as input data.
- Does not cover methods that predict protein secondary structure without using an artificial intelligence model.
- Does not cover AI models that do not include multiple sub-models like Partial Least Squares or XGBoost, as specified in ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 3.
- Does not cover predicting protein tertiary or quaternary structures, only secondary structures like alpha-helix and beta-sheet.
- Does not cover methods that do not involve a training phase using a dataset of known FTIR spectra and corresponding secondary structures.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in combining specific machine learning techniques, including multiple sub-models like XGBoost and Lasso regression within a 'stacking ensemble regressor,' to accurately interpret complex FTIR spectra and predict protein secondary structures. This approach allows for a more robust and precise prediction than simpler models.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Quality control in food manufacturing for protein-rich ingredients
Development of new plant-based protein products
Characterization of protein ingredients for nutritional supplements
Research into protein functionality in biopharmaceuticals
Why it matters
The bigger picture
Understanding protein secondary structure is crucial in fields like food science, pharmaceuticals, and materials science, as it directly influences a protein's function, stability, and interaction with other molecules. This patent offers a rapid, non-destructive way to gain this insight, potentially speeding up research and development cycles. For instance, in developing plant-based foods, knowing the protein structure helps engineers formulate products with specific textures, like the chewiness of meat alternatives.
Filed
November 5, 2020
Granted
March 30, 2021
Market context
Who's building on this
Companies in this space
Companies in the food technology and biotechnology sectors, especially those focused on plant-based proteins and ingredient innovation, are likely building on similar techniques. Notco Delaware LLC, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a prominent player in developing AI-driven plant-based food products. Other food science research institutions and AI-driven materials science companies would also be active in this space.
Market impact
This patent contributes to the growing trend of applying artificial intelligence and advanced spectroscopy to ingredient analysis, particularly in the food industry. It enables faster, more efficient characterization of novel protein sources, which can accelerate product development cycles and improve quality control. This capability supports the expansion of the plant-based food market by providing tools to engineer desired textures and functionalities in alternative protein products.
Claim 1 — Plain English
What this patent covers
The patent outlines a computer-based method for predicting the secondary structure of a specific protein in an ingredient. First, it collects two types of digital data: Fourier Transform Infrared Spectroscopy (FTIR) spectra from many ingredients and their known protein secondary structures (Claim 1). The FTIR data is then transformed into a 'quantized' format. This prepared data trains an artificial intelligence (AI) model. Once trained, the AI model receives the FTIR spectrum of a new, specific ingredient and predicts its protein secondary structure. For example, a food company could use this to quickly determine if a new plant-based protein ingredient has the desired alpha-helix or beta-sheet structures for a specific texture or function in a product.
The clever bit
The novelty lies in combining specific machine learning techniques, including multiple sub-models like XGBoost and Lasso regression within a 'stacking ensemble regressor,' to accurately interpret complex FTIR spectra and predict protein secondary structures. This approach allows for a more robust and precise prediction than simpler models.
What it does not cover
- Does not cover predicting protein secondary structure without using FTIR spectra as input data.
- Does not cover methods that predict protein secondary structure without using an artificial intelligence model.
- Does not cover AI models that do not include multiple sub-models like Partial Least Squares or XGBoost, as specified in Claim 3.
- Does not cover predicting protein tertiary or quaternary structures, only secondary structures like alpha-helix and beta-sheet.
- Does not cover methods that do not involve a training phase using a dataset of known FTIR spectra and corresponding secondary structures.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
23/40
Moderately cited
Claim breadth
16/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$112K – $359K
Midpoint $225K · 14.3 yr remaining · industry ×1.5
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
24 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
O'Hara, N., Nun, I., Patel, A., Berning, J. C., Yusuf, A., Villanueva, F., Pichara, K., & Contreras, R. (2021). AI Model Predicts Protein Shapes from Infrared Light Data (U.S. Patent No. 10,962,473). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10962473/protein-secondary-structure-prediction
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
Embed
Add this patent to your site
Drop this plain-English patent card into any blog post or article — free, no signup. It always links back to the full breakdown here.
<div data-patentlens-widget data-patent-number="US10962473"></div> <script src="https://patentbrief.org/embed.js" async></script>
Stay in the loop
Get a weekly digest of new patents.
One email per week. No spam. Unsubscribe anytime.
Keep exploring
Related patents you should know
US 4683195 · 1987
How to Make Billions of Copies of a DNA Segment
This patent describes the Polymerase Chain Reaction (PCR), a method to rapidly create many copies of a specific piece of DNA or RNA, enabling its detection and analysis.
Cetus Corp
US 8697359 · 2014
How to Edit Genes in Human Cells Using an Engineered CRISPR System
This patent describes an engineered CRISPR-Cas9 system for precisely cutting DNA in eukaryotic cells to change how genes work, opening the door for gene editing in complex organisms.
Massachusetts Institute of Technology
US 7657849 · 2010
How the iPhone's Slide-to-Unlock Gesture Works
Apple's 2010 patent describes unlocking a device by dragging a specific graphical image across the touchscreen along a predefined path, a gesture that became iconic with the original iPhone.
Apple Inc
US 4733665 · 1988
How Doctors Implant a Permanent Stent Using a Balloon
This patent describes the method for placing a permanent, expandable wire mesh tube inside a blood vessel or other body tube using a balloon-tipped catheter to widen it and keep it open.
Expandable Grafts Partnership
US 4965188 · 1990
How to Make Many Copies of a DNA Piece with Heat
This patent describes the Polymerase Chain Reaction (PCR) method, a technique to make millions of copies of a specific DNA segment using a heat-resistant enzyme and repeated temperature changes.
Cetus Corp
US 4235871 · 1980
How to Encapsulate Active Materials in Lipid Bubbles Efficiently
This patent describes a method for trapping biologically active substances inside tiny, multi-layered fat bubbles called liposomes, using a specific water-in-oil emulsion and gel-forming process to improve how much material gets captured.
Individual
Semantically similar
You might also find these interesting
US 20210174903 · Protein Evolution
How to Predict Protein Shapes Better Using Lab Tests and Computers
US 20210280268 · Dnastar
How Computers Predict Protein Shapes Faster Using Smart Templates
US 11544573 · 2023 · Google LLC
How Projection Neural Networks Speed Up AI Predictions
US 20220383078 · Huawei Technologies Co
Adapting AI Models to Fit Device Resources
More to explore
More in Biotech & Medicine
US 4683195 · 1987 · Cetus Corp
How to Make Billions of Copies of a DNA Segment
US 8697359 · 2014 · Massachusetts Institute of Technology
How to Edit Genes in Human Cells Using an Engineered CRISPR System
US 4733665 · 1988 · Expandable Grafts Partnership
How Doctors Implant a Permanent Stent Using a Balloon
US 4965188 · 1990 · Cetus Corp
How to Make Many Copies of a DNA Piece with Heat
New to patents?
Common Questions
Frequently Asked Questions
What does AI Model Predicts Protein Shapes from Infrared Light Data cover?
This patent describes a computer method that uses an artificial intelligence model to predict the detailed 3D shapes of proteins within a food ingredient by analyzing how the ingredient absorbs infrared light.
Who owns patent US 10962473?
Notco Delaware owns this patent, granted in 2021.
When does this patent expire?
This patent is expected to expire on November 5, 2040, when the invention enters the public domain.
What is patent US 10962473 cited by?
This patent has been cited by 14 later patents that build on its ideas.
What problem does this patent solve?
Understanding protein secondary structure is crucial in fields like food science, pharmaceuticals, and materials science, as it directly influences a protein's function, stability, and interaction with other molecules. This patent offers a rapid, non-destructive way to gain this insight, potentially speeding up research and development cycles. For instance, in developing plant-based foods, knowing the protein structure helps engineers formulate products with specific textures, like the chewiness of meat alternatives.
What does this patent NOT cover?
Does not cover predicting protein secondary structure without using FTIR spectra as input data.
Patent monitoring