{
  "patent_number": "US 10962473",
  "country": "US",
  "title": "AI Model Predicts Protein Shapes from Infrared Light Data",
  "original_title": "Protein secondary structure prediction",
  "summary": "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.",
  "what_it_does": "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.",
  "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."
  ],
  "filed": "2020-11-05",
  "granted": "2021-03-30",
  "expires": "2040-11-05",
  "status": "active",
  "holder": "Notco Delaware",
  "holder_url": "https://patentbrief.org/company/notco-delaware",
  "inventors": [
    {
      "name": "Nathan O'Hara",
      "url": "https://patentbrief.org/inventor/nathan-ohara"
    },
    {
      "name": "Isadora Nun",
      "url": "https://patentbrief.org/inventor/isadora-nun"
    },
    {
      "name": "Aadit Patel",
      "url": "https://patentbrief.org/inventor/aadit-patel"
    },
    {
      "name": "Julia Christin Berning",
      "url": "https://patentbrief.org/inventor/julia-christin-berning"
    },
    {
      "name": "Adil Yusuf",
      "url": "https://patentbrief.org/inventor/adil-yusuf"
    },
    {
      "name": "Francisca Villanueva",
      "url": "https://patentbrief.org/inventor/francisca-villanueva"
    },
    {
      "name": "Karim Pichara",
      "url": "https://patentbrief.org/inventor/karim-pichara"
    },
    {
      "name": "Rodrigo Contreras",
      "url": "https://patentbrief.org/inventor/rodrigo-contreras"
    }
  ],
  "times_cited": 14,
  "tags": [
    "biotech",
    "food_science",
    "software",
    "ai_ml",
    "materials"
  ],
  "abstract": "An artificial intelligence model receives a FTIR spectrum of a given ingredient to predict its protein secondary structure. The model includes three artificial modules, which generate three predicted values corresponding to structural categories (e.g., α-helix, β-sheet, and other) of the predicted secondary structure. Proteins may be compared for similarity based on predicted values corresponding to the structural categories of the predicted secondary structure.",
  "url": "https://patentbrief.org/patent/us/10962473/protein-secondary-structure-prediction",
  "markdown_url": "https://patentbrief.org/patent/us/10962473/protein-secondary-structure-prediction/md",
  "google_patents_url": "https://patents.google.com/patent/US10962473",
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}