Patents by Inventor Ole Schulz-Trieglaff

Ole Schulz-Trieglaff has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Deep learning-based variant classifier
    Patent number: 12217832
    Abstract: The technology disclosed directly operates on sequencing data and derives its own feature filters. It processes a plurality of aligned reads that span a target base position. It combines elegant encoding of the reads with a lightweight analysis to produce good recall and precision using lightweight hardware. For instance, one million training examples of target base variant sites with 50 to 100 reads each can be trained on a single GPU card in less than 10 hours with good recall and precision. A single GPU card is desirable because it a computer with a single GPU is inexpensive, almost universally within reach for users looking at genetic data. It is readily available on could-based platforms.
    Type: Grant
    Filed: May 9, 2023
    Date of Patent: February 4, 2025
    Assignees: Illumina, Inc., Illumina Cambridge Limited
    Inventors: Ole Schulz-Trieglaff, Anthony James Cox, Kai-How Farh
  • DEEP LEARNING-BASED VARIANT CLASSIFIER
    Publication number: 20230386611
    Abstract: The technology disclosed directly operates on sequencing data and derives its own feature filters. It processes a plurality of aligned reads that span a target base position. It combines elegant encoding of the reads with a lightweight analysis to produce good recall and precision using lightweight hardware. For instance, one million training examples of target base variant sites with 50 to 100 reads each can be trained on a single GPU card in less than 10 hours with good recall and precision. A single GPU card is desirable because it a computer with a single GPU is inexpensive, almost universally within reach for users looking at genetic data. It is readily available on could-based platforms.
    Type: Application
    Filed: May 9, 2023
    Publication date: November 30, 2023
    Inventors: Ole SCHULZ-TRIEGLAFF, Anthony James COX, Kai-How FARH
  • Deep learning-based variant classifier
    Patent number: 11705219
    Abstract: The technology disclosed directly operates on sequencing data and derives its own feature filters. It processes a plurality of aligned reads that span a target base position. It combines elegant encoding of the reads with a lightweight analysis to produce good recall and precision using lightweight hardware. For instance, one million training examples of target base variant sites with 50 to 100 reads each can be trained on a single GPU card in less than 10 hours with good recall and precision. A single GPU card is desirable because it a computer with a single GPU is inexpensive, almost universally within reach for users looking at genetic data. It is readily available on could-based platforms.
    Type: Grant
    Filed: January 14, 2019
    Date of Patent: July 18, 2023
    Assignees: Illumina, Inc., Illumina Cambridge Limited
    Inventors: Ole Schulz-Trieglaff, Anthony James Cox, Kai-How Farh