Patents by Inventor Nathan Henry Lazar

Nathan Henry Lazar 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).

  • Publication number: 20250095392
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.
    Type: Application
    Filed: July 19, 2024
    Publication date: March 20, 2025
    Inventors: Nathan Henry LAZAR, Conor Austin Forsman TILLINGHAST, James Douglas JENSEN, James Benjamin TAYLOR, Berton Allen EARNSHAW, Marta Marie FAY, Renat Nailevich KHALIULLIN, Jacob Carter COOPER, Imran Saeedul HAQUE, Seyhmus GULER, Kyle Rollins HANSEN, Safiye CELIK
  • Publication number: 20250095146
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.
    Type: Application
    Filed: July 22, 2024
    Publication date: March 20, 2025
    Inventors: Nathan Henry LAZAR, Conor Austin Forsman TILLINGHAST, James Douglas JENSEN, James Benjamin TAYLOR, Berton Allen EARNSHAW, Marta Marie FAY, Renat Nailevich KHALIULLIN, Jacob Carter COOPER, Imran Saeedul HAQUE, Seyhmus GULER, Kyle Rollins HANSEN, Safiye CELIK
  • Patent number: 12079992
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.
    Type: Grant
    Filed: December 21, 2023
    Date of Patent: September 3, 2024
    Assignee: Recursion Pharmaceuticals, Inc.
    Inventors: Nathan Henry Lazar, Conor Austin Forsman Tillinghast, James Douglas Jensen, James Benjamin Taylor, Berton Allen Earnshaw, Marta Marie Fay, Renat Nailevich Khaliullin, Jacob Carter Cooper, Imran Saeedul Haque, Seyhmus Guler, Kyle Rollins Hansen, Safiye Celik
  • Patent number: 12073638
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.
    Type: Grant
    Filed: December 21, 2023
    Date of Patent: August 27, 2024
    Assignee: Recursion Pharmaceuticals, Inc.
    Inventors: Nathan Henry Lazar, Conor Austin Forsman Tillinghast, James Douglas Jensen, James Benjamin Taylor, Berton Allen Earnshaw, Marta Marie Fay, Renat Nailevich Khaliullin, Jacob Carter Cooper, Imran Saeedul Haque, Seyhmus Guler, Kyle Rollins Hansen, Safiye Celik