Patents by Inventor William Paul Bone

William Paul Bone 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).

  • UTILIZING A CLINICAL-PHENOMICS CAUSAL DISCOVERY FRAMEWORK TO GENERATE CAUSAL DISCOVERY PREDICTIONS
    Publication number: 20260024614
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that analyze gene perturbation machine learning embeddings and clinical observation data sets utilizing machine learning, explainability models, and causal discovery models to generate causal predictions between one or more genes and clinical outcomes. Indeed, in one or more implementations, the disclosed systems identify gene perturbation embeddings generated from cells exposed to perturbations. For instance, the disclosed systems select a cluster of genes from a plurality of genes by applying a clustering model to the gene perturbation embeddings. In some instances, the disclosed systems select gene targets from the cluster of genes by using a machine learning classification model trained on a plurality of features of the clinical observation data set.
    Type: Application
    Filed: September 30, 2025
    Publication date: January 22, 2026
    Inventors: Hayley Jeton DONNELLA, Seyed Ali MADANI TONEKABONI, William Paul BONE
  • UTILIZING A CLINICAL-PHENOMICS CAUSAL DISCOVERY FRAMEWORK TO GENERATE CAUSAL DISCOVERY PREDICTIONS
    Publication number: 20250378905
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that analyze gene perturbation machine learning embeddings and clinical observation data sets utilizing machine learning, explainability models, and causal discovery models to generate causal predictions between one or more genes and clinical outcomes. Indeed, in one or more implementations, the disclosed systems identify gene perturbation embeddings generated from cells exposed to perturbations. For instance, the disclosed systems select a cluster of genes from a plurality of genes by applying a clustering model to the gene perturbation embeddings. In some instances, the disclosed systems select gene targets from the cluster of genes by using a machine learning classification model trained on a plurality of features of the clinical observation data set.
    Type: Application
    Filed: June 10, 2024
    Publication date: December 11, 2025
    Inventors: Hayley Jeton DONNELLA, Seyed Ali MADANI TONEKABONI, William Paul BONE
  • Utilizing a clinical-phenomics causal discovery framework to generate causal discovery predictions
    Patent number: 12494266
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that analyze gene perturbation machine learning embeddings and clinical observation data sets utilizing machine learning, explainability models, and causal discovery models to generate causal predictions between one or more genes and clinical outcomes. Indeed, in one or more implementations, the disclosed systems identify gene perturbation embeddings generated from cells exposed to perturbations. For instance, the disclosed systems select a cluster of genes from a plurality of genes by applying a clustering model to the gene perturbation embeddings. In some instances, the disclosed systems select gene targets from the cluster of genes by using a machine learning classification model trained on a plurality of features of the clinical observation data set.
    Type: Grant
    Filed: June 10, 2024
    Date of Patent: December 9, 2025
    Assignee: Recursion Pharmaceuticals, Inc.
    Inventors: Hayley Jeton Donnella, Seyed Ali Madani Tonekaboni, William Paul Bone