Patents by Inventor Nicholas Tatonetti

Nicholas Tatonetti 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: 20210194699
    Abstract: A system for collecting and distributing a digital audiovisual item captured by a sensor using a blockchain server is disclosed. The system can comprise a security module that is coupled to the sensor and adapted to generate a private cryptographic key. The system can further include a blockchain-enabled hardware, coupled to the sensor and the security module. The hardware can generate a set of original hashes corresponding to the captured digital audiovisual item and an identifier having an address to the original hashes, embed information corresponding to the identifier in the captured digital audiovisual item to create a blockchain identified audiovisual item, and post the set of original hashes to a public blockchain using the private cryptographic key. Methods for collecting and distributing a digital audiovisual item captured by a sensor using a blockchain server are also provided.
    Type: Application
    Filed: December 8, 2020
    Publication date: June 24, 2021
    Applicant: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
    Inventors: Nicholas TATONETTI, Siddhartha SHRIVASTAVA
  • Publication number: 20210134402
    Abstract: System and methods for predicting success rates of clinical trials are disclosed. The system can comprise one or more processors and one or more computer-readable non-transitory storage media coupled to the one or more of processors including instructions operable when executed by one or more of the processor. The system is configured to cause the system to construct a training set using a data source, a performance score and a robustness score of the training set based on selected features, a random forest model based on the calculated performance and robustness scores; and calculate a toxicity score of the pharmaceuticals by applying the random forest model to a genome which is affected by the pharmaceuticals. Methods for predicting success rates of clinical trials and pharmaceuticals are also provided.
    Type: Application
    Filed: October 30, 2020
    Publication date: May 6, 2021
    Applicant: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
    Inventors: Nicholas TATONETTI, Yun HAO
  • Patent number: 10354202
    Abstract: An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose.
    Type: Grant
    Filed: April 4, 2016
    Date of Patent: July 16, 2019
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Nicholas Tatonetti, Russ B. Altman, Guy Haskin Fernald
  • Publication number: 20160283650
    Abstract: Techniques for predicting synthetic lethality in a first species using experimentally derived interactions from at least a second species. An example method can include generating a first biological network for the first species and a second biological network for the second species that include node information representing genes and edge information representing physical interactions between gene-protein products. The method can include determining and normalizing one or more network parameters to permit comparisons between the first and second biological networks. The method can further include training a synthetic lethality model with the experimentally derived synthetic lethality data and applying the synthetic lethality model to the first biological network to predict one or more synthetic lethality pairs.
    Type: Application
    Filed: February 26, 2016
    Publication date: September 29, 2016
    Inventors: Alexandra Jacunski, Nicholas Tatonetti
  • Publication number: 20160217395
    Abstract: An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose.
    Type: Application
    Filed: April 4, 2016
    Publication date: July 28, 2016
    Applicant: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Nicholas Tatonetti, Russ B. Altman, Guy Haskin Fernald
  • Patent number: 9305267
    Abstract: An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose.
    Type: Grant
    Filed: January 10, 2013
    Date of Patent: April 5, 2016
    Assignee: The Board of Trustees of the Leland Stanford Junior University
    Inventors: Nicholas Tatonetti, Russ B. Altman, Guy Haskin Fernald