Patents by Inventor Ciarán Mark LEE

Ciarán Mark LEE 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).

  • Patent number: 11379747
    Abstract: A method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.
    Type: Grant
    Filed: July 24, 2020
    Date of Patent: July 5, 2022
    Assignee: BABYLON PARTNERS LIMITED
    Inventors: Jonathan George Richens, Ciarán Mark Lee, Saurabh Johri
  • Patent number: 11017572
    Abstract: A computer-implemented method of generating a PGM with causal information, said graphical model containing the causal relationship between a first variable and a second variable, the method comprising: receiving data at a processor, said data showing a correlation between the first variable and a second variable; producing a third variable by reducing the dimensionality of the graphical representation of the two dimensional data defined by the first variable and the second variable, determining determine the causal relationship between the first and third variables and the second and third variable, the causal discovery algorithm being able to determine if the first variable causes the third variable, the third variable causes the first variable, the second variable causes the third variable and the third variable causes the second variable; and outputting a graphical model indicating the direction of edges in a graphical representation of said PGM.
    Type: Grant
    Filed: February 28, 2019
    Date of Patent: May 25, 2021
    Assignee: Babylon Partners Limited
    Inventors: Ciarán Mark Lee, Christopher Robert Hart, Jonathan George Richens, Saurabh Johri
  • Patent number: 11017905
    Abstract: A method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.
    Type: Grant
    Filed: July 23, 2019
    Date of Patent: May 25, 2021
    Assignee: Babylon Partners Limited
    Inventors: Jonathan George Richens, Ciarán Mark Lee, Saurabh Johri
  • Publication number: 20210110287
    Abstract: A computer implemented method of performing inference on a generative model, wherein the generative model in a probabilistic program form, said probabilistic program form defining variables and probabilistic relationships between variables, the method comprising: providing at least one of observations or interventions to the generative model; selecting an inference method, wherein the inference method is selected from one of: observational inference, interventional inference or counterfactual inference; performing the selected inference method using an approximate inference method on the generative model; and outputting a predicted outcome from the results of the inference; wherein approximate inference is performed by inputting an inference query and the model, observations, interventions and inference query are provided as independent parameters such that they can be iterated over and varied independently of each other.
    Type: Application
    Filed: July 31, 2020
    Publication date: April 15, 2021
    Inventors: Iurii Perov, Logan Christopher Sherwin Graham, Konstantinos Gourgoulias, Jonathan George Richens, Ciarán Mark Lee, Adam Philip Baker, Saurabh Johri
  • Publication number: 20200279655
    Abstract: A method for providing a computer-implemented medical diagnosis includes receiving an input from a user comprising at least one symptom of the user. The method also includes providing the at least one symptom as an input to a medical model, the medical model being retrieved from memory. The medical model includes a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases. The method also includes performing inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease. The method also includes outputting an indication that the user has a disease from the Bayesian inference, wherein the inference is performed using a counterfactual measure.
    Type: Application
    Filed: July 23, 2019
    Publication date: September 3, 2020
    Inventors: Jonathan George RICHENS, Ciarán Mark LEE, Saurabh JOHRI
  • Publication number: 20200279417
    Abstract: A computer-implemented method of generating a PGM with causal information, said graphical model containing the causal relationship between a first variable and a second variable, the method comprising: receiving data at a processor, said data showing a correlation between the first variable and a second variable; producing a third variable by reducing the dimensionality of the graphical representation of the two dimensional data defined by the first variable and the second variable, determining determine the causal relationship between the first and third variables and the second and third variable, the causal discovery algorithm being able to determine if the first variable causes the third variable, the third variable causes the first variable, the second variable causes the third variable and the third variable causes the second variable; and outputting a graphical model indicating the direction of edges in a graphical representation of said PGM.
    Type: Application
    Filed: February 28, 2019
    Publication date: September 3, 2020
    Inventors: Ciarán Mark LEE, Christopher Robert HART, Jonathan George RICHENS, Saurabh JOHRI