Patents by Inventor David LOUGHRAN
David LOUGHRAN 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: 11893511Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.Type: GrantFiled: November 25, 2020Date of Patent: February 6, 2024Inventors: Adam Grossman, Lauren Caston, Ryan Irvine, David Loughran, Robert Thomas Reville
-
Publication number: 20220360223Abstract: A system for adjusting various parameters of an active electronic component based on sensed characteristics of the active electronic component and/or characteristics of the input or output power.Type: ApplicationFiled: May 3, 2022Publication date: November 10, 2022Inventors: Denpol Kultran, Harry Bourne Marr, JR., Ryan Scott Ligon, Steven Deward Gray, Jonathan David Loughran
-
Publication number: 20210279421Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.Type: ApplicationFiled: January 27, 2021Publication date: September 9, 2021Applicant: Praedicat, Inc.Inventors: Andrea Melissa Boudreau, Lauren Caston, Naresh Chebolu, Adam Grossman, Liyang Hao, David Loughran, Michael Ragland, Robert Reville, Chun-Yuen Teng
-
Publication number: 20210271992Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.Type: ApplicationFiled: November 25, 2020Publication date: September 2, 2021Inventors: Adam Grossman, Lauren Caston, Ryan Irvine, David Loughran, Robert Thomas Reville
-
Patent number: 10909323Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.Type: GrantFiled: March 9, 2020Date of Patent: February 2, 2021Assignee: Praedicat, Inc.Inventors: Andrea Melissa Boudreau, Lauren Caston, Naresh Chebolu, Adam Grossman, Liyang Hao, David Loughran, Michael Ragland, Robert Reville, Chun-Yuen Teng
-
Patent number: 10853734Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.Type: GrantFiled: March 2, 2020Date of Patent: December 1, 2020Assignee: Praedicat, Inc.Inventors: Adam Grossman, Lauren Caston, Ryan Irvine, David Loughran, Robert Thomas Reville
-
Publication number: 20200293718Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.Type: ApplicationFiled: March 9, 2020Publication date: September 17, 2020Inventors: Andrea Melissa Boudreau, Lauren Caston, Naresh Chebolu, Adam Grossman, Liyang Hao, David Loughran, Michael Ragland, Robert Reville, Chun-Yuen Teng
-
Patent number: 10585990Abstract: Examples of the disclosure are directed to systems and methods of using natural language processing techniques to automatically assign metadata to articles as they are published. The automatically-assigned metadata can then feed into the algorithms that calculate updated causation scores for agent-outcome hypotheses, powering live visualizations of the data that update automatically as new scientific articles become available.Type: GrantFiled: March 15, 2019Date of Patent: March 10, 2020Assignee: Praedicat, Inc.Inventors: Andrea Melissa Boudreau, Lauren Caston, Naresh Chebolu, Adam Grossman, Liyang Hao, David Loughran, Michael Ragland, Robert Reville, Chun-Yuen Teng
-
Patent number: 10579930Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.Type: GrantFiled: July 22, 2016Date of Patent: March 3, 2020Assignee: Praedicat, Inc.Inventors: Adam Grossman, Lauren Caston, Ryan Irvine, David Loughran, Robert Thomas Reville
-
Publication number: 20170161837Abstract: Embodiments of the disclosure are directed toward a latent risk assessment user interface, including generating one or more curves that indicate expected future losses due to one or more agents. Embodiments of the disclosure can visualize such losses by mapping estimated litigation losses over time in a loss-time curve and/or generating an exceedance probability curve that indicates total losses at various probabilities. Further, the latent risk assessment user interface can allow a user to select multiple agents and/or multiple companies for aggregation/comparison in a single user interface. Such a user interface can be useful for an insurance company or reinsurer with a portfolio of many companies across diverse industries that utilize the same agents. For example, the latent risk assessment user interface can display estimated future litigation losses over time due to use of BPA and carbon nanotubes for a portfolio of multiple companies, displayed in a single aggregated visualization.Type: ApplicationFiled: December 4, 2015Publication date: June 8, 2017Applicants: Praedicat, Inc., Praedicat, Inc.Inventors: David LOUGHRAN, Robert Thomas REVILLE, Grant CAVANAUGH, Naresh CHEBOLU
-
Publication number: 20170161839Abstract: Embodiments of the disclosure are directed toward a latent risk assessment user interface, including generating one or more curves that indicate expected future losses due to one or more agents. Embodiments of the disclosure can visualize such losses by mapping estimated litigation losses over time in a loss-time curve and/or generating an exceedance probability curve that indicates total losses at various probabilities. Further, the latent risk assessment user interface can allow a user to select multiple agents and/or multiple companies for aggregation/comparison in a single user interface. Such a user interface can be useful for an insurance company or reinsurer with a portfolio of many companies across diverse industries that utilize the same agents. For example, the latent risk assessment user interface can display estimated future litigation losses over time due to use of BPA and carbon nanotubes for a portfolio of multiple companies, displayed in a single aggregated visualization.Type: ApplicationFiled: October 20, 2016Publication date: June 8, 2017Inventors: David LOUGHRAN, Robert Thomas REVILLE, Grant CAVANAUGH, Naresh CHEBOLU
-
Publication number: 20160328652Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.Type: ApplicationFiled: July 22, 2016Publication date: November 10, 2016Inventors: Adam GROSSMAN, Lauren CASTON, Ryan IRVINE, David LOUGHRAN, Robert Thomas REVILLE
-
Patent number: 9430739Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.Type: GrantFiled: December 19, 2013Date of Patent: August 30, 2016Assignee: Praedicat, Inc.Inventors: Adam Grossman, Lauren Caston, Ryan Irvine, David Loughran, Robert Thomas Reville
-
Publication number: 20150339605Abstract: Examples of the disclosure are directed to methods of generating an event set of prospective litigation scenarios and further generating a visualization of a subset of the event set that constitutes a worst-case mass litigation associated with a common agent. A prospective litigation scenario may describe a claim that a particular plaintiff or class of plaintiffs suffered a harm caused by an agent in an exposure setting, and a particular defendant or class of defendants are identified as potentially liable. Further, a prospective litigation scenario may be paired with a liability risk score indicating the likelihood that a particular defendant or class of defendant might be held financially responsible given the claim.Type: ApplicationFiled: May 20, 2014Publication date: November 26, 2015Applicant: PRAEDICAT, INC.Inventors: Robert Thomas REVILLE, David LOUGHRAN
-
Publication number: 20150178628Abstract: Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.Type: ApplicationFiled: December 19, 2013Publication date: June 25, 2015Applicant: PRAEDICAT, INC.Inventors: Adam GROSSMAN, Lauren CASTON, Ryan IRVINE, David LOUGHRAN, Robert Thomas REVILLE