Patents by Inventor James S. Burns

James S. Burns 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: 20220020466
    Abstract: Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies.
    Type: Application
    Filed: September 22, 2021
    Publication date: January 20, 2022
    Inventors: Brian D. Athey, Gerald A. Higgins, Alex Ade, Alexandr Kalinin, Narathip Reamaroon, James S. Burns
  • Patent number: 10867702
    Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
    Type: Grant
    Filed: December 23, 2019
    Date of Patent: December 15, 2020
    Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
  • Publication number: 20200294623
    Abstract: Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies.
    Type: Application
    Filed: January 22, 2020
    Publication date: September 17, 2020
    Inventors: Brian D. Athey, Gerald A. Higgins, Alex Ade, Alexandr Kalinin, Narathip Reamaroon, James S. Burns
  • Publication number: 20200234810
    Abstract: Methods for identifying patients diagnosed with treatment resistant or refractory depression, pain or other clinical indications who are eligible to receive N-methyl-D-aspartate receptor antagonist, glycine receptor beta (GLRB) modulator, or ?-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-based therapies to include determining the appropriate medication, an optimal dose for each patient, and determining which patients are not eligible to receive the therapy. The pharmacogenomic clinical decision support assays include targeted single nucleotide polymorphisms and clinical values or a combination of targeted single nucleotide polymorphisms, targeted ketamine-specific expansion and contraction of topologically associated domains, and clinical values. The methods described herein allow for a more effective determination of which patients will experience drug efficacy and which patients will experience adverse drug events.
    Type: Application
    Filed: January 22, 2020
    Publication date: July 23, 2020
    Inventors: Brian D. Athey, Alex Ade, Gerald A. Higgins, Alexandr Kalinin, Narathip Reamaroon, James S. Burns
  • Publication number: 20200233410
    Abstract: Aspects of the present disclosure generally pertains to freight trailers having onboard power. Aspects of the present disclosure more specifically are directed toward an electric freight trailer that can recycle the trailer braking energy of the towing vehicle, and also provide additional thrust to propel the freight trailer. Aspects of the present disclosure are also directed toward an electric freight trailer that can propel itself independently of a tractor, including steering and braking. Aspects of the disclosure are also directed toward additional subsystems that can utilize the trailer energy source.
    Type: Application
    Filed: January 22, 2020
    Publication date: July 23, 2020
    Inventors: James S. BURNS, Thomas L. BARTLEY
  • Publication number: 20200135337
    Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
    Type: Application
    Filed: December 23, 2019
    Publication date: April 30, 2020
    Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
  • Patent number: 10553318
    Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
    Type: Grant
    Filed: February 5, 2019
    Date of Patent: February 4, 2020
    Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
  • Patent number: 10344583
    Abstract: Provided is an acoustic housing including a cover including a first perimeter defining an open cover portion, the cover having a cover length, and a cover height, and a body including a second perimeter defining an open body portion, wherein either the first or second or both perimeters are chamfered, configured to receive one or more electrical components and to sealingly engage with the first chamfered perimeter, the body having a body length, a body height, and an under-surface, and the body including an engagement portion projecting from the under-surface and having an engagement length, an engagement height, and an engagement surface configured to engage an outer surface of a tubular.
    Type: Grant
    Filed: August 29, 2017
    Date of Patent: July 9, 2019
    Assignee: ExxonMobil Upstream Research Company
    Inventors: Katie M. Walker, Thomas M. Smith, Henry Alan Wolf, James S. Burns, Timothy R. Bragg, Mark M. Disko, Yibing Zhang
  • Publication number: 20190172584
    Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
    Type: Application
    Filed: February 5, 2019
    Publication date: June 6, 2019
    Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
  • Patent number: 10249389
    Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc.
    Type: Grant
    Filed: May 11, 2018
    Date of Patent: April 2, 2019
    Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
  • Publication number: 20180330824
    Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc.
    Type: Application
    Filed: May 11, 2018
    Publication date: November 15, 2018
    Inventors: Brian D. Athey, Ari Allyn-Feuer, Gerald A. Higgins, James S. Burns, Alexandr Kalinin, Brian Pauls, Alex Ade, Narathip Reamaroon
  • Patent number: 10100635
    Abstract: A system for downhole telemetry is provided herein. The system employs a series of communications nodes spaced along a tubular body in a wellbore. Each communications node is associated with a sensor that senses data indicative of a formation condition or a wellbore parameter along a subsurface formation. The data is stored in memory until a logging tool is run into the wellbore. The data is transmitted from the respective communications nodes to a receiver in the logging tool. The data is then transferred to the surface. A method of transmitting data in a wellbore is also provided herein. The method uses a logging tool to harvest data in a wellbore from a plurality of sensor communications nodes.
    Type: Grant
    Filed: December 18, 2013
    Date of Patent: October 16, 2018
    Assignee: ExxonMobil Upstream Research Company
    Inventors: Stuart R. Keller, Timothy I. Morrow, James S. Burns, Max Deffenbaugh, Mark M. Disko, David A. Stiles
  • Publication number: 20180066510
    Abstract: Provided is an acoustic housing including a cover including a first perimeter defining an open cover portion, the cover having a cover length, and a cover height, and a body including a second perimeter defining an open body portion, wherein either the first or second or both perimeters are chamfered, configured to receive one or more electrical components and to sealingly engage with the first chamfered perimeter, the body having a body length, a body height, and an under-surface, and the body including an engagement portion projecting from the under-surface and having an engagement length, an engagement height, and an engagement surface configured to engage an outer surface of a tubular.
    Type: Application
    Filed: August 29, 2017
    Publication date: March 8, 2018
    Inventors: Katie M. Walker, Thomas M. Smith, Henry Alan Wolf, James S. Burns, Timothy R. Bragg, Mark M. Disko, Yibing Zhang
  • Patent number: 9644466
    Abstract: A method of recovering hydrocarbons includes forming a first electrode by creating a first hydraulic fracture within the subsurface formation and pumping a first electrically conductive material into the first hydraulic fracture; forming a second electrode by creating a second hydraulic fracture within the subsurface formation and pumping a second electrically conductive material into the second hydraulic fracture; electrically connecting a first power transmitting mechanism to the first electrode; electrically connecting a second power transmitting mechanism to the second electrode; and heating the subsurface formation between the first electrode and the second electrode by transmitting an electrical current via the first power transmitting mechanism to the first electrode and via the second power transmitting mechanism to the second electrode and by flowing the electrical current from the first electrode to the second electrode.
    Type: Grant
    Filed: October 15, 2015
    Date of Patent: May 9, 2017
    Assignee: ExxonMobil Upstream Research Company
    Inventors: William A. Symington, Erik H Clayton, Robert D. Kaminsky, Larry J Manak, James S. Burns
  • Patent number: 9568978
    Abstract: Since the maximum power consumption is largely a concern at the power supply domain, a limited number of nodes may be allowed to consume their maximum power consumption by preventing other nodes from consuming their maximum power consumption. This approach may be used either instead of or in cooperation with existing maximum power consumption regulators.
    Type: Grant
    Filed: September 26, 2013
    Date of Patent: February 14, 2017
    Assignee: Intel Corporation
    Inventors: James S. Burns, James W. Alexander, Muralidhar Rajappa
  • Patent number: 9557804
    Abstract: A method and apparatus for dynamic power limit sharing among the modules in the platform. In one embodiment of the invention, the platform comprises a processor and memory modules. By expanding the power domain to include the processor and the memory modules, dynamic sharing of the power budget of the platform between the processor and the memory modules is enabled. For low-bandwidth workloads, the dynamic sharing of the power budget offers significant opportunity for the processor to increase its frequency by using the headroom in the memory power and vice versa. This enables higher peak performance for the same total platform power budget in one embodiment of the invention.
    Type: Grant
    Filed: September 28, 2015
    Date of Patent: January 31, 2017
    Assignee: Intel Corporation
    Inventors: Ankush Varma, Krishnakanth V. Sistla, Cesar A. Quiroz, Vivek Garg, Martin T. Rowland, Inder M. Sodhi, James S. Burns
  • Patent number: 9547027
    Abstract: In one embodiment, the present invention includes a processor having multiple cores to independently execute instructions, a first sensor to measure a first power consumption level of the processor based at least in part on events occurring on the cores, and a hybrid logic to combine the first power consumption level and a second power consumption level. Other embodiments are described and claimed.
    Type: Grant
    Filed: March 30, 2012
    Date of Patent: January 17, 2017
    Assignee: Intel Corporation
    Inventors: Ankush Varma, Krishnakanth V. Sistla, Martin T. Rowland, Vivek Garg, James S. Burns
  • Patent number: 9502082
    Abstract: Methods, apparatuses, and systems may provide a sensor to monitor a power consumption of a non-volatile random access memory (RAM) and a volatile RAM. A switch, connected to an output of the sensor, controls power to the non-volatile RAM, and a voltage regulator regulates a voltage of the non-volatile RAM and the volatile RAM. One or more memory slots receive the non-volatile RAM and the volatile RAM, and a processor receives information from the sensor, and controls the voltage regulator based on the received information. The voltage regulator comprises a plurality of registers to store power consumption information of the non-volatile RAM and the volatile RAM.
    Type: Grant
    Filed: June 24, 2015
    Date of Patent: November 22, 2016
    Assignee: Intel Corporation
    Inventors: Thi Dang, James S. Burns, Russell J. Wunderlich, Jagannath Coimbatore Premkumar
  • Publication number: 20160145987
    Abstract: A method of recovering hydrocarbons includes forming a first electrode by creating a first hydraulic fracture within the subsurface formation and pumping a first electrically conductive material into the first hydraulic fracture; forming a second electrode by creating a second hydraulic fracture within the subsurface formation and pumping a second electrically conductive material into the second hydraulic fracture; electrically connecting a first power transmitting mechanism to the first electrode; electrically connecting a second power transmitting mechanism to the second electrode; and heating the subsurface formation between the first electrode and the second electrode by transmitting an electrical current via the first power transmitting mechanism to the first electrode and via the second power transmitting mechanism to the second electrode and by flowing the electrical current from the first electrode to the second electrode.
    Type: Application
    Filed: October 15, 2015
    Publication date: May 26, 2016
    Inventors: William A. Symington, Erik H. Clayton, Robert D. Kaminsky, Larry J. Manak, James S. Burns
  • Patent number: 9335813
    Abstract: A method and system for dynamic or run-time reallocation of leakage current and dynamic power supply current of a processor. In one embodiment of the invention, the processor uses the variation in the leakage current of the processor to reduce the maximum current dissipation or power supply current of the processor (ICCmax). By reducing the maximum current dissipation, the system cost can be reduced as a less expensive power delivery system is required in one embodiment of the invention.
    Type: Grant
    Filed: May 28, 2013
    Date of Patent: May 10, 2016
    Assignee: Intel Corporation
    Inventors: James S. Burns, Ashish V. Choubal, Arvind Raman, Johan G. Van De Groenendaal