Patents by Inventor Timothy Mark Frazier

Timothy Mark Frazier 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: 11126667
    Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.
    Type: Grant
    Filed: April 12, 2019
    Date of Patent: September 21, 2021
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Brent Arthur Enck, Sampanna Shahaji Salunke, Uri Shaft, John Branson Bley, Timothy Mark Frazier, Sumathi Gopalakrishnan
  • Publication number: 20210286611
    Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
    Type: Application
    Filed: May 27, 2021
    Publication date: September 16, 2021
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Patent number: 11048612
    Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.
    Type: Grant
    Filed: August 17, 2020
    Date of Patent: June 29, 2021
    Assignee: Oracle International Corporation
    Inventors: Sampanna Shahaji Salunke, Dustin Garvey, Uri Shaft, Brent Arthur Enck, Timothy Mark Frazier, Sumathi Gopalakrishnan, Eric L. Sutton
  • Patent number: 11023221
    Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
    Type: Grant
    Filed: April 21, 2020
    Date of Patent: June 1, 2021
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Publication number: 20200379882
    Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.
    Type: Application
    Filed: August 17, 2020
    Publication date: December 3, 2020
    Applicant: Oracle International Corporation
    Inventors: Sampanna Shahaji Salunke, Dustin Garvey, Uri Shaft, Brent Arthur Enck, Timothy Mark Frazier, Sumathi Gopalakrishnan, Eric L. Sutton
  • Patent number: 10855548
    Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.
    Type: Grant
    Filed: February 15, 2019
    Date of Patent: December 1, 2020
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Neil Goodman, Sampanna Shahaji Salunke, Brent Arthur Enck, Sumathi Gopalakrishnan, Amit Ganesh, Timothy Mark Frazier
  • Patent number: 10789065
    Abstract: Techniques for analyzing, understanding, and remediating differences in configurations among many software resources are described herein. Machine learning processes are applied to determine a small feature set of parameters from among the complete set of parameters configured for each software resource. The feature set of parameters is selected to optimally cluster configuration instances for each of the software resources. Once clustered based on the values of the feature set of parameters, a graph is generated for each cluster of configuration instances that depicts the differences among the configuration instances within the cluster. An interactive visualization tool renders the graph in a user interface, and a management tool allows changes to the graph and changes to the configuration of one or more software resources.
    Type: Grant
    Filed: May 7, 2018
    Date of Patent: September 29, 2020
    Assignee: Oracle lnternational Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Timothy Mark Frazier, Shriram Krishnan, Sr., Uri Shaft, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20200267057
    Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.
    Type: Application
    Filed: February 15, 2019
    Publication date: August 20, 2020
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Neil Goodman, Sampanna Shahaji Salunke, Brent Arthur Enck, Sumathi Gopalakrishnan, Amit Ganesh, Timothy Mark Frazier
  • Patent number: 10747642
    Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.
    Type: Grant
    Filed: October 20, 2018
    Date of Patent: August 18, 2020
    Assignee: Oracle International Corporation
    Inventors: Sampanna Shahaji Salunke, Dustin Garvey, Uri Shaft, Brent Arthur Enck, Timothy Mark Frazier, Sumathi Gopalakrishnan, Eric L. Sutton
  • Publication number: 20200249931
    Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
    Type: Application
    Filed: April 21, 2020
    Publication date: August 6, 2020
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Patent number: 10664264
    Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
    Type: Grant
    Filed: July 23, 2018
    Date of Patent: May 26, 2020
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan
  • Publication number: 20200125601
    Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.
    Type: Application
    Filed: April 12, 2019
    Publication date: April 23, 2020
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Brent Arthur Enck, Sampanna Shahaji Salunke, Uri Shaft, John Branson Bley, Timothy Mark Frazier, Sumathi Gopalakrishnan
  • Publication number: 20200125474
    Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.
    Type: Application
    Filed: October 20, 2018
    Publication date: April 23, 2020
    Applicant: Oracle International Corporation
    Inventors: Sampanna Shahaji Salunke, Dustin Garvey, Uri Shaft, Brent Arthur Enck, Timothy Mark Frazier, Sumathi Gopalakrishnan, Eric L. Sutton
  • Patent number: 10592230
    Abstract: Techniques are described herein for scalable clustering of target resources by parameter set. In some embodiments, a plurality of parameter sets of varying length are received, where a parameter set identifies attributes of a target resource. A plurality of signature vectors are generated based on the plurality of parameter sets such that the signature vectors have equal lengths. A signature vector may map to one or more parameter sets of the plurality of parameter sets. A plurality of clusters are generated based on the similarity between signature vectors. Operations may be performed on a target resource based on one or more nodes in the plurality of clusters.
    Type: Grant
    Filed: August 7, 2019
    Date of Patent: March 17, 2020
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Timothy Mark Frazier, Shriram Krishnan, Uri Shaft, Amit Ganesh, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Patent number: 10496396
    Abstract: Techniques are described herein for scalable clustering of target resources by parameter set. In some embodiments, a plurality of parameter sets of varying length are received, where a parameter set identifies attributes of a target resource. A plurality of signature vectors are generated based on the plurality of parameter sets such that the signature vectors have equal lengths. A signature vector may map to one or more parameter sets of the plurality of parameter sets. A plurality of clusters are generated based on the similarity between signature vectors. Operations may be performed on a target resource based on one or more nodes in the plurality of clusters.
    Type: Grant
    Filed: July 20, 2018
    Date of Patent: December 3, 2019
    Assignee: Oracle International Corporation
    Inventors: Dustin Garvey, Timothy Mark Frazier, Shriram Krishnan, Uri Shaft, Amit Ganesh, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20190361693
    Abstract: Techniques are described herein for scalable clustering of target resources by parameter set. In some embodiments, a plurality of parameter sets of varying length are received, where a parameter set identifies attributes of a target resource. A plurality of signature vectors are generated based on the plurality of parameter sets such that the signature vectors have equal lengths. A signature vector may map to one or more parameter sets of the plurality of parameter sets. A plurality of clusters are generated based on the similarity between signature vectors. Operations may be performed on a target resource based on one or more nodes in the plurality of clusters.
    Type: Application
    Filed: August 7, 2019
    Publication date: November 28, 2019
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Timothy Mark Frazier, Shriram Krishnan, Uri Shaft, Amit Ganesh, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20190339965
    Abstract: Techniques for analyzing, understanding, and remediating differences in configurations among many software resources are described herein. Machine learning processes are applied to determine a small feature set of parameters from among the complete set of parameters configured for each software resource. The feature set of parameters is selected to optimally cluster configuration instances for each of the software resources. Once clustered based on the values of the feature set of parameters, a graph is generated for each cluster of configuration instances that depicts the differences among the configuration instances within the cluster. An interactive visualization tool renders the graph in a user interface, and a management tool allows changes to the graph and changes to the configuration of one or more software resources.
    Type: Application
    Filed: May 7, 2018
    Publication date: November 7, 2019
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Timothy Mark Frazier, Shriram Krishnan, SR., Uri Shaft, Prasad Ravuri, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan
  • Publication number: 20190138290
    Abstract: Techniques are described herein for scalable clustering of target resources by parameter set. In some embodiments, a plurality of parameter sets of varying length are received, where a parameter set identifies attributes of a target resource. A plurality of signature vectors are generated based on the plurality of parameter sets such that the signature vectors have equal lengths. A signature vector may map to one or more parameter sets of the plurality of parameter sets. A plurality of clusters are generated based on the similarity between signature vectors. Operations may be performed on a target resource based on one or more nodes in the plurality of clusters.
    Type: Application
    Filed: July 20, 2018
    Publication date: May 9, 2019
    Applicant: Oracle International Corporation
    Inventors: DUSTIN GARVEY, TIMOTHY MARK FRAZIER, SHRIRAM KRISHNAN, URI SHAFT, AMIT GANESH, PRASAD RAVURI, SAMPANNA SHAHAJI SALUNKE, SUMATHI GOPALAKRISHNAN
  • Publication number: 20190102155
    Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.
    Type: Application
    Filed: July 23, 2018
    Publication date: April 4, 2019
    Applicant: Oracle International Corporation
    Inventors: Dustin Garvey, Amit Ganesh, Uri Shaft, Prasad Ravuri, Long Yang, Sampanna Shahaji Salunke, Sumathi Gopalakrishnan, Timothy Mark Frazier, Shriram Krishnan