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公开(公告)号：US20200249931A1
公开(公告)日：2020-08-06
申请号：US16854635
申请日：2020-04-21
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
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.
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Patent Agency Ranking
公开(公告)号：US10664264B2
公开(公告)日：2020-05-26
申请号：US16042971
申请日：2018-07-23
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
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.
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公开(公告)号：US11023221B2
公开(公告)日：2021-06-01
申请号：US16854635
申请日：2020-04-21
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
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.
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公开(公告)号：US20190102155A1
公开(公告)日：2019-04-04
申请号：US16042971
申请日：2018-07-23
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
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.
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公开(公告)号：US12131142B2
公开(公告)日：2024-10-29
申请号：US17332649
申请日：2021-05-27
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
CPC classification number: G06F8/65 , G06F8/60 , G06F8/61 , G06F9/5055 , G06F16/906 , G06N20/00 , G06N5/022
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.
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公开(公告)号：US20210286611A1
公开(公告)日：2021-09-16
申请号：US17332649
申请日：2021-05-27
Applicant: Oracle International Corporation
Inventor： Dustin Garvey , Amit Ganesh , Uri Shaft , Prasad Ravuri , Long Yang , Sampanna Shahaji Salunke , Sumathi Gopalakrishnan , Timothy Mark Frazier , Shriram Krishnan
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.
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