公开(公告)号：US10409789B2

公开(公告)日：2019-09-10

申请号：US15707500

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Michael Zoll ,  Yaser I. Suleiman ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/30 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N7/00 ,  H04L12/24 ,  G06N20/20 ,  G06N20/00 ,  H04L12/26

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

公开(公告)号：US10997135B2

公开(公告)日：2021-05-04

申请号：US15707536

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Michael Zoll ,  Yaser I. Suleiman ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: H04L12/24 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  G06N7/00 ,  G06N20/00 ,  H04L12/26

Abstract: Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

公开(公告)号：US11455284B2

公开(公告)日：2022-09-27

申请号：US16564910

申请日：2019-09-09

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Michael Zoll ,  Yaser I. Suleiman ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/30 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  H04L41/142 ,  G06N7/00 ,  H04L41/069 ,  G06N20/00 ,  H04L41/0695 ,  H04L41/0823 ,  H04L43/02

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

公开(公告)号：US11308049B2

公开(公告)日：2022-04-19

申请号：US15707454

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Yaser I. Suleiman ,  Michael Zoll ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/21 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  H04L41/142 ,  G06N7/00 ,  H04L41/069 ,  G06N20/00 ,  H04L41/0695 ,  H04L41/0823 ,  H04L43/02

Abstract: Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

公开(公告)号：US10909095B2

公开(公告)日：2021-02-02

申请号：US15707417

申请日：2017-09-18

Applicant: Oracle International Corporation

Inventor： Yaser I. Suleiman ,  Michael Zoll ,  Subhransu Basu ,  Angelo Pruscino ,  Wolfgang Lohwasser ,  Wataru Miyoshi ,  Thomas Breidt ,  Thomas Herter ,  Klaus Thielen ,  Sahil Kumar

IPC: G06F16/00 ,  G06F16/215 ,  G06K9/62 ,  G06F11/34 ,  G06N20/20 ,  H04L12/24 ,  G06N7/00 ,  G06N20/00 ,  H04L12/26

Abstract: Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.