The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 28, 2018

Filed:

Jan. 19, 2017
Applicant:

Amdocs Development Limited, Limassol, CY;

Inventors:

Dan Halbersberg, Tel Aviv, IL;

Vivi Miranda, Raanana, IL;

Eitan Gal, Tel-Aviv, IL;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 11/00 (2006.01); G06F 11/07 (2006.01);
U.S. Cl.
CPC ...
G06F 11/079 (2013.01); G06F 11/0751 (2013.01); G06F 11/0787 (2013.01);
Abstract

A system, method, and computer program product are provided for automatic root cause analysis. In operation, a root cause analysis system identifies at least one event associated with one or more records for which to perform a root cause analysis. The root cause analysis system performs a root cause analysis of the event by automatically generating a decision tree based on all records in the current time-window such that each leaf in the decision tree represents the probabilities for class labels of a target variable and each branch in the decision tree represents a feature that leads to a corresponding class label probability. The root cause analysis system automatically generates the decision tree by automatically selecting at each step the feature that maximizes information gain based on a current subset of data. The root cause analysis system then classifies which conditioned feature is a causal factor and which is a root cause of the event by using a conditional entropy equation on each branch leading to the tree leaf. The root cause analysis is repeatedly performed on sequential time-window sets of records gathered, per a sufficiently small time window for near-real-time root cause detection, yet sufficiently large records set for statistical significance.


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