Machine learning patterns of failure in broadband networks

Abstract

A method of diagnosing a potential fault on a broadband connection line is provided. The method comprises receiving line data associated with a plurality of connection lines, analysing, by a machine learning algorithm, the line data, and classifying, by the machine learning algorithm, the plurality of connection lines into a plurality of clusters based on the analysing of the line data. Here, each of the plurality of clusters comprises a subset of the plurality of connection lines. The method further comprises assigning, by the machine learning algorithm, a label to each cluster of the plurality of clusters, each label indicating the overall connection line performance of the connection lines in that cluster, and generating a metric score for each of a plurality of metrics associated with the performance of that connection line. For each of the plurality of connection lines, deviating metric scores are identified from among the generated metric scores for that connection line, wherein a deviating metric score is a metric score indicates a potential fault on that connection line. For each identified deviating metric score, a recommendation is generated based on one or more of: the label of the cluster to which the connection line associated with that deviating metric score has been assigned, the type of metric associated with that deviating metric score, or the magnitude of the deviating metric score. The generated recommendation is then outputted for each connection line identified as having a deviating metric score.

Dependencies are inferred from the claim text.