Patent ID: 11936542
Assignee: SAMSUNG ELECTRONICS CO., LTD.
Field: Digital communication (Electrical engineering)
Classification: CPC H | IPC H

Claim 4:
5. A method of solving a problem of a network, the method comprising:
detecting one or more anomaly samples of a quality indicator indicating a quality of the network;
determining a cause of each of the one or more anomaly samples through pattern matching;
determining a representative cause representing causes of the one or more anomaly samples;
performing a time-series analysis on an indicator associated with the representative cause during a time interval comprising the one or more anomaly samples; and
proposing a solution corresponding to the representative cause and a result of the time-series analysis,
wherein the performing of the time-series analysis comprises:
determining a time corresponding to a predetermined sample prior to a time of a first anomaly sample among the anomaly samples as a start time at which the time-series analysis is to be performed, and
analyzing a trend, a seasonality or a residual component of the associated indicator during a target interval starting from the start time at which the time-series analysis is to be performed,

wherein the target interval starting from the start time at which the time-series analysis is to be performed is a first time interval,
wherein the performing of the time-series analysis comprises performing a time-series analysis on the indicator associated with the representative cause during a second time interval comprising the first time interval,
wherein the determining of the time as the start time comprises determining a time corresponding to a predetermined sample prior to a start time of the first time interval as a start time of the second time interval, and
wherein the determining of the cause for each of the one or more anomaly samples comprises:
inputting the one or more anomaly samples to a neural network that is trained based on a plurality of pieces of training data associated with a corresponding relationship between an anomaly sample and a cause; and
determining a cause of each of the one or more anomaly samples from an output of the neural network.