| CPC H04N 21/44218 (2013.01) [G06F 1/28 (2013.01); H04N 21/4667 (2013.01); H04N 21/482 (2013.01)] | 18 Claims |
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11. A method of state-based performance evaluation of televisions via machine learning, comprising:
constructing, by a computing system comprising one or more processors coupled with memory, an array of power values from time series data collected by a power sensor coupled with a television;
receiving, by the computing system via a network, a model from a remote computing system, wherein the remote computing system is configured to:
receive log files of data collected from a plurality of power sensors coupled with a plurality of televisions;
apply a data cleaning technique to the data of the log files to generate a cleaned data set;
apply a normalization technique to the cleaned data set to generate a normalized data set;
remove, from the normalized data set, spikes via a filter to generate a filtered data set;
interpolate missing values in the filtered data set to generate a training data set; and
train, via machine learning, the model with the training data set;
inputting, by the computing system, the array of power values into the model trained with machine learning based on the log files of data collected from the plurality of power sensors coupled with the plurality of televisions;
determining, by the computing system, based on output from the model generated with the array of power values, the television is active; and
executing, by the computing system, responsive to the determination that the television is active, an action to evaluate a performance of content rendered by the television.
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