Patent ID: 11568312

Abstract:
Systems and methods for increasing the training value of input training datasets are described herein. In an embodiment, a server computer receives a plurality of input training datasets, each of the input training datasets comprising values for a plurality of parameters, a value indicating whether failure has occurred, and another value indicating the time of failure or the time of observation if no failure has occurred. For each input training dataset, the server computer generates a plurality of month-specific training datasets, each of which comprising a first value indicating a number of previous months where failure has not occurred and a second value indicating whether failure occurred during a month corresponding to the month-specific training data. The server computer trains a machine learning model using the plurality of month-specific training datasets. When the server computer receives a particular input dataset, the server computer generates a plurality of month-specific input datasets from the particular input dataset and uses the machine learning model to compute a plurality of month-specific likelihoods of failure of the particular item from the plurality of month-specific input datasets. This process allows a machine learning model to train off of both complete and incomplete datasets, giving the machine learning model access to current data and allowing for earlier implementation of machine learning in new business areas.