Machine Learning algorithms are empowering a lot of software applications today. We address a business need where the
Machine Learning model can help to resolve customer issues which arise during printing and scanning on devices or installing
a software driver. The model is based on Bayesian Network diagnoses the problem and subsequently resolves it by suggesting
a sequence of steps which increase the probability of fix. This is applied in the context of an installer or driver or a diagnostics
tool (referred to as *component*) to arrive at a resolution. The model factors in the current state of the print system which
comprises of Operating System, Printer model, localization etc. The resolution can be a static sequence which provides a
sequence of steps based upon the initial state of the customer or it can be a dynamic sequence wherein user provides feedback
against each action suggested by the model and the model takes that feedback into account and suggests the next step. We
have a prototype for this model and are applying it as part of Machine Learning service of Print Scan Doctor (PSDr) and are
evaluating the possibility of other applications.
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INC, HP, "SEQUENCE PREDICTION FOR PRINT/SCAN ISSUES USING BAYESIAN NETWORKS", Technical Disclosure Commons, (April 03, 2019)