Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.
Recommended Citation
INC, HP, "SEQUENCE PREDICTION FOR PRINT/SCAN ISSUES USING BAYESIAN NETWORKS", Technical Disclosure Commons, (April 03, 2019)
https://www.tdcommons.org/dpubs_series/2117