Abstract: Example techniques for processing service notes are described. In an example, labeled service notes, associated with fuser units of a plurality of image rendering devices, are processed to generate a vector corresponding to each of the labeled service notes, a labeled service note comprising natural language text describing an error event and a corresponding service activity associated with a fuser unit, wherein the labeled service note is assigned a label based on a category of failure of the fuser unit. Based on the processing, a relationship between vectors and labels corresponding to the labeled service notes is generated.

Abstract: Example techniques for processing service notes are described. In an example, labeled service notes, associated with fuser units of a plurality of image rendering devices, are processed to generate a vector corresponding to each of the labeled service notes, a labeled service note comprising natural language text describing an error event and a corresponding service activity associated with a fuser unit, wherein the labeled service note is assigned a label based on a category of failure of the fuser unit. Based on the processing, a relationship between vectors and labels corresponding to the labeled service notes is generated.

Abstract: A device monitoring system comprising a computation engine to obtain, for each of a plurality of devices, an actual failure condition indicating actual device failure and a probable failure condition predicted by a health monitoring device. The health monitoring device to monitor health of the plurality of devices, the probable failure condition indicating when the device is predicted to stop functioning. The computation engine is to compute a failure prediction gap for each of the plurality of devices. The failure prediction gap indicating a difference between the probable failure condition and the actual failure condition. A performance evaluation engine to compute a saving factor based at least on cost parameters and an average of the failure prediction gap computed for the plurality of devices and initiate discontinuance of usage of the health monitoring device based on a comparison of the saving factor with a threshold.