Abstract:
Disclosed is a diagnostic method and system including the processing of historical event logs generated by one or more devices. According to an exemplary embodiment, a diagnostic system includes an event log acquisition module, an event classification module classifying event logs acquired, and a diagnostic module generating a labeled version of the historical event log including labels provided by the classification module. The event classification module is trained using supervised machine learning techniques.
Abstract:
Disclosed is a diagnostic method and system including the processing of historical event logs generated by one or more devices. According to an exemplary embodiment, a diagnostic system includes an event log acquisition module, an event classification module classifying event logs acquired, and a diagnostic module generating a labeled version of the historical event log including labels provided by the classification module. The event classification module is trained using supervised machine learning techniques.
Abstract:
A device management application (DMA) contacts printing devices connected to a computer network to obtain the device type, configuration parameters, and configuration settings from the printing devices. The device type, the configuration parameters, and the configuration settings are stored for each of the printing devices in a nested hash table. The configuration settings are grouped for each of the printing devices by device type in a working file, such as an XML tree that has leaf nodes for each configuration parameter of each different device type group. These methods identify the most common configuration settings for each of the configuration parameters in the leaf nodes, and map the most common configuration settings for the configuration parameters to configuration setup files for each the device type group. The configuration setup files are then deployed to the printing devices.
Abstract:
A method for process direction registration in an inkjet printer includes ejecting ink drops from a first inkjet at less than a maximum operating rate onto an image receiving surface moving in a process direction. The method includes generating image data samples of the image receiving surface including the ink drops. The method further includes identifying a center of the ink drops in the process direction with reference to the image data samples and storing a time offset value in a memory to correct an identified process direction offset between the identified center of the ink drops and another identified center of ink drops that are ejected by another inkjet.
Abstract:
Methods, systems, and processor-readable media for remotely providing a device status alert. In an example embodiment, data indicative of the status of one or more devices can be subject to an HMM (Hidden Markov Model) and a dynamic programming algorithm to determine the latent state of the device (or devices). A status alert model can be trained based on such data and can be expanded with respect to a wide range of devices including utilizing semi-supervised learning. The alert status model can then be integrated into a device management application that provides a status alert regarding one or more of such devices based on the status alert model.
Abstract:
A method for process direction registration in an inkjet printer includes ejecting ink drops from a first inkjet at less than a maximum operating rate onto an image receiving surface moving in a process direction. The method includes generating image data samples of the image receiving surface including the ink drops. The method further includes identifying a center of the ink drops in the process direction with reference to the image data samples and storing a time offset value in a memory to correct an identified process direction offset between the identified center of the ink drops and another identified center of ink drops that are ejected by another inkjet.