Abstract: Methods and systems for automatically organizing devices in a network are disclosed. Information may be collected for a plurality of devices in a predetermined area over a predetermined period of time. The information may include device location information and device information for one or more user groups. Each user group may include one or more users. An average preference for each user group may be determined for each device based on the device usage information. The plurality of devices may be organized automatically into a plurality of clusters based on at least the determined average preferences. Each cluster may include one or more devices.

Abstract: A method for producing an activity report is disclosed. The method includes identifying at least one device in a system, generating the activity report with the activity report including information about the at least one device in the system, delivering the generated activity report to a remote location from the at least one device and providing recommendations about the at least one device generated by a recommendation system utilizing the information from the activity report.

Abstract: A system and method are provided for acquiring and processing device usage data and applying a computational knowledge thereto for proactively determining customer state, including inferring device usage patterns, and accordingly proposing at least one action, if any, to be undertaken. In particular, computational knowledge represented by a set of rules is applied to the processed device data for analyzing the processed data and describing at least one feature or characteristic relating to the processed data using keywords. A rules database is accessed and at least one rule is selected from a set of rules. The at least one selected rule includes keywords which substantially match the keywords used to describe the feature or characteristic of the processed data. The at least one selected rule is then correlated to at least one proposed action using the rules database. The at least one determined rule and/or at least one proposed action are then outputted.

Abstract: A computerized method is presented for the management of job distribution among a population of networked production machines through balancing of the usage load over the population of networked machines based upon wear data. The method includes initializing a production server, which communicates with the networked production machines. The wear rate for each monitored component within each machine is synchronized, as is the residual life for each monitored component. The residual life value for each networked production machine is communicated to the production server. A determination is made as to whether a job request has arrived, and production machine(s) are selected to perform the job, with selection based on the state of wear of each networked production machine and the wear policy being applied to the network. The residual life values for the selected production machine(s) are updated based on the requirements of the job.

Abstract: A computer-implemented method collects operational state and usage data from a population of networked machines in communication with a service center for the purpose of adjusting service schedules. The method includes applying domain knowledge to develop one or more hypotheses defining a correlation between usage/environment and the operational health of the machine(s). The method gathers usage data from the machines, deletes unrelated usage data at a preprocessing stage, and identifies suitable data mining tasks and techniques for analysis of the data. Data mining tools are applied to discover knowledge pertaining to the one or more hypotheses. Discovered knowledge is interpreted and a determination is made on whether to refine the one or more hypotheses. When a determination is made not to continue refining the one or more hypotheses, the manufacturer's service schedule is adjusted and feedback is provided to the machine population.

Abstract: Methods and systems where remotely accessible machine-provided data is provided to a customer support (e.g., “help desk”) enterprise system wherein the data automatically satisfies a need for most information traditionally provided for analysis manually during a conversation. Advanced data enables help desk personnel to expeditiously interact with customers and gather specific data useful for isolating a problem and correcting it. Data can convey information regarding a machine's model type, location, serial number, usage history (e.g., counter data), fault codes, etc. Data can be provided directly by the machine over networks to customer support facilities.

Abstract: The Device Model Agent (DMA), the device side technology module in Device Centric Services (DCS) system, is the main focus of this invention. DMA is a thin, efficient applications/services execution environment. DMA provides an embedded services platform for enabling system management applications and services. This allows a flexible, extensible, dynamic services management module allowing networked services to be designed, added, and managed within the system without modifications to devices, DCS system, or DMA itself. While this invention specifically describes the integration of benefits from DMA into document system devices, the concepts are equally applicable in other domains. The DMA runtime environment is a thin software interface layer that resides on a document system device between the Java runtime environment and embedded web server.