Patent Application
20190272209
This application relates generally to maintenance of document processing devices. The application relates more particularly to predicting device failures for multifunction peripherals to facilitate prophylactic device repair and part availability.
In an example embodiment a system and method for predicting device failures and generating proposed resolutions for such an error when occurs includes receiving device status data from each of a plurality of identified multifunction peripherals into a memory. Service history data for each of the multifunction peripherals is stored, the service history data including data corresponding to a plurality of data patterns associated with prior device failures associatively with resolutions implemented to address such failures. Patterns are detected in received device status data. Device failure is predicted for at least one identified multifunction peripheral in accordance with detected patterns and service history data. The predicted device failure is reported along with at least one proposed resolution to address a device error predicted by the predictive device failure data.
Document processing devices include printers, copiers, scanners and e-mail gateways. More recently, devices employing two or more of these functions are found in office environments. These devices are referred to as multifunction peripherals (MFPs) or multifunction devices (MFDs). As used herein, MFP means any of the forgoing.
Given the expense in obtaining and maintain MFPs, MFPs are frequently shared by users and monitored by technicians via a data network for example using Simple Network Management Protocol (SNMP). MFP devices are complex devices that are subject to failures. When devices fail, an end user will initiate a service call. Device can be particularly frustrating for device users. They can result in periods when a MFP is out of service, leaving users without a powerful office tool and causing user frustration when a job must wait or an alternative MFP used, such as one that is not conveniently located or one without needed capabilities that were available on the out of service MFP.
Not only are failed devices a burden on end users, they can provide significant financial cost to MFP providers. A common business model for MFPs is one wherein a distributor enters into an end user agreement where the distributer provides a device at little or no upfront cost to the end user. User charges are based a cost per page. This cost reflects device usage charges, as well as maintenance costs. Significant human resource costs are associated with receiving a service call, logging a call, scheduling a service time, dispatching a service technician, and diagnosing and repairing the device. Such service costs can lower the distributor's profitability, increase the end user's cost per page, or both.
Various embodiments will become better understood with regard to the following description, appended claims and accompanying drawings wherein:
The systems and methods disclosed herein are described in detail by way of examples and with reference to the figures. It will be appreciated that modifications to disclosed and described examples, arrangements, configurations, components, elements, apparatuses, devices methods, systems, etc. can suitably be made and may be desired for a specific application. In this disclosure, any identification of specific techniques, arrangements, etc. are either related to a specific example presented or are merely a general description of such a technique, arrangement, etc. Identifications of specific details or examples are not intended to be, and should not be, construed as mandatory or limiting unless specifically designated as such.
Turning to
Server 116 accumulates MFP data such as device error logs, device usage, such as number of print jobs or device page count, mechanical wear and tear tracking, forced shutdowns, copy interruptions or environmental factors such as temperature, humidity, ground stability, barometric pressure, and the like. Historical data corresponding to data patterns associated with prior device failures is stored associatively with prior solutions used to address each error. Server 116 monitors data incoming data patterns for monitored MFPs relative to historical data patterns to predict likely device failures in advance of an actual failure, along with one or more proposed solutions to the predicted device error based upon prior resolutions associated with a failure for the same or similar data pattern. More than one solution may suitably be determined. A ranking is suitably given to multiple possible resolutions. For example, prior, higher ranked resolutions may include mechanical adjustment and part replacement. Thus, a technician can order a needed part in the event mechanical adjustment does not address problem once it occurs.
Predictive device error information, along with one or more proposed resolutions used in the past for similar errors, is suitably be communicated to a service center or service technician via a digital device, such as tablet computer 120 of service technician 124. Server 116 suitably associates suggested maintenance procedures and required part information with identified devices predicted to fail. A suitable check of existing inventory, such as local inventory 128 is made. If sufficient parts are not available, an order for required parts is suitably sent to a parts supplier such as warehouse 132. When a predicted failure does occur, pattern data associated with that failure is fed back into the system to further refine the historical pattern data, along with data corresponding to the resolution that was ultimately used to remedy the problem. With such machine learning, each new failure situation and resolution will further refine the system for predicting and addressing future failures.
Turning now to
Processor 202 is also in data communication with a storage interface 208 for reading or writing to a storage 216, suitably comprised of a hard disk, optical disk, solid-state disk, cloud-based storage, or any other suitable data storage as will be appreciated by one of ordinary skill in the art.
Processor 202 is also in data communication with a network interface 210 which provides an interface to a network interface controller (NIC) 214, which in turn provides a data path to any suitable wired or physical network connection 220, or to a wireless data connection via wireless network interface 218. Example wireless connections include cellular, Wi-Fi, Bluetooth, NFC, wireless universal serial bus (wireless USB), satellite, and the like. Example wired interfaces include Ethernet, USB, IEEE 1394 (FireWire), Lightning, telephone line, or the like. Processor 202 is also in data communication with user interface 219 for interfacing with displays, keyboards, touchscreens, mice, trackballs and the like.
Processor 202 can also be in data communication with any suitable user input/output (I/O) interface 219 which provides data communication with user peripherals, such as displays, keyboards, mice, track balls, touch screens, or the like.
Also in data communication with data bus 212 is a document processor interface 222 suitable for data communication with NFP functional units. In the illustrated example, these units include copy hardware 240, scan hardware 242, print hardware 244 and fax hardware 246 which together comprise NFP functional hardware 250. It will be understood that functional units are suitably comprised of intelligent units, including any suitable hardware or software platform.
Turning now to
Device management system 404 provides device state information 408 for application of machine learning and analysis for predictive device failures by a suitable machine learning platform 412 such as Microsoft Azure. Additional information 416 for such prediction, such as device service log information, is provided by a suitable CMMS (Computerized Maintenance Management System (or Software)) 420, and is sometimes referred to as Enterprise Asset Management (EAM). By way of particular example a CMMS system 420 can be based on CMMS Software, Field Service Software, or Field Force Automation Software provided by Tessaract Corporation.
By way of particular example, a determination of likeliness of a forthcoming service call can be utilized to schedule device maintenance. Such scheduling is suitably integrated with service calls already scheduled or with servicing of two or more geographically proximate devices to minimize travel time needed for technician on-site visits. Suitable machine learning systems are built on available third party platforms such as R-Script, Microsoft Azure, Google Next, Kaggle.com or the like.
While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the spirit and scope of the inventions.
