Multi-Function Device Anomaly Detection With Automated Resolution
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Solution Overview
Problem
Existing networked multi-function devices (MFDs) face burdensome technical support issues due to the need for extensive human intervention, fragmented information retrieval, and inefficient automated support systems, leading to time-consuming and costly troubleshooting.
Innovation Solution
A system and method for MFDs that utilize a trained support model to detect anomalies through MFD monitoring data, identify solutions, and provide automated support via a user interface, including automatic solution implementation and user instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated support systems are implemented, then productivity is improved, but the system complexity increases
Solution Approach 1:
The support system is segmented into multiple specialized models: anomaly detection model for identifying issues, root cause analysis model for determining causes, and solution recommendation model for providing fixes. This segmentation allows each component to specialize in one function, improving overall productivity while managing complexity through modular design.
Solution Approach 2:
A centralized server acts as an intermediary between distributed MFDs and support personnel. The server hosts the trained support models and receives monitoring data from multiple devices, processing anomalies centrally. This intermediary approach enables automated support across many devices without requiring complex local intelligence at each MFD.
2Loss of time
If manual support processes are used, then device complexity is reduced, but loss of time increases
Solution Approach 1:
Support models are trained in advance on historical ticketing data, device monitoring data, and documentation before deployment. This preliminary training enables the models to rapidly analyze and resolve anomalies without requiring time-consuming manual research during actual support incidents, significantly reducing troubleshooting time.
Solution Approach 2:
The system continuously monitors MFD performance data and feeds this information back to the anomaly detection and analysis models. This real-time feedback loop enables automated detection and resolution of issues as they occur, preventing escalation and reducing the time support personnel need to intervene.
3Reliability
If extensive documentation and ticketing systems are used, then reliability of support is improved, but ease of operation deteriorates
Solution Approach 1:
The system enables MFDs to perform self-diagnosis and self-resolution through automated anomaly detection and solution implementation. Devices can automatically order supplies, adjust settings, or reboot themselves based on model recommendations, eliminating the need for users to navigate complex ticketing systems or documentation while maintaining high support quality.
Solution Approach 2:
The support models are designed to handle multiple types of anomalies across different MFD functions (printing, scanning, copying, faxing) using a single unified system. This universal approach maintains reliability by applying consistent analytical methods while simplifying operation for users who face a single point of contact regardless of the specific issue.
4Measurement precision
If human intervention is increased, then measurement precision of support accuracy is improved, but loss of time increases
Solution Approach 1:
Support models are pre-trained on extensive historical data including ticketing records, device monitoring data, and technical documentation before deployment. This preliminary training enables the models to achieve high detection accuracy comparable to expert human analysts, while eliminating the time required for human researchers to study and analyze each new anomaly.
Solution Approach 2:
The anomaly detection and analysis models operate continuously and automatically as MFDs generate monitoring data, providing real-time precision detection without interruption. This continuous automated operation maintains high accuracy while eliminating delays associated with human availability, scheduling, and manual analysis processes.
Data Source
AI summary
A multi-function device (MFD), comprising: one or more MFD sensors configured to obtain MFD sensor data; a processor configured to: (i) receive MFD monitoring data, the MFD monitoring data comprising one or more of the MFD sensor data and user input data received via the user interface; (ii) direct transmission of the received MFD monitoring data to a remote central processor, the remote central processor configured to detect, from the received MFD monitoring data, an anomaly in the MFD, the remote central processor further comprising a trained support model configured to identify a solution to the detected anomaly; (iii) receive the identified solution from the remote central processor; and a user interface configured to provide the identified solution.