Multi-Function Device Self-Diagnosis for Automated Support
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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
An MFD system equipped with sensors to gather data, a processor to detect anomalies, and a trained support model to identify and provide solutions, including automatic implementation or user interface instructions, leveraging historical data and user queries for efficient support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated support systems are implemented, then support efficiency is improved, but the system complexity increases
Solution Approach 1:
The MFD system performs self-diagnosis and self-support by automatically detecting anomalies through sensors, analyzing monitoring data, and identifying solutions without requiring human intervention. The system serves itself by implementing corrective actions autonomously, such as ordering supplies or adjusting settings, which resolves support issues while maintaining high efficiency without proportionally increasing complexity.
Solution Approach 2:
A trained support model acts as an intermediary between the MFD's monitoring data and the support solutions. This AI-based mediator processes anomaly detection data, retrieves relevant information from knowledge bases, and generates appropriate solutions, thereby simplifying the overall system architecture while maintaining high support efficiency through intelligent mediation.
2Loss of information
If extensive form filling and ticketing systems are used, then support information is captured, but the time required for support increases
Solution Approach 1:
The MFD automatically generates and transmits support information, including anomaly details and contextual data from monitoring sensors, without requiring user form filling. The system self-documented the issue by collecting relevant monitoring data and ticketing history, eliminating time-consuming manual information capture while ensuring complete information is captured for support resolution.
Solution Approach 2:
The system performs preliminary data collection and organization before support is needed by continuously monitoring MFD operations and pre-processing information. When an anomaly occurs, the relevant support information is already captured and structured in the knowledge base, eliminating the need for time-consuming form filling at the moment of support request.
3Reliability
If human intervention is required for support, then complex troubleshooting can be handled, but support costs increase
Solution Approach 1:
The MFD system autonomously handles troubleshooting by detecting anomalies, querying the trained support model for solutions, and implementing corrective actions without human intervention. This self-service capability maintains reliable troubleshooting for common issues while eliminating support costs. The system only escalates to human experts when necessary, optimizing the balance between troubleshooting reliability and cost efficiency.
Solution Approach 2:
The patent replaces the mechanical human support system with an AI-based support model that processes anomaly data and generates solutions. This substitution maintains effective troubleshooting capability through intelligent analysis while dramatically reducing support costs by eliminating the need for human support personnel for routine issues.
4Loss of information
If legacy documentation and knowledge bases are searched manually, then solutions can be found, but the process becomes time-consuming
Solution Approach 1:
The trained support model continuously learns from support interactions and anomaly patterns, refining its ability to retrieve relevant solutions from the knowledge base. The system provides feedback by analyzing which solutions are most effective and uses this information to improve future solution retrieval, reducing search time while ensuring accurate solution delivery through iterative learning.
Solution Approach 2:
The manual search process through legacy documentation is replaced by an AI-based support model that automatically queries the knowledge base using the detected anomaly as input. This substitution retrieves relevant solutions instantaneously through intelligent pattern matching and natural language processing, eliminating time-consuming manual searches while ensuring comprehensive solution retrieval.
Data Source
AI summary
A multi-function device (MFD) (100) configured to detect an MFD anomaly and provide support, comprising: one or more MFD sensors (390) configured to obtain MFD sensor data; a processor (320) 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 from a user of the MFD; (ii) detect, from the received MFD monitoring data, an anomaly in one or more of the one or more MFDs; (iii) identify, by a trained support model analyzing the detected anomaly, a solution to the detected anomaly; and a user interface (340) configured to provide the identified solution to the user.