Alert Categorization System for Managed Printing
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Solution Overview
Problem
In managed printing environments, existing systems face challenges in effectively filtering and categorizing device alerts due to the influx of redundant alerts and varying business rules across different fleets and customer sites, leading to difficulties in maintaining accurate alert categorization and response.
Innovation Solution
An alert processing system that receives and processes alerts in natural language, using a language module to translate and identify the source language, and an alert categorization module to categorize alerts based on a predetermined model, with an update module that automatically updates a reference list for uncategorized alerts, allowing for efficient filtering and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rule-based filtering systems are used to automatically filter alerts, then filtering efficiency is improved, but the system cannot cover all types of alerts and requires substantial information and experience to formulate and maintain
Solution Approach 1:
The system enables automatic self-learning through machine learning algorithms that continuously analyze alert patterns and update filtering rules without human intervention. The categorization model automatically adapts to new alert types by learning from historical data, eliminating the need for manual rule formulation and maintenance while improving both filtering efficiency and coverage of alert types.
Solution Approach 2:
The system dynamically adjusts filtering parameters and categorization criteria based on learned patterns from alert data. By changing the parameters of the categorization model through continuous learning, the system can adapt to varying alert types and business rules across different fleets and customer sites, resolving the contradiction between efficient filtering and comprehensive coverage.
2Measurement precision
If customized rules are created for different fleets and customer sites, then alert categorization accuracy is improved, but the complexity of maintaining and propagating rule changes increases
Solution Approach 1:
The machine learning-based categorization model automatically learns and adapts to customized business rules for different fleets and customer sites through continuous training on site-specific alert data. This self-learning capability eliminates the need for manual rule creation and propagation, maintaining high categorization accuracy while reducing maintenance complexity to minimal retraining operations.
Solution Approach 2:
The system uses a universal machine learning framework that can handle multiple fleets and customer sites with different business rules through a single adaptable model. The categorization model serves multiple functions across diverse environments by learning from each site's data, providing accurate customization without requiring separate rule sets for each fleet or customer site.
3Adaptability or versatility
If operators manually sort and schedule alert responses, then flexibility in handling diverse alert types is improved, but the time and resources required for processing increase
Solution Approach 1:
The system implements automated alert processing through machine learning models that independently categorize, prioritize, and route alerts based on learned patterns. This self-service automation maintains the flexibility of manual handling by adapting to diverse alert types while dramatically reducing processing time and operator workload through autonomous decision-making.
Solution Approach 2:
The system replaces the mechanical process of manual alert sorting and scheduling with an automated machine learning-based processing system. This substitution maintains adaptability through intelligent algorithms while eliminating the time loss associated with manual operations, providing both flexibility and efficiency simultaneously.
Data Source
AI summary
An alert processing system and method are adapted for processing device alerts. The system includes a routing device in communication with a printer. The routing device receives at least one alert description in a source language transmitted from the printer. The routing device identifies a set of words derived from the alert description related to a condition of the associated device. The routing device compares the set of words, in a target language, to a categorization model and, based on the comparison, categorizes the set of words into to one of a predetermined set of alert categories.