Dynamic Incident Report Grouping via Data Stream Feature Extraction
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Solution Overview
Problem
The existing case management systems for incident reports in multi-tenant software platforms are inefficient due to their static nature, leading to duplicate efforts by agents in resolving similar issues, as incident reports are not always categorized correctly, resulting in wasted computing resources and time.
Innovation Solution
An automated incident report grouping system that dynamically adapts to current issues by extracting features from reports, grouping similar incidents, and periodically recomputing the model to identify new features and remove stale ones, ensuring that similar reports are routed to the same agents for efficient resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static case management system uses predefined products, categories, and groups to organize incident reports, then the system structure is simple and easy to maintain, but incident reports related to the same root cause are routed to different agents, causing duplicate efforts and reducing productivity
Solution Approach 1:
The patent implements dynamic grouping by continuously monitoring incident report data streams and automatically adjusting groupings based on identified root causes. Instead of static predefined categories, the system dynamically creates and updates incident groups as new patterns are detected, allowing the organization structure to adapt automatically to changing issue patterns and improve agent efficiency.
Solution Approach 2:
The system incorporates feedback loops where incident resolution outcomes and root cause analyses are continuously fed back into the grouping mechanism. This feedback enables the system to learn from past incidents and refine future groupings, ensuring that similar incidents are consistently routed to the same agents while adapting to new patterns over time.
2Productivity
If incident reports are categorized using static predefined categories, then the categorization process is fast and simple, but the same issue may be categorized differently, leading to duplicate agent efforts and waste of computing resources
Solution Approach 1:
The patent replaces manual static categorization with automated machine learning-based root cause identification. The system uses computational algorithms to analyze incident report content, identify underlying root causes, and automatically group incidents accordingly. This substitution of mechanical categorization with intelligent automation improves both accuracy and efficiency while reducing duplicate agent efforts.
Solution Approach 2:
The system changes the fundamental parameter used for grouping from static category labels to dynamic root cause identifiers. By analyzing the actual content and patterns of incident reports, the system identifies root causes as the primary grouping parameter, ensuring that incidents with the same underlying issue are consistently grouped together regardless of variations in user descriptions or initial categorization attempts.
3Adaptability or versatility
If the case management system uses fixed grouping structures, then system maintenance is easier, but the system cannot adapt to new issues or evolving problem patterns, reducing adaptability
Solution Approach 1:
The patent implements self-service through automated root cause identification and dynamic grouping mechanisms that continuously monitor incident data streams and automatically adapt their structure. The system serves itself by detecting new patterns, creating appropriate groups, and routing incidents without requiring manual reconfiguration, thereby maintaining high adaptability while managing complexity through automation.
Solution Approach 2:
The system performs preliminary analysis of incident reports to identify root causes and establish appropriate groupings before agents need to process the incidents. By proactively organizing incidents into meaningful groups based on detected patterns, the system prepares the data structure in advance, enabling agents to work efficiently from the start and allowing the system to adapt to new issues as they emerge.
Data Source
AI summary
Systems, methods, and other embodiments associated with grouping data using data streams are described. In one embodiment, a method includes publishing data into a data stream. The example method may also include evaluates phrases within data in the data stream to identify a set of features having data divergence amongst the data above a divergence threshold. The example method may also include computing a model correlating data to the set of features. The example method may also include applying the model to data to compute feature vectors for the data. The example method may also include comparing the feature vectors to identify and group similar data.