Digital Fingerprint Analysis for Incident Resolution
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Solution Overview
Problem
In enterprise networks and systems, different customer service agents and users perform tasks and resolve incidents using varying sequences of steps, leading to inefficiencies and inconsistencies in resolution times and quality.
Innovation Solution
A system that tracks, analyzes, and records action paths taken by agents to resolve incidents, identifying the most efficient sequence of steps and storing them in databases for future reference, allowing for the generation of recommended shortest action paths and digital fingerprints for users, which can be used to train new users and anticipate future actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different customer service agents use different sequences of steps to resolve incidents, then agents can apply their individual expertise and judgment, but resolution efficiency and consistency deteriorate due to varying numbers of steps and approaches
Solution Approach 1:
The system collects and analyzes action paths from multiple agents resolving similar incidents, then provides feedback in the form of recommended action paths to agents. This feedback loop enables agents to learn from others' experiences while maintaining flexibility to adapt recommendations to specific incident contexts, thereby improving overall resolution efficiency without eliminating individual judgment.
Solution Approach 2:
The system performs preliminary analysis of historical action paths and incident data to generate recommended action paths before agents begin resolving new incidents. By preparing these recommendations in advance based on patterns from similar past incidents, the system enables agents to start with an optimized approach rather than developing sequences from scratch, improving consistency and efficiency.
2Productivity
If the system tracks and analyzes all action paths taken by agents, then the most efficient sequences can be identified and reused, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential elements from complex action paths - specifically the sequence of actions taken and their outcomes - while discarding unnecessary details such as specific agent identities, timing metadata, and contextual information not relevant to pattern recognition. This extraction enables efficient analysis and comparison of action paths without being overwhelmed by the full complexity of raw data.
Solution Approach 2:
The system transforms action path data into standardized parameters and metrics that facilitate comparison and analysis. By converting diverse agent actions into uniform parameter representations (such as action sequences, step counts, and outcome indicators), the system can efficiently process and compare data without requiring complex handling of raw, unstructured information.
3Loss of time
If action paths are analyzed to determine the shortest sequence of steps, then resolution time is reduced, but the quality and completeness of analysis may deteriorate if important but time-consuming steps are overlooked
Solution Approach 1:
The system generates multiple candidate action paths of varying lengths and complexities, treating them as disposable options to evaluate. Rather than committing to a single shortest path, the system can quickly generate and discard multiple alternatives, selecting the one that best balances speed and quality for each specific incident context, thereby avoiding the pitfall of always choosing the shortest path regardless of quality.
Solution Approach 2:
The system analyzes not only the minimal necessary steps but also includes additional contextual actions and best practices in its recommendations. By providing slightly more comprehensive action paths that include preventive measures and quality-check steps, the system ensures that while resolution time is optimized, important quality and completeness considerations are not sacrificed.
Data Source
AI summary
The disclosed techniques generally relate to the use of action paths comprising sequences of steps performed by a user to efficiently perform tasks or resolve incidents. Action paths as discussed herein may be used to achieve more efficient outcomes, to train new employees, or to anticipate the future needs of a user.