Intelligent Capability Extraction for Request Routing
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Solution Overview
Problem
Current request management systems inaccurately convey agent capabilities, leading to inefficient distribution of requests and failure to recognize frequently used capabilities, which can result in prolonged resolution times and inefficient resource allocation.
Innovation Solution
Implementing intelligent capability extraction and assignment using text-based pattern recognition and machine learning techniques to analyze resolution descriptions and identify actual capabilities used by agents, updating lists to reflect accurate agent capabilities and distributing requests accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual capability listing by agents or managers is used, then the process is simple to implement, but the accuracy of agent capabilities is poor
Solution Approach 1:
The system automatically extracts agent capabilities from resolution descriptions without requiring manual input from agents or managers. The text analysis system processes resolution texts autonomously to identify and categorize capabilities, eliminating the need for self-declared capability lists while improving accuracy.
Solution Approach 2:
The manual mechanical process of listing capabilities by agents or managers is replaced with an automated text analysis system that uses pattern recognition and machine learning to extract capabilities from resolution descriptions, significantly improving accuracy while reducing manual effort.
2Productivity
If manual capability listing is used, then implementation is straightforward, but frequently used capabilities are not identified
Solution Approach 1:
The system analyzes resolution descriptions to extract not only what capabilities are used but also how frequently they are used. This feedback mechanism provides quantitative data on capability usage patterns, enabling the system to identify frequently used capabilities and optimize request routing accordingly.
Solution Approach 2:
The static manual capability listing process is replaced with a dynamic text analysis system that continuously processes resolution descriptions to extract and quantify capability usage frequencies, transforming lost information into actionable insights for improving productivity.
3Productivity
If capabilities are not accurately identified, then the system is simple to maintain, but request distribution efficiency decreases
Solution Approach 1:
The simple but inefficient manual capability assignment process is replaced with an automated text analysis system that uses pattern recognition and machine learning to accurately extract capabilities from resolution descriptions, significantly improving request distribution efficiency despite the increased system complexity.
Solution Approach 2:
The system automatically performs capability extraction and request routing without requiring manual intervention to maintain accuracy. The text analysis system self-updates capability information from resolution descriptions, maintaining high resolution speed while managing the complexity through automation.
4Ease of operation
If agent capabilities are inaccurately represented, then data collection is simple, but resource allocation efficiency worsens
Solution Approach 1:
The simple process of collecting self-declared capability data is replaced with an automated text analysis system that extracts actual capability usage from resolution descriptions, significantly improving resource allocation efficiency by matching requests to agents based on demonstrated rather than declared capabilities.
Solution Approach 2:
The system uses resolution descriptions as feedback to continuously learn and update agent capabilities. By analyzing actual performance data from resolved requests, the system accurately represents agent capabilities, enabling efficient resource allocation while maintaining ease of operation through automation.
Data Source
AI summary
A system may include persistent storage containing representations of requests associated with a managed network. The persistent storage may include lists of capabilities associated with agents, and each request may include a textual description of a situation experienced by a user and a resolution of the situation by a particular agent. A computing device may obtain a set of requests from the persistent storage, apply an unsupervised machine learning clustering technique to textual descriptions included in the set of requests, and arrange the requests into groups such that each group contains requests including textual descriptions with at least a threshold degree of similarity to one another. The computing device may perform, for the requests in a particular group, a textual analysis on associated resolutions to identify capabilities used by agents to resolve the requests, and update the lists of capabilities to associate the capabilities with agents that used them.