Issue Routing via NLP Recognition Scores
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Solution Overview
Problem
Large-scale organizations face inefficiencies in issue resolution due to duplication of effort across teams and incorrect routing of issues, particularly when clients report issues without proper terminology, leading to delayed resolution and misallocation of resources.
Innovation Solution
A method utilizing Natural Language Processing (NLP) to analyze and reroute issues based on recognition scores, where weights are applied to teams' profiles to accurately redirect issues to the most capable individual or team, optimizing issue assignment and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If NLP is used to route issues automatically, then routing speed is improved, but routing accuracy deteriorates due to imprecise issue descriptions
Solution Approach 1:
The system implements feedback by analyzing the review chain of previously assigned issues and using this information to adjust recognition scores. When an issue is reassigned from one team to another, the system learns from this feedback loop, updating the recognition scores to reflect actual resolution patterns. This feedback mechanism continuously improves routing accuracy while maintaining automated routing speed.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing recognition scores for each team based on their historical performance. Before routing a new issue, the system has already prepared the weighted recognition scores and team profiles, enabling fast automated routing decisions without compromising accuracy. This preliminary preparation of routing data allows the system to handle incoming issues efficiently.
2Measurement precision
If issues are reassigned multiple times to find the correct team, then routing accuracy is improved, but resolution time deteriorates
Solution Approach 1:
The system uses feedback from the review chain analysis to learn from past reassignment patterns. By examining which teams successfully resolved issues after reassignment, the system updates recognition scores to prevent future reassignments. This feedback loop reduces the need for multiple reassignments over time, thereby decreasing resolution time while maintaining high routing accuracy.
Solution Approach 2:
The system replaces the manual mechanical process of iterative reassignment with an automated intelligent routing mechanism. Instead of physically moving issues through multiple teams based on trial and error, the system uses NLP analysis and recognition score algorithms to directly route issues to the most appropriate team from the beginning. This substitution of automated intelligence for manual iteration significantly reduces resolution time.
3Adaptability or versatility
If more teams are assigned to handle diverse issues, then coverage is improved, but resource allocation efficiency deteriorates due to duplication of effort
Solution Approach 1:
The system replaces manual monitoring and coordination mechanisms with automated NLP analysis and recognition score-based routing. Instead of relying on human teams to manually track and coordinate their workloads, the system automatically analyzes issue descriptions and directs them to the most appropriate team based on historical performance data. This substitution eliminates duplication of effort while maintaining comprehensive issue coverage across multiple specialized teams.
Solution Approach 2:
The system uses feedback from resolved issues to continuously optimize resource allocation. By analyzing the review chain and updating recognition scores based on actual resolution outcomes, the system learns which teams are most effective for specific types of issues. This feedback-driven optimization ensures that issues are routed to the right teams with high precision, preventing duplication of effort across teams while maintaining broad issue coverage.
Data Source
AI summary
Systems and methods are provided for assigning an issue for resolution using natural language processing (NLP) and updating recognition scores for individual/teams accurately redirecting an issue to a different individual/team having a greater ability to resolve it. An issue is analyzed using NLP, and the text is compared to each individual/team's corpus of issues to derive a match percentage. A list is built which ranks individuals/teams by the match percentage. Weights are applied to each individual/team in the list, based on their corresponding recognition scores in their profiles in a profile database. The recognition scores indicate an ability to recognize correct reassignment with a degree of accuracy above a threshold. The list is reordered based on the applied weights, and the issue is assigned to the individual/team having a highest rank.