Chat Bot Design Adjustment via Escalation Log Similarity
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Solution Overview
Problem
Existing chat bot designs lack effective integration with escalation logs, making it difficult to automatically extract actionable insights and improve bot performance, leading to sub-optimal design and missed automation opportunities.
Innovation Solution
A system that computes similarity scores between bot response nodes and escalation logs, generating a sorted list of bot response nodes to identify potential design issues and missed content opportunities, thereby enhancing bot containment and automation capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chat bot designs are developed without integrating escalation logs, then bot design process is simpler and faster, but bot performance and containment are sub-optimal
Solution Approach 1:
The system performs preliminary analysis by computing similarity scores between bot response nodes and escalation logs before finalizing bot design. This allows designers to proactively identify and address potential performance issues by matching escalation patterns with response nodes, ensuring improved bot containment before deployment.
Solution Approach 2:
The system establishes a feedback loop where escalation logs are continuously analyzed and matched with bot response nodes using similarity scoring. This feedback mechanism identifies sub-optimal design elements and missed content opportunities, enabling iterative improvements to bot performance based on actual escalation data.
2Loss of information
If manual analysis of escalation logs is performed, then actionable insights can be extracted, but time and resources are consumed
Solution Approach 1:
The system enables self-service analysis by automatically computing similarity scores between bot response nodes and escalation logs without requiring manual intervention. The automated matching process identifies patterns and generates sorted lists of response nodes needing improvement, eliminating the need for time-consuming manual log analysis while preserving all actionable insights.
Solution Approach 2:
The system replaces manual mechanical analysis of escalation logs with an automated computational approach. By using similarity scoring algorithms and automated matching processes, the system substitutes human analysts with a computational mechanism that processes logs more quickly and consistently, maintaining insight extraction quality while reducing time consumption.
3Measurement precision
If bot response nodes are not prioritized based on similarity scores, then design review is faster, but key design issues are missed
Solution Approach 1:
The system segments bot response nodes into priority levels based on their similarity scores with escalation logs. By sorting response nodes according to their match strength with escalation patterns, the system creates a prioritized review list that separates high-impact design issues from lower-priority items, enabling focused review of the most critical nodes first.
Solution Approach 2:
The system introduces similarity scores as a new parameter to prioritize bot response nodes during design review. By computing and sorting response nodes based on this parameter, the system transforms the review process from a non-differentiated approach to one that systematically identifies and addresses the most problematic nodes, improving both accuracy and efficiency.
Data Source
AI summary
An example system includes a processor to receive a bot design and escalation logs associated with a chat bot configured based on the bot design. The processor can compute a similarity score between each of a number of bot response nodes in the bot design and the escalation logs. The processor can generate a sorted list of the bot response nodes in the bot design based on the similarity scores.