Conversational Agent Workflow Clustering for Response Accuracy
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Solution Overview
Problem
Automated conversational agents, such as chatbots, face limitations in providing effective assistance due to the variety of customer requests, leading to frequent overriding by human agents, which is inefficient and delays customer service.
Innovation Solution
A method and apparatus that automatically extract workflows from conversational transcripts, cluster similar conversations, and train conversational agents using these workflows to improve their ability to handle specific customer requests, reducing the need for manual interpretation and live agent intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated conversational agents are deployed to handle customer requests, then productivity is improved, but reliability deteriorates due to inability to handle variety of requests
Solution Approach 1:
The system performs preliminary training of conversational agents using historical conversation data and extracted workflows before actual customer interactions occur. This advance preparation enables agents to handle a broader variety of requests reliably without requiring real-time human intervention.
Solution Approach 2:
The system continuously monitors and analyzes conversation outcomes, using feedback from both successful automated responses and human agent interventions to refine and retrain conversational agents. This iterative feedback loop improves response accuracy over time while maintaining high productivity.
2Reliability
If live agents manually monitor and override automated agent responses, then reliability is improved, but productivity deteriorates due to resource waste and delays
Solution Approach 1:
Conversational agents are trained to autonomously learn from historical data and automatically improve their response quality without requiring continuous manual monitoring or overriding by live agents. This self-service capability maintains high reliability while eliminating the productivity cost of constant human intervention.
Solution Approach 2:
The system performs preliminary extraction of workflows and training of conversational agents before production use. This advance preparation reduces the need for real-time human intervention, allowing agents to handle most requests independently while maintaining quality standards.
3Reliability
If conversational agents are trained using manual interpretation of responses, then reliability is improved, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The system replaces manual interpretation and training processes with automated machine learning algorithms that automatically extract workflows, identify patterns, and train conversational agents from historical conversation data. This substitution eliminates time-consuming manual labor while maintaining or improving training accuracy through computational analysis.
Solution Approach 2:
The system creates trained conversational agents by copying and analyzing patterns from historical successful conversations and workflows. This automated copying process enables rapid replication of effective interaction patterns without requiring manual training for each agent, significantly reducing training time while maintaining quality.
Data Source
AI summary
A computer-implemented method and an apparatus for facilitating training of conversational agents are disclosed. The method includes automatically extracting a workflow associated with each conversation from among a plurality of conversations between agents and customers of an enterprise. The workflow is extracted, at least in part, by encoding one or more utterances associated with the respective conversation and mapping the encoded one or more utterances to predefined workflow stages. A clustering of the plurality of conversations is performed based on a similarity among respective extracted workflows. The clustering of the plurality of conversations configures a plurality of workflow groups. At least one conversational agent is trained in customer engagement using a set of conversations associated with at least one workflow group from among the plurality of workflow groups.