Dialog System Node Generation from Agent Response Clusters
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Solution Overview
Problem
Automated dialog systems, such as chatbots, often fail to assist users when they cannot determine the user's goal, leading to a lack of effective interaction and the need for human agent intervention, which can disrupt the conversation flow and limit the system's ability to maintain relevance over time.
Innovation Solution
A computer-implemented method to modify the dialog system's execution graph by clustering agent responses into types, generating Boolean expressions for these responses, and adding child nodes with entry conditions and actions to replicate human agent responses, allowing the system to improve its interaction and adapt to new technologies and terminology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated dialog systems escalate to human agents when unable to determine user goals, then the system can handle complex cases, but conversation flow is disrupted and the system cannot maintain relevance over time
Solution Approach 1:
The system performs preliminary actions by analyzing conversation data and proactively adding child nodes to the execution graph before escalation occurs. This allows the system to learn from historical escalation cases and automatically expand its capability to handle similar future cases without human intervention, thereby maintaining automation continuity while improving system coverage.
Solution Approach 2:
The system creates copies of human agent responses by clustering agent responses into types and generating child nodes that replicate successful human intervention patterns. These copied responses are stored as Boolean expressions representing dialog state feature value sets, allowing the automated system to reproduce effective human agent behavior for similar future cases.
2Reliability
If the dialog system executes predefined responses, then response consistency is maintained, but the system cannot adapt to new technologies and terminology
Solution Approach 1:
The system implements feedback by continuously analyzing conversation data from escalated cases, identifying patterns in human agent responses, and using this feedback to automatically add new child nodes to the execution graph. This closed-loop learning process allows the system to maintain response consistency for known patterns while adapting to new technologies and terminology through continuous data-driven expansion of its response repertoire.
Solution Approach 2:
The execution graph is transformed from a static predefined structure to a dynamic structure that automatically evolves by adding child nodes based on analyzed conversation data. This dynamic adaptation allows the system to maintain reliability for established patterns while becoming increasingly adaptable to new scenarios over time.
Data Source
AI summary
The computer receives a group of conversation data associated with the escalation node, identifies agent responses in the conversation data, and clusters them into agent response types. The computer identifies dialog state feature value sets for the conversations. The computer identifies feature value set associations with response types, and generates, Boolean expressions representing the feature value sets associated with each of the response types. The computer makes a recommendation to add to at least one child node for the escalation node, with the child node corresponding to one of the response types. The child node has, as an entry condition, the Boolean expression for the response type to which the child node corresponds. The child node has as an action, which according to some aspects, provides a response representative of the cluster of agent responses for the response type to which the child node corresponds.