Adaptive Conversation Flow for Cognitive Interaction Services
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Solution Overview
Problem
Existing cognitive interaction engines rely on fixed rules for conversation flow, which limits adaptability and responsiveness to user behavior, failing to effectively manage repetitive interactions and user intent variations.
Innovation Solution
A computer-implemented method that monitors user interactions and machine responses to identify predefined patterns and thresholds, allowing for adaptive response adjustments based on defined actions, enabling dynamic conversation flow management through a cognitive interaction service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed rules for conversation flow are used, then system reliability is improved, but adaptability deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static fixed rules to dynamic adaptive response selection. The system monitors conversation patterns in real-time and adjusts responses based on detected patterns, making the conversation flow adaptable while maintaining reliability through structured pattern definitions.
Solution Approach 2:
The patent implements feedback mechanisms by monitoring user interactions and machine responses to identify patterns. This feedback loop enables the system to adapt subsequent responses based on detected conversation patterns, resolving the contradiction between fixed rules and adaptability.
2Adaptability or versatility
If pattern monitoring and adaptive response adjustment are implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex task of adaptive conversation management into distinct components: pattern definition, interaction monitoring, pattern matching, and response adaptation. This modular approach reduces system complexity while maintaining adaptability.
Solution Approach 2:
The patent uses preliminary action by pre-defining conversation patterns and corresponding actions before actual conversations occur. This preparation reduces the complexity of real-time decision-making, as the system only needs to match observed patterns against predefined templates rather than generating responses from scratch.
3Productivity
If maximum repetition thresholds are enforced, then user engagement is improved, but loss of information increases
Solution Approach 1:
The patent implements feedback by monitoring conversation patterns and detecting when maximum repetition thresholds are reached. This enables the system to adapt responses appropriately, maintaining user engagement while preserving important conversation context through pattern-aware response selection.
Solution Approach 2:
The patent applies parameter changes by adjusting response behavior based on detected conversation patterns and repetition counts. The system modifies response parameters dynamically, selecting from different response types based on pattern matching results, thereby balancing engagement with information preservation.
Data Source
AI summary
Conversation flow is adapted based on user interactions in a cognitive interaction between a user and a machine carried out at a gateway to a cognitive interaction service. A series of cognitive interactions are received during a conversation flow. A determination is made that a pattern of user cognitive interactions meets a defined pattern and that a plurality of machine responses meets a defined threshold of a maximum repetition of responses to the defined pattern. An indication is provided to the cognitive interaction service to adapt a type of subsequent response according to a defined action for the defined pattern.