Natural Language Conversation Pausing and Resuming System
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Solution Overview
Problem
Existing natural language conversation systems lack the ability to pause and resume conversations seamlessly, leading to disruptions and loss of contextual data.
Innovation Solution
A computer system-based approach that allows for the pausing and resuming of natural language conversations by sending data to associated devices, storing contextual data, and using triggers to manage the conversation state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language conversation systems operate continuously without pause functionality, then the conversation flow remains uninterrupted and user experience is improved, but the system cannot accommodate user needs to pause or interrupt the conversation
Solution Approach 1:
The system dynamically transitions between active conversation state and paused state based on user triggers. The conversation management system can switch between processing audio data and maintaining paused state, allowing flexible adaptation to user needs while preserving conversation context for resumption.
Solution Approach 2:
The system introduces a pause indication as an intermediary state between active conversation and termination. This intermediary allows the conversation to be temporarily suspended without losing context, enabling users to pause when needed while maintaining the ability to resume from the exact point of interruption.
2Loss of information
If the system stores complete conversation history for resumption, then contextual data is preserved accurately, but data processing overhead and storage requirements increase
Solution Approach 1:
The system extracts only the essential pause trigger information and conversation state at the point of interruption, rather than storing complete conversation history. This selective extraction preserves the necessary contextual data for accurate resumption while significantly reducing storage requirements and processing overhead.
Solution Approach 2:
The system performs preliminary action by capturing and storing the conversation state at the exact moment of pause trigger. This preliminary capture of contextual data (including audio data and conversation parameters) enables efficient resumption without requiring storage of the entire conversation history, reducing both data volume and processing requirements.
3Quantity of substance
If the system processes all audio data continuously, then complete conversation data is captured, but processing resources are consumed even when user pauses the conversation
Solution Approach 1:
The system transitions from continuous processing to periodic processing. During paused state, the system stops processing audio data and only resumes processing when the user triggers conversation resumption. This periodic action maintains data completeness for the active conversation portions while significantly reducing processing resource consumption during paused intervals.
Data Source
AI summary
Techniques for computer system-based conversations are described. In an example, a system receives, from a first device, first data corresponding to a first interaction in a conversation that requests a function. The system causes the first device to output a first response to the first interaction. Prior to an execution of the function, the system determines that the conversation is to be paused and causes the first device to output a first indication that the conversation is paused. Upon determining that the conversation is to be resumed, the system causes a second device to output a second indication that the conversation is resumed. The second device can be the same or different from the first device. The system receives, from the second device, second data corresponding to a second interaction in the conversation and causes the execution of the function based at least in part on the second data.