Generative Response Engine Interaction During Long-Running Tasks
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Solution Overview
Problem
Current generative response engines require users to wait for the completion of a processing cycle before interacting further, limiting user interaction and adaptability during tasks, especially for long-running processes.
Innovation Solution
Allow users to interact with the generative response engine through a conversational interface while the model is processing, enabling real-time interaction, including providing intermediate responses, clarifications, and managing complex tasks through a sequence of subtasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the generative response engine completes its entire processing cycle before allowing further interaction, then the system ensures complete and consistent task execution, but the user experience deteriorates due to inability to interact during processing and long waiting times
Solution Approach 1:
The patent segments the processing cycle into distinct phases: an initial processing phase where the model works on the task, and subsequent interaction phases where users can provide follow-up inputs. This segmentation allows the system to maintain complete task execution while enabling user interaction during non-critical processing intervals, thereby reducing user waiting time without compromising task consistency.
Solution Approach 2:
The system performs preliminary processing actions to generate an initial response or intermediate results before the complete processing cycle finishes. This preliminary action provides users with early feedback and enables interaction during the remaining processing time, effectively reducing the perceived waiting time while the background processing continues to complete the full task.
2Adaptability or versatility
If the system waits for complete processing before accepting new inputs, then task execution consistency is maintained, but adaptability to new user inputs during task execution is reduced
Solution Approach 1:
The patent implements dynamic input acceptance where the system's responsiveness to new inputs changes based on the processing phase. During the initial phase, the system accepts follow-up inputs to improve adaptability. As processing progresses toward completion, the system dynamically adjusts to maintain consistency, selectively accepting or rejecting new inputs based on their potential impact on task integrity.
Solution Approach 2:
The system introduces an intermediary mechanism that evaluates follow-up inputs against the current processing state. This intermediary layer determines whether new inputs should be accepted, modified, or rejected, thereby maintaining task execution consistency while still allowing adaptability to relevant user needs. The intermediary acts as a buffer that reconciles the conflicting requirements of consistency and adaptability.
3Device complexity
If the generative response engine processes tasks sequentially without intermediate responses, then system complexity is reduced, but user feedback and clarification opportunities are lost
Solution Approach 1:
The patent implements a feedback mechanism where the system provides intermediate responses or status updates during task processing, and explicitly seeks user feedback through clarification requests. This feedback loop enables the system to correct misunderstandings, adjust processing direction, and improve final output quality without requiring complex architectural changes, maintaining relative system simplicity while recovering valuable user feedback information.
Solution Approach 2:
The system employs periodic action by providing intermediate responses at scheduled intervals or at key processing milestones rather than waiting for complete processing. This periodic output strategy maintains simpler system architecture compared to continuous intermediate communication, while still capturing essential user feedback opportunities at critical decision points in the processing flow.
Data Source
AI summary
The present technology provides an interaction paradigm whereby a prompt source can continue to interact with the generative response engine through a conversational interface while the generative response engine is processing a task, especially a long-running task. A prompt source can provide additional prompts to modify or clarify the task. The prompt source can also provide additional tasks or subtasks. The generative response engine can also provide intermediate responses in the conversational interface. For example, the generative response engine can respond to prompts provided by the prompt source during the performance of the long-running task. The generative response engine can also determine that it should ask for additional details or clarification, and in response to such a determination, the generative response engine can provide intermediate responses in the conversation interface to encourage further input from the prompt source.