Input Understanding System Uncertainty Thresholds
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Solution Overview
Problem
Input understanding systems face challenges in balancing the need for user clarification with the risk of alienating users due to repeated requests for confirmation, while also risking errors from misinterpreting user intentions without sufficient clarification.
Innovation Solution
An input understanding system that analyzes user inputs using an input recognition component, understanding component, and context component to determine uncertainty values and costs of misclassification, allowing it to decide whether to seek clarification or execute an action based on generated uncertainty and cost values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system repeatedly engages the user for confirmation or clarification, then the accuracy of user intention inference is improved, but the user experience deteriorates due to alienation
Solution Approach 1:
The system dynamically changes the parameter of clarification frequency based on uncertainty thresholds. When uncertainty exceeds a threshold, clarification is requested; when below, the system proceeds with best-guess interpretation. This adaptive parameter adjustment resolves the contradiction by optimizing the balance between accuracy and user experience in different contexts.
Solution Approach 2:
The system implements dynamic decision-making where the choice to seek clarification is not static but adapts based on real-time uncertainty assessment. The dialog policy component dynamically evaluates uncertainty values and adjusts clarification requests accordingly, allowing the system to be more autonomous when confident and more cautious when uncertain, thus improving both accuracy and user experience.
2Ease of operation
If the system omits confirmation or clarification steps, then the user experience is improved by reducing alienation, but the accuracy of user intention evaluation deteriorates
Solution Approach 1:
The system adjusts the parameter of clarification request frequency based on uncertainty levels. When uncertainty is low, the system omits clarification steps to improve user experience. When uncertainty is high, clarification is requested to maintain accuracy. This dynamic parameter adjustment resolves the contradiction.
Solution Approach 2:
The system uses feedback from uncertainty evaluation to determine whether clarification is needed. The uncertainty value generated by analyzing input features feeds back into the dialog policy decision, creating a closed-loop system that adapts clarification requests based on actual confidence levels, thus maintaining accuracy while minimizing unnecessary user interactions.
3Reliability
If the system seeks clarification for every uncertain input, then the accuracy of action execution is improved, but the productivity deteriorates due to increased interaction time
Solution Approach 1:
The system changes the parameter of clarification request frequency based on uncertainty thresholds and action criticality. For high-uncertainty inputs involving critical actions, clarification is requested to ensure reliability. For low-uncertainty or non-critical actions, the system proceeds without clarification to maintain productivity. This selective approach resolves the contradiction.
Solution Approach 2:
The dialog policy dynamically adjusts clarification requests based on real-time assessment of uncertainty and action importance. The system is more likely to seek clarification for high-stakes actions with high uncertainty, while being more autonomous for routine actions, thus optimizing the balance between reliability and productivity dynamically.
4Productivity
If the system takes action based on best current belief without clarification, then the productivity is improved by reducing interaction steps, but the reliability deteriorates due to potential misinterpretation
Solution Approach 1:
The system adjusts the parameter of clarification request based on uncertainty thresholds. When uncertainty is below the threshold, the system takes action without clarification to maintain productivity. When uncertainty exceeds the threshold, clarification is requested to ensure reliability. This dynamic threshold-based approach resolves the contradiction.
Solution Approach 2:
The system uses feedback from uncertainty evaluation to determine whether to proceed with action or request clarification. The uncertainty value feeds back into the dialog policy decision, creating an adaptive system that maintains productivity when confident and ensures reliability when uncertain, thus resolving the contradiction between these two objectives.
Data Source
AI summary
Examples of the present disclosure improve decision-making for input understanding to assist in determining how to best respond to a user input. A received input is analyzed using an input recognition component, input understanding component and input context component. Potential response options are determined. If uncertainty exists with respect to responding to the received input, an uncertainty value and a cost of misclassification are generated for the potential response options to assist in making a decision as to how to best respond to the received input. The uncertainty value is determined for a potential response and parameters associated with the potential response and the cost of misclassification is a cost associated with pursuing a potential response if the potential response turns out to be incorrect. A response is selected to transmit to a user based on analyzing the generated uncertainty value and the generated cost of misclassification for the potential responses.