Automation Preference Feedback Using Impact-Score Triggers
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Solution Overview
Problem
Current automation systems face challenges in understanding and adapting to user preferences, particularly in identifying exceptions to general patterns, which can lead to inefficiencies, such as energy savings, due to their inability to solicit and process relevant user feedback effectively.
Innovation Solution
The system calculates an impact score to determine when to solicit user feedback, focusing on feedback that is likely to make the largest performance improvements, and uses this feedback to differentiate between signal noise and contextual exceptions, optimizing automation settings based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system continuously solicits user feedback to improve automation settings accuracy, then the preference identification accuracy improves, but user burden and system complexity increase
Solution Approach 1:
The system implements a feedback mechanism where automation settings are adjusted based on explicitly provided or learned user preferences. The system solicits feedback when pattern matching confidence is below a threshold, creating a closed-loop system that continuously improves preference identification accuracy while managing user interaction.
Solution Approach 2:
The system changes the parameter of feedback solicitation frequency based on pattern matching confidence levels. When confidence is high, no feedback is solicited; when confidence is below a threshold, feedback is solicited. This dynamic parameter adjustment optimizes both accuracy improvement and user burden reduction.
2Adaptability or versatility
If the system solicits feedback frequently to capture exceptions to general patterns, then the system's adaptability to user preferences improves, but user time and willingness to provide feedback decrease
Solution Approach 1:
The system performs preliminary pattern matching to identify general user preferences before soliciting feedback. By pre-processing the data and only requesting feedback when pattern confidence is low, the system captures exceptions efficiently without requiring users to continuously provide feedback, thus preserving user time while maintaining adaptability.
Solution Approach 2:
The system applies partial feedback solicitation rather than continuous feedback requests. Feedback is solicited only in specific situations where pattern matching confidence is below a threshold, representing a partial action that is sufficient to capture exceptions without excessive user burden.
3Productivity
If the system requests feedback only when needed based on impact scoring, then feedback processing efficiency improves, but the system's ability to capture all potential preferences may be reduced
Solution Approach 1:
The system uses a feedback mechanism triggered by impact scoring to determine when feedback solicitation is most valuable. By calculating the potential improvement from feedback and only soliciting when the impact score exceeds a threshold, the system maximizes feedback processing efficiency while maintaining reliable preference capture through targeted feedback requests.
Data Source
AI summary
The technology described herein solicits user feedback in order to improve the processing of contextual signal data to identify automation setting preferences. Users have limited availability or willingness to provide explicit feedback. The technology calculates an impact score that measures a possible improvement to the automation system that could result from receiving feedback. Feedback is solicited when the impact score exceeds a threshold. Other rules can be provided in conjunction with the impact score to determine when feedback is solicited, such as a daily cap on feedback solicitations.