Selective Feedback for Automation Preference Exceptions
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Solution Overview
Problem
Current automation systems face challenges in understanding and adapting to individual and group user preferences, particularly in identifying exceptions to general patterns, which can lead to inefficiencies in settings like thermostats and entertainment systems.
Innovation Solution
The system solicits user feedback through an impact score calculation to determine when feedback is most valuable, focusing on exceptions and limiting solicitations to optimize automation settings, such as temperature and entertainment choices, by asking users about their preferences in specific contextual situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feedback is continuously solicited to improve automation setting accuracy, then the precision of preference identification improves, but user burden and system complexity increase
Solution Approach 1:
The system implements selective feedback solicitation where feedback requests are triggered only when the automation system detects a pattern exception or uncertainty. The feedback mechanism includes presenting multiple possible explanations to the user and selectively requesting confirmation only for the most likely exceptions, rather than continuously soliciting feedback for all decisions.
Solution Approach 2:
The system applies partial feedback solicitation by focusing only on critical exception cases rather than all automation decisions. It calculates an impact score to determine which feedback opportunities are most valuable, soliciting feedback selectively for high-impact cases while avoiding low-impact situations, thereby reducing overall system complexity.
2Adaptability or versatility
If feedback is frequently solicited to capture exceptions, then the adaptability to user preferences improves, but user time and willingness to provide feedback decrease
Solution Approach 1:
The system performs preliminary analysis by calculating an impact score for each potential feedback opportunity before soliciting user feedback. It pre-identifies and ranks exception cases based on their potential impact on automation accuracy, preparing targeted feedback requests in advance rather than indiscriminately asking for feedback on all exceptions.
Solution Approach 2:
The system implements periodic feedback solicitation with constraints such as limiting the number of feedback requests per day or per session. It schedules feedback opportunities strategically rather than continuously, allowing users adequate time between feedback requests while still capturing sufficient exception data for adaptability.
3Measurement precision
If the system attempts to understand all user preferences including exceptions, then the automation accuracy improves, but the difficulty of preference modeling increases
Solution Approach 1:
The system segments preference patterns into distinct categories: general patterns (applicable most of the time) and exception patterns (specific contextual deviations). It maintains separate models for each type, with the exception model triggered only when pattern-matching confidence falls below a threshold or when explicit feedback indicates an exception occurred.
Solution Approach 2:
The system dynamically adjusts the confidence threshold parameter that determines when to switch from general pattern application to exception handling. It modifies its operational parameters based on accumulated feedback, learning to recognize exceptions more accurately over time and adjusting the sensitivity of exception detection to optimize between accuracy and false positives.
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.