Lighting Controller Feedback Segmentation
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Solution Overview
Problem
Existing lighting control systems using machine learning struggle to differentiate between user feedback directed at lighting transitions and feedback directed at the final light settings, leading to potential misinterpretation of user preferences.
Innovation Solution
A method that involves controlling lighting devices to render a first and second light effect, receiving user feedback within a predetermined time frame, and associating the feedback with either the transition or the second light effect based on the timing of the feedback, thereby improving the training of the machine learning system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the system uses machine learning to automatically control lighting devices based on user feedback, then the system can learn user preferences and automate lighting control actions, but the system may misinterpret user feedback by confusing feedback about transitions with feedback about final light settings
Solution Approach 1:
The patent segments the feedback reception period into distinct time intervals: a first period during which feedback is associated with transitions, and a second period during which feedback is associated with final light settings. This temporal segmentation allows the system to accurately distinguish between feedback about transition processes and feedback about destination states, resolving the ambiguity in feedback interpretation while maintaining automated control capabilities.
2Speed
If the system transitions between light effects quickly to respond to user preferences, then the system responsiveness is improved, but user feedback may be ambiguous about whether the feedback is about the transition speed or the final light effect
Solution Approach 1:
The patent implements preliminary action by establishing clear temporal boundaries before feedback occurs. The system pre-defines the first feedback period (associated with transitions) and the second feedback period (associated with final settings) before receiving user feedback. This preliminary temporal structuring ensures that even when transitions occur quickly, the system can unambiguously identify whether feedback targets the transition process or the final state, maintaining both speed and feedback accuracy.
Data Source
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AI summary
A method for training a machine for automatizing lighting control actions, wherein the method comprises the steps of: controlling one or more lighting devices to render a first light effect based on a first set of control parameters; controlling the one or more lighting devices to render a second light effect based on a second set of control parameters by transitioning over a transitioning time period from the first light effect to the second light effect; receiving a feedback from a user during a feedback time period; wherein if the feedback has been received within a predetermined time period during the feedback time period; associating the feedback with the transition; and wherein if the feedback has been received after the predetermined time period during the feedback time period; associating the feedback with the second light effect; and wherein the method further comprises training the machine based on the associated feedback.