Smart Home Device Control Using Near Real-Time User Feedback Learning
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Solution Overview
Problem
Smart home devices often perform actions automatically based on predefined rules, but user feedback, both explicit and implicit, is not effectively integrated to adjust these actions in real-time, leading to potential dissatisfaction and inefficiencies.
Innovation Solution
A system that utilizes near real-time modeling to analyze context data from the environment to determine user reactions to automatic actions, adjusting directives accordingly by using machine learning models to generate scores indicating positive or negative user reactions, thereby refining when and how actions are performed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic actions are performed based on predefined rules, then device automation and efficiency are improved, but user satisfaction deteriorates due to inability to adapt to user feedback
Solution Approach 1:
The system implements feedback loops where user reactions (both explicit and implicit) to automatic actions are continuously collected and processed. Machine learning models analyze this feedback in near real-time to adjust future automatic actions, enabling the system to adapt its behavior while maintaining automation. This resolves the contradiction by making the automated system responsive to user preferences without requiring manual reconfiguration.
Solution Approach 2:
The patent transforms static predefined rules into dynamic adaptive policies through machine learning models. These models continuously update their parameters based on incoming user feedback, allowing the system to evolve its behavior over time. The automation level and action timing become dynamic rather than fixed, enabling adaptation while preserving automated operation.
2Adaptability or versatility
If machine learning models analyze context data in near real-time, then adaptability to user preferences is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between raw context data and control decisions. These models process and interpret complex multi-modal data (sensor readings, user interactions, environmental context) and translate them into actionable insights that adjust automatic actions. This intermediary layer manages complexity by encapsulating the analytical logic within trained models rather than requiring complex rule-based systems.
Solution Approach 2:
The system uses machine learning models that are trained on historical data to create simplified representations of complex user preferences and environmental patterns. Once trained, these model copies can make rapid predictions about user reactions without requiring real-time analysis of all raw data, reducing computational complexity while maintaining adaptability.
3Adaptability or versatility
If user feedback is collected and analyzed, then user satisfaction is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical user feedback and context data before deployment. This offline training phase captures user preferences and environmental patterns in advance, enabling the models to make rapid predictions during real-time operation without requiring extensive processing of raw feedback data at the moment of decision-making.
Solution Approach 2:
The patent implements continuous learning where the machine learning models are continuously updated with new user feedback while maintaining their predictive capabilities. This allows the system to learn from all available data over time rather than requiring batch processing, enabling adaptive behavior improvement without interrupting or significantly delaying automated actions.
Data Source
AI summary
Systems and methods for device control using near real time learning are disclosed. For example, an automatic action is performed by a target device in response to a predefined condition being met. Thereafter, context data is gathered and utilized to determine whether a negative user reaction has been provided in response to performance of the automatic action. When a negative user reaction is determined, mitigating actions may be taken close in time to when the context data is received to prevent further negative user reactions.