Ambient Computing Privacy Processor Power Management
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Solution Overview
Problem
Mobile computing devices face challenges in reducing power consumption to extend battery life, especially when maintaining processing components in an active state to process sensor data, which consumes significant power and reduces mobility.
Innovation Solution
An ambient computing system that uses a machine learning engine to determine the optimal power state of components based on sensor data, such as audio, touch, and location data, allowing for the disabling of unnecessary sensors and processing components to conserve power, thereby enhancing privacy and extending battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processing components are maintained in an active state to process sensor data, then the device can perform actions based on sensor data, but power consumption increases significantly
Solution Approach 1:
The patent implements dynamic power state adjustment of processing components based on ambient conditions detected by sensors. The system transitions components between active and low-power states adaptively, allowing the device to maintain productivity when needed while conserving energy during periods of low activity or when privacy conditions are met.
Solution Approach 2:
The system uses sensor data to automatically determine when to activate or deactivate processing components without requiring user intervention. The ambient computing system self-regulates its power state based on environmental conditions, achieving energy efficiency through autonomous decision-making.
2Duration of action of moving object
If sensors and processing components are disabled to conserve power, then battery life is extended, but the device cannot respond to environmental changes
Solution Approach 1:
The system implements periodic monitoring of ambient conditions using low-power sensors, allowing the device to remain in sleep mode for extended periods while still detecting environmental changes. When sensors detect relevant conditions, the system periodically activates processing components to respond appropriately, balancing battery conservation with environmental awareness.
Solution Approach 2:
The system performs preliminary detection using low-power sensors to identify conditions that may require full processing capability. By detecting ambient conditions in advance using minimal power, the system can prepare for upcoming tasks without continuously running high-power components, thus extending battery life while maintaining responsiveness.
3Object-affected harmful factors
If components are disabled in private contexts to improve privacy, then user privacy is protected, but the device cannot perform useful actions
Solution Approach 1:
The system applies different privacy and power management policies to different sensors and processing components based on the detected context. Rather than disabling all components uniformly, the system selectively disables only those components that would compromise privacy in the current context, while keeping other components active to maintain useful functionality.
Solution Approach 2:
The system dynamically changes the operational parameters of processing components based on privacy context. When private conditions are detected, the system modifies which components are active and which remain disabled, adjusting the functional state of the device to balance privacy protection with useful action capability.
Data Source
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AI summary
Methods, systems, and apparatus, for handling applications in an ambient computing system with a privacy processor. One of the methods includes to remain in a monitoring power state until a controller receives an interrupt indicating that one or more sensor signals are present. The one or more sensor signals are provided as input to a machine learning engine. An inference pass is performed by the machine learning engine to generate an output representing a particular context that is specific to a particular user. It is determined that one or more components of an ambient computing system should be disabled based on the on the particular context for the particular user. In response, the one or more components of the ambient computing system are disabled.