AI Engine for Variable-State Computing Device Context Adaptation
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Solution Overview
Problem
Current AI technologies lack the ability to efficiently manage the operation of variable-state computing devices, as they fail to adapt to changing contexts and user preferences, leading to suboptimal performance, power consumption, and resource allocation.
Innovation Solution
An AI engine is implemented to determine the context of a device based on various factors, including workload, environmental conditions, and user behavior, and adjusts operational parameters of hardware components to optimize efficiency, power usage, and performance by learning from usage patterns and predicting future states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional AI technologies are used to manage computing devices, then the device can perform basic operations, but it fails to adapt to changing contexts and user preferences, leading to suboptimal performance and power consumption
Solution Approach 1:
The patent implements a dynamic state representation system where the computing device transitions between multiple operational states (e.g., low-power idle state, active processing state, predictive pre-loading state). The AI engine dynamically adjusts operational parameters based on the current state and predicted future states, enabling the system to adapt to changing contexts while optimizing performance for each specific state rather than using a static one-size-fits-all approach
Solution Approach 2:
The patent employs predictive pre-loading and pre-processing mechanisms where the AI engine analyzes usage patterns and predicts future computational needs before they occur. This allows the system to proactively prepare data and resources in advance, so when actual computational tasks arise, the device can execute them at optimal performance levels without waiting for resource allocation delays
2Loss of energy
If traditional AI technologies manage device operations, then basic functionality is maintained, but power consumption is not optimized leading to increased energy usage
Solution Approach 1:
The patent implements a multi-parameter optimization system that dynamically adjusts multiple operational parameters simultaneously including CPU frequency, memory allocation, I/O operation scheduling, and component power states. The AI engine evaluates the interrelationships between these parameters and makes coordinated adjustments to achieve optimal power efficiency while maintaining user-perceived performance, recognizing that changing a single parameter in isolation can lead to suboptimal overall system behavior
Solution Approach 2:
The patent enables the computing device to autonomously manage its own power consumption through self-learning mechanisms. The AI engine continuously monitors system behavior, learns from usage patterns, and automatically adjusts operational parameters without requiring user intervention or manual power management settings. This self-service approach allows the system to optimize power efficiency adaptively while maintaining ease of operation for users
3Productivity
If hardware components operate without contextual awareness, then device structure remains simple, but resource allocation is suboptimal reducing overall efficiency
Solution Approach 1:
The patent implements a universal state representation framework that can model and manage diverse hardware components (CPU, GPU, memory, storage, I/O devices) using a common set of state variables and transition rules. This universal approach allows the AI engine to apply the same contextual awareness mechanisms across different component types, achieving efficient resource allocation for the entire system without requiring separate complex control logic for each individual component
Data Source
AI summary
Methods and apparatus to manage operation of variable-state computing devices using artificial intelligence are disclosed. An example computing device includes a hardware platform. The example computing device also includes an artificial intelligence (AI) engine to: determine a context of the device; and adjust an operation of the hardware platform based on an expected change in the context of the device. The adjustment modifies at least one of a computational efficiency of the device, a power efficiency of the device, or a memory response time of the device.