Predictive Policy Selection for Electronic Device Unresponsiveness
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Solution Overview
Problem
Existing methods for predicting and preventing sluggishness in electronic devices are limited by their reliance on software policies that are triggered only after sluggishness occurs, failing to prevent abnormal system states and being inaccurate due to turbulence in data parameters.
Innovation Solution
A method that uses a processor to obtain device parameters and user usage patterns, applies a 4D clustering model to identify the cause of sluggishness, and applies policies to prevent unresponsive states by determining a policy based on rewards and user usage patterns to avoid predicted sluggishness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of data parameters is increased to improve prediction accuracy, then the prediction model becomes more comprehensive, but turbulence and variances in data parameters curtail prediction accuracy
Solution Approach 1:
The patent extracts and removes extraneous data parameters from the prediction model. The system identifies and eliminates parameters that do not contribute meaningfully to prediction accuracy, thereby reducing turbulence and variances in the data set while maintaining comprehensive coverage of relevant factors.
Solution Approach 2:
The patent changes the parameters by applying weighting mechanisms and selection criteria to identify the most relevant parameters. Instead of using all available parameters equally, the system transforms the parameter set by assigning different weights based on their contribution to prediction accuracy, thereby reducing the negative impact of turbulent parameters.
2Speed
If software policies are applied to reduce sluggishness, then system responsiveness is improved, but the policies can only be triggered after sluggishness has already occurred
Solution Approach 1:
The patent implements preliminary action by using the prediction model to identify potential sluggishness conditions before they actually occur. The system proactively applies software policies based on predicted states, preventing sluggishness rather than reacting to it after detection, thereby eliminating the time delay between sluggishness occurrence and policy intervention.
Solution Approach 2:
The patent establishes a feedback loop where the prediction model continuously monitors system parameters and provides early warnings of potential sluggishness. This feedback mechanism enables the system to adjust policies proactively based on predicted future states, improving responsiveness while reducing the time lag associated with reactive policies.
3Reliability
If existing software policies are used to manage system parameters, then sluggishness is addressed, but the electronic device cannot be prevented from entering or re-entering abnormal system states
Solution Approach 1:
The patent applies preliminary action by predicting abnormal system states before they occur and implementing preventive policies in advance. The system uses the prediction model to identify trends leading to abnormal states and intervenes proactively, preventing the device from entering abnormal states in the first place and reducing the likelihood of re-entry.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically adjust its own parameters and policies based on predictions. The electronic device monitors its own state, predicts potential abnormalities, and applies corrective policies autonomously without external intervention, thereby improving its ability to prevent and recover from abnormal states.
Data Source
AI summary
Provided are a method and apparatus for controlling an unresponsive state of an electronic device. The method includes: obtaining a plurality of device parameters related to at least one of a hardware module and a software module of the electronic device, and a user usage pattern parameter based on receiving at least one user input to the electronic device; predicting the unresponsive state of the electronic device based on the collected plurality of device parameters and the collected user usage pattern parameter; and avoiding the predicted unresponsive state of the electronic device by applying a policy selected from among a plurality of policies associated with the plurality of the device parameters to a device parameter related to the predicted unresponsive state of the electronic device.