Firmware Sliding Time-Window Event Thresholding
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Solution Overview
Problem
Computing devices lack built-in methods to determine when critical actions should be taken in response to increasing error rates, as they are unable to link error frequencies to specific remedial actions, leading to inefficiencies and potential system failures.
Innovation Solution
A firmware-based method that uses a sliding time window to track the frequency of specified system events, allowing for the comparison of occurrences within a set duration to pre-defined threshold values, and invokes defined actions when criteria are met, independent of the operating system or third-party software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If error monitoring is implemented at the operating system level, then error detection capability is improved, but system efficiency deteriorates and introduces OS dependencies
Solution Approach 1:
The patent introduces firmware as an intermediary layer between hardware and operating system to perform error monitoring and threshold evaluation. This mediator handles error counting and threshold comparison independently of the OS, preventing OS-level processing overhead while maintaining accurate error detection. The firmware acts as a bridge that resolves the contradiction by enabling precise measurement without sacrificing system productivity.
2Reliability
If comprehensive error monitoring and threshold evaluation are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the error monitoring function into distinct modular components: error event detection, counting mechanism, threshold evaluation, and action invocation. Each component is implemented as a separate firmware module that can be independently configured and maintained. This segmentation reduces overall system complexity by breaking down the comprehensive monitoring task into manageable, reusable units while maintaining high reliability through systematic error handling.
Solution Approach 2:
The patent implements preliminary action by pre-defining threshold values and associated remedial actions during system configuration. The firmware evaluates error counts against these pre-established thresholds and automatically invokes predetermined remedial actions when thresholds are exceeded. This approach simplifies the monitoring system by eliminating the need for complex real-time decision-making logic, thereby improving reliability without significantly increasing device complexity.
3Measurement precision
If error counting is performed continuously, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent implements periodic action by evaluating error counts against thresholds only at specific intervals - namely, when error events occur or when predefined time periods elapse. Rather than continuously monitoring and processing error data, the firmware performs threshold evaluation periodically, which maintains accurate error rate measurement while significantly reducing the energy consumption associated with constant firmware processing. This periodic evaluation approach resolves the contradiction between measurement precision and energy usage.
Data Source
AI summary
A scalable method of determining in a firmware environment if the rate of occurrence of a detectable specified type of system event that occurs to a system component or discrete functional unit, has met a criteria with respect to a pre-selected threshold. When the meeting of the threshold criteria is detected, a previously defined action associated with the threshold criteria for the particular event can be invoked by the firmware. Embodiments may establish a sliding time-window that includes a currently detected type of system event and extends back a set duration in the past. Any occurrences of the specified event taking place earlier than the established time-window may be discarded while occurrences of the events during the specified time-window are added together with the newly detected event and compared to a threshold value to see if the threshold criteria has been met.