Anomaly Detection for Firmware Release Metrics
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Solution Overview
Problem
Existing techniques for detecting anomalies caused by firmware releases in electronic devices often result in high false positive rates and are unable to report results of statistical significance due to small sample sizes.
Innovation Solution
A method that compares statistical properties of metric values collected before and after a firmware release on an electronic device, without relying on data from other devices, to detect anomalies and trigger remedial actions automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques are used to detect anomalies caused by firmware releases, then anomaly detection can be performed, but the false positive rate becomes high and statistical significance cannot be reported due to small sample size
Solution Approach 1:
The patent segments the anomaly detection process into distinct phases: collecting baseline metrics before firmware release, collecting metrics after release, and comparing the two datasets. This segmentation allows for focused statistical analysis on the specific impact of the firmware release without being diluted by unrelated data, thereby improving measurement precision and reducing false positives.
Solution Approach 2:
The patent performs preliminary data collection and statistical property determination before the firmware release. By establishing baseline statistical properties (mean, standard deviation) in advance, the system is prepared to compare against these baselines after the release, enabling rapid and accurate anomaly detection without requiring large post-release sample sizes to achieve statistical significance.
2Reliability
If data from multiple electronic devices is collected for anomaly detection, then statistical significance can be improved, but the complexity of managing and processing the data increases
Solution Approach 1:
The patent extracts and focuses on a single electronic device's data for anomaly detection, rather than aggregating data from multiple devices. By isolating the analysis to one device with before-and-after firmware release data, the system achieves statistical significance through temporal comparison while avoiding the complexity of multi-device data management and correlation.
Solution Approach 2:
Instead of expanding the approach to multiple devices (horizontal dimension), the patent deepens the analysis dimension by collecting and comparing statistical properties at different time points (before and after firmware release) for the same device. This temporal dimension provides sufficient statistical power without increasing device management complexity.
3Reliability
If manual intervention is used for anomaly detection, then human expertise can be applied, but the processing speed and automation capability are reduced
Solution Approach 1:
The patent implements a self-service anomaly detection system that automatically collects metrics, determines statistical properties, compares before-and-after data, and triggers remedial actions without requiring human intervention. The system uses its own processed data to make detection decisions, enabling rapid automated response while maintaining the reliability of statistical analysis through programmable comparison logic.
Solution Approach 2:
The patent establishes a feedback loop where the system continuously monitors metric values, compares them against baseline statistical properties, and automatically triggers remedial actions when anomalies are detected. This closed-loop feedback mechanism enables both high-speed automated detection and reliable statistical-based decision-making, eliminating the need for manual analysis while maintaining accuracy.
Data Source
AI summary
A first set of values reported by an electronic device and not reported by another electronic device over a first period of time that is prior to a firmware release to the electronic device is received. The first set of values is associated with a metric. A set of statistical properties associated with the first set of values is determined. A second set of values reported by the electronic device and not reported by another electronic device over a second period of time that is after the firmware release is received. The second set of values is associated with the metric. A set of statistical properties associated with the second set of values is determined. The set of statistical properties associated with the first set of values and the set of statistical properties associated with the second set of values is compared to detect an anomaly.