Compliance Deviation Detection With Adaptive Operational Protocols
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Solution Overview
Problem
Deviations from compliance metrics cause undue stress on electronic systems, leading to increased resource usage and potential obsolescence.
Innovation Solution
A method and system utilizing trained models to analyze sensor measurements, correlate deviations with database records, and generate operational protocols to reduce deviations from compliance metrics, deploying these protocols to mitigate impacts on electronic systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used to detect compliance deviations, then deviations can be identified, but electronic systems experience increased processing resource usage and bandwidth consumption to address these deviations
Solution Approach 1:
The system performs preliminary actions by training machine learning models offline using historical sensor data and compliance violations. The trained models are then deployed to edge devices where they autonomously predict compliance violations in real-time without requiring continuous cloud processing, thereby reducing runtime processing resource usage while maintaining monitoring accuracy
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw sensor data and compliance violation detection. These models process and interpret sensor measurements locally, filtering out normal variations and only flagging genuine compliance issues, which reduces the amount of data that needs to be transmitted and processed by central electronic systems
2Reliability
If continuous monitoring and analysis of sensor measurements are performed to detect deviations, then compliance violations are identified timely, but bandwidth consumption increases to transmit and process the data
Solution Approach 1:
The system extracts only the essential features from raw sensor measurements that are relevant to compliance violation detection. The machine learning models process sensor data locally and extract only the necessary compliance-related information, transmitting minimal processed data rather than continuous raw sensor streams, thereby reducing bandwidth consumption while maintaining timely detection capability
Solution Approach 2:
Edge devices perform self-service by autonomously analyzing their own sensor measurements using locally deployed machine learning models. The devices independently detect compliance violations and generate alerts without requiring constant communication with central systems, reducing bandwidth consumption while ensuring timely local detection and response
3Reliability
If operational protocols are frequently updated to address compliance deviations, then system compliance improves, but system complexity increases to manage and deploy the protocols
Solution Approach 1:
The system implements feedback mechanisms where machine learning models continuously monitor compliance metrics and automatically adjust operational parameters within predefined boundaries. This closed-loop control enables the system to maintain compliance through automated adjustments rather than frequent manual protocol updates, reducing the complexity of protocol management while improving sustained compliance
Solution Approach 2:
The patent introduces dynamic operational parameters that can automatically adjust within defined ranges based on real-time sensor measurements and model predictions. This dynamic adaptation allows the system to respond to compliance variations continuously without requiring discrete protocol updates, simplifying protocol management while maintaining high compliance levels
Data Source
AI summary
A device receives sensor measurements associated with a location and inputs to a first trained model that processes given sensor measurements and outputs indications of deviations from compliance metrics in the given sensor measurements. An indication of such a deviation is received from the first model, and the device correlates with one or more database records associated with the location. The indication of the deviation and the database record(s) are input to a second trained model that processes the deviation and correlated database records, and outputs scores indicative of respective impact of the deviation on the correlated database records. Such a score is received from the second model, and when the score does not meet a given compliance threshold score, the device generates and/or updates an operational protocol to reduce the deviation, and electronically deploys the operational protocol, in association with the location, to reduce such deviations at the location.