Context-Aware Enterprise Protection Using Multi-Sensor ML Correlation
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Solution Overview
Problem
Conventional enterprise protection systems rely on isolated sensor data analysis and generic settings, failing to consider data from multiple sensors in combination, leading to inefficient and non-contextualized alarm triggering.
Innovation Solution
A system utilizing machine learning to analyze sensor data from multiple sources, normalizing it to an enterprise-specific format, and continuously tuning sensor settings and thresholds through feedback loops to provide context-aware responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use isolated sensor data analysis with generic settings, then system simplicity is maintained, but detection accuracy and contextual awareness deteriorate
Solution Approach 1:
The patent combines multiple sensor data sources and integrates them with enterprise context information (workflows, assets, locations) to create a unified analysis system. This merging enables contextual awareness and improved detection accuracy by evaluating sensor readings in conjunction with enterprise-specific information rather than in isolation.
Solution Approach 2:
The system creates a multi-functional platform that handles diverse sensor types, multiple data formats, various notification channels, and different enterprise contexts. This universal architecture processes heterogeneous data sources through a common machine learning framework, achieving both complexity management and enhanced detection capabilities.
2Measurement precision
If machine learning models analyze multiple sensor data sources with enterprise context, then contextual awareness and detection accuracy improve, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data, normalizing multiple data formats, and pre-loading enterprise context information before analysis is needed. Machine learning models are trained in advance on historical data, so when real-time analysis is required, the computational burden is reduced to inference rather than full training, decreasing energy consumption during operation.
Solution Approach 2:
The patent applies local quality by customizing analysis depth and processing intensity based on specific enterprise needs, sensor types, and risk profiles. Different sensor combinations receive tailored analysis approaches, and enterprise-specific parameters are localized to relevant contexts, optimizing computational energy usage by avoiding uniform high-intensity processing across all data streams.
3Ease of operation
If generic alarm thresholds are used, then system ease of operation is maintained, but response effectiveness and enterprise-specific customization deteriorate
Solution Approach 1:
The system implements dynamic alarm thresholds that automatically adjust based on enterprise-specific parameters, historical data patterns, and contextual information. Rather than static generic thresholds, the machine learning models continuously refine threshold values to match enterprise operations, maintaining ease of operation through automation while achieving high adaptability to specific organizational needs.
Solution Approach 2:
The patent enables self-service through automated machine learning model training and threshold optimization. The system autonomously learns enterprise-specific patterns from historical data and automatically configures appropriate alarm thresholds without requiring manual customization, thus maintaining ease of operation while achieving enterprise-specific adaptability through self-configuration.
Data Source
AI summary
Arrangements for providing context aware response functions are provided. In some examples, sensor data may be received from a plurality of sensors in a sensor data farm associated with an enterprise organization. The sensor data may be received from various types of sensors and/or from sensors from various vendors or manufacturers. a machine learning model may be executed to analyze the sensor data. Based on an output of the machine learning model, a determination may be made as to whether one or more enterprise-specific thresholds have been met or exceeded. If not, additional, subsequently received data may be analyzed. If so, one or more notifications or notification actions may be identified. In some examples, the notification or notification actions may be executed and response data may be received. The response data may be used to update and/or validate the machine learning model.