Building Risk Scoring with Dynamic and Baseline Threat Analysis
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Solution Overview
Problem
Current building security systems face challenges in efficiently processing and analyzing the high volume of data from various sources, leading to resource-intensive operations and the need for numerous security operators to monitor alarms, which are often dynamic and diverse, including threats such as intrusions, fires, and external events like crimes and natural disasters.
Innovation Solution
A building management system utilizing a Natural Language Processing (NLP) engine to categorize and standardize threat events from diverse data sources, including geolocation-based threat assessment and expiry time prediction, to automate threat analysis and reduce operator workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a security platform processes a large volume of data from multiple data sources to provide comprehensive threat information, then the completeness and coverage of threat detection is improved, but the resource consumption and operational complexity increase significantly
Solution Approach 1:
The patent introduces an intermediary processing layer that standardizes threat data from multiple diverse sources into a common format. This intermediary layer includes normalization components that convert different data schemas, threat taxonomies, and formats into a unified structure, reducing the complexity of processing heterogeneous data sources while maintaining comprehensive threat detection coverage
Solution Approach 2:
The security platform is segmented into modular components including data ingestion modules, normalization modules, analysis modules, and response modules. Each module handles specific aspects of threat processing independently, allowing the system to manage complex multi-source data through divided functional responsibilities rather than monolithic processing
2Measurement precision
If multiple security operators and analysts are deployed to review and monitor various alarms, then the thoroughness of alarm review is improved, but the operational cost and resource requirements increase
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computational analysis systems. Machine learning models and algorithmic analysis engines automatically process and prioritize alarms, substituting human operators for routine monitoring tasks while maintaining or improving review thoroughness through consistent application of analysis criteria across all alerts
Solution Approach 2:
The system implements self-service capabilities where the security platform automatically prioritizes, filters, and routes alarms based on predefined criteria and learned patterns. The system serves itself by autonomously managing the alarm workflow, reducing the need for human operators to manually review every alert while maintaining high thoroughness through automated decision-making
3Speed
If the security platform processes dynamic and diverse threat data in real-time, then the responsiveness to emerging threats is improved, but the computational resources and processing power required increase
Solution Approach 1:
The patent implements preliminary action through pre-computed threat models, pre-defined response protocols, and pre-indexed threat data. By preparing analysis frameworks and decision trees in advance, the system can rapidly respond to emerging threats by applying pre-prepared analytical structures rather than building analysis from scratch, reducing real-time computational requirements while maintaining fast response speeds
Solution Approach 2:
The security platform employs periodic action through batch processing of certain data types, scheduled updates of threat models, and interval-based analysis of lower-priority alerts. This periodic processing approach allows the system to balance real-time responsiveness for critical threats with resource-efficient periodic analysis for less urgent data, optimizing the trade-off between response speed and computational resource consumption
Data Source
AI summary
A building management system includes one or more computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive threats, the threats each indicating an incident affecting a dynamic risk score associated with an asset, wherein one or more of the threats are current threats that are active at a current point in time and one or more of the threats are historic threats that were active at one or more past times. The instructions cause the one or more processors to generate, based on the one or more current threats, the dynamic risk score at the current point in time, generate, based on the one or more historic threats, a baseline risk score, and cause a user interface to display an indication of the dynamic risk score at the current point in time and an indication of the baseline risk score.