Cognitive Bayesian Reasoning for Real-Time Property Intrusion Detection
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Solution Overview
Problem
Existing probabilistic modeling systems fail to efficiently process complex, dynamic sensor data in real-time environments due to assumptions of stationary distributions and static model construction, leading to inefficiencies and time constraints, especially when context changes such as sensor failures or changes in causal interactions occur.
Innovation Solution
A neuromorphic apparatus with a Cognitive Bayesian Reasoning System (CBRS) that integrates adaptive control logic, model compilation, and runtime environments to dynamically update probabilistic and predicate logic models, enabling real-time data interpretation and reaction in complex environments using a Rules Engine and support software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If probabilistic modeling systems use stationary distribution assumptions and static model construction, then model simplicity is maintained, but real-time processing efficiency of complex sensor data deteriorates
Solution Approach 1:
The patent implements dynamic model construction where the probabilistic model adapts its structure and parameters based on incoming sensor data and contextual changes. The system transitions from static to dynamic modeling, allowing the model to evolve during runtime to maintain relevance and processing efficiency in changing environments.
Solution Approach 2:
The system performs preliminary model compilation and preparation before real-time data processing. By pre-compiling probabilistic models and prediction rules, the system reduces computational overhead during runtime, enabling faster real-time processing without sacrificing model sophistication.
2Adaptability or versatility
If the system dynamically updates probabilistic models in real-time, then adaptability to context changes improves, but computational time and processing load increase
Solution Approach 1:
The system pre-compiles probabilistic models and prediction rules before real-time operation. This preliminary action prepares the computational framework in advance, reducing the time required for dynamic updates during runtime while maintaining adaptability to context changes.
Solution Approach 2:
The model updating process is segmented into discrete, manageable components. By dividing the probabilistic model into modular segments that can be independently updated, the system reduces overall computational time while maintaining adaptability to specific context changes without requiring complete model reprocessing.
3Measurement precision
If the system processes rapidly changing sensor data with full model reassessment, then measurement precision improves, but processing speed decreases
Solution Approach 1:
The system applies partial model reassessment rather than complete reprocessing of all sensor data. By selectively updating only the portions of the probabilistic model that are relevant to current context changes, the system maintains measurement precision for critical parameters while reducing overall processing time through focused rather than exhaustive analysis.
Data Source
AI summary
A cognitive Bayesian reasoning system to identify an intrusion event. The system includes agents for performing reasoning functions, such as inference, inductive reasoning, abductive reasoning, deductive reasoning, and causal reasoning. Simple logic cells are provided for implementing the reasoning agents. These logic cells may be one, or more, of “OR”, “AND”, “NAND (not AND)”, “NOR (not OR)”, and “X-OR (Exclusive OR).” The agents are implemented in the simple logic cells to reason from processed data changes to identify the intrusion event.