Adaptive Linguistic Model for Neuro-Linguistic AI Feature Selection
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Solution Overview
Problem
Current surveillance and monitoring systems require predefined rules to detect activities, making them rigid and incapable of recognizing behaviors outside these rules, limiting their adaptability and effectiveness in dynamic environments.
Innovation Solution
A computer-implemented method that optimizes feature selection by generating an adaptive linguistic model using probabilistic clusters and optimization parameters based on input data from sensors, allowing the system to learn and recognize patterns without pre-defined rules, enabling the detection of abnormal activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined rules are used to detect activities, then the system can reliably detect known behaviors, but the system cannot recognize behaviors outside these rules, limiting adaptability
Solution Approach 1:
The system transitions from static predefined rules to dynamic probabilistic models that continuously learn from data. The monitoring system adapts its detection criteria based on observed patterns, allowing it to recognize both known and novel behaviors while maintaining reliability through statistical validation.
Solution Approach 2:
The system changes the fundamental parameters of detection from fixed rule-based thresholds to adaptive probabilistic parameters. By organizing input data into probabilistic clusters and generating feature words based on optimization parameters, the system can adjust its detection sensitivity and scope dynamically while maintaining reliable performance.
2Extent of automation
If the system trains itself based on provided definitions or rules, then it can learn specified behaviors, but it requires rules to be defined in advance, maintaining rigidity
Solution Approach 1:
The system performs self-service by automatically organizing input data into probabilistic clusters and generating feature words without requiring external rule definitions. The monitoring system autonomously learns patterns from raw data, identifying both normal and abnormal behaviors through unsupervised learning mechanisms.
Solution Approach 2:
The system performs preliminary organization of input data into probabilistic clusters before detection. By pre-processing data through clustering and feature word generation based on optimization parameters, the system prepares structured representations that enable both automated learning and flexible detection of unforeseen behaviors.
3Measurement precision
If hard-coded descriptions of rules are included, then the system can detect conforming behaviors, but behaviors not matching predefined rules go undetected
Solution Approach 1:
The system replaces static hard-coded rules with dynamic probabilistic clustering that continuously adapts to observed data patterns. This allows the system to maintain precise detection of known behaviors while simultaneously expanding detection coverage to include novel and unexpected behaviors through ongoing learning.
Solution Approach 2:
The probabilistic clustering framework serves multiple functions simultaneously: it detects conforming behaviors with high precision, identifies novel patterns, and adapts to different sensor types and data formats. This universal approach eliminates the need for separate rule sets for different behavior types while maintaining detection precision across diverse scenarios.
Data Source
AI summary
Techniques are disclosed to optimize feature selection in generating betas for a feature dictionary of a neuro-linguistic Cognitive AI System. A machine learning engine receives a sample vector of input data to be analyzed by the neuro-linguistic Cognitive AI System. The neuro-linguistic Cognitive AI System is configured to generate multiple betas for each of a plurality of sensors. The machine learning engine identifies a sensor specified in the sample vector and selects optimization parameters for generating betas based on the identified sensor.