Activity Map Machine Learning for Anomalous Behavior Detection
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Solution Overview
Problem
Existing systems struggle to effectively detect and respond to anomalous behavior in monitored physical environments, such as construction, manufacturing, or fulfillment centers, using conventional methods.
Innovation Solution
A machine learning-based approach that utilizes activity maps generated from sensor data to identify anomalous behavior by applying these maps to a trained model, followed by automated actions to mitigate such behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional detection methods are used, then system complexity is reduced, but detection accuracy and reliability deteriorate
Solution Approach 1:
The patent replaces conventional mechanical/deterministic detection systems with a machine learning-based system that uses neural networks to analyze activity maps. This substitution enables the system to learn complex patterns from data and improve detection accuracy automatically, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where detected anomalies are fed back into the machine learning model for continuous training and improvement. This feedback loop allows the system to progressively improve its detection accuracy while managing complexity through automated learning rather than manual system redesign.
2Reliability
If machine learning models are applied to detect anomalous behavior, then detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary processing by pre-computing and storing activity maps from sensor data before feeding them into the machine learning model. This preliminary action reduces the computational burden during real-time anomaly detection, as the model only needs to analyze the pre-processed activity maps rather than raw sensor data.
Solution Approach 2:
The patent segments the detection process into distinct stages: sensor data collection, activity map generation, and anomaly detection using machine learning models. This segmentation allows each component to be optimized independently, reducing overall computational energy requirements while maintaining high detection accuracy.
3Speed
If real-time monitoring is implemented, then response time to anomalies improves, but data processing volume and system complexity increase
Solution Approach 1:
The system extracts and processes only the essential information from sensor data by generating activity maps that summarize object behavior patterns. This extraction approach reduces the data volume that needs to be stored and processed in real-time while maintaining the ability to detect anomalies quickly, thus improving response time without proportionally increasing data handling complexity.
Data Source
AI summary
Techniques are provided for machine learning-based detection of anomalous object behavior in a monitored physical environment. One method includes obtaining activity maps comprising data characterizing objects of a respective object type within a monitored physical environment; applying the activity maps to a machine learning model trained to generate one or more predicted activity maps; comparing the one or more predicted activity maps to corresponding activity maps; and, in response to a result of the comparison indicating anomalous object behavior, initiating at least one automated action. A given activity map may comprise multiple cells, wherein a given cell is mapped to a corresponding portion of the monitored physical environment. The given activity map may correspond to a particular object type and the given cell may comprise aggregated data characterizing objects of the particular object type in the given cell.