Machine Learning Anomaly Detection System
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Solution Overview
Problem
As datasets grow in size and complexity, automatic identification and handling of anomalies within large volumes of data have emerged as critical challenges, necessitating intelligent anomaly detection systems that can consider complex patterns in high-dimensional data.
Innovation Solution
A computerized system and method that processes and detects anomalies in input data by assembling signals from event data items, calculating anomaly scores through comparisons with past signals, generating alerts based on these scores, and presenting them on a display, while allowing or reversing data transfers between separate computer systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection methods are used, then the system is simple to implement, but it cannot effectively identify anomalies in large volumes of high-dimensional data
Solution Approach 1:
The patent segments the anomaly detection process into multiple independent machine learning models including unsupervised learning for pattern recognition, supervised learning for classification, and reinforcement learning for optimization. Each model handles specific aspects of anomaly detection in high-dimensional data, improving overall accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent transforms the anomaly detection problem from traditional statistical methods into a multi-dimensional machine learning framework. By incorporating multiple ML models that operate in different feature spaces and dimensionalities, the system can effectively analyze complex high-dimensional datasets while maintaining interpretability through the structured integration of various learning approaches.
2Measurement precision
If multiple machine learning models are used to detect anomalies, then detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements preliminary action by using unsupervised learning models to pre-process and identify potential anomaly patterns before applying more computationally intensive supervised and reinforcement learning models. This hierarchical approach filters data early in the pipeline, reducing the volume of data that requires extensive processing while maintaining high detection accuracy.
Solution Approach 2:
The system applies partial action by selectively deploying different machine learning models based on the specific characteristics of the data and the type of anomalies being detected. Not all models are applied to all datasets uniformly; instead, the system optimizes model selection and application depth to achieve sufficient accuracy without unnecessary computational overhead.
3Speed
If real-time anomaly detection is implemented, then timely alerts are generated, but the system requires high computational power
Solution Approach 1:
The patent implements periodic action by processing data in continuous batches or time windows rather than analyzing every data point in real-time. The machine learning models are applied periodically to aggregated data segments, enabling timely anomaly detection while significantly reducing instantaneous computational energy consumption compared to true real-time processing of every incoming data point.
Data Source
AI summary
A computerized system and method may process and detect anomalies in input data using of machine learning models and techniques. A computerized system comprising one or more processors, a memory, and a communication interface to communicate via a communication network with remote computing devices, may be used for assembling a signal based on event data items; calculating an anomaly score for the signal, which may describe a change or difference between the signal and past signals; generating an alert based on the calculated score; presenting the alert on an output computer display; and allowing or reversing data transfers performed over a communication network between physically separate computer systems based on the anomaly score. Some embodiments of the invention may include performing peer anomaly detection context anomaly detection as two separate and distinct anomaly detection procedures, using separate and distinct machine learning models and algorithms.