LLM-Based Anomaly Detection for Unstructured Data Clustering
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Solution Overview
Problem
Existing systems struggle to identify anomalous data in unstructured data sets due to the lack of understanding of context or meaning by computers, leading to inefficiencies and errors in data analysis.
Innovation Solution
A combination of a trained large language model (LLM) and a cluster analyzer is used to extract keywords from unstructured data, generate vectors in an n-dimensional space, and identify clusters, allowing for the automatic detection of outliers or anomalous data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional comparison methods using predetermined data sets are used to identify anomalous data in unstructured data, then the system can detect outliers, but it requires significant effort to gather data, large amounts of memory, and high processing resources
Solution Approach 1:
The patent replaces traditional mechanical comparison methods (using predetermined data sets and histograms) with a machine learning-based approach. The system uses a trained neural network model to automatically detect anomalies in unstructured data, substituting the need for manual data gathering and complex processing with an automated AI-driven system that consumes significantly fewer resources.
Solution Approach 2:
The system employs self-service principles by using the data itself to train the anomaly detection model. The neural network learns patterns from the unstructured data without requiring external predetermined data sets or manual feature engineering. The model automatically adapts to the data distribution and identifies anomalies based on learned patterns, eliminating the need for extensive manual data collection and processing resource consumption.
2Productivity
If computers analyze unstructured data without understanding context or meaning, then processing is simple, but the system cannot accurately identify anomalous data
Solution Approach 1:
The patent replaces simple mechanical processing with a neural network-based system that understands context and meaning in unstructured data. The trained model processes data semantically, enabling accurate anomaly detection while maintaining efficient processing speeds through automated pattern recognition rather than manual analysis.
Solution Approach 2:
The system changes the parameter of data representation from raw unstructured format to transformed feature vectors that capture semantic meaning. The neural network converts unstructured data into a standardized representation that preserves contextual information, enabling both accurate anomaly detection and efficient processing by transforming the data into a more manageable format.
3Reliability
If predetermined data sets are used for comparison to identify outliers in unstructured data, then outlier detection is possible, but it requires significant effort to gather and process the data
Solution Approach 1:
The system performs preliminary action by pre-training the neural network model on a representative sample of unstructured data before actual anomaly detection. This preliminary training phase allows the model to learn patterns and characteristics of normal data, enabling rapid and accurate anomaly detection without requiring extensive data gathering during the actual detection process. The model is prepared in advance to handle new data efficiently.
Solution Approach 2:
The system uses self-service by automatically learning from the data itself without requiring external predetermined data sets. The neural network trains on the unstructured data to understand its patterns and characteristics, eliminating the need for manual data gathering and processing. The model adapts to the specific data distribution and provides accurate anomaly detection with minimal time investment.
Data Source
AI summary
In some aspects, the disclosure is directed to methods and systems for automatic detection of outliers in unstructured and semi-structured data. In some implementations, unstructured or semi-structured data may be provided to a trained large language model (LLM), which may be used to summarize or extract important tokens or keywords from the data. The extracted tokens or keywords may be used to generate a vector in an n-dimensional space, and compared to other vectors generated from tokens or keywords extracted from other unstructured or semi-structured data. A cluster analyzer may identify clusters or groups of vectors within the n-dimensional space, and may identify outliers or vectors lying outside of the identified clusters or groups.