Data Analysis Pipeline Engine for Unstructured Risk Data Clustering
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Solution Overview
Problem
Conventional data intelligence systems lack comprehensive logic and infrastructure for efficient data analysis pipelines, leading to reduced accuracy, scalability issues, and inability to handle large datasets effectively, particularly in processing unstructured data, resulting in poor data quality and inflexibility.
Innovation Solution
A data analysis pipeline engine employing unsupervised learning, clustering, topic modeling, and Large Language Models (LLMs) for data preprocessing, dimensionality reduction, and graph-based reasoning to segment and analyze large datasets, enabling efficient categorization and summarization of unstructured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data intelligence systems process large datasets without clustering and topic modeling, then they can maintain simple system architecture, but they suffer from reduced accuracy, scalability problems, and inability to handle large datasets effectively
Solution Approach 1:
The patent segments large datasets into smaller, manageable clusters using unsupervised clustering techniques. This segmentation allows the system to process and analyze data in organized groups, improving measurement precision while maintaining tractable system complexity through structured data organization.
Solution Approach 2:
The patent introduces topic modeling as an intermediary layer between raw data and analysis. This intermediary extracts meaningful themes and patterns from unstructured data, enhancing analysis accuracy without requiring the entire system to directly handle the complexity of raw large-scale datasets.
2Productivity
If data intelligence systems process vast amounts of unstructured data in its entirety, then they can ensure comprehensive data coverage, but they face computational intensity and scalability issues
Solution Approach 1:
The patent extracts essential features, patterns, and topics from vast amounts of unstructured data using topic modeling and clustering. This extraction process identifies and isolates the most relevant information, enabling efficient processing that maintains comprehensive coverage while reducing computational burden through selective focus on key data elements.
3Reliability
If conventional systems analyze large datasets without graph-based reasoning and AI agents, then they can maintain simpler processing logic, but they lack context-aware analysis and comprehensive data feature assessment
Solution Approach 1:
The patent introduces graph-based reasoning and AI agents as intermediary components that enhance data feature assessment. These intermediaries build reasoned knowledge graphs that capture contextual relationships and dependencies, improving reliability of assessments without requiring the entire system to be fundamentally restructured, as the complex reasoning is contained within these modular intermediary components.
Data Source
AI summary
Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.