Automated Data Profile Segmentation Using Small Text Variations
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Solution Overview
Problem
Current systems for data profile segmentation struggle with accurately identifying and classifying small data sets, leading to inaccurate classification and laborious analysis, especially when data is limited to a few words, and fail to generate actionable insights or descriptive names for personas.
Innovation Solution
An automated machine learning system for attribute-based clustering of data profiles, using a sequence prediction architecture with conditional random field models to extract features, vectorize information components, and perform hierarchical clustering, generating descriptive names and database queries for optimized cluster representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cluster analysis is used to discover groups of similar profiles, then classification accuracy improves, but large dataset requirements worsen the system's applicability to small data sets
Solution Approach 1:
The patent segments text data into small components (words, phrases, entities) and processes them individually through specialized models. This segmentation allows the system to extract meaningful features from limited text data without requiring large volumes, thereby maintaining classification accuracy while reducing data volume requirements.
Solution Approach 2:
The patent transforms text data into vector representations in a multi-dimensional space, enabling cluster analysis to operate effectively even with small datasets. By converting textual information into numerical vectors with multiple dimensions, the system can capture nuanced relationships and perform accurate classification without needing large amounts of raw text data.
2Device complexity
If traditional classification systems are used with limited text data, then system complexity is reduced, but classification accuracy deteriorates
Solution Approach 1:
The system segments text processing into distinct stages: component extraction, classification, vectorization, and clustering. Each stage uses specialized models tailored to its specific task, allowing accurate classification of limited text data while keeping each individual component relatively simple and manageable.
Solution Approach 2:
The patent introduces intermediate representations (vector embeddings) that bridge raw text data and final classification results. These intermediate vectors capture semantic meaning and relationships, enabling accurate classification of small text datasets without requiring overly complex direct classification systems.
3Loss of information
If laborious analysis and deconstruction is performed to understand clusters, then actionable insight quality improves, but time consumption worsens
Solution Approach 1:
The patent performs preliminary actions by automatically generating descriptive names and summaries for clusters before any human analysis is needed. This preliminary characterization of clusters provides immediate actionable insights and reduces the time required for subsequent analysis, while maintaining high information quality through automated feature extraction and synthesis.
Solution Approach 2:
The system performs self-service by automatically generating cluster descriptions, names, and interpretations without requiring manual analysis. The automated generation of actionable insights from cluster data eliminates the need for laborious human deconstruction, reducing analysis time while preserving insight quality through sophisticated natural language processing.
Data Source
AI summary
Systems and methods described herein enable effective and accurate modeling of a set of existing data profiles, perform categorization of the data profiles in an explainable way such that actions can be taken on the information to have predictable results. The systems and methods further facilitate means to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the data profiles.