Automated Text Type Classification via Multi-Analysis Inference
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Solution Overview
Problem
Existing text processing methods lack efficiency in inferring type classifications for terms in documents, requiring manual processing and often resulting in inaccurate or time-consuming classification of integer, floating point, string, and other value types.
Innovation Solution
A device and method that utilize name-based, context-based, synonym-based, and value-based analyses to infer type classifications for terms in text, comparing terms to sets of indicators and providing confidence scores for accurate classification, thereby automating the process and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing methods are used to classify terms in text documents, then classification accuracy can be maintained through human judgment, but processing time and labor costs increase significantly
Solution Approach 1:
The system enables automated self-service classification by equipping the text processing system with intelligence to automatically infer type classifications for terms using multiple analysis techniques (name-based, context-based, synonym-based, value-based), eliminating the need for manual human intervention while maintaining high accuracy through confidence scoring mechanisms
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational analysis systems that use algorithmic approaches (name-based analysis, context-based analysis, synonym-based analysis, value-based analysis) to infer type classifications, substituting human cognitive work with machine-based intelligent processing
2Productivity
If automated text processing methods are implemented to infer type classifications, then processing efficiency and speed improve, but classification accuracy may deteriorate due to lack of human judgment
Solution Approach 1:
The system implements feedback mechanisms through confidence scoring, where each inferred type classification is accompanied by a confidence score indicating the reliability of the classification. This feedback allows users to review and correct low-confidence classifications, thereby maintaining high accuracy while preserving automated processing efficiency
Solution Approach 2:
The patent employs multiple analysis parameters and techniques (name-based analysis comparing terms to type indicators, context-based analysis examining modifiers, synonym-based analysis using synonym dictionaries, value-based analysis looking at associated values) to infer type classifications, changing and combining multiple parameters to achieve both efficiency and accuracy in automated processing
3Measurement precision
If multiple analysis techniques are used to infer type classifications, then classification accuracy improves through comprehensive analysis, but system complexity increases
Solution Approach 1:
The patent segments the complex classification task into four distinct analysis modules: name-based analysis (comparing terms to type indicators), context-based analysis (examining modifiers and surrounding text), synonym-based analysis (using synonym dictionaries), and value-based analysis (looking at associated values). Each module handles a specific aspect of classification, making the overall complex system manageable through functional segmentation
Solution Approach 2:
The system achieves universality by designing a multi-functional analysis framework that can handle various types of terms and classification scenarios using the same four analysis techniques. The system is versatile enough to process different domain-specific texts while maintaining consistent classification accuracy through its comprehensive analytical approach
4Extent of automation
If automated type classification systems are deployed, then manual processing requirements are reduced, but the need for sophisticated analysis algorithms and processing power increases
Solution Approach 1:
The system achieves high-level automation by enabling self-service type classification inference, where the system automatically performs all four analysis techniques (name-based, context-based, synonym-based, value-based) and determines type classifications without human intervention, maximizing automation while managing algorithmic complexity through modular design
Data Source
AI summary
A device may obtain text to be processed to infer type classifications associated with terms in the text. The type classifications may indicate types of values that the terms are intended to represent. The device may infer type classifications corresponding to terms in the text by performing a type classification technique. The type classification technique may include a name-based analysis, a context-based analysis a synonym-based analysis, or a valued-based analysis. These analyses may compare information, associated with the terms in the text, to type indicators that indicate the type classifications. The device may provide information that identifies a type relationship between a particular type classification and a particular term based on inferring the one or more type classifications.