Logical Inference Model for Unstructured Text Analysis
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Solution Overview
Problem
Machine learning algorithms and AI models struggle to effectively analyze unstructured text due to limitations in contextual awareness, handling of nuances in human language, and ambiguity, which hinders their ability to generate meaningful insights from complex text data.
Innovation Solution
The implementation of an automated reasoning via natural intelligence (ARNI) system that employs a holistic logical inference model. This system applies contextual analysis and phrase recognition, combining induction heuristics with deductive techniques to generate meaning from unstructured text, mimicking human logical reasoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning algorithms and AI models are used to analyze unstructured text, then automation and processing speed are improved, but contextual awareness and accuracy in understanding human language nuances deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the ML algorithm and the unstructured text data. This intermediary performs contextual analysis, phrase recognition, and semantic interpretation before passing processed information to the ML model, thereby maintaining both automation speed and contextual accuracy
Solution Approach 2:
The text analysis process is segmented into multiple stages: initial text processing, contextual analysis, phrase recognition, semantic interpretation, and final ML classification. Each segment handles specific aspects of language understanding, allowing the system to maintain high speed while improving accuracy at each stage
2Extent of automation
If machine learning models process unstructured text data, then automation extent is improved, but ability to handle language ambiguity and synonyms deteriorates
Solution Approach 1:
The system employs dynamic processing where the analysis depth and methods adapt based on the detected complexity of language nuances. When synonyms, ambiguity, or contextual dependencies are detected, the system dynamically adjusts its processing approach to handle these cases with specialized algorithms while maintaining automated operation
Solution Approach 2:
The patent changes key processing parameters based on the detected language characteristics. When ambiguity or nuanced synonyms are identified, the system adjusts parameters such as analysis depth, contextual window size, and interpretation thresholds to appropriately handle these challenging cases while maintaining automation
3Measurement precision
If contextual analysis and phrase recognition are applied to unstructured text, then meaning generation accuracy is improved, but processing complexity increases
Solution Approach 1:
The complex contextual analysis system is segmented into modular components: tokenization module, phrase recognition module, contextual analysis module, and meaning generation module. Each module performs a specific function and can be independently optimized, maintaining high accuracy while managing system complexity through modular architecture
Data Source
AI summary
Disclosed herein are systems, methods, and computer-readable media for a holistic logical inference model. Unstructured data, which can include text, is received at an automated reasoning via natural intelligence (ARNI) system. A logical inference model is applied to at least one or more portions of the unstructured data. Meaning is generated from the at least one or more portions of the unstructured data based on an induction heuristic model.