Structured Text Mapping Using NLP Similarity and Categorization
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Solution Overview
Problem
Interacting with and analyzing large volumes of text data is time-consuming and prone to errors due to human biases and inconsistencies, particularly in domains like model-based systems engineering and litigation discovery, where conventional natural language processing techniques are limited in usability.
Innovation Solution
A text analysis system comprising an NLP layer with modules for syntax and verb usage, categorization, similarity analysis, and topic analysis, and an output module that generates human-consumable outputs, such as reports and annotations, to facilitate efficient mapping and organization of textual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual text analysis is performed by multiple individuals, then diverse perspectives and insights can be obtained, but analysis time increases and results become inconsistent due to varying perspectives and biases
Solution Approach 1:
The system creates a virtual copy of the text analysis process through AI agents. Instead of requiring multiple human analysts to review the same text, the system deploys multiple AI agents that can independently analyze the text data, each representing different analytical perspectives. These agents process the text simultaneously, eliminating the time consumption of human collaboration while maintaining diverse analytical approaches through the agents' different training frameworks and evaluation criteria.
Solution Approach 2:
The patent replaces the mechanical process of human text analysis with an automated AI-based system. Human analysts manually reading and interpreting text is substituted by AI agents that automatically process text data using natural language processing capabilities. This substitution eliminates the time variability and inconsistency inherent in human analysis while preserving analytical depth through the agents' sophisticated processing frameworks.
2Adaptability or versatility
If manual text analysis is performed, then flexibility in interpretation is maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The system incorporates feedback mechanisms where AI agents continuously refine their analysis based on evaluation criteria and can request clarification when uncertain. The agents receive feedback about their analysis accuracy and adjust their interpretations accordingly, maintaining high flexibility in analysis while operating at automated speed. This feedback loop ensures that the system adapts to nuanced text data without sacrificing efficiency for flexibility.
Solution Approach 2:
The system allows dynamic adjustment of analysis parameters such as evaluation criteria, focus areas, and processing depth. Users can modify these parameters to change the nature of analysis without retraining the underlying AI models. This parameter flexibility enables the system to adapt to different analytical needs while maintaining consistent, error-free processing speeds characteristic of automated systems.
3Productivity
If AI agents are used for text analysis, then analysis speed and consistency are improved, but system complexity increases
Solution Approach 1:
The system divides the text analysis function into separate AI agent instances, each responsible for specific analytical tasks. Instead of one complex monolithic system, the analysis is segmented into multiple independent agents that can process different aspects of the text simultaneously. This segmentation reduces the complexity of any single component while maintaining high overall productivity through parallel processing.
Solution Approach 2:
The AI agents are designed with universal capabilities to handle multiple types of text analysis tasks using a common framework. The same agent infrastructure can perform various analysis functions by adjusting evaluation criteria and processing parameters, rather than requiring separate specialized systems for each analytical task. This multi-functionality reduces overall system complexity while maintaining high productivity across different analysis types.
Data Source
AI summary
A system for facilitating text analysis is configurable to (i) receive input text data comprising a set of reference text and at least a first set of text, wherein the set of reference text and the first set of text each comprise structured components; process the input text data utilizing a syntax and verb usage module of a natural language processing (NLP) layer; generate a mapping of structured components of the first set of text to structured components of the set of reference text by processing output of the syntax and verb usage module utilizing a similarity analysis module or a categorization module of the NLP layer; and generate an output depicting one or more aspects of the mapping.