Prompt-Guided Data Extraction From Segmented Unstructured Text
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Solution Overview
Problem
Extracting structured data from unstructured text is challenging due to variability and complexity, often resulting in incomplete, inconsistent, and ambiguous information, and poses significant computational challenges.
Innovation Solution
A computer-implemented method using a processor to receive text data, segment it into categories, generate prompts for field extraction, validate the extracted values, and route them to appropriate databases or reviewer platforms based on category-specific rules using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text processing methods are used to extract structured data from unstructured text, then the process is simple to implement, but the extraction accuracy is low and data quality is poor (incomplete, inconsistent, and ambiguous)
Solution Approach 1:
The patent segments the unstructured text into multiple portions and processes each portion separately through machine learning models. This segmentation approach improves extraction accuracy by focusing on specific data types in each portion while managing complexity through modular processing of distinct text segments.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the unstructured text and the structured data extraction process. These models act as mediators that transform unstructured text into structured data, improving accuracy while the modular architecture of multiple models handles different data types independently to manage overall system complexity.
2Measurement precision
If machine learning models are used to improve data extraction accuracy, then extraction quality improves, but computational challenges and processing time increase significantly
Solution Approach 1:
The patent divides the text processing task into multiple segments handled by different machine learning models. Each model processes a specific portion of text for particular data types, which improves overall extraction accuracy while reducing the computational burden on any single model, thereby optimizing processing time.
Solution Approach 2:
The patent applies machine learning models selectively to specific portions of text that contain particular data types, rather than applying a single comprehensive model to the entire text. This partial action approach maintains high extraction accuracy for target data while reducing overall processing time by avoiding unnecessary computation on irrelevant text segments.
3Loss of information
If comprehensive data extraction is performed on all text portions, then data completeness improves, but computational resources and processing complexity increase
Solution Approach 1:
The patent segments text and applies specialized machine learning models to each segment based on the data types present. This ensures comprehensive extraction of relevant information in each segment while maintaining system complexity at manageable levels through modular, targeted processing rather than monolithic comprehensive analysis.
Solution Approach 2:
The patent creates a multi-functional processing system where multiple machine learning models handle different data types across different text portions. Each model is specialized for specific data extraction tasks, achieving comprehensive data completeness through the collective capability of multiple specialized components rather than a single complex system.
Data Source
AI summary
This disclosure describes techniques for automated data extraction, validation, and routing based on unstructured text data. In some cases, the techniques described herein include receiving text data, segmenting the text data into multiple segments, assigning each segment to a category, generating a prompt for each segment based on the segment's category, extracting field values from each segment using the generated prompt, validating or rejecting the extracted field values based on category-specific validation rules, and routing the validated field values to category-specific target databases and/or reviewer platforms based on the validation results.