Indexing Engine for Unstructured Text Segmentation
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Solution Overview
Problem
Conventional systems fail to provide structured or unstructured text results directly usable for decision-making, as they lack efficient indexing and flexible querying capabilities, especially for large organizations with vast amounts of unstructured or semi-structured data.
Innovation Solution
A system comprising an indexing engine that identifies and encodes regions within documents, a querying engine that evaluates constraints based on linguistic structures and taxonomies, and an output engine that formats results interactively, allowing users to query and display relevant information effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional search engines are used to prune down relevant documents, then the number of relevant documents is reduced, but the results remain in unstructured format that cannot be used directly for decision making
Solution Approach 1:
The patent segments unstructured text into structured regions (headers, paragraphs, lists, tables) and extracts specific data elements from each region. This segmentation transforms raw unstructured text into organized, decision-ready information while preserving the original document structure for reference.
Solution Approach 2:
The patent introduces an intermediary extraction layer between the search engine and the user. This intermediary processes search results by extracting and structuring key information, converting unstructured search results into a format suitable for decision-making without losing the connection to original documents.
2Productivity
If information extraction systems are used without an index, then extraction can be performed, but fast interactive querying cannot be provided
Solution Approach 1:
The patent performs preliminary indexing of text regions and data elements before querying occurs. By pre-organizing the unstructured text into indexed regions with metadata during the extraction phase, the system enables fast interactive querying without requiring complex architecture during the querying phase.
Solution Approach 2:
The patent merges the indexing function with the information extraction process. Instead of separate indexing and extraction systems, the patent combines them into a unified approach where extraction inherently creates the index structure, simplifying the overall system architecture while maintaining fast querying capability.
3Quantity of substance
If conventional systems are used to handle vast amounts of unstructured text, then all information is available, but it becomes hard to read and use
Solution Approach 1:
The patent extracts only the most relevant and decision-critical information from vast amounts of unstructured text. By taking out key data elements, statistics, and conclusions while leaving the bulk of raw text behind, the system maintains information availability while dramatically improving readability and usability.
Solution Approach 2:
The patent applies different processing qualities to different parts of the text. Critical decision-making information receives structured extraction and formatting, while less important text remains in its original form or is summarized. This local quality approach optimizes readability for the most important information while preserving the completeness of the source material.
Data Source
AI summary
A system for indexing unstructured or semi-structured data is disclosed. The system may identify regions within the data, such as “Abstract” or “References”. The system may identify linguistic units such as sentences, noun groups, verb groups. The system may also identify concepts such as companies, people, diseases, amounts, and so forth. The query results may be formatted so that similar results from different documents, or from the same document, are clustered together.