International business machines corporation (20240202358). SMART IDENTIFICATION OF INDICATOR TEXT WITH FULL-TEXT SEARCH OR OPTIMIZED DOCUMENT ANALYSIS simplified abstract
Contents
SMART IDENTIFICATION OF INDICATOR TEXT WITH FULL-TEXT SEARCH OR OPTIMIZED DOCUMENT ANALYSIS
Organization Name
international business machines corporation
Inventor(s)
Thomas Hampp-bahnmueller of Stuttgart (DE)
Michael Baessler of Bempflingen (DE)
Yannick Saillet of Stuttgart (DE)
SMART IDENTIFICATION OF INDICATOR TEXT WITH FULL-TEXT SEARCH OR OPTIMIZED DOCUMENT ANALYSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202358 titled 'SMART IDENTIFICATION OF INDICATOR TEXT WITH FULL-TEXT SEARCH OR OPTIMIZED DOCUMENT ANALYSIS
The abstract describes a method for optimizing unstructured document analysis by operating a document system with a full-text index, identifying documents with specific data elements, selecting a sample of documents, determining data elements in the sample, creating an indicator context expression, generating a search query using the indicator context expression, and identifying compliant documents in the system.
- Simplified Explanation:
The patent application outlines a process to improve the analysis of unstructured documents by efficiently identifying and retrieving relevant information based on specific data elements.
- Key Features and Innovation:
- Utilizing a full-text index in a document system - Identifying documents with specific data elements - Creating indicator context expressions for data elements - Generating search queries for efficient retrieval of information
- Potential Applications:
- Information retrieval systems - Data mining and analysis tools - Document management software
- Problems Solved:
- Enhancing the efficiency of unstructured document analysis - Improving information retrieval accuracy - Streamlining data mining processes
- Benefits:
- Increased productivity in analyzing large volumes of unstructured data - Enhanced accuracy in retrieving relevant information - Improved decision-making based on extracted data
- Commercial Applications:
Optimizing unstructured document analysis technology can be applied in various industries such as legal, healthcare, finance, and research for efficient data extraction and analysis.
- Prior Art:
Researchers can explore existing patents and publications related to document analysis, data mining, and information retrieval systems to understand the prior art in this field.
- Frequently Updated Research:
Stay updated on advancements in natural language processing, machine learning, and artificial intelligence to enhance the capabilities of unstructured document analysis technology.
Questions about unstructured document analysis: 1. How does this technology improve the efficiency of data extraction from unstructured documents? 2. What are the potential challenges in implementing this method in real-world applications?
Original Abstract Submitted
several aspects for optimizing unstructured document analysis comprise operating a document system, where the document system comprises a plurality of documents comprising unstructured content and a full-text index; receiving a request to identify documents comprising a type of data elements; selecting a sample out of the plurality of documents; determining data elements of the type in the sample of documents; determining an indicator context expression for the type of data elements out of the determined data elements of the type; determining a query for searching, using a search engine, the full-text index using the indicator context expression; and determining the documents in the document system being compliant to the determined query.
