18068022. SMART IDENTIFICATION OF INDICATOR TEXT WITH FULL-TEXT SEARCH OR OPTIMIZED DOCUMENT ANALYSIS simplified abstract (International Business Machines Corporation)
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 18068022 titled 'SMART IDENTIFICATION OF INDICATOR TEXT WITH FULL-TEXT SEARCH OR OPTIMIZED DOCUMENT ANALYSIS
Simplified Explanation: The patent application focuses on optimizing the analysis of unstructured documents by using a document system with a full-text index to identify documents containing specific data elements.
Key Features and Innovation:
- Utilizing a document system with a full-text index for analyzing unstructured content.
- Selecting a sample of documents to identify data elements and determine indicator context expressions.
- Generating queries for searching the full-text index using indicator context expressions to find compliant documents.
Potential Applications: This technology can be applied in various industries such as legal, healthcare, and finance for efficient document analysis and information retrieval.
Problems Solved: This technology addresses the challenges of analyzing unstructured documents and extracting specific data elements accurately and efficiently.
Benefits:
- Improved document analysis and information retrieval.
- Enhanced efficiency in identifying specific data elements within unstructured content.
- Streamlined search processes for finding relevant documents.
Commercial Applications: The technology can be used in document management systems, information retrieval software, and data analysis tools to enhance productivity and accuracy in document analysis tasks.
Prior Art: Researchers can explore prior art related to document analysis, information retrieval, and data extraction techniques to understand the existing technologies in this field.
Frequently Updated Research: Stay updated on the latest advancements in document analysis, natural language processing, and information retrieval to enhance the capabilities of this technology.
Questions about Unstructured Document Analysis: 1. How does this technology improve the efficiency of document analysis tasks? 2. What are the potential challenges in implementing this technology in different industries?
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.
