18065621. TRANSMFORMING TABLE-TO-TEXT USING AGGLOMERATIVE CLUSTERING simplified abstract (International Business Machines Corporation)

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TRANSMFORMING TABLE-TO-TEXT USING AGGLOMERATIVE CLUSTERING

Organization Name

International Business Machines Corporation

Inventor(s)

Kunal Sawarkar of Franklin Park NJ (US)

TRANSMFORMING TABLE-TO-TEXT USING AGGLOMERATIVE CLUSTERING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18065621 titled 'TRANSMFORMING TABLE-TO-TEXT USING AGGLOMERATIVE CLUSTERING

The abstract describes a method and system for generating inferential text based on structured hierarchical or multidimensional tables in a particular domain using a machine learning model.

  • Ingesting a data set with structured tables
  • Processing the data set with a machine learning model
  • Generating inferential natural language text
  • Outputting the text in a sequence format

Potential Applications: - Automated report generation - Data analysis in various industries - Natural language processing tasks

Problems Solved: - Streamlining data interpretation - Enhancing data-driven decision-making processes

Benefits: - Increased efficiency in data analysis - Improved accuracy in generating textual insights - Automation of repetitive tasks

Commercial Applications: Title: "Automated Text Generation System for Data Analysis" This technology can be utilized in industries such as finance, healthcare, and marketing for automated report generation, trend analysis, and customer insights.

Prior Art: Researchers in the field of natural language processing have explored similar methods for generating text from structured data sets using machine learning models. Further investigation can be done in academic journals and patent databases.

Frequently Updated Research: Stay updated on advancements in natural language processing, machine learning, and data analysis techniques to enhance the performance of the inferential text generation system.

Questions about the technology: 1. How does this method differ from traditional text generation techniques? 2. What are the potential limitations of using machine learning models for generating inferential text?


Original Abstract Submitted

A method and system of generating inferential text are provided. The method includes ingesting a data set that includes at least one structured hierarchical or multidimensional table for a particular domain. The method includes processing the ingested data set that includes the at least one structured hierarchical or multidimensional table for the particular domain by applying a generated machine learning model. The method includes generating inferential natural language text based on applying the machine learning model. The method includes outputting the generated inferential natural language text in a sequence format.

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