17969862. MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS simplified abstract (Dell Products L.P.)

MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS

Organization Name

Dell Products L.P.

Inventor(s)

Saheli Saha of Kolkata (IN)

Prerit Jain of New Delhi (IN)

Ramakanth Kanagovi of Bengaluru (IN)

Prakash Sridharan of Bengaluru (IN)

MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17969862 titled 'MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS

Simplified Explanation

The apparatus described in the abstract utilizes machine learning models to generate a synthesized document based on search terms and keywords extracted from a set of documents. Here is a simplified explanation of the patent application:

• The processing device receives a request to generate a synthesized document with specific search terms.
• It extracts keywords from a set of documents using a machine learning model.
• Based on the similarity of the search terms and extracted keywords, it selects content for inclusion in the synthesized document.
• Another machine learning model is used to determine a set of terms for a second section of the document.
• Content for the second section is selected based on the similarity of the determined terms and extracted keywords from the set of documents.

Potential Applications

This technology could be applied in content generation, automated document summarization, and information retrieval systems.

Problems Solved

This technology helps in efficiently generating relevant content based on search terms and extracted keywords, saving time and effort in manual content curation.

Benefits

The benefits of this technology include improved document synthesis, enhanced search result relevance, and increased productivity in content creation tasks.

Potential Commercial Applications

Potential commercial applications of this technology include AI-powered content generation tools, automated report writing software, and intelligent search engines.

Possible Prior Art

One possible prior art for this technology could be existing machine learning models used for text summarization and keyword extraction in natural language processing applications.

How does this technology handle multi-language documents and search terms?

This article does not address how the apparatus deals with documents and search terms in multiple languages. It would be interesting to know if the machine learning models are language-agnostic or if they are trained on specific languages.

What is the scalability of this technology for processing a large volume of documents?

The scalability of the apparatus for handling a large number of documents and search terms is not discussed in this article. It would be valuable to understand the performance and efficiency of the system when dealing with a significant amount of data.

Original Abstract Submitted

An apparatus comprises a processing device configured to receive a request to generate a synthesized document comprising one or more search terms, and to extract, utilizing a first machine learning model, keywords from a set of documents. The processing device is also configured to select first content for inclusion in a first section of the synthesized document based on a similarity of the search terms and the extracted keywords from corresponding first sections of the set of documents, and to determine, utilizing a second machine learning model that takes as input the selected first content, a set of terms for a second section of the synthesized document. The processing device is further configured to select second content for inclusion in the second section of the synthesized document based on a similarity of the determined set of terms and the extracted keywords from corresponding sections of the set of documents.