Dell products l.p. (20240135088). MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS simplified abstract
Contents
- 1 MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the apparatus handle conflicting search terms and extracted keywords?
- 1.11 What is the accuracy rate of the machine learning models used in the apparatus?
- 1.12 Original Abstract Submitted
MACHINE LEARNING-BASED GENERATION OF SYNTHESIZED DOCUMENTS
Organization Name
Inventor(s)
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 20240135088 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. The document is divided into sections, with content selected based on the similarity of the search terms and keywords.
- The apparatus uses a processing device to receive a request for a synthesized document with search terms.
- It extracts keywords from a set of documents using a machine learning model.
- The apparatus selects content for the document based on the similarity of search terms and extracted keywords.
- A second machine learning model is used to determine a set of terms for another section of the document.
- Content for the second section is selected based on the determined set of terms and extracted keywords.
Potential Applications
The technology described in the patent application could be applied in various fields such as content generation, information retrieval, and document summarization.
Problems Solved
This technology helps in automating the process of synthesizing documents based on search terms, saving time and effort in manual content curation.
Benefits
The benefits of this technology include improved efficiency in document generation, accurate selection of relevant content, and enhanced user experience in information retrieval.
Potential Commercial Applications
The technology could be utilized in content creation platforms, search engines, and knowledge management systems to streamline document synthesis processes.
Possible Prior Art
One possible prior art for this technology could be existing document summarization algorithms that use machine learning models to extract key information from text.
Unanswered Questions
How does the apparatus handle conflicting search terms and extracted keywords?
The abstract does not specify how the apparatus resolves conflicts between search terms and extracted keywords when selecting content for the synthesized document.
What is the accuracy rate of the machine learning models used in the apparatus?
The abstract does not mention the accuracy rate of the machine learning models employed in the apparatus for extracting keywords and determining content for the synthesized document.
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.
