International business machines corporation (20240177029). ADAPTABLE AND EXPLAINABLE APPLICATION MODERNIZATION DISPOSITION simplified abstract

ADAPTABLE AND EXPLAINABLE APPLICATION MODERNIZATION DISPOSITION

Organization Name

international business machines corporation

Inventor(s)

Anup Kalia of White Plains NY (US)

Mihir Choudhury of Jersey City NJ (US)

Jin Xiao of White Plains NY (US)

Divya Sankar of West Nyack NY (US)

John Rofrano of Mahopac NY (US)

Venkata Nagaraju Pavuluri of New Rochelle NY (US)

Lambert Pouguem Wassi of Yonkers NY (US)

Maja Vukovic of New York NY (US)

Michele Merler of New York City NY (US)

ADAPTABLE AND EXPLAINABLE APPLICATION MODERNIZATION DISPOSITION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240177029 titled 'ADAPTABLE AND EXPLAINABLE APPLICATION MODERNIZATION DISPOSITION

Simplified Explanation

The method described in the abstract involves extracting structured information from a natural language problem statement related to application modernization needs. This information is then used to generate standardized technical entities, business entities, and dispositions, which are further utilized to recommend dispositions for technical entities based on business constraints.

• Neural word segmentation method used to extract information from natural language problem statement
• Machine learning models employed to generate standardized technical entities, business entities, and dispositions
• Recommended dispositions provided based on business constraints and mentions of technical entities in the problem statement

Potential Applications

This technology could be applied in various industries where application modernization is required, such as software development, IT consulting, and digital transformation services.

Problems Solved

This technology helps in efficiently analyzing and addressing application modernization needs by automating the process of extracting and structuring information from natural language problem statements.

Benefits

- Streamlines the application modernization process - Improves accuracy and consistency in recommending dispositions for technical entities - Enhances decision-making based on standardized information extracted from natural language problem statements

Potential Commercial Applications

Optimizing application modernization processes in software development companies Enhancing IT consulting services by providing automated recommendations for application modernization needs

Possible Prior Art

There may be prior art related to natural language processing techniques for extracting structured information from problem statements in various domains.

How does this technology compare to existing manual methods of analyzing application modernization needs?

This article does not provide a direct comparison between the automated method described and traditional manual approaches in the application modernization domain.

What are the potential limitations or challenges of implementing this technology in real-world scenarios?

The article does not address the potential obstacles or constraints that may arise when implementing this technology in practical settings, such as data privacy concerns or compatibility issues with existing systems.

Original Abstract Submitted

a method includes receiving a natural language problem statement corresponding to application modernization needs of a user, the natural language problem statement including at least one technical entity, business constraint and disposition information; providing structured information by extracting information from the natural language problem statement using a neural word segmentation method; generating standardized technical entities, standardized business entities, and standardized dispositions by inputting the structured information to at least one machine learning model; and generating at least one recommended disposition of at least one technical entity to a second technical entity based at least on a business constraint corresponding to the natural language problem statement using the standardized technical entities, business entities, and dispositions. optionally, the at least one recommended disposition corresponds to one or more possible target environments along with explanation generated based on the business constraints and mentions of technical entities present in the natural language problem statement.