This inventor holds 3 USPTO granted patents. Top assignee: General Electric Company. Active years: 2018-2020.
Company Filing History:
Years Active: 2018-2020
Title: Shaddy Abado: Innovator in Industrial Data Processing
Introduction
Shaddy Abado is a notable inventor based in Lisle, IL (US). He has made significant contributions to the field of industrial data processing, holding a total of 3 patents. His work focuses on enhancing the efficiency and accuracy of data analysis in industrial settings.
Latest Patents
Abado's latest patents include an "Apparatus and method for screening data for kernel regression model building." This invention involves receiving raw data from industrial machines equipped with sensors. The data is processed at a central processing center, where an unsupervised kernel-based algorithm is applied to learn characteristics of the data. This method effectively determines a class of acceptable data, ensuring that the data reflects healthy machine operation.
Another significant patent is the "Apparatus and method for tag mapping with industrial machines." This system associates equipment sensor tags with analytic program tags. It normalizes equipment and analytic tag descriptions to create matrices that enhance the association between the tags and their respective content. This innovation streamlines the process of data analysis in industrial environments.
Career Highlights
Shaddy Abado is currently employed at General Electric Company, where he continues to develop innovative solutions for industrial data processing. His expertise in this area has positioned him as a valuable asset to his team and the company.
Collaborations
Abado collaborates with talented coworkers, including Abhinav Saxena and Charmin Patel. Together, they work on advancing technologies that improve industrial data management and analysis.
Conclusion
Shaddy Abado's contributions to industrial data processing through his patents and work at General Electric Company highlight his role as an influential inventor. His innovative approaches are paving the way for more efficient data analysis in industrial applications.
