The patent process is representative of a nationwide means for innovations and new ideas to be recognized. The U.S. Patents Office, since its inception in 1790, has issued nearly five million patents. These patents span from the U.S. Patent #1, which was for an improvement \"in the making of Pot ash and Pearl ash by a new Apparatus and Process\" to today's patents which deal with technologies and mediums that were unimaginable at the Patent Offices' inception. The purpose of this study is to determine what social and economic factors at the federal level have the highest impact on national productivity measured by the number of patents applied for and/or granted each year. Using Machine Learning algorithms and predictive analysis on fifty years worth of data to determine what macroeconomic and educational factors have the most impact on patents. The first part of this study describes the methods and algorithms used during this research. The second part of this study discusses the results and what those results reveal about the impact of education and economic factors as they relate to national creativity / intellectual productivity. The goal of this study is to determine what factors affect national intellectual productivity in a given year. This data will be useful for governments, both local and federal, when faced with educational and economic issues.
","abstract_html":"<p>The patent process is representative of a nationwide means for innovations and new ideas to be recognized. The U.S. Patents Office, since its inception in 1790, has issued nearly five million patents. These patents span from the U.S. Patent #1, which was for an improvement "in the making of Pot ash and Pearl ash by a new Apparatus and Process" to today's patents which deal with technologies and mediums that were unimaginable at the Patent Offices' inception. The purpose of this study is to determine what social and economic factors at the federal level have the highest impact on national productivity measured by the number of patents applied for and/or granted each year. Using Machine Learning algorithms and predictive analysis on fifty years worth of data to determine what macroeconomic and educational factors have the most impact on patents. The first part of this study describes the methods and algorithms used during this research. The second part of this study discusses the results and what those results reveal about the impact of education and economic factors as they relate to national creativity / intellectual productivity. The goal of this study is to determine what factors affect national intellectual productivity in a given year. This data will be useful for governments, both local and federal, when faced with educational and economic issues.</p>","abstract_has_math":false,"creators":["Fazenbaker, Canon"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kayvan Najarian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-01-01T08:00:00Z","date_published":"2009-01-01T08:00:00Z","updated_at":"2026-07-24T05:54:11Z","subjects":["Intellectual Productivity Weka Machine Learning Educational Economic","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/642"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/642","href":"https://scholarscompass.vcu.edu/etd/642","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/80HS-8D87","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kayvan Najarian"]},{"key":"dc:creator","label":"Author","values":["Fazenbaker, Canon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-12-18T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Intellectual Productivity Weka Machine Learning Educational Economic","Computer Sciences","Physical Sciences and Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© The Author"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.25772/80HS-8D87","https://scholarscompass.vcu.edu/etd/642"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The patent process is representative of a nationwide means for innovations and new ideas to be recognized. The U.S. Patents Office, since its inception in 1790, has issued nearly five million patents. These patents span from the U.S. Patent #1, which was for an improvement \"in the making of Pot ash and Pearl ash by a new Apparatus and Process\" to today's patents which deal with technologies and mediums that were unimaginable at the Patent Offices' inception. The purpose of this study is to determine what social and economic factors at the federal level have the highest impact on national productivity measured by the number of patents applied for and/or granted each year. Using Machine Learning algorithms and predictive analysis on fifty years worth of data to determine what macroeconomic and educational factors have the most impact on patents. The first part of this study describes the methods and algorithms used during this research. The second part of this study discusses the results and what those results reveal about the impact of education and economic factors as they relate to national creativity / intellectual productivity. The goal of this study is to determine what factors affect national intellectual productivity in a given year. This data will be useful for governments, both local and federal, when faced with educational and economic issues.
"]},{"key":"dc:title","label":"Title","values":["EXPLORING IMPACT OF EDUCATIONAL AND ECONOMIC FACTORS ON NATIONAL INTELLECTUAL PRODUCTIVITY USING MACHINE LEARNING METHODS"]}]}],"canonical_facts":{"dc:contributor":["Kayvan Najarian"],"dc:creator":["Fazenbaker, Canon"],"dc:date.available":["2014-12-18T08:00:00Z"],"dc:description.abstract":["The patent process is representative of a nationwide means for innovations and new ideas to be recognized. The U.S. Patents Office, since its inception in 1790, has issued nearly five million patents. These patents span from the U.S. Patent #1, which was for an improvement \"in the making of Pot ash and Pearl ash by a new Apparatus and Process\" to today's patents which deal with technologies and mediums that were unimaginable at the Patent Offices' inception. The purpose of this study is to determine what social and economic factors at the federal level have the highest impact on national productivity measured by the number of patents applied for and/or granted each year. Using Machine Learning algorithms and predictive analysis on fifty years worth of data to determine what macroeconomic and educational factors have the most impact on patents. The first part of this study describes the methods and algorithms used during this research. The second part of this study discusses the results and what those results reveal about the impact of education and economic factors as they relate to national creativity / intellectual productivity. The goal of this study is to determine what factors affect national intellectual productivity in a given year. This data will be useful for governments, both local and federal, when faced with educational and economic issues.
"],"dc:identifier":["https://doi.org/10.25772/80HS-8D87","https://scholarscompass.vcu.edu/etd/642"],"dc:rights":["© The Author"],"dc:subject":["Intellectual Productivity Weka Machine Learning Educational Economic","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["EXPLORING IMPACT OF EDUCATIONAL AND ECONOMIC FACTORS ON NATIONAL INTELLECTUAL PRODUCTIVITY USING MACHINE LEARNING METHODS"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T05:54:11Z"}