Mountain View, CA, United States of America

David Paul Woodruff

USPTO Granted Patents = 22 

Average Co-Inventor Count = 1.7

ph-index = 3

Forward Citations = 30(Granted Patents)


Company Filing History:


Years Active: 2012-2021

where 'Filed Patents' based on already Granted Patents

22 patents (USPTO):

Title: The Innovations of David Paul Woodruff

Introduction: David Paul Woodruff, based in Mountain View, California, is a prolific inventor with an impressive portfolio of 22 patents. His work primarily focuses on advanced data representation techniques that have significant implications in semantic document analysis.

Latest Patents: One of Woodruff's notable recent patents is centered on the lower-dimensional subspace approximation of a dataset. This invention outlines a method for determining a lower-dimensional representation of datasets, which enables improved semantic document analysis. The process involves analyzing a matrix of input data points that record term frequency within a set of documents. Through complex sampling techniques based on rank parameters and accuracy tolerances, Woodruff's method produces a compressed matrix that facilitates the calculation of similarity metrics. This innovation allows for effective clustering of documents based on their similarity, enhancing data organization and retrieval strategies.

Career Highlights: Woodruff has had a distinguished career, holding positions at esteemed organizations such as IBM and Iowa State University. His tenure in these institutions has allowed him to contribute significantly to innovations in data processing and representation.

Collaborations: Throughout his career, Woodruff has collaborated with notable colleagues, including Kenneth Lee Clarkson and Srikanta Tirthapura. Their combined expertise has furthered the development of methodologies that enhance data analysis capabilities.

Conclusion: David Paul Woodruff's contributions to the field of data representation and analysis are profound, as evidenced by his numerous patents and collaborations. His work continues to push the boundaries of innovation, illustrating the importance of interdisciplinary efforts in tackling complex problems in technology and data science.

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