This inventor holds 2 USPTO granted patents and 3 published patent applications. Top assignees: E.i. Dupont De Nemours and Company, Nodality, Inc.. Active years: 2008-2012.
Company Filing History:
Years Active: 2008-2012
Title: Herbert Alan Holyst: Innovator in Multidimensional Data Representation
Introduction
Herbert Alan Holyst is a notable inventor based in Morton, PA (US). He has made significant contributions to the field of data representation, holding a total of 2 patents. His work focuses on innovative methods for analyzing and visualizing complex data sets.
Latest Patents
Holyst's latest patents include a "Method and apparatus for representing multidimensional data." This invention relates to techniques for effectively representing multidimensional data, particularly in a manner that facilitates the comparison and differentiation of various data sets. One application of this invention is in the representation of flow cytometric data. Additionally, he has developed a "Method of discovering patterns in symbol sequences." This method involves creating a master offset table for two sequences of symbols, allowing for the identification of patterns based on the differences in the positions of symbols across the sequences. Both patents also include provisions for computer-readable media that enable the implementation of these methods.
Career Highlights
Throughout his career, Holyst has worked with prominent companies such as E.I. DuPont De Nemours and Company and Nodality, Inc. His experience in these organizations has contributed to his expertise in data representation and analysis.
Collaborations
Some of Holyst's notable coworkers include Allan Robert Moser and Wade Thomas Rogers. Their collaboration has likely enriched the innovative processes and projects they have undertaken together.
Conclusion
Herbert Alan Holyst's contributions to the field of data representation through his patents demonstrate his innovative spirit and commitment to advancing technology. His work continues to influence how multidimensional data is analyzed and understood.
