Charlotte, NC, United States of America

Patrick W Fink

USPTO Granted Patents = 19 

Average Co-Inventor Count = 3.5

ph-index = 3

Forward Citations = 58(Granted Patents)


Company Filing History:


Years Active: 2015-2020

where 'Filed Patents' based on already Granted Patents

19 patents (USPTO):

Patrick W Fink is a prolific inventor from Charlotte, North Carolina, who has made significant contributions to the field of machine learning and medical diagnostics. Mr. Fink has been awarded a total of 19 patents, covering a broad range of technologies.

One of Mr. Fink's latest patents is entitled "Generating training data for machine learning." This patent describes a computer-implemented method that involves generating training data to be used by a machine learning model. The method includes receiving a rule that includes at least one token and at least two dictionaries, one of which is domain-specific. For each token, the method randomly selects one or more words from the dictionaries to create a candidate statement that conforms to the rule. The candidate statement is then filtered based on a domain-specific model before being included in the training data.

Mr. Fink's other recent patent is entitled "Determining correlation between medical symptoms and environmental factors." This patent describes a method, processing device, and computer program product for identifying correlations between medical conditions and environmental factors. The method involves analyzing unstructured text to identify medical condition information for at least one subject, obtaining times and geographic locations corresponding to the medical condition occurrences, retrieving environmental information corresponding to the times and locations, and determining correlations between the medical condition information and the environmental information.

Mr. Fink works for International Business Machines Corporation, commonly known as IBM. He is part of a team of talented inventors that includes Philip E Parker and Kristin E McNeil. The team's work in machine learning and medical diagnostics has the potential to revolutionize these fields and improve patient outcomes.

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