As part of the recent IIeX North America, I wrote a short article about the recent patent award granted by the USPTO for our approach to enhanced machine-learning. Our “system and method of classifier ranking for incorporation into enhanced machine learning” is colloquially referred to as CoderRank.
CoderRank is a big idea. CoderRank is to text analytics what Google’s PageRank has been to search. Just as Google said not all web pages are created equal, links on some pages rank higher than others, I argue that not all human coders are created equal; the accuracy of observations by some coders invariably rank higher than others. The major idea is that when training machines for text analysis, greater reliance should be placed on the specific inputs of those humans most likely to create a valid observation. I proposed a unique way to measure and rank humans on trust and knowledge vectors, and called it CoderRank. The U.S. Patent and Trademark Office agreed it was a novel approach to machine-learning and issued a patent March 1, 2016. Not bad for a political scientist.