US Patent Issued to Fair Isaac on Oct. 14 for "Machine learning uncertainty quantification and modification" (California Inventors)

ALEXANDRIA, Va., Oct. 21 -- United States Patent no. 12,443,843, issued on Oct. 14, was assigned to Fair Isaac Corp. (Minneapolis).

"Machine learning uncertainty quantification and modification" was invented by Scott Michael Zoldi (San Diego), Jeremy Mamer Schmitt (Encinitas, Calif.) and Maria Edna Derderian (San Diego).

According to the abstract* released by the U.S. Patent & Trademark Office: "Computer-implemented machines, systems and methods for providing insights about uncertainty of a machine learning model. A method includes determining an uncertainty value associated with a first machine learning model output of a first machine learning model. The method further includes generating a confidence interval for the first machine learn...