US Patent Issued to First Principles AI on Feb. 17 for "Computationally efficient framework for sequence-to-sequence modeling and reinforcement learning with deep history" (Washington Inventor)

ALEXANDRIA, Va., Feb. 17 -- United States Patent no. 12,554,995, issued on Feb. 17, was assigned to First Principles AI Inc. (Auburn, Wash.).

"Computationally efficient framework for sequence-to-sequence modeling and reinforcement learning with deep history" was invented by Jason Michael Nett (Auburn, Wash.).

According to the abstract* released by the U.S. Patent & Trademark Office: "Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and utilizing a tree-based machine learning model to perform sequence modeling tasks. In one aspect, a method comprises: obtaining a collection of training examples, wherein each training example comprises a sequence of data elements; using the coll...