No. 63: Reinforcement Learning Patents: A Transatlantic Review

Abstract

One of the most difficult problems with developing scalable artificial intelligence is tracking technical innovation. As such, this research will provide empirical data patents with legal claims to state of the art in AI technologies, reinforcement learning. A keystone architecture in the machine learning paradigm, reinforcement learning technologies power trading algorithms, driverless cars, and space satellites.

In competitive global markets, owning and protecting legal rights to reinforcement learning technologies may mean the difference between market dominance and irrelevance for the transatlantic firm. Aggregating patent data for reinforcement learning technologies from both the United States Patent and Trademark Office (USPTO) and the European Patent Office (EPO), this research offers a transatlantic perspective on reinforcement learning innovation and intellectual property protections.

Details

Author(s):
  • Brian Haney
Publish Date:
September 26, 2020
Publication Title:
TTLF Working Papers
Publisher:
Stanford Law School
Format:
Working Paper
Citation(s):
  • Brian Haney, Reinforcement Learning Patents: A Transatlantic Review, TTLF Working Papers No. 63, Stanford-Vienna Transatlantic Technology Law Forum (2020).
Related Organization(s):

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