The Library of Babel for Prior Art: Using Artificial Intelligence to Mass Produce Prior Art in Patent Law

Artificial intelligence is playing an increasingly important role in the invention and innovation processes of our society. To date, though, much of the academic discussion on the interaction of artificial intelligence and the patent system focuses on the patentability of inventions produced by artificial intelligence. Little attention has been paid to organizations that are seeking to use artificial intelligence to defeat the patentability of otherwise patent-worthy inventions by mass producing prior art. This Note seeks to highlight the consequences of allowing mass-produced, AI-generated prior art to render valuable inventions unpatentable. Specifically, this Note concludes that AI-generated prior art decreases the incentive for researchers to disclose valuable knowledge through the patent system without providing an adequate substitute source of such knowledge. This Note also examines a number of patent law doctrines that should, but likely will not, prevent deficient AI-generated prior art from rendering valuable inventions unpatentable. To resolve these issues, this Note proposes a solution that modifies the current novelty inquiry and breathes new life into the patent law doctrine of conception. This solution advances the patent system’s purpose of promoting technological advancement while still allowing artificial intelligence to play a large role in that technological advancement

Automated Generation of Timestamped Patent Abstracts at Scale to Outsmart Patent-Trolls

The fundamental idea of patent systems is to protect inventors who have invested resources during the development of their invention. Patent trolls abuse these systems by filing obvious patents with significantly less cost and usually without the intention to produce or offer the invention. Instead, patent trolls sue other companies that allegedly violate their obvious patents. We propose a method that challenges patent trolls by generating large amounts of obvious patent abstracts automatically. In contrast to prior art, our approach generates abstracts for any patent category and achieves high diversity in content and structure of the resulting abstracts. Furthermore, we timestamp the generated abstracts using a decentralized timestamping service so that users can prove that a generated abstract existed at a certain point in time. In a survey, we found that the quality of the generated abstracts, using criteria defined by the European Patent Office, was 6% higher compared to prior art.publishe