Carnegie Mellon University

AI & Patent Data Workshop @ JURIX 2020

To be held online on December 9, 2020

(9:00 AM - 5:00 PM EST, 6:00 AM - 2:00 PM PST, 3:00 PM - 11:00 PM CET)


Up To Date Program (with Microsoft Teams links)

Abbreviated Program

We invite paper submissions on research and tools involving AI processing of patent data. 

Patents are increasingly of interest to many industries beyond traditional technology sectors. Many companies, universities, and government entities own portfolios of hundreds or thousands of patents. Extensive amounts of patent data must be assessed when the a jurisdiction’s patent office examines pending patent applications, when judges preside over patent litigation, when firms decide what types of R&D they want to invest in, when firms decide what types of technology they want to license, when patent owners determine whether to sue competitors for patent infringement, and when companies assess how to avoid infringing patents.

There are millions of existing patents documents, and each year 300,000 new patents are granted in the U.S. alone. There are a wide variety of patent tasks that remain primarily manual, requiring patent agents and patent attorneys to filter and analyze huge amounts of data, thereby consuming valuable time, money, and other resources. Artificial intelligence (AI) has tremendous potential to facilitate the processing of patent documents and related patent data. The workshop addresses the use of AI techniques for patents. This includes the use of Machine Learning and Natural Language Processing in patent examination, extracting meaning and information from the text of patents, evaluating patent portfolios, patent litigation analytics, patent citation analysis, and evaluating patent licenses.

However, it is often not straightforward to automate these patent processing tasks, especially those which rely on reading and assessing the meaning of patents. The text of patents are a hybrid legal and technical document with a style distinct from documents such as statutes, judicial opinions, or contracts. Patents frequently contain idiosyncratic terms and language structure dictated by either the relevant scientific field or various legal requirements. This unique format often requires novel AI techniques tailored to the patent domain. The workshop is intended to popularize new methods for addressing longstanding problems in automating patent processing. Our goal is to build a stronger community of researchers exploring these methods, to find synergies among related approaches and alternatives, and to promote opportunities for collaboration.


Patents are legal documents in many ways are comparable to contracts, wills, and other legal instruments. Patents are typically written by attorneys who employ drafting strategies designed to effectuate various legal objectives. Patent claims are analogous to statutes; claims define the legal rights that the patent confers on its owner, and claim meaning is interpreted through adherence to judicially-created canons of construction. Patent claims provide precisely delineated rights that may be enforced against any infringer in the patent’s jurisdiction, whether or not infringement is intentional. Despite these similarities, patents are different from other legal documents, most notably because they mix technical description with legal drafting. Patents therefore require AI techniques customized to this domain.

There is rapidly-growing community of researchers who apply AI techniques to the patent field. The emergence of readily available AI tools has democratized much of the analysis that a decade ago would have required in-depth knowledge of computer programming or econometrics.

Workshop Format

Paper presentations, invited talks, and “moonshot” discussions describing the most pressing but unaddressed needs in AI processing of patent data.

Registration is free. Please register here.


Practitioners, legal scholars, economists, AI / NLP researchers, patent office representatives, industry software developers.

Programming Committee

Dean Alderucci, Carnegie Mellon University
Sean Tu, West Virginia University College of Law 

Important Dates

Submission deadline 9 November 2020
Notification of acceptance 16 November 2020
Camera ready paper 26 November 2020
Workshop 9 December 2020

Please email your submissions and requests for further information to Dean Alderucci