Automatic Discovery of Prior Art: Big Data to the Rescue of the Patent System

23 Pages Posted: 19 May 2017

See all articles by Amir H. Khoury

Amir H. Khoury

Tel Aviv University - Buchmann Faculty of Law

Ron Bekkerman

Cherre Inc

Date Written: April 2, 2016

Abstract

In this research, we offer a fresh approach as to determining prior art. We do this by using Big Data methods. More specifically, we apply a model which constructs the semantic space of patents, in which all published patents and patent applications are arranged according to semantic similarities between each other. Our model provides a clear indication of how closely patents stand in relation to existing technologies, which we refer to as Near Inventions (“NI”). Our model exposes a certain level of deficiency when it comes to the disclosure, by patent applicants, of NIs. One conclusion which we draw from this approach is that there is no consistency among applicants when it comes to citing NIs. Another conclusion is that the more “densely populated” the semantic neighborhood of an invention is, the more rigorous the examination needs to be regarding its patentability.

Keywords: Patents, Big Data, Determining Prior Art, Semantic Space of Patents, Proximity of Patents to Existing Technology, Near Inventions, Patent Examination, Intellectual Property, Patent Disclosure

Suggested Citation

Khoury, Amir H. and Bekkerman, Ron, Automatic Discovery of Prior Art: Big Data to the Rescue of the Patent System (April 2, 2016). 16 J. MARSHALL REV. INTELL. PROP. L. 44 (2016), Available at SSRN: https://ssrn.com/abstract=2970693

Amir H. Khoury (Contact Author)

Tel Aviv University - Buchmann Faculty of Law ( email )

Ramat Aviv
Tel Aviv, 69978
Israel
(972)-36405263 (Phone)
(972)-3640 5349 (Fax)

Ron Bekkerman

Cherre Inc

575 8th Ave
New York, NY 10018
United States

HOME PAGE: http://cherre.com/

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