Van Lindberg

Van Lindberg
Member, Python Software Foundation

Website | @VanL

Van Lindberg is an open source and intellectual property lawyer based out of San Antonio. Van’s professional work focuses on the intersection of technology and law, with particular expertise in the area of open source. Over his career, he has helped businesses with everything from open source compliance to business strategy and represents companies in high-stakes IP litigation and inter partes review proceedings before the Patent Trial and Appeal Board. Van has represented companies on Capitol Hill, before Congress, and in industry associations; has led teams through successful mergers and acquisitions and restructurings; and has organized employee agreements to create greater employee satisfaction and promote higher compliance with internal policies.

Van is a regular speaker on everything from community dynamics to graph theory and has testified in Congressional proceedings as an expert on both copyright and encryption policy. In 2012, he was named one of “America’s top 12 techiest attorneys” by the American Bar Association Journal. He is the author of Intellectual Property and Open Source.


IT Leaders Summit
Location: F 150
Van Lindberg (Python Software Foundation)
Average rating: ****.
(4.00, 3 ratings)
We are more than a decade into the widespread use of open source in business, but there is too much focus on the compliance only, and the "risks" of using open source code. This talk is about moving beyond compliance and making the positive case for using open source in business based on reduced cost, improved time-to-market, and yes, freedom. Read more.
Open Data
Location: F150
Tags: patents, nlp, graphs
Van Lindberg (Python Software Foundation)
Average rating: ****.
(4.00, 2 ratings)
Finding the right piece of "prior art" - technical documentation that described a patented piece of technology before the patent was filed - is like finding a needle in a very big haystack. This session will talk about making that process faster and more accurate through the use of natural language processing, graph theory, machine learning, and lots of Python. Read more.