Amster Rothstein and Ebenstein, LLP - Intellectual Property Law

Key Takeaways from the AUTM 2020 Software Course

November 3, 2020
Author(s): Charles R. Macedo

Amster, Rothstein & Ebenstein was honored to participate and sponsor the AUTM 2020 Software Course. Our firm has been (and continues to be) a longtime supporter of AUTM.


The five sessions of this year’s course focused on different aspects of issues facing tech transfer offices (TTOs) in developing, protecting and monetizing software and data rights.

Session 1: Open Source Software

In the first session, Drew Bennet from University of Michigan, George Chellapa from University of Chicago and Chris Ghere from University of Minnesota focused on open source software issues, and provided insights on such useful topics as:

For an interesting example of where failing to comply with open source restrictions went awry in a case from 2008 that still is relevant today, see partner Charley Macedo’s Oxford University Press article, Copying of Open Source Software in Violation of Artistic License Was Not Licensed, which was published in the Journal of Intellectual Property Law and Practice.
Session 2: Patenting Software Current Insights

In the second session, Cindy Chepanoske from Carnegie Mellon University, headed up a panel consisting of Christina McDonough (also from Carnegie Mellon) and David Bailey from the University of Michigan, as well as Imelda Oropeza from Stanford University and Louisa Salomon from Johns Hopkins Technology Ventures. 

This panel offered insights into patenting artificial intelligence and machine learning technologies, and discussed a real world example of a portfolio selected to test the edges of patenting bioinformatics, as well as how TTOs can navigate with stakeholders in making at times difficult decisions on how to proceed (or not to proceed) with software related inventions.

Check out a courtesy copy of our Practical Law white paper, “Protecting Artificial Intelligence Innovations as Intellectual Property: Opportunities and Pitfalls,” (last updated in September 2020), in which we explain the basic components of machine learning solutions, and the intellectual property protections and challenges that are available (or not) for each component.

Session 3: Licensing Models

In the third session, Drew Bennet from the University of Michigan, Laura Dorsey from University of Washington and Andrew Morrow from University of Minnesota shared their insights on various options available for licensing software directly or through an app store.

For a useful checklist on key statutes and regulations that should be considered when launching a mobile application, see our white paper for Practical Law, “Mobile Device And Applications Key Laws Chart.”

Session 4:  Dealing with Data

In this fourth session, our panelists explored the concept of data as the new “oil.” Charley was honored to join this panel led by Dan Dardani from MIT, along with his colleague Myron Kassabara, Cindy Chepanoske from Carnegie Mellon University, and Dr. Dinesh Divakaran from Duke.  

The panel discussed:
For more on data issues for machine learning, check out ARE Law’s video recap from our panel at the Licensing Executive Society 2020 Annual Meeting.

Session 5: Roundtable Discussion
In this final session, Dr. Charles Williams from University of Washington led a roundtable discussion addressing open questions that arose from the first four sessions.  

Each of these sessions are available in AUTM’s Learning Center. 

If you have any questions or would like to further discuss any of the topics we covered this month, reach out to Charley at [email protected] and connect with him on LinkedIn

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