What Does Big Data Mean for Intellectual Property Protection?Publication Date: October 10, 2014 3 minute read
According to Business News Daily, the “Big Data industry is expected to grow by leaps and bounds in the next few years. A recent study revealed that the revenue from Big Data is expected to grow from its current $5 billion mark to more than $50 billion by 2017.”
Law professor Michael Mattioli with co-author Todd Vare examines the connection between big data and intellectual property law in “Big Business, Big Government and Big Legal Questions.” In this article, the authors explain how current intellectual property (IP) laws protect data and the associated methods of collecting, analyzing, and reusing data. They then question how—or if—IP law can adequately protect investments in big data.
Below are excerpts from “Big Business, Big Government and Big Legal Questions:”
To be effective, most big data advocates emphasise that the data must be shared, which includes the sharing of the means of creating, compiling and analysing such data. There are at least two major legal impediments to this advocated sharing, however: privacy and intellectual property. This article focuses on the nexus of intellectual property and big data.
How Intellectual Property Protects Data Generally
The patent system has historically been a poor protector of data and data processing, and recent case law suggests that it’s even poorer today. Data per se is not patentable.
What about data compilations that are somehow manipulated, analysed and reused in innovative ways – the very epitome of big data? The case law suggests that merely assembling, organizing or manipulating data is not itself eligible for patenting.
Data itself is not copyrightable. Thus, even where the data compilation is copyrightable, one can extract individual datums from the compilation without violating the copyright laws. However, one cannot copy the entire database, since this would involve copying the entire protection expression (i.e., the compilation) provided it is selected, coordinated, and arranged in a creative, original way.
The definition of “trade secret” would include data, data compilations and processes that compile, organize, manipulate or analyse data. The critical components to demonstrating that data, data compilations, or data processing are protectable trade secrets are that (a) there is economic value and (b) there are reasonable efforts to keep the subject matter secret.
Big data and big data processing likely fits well within the expansive definition of trade secret law.
Can Big Data Thrive Under Today’s IP Legal Framework?
The widespread excitement that big data has inspired should be tempered by the fact that secrecy, rather than disclosure, is the most powerful legal tool to protect many investments in this new arena. Data cannot be reused meaningfully on a large scale, after all, if there is insufficient information describing its provenance and pedigree.
Some might conclude that, unless our intellectual property system is somehow modified, the grand vision of big data will never be realised. A primary goal of our IP system is to incentivise technological disclosures, after all, and big data is currently channelled toward secrecy.
Read the full article, Big Business, Big Government and Big Legal Questions (Managing Intellectual Property, September 26, 2014) [Note: A subscription is required to read this article.]
Michael Mattioli is an Associate Professor of Law at the Indiana University Maurer School of Law. He teaches and writes on intellectual property, with a special emphasis on patents, and on contract law. Professor Mattioli's research examines how new forms of knowledge-sharing and collective licensing influence patenting, industrial organization, and the process of innovation itself.
Professor Mattioli is the author of “Disclosing Big Data,” forthcoming in the Minnesota Law Review, volume 99, issue 2.
About Michael Mattioli
Michael Mattioli is an Associate Professor of Law at the Indiana University Maurer School of Law. He teaches and writes on intellectual property, with a special emphasis on patents, and on contract law. Professor Mattioli's research examines how new forms of knowledge-sharing and collective licensing influence patenting, industrial organization, and the process of innovation itself. He is also an active affiliated faculty member of the Center for Intellectual Property Research.