Patents are key to data sharing---that unlocks innovation. The alternative is secrets, monopolies and no investor confidence.

D. Need of Patent Rights for Machine Learning Inventions in the Era of Big Data

The current system, on its surface, does not provide adequate encouragement for data sharing. If anything, companies have strong incentives to avoid disclosure of their training data, machine learning model, and output data.

Despite these concerns, data reuse may enable social impacts and advances that would not be otherwise possible. Previous studies have pointed out that one of the major barriers preventing advances in machine learning is the lack of data sharing between institutions and industries. 85 Data scientists have demonstrated that they were able to predict flu trends with data extracted from Twitter. 86 Foursquare’s location database provides Uber with the requisite data to pinpoint the location of users based on venue names instead of addresses.87 Information about fraudulent Twitter accounts may enable early detection of financial frauds.88 The possibilities that cross-industry data sharing may bring are endless.

To encourage free sharing of data, companies should have a reliable method of protecting their investments in machine learning. At the same time, protection based on non-disclosure of data would defeat of purpose of promoting data sharing. Hence protection over computation methods involved with machine learning maintains the delicate balance between promoting data sharing and protecting innovation.

Protection over inventions in the machine learning algorithm provides one additional merit other than allowing data sharing and avoiding the sort of excessive protection that leads to a competitor-free road and data network effects. It incentivizes innovators to focus on the core technological blocks to the advancement of technology, and encourages disclosure of such know-how to the machine learning community.

Then what are the key obstacles in obtaining patents in machine learning inventions? While there are arguments that the definiteness requirement of patent law is the primary hurdle against patent protection of machine learning models due to reliance on subjective judgment, there is no evidence that the underlying inventions driving big data faces the same challenge. 89 Definiteness may be overcome by providing reasonable certainty for those skilled in the art of defining what the scope of the invention is at the time of filing.90 There is no inherent reason why specific solutions for data cleaning, enhancement of computation efficiency, and similar inventions would be deemed indefinite by nature.

Since the United States Supreme Court invalidated a patent on computer implemented financial transaction methods in the 2014 Alice decision, the validity of numerous software and business method patents were challenged under 35 U.S.C. §101.91 As of June 8th, 2016, federal district courts invalidated 163 of the 247 patents that were considered under patentable subject matter—striking down 66% of challenged patents.92 The U.S. Court of Appeals for the Federal Circuit invalidated 38 of the 40 cases it heard.93

Arguably, the public benefits more from such high rates of post-issuance invalidity. The public still has access to the disclosures from the patents and patent applications. In reliance on granted patents, companies may have already invested in growing related businesses, catering to the need of consumers. At the same time, the patent holder’s monopolistic rights have been shortened as the result of litigation. Effectively, the price that the public pays to inventors in exchange for the benefits of disclosure is reduced.

Yet the high degree of invalidity raises several concerns for the software industry. Smaller entities, lacking market influence and capital, have difficulty competing against established corporations without the monopolistic rights granted through the patent system. Investors become hesitant to infuse capital into startups for fear that invalidity decreases the worth of patents. Reliance on trade secret has its own limitations due to the disclosure dilemma—the inventor needs to disclose the secret to lure inventors, but risks losing secrecy in the process. Copyright law does not provide appropriate protection. The restrictions imposed by the merger doctrine and scènes à faire doctrine constrain copyright protection of software. Though copyright provides an alternative method of protecting literal copying of code, it does little to protect the underlying software algorithms and innovation.

Ultimately, the increase of alliances and collaboration provides incentives for parties to obtain patent rights. Reliance on trade secret or copyright are not suitable methods of protecting their intellectual property. Furthermore, market power or network effects alone cannot sufficiently mitigate the risks involved with operating a business. Patents become even more important for startups since patents provide investors with assurance that in the worst case, the patents may still serve as potential collateral.