Research into the human microbiome has resulted in such unprecedented amounts of data that challenges related to both interpretation and management have emerged. Somewhat paradoxically, current statistical methods have made it such that it is more difficult and less likely to identify statistically significant results from large data sets. We see much potential for the expansion of intellectual property protection in the area of big data related to the microbiome, not only as a result of recent research advances and the desire to better and more efficiently handle big data, but also because of several recent Federal Circuit decisions in intellectual property law that provide some promising guidance for protecting software and computing information that may be required for managing and interpreting microbiome-related data. Moreover, the United States Patent and Trademark Office (USPTO) has provided examples for crafting potentially patent eligible claims in this space, a promising step forward in what has become a difficult area for IP protection. The Human Microbiome Project and the Introduction of Big Data When The Human Microbiome Project (MHP) began in 2008, researchers had hoped to describe and characterize the microbial communities living in association with humans. In particular, the MHP aimed to describe the microorganism population of five areas of the human body: the mouth, skin, vagina, gut, and nose/lung. Understanding the normal composition of these communities would, researchers hoped, help to shed light on how various external influences (disease, diet, medication, etc.) might affect the composition. However, taking this further to find causative relationships between the microbiome and disease where only correlations have been shown has remained a rather difficult task. In particular, the HMP resulted in large, and at times unwieldy, sets of data. Although these data sets contained a plethora of information, they were difficult to comb through and understand to their fullest extent.

Since the beginnings of the MHP, microbiome related research has ballooned, with scientists regularly seeking to make connections between an individual’s microbiome and the likelihood of developing a certain disease or condition, such as, for example, obesity, or even exhibiting certain psychological conditions. As research in the area expands, many researchers continue to call for better ways to manage and curate the resulting large datasets that have resulted from these investigations. Additionally, it is apparent to many in the field that there are shortcomings in data interpretation that have prevented an understanding of causative relationships between microbial communities rather than only correlative ones.

As the costs related to sequencing technologies have decreased dramatically, it has become increasingly more possible to produce large-scale datasets that are the product of sequencing microbial communities. Sequencing communities to understand their composition has eliminated the historical concern of in vivo culture conditions, particularly because so many microorganisms are ‘unculturable’ under current lab conditions, thereby excluding them from analyses of microbial community compositions.

Handling Big Data and Associated Challenges Large datasets often seem like a veritable treasure trove of information for researchers, but challenges abound with regards to interpretation. It is possible to think of these datasets as large tangled networks that require appropriate methods, treatment, and attention to reveal their hidden secrets. As mentioned above, a somewhat paradoxical situation arises with large datasets: the larger the dataset, the more difficult it is to extract statistically significant information. This is due to multiple hypothesis corrections, and it may only be solved through the development of novel statistical methods for analysis and interpretation. As such, novel statistical methods and computing analysis approaches are needed to better deal with these large datasets. This, in turn, requires an intellectual property landscape amenable to protecting innovations in this space.

Intellectual Property and Big Data The legal landscape with respect to intellectual property protection surrounding software is rapidly evolving. Many have interpreted the Supreme Court decision in Alice Corp. v. CLS Bank International to suggest that software and algorithm-based innovations are not patent eligible under 35 U.S.C. §101. This has resulted in countless challenges for those seeking to patent advancements in software and computing. There have, however, been several important decisions issued by The United States Court of Appeals for the Federal Circuit that have provided some hope for the patent eligibility of software-related innovations. First, there is the Enfish decision, which focused on improvements in the prior art instead of the abstract nature of the software-related claims. The claims were ruled patent eligible under 35 U.S.C. §101. Second is the BASCOM decision, which stated that a District Court decision holding a computer network filtering system as patent ineligible had incorrectly applied the framework for patent subject matter eligibility put forth in the Alice decision, and that the filtering system was, in fact, patent eligible. Third is the McRO decision, which stated that lip synchronization software claims were directed towards an improvement in already existent technology and were not merely directed towards an abstract idea. Similar to the Enfish decision, the McRO decision focused on an improvement in the prior art instead of the abstract nature of the software-related claims.

In addition to these promising decisions regarding patent eligibility of software-related claims, the United States Patent and Trademark Office (USPTO) has issued a memo on Recent Subject Matter Eligibility Decisions to provide guidance in drafting claims that may have been rendered patent ineligible under the Alice framework. Moreover, the USPTO has signaled it will continue to provide guidance in the future and has continued to emphasize the importance of its subject matter eligibility court case guidance chart, which provides claims drafters guidance on which cases should be cited as precedential and which should not.

Conclusion and Outlook: Advancing Big Data from the Microbiome in the Current Legal Landscape We see a promising movement towards patents incorporating advancements pertaining to computing and including large data sets and sequencing. In several granted patents (see Appendix 1), we have also begun to note research that relies on and is indicative of advancements related to big data and sequencing. Although the patents in Appendix 1 do not include big data claims, the claims that are presented have often resulted from and rely on these kinds of advancements. As patents related to software and computing become more accessible, we expect a corresponding expansion in patent applications relying on big data. Taken together, we believe that the Federal Circuit’s and USPTO’s actions suggest a more navigable landscape for patent-eligible claims related to software and computing—an area from which microbiome research could greatly benefit and progress. In the developing legal landscape, we believe that microbiome researchers will be better armed to develop novel innovations that tackle the complexities of big data. The focus will have to be on advancements over prior art instead of the abstract nature of the computing advances. These advances will not only help to interpret the data that are already available, but will also help researchers to ask more probing questions. Currently, many investigations have revealed strong correlations between changes in the microbiome and disease. Further advancements in the management of big data will hopefully bring microbiome research into a new era—one that addresses causative relationships that have, thus far, remained largely elusive.

Patent Number Inventor Assignee Title Publication Date
US9040302 B2 Klaenhammer, et al. NC State University and University of Virginia Patent Foundation Genetically modified Streptococcus thermophilus bacterium May 25, 2011
US8951512 B2 Blaser, Cho, and Cox New York University Methods for treating bone disorders by characterizing and restoring mammalian bacterial microbiota February 10, 2015
US9057070 B2 Mazmanian, Lee,and Kasper California Institute of Technology and The Brigham and Women’s Hospital, Inc. Generation of Bacteriodes fragilis capsular polysaccharide A-only producing mutant strain June 16, 2015