In the world of technology we live in, a huge amount of benefit is created when people apply certain well-known techniques to solve problems and create value to the broader community. Such techniques are often the result of painstakingly long and laborious research, driven primarily by academic institutions with private industry either funding such research directly or by co-opting them in their own work. When the industry as a whole recognizes a certain methodology, it gains popular usage.
In information retrieval, searching and retrieving relevant content from unstructured text has been a vexing problem, and we’ve had decades of the brightest minds applying their collective intelligence and the rigors of peer review to validate and establish the most effective way to solve a retrieval problem. And, research forums such as TREC, SIGIR and other information retrieval conferences establish a venue for advancing the state of the art. So, when Recommind announced that they have been issued a patent on Predictive Coding, I took notice, especially since it touches a nerve with those who believe research should be openly shared.
The patent lists six claims that describe a workflow whereby humans review and code a document and the coding decisions applied to the document sample are projected or applied to the larger collection of documents. Anyone who has even the slightest exposure to information retrieval research will recognize this as a very common interactive relevance feedback mechanism. Relevance feedback as a way to perform information retrieval has been studied for well over forty years, with a paper as early as 1968 by Rocchio J.J., titled Relevance Feedback in Information Retrieval. It falls under a category of methods broadly known as machine learning.
Any supervised machine learning system involves creating a training sample and using that sample to project into a larger population. The fact that one could claim patentable ideas on something that is so widely known and used is puzzling. Any workflow that employs machine learning would include the steps of creating an initial control set, coding that by human review, and applying the learned tags to a larger population. In fact, the Wiki article Learning to rank describes precisely the workflow that is claimed in the patent and as part of our participation in the TREC Legal Track 2009, Clearwell submitted a paper with iterative sampling based evaluation and automatic expansion of initial query. In that paper, we describe exactly the workflow postulated by the six claims of the patent.
In terms of other prior art that would potentially invalidate the patent, the list is long. Let’s start with Text Classification. Text Classification using Support Vector Machines (SVM) was first published by Thorsten Joachims in 1998, in the Proceedings of Sixteenth International Conference on Machine Learning, as well as his book Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, published by The Springer International Series in Engineering and Computer Science. Now a well-recognized Professor of Computer Science at Cornell University, that work is widely cited as a seminal work on the area of machine learning and text classification. Interestingly, this work was cited by the Patent Examiner as prior art, but the inventors missed listing it. Nevertheless, that work and further work by several academics such as Leopold and Kindermann has already established the use of Support Vector Machines as a useful technique for machine learning. To claim the novelty of its use in automatically coding documents is, in my opinion, a hollow claim.
Another technology mentioned in passing is Latent Semantic Indexing (LSI). This is proposed as a retrieval technique by Deerwester, S., Dumais, S.T., Furnas, G.W.,Landauer, T.K., Harshman R. in their paper, Indexing by Latent Semantic Analysis, in Journal of the ASIS, 41(6):391-407, 1990. The use of LSI for semantic analysis, concept searching and text classification is also very widespread, and once again, it seems ridiculous to claim that it is something novel or innovative.
Next, let’s examine the use of sampling to validate the initial control set. Use of sampling for validation of a control set of documents is in fact such a widely known technique that most e-discovery productions employ sampling. In fact, the Sedona Commentary on Achieving Quality and the EDRM Search Guide recommend use of sampling to validate automated searches. Furthermore, several E-discovery opinions such as Judge Grimm’s opinion in Victor Stanley [Victor Stanley, Inc. v. Creative Pipe, Inc. , 2008 WL 2221841 (D. Md., May 29, 2008)] suggests that any technique that reduces the universe of documents produced must employ sampling to validate automated searches.
In short, we think the claims issued in the patent and the associated workflow are so commonly used that the workflow is neither novel nor non-obvious to a trained practitioner, and there is enough prior art on each of the individual technologies to warrant a re-examination and eventual invalidation of the patent. In any event, it is fairly easy for anyone to pick up existing prior art and devise a similar workflow that achieves the same or better outcome, and attempt to enforce the patent will likely be challenged.
But there is an even bigger issue at stake here beyond the status of Recommind’s patent: namely, shouldn’t the e-discovery vendor community continue to work, as it has for years, toward what is in the best interest of the legal community and, more broadly, the justice system? Recommind’s thinly veiled threats about requiring industry participants to license their technology are an affront to those who have invested years developing the technology and practicing the approach in real-world e-discovery cases. Spend a few minutes trolling (no pun intended) around on archive.org and you’ll see that early predictive coding companies like H5 were practicing machine learning and predictive workflows in e-discovery over two years before Recommind announced their first version of Axcelerate.
Wouldn’t a better outcome be for corporations and law firms to benefit from the innovation that comes from free competition in the marketplace, while still honoring the sort of novel, non-obvious innovation that warrants patent protection? Legitimate patents that actually encourage and protect investments by an organization are fine, but process patents that attempt to patent a workflow are bad for business. With such an approach, the full promise of automated document review (which, as any truly honest vendor should admit, still has much more room to grow and develop) can be fully realized in a way that both provides vendors with the fair and just economic rewards they deserve while helping the legal system become radically more efficient.