Patents and Innovation in Electronic Discovery

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.

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Source: eDiscovery 2.0
By: Venkat Rangan