Last week, Law Technology News reporter Evan Koblentz called me to ask about a new patent issued to Recommind for a method of “predictive coding.” At the time, I had only glanced at the patent and told the reporter that I was in no position to comment on its substantive claims over the phone. I did wish Recommind well with its patent and its business—just as I would with respect to other competitors.

As background, I also explained that getting a patent awarded was not the end of the process. Rather, to enforce the patent, one has to meet a number of additional challenges, including proving that the patented device or process was new and innovative. A patent based on works or ideas already in circulation, often referred to as “prior art,” is subject to challenge and revocation.

In response to further questions, I told the reporter that we were “puzzled” as to how a company could get a patent involving a process that had been around academia for more than 40 years. Before the call, I had spoken with Dr. Jeremy Pickens, our Senior Applied Research Scientist, to ask him for his thoughts on the patent. Prior to joining Catalyst, Jeremy’s research at the FX Palo Alto Lab led to six patents in the field of search and information retrieval, including two for collaborative exploratory search systems.

Jeremy had taken a quick look at the patent and wondered how it got it through. You can read his comments about prior research and the state of the industry.

I had to laugh when, after the LTN article came out last week (Recommind Intends to Flex Predictive Coding Muscles), my comments were interpreted as “grumpy” by the good folks at Above the Law.

I found myself smiling because I have been called a lot of things but never grumpy. And, other than being an interested observer, I didn’t feel happy or unhappy about the Recommind patent. As I said to Evan Koblentz, I wish the Recommind people well with their patent and their business—they are doing a lot of exciting things in the industry and deserve their success.

But, for the record (as I used to say when I was a lawyer), the concepts and processes underlying predictive coding are not new. Perhaps Recommind has added a new wrinkle to the process but not much more than that, so far as we can see.

Who Invented ‘Predictive Coding’ Anyway?

The phrase “predictive coding” isn’t new in the industry and was not coined by Recommind. Even before Recommind filed the application for its patent, the Bank of America had already filed an application for a patent on “Predictive Coding of Documents in an Electronic Discovery System” (with the provision application filed on March 27, 2009). Others have used the term for a variety of processes as well. For examples, just Google the phrase.

Although Recommind tried to bull its way through a trademark for the term, the effort failed. As Evan Koblentz later reported on the ALM blog EDD Update, the government rejected Recommind’s attempt to trademark a phrase that was descriptive and already in use by others. (Ironically, the same government agency that granted the patent rejected the trademark.)

Goodbye trademark.

More to the point, the techniques behind predictive coding aren’t new. As Dr. Pickens points out in his post, they go back to the 1970s when search scientists introduced the concept of “relevance feedback” into the lexicon. They realized the simple truth that computer-based search algorithms could be made more effective through an iterative process involving human feedback. And, that work has continued to evolve over the past 40 years.

So, we remain puzzled as to how Recommind could claim a patent around the work of so many others.

The inventor of the Internet.

Ultimately, we are observers in this process because we use different math and techniques than Recommind and many of the others in the market. Specifically, we were one of the first to use a more modern set of algorithms, called non-negative matrix factorization, to analyze document themes and similarities.

This technique, developed at the Massachusetts Institute of Technology, is used widely for facial recognition as well as text analysis. We work closely with Dr. Michael Berry from the University of Tennessee’s Center for Intelligent Systems and Machine Learning, who is a leading proponent of the technique for mathematical search analysis.

Not Grumpy Here

So, we at Catalyst are certainly not grumpy—life’s good here. Nor are we unhappy about Recommind’s patent. Like other bystanders, we will enjoy watching Recommind try to enforce its patent against whomever they suspect might be using similar techniques. I am sure it will make for a great show, perhaps even worthy of truTV.

For the record, we didn’t invent predictive coding or the techniques around relevance feedback. Nor did Recommind. Check with Al Gore. Maybe he did.