I had the opportunity to speak with Owen Byrd, the Chief Evangelist and General Counsel for the Legal Analytics company Lex Machina. Based in Menlo Park, California, Lex Machina is one of the many legal technology startup companies that has recently sprouted in the Silicon Valley area. As a leader in the intersection of legal analytics and IP law, I wanted to hear Owen’s thoughts on Lex Machina and the recent growth of legal technology startup companies. Here is what he had to say:
Tell me a bit about Lex Machina. Where did the idea for Lex Machina come from and what has it turned into since the early days of the company?
Lex Machina began as a public interest project at Stanford Law School. A number of big law firms and technology companies donated a couple of million dollars to what was then called the “IP Litigation Clearing House,” which Professor Mark Lemley spearheaded to create the first electronic set of patent litigation analytics and outcomes. After the money was donated and was turned into a public interest project Lex Machina spun out of a private venture in 2010, as so often happens at Stanford when someone says, “Hey I think there might be a business here”.
Unlike traditional legal research that requires top-down control, Lex Machina provides a novel legal resource called legal analytics. We mine litigation data and then clean, code, tag, and build meta-datasets. This data then enables lawyers to make data-driven decisions in their strategy and tactics that they employ for their cases, transactions, and even for obtaining new clients.
Legal technology has been slow to develop relative to technological advancements in other industries. Why do you think this is?
The legal services industry organizes itself around precedent and so by nature change is slow. Practicing law is a very old profession and has been done the same way for a long time. Personally, I think that the emergence of legal analytics is as big a moment and analogous to the moment when legal research switched from books to computers.
I like to use the “Moneyball” analogy. Analytics were applied to the Oakland A’s and transformed their ability to win even though they had a smaller market and payroll. Now, every team employs Moneyball analytics. The same is true for law as we are able to turn lawyers into Moneyball lawyers. You still need great legal research and legal reasoning skills but analytics supplements those skills. For example, let’s say that you are a patent lawyer and your client is suing in Delaware for patent infringement. As a lawyer you determine that you will have a better chance at winning the case using a jury in Chicago. Typically, you would do some legal research and use your best legal reasoning to persuade the judge that there is a basis for transferring your client’s case to Chicago. A Moneyball lawyer is going to do all that but they will also go into Lex Machina to unpack how that judge has previously treated motions to transfer in patent cases and extract from the data where that judge’s preferences lie. Everyone, including judges, have subconscious biases and we at Lex Machina know to the second decimal point how judges behave. In our example, if the case in Delaware had been assigned to Judge Robinson and you need to persuade her to move the case to Chicago, you can go into Lex Machina, push a button, and find the 20 patent cases that have succeeded in motions to transfer under Judge Robinson. You can then use that data to uncover how best to approach your argument for a motion to transfer. The data allows you can determine how to speak to Judge Robinson in terms that are more favourable to her, thereby increasing your odds of winning.
Companies such as LexisNexis and Thomson Reuters have been developing legal technology for a long time. What can a start-up culture for legal technology offer that customers might not be able to get from more mainstream legal technology companies?
Just like any industry, big incumbents get big by doing something well. Both LexisNexis and Thomson Reuters provide excellent online legal research environments. They are able to structure their information similar to a law library and train lawyers how to perform legal research using their technology. This strategy has allowed them to become very good at what they do. As startups, however, we are not burdened with that history. We can be nimble and provide services that haven’t yet been done, and try to deliver those services in a saleable market so that we have a successful venture. The rise of e-discovery and the use of algorithms to identify which documents in discovery are relevant to a case was largely driven by small startups. There are also companies like Serengeti, Sky Analytics, and TyMetrix that are now doing analytics around billing data. These services all developed through startups that took a fresh look at existing needs and came up with answers for those needs that had not yet been proposed by the big incumbents. Therefore, just like any industry, there is going to be transformation that is driven by startups who are not afraid to tackle big problems with new approaches.
What is the long-term goal for Lex Machina? Where do you see it in 10 or 20 years?
We want to bring legal analytics to the law. We have had a great run with our beachhead market of patent litigation. In some respects that was because of luck. If the project at Stanford from which we grew was in a different area of law, it is not clear that it would have provided the same platform for transition into a private sector venture. We have also provided access to data about copyright, trademark, and antitrust, and are now building the Robust Analysts tools for copyright and trademark, which is a platform that we currently provide for patent litigators.
Aside from our IP analytics we have also studied another half a dozen federal subjects. Our focus on federal subjects is largely because we collect our data using Pacer, the federal court reporting system. Whether it is tax, securities, employment law, or another federal subject, we hope next year to move past IP by providing legal analytics to the rest of the federal subjects. In 10 or 20 years, I hope that we are providing analytics for every subject in law, including state law subjects, and that every attorney has his or her specialty empowered with analytics. While there will probably still be those big research incumbents, we hope that there will also be a big legal analytic incumbent called Lex Machina.
Thanks again for meeting with me today, Owen. Is there anything else you would like to add?
Considering that many of your readers are in academia I’d like to make two more points. First, because of our Stanford heritage, we still have a large public interest component to our business. We provide free access to our platform to members of Congress and their staff, to the federal judiciary and their clerks, and to academics and students who are studying IP. We are here to do good and do well, and our public interest program helps us fulfill part of our mission.
Second, I find it interesting that the information about trends in patent litigation is of great interest to academics and journalists. We are glad that we can provide that sort of information. However, we are going to succeed as a private venture not by looking at those trends but by allowing lawyers to make data-driven decisions and to predict what sort of approaches will affect outcomes and strategies in cases. While trends get a lot of attention in articles, it is the application of analytics and its data to the day-to-day practice of law that holds the most hope in transforming and improving the practice.
Peter Neufeld is the Features Editor of the IPilogue and a J.D. Candidate at Osgoode Hall Law School. Peter met with Owen Byrd when he was a Visiting Researcher at Stanford Law School’s Codex: The Stanford Center for Legal Informatics as part of Osgoode’s Intellectual Property Law Intensive Program.