A few days ago, I walked into my local Walgreens to buy a few household odds and ends. Along with my receipt, the cashier handed me a stack of coupons. As I exited the store, I glanced down at them and noticed that one was for my favorite brand of detergent and another for my dog’s favorite treats. I didn’t buy either of those items that day, but Walgreens, like other large corporations, makes routine use of the data it collects about me to know what I’m apt to purchase in the future.
Known by the term “big data,” Netflix uses similar information to decide what shows to suggest for its users. Starbucks uses big data to determine optimal locations for new stores (even if it’s directly across the street from an existing store). Even more beneficially, Microsoft is working on using the data it collects on Bing searches to predict a diagnosis of pancreatic cancer.
But where do law firms fall when it comes to harnessing the insights provided by big data?
Legal Tech’s Beginnings
The very first wave of legal technology consisted of software relating to billing, timekeeping, and docketing. As with any other technical advances, these tools improved lawyers’ efficiency.
The second wave came with the digitization of case law for attorneys conducting research. Companies like Westlaw provide vast libraries of case law, agency determinations, law review articles, and other publications. These libraries were initially searchable with Boolean connectors; however, in the last few years, the search capabilities have transitioned to a Google-like natural language style.
While Westlaw charges for the use of its database, some believe the wealth of case law should be available to the public in digital form for free. Ravel Law, for instance, has partnered with Harvard Law School to digitize Harvard’s entire collection of U.S. case law in order to make it freely available online.
The third wave is the legal world’s use of big data.
Predictive Analytics and the Law
The use of big data for legal teams is in its nascent stage. Unlike marketing and other industries, the legal field has been slow to appreciate the benefits of big data insights, but it’s getting the hang of it.
As anyone who’s worked at a law firm can attest, lawyers at a firm use each others’ experience to inform their strategy in a given case. Firm-wide emails asking if anyone has appeared in front of a particular judge or tribunal are routine and allow lawyers to benefit from the insights their colleagues provide.
Big data analytics allow lawyers to gather this same information, but on a much larger scale. For instance, analytics platforms allow attorneys to view their judge’s complete history, including every decision issued and every case cited, to identify the legal precedent the judge finds most persuasive.
While this type of analytics can’t tell an attorney whether this judge is particular about staying behind a podium during cross examination or likes his motions in a particular font size, it does allow an attorney to craft an argument using a judge’s favorite case. In addition, such analytics can inform an attorney’s strategy in litigating a particular case in terms of filing motions that a judge is likely to grant, rather than spending a client’s time and money on motions that a judge hardly ever accepts.
Predictive analytics aren’t just being used by litigators, either. In-house counsel at some of the world’s top corporations are using big data to protect their company’s innovations. Juristat provides behavioral analytics to attorneys about their patent examiners at the United States Patent and Trademark Office (USPTO). With this information, attorneys who specialize in getting patents for their clients can mold their strategy to the specific examiner evaluating their client’s patent application.
“Not only can patent prosecutors use big data to inform their prosecution strategy for a particular examiner, but it also allows prosecutors to predict where their applications will be assigned within the USPTO for evaluation,” says Drew Winship, CEO of Juristat. “This allows prosecutors to use specific language to target their applications toward preferred classes and technology centers.”
The Future of Legal Tech: Artificial Intelligence
Big data analytics are a step in the path toward more advanced technology becoming prevalent in the legal industry. Often criticized for slow acceptance of new technology, law firms and in-house departments will soon have no choice but to adapt as their rivals gain a competitive edge with predictive analytics that have been prevalent in marketing departments for years.
There is a step beyond predictive analytics, however. IBM has already developed “Ross,” using its artificial intelligence software, Watson. Ross is an artificial attorney that has been “hired” by law firms such as BakerHostetler and Latham & Watkins.
Currently, Ross is a specialist in bankruptcy law, able to answer simple questions asked in plain English with citations to authority in its response. But Ross is also learning. The more questions asked of Ross, the more accurate Ross’s answers become. Finally, the never-tiring Ross constantly monitors for changes in the law and alerts attorneys to relevant updates.
What started with technology to help attorneys perform legal research, manage their billing, and perform routine discovery tasks, has slowly caught up to what software engineers have known for decades – that most tasks, even complex ones, traditionally performed by human labor are teachable to computers.
As has been observed by others in the tech industry, the future of computing is likely to involve less traditional coding and more training of neural networks. As machine learning becomes more prevalent and accessible by the masses, it’s likely that it will have a significant impact on nearly every industry, including law. Document review, traditionally performed by junior associates, is already being automated by software that looks for key words and phrases to flag relevant or privileged documents. The junior associate of the future may well be in charge of training such software to look for documents relevant to a particular case. Thus, young lawyers may need to be versed in not just the law, but machine learning and neural networks as well.