In my opinion, content is still king when it comes to legal research, but analytical tools make deciphering all the content so much better, and help researchers find the relationships between issues that might otherwise go unnoticed. Ravel is introducing a new analytics platform today which identifies patterns in millions of court decisions to access the possible outcomes, and help the litigation researcher deduce the best arguments or actions to take in his or her individual case, based upon the way specific judges or courts previously ruled on similar issues. In simpler terms, it allows you to better know your Judge or Court.
Daniel Lewis walked me through examples of the tool, ranging from specific issues in front of individual courts and judges, to much more complicated and academic research of how broader issues are handled differently over time, or regions. From what I have seen, there is a lot of potential for practicing attorneys and research academics alike with the Court Analytics tool.
The image below shows the layout of the Court Analytics platform. There's a lot more to see from the tool, and Jean O'Grady will present a webinar later today (1 PM ET) to demonstrate it.
The press release is listed below with more information.
Ravel Law Launches New Analytics for US Court System
First Platform to Offer Analytics for All Federal and State Courts
SAN FRANCISCO, CA – DECEMBER 5, 2016 – Ravel Law, a legal research and analytics platform, today announced the launch of Court Analytics, a first-of-its-kind offering that provides an unprecedented view into the caselaw and decisions of state and federal courts.
Ravel’s Court Analytics answers critical questions that litigators face in developing legal strategy. By analyzing millions of court opinions to identify patterns in language and case outcomes, Ravel empowers litigators to make data-driven decisions when comparing forums, assessing possible outcomes, and crafting briefs using the most important cases and rules. Complex projects that used to take hours or days of research can now be done in minutes, with answers that deliver richer intelligence and detail.
“Court Analytics offers law firms a truer understanding of how courts behave and how cases are tried. Attorneys can inform their strategy with objective insights about the cases, judges, rules and language that make each jurisdiction unique. The future of litigation will be different, and we’re already seeing changes – with top attorneys combining their art of lawyering with our science, to advance their arguments in the most effective way possible,” said Daniel Lewis, co-founder and chief executive officer of Ravel Law.
Court Analytics applies data science, natural language processing, and machine learning to evaluate millions of court decisions spanning hundreds of years from over 400 federal and state courts. Its features include:
· Search and filter caselaw by court, 90+ motion types, keywords, and topics.
· Predict possible outcomes by identifying how courts and judges have ruled on similar cases or or motion types in the past.
· Uncover the key cases, standards, and language that make each court unique.
With Court Analytics, lawyers can take advantage of never-before-seen insights, such as:
· Judge Susan Illston in the Northern District of California grants 60% of motions to dismiss, which makes her 14% more likely to grant than other judges in the district.
· The Second Circuit is most likely to turn to the 9th Circuit for persuasive caselaw, and then to the 5th and 7th Circuits.
· Measured by citations, Judge Richard Posner truly is the most influential judge on the 7th Circuit. One of Posner’s most widely cited decisions is Bjornson v. Astrue, an appeal from a district court decision affirming the denial of social security disability benefits by an administrative law judge. The most important passage of that decision is on page 644, as it deals with the Administrative Law Judge’s “opaque boilerplate.”
· The California Court of Appeals has ruled on more than 1,000 cases that deal with the right of privacy. The two most important precedential decisions the courts rely on in such cases are California Supreme Court cases: White v. Davis and Hill v. National Collegiate Athletic Association.
Court Analytics adds to Ravel’s analytical research suite, alongside the award-winning Judge Analytics tool, which identifies the rules, cases, and specific language that a judge commonly cites, and Case Analytics, which finds key passages within cases and shows how they are interpreted. Using the Ravel platform, attorneys can gain insights customized to the unique circumstances of their case at every step of their research process. All three features are available today (www.ravellaw.com) via paid subscription (for individuals and organizations).
Ravel’s subscription-based services are enhanced by the “Caselaw Access Project,” the company’s ongoing collaboration with Harvard Law School to digitize the school’s entire collection of U.S. caselaw, one of the largest collections of legal materials in the world. Through this project, millions of court decisions are being digitized and added to the Ravel platform. This database of American law serves as an underlying data set that people can search and view for free in Ravel, in addition to using Ravel’s paid technologies to derive insights.
Ravel will be hosting a launch webcast to share more details on Court Analytics on Monday, December 5, 2016, 11:00 AM PST (2 pm EST). Register here to learn more: https://attendee.gotowebinar.com/register/7457269768975514627?source=CMS
About Ravel Law
Ravel is a legal search, visualization, and analytics platform. Ravel empowers lawyers to do data-driven research, with analytics and interfaces that help them sift through vast amounts of legal information to find what matters. Established by lawyers in 2012, Ravel spun out of interdisciplinary work between Stanford University’s law school, computer science department, and d.school. Ravel is based in San Francisco, and is funded by New Enterprise Associates, North Bridge Venture Partners, Ulu Ventures, Experiment Fund, and Work-Bench.