Partnering to introduce legal analytics with artificial intelligence

An iterative approach to predictive analytics

November 26, 2018

 

“Legal decisions are being made today like they were 100 years ago. With AI technology, we can do better than that."--Johnathan Klein, CEO and Founder of Ex Parte

Every day companies make important legal decisions. Do we fight this or settle? Does our patent cover that? Which state should we file in? Litigation is a $250 billion-dollar-a-year industry but legal decisions are still often made by asking colleagues and following gut feelings.

This process bothered Ex Parte founder Johnathan Klein when he was in law school. The people who were paid to analyze cases really couldn’t factor all the variables. Later, when Johnathan served as general counsel for MicroStrategy, he noted that more data would have been helpful to make informed decisions. When AI started disrupting other industries, Klein wondered if it was time to change legal analytics for good.

Early on, EastBanc Technologies was tapped to create a proof of concept for Ex Parte. They asked us: Could AI predict the probability of winning or losing certain cases based on historic trends and patterns? Intrigued, we looked to decades of carefully kept historic case data and recommended a low-risk approach to the data analytics, Minimal Variable Prediction (MVP).

MVP is a technique that EastBanc Tech has used before with success. Rather than wading into a giant data lake and asking lots of big questions, we start small, with a single inquiry, and follow “breadcrumbs” to find related data.

Focusing on a single initial data point – one particular type of litigation - we built a predictive analytics engine to test the assumption. With each iteration, we moved closer to answering the legal questions of tomorrow.

The initial challenge was getting semi-structured court data out of PDF case files into a format that a machine understands. From there, the engine applied machine learning to automate analytical modeling and find hidden patterns and insights within the data. Next, deep learning was applied. AI was key to extracting specific information from transactional data, such as case wins, losses, and correlating factors, then reporting that information contextually to predict outcomes in future patent appeal cases.

“Other fields have been transformed by analytics, but law has been slow to adapt to changes in technology. Better decision making is a powerful motivator,” says Johnathan Klein. “We anticipate being able to expand Ex Parte’s services to help companies predict the outcome of litigation based on thousands of different variables. In a similar way, law firms could use our service to market themselves based on predicted success rates. EastBanc Tech’s approach is the key to all of that.”

 

“You don’t have to understand the technology to appreciate what Ex Parte can do. EastBanc Tech never questioned if this could be done, they just went to work to solve how to make it happen.”--Johnathan Klein

 

The EastBanc Tech approach makes analytics cost-effective, straight forward, and quick to implement. Instead of going “all-in” for the big bang, EastBanc Tech’s iterative approach focuses on assembling small, relevant data sets, asking the right questions and pursuing answers that are meaningful to the customer, whether they are looking for data insights within their company, or looking to apply analytics to an industry, like Ex Parte.

Developing the Ex Parte MVP was a unique challenge for EastBanc Tech. Our resourceful team was called on to solve problems along the way. We didn’t just apply machines learning, we also had to solve design, architecture, extraction, and quality assurance issues as well. That’s where EastBanc Tech’s talent base really shined.

Says Klein, “When I needed someone with specific expertise for part of our development, they didn’t just give me anyone. They sent a rock star in AI with a PhD in the field, who accelerated our progress.”

“EastBanc Tech has been a true partner, fully invested in our mission. Their people are living and breathing Ex Parte, which has been enormously consequential.”

Partner Technologies

Microsoft Machine Learning, Azure, SQL Azure, Power BI, R, among others.

Results:

  • The Ex Parte prototype proved successful.  They can refine and iterate to add new data sets and test other inquiries for an ongoing stream of actionable, affordable legal predictions.
  • Eventually the Ex Parte engine can be used to make decisions based on the law firm, lawyer, judge and other hidden drivers. One day it may even be able to offer real-time analytics in cases.
  • The business impact of this technology is significant. Clients who can predict the likelihood of success or failure in a case are looking at costs savings of hundreds of thousands or millions of dollars on litigation alone.

Learn more about EastBanc Tech’s Minimal Viable Prediction approach here.  

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