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Big data, big trademark questions

In an INTA Annual Meeting session yesterday, panelists demonstrated how trademark practitioners—particularly litigators—can use big data and analytics tools to grow their practices, provide fact-based predictions to clients and streamline the legal research process.

Daniel Lewis of Ravel Law, Ian C. Ballon of Greenberg Traurig, Darren Schleicher of Lex Machina and Alex Butler of Bloomberg BNA contributed the perspectives of both data analysts and litigators.

Massive databases such as PACER and LexisNexis catalogue comprehensive records and statistics of cases, but this volume of information can be unwieldly, requiring practitioners and their teams to spend many hours combing through records to manually find and analyze relevant data. However, harnessing this information through analytics tools can be useful at every stage of a case.

As attorneys and law firm business developers formulate strategies to grow their businesses, data can provide insights into “who are these companies using, who has a good track record, then drill down into the actual cases and dockets that are interesting to see changes trends over time,” said Butler. In-house counsel, he said, can also use this data to guide their choice of outside counsel, based on the past performances of firms handling matters similar to what they expect to encounter.

Data allows attorneys to demonstrate their expertise on various judges and venues, with specific regard to the client’s industry and the nature of the case at hand. Instead of providing anecdotal descriptions about the speed of a venue, or their personal impression of a judge, data gives attorneys an opportunity to prove that they know what they are talking about. Lex Machina, a legal analytics company owned by LexisNexis, provides features such as a timeline predictor, which, depending on the stipulations a user enters, will return visualizations for the average duration of a case of the specified nature.

Even a specific judge’s decisions have become far more predictable through data analytics. Lewis said that the tool analyzes patterns of language used by judges, so that litigators can see “what’s resonated with the judge before, and how can you tailor your argument to grab them.”

This and other insights accessible through analytics could theoretically be mined manually, but that process is made far more efficient through technological tools. These facts still require interpretation, but analytics tools are “exploratory”: they should not be seen as “replacing human reasoning, but as supplementing it with data,” said Lewis. 

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