Against the backdrop of recent government court battles over privileged data, one legal technology company is looking to streamline how attorneys can identify privileged documents.

In late August 2018, e-discovery managed service provider QDiscovery released QPrivAlert, an application that links into Relativity's e-discovery and analytics technology to allow users to quickly identify potentially privileged documents in their data sets.

David Barrett, CEO of QDiscovery, noted that e-discovery practitioners can already use “technology assisted review (TAR) and use the advanced analytics within Relativity,” to identify potentially privileged content.

But the impetus to launch QPrivAlert, he explained, was to give e-discovery practitioners “an opportunity to potentially identify privileged documents much quicker.”

QPrivAlert works by automatically identifying certain fields of information in documents and grouping them accordingly. For example, it can show, on its proprietary dashboard, what emails are sent or received by a specific recipient, such as an attorney.

While Relativity already allows users to identify and group documents by certain fields, QPrivAlert adds additional fields, such as domain names, which can alert users to all emails sent or received from a law firm.

Barrett stressed that while the tool “makes it easier to get a high level overview of potentially privileged communication,” it does not definitively identify privileged content.

“We are not saying that QPrivAlert can replace a privilege review, we are saying that QPrivAlert allows for a workflow that automatically identifies potentially privileged information … so that the case team can choose how they want to do the review, but at the end of the day there needs to be a decision of whether the document is privileged or not.”

The release of QPrivAlert comes at a time when use of technology to help speed up privileged review has become a hotly debated topic, spurred mainly by the U.S. government's proposal in April to use TAR for the privilege review in the United States v. Cohen.

Government attorneys asked the judge overseeing the case to appoint former U.S. Magistrate Judge Frank Maas of the Southern District of New York as special master for the review, which concerned evidence collected from President Donald Trump's former personal attorney Michael Cohen.

While another special master was ultimately selected, Maas had argued that an AI-based technology-assisted review (TAR) processes developed by Maura Grossman, research professor in the David R. Cheriton School of Computer Science at the University of Waterloo, Ontario, could automate and speed up the identification of privileged documents.

The process employed by Grossman works similar to the QPrivAlert tool in that it extracts certain correspondence information from all the documents in the data set. Maas told Legaltech News that when reviewing documents, the TAR process “considers metadata as part of the algorithm, so it looks at things like the 'to' field, the 'from' field, the 'bcc' and 'cc' fields, and so forth.”

Once all communications between Cohen and his clients are found, he added, one could train a TAR e-discovery tool “through an iterative process to discriminate between privileged and non-privileged communications,” Maas added.

To be sure, the development and release of QPrivAlert was not meant to capitalize on the interest of the Cohen case's privilege review, but instead was fueled by client demand for a quicker way to identify potentially privileged content. Still, Barrett said the publicity of the case is an added benefit for the new tool. “I think it's very possible there will be some additional interest in this tool in light of that privilege process and case”