“Disrupt” for Lawyers – Think the Unthinkable to Spark Transformation in the Legal Industry

If you missed the dynamic keynote at LegalTech, here¹s your chance to hear Luke explain how to combine fluid creativity with analytical rigor in a simple process for successful disruption. Luke explores how we can rethink the habits that have made lawyers successful in the past, and challenge the conventional wisdom and legal industry paradigms that have defined that world in the past. No ideas are off the table and no cows are sacred to succeed in today¹s fast-changing marketplace, lawyers must think the unthinkable.

House of Representatives Passes the Innovation Act — What You Need to Know

They may be derided as a ‘do-nothing’ Congress, but both the Senate and House are taking up important new legislation to reform patent litigation.

The Innovation Act

On December 5, the House of Representatives passed H.R. 3309, a piece of patent reform legislation known as the “Innovation Act of 2013.” The bill passed 325-91, with most of the opposition coming from Democrats, but with some Republican opponents as well. The bill would amend U.S. Code Title 35 (the section that governs patent law) and the “America Invents Act,” which Congress passed in 2011 (but which most folks agree did not accomplish nearly enough in the way of true reform).

The legislation now moves to the Senate, where lawmakers will have to dealt with it in conjunction with the “Patent Transparency and Improvements Act of 2013” (S. 1720), the Senate’s version of similar patent reform. (The Senate bill proposes less sweeping reform than the House version; on December 16, the Senate Judiciary Committee began hearings on the legislation.)

H.R. 3309

Here’s my take on the major provisions of H.R. 3309, most of which would change the patent system to eliminate some of the more egregious litigation tactics adopted by patent “trolls”:

  • The Innovation Act would require plaintiffs to plead with more specificity in the complaint how the defendant allegedly infringed the plaintiff’s patent. The plaintiff must identify each patent and claim asserted, each accused device or process, and how each claim corresponds to each accused device or process.
  • The bill contains limits on discovery in the pre-claim construction phase of ligation. Until a claim construction decision is issued, discovery is limited to determining the meaning of claim terms. However, the judge or the parties may agree to expand the scope of discovery as necessary.
  • The legislation proposes a fee-shifting rule, which would require a losing plaintiff to pay the attorneys’ fees incurred by a winning defendant. The law would create a presumption that attorneys’ fees are awarded to the prevailing party, unless the court finds that the non-prevailing party’s position was “reasonably justified . . . or that special circumstances make an award unjust.”
  • The act would address a more recent, and increasingly common troll tactic of suing end users of technology (such as restaurants or hotels offering their customers Wi-Fi access) rather than the technology provider (such as the manufacturer of the Wi-Fi equipment). These end users — often small independent businesses or franchisees of chain companies — can be easily intimidated into paying a settlement regardless of the merits of the case. The Innovation Act would allow technology manufacturers to step in and fight these lawsuits on their customers’ behalf. Similarly, the bill provides that a suit against a customer must be stayed if the manufacturer is involved in a separate patent action related to the same product, and the customer agrees to be bound by the decision in that other action.
  • More transparency in patent ownership will be required, as plaintiffs must identify any entity that has a financial interest in the patent being litigated.

Stay tuned for further updates here, as the House and Senate move forward with this much-needed patent reform legislation, The Innovation Act.

New ITC eDiscovery Rules Clear Path for Swifter, Less Costly Patent Infringement Investigations

Section 337 of the Tariff Act of 1930 (19 U.S.C. § 1337) authorizes the U.S. International Trade Commission (USITC) to investigate complaints of certain unfair practices in import trade. These investigations most often involve allegations of patent or registered trademark infringement, although other forms of unfair competition may also be asserted, such as misappropriation of trade secrets, trade dress infringement, passing off, false advertising, and violations of the antitrust laws.

Section 337 investigations could be an attractive alternative to civil patent litigation for some corporations. The investigations, presided over by an administrative law judge, typically progress much faster than litigation filed in federal district courts, even in “rocket docket” jurisdictions. Most matters are targeted for resolution within 12 to 18 months, and under appropriate circumstances expedited temporary relief is available.

But the promise of this alternative has not been fully realized, for a couple of reasons. First, patent holders cannot obtain monetary relief in Section 337 investigations — the primary remedies available are exclusion orders that stop infringing imports from entering the United States, and cease-and-desist orders against persons engaged in unfair acts.

Second, discovery in Section 337 investigations has not been held in check by limitations like those in the Federal Rules of Civil Procedure. By the ITC’s own acknowledgement, the agency rules “contain[ed] no limitations on e-discovery and provide[d] little guidance on when it would be appropriate for an administrative law judge to limit discovery generally.” Within this loose framework, ediscovery became a free-for-all, and matters became needlessly and disproportionately expensive.

That is, until now. Earlier this summer the ITC adopted amendments to Section 210.27 of the ITC rules of procedure (19 C.F.R. § 210). The amendments are intended to “reduce expensive, inefficient, unjustified, or unnecessary discovery practices in agency proceedings while preserving the opportunity for fair and efficient discovery for all parties.” Under amended Rule 210.27, the scope of discovery changes dramatically so that it closely comports with Federal Rule of Civil Procedure 26(b)(1). Under the revised rule, a party can object to an e-discovery request if they are requested to produce data that is “not reasonably accessible because of undue burden or cost.” If the requesting party challenges a claim of undue burden or cost, it can file a motion to compel, at which point the producing party must make a showing supporting their claim. The rule also allows an administrative law judge to place limitations on discovery if the information being sought is duplicative or can be obtained through less costly means.

In adopting the new ediscovery rule, Amended Rule 210.27, the ITC has provided its administrative judges with the valuable discretion to manage both the cost and burden of discovery. Moreover, it rejected additional proposed provisions to limit that discretion, including such proposals as limiting e-discovery to five custodians per party and expressly defining categories of data that would be considered not reasonably accessible.

In theory, the revisions to Rule 210.27 should improve the ITC’s timely, but often costly, patent dispute resolution process. However, these additional efficiencies might be offset by the fact that administrative law judges will now have to wrestle with electronic discovery disputes and motion practice similar to what now exists in federal district courts. If the ITC can develop a process to efficiently limit unnecessary motion practice and quickly adjudicate appropriate disputes, more patent holders may forgo the potential of monetary damages and instead pursue injunctive relief with the ITC.

DiscoverReady clients who pursue patent challenges with the ITC should benefit from these amendments and new ITC ediscovery rules. For almost a decade, we have been helping clients manage the substantial cost and burden of the ITC discovery process. Through our consulting services, Dynamic Data Analysis™ team, Samplyzer™ product offering, and other analytic tools and services, we have a proven track record of making ediscovery more efficient, less expensive, and more valuable in resolving ITC complaints. Under the amended rules, we will be able to make even better use of these tools to help our clients limit discovery to a reasonable scope and cost.

Making Sure Your Predictive Coding Solution Doesn’t Cost More…

Depending on how it’s used, predictive coding may result in a discovery process that is less expensive than a process built around traditional search term based culling and manual document review. I emphasize “may” because the notion that predictive coding is an automatic cost saver is one of the biggest misconceptions currently permeating the marketplace.

Four Main Cost Drivers of Predictive Culling When Planning a Workflow

Reflecting the vast majority of the reported court decisions, litigants primarily are using predictive coding technology for the limited purpose of culling the data in lieu of search terms. Following the culling process, in both a predictive coding and search term based workflow, parties are then manually reviewing for responsiveness, privilege and confidentiality the documents that “hit” or are identified as likely relevant. However, there are four primary cost drivers associated with a predictive “culling” based process for which a party must account when planning its workflow:

  1. There are additional technology and consulting costs for using predictive coding. In addition to paying your “normal” processing fee to prepare data for the hosting platform, a workflow built around predictive coding typically requires an additional processing fee for the technology itself. You will incur these charges regardless of whether you are pairing a predictive coding technology (such as Equivio Relevance) with a separate hosted review platform or whether the technology is incorporated into a single offering (such as Relativity Assisted Review or Recommind). While these costs are relatively small on a per unit basis, they can result in six figure fees when applied to a significant data collection. On top of that, you can expect to pay significant costs to the technologists and other consultants necessary to devise and implement a predictive coding workflow both before and after the processing fees are incurred.
  2. Predictive coding can result in the review of a much larger “potentially relevant” document set. When predictive coding is used to cull the data in lieu of keywords, there is no guarantee that it will result in fewer documents to review. In fact, it is possible that predictive coding will generate a much larger “potentially relevant” document set — and, along with it, a larger legal bill. This occurs because the predictive coding technology often identifies documents as potentially relevant even though the documents do not contain any agreed-upon search terms .
  3. Your subject matter experts are going to be reviewing more documents with predictive coding. Many corporations and law firms now use review vendors to conduct the majority of their document review, with outside counsel serving as subject matter experts (SMEs) that provide guidance and ensure the quality of the review team’s efforts. In a predictive coding workflow, the SMEs will continue to perform the quality-control and oversight function, but often also are responsible for performing first-pass review necessary to establish a control set and train the predictive coding technology. While some predictive coding providers will suggest as a rule that this process will require your SMEs to review 5,000 documents or fewer, in practice we have seen that SMEs review many times that number depending on the prevalence of relevant document in the data set and the margin of error you hope to achieve. Add to this the fact that there is an increasing trend for the SMEs to review a larger proportion of likely relevant documents as part of the manual review process, and in some cases SME fees may exceed the total costs of a traditional review of search-term results by a review vendor.
  4. You’re going to spend significant time and money negotiating a predictive coding protocol. At this point in its life cycle, neither litigants nor the courts have developed established ESI protocols or templates defining how parties can or should implement predictive coding. Many parties (or at least plaintiffs) currently suggest using the Actos protocol as a starting point for negotiation, but it requires a level of joint training and review that in most cases is impracticable and impossible to implement. As such, parties are spending an inordinate amount of time and money negotiating and developing ESI protocols on a case by case basis. While this cost will dissipate as the use of predictive coding becomes more prevalent, it likely will be part of any predictive coding process for the foreseeable future.

How to Minimize Predictive Coding Costs

So given these predictive coding cost drivers, are we suggesting that clients should uniformly shun predictive coding and hold fast to search term based culling and manual document review? Absolutely not. Here at DiscoverReady, we are huge proponents of predictive coding. We have been recognized as leaders in providing automated discovery solutions, and believe it’s critical that we actively work with our clients to identify and develop discovery solutions that employ appropriate technology based on the requirements of each case. As such, we take steps on every project to accurately account, budget for, and where appropriate mitigate, the impact of these cost drivers. These steps include:

  • Creating statistical samples to compare the prevalence of relevant data in a client’s data set as identified by a search term and predictive coding based process. This allows us to accurately project the volume of documents the SMEs will have to review to train the predictive coding technology, as well as the volume of documents identified as likely relevant and potentially subject to review as a result of each process.
  • Preparing budgets based on the result of the statistical sampling that give our clients visibility into the process .
  • Consulting on methodologies to modify either the proposed training criteria for the predictive coding tool or search term syntax while validating the results with real time measurements of the precision and recall of each process.
  • Relying on our extensive experience designing and implementing predictive coding based workflows, and leveraging our knowledge (and templates) from other matters to reduce the fees associated with negotiating and implementing an ESI process built around predictive coding.

Armed with these steps, we can help our clients identify the matters where predictive coding based culling is appropriate….and also those cases where the costs ultimately may be more than they bargained for.

1Recognizing that this is a topic for a separate blog, the likelihood of a predictive coding process generating more documents to review hinges on two important points.

  • First, predictive coding likely will result in increased costs only if you would have been able to negotiate very narrow, targeted search terms that would have returned a very high percentage of relevant documents. These narrowly construed search terms would exclude from the review population a significant portion of likely irrelevant data — as well as data that predictive coding technology might deem as potentially relevant. This scenario obviously does not frequently arise when you are dealing with very broad search terms.
  • Second, and relatedly, if you appropriately utilize a predictive coding process, you can expect a smaller percentage of likely irrelevant documents in your review set. In a scenario where search-term-based culling would result in a smaller review population, the additional documents identified through the predictive coding process represent likely relevant documents that either do not contain a search term or otherwise would have been excluded from the review population.

You address each of these factors and ensure a smaller review population that is more likely to contain relevant data by combining search term and predictive coding culling methodologies. This is accomplished by applying broad search terms to the document collection, and then utilizing predictive coding to further cull the data set to exclude irrelevant data that hit on the keyword searches. However, it’s important to recognize that with the limited exception of the very recent In re Biomet M2a Magnum Hip Implant Products Liability Litigation (MDL 2391), the reported decisions and litigants are not contemplating a hybrid search term/predictive coding based approach. Moreover, given that the whole thrust of predictive coding is to utilize a culling methodology that presumably is superior to search terms, an opposing litigant who is championing the use of predictive coding likely will oppose (perhaps unsuccessfully in light of Biomet) the idea of applying search terms to the data prior to application of the predictive coding technology.

Order Highlights Potential Costs of Predictive Coding

Predictive coding continues to gain momentum as a hedge against spiraling discovery costs. But if you assume that its usage will dramatically lower costs every time, you might be surprised by a recent patent infringement case in California. Although Staples may have an “easy” button, there is no such thing as an automatic “cheap” button when it comes to predictive coding.

The February 1 order issued by Judge Anthony Battaglia in Gabriel Technologies Corp. et  al. v. Qualcomm Inc. et al., reveals the potentially high costs associated with predictive coding, and demonstrates that predictive coding might not always be a huge cost saver when compared to manual review. The order, which awards sanctions to Qualcomm based on a finding that Gabriel’s patent infringement claims were frivolous, is not a discussion of predictive coding process and methodology in the vein of Da Silva Moore, Kleen, or the widely disseminated Actos protocol. However, in granting Qualcomm’s request for attorneys’ fees, the order provides some rare visibility into the costs that can be associated with predictive coding technologies. Judge Battaglia’s award granted Qualcomm:

  • $2.8 million for fees associated with computer assisted, algorithm driven document review, and
  • $392,000 for contract attorneys who reviewed the documents that the predictive coding technology determined were likely to be responsive based on the training it received.

This award of almost $3.2 million (which presumably does not include the fees incurred by Qualcomm for its outside counsel to develop and implement the solution and to supervise and review the results of the contract attorneys’ efforts) has been cited widely as an example of the “costs” of predictive coding. However, a careful analysis of the way predictive coding was used by Qualcomm illustrates not just the mere fact that predictive coding can be expensive — which it can be — but how the method of implementing the predictive coding solution significantly drives the cost.

The Implementation of Predictive Coding in Qualcomm:

As Judge Battaglia’s order explains:

Over the course of this litigation, Defendants collected almost 12,000,000 records — most in the form of Electronically Stored Information (ESI) … Rather than manually reviewing the huge volume of resultant records, Defendants paid [their e-discovery vendor] to employ its proprietary technology to sort these records into responsive and non-responsive documents … the [vendor’s] algorithm made initial responsiveness determinations for more than one million documents.

Following this process, contract attorneys manually reviewed the subset of likely responsive documents for “confidentiality, privilege, and relevance.” The court recognized that “the review performed by the [vendor] and [contract attorneys] accomplished different objectives, with the [vendor’s] electronic process minimizing the overall work for [the contract attorneys].

Although some of the more granular details are not set out in the order, some pertinent facts about the use of predictive coding are as follows:

  • Qualcomm incurred a “predictive coding charge” for the 12 million collected files (likely on a per-gigabyte or per-file basis).
  • Qualcomm used the predictive coding technology (in lieu of search terms or other more traditional approaches) to cull down the data set, and then subjected the subset of documents determined to have potential relevance to a human review for further analysis (a technique DiscoverReady endorses, and refers to as “Predictive CullingTM”).
  • Of the 12 million files that Qualcomm subjected to Predictive Culling, the software tool determined that slightly more than one million files – less than ten percent of the population – were likely to be responsive.
  • Contract attorneys then reviewed the likely responsive documents.

Object Lessons from Qualcomm:

The order in Qualcomm does not disclose particulars about the exact issues in dispute, the basis for Qualcomm’s large document collection, or the “traditional” search and review methodologies that may have been considered. In the absence of those details and the important context they would provide, it is difficult to evaluate the “merits” of predictive coding as applied to the matter or conduct a cost/benefit analysis of the technology and process. However, there are some important take-aways from the order:

  • The volume of data subjected to predictive coding will materially impact your technology costs. Significant cost savings can be achieved by identifying and culling out patently non-responsive documents (e.g., emails from non-business, non-relevant, and “spam” senders, non-relevant file types) and documents that are not appropriate for the predictive coding process (e.g., low text files, files without OCR or metadata) prior to application of the predictive coding technology.
  • Explore how to take full advantage of  automated review technology (even beyond predictive coding). In a very high-volume matter like Qualcomm, consider using analytical software tools not only to cull the data for responsiveness, but also to make presumptive privilege determinations (using a tool like PrivBank™) and issue coding decisions (using i-Decision™ for example). Such techniques can further reduce the number of documents sent to manual review.
  • A litigant cannot estimate the total cost of using predictive coding (or compare the cost of predictive coding to a more traditional review alternative) until it gathers information about the prevalence of relevant information (or “richness”) in the data set. In a collection of 12 million documents, for example, if the richness of responsive documents is one percent, the total costs of a predictive culling process will be materially different than if the richness was twenty percent. Before embarking down a document review path – whether using traditional or predictive coding methods – take a statistically appropriate sample and estimate the level of richness. Because this sampling process plays such an important role in both estimating and validating discovery efforts, DiscoverReady recently developed and released its Samplyzer™ tool.
  • Finally, recognize that the predictive coding process will require significant human involvement, both in training the technology and evaluating its results. These costs should be factored into your assessment of potential solutions. Consider both the volume of manual “eyes-on” document review that will be required to train the system and then evaluate the output of the application (which can be estimated using the sampling processes discussed above) and the cost of the human resources that will be conducting the manual review. Is it your outside counsel doing the review, a combination of your outside counsel and opposing counsel (as anticipated by Actos), or contract reviewers?  All these factors will have a material impact on costs.

Predictive coding is a proven driver of efficiency in discovery. But effective application of the technology still requires human expertise and judgment. As Gabriel Technologies highlights, the cost advantage of predictive coding depends on a multitude of details — and sometimes cost savings may not exist at all.