SUCCESS STORY
Major food & beverage
manufacturer
Industry• Food & beverageSolution• OpenText™ Insight Predict

Major food & beverage manufacturer
serves up high-efficiency review
Corporation almost doubles recall with continuous active learning (CAL)
approach from OpenText Insight Predict
CAL achieves on average a 2:1 efficiency
ratio compared to 10:1 rates for keyword
search and other TAR systems.

Results
Achieved an estimated recallof 75 percent
Found relevant documents faster,easier and cheaper using CAL
Met tight 30-day
production deadline

Major food & beverage manufacturer serves up high-efficiency review

A leading food and beverage manufacturer faced a potential
class action related to allegations of deceptive food packagelabeling and ingredients. The client and its law firm needed
to review approximately 175,000 documents and produce therelevant documents to the plaintiff within 30 days to determine
whether the litigation would be certified as a class action.
Even when inefficiencies and coding mistakes initially hindered thereview process, OpenText™ Insight Predict corrected for inefficiencies
and significantly outperformed other search and review methods witha continuous active learning (CAL) approach.
Continuous active learning: CAL continuously integrates thejudgments made by the review team and then uses those judgments
to constantly re-rank the entire document collection. Unlike earlier
generation TAR systems based on simple passive learning (SPL) andsimple active learning (SAL), no control set is needed, thus eliminating
the time and costs associated with a senior lawyer reviewing
thousands of documents to build a control set, train and then test theresults. CAL also accommodates rolling uploads without requiringa re-training of the system, thus saving even more time and moneycompared to other TAR tools.
High efficiency: CAL can achieve, on average, a 2:1 efficiency ratio.
In the perfect world of a simulation, a lawyer would have to review
two documents to find one relevant document during the course of
the review. Efficiency is another way to look at precision; an efficiency
rate of 2:1 equates to 50 percent precision. Keyword search, and evenother TAR systems based on active learning, typically achieve an
average of 10:1 efficiency rates.

Even when inefficiencies and coding mistakes initially hindered
the review process, OpenText Insight Predict corrected for
inefficiencies and significantly outperformed other methods.
Contextual diversity: The integration of contextual diversity intoCAL, which is unique to Insight Predict, ensures that legal teams do
not miss pockets of potentially relevant documents. The contextual
diversity algorithm identifies documents based on how significant andhow different they are from the ones already seen, and then selects
exemplar documents that are the most representative of thoseunseen topics for human review.
Before integrating a CAL approach, the law firm for the food andbeverage manufacturer reviewed the first 8,500 documents that
had been prioritized using key custodian, date and search terms.
During the course of its priority review for production, however,the firm faced a number of obstacles.
Because the attorneys had not used CAL on previous cases and werethus unfamiliar with how it works, they determined that they needed
to review each and every one of the 8,500 documents based on thebelief that they could use this set to “seed” the system for further
review later on.

Major food & beverage manufacturer serves up high-efficiency review

Also, the coding itself was conducted by a review team that had notbeen properly trained on the subject matter and issues related to thecase. In addition to miscoding nonrelevant documents as relevant, thereviewers also missed significant pockets of unreviewed information.
Further, the client did not implement recommended QC process to checkthe attorneys’ coding calls. Unfortunately, that decision meant that thecoding errors were not identified until the second phase of the review.
During the second phase, an additional 165,000 documents wereloaded into the Insight Predict system. In just five days, an additional
3,640 documents were identified as relevant. When the batches
started consistently containing few relevant documents, the team took
another random sample for an updated recall estimate. This sample
showed the team had only reached an estimated recall of 40 percent—
substantially below its goals for a final production. It was at this pointthat the review team and OpenText analysts and data science experts
discovered the coding discrepancies that had resulted from lack ofreviewer training. The team “paused” for retraining, and then thethird phase of the review commenced.
OpenText simulations illustrated the Insight Predict algorithm canquickly self-correct after large-scale coding revisions, and a review
does not need a large seed set coded by a subject matter expert
to jumpstart the machine learning. In fact, the simulations showedthat the review would have been slightly more efficient had the teamstarted off by coding a 100 document random sample instead of thetargeted 8,500 document set.

Back on track, the review team found an additional 5,400 responsive
documents. At the point in the review when the Insight Predict batchrichness, or precision, was less than 10 percent for two consecutive
days, the team determined it should stop the review. A final sample
indicated 89.4 percent recall.
By using Insight Predict to prioritize a document review, review teams
found more relevant documents more quickly, with less effort and at
substantially lower cost. Even with the inefficiencies introduced by
the client’s workflow and inadequate reviewer training, Insight Predictproduced exceptional results. The teams reached the 80 percent
recall level after reviewing only about 36,000 documents with Insight
Predict, whereas with a standard linear review, they would have had toreview over 130,000 documents to reach that same recall level.
The client’s use of CAL to accelerate the review resulted in the following:• Comparatively high efficiency rate of 3.1:1 at 75 percent recall
despite inefficient workflow in the first two phases of the review.
• Estimated recall of 89.4 percent for responsive production.
• All production timelines met.

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