The case initially had over 1.5 million documents. After filtering, keyword and concept searching, about 600,000 documents were left to review. When you start a case, visualise that you have a huge mountain of documents to look at.
With active learning running in the background, the system watches and analyses the decisions that you make, right from the very first document that is reviewed and tagged relevant or not relevant.
All the while, active learning is still working in the background, analysing your decisions and improving its own accuracy.
In a typical review, the two document mountains will be fairly close together but, as the review goes on, you want them to move further and further apart. You want the valley between the documents to become more and more sparse, as these are the documents that the system is not sure about.
As your mountains separate, so does the risk of relevant or hot documents remaining unreviewed and lost within the not-relevant documents.
You carry on with prioritised review until the flow of relevant documents that the system is serving you dries up. Then you would move to a coverage review.