BACKGROUND OF THE INVENTION

Field of the Invention

The present invention is directed generally to methods of identifying relevant documents within a document corpus.

Description of the Related Art

Electronic Discovery (“E-Discovery”) is a field that addresses identification and production of electronic evidence (referred to as “documents”) relevant to a digital investigation or litigation. The process of identifying documents relevant to a legal dispute typically involves three phases:1. A document collection phase during which documents are harvested from information systems and indexed in a searchable database to establish a document corpus;2. An Early Case Assessment (“ECA”) phase during which queries and analytic operations are run against the document corpus to eliminate irrelevant documents and narrow the potentially relevant document universe prior to a human review phase; and3. A human review phase during which attorneys make human determinations as to the relevance of each document in the document corpus.

Mounting document corpora have made human review increasingly time consuming and costly. Each relevance determination made by an attorney through human review costs approximately $1.25 based on industry averages. In a modern litigation, initial document corpora regularly exceed 10 million (“MM”) potentially relevant documents, of which less than 1% are often deemed relevant. Because of the significant time and cost associated with human review, eliminating irrelevant documents from the document corpus prior to human review is a high priority. As a result, automated methods for reducing the document corpus prior to human review have become essential to the successful execution of an E-Discovery project.

Various document retrieval methods have been established for identifying a subset of documents that require human review, including conceptual analytics techniques (e.g., Latent Semantic Indexing), Boolean searching, and metadata-based analytics (e.g., communication analysis). Most document retrieval methods result in a binary classification (positive or negative) and, as a result, may be validated (or invalidated) through statistical sampling to estimate a recall rate and a precision value for the results.

A perfect E-Discovery document retrieval model would identify all relevant documents within the larger document corpus (or have a recall rate=1.0) and without generating any false positives (or have a precision value=1.0). In such a scenario, attorneys would not be required to review any irrelevant documents, resulting in maximum time and cost savings.

