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AssigneeDiscoverReady LLC
InventorsYerachmiel Tzvi Messing, David Matthew Shub, James Kenneth Wagner, Jr.
Estimating the term…
About 5 years 5 months of term left · ends 1 Apr 2032 (estimated)
A litigation discovery document system is provided to help identify documents that might be privileged. In particular, a system is described in which documents within a document set are compared against one or more data sources which store data that indicate whether a particular document is privileged or potentially privileged. Human reviewers can confirm whether such determinations are accurate. Analytic reports can be provided which characterize the underlying methodology used to make such determinations. Related apparatus, systems, techniques and articles are also described.
A method for implementation by one or more data processors of at least one computing system comprising:
A non-transitory computer program product comprising instructions, which when executed by one or more data processors, result in operations comprising:
A system comprising:
A method for implementation by one or more data processors forming part of at least one computing system, the method comprising:
Dependencies are inferred from the claim text.
The subject matter described herein relates to techniques for identifying and classifying potentially privileged and privileged documents.
Technology has exponentially facilitated the capacity to generate, share and store information, acting as the backbone of our knowledge-based economy. But that same technological capacity that supports our economy is negatively impacting our justice system. It has created enormous problems for trial lawyers and their clients, who are regularly challenged to sift through terabytes of information to find relevant, privileged or confidential material in a way that is accurate, consistent and yet cost-effective.
When one case can require sorting through a terabyte of data, the result can be 75 million pages for review, taking up to 375,000 hours of time. Studies have estimated that document review accounts for 50 to 90 percent of the cost involved in litigation matters, and one report has suggested that 70 percent or more of the cost of discovery is now attributable solely to human review of documents for responsiveness and privilege.
FIG. 1 is a process flow diagram illustrating a method for characterizing whether documents are potentially privileged;
FIG. 2 is a system diagram illustrating a system for identifying whether documents are potentially privileged;
FIG. 3 is a screenshot illustrating domain comparison search results and generated keyword search strings;
The current subject matter describes methods, systems, apparatuses, and articles (sometimes referred to herein as “PrivBank” or the “PrivBank system”—PrivBank being a registered trademark of DiscoverReady LLC) for collecting information and insight gained from reviewing more than 10 million electronic files (100 million pages) for privileged material over hundreds of cases and from thousands of hours of independent research. The current subject matter can be implemented in connection with a wide variety of platforms including, without limitation, the platform (and related systems, methods, and articles) described in co-pending application Ser. No. 13/014,643, the contents of which are hereby fully incorporated by reference, as well as the i-Decision® platform by DiscoverReady, LLC.
With reference to FIG. 1, a method 100 is illustrated in which, at 110, a set of documents is imported/received. Thereafter, at 120, attributes of each of the documents are compared with a plurality of predefined indicators that characterize a likelihood of the document containing such attributes being privileged. Based on this comparison, at 130, it is determined which of the documents are potentially privileged based on the comparison. Data can then be provided, at 140, that identifies which of the documents are potentially privileged.
The attributes can comprise content of the documents and/or contextual information associated with the document. Example attributes include, but are not limited to: terms within a document, metadata, document ID, sending entity, receiving entity, copied entity, blind copied entity, subject line, date and/or time sent, date and/or time received, file name, file size, file checksum, reviewed, hash, attachment, confidentiality, author, title, file type, document extension, pre-defined category, privilege, and pre-defined issues.
Details of database functions independent of the retrieved data types: document management systems
Clustering; Classification: into predefined classes
Computing and data processing
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