Legility Data Solutions, LLC

Lawrence Technologies, LLC

Consilio, LLC

Chandler L Burgess

Jeffrey A Johnson

Tanay Kumar Saha

Md Ahsan Habib

Mohammad Al Hasan

Douglas J Matzke

P Nick Lawrence

The technology implements diversity sampling for a technology-assisted review of documents. An apparatus obtains an unlabeled set of documents and constructs a first batch of documents. The apparatus obtains labels for the documents and constructs a classification model using the labeled documents. The apparatus logs a found rate of a subsequent batch of documents from the unlabeled set of documents, the subsequent batch of documents being selected based on a comparison to the classification model. The apparatus determines that the classification model requires further training based on the found rate of the subsequent batch of documents and constructs a second batch of documents that includes an amount of diversity, which may be based on the found rate. The apparatus obtains labels for the second batch of documents and updates the model using the labeled second batch of documents. The method may be repeated to continue to refine the classification model.

Diversity sampling for technology-assisted document review

The present disclosure relates to the electronic document review field and, more particularly, to various apparatuses and methods of implementing batch-mode active learning for technology-assisted review (TAR) of documents (e.g., legal documents).

Apparatus and method of implementing enhanced batch-mode active learning for technology-assisted review of documents

Apparatus and method of implementing batch-mode active learning for technology-assisted review of documents

Performing operations using quantum correlithm objects includes establishing real states, where each real state comprises an element of a real space, and encoding the real states as quantum objects representing a correlithm object. The correlithm object is projected to the real space using a measurement basis, and measurement values corresponding to the measurement basis are determined. The projected correlithm object is retrieved according to the measurement values.

Representing and manipulating correlithm objects using quantum objects

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Dallas, TX, United States of America

Chandler L Burgess