Veritas Technologies LLC

Symantec Corporation

Abbyy Production LLC

Nelson Murray Wiggins

Gary Steven Lehrman

Kurt Leafstrand

Michael J Coyle

Karen Williams

Chris Foote

Ben Heymink

Venkat Rangan

Malay Desai

Chitrang Shah

Rohit Dinakar

Virali Sheth

Terence Chang

Tatiana Parfentieva

Anil Khadka

Donna Lynn Marcelino

A count of documents similar to a reference document is determined based on a plurality of similarity ratings. Each similarity rating may be based on a number of co-occurring terms between the reference document and the corresponding similar documents. A graphical user interface (GUI) may be provided. The GUI may include a GUI element that is associated with the similar documents. Furthermore, the GUI element may include a visual representation of a number of documents for each similarity rating that are retrievable based on a selection of the corresponding similarity rating. The GUI element may be provided prior to retrieving one of the similar documents.

User interface for finding similar documents

A disclosed computer-implemented method for displaying search results may include (1) receiving a query to search a document database that stores a document family containing a parent document and a child document embedded within the parent document, (2) in response to receiving the search query, identifying (a) an individual relevance score that quantifies the relevance of the parent document to the search query and (b) a family relevance score that quantifies the overall relevance of the entire document family to the search query, and (3) simultaneously depicting both the individual relevance score and the family relevance score in response to the search query. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for displaying search results

Portion of a display screen with a transitional graphical user interface

Display screen with graphical user interface

A computing device identifies concept terms related to an input phrase based on data in a data set. The input phrase defines an initial scope of a concept search. The computing device presents the concept terms in a graphical user interface (GUI) and a GUI element in the GUI to represent the input phrase. Upon a selection of at least one concept term, the computing device presents a visual representation of a relationship between the selected concept term(s) and the input phrase in the GUI using the GUI element, and a count of documents available to be retrieved in the GUI based on the relationship.

User interface for transparent concept search

A computer-implemented method for expanding search results may include (1) determining that a user is attempting to perform a search, (2) presenting an option to the user to include, within results of the search, a document family associated with a found document, (3) determining that the user has selected to include the document family associated with the document, and (4) including the document family in the search results in response to determining that the user has selected to include the document family associated with the document. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for expanding search results

A computing device determines counts of documents that are similar to a reference document for a set of similarity ratings. Each similarity rating is based on a number of co-occurring terms between the reference document and corresponding similar documents. The computing device present the reference document and a GUI element pertaining to the documents similar to the reference document in a graphical user interface (GUI). Upon a selection of the GUI element, the computing device presents a visual representation of a correlation between the counts of similar documents and the similarity ratings in the GUI. The visual representation is provided prior to displaying at least one of the similar documents.

An exemplary predictive coding system can be programmed to update a plurality of training documents based on a portion of a training document selected by a user. The predictive coding system generates a machine learning engine based on the updated plurality of training documents. The predictive coding system predicts a classification for one or more remaining documents from the plurality of training documents using the machine learning engine.

Enhancing training of predictive coding systems through user selected text

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San Jose, CA, United States of America

Nelson Murray Wiggins