Thomson Reuters Enterprise Centre Gmbh

Khaled Ammar

Brian Alexander Zubert

Isabelle Moulinier

Tonya Custis

Gayle McElvain

Julian Brooke

Sakif Hossain Khan

Afsaneh Fazly

Hector Martinez Alonso

Kanika Madan

Diane Erickson

Gayle Mcelvain

Brian Zubert

The present disclosure relates to systems and methods for analyzing citationally related content and identifying, based on the analysis, a risk of impliedly overruled content. Embodiments provide for receiving case law data from a document source, for extracting a case triple that includes a first case overruling or abrogating a second case, and a third case citationally related to the second case. Features may be generated from case triple, such as natural processing language features comparing the language in the various cases of the triple, and feeding the generated features to a main classifier. In embodiments, the main classifier classifies the case triple into a class indicating the risk probability that the second case is impliedly overruled by the first case.

Systems and methods for identifying a risk of impliedly overruled content based on citationally related content

In some aspects, a method includes performing optical character recognition (OCR) based on data corresponding to a document to generate text data, detecting one or more bounded regions from the data based on a predetermined boundary rule set, and matching one or more portions of the text data to the one or more bounded regions to generate matched text data. Each bounded region of the one or more bounded regions encloses a corresponding block of text. The method also includes extracting features from the matched text data to generate a plurality of feature vectors and providing the plurality of feature vectors to a trained machine-learning classifier to generate one or more labels associated with the one or more bounded regions. The method further includes outputting metadata indicating a hierarchical layout associated with the document based on the one or more labels and the matched text data.

Representative document hierarchy generation

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Waterloo, Canada

Khaled Ammar