Thomson Reuters Enterprise Centre Gmbh

Matthew A Surprenant

Tonya Custis

Gayle McElvain

Scott Francis

Erik Lindberg

Steven Sidwell

Ryan Kelly

Eric Milles

Eric Holten

Sameena Shah

Armineh Nourbakhsh

Xiaomo Liu

Michael Dahn

Thomas W Vacek

Merine Thomas

Quanzhi Li

Xin Shuai

Mike Dahn

Matt Makosky

Tom Vacek

Matthew Makosky

Gayle Mcelvain

The present disclosure relates to systems and methods for generating contextually, grammatically, and conversationally correct answers to input questions. Embodiments provide for linguistic and syntactic structure analysis of a submitted question in order to determine whether the submitted question may be answered by at least one headnote. The question is then further analyzed to determine more details about the intent and context of the question. A federated search process, based on the linguistic and syntactic structure analysis, and the additional analysis of the question is used to identify candidate question-answer pairs from a corpus of previously created headnotes. Machine learning models are used to analyze the candidate question-answer pairs, additional rules are applied to rank the candidate answers, and dynamic thresholds are applied to identify the best potential answers to provide to a user as a response to the submitted question.

Systems and methods for generating a contextually and conversationally correct response to a query

The present disclosure is directed towards systems and methods for assisting with legal research and for aiding in the discovery of information and documents relevant to a user's research focus. The inventive systems receive identifications of relevant documents explicitly or implicitly from a user's research session and, based on issues relevant to those documents and other connections to those documents, recommends similar documents to the user for their consideration. Recommendations may be ranked and filtered and the recommendations and their ranking may be based at least in part on aggregated research session information from previous research sessions.

Legal research recommendation system

The present disclosure is directed towards systems and methods for assisting with legal research and for aiding in the discovery of information and documents relevant to a user's research focus or input text. The inventive systems receive identifications of relevant documents explicitly or implicitly from a user's research session or input text and, based on issues relevant to those documents, texts and other connections to those documents and texts, recommends similar or helpful documents to the user for their consideration. Recommendations may be ranked and filtered.

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Saint Paul, MN, United States of America

Matthew A Surprenant