Thomson Reuters Enterprise Centre Gmbh

Thomson Reuters (grc) LLC

David Von Rickenbach

Nikolai Nefedov

Hella-Franziska Hoffmann

David Oliver

Johannes Schleith

Elizabeth Roman

Daniella Tsar

Kim Hau

Sanzio Monti

Dorcas Mbwiti

Guillaume Mosching

Josh Lemaitre

John G Kelly

Mans Olof-Ors

Marc Kukura

Mark Brusadelli

David Harper

The present disclosure is directed towards systems and methods for detecting deviations from a standard document in a document being analyzed. The inventive systems and methods include performing a first level analysis to detect portions of a standard that are identical to, similar to, deleted from, and added to a document being evaluated. A second level analysis may be applied to those portions of the standard that are similar, but not identical to, portions of the document being evaluated to assist a user in identifying similarities and differences between the two portions of text.

Systems and methods for document deviation detection

Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media for extracting taxonomies based on hierarchical clustering on graphs related to a corpus of documents and using said taxonomies for classifying and labeling documents. The hierarchical clustering of graphs include the adaptive pruning of nodes at each hierarchy based on betweenness centrality of nodes to form clusters that have modularity score exceeding a minimum modularity threshold.

Hierarchical clustering on graphs for taxonomy extraction and applications thereof

The present disclosure relates to systems and methods for enhanced mapping of transaction data to a target document, and for classifying line items of the mapped transaction data, using classification algorithms. Embodiments provide a system including a column mapping module to receive a target scheme specifying a target structure for the target document, receive transaction data having a source structure, and map at least one source column to at least one target column of the target columns based on application of classification algorithms to features identified from the source transaction data. The system also includes a row classification module to classify the rows of the mapped transaction data into classification categories. The system also includes a validation handler to receive validation input from a user, validating the column mapping and/or the row classification. The validating including accepting the recommendation or rejecting the recommendation and selecting a correct choice.

Systems and methods for enhanced mapping and classification of data

Exemplary embodiments of the present disclosure relate to systems and methods for providing information about a sector and geographic focus of a company and enables user to identify the top product/brands of a company. Trademark records can be leveraged and combined with other sources of data, which can be presented to a user in an integrated graphical user interface, to facilitate combinatorial business intelligence for one or more companies. Analytical data associated with trademark records corresponding to the company included in the search can be generated to provide a ranked list of trademarked names/brands for the company and a combined or integrated view can be generated in a graphical user interface that includes the ranked list of the trademarked names/brands for the company and also includes the recent news or other information related to the trademark names/brands.

Combinatorial business intelligence

Growing community of inventors

Baar, Switzerland

David Von Rickenbach