Fmr Corp.

Deepak Venkatachalapathi

Seth Warren Brooks

Dmitry Bisikalo

Biju Kozhikode Kizhakhemadtil

Aishwarya Vijay Wesanekar

Suresh Nalliakodan

Rijesh Panicker

Subramaniam Vaithiyalingam

Sharon Botwinik

Methods and apparatuses are described in which unstructured computer text is analyzed for generation of an enforcement action database. A computing device receives a digital document comprising a corpus of unstructured text and converts each sentence into tokens. The computing device identifies entities in the tokens and classifies each sentence as relevant or not relevant to an enforcement action. For each relevant sentence, the computing device generates a record in a first data structure and generates a record in a second data structure. The computing device maps the records in the first data structure to the records in the second data structure based upon an enforcement attribute to generate a third data structure. The computing device aggregates the records in the third data structure based upon a name of the entity and a type of the entity in the sentence to determine an aggregated first enforcement attribute for each entity.

Automated analysis of unstructured computer text for generation of an enforcement action database using machine learning

Computerized systems and methods for identifying a table in a document include: removing from a document content other than text characters and associated size, position and format information; converting each text character into a block covering the corresponding text character; converting each page of the document into a corresponding image file; drawing a set of horizontal lines spanning a width of the document, each block super-scored and under-scored by at least one of the horizontal lines; drawing a set of vertical lines spanning all or a portion of a length of the document; removing a subset of redundant vertical lines; and determining, based on the set of horizontal lines and the subset of vertical lines, (i) a set of table coordinates corresponding to a table in the document, and (ii) one or more sets of cell coordinates corresponding to one or more cells in the table.

Systems and methods for table extraction in documents

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Chennai, India

Deepak Venkatachalapathi