Deckard Technologies, Inc.

Nickolas Del Pego

Gregory Gordon Rose

Craig Brown

Anthony Moriarty

Jessica Flanagan

Weikai Xie

Claire Hardgrove

Provided herein are media, systems, and methods that identify the address of a rental property from public sources (e.g., rental listings, maps, and county property records). A data mining task process may be performed, by each of a plurality of first data ingestion interfaces to a unique external property data source, to determine at least one property record depiction, each property record depiction associated with a property record. A first machine learning algorithm may be applied to the at least one rental property depiction and the at least one property record depiction from each data source to identify one or more common property records, wherein each common property record comprises a property record that refers to the rental property. A data mining task process may be performed, by a second data ingestion interface to the one or more common property records to determine the street address of the rental property.

Identifying and validating rental property addresses

Described are medias, systems, and computer-implemented methods to detect improper residency status by performing a data mining task to data source to detect one or more improper residency indicia, applying a machine learning algorithm to identify an initial candidate, calculating a probability that the initial candidate has an improper residency status, and validating the detection. Further described are medias, systems, and computer-implemented methods to detect improper occupancy tax status. Further described are medias, systems, and computer-implemented methods to detect an improper homeowner exemption.

Detecting and validating improper homeowner exemptions through data mining, natural language processing, and machine learning

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Escondido, CA, United States of America

Nickolas Del Pego