First American Financial Corporation

Mark M Fleming

Yan Qiu

David Harry Blackman

Techniques are described for collecting descriptions of an entity from different data sources and using a numeric comparison and textual centrality measure to automatically select a best description. In one implementation, a method includes: retrieving a real property description dataset, the real property description dataset including descriptions from multiple data sources that describe the real property; extracting, from each of the descriptions, numbers that identify the property; performing a numerical comparison of the numbers extracted from each of the descriptions to determine if any descriptions needs to be discarded from further consideration; applying a text cleaning process to normalize the descriptions; and performing a textual centrality measure of remaining descriptions to determine a level agreement of each of the remaining descriptions with each of the other remaining descriptions; and using at least the textual centrality measure to select a description. The selected description may be used to populate a document.

System and method for automated selection of best description from descriptions extracted from a plurality of data sources using numeric comparison and textual centrality measure

Some implementations of the disclosure are directed to retrieving a property description describing a real property; generating a numeric representation of the property description; determining one or more keywords of a keyword list present or absent in the property description; generating one or more values corresponding to a presence or absence of the one or more keywords in the property description; combining the numeric representation of the property description with the one or more values to create a combination; and computing, based on the combination, a most likely classification of the property description from a plurality of classifications.

System and method for automated classification of structured property description extracted from data source using numeric representation and keyword search

Some implementations of the disclosure are directed to retrieving a structured description; using a model trained on a plurality of structured descriptions to generate a numeric representation of the retrieved description; searching the retrieved description for a plurality of keywords in a keyword list to generate one or more values corresponding to a presence of the plurality of searched keywords; combining the numeric representation of the structured description with the generated one or more values to create a combination; using the combination to compute a most likely classification of the description from a plurality of classifications; and returning the most likely classification.

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Santa Ana, CA, United States of America

Mark M Fleming