- Washington DC, US John David FLEIG - Hilliard OH, US Dmitrii Arkadyevich POLSHAKOV - Columbus OH, US Jeffrey Michael WILSON - Upper Arlington OH, US Rodney Laroy FULFORD - Reynoldsburg OH, US Yi DENG - Powell OH, US Philippe Yves Bertrand AYALA - Columbus OH, US Donald Eugene SWARTWOUT - Powell OH, US Nicholas Thady COCKROFT - Columbus OH, US
International Classification:
G06N 20/20 G06N 5/04
Abstract:
In some embodiments, a computer implemented method for identifying conflicting prior art is provided. The method may include: receiving a set of target conflict citations from a database; generating a first data set based on the conflict citations; decorating the first set of data with one or more features from the set of target conflict citations; generating a training set based on the first data set; training multiple data models using the training data set to identify one or more conflict citations; selecting a data model from the multiple data models; receiving a search document; generating a data set of potential prior art related to the received search document: generating, by the selected model, a ranked list of potential conflict citations based on the potential prior art; and outputting the ranked list.
Systems And Methods For Performing A Computer-Implemented Prior Art Search
- Washington DC, US John David FLEIG - Hilliard OH, US Dmitrii Arkadyevich POLSHAKOV - Columbus OH, US Jeffrey Michael WILSON - Upper Arlington OH, US Rodney Laroy FULFORD - Reynoldsburg OH, US Yi DENG - Powell OH, US Philippe Yves AYALA - Columbus OH, US Donald Eugene SWARTWOUT - Powell OH, US Christopher Ryan Gessner - Columbus OH, US
In some embodiments, a computer-implemented method for retrieving a similar document from a corpus of documents is provided. The method may include receiving a search document comprising a set of words; applying a first encoder to generate a first vector; applying a second encoder to generate a second vector; determining a first similarity between the first vector of the search document and the first vector of each document of the corpus of documents; determining a second similarity between the second vector of the search document and the second vector of each document of the corpus of documents; generating a first ranked list of documents based on the first similarity; generating a second ranked list of documents based on the second similarity; applying a voting algorithm to determine a score associated with each document; and outputting a third ranked list of documents based on the determined score.