Abstract: 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.

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.

Abstract: 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.

Abstract: 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.

Abstract: Large amounts of data resulting from periodic sampling and capture of data corresponding to variable operational parameters of one or more systems, such as a plurality of vehicles, and stored in a large number of rows of data corresponding to the number of captured samples are summarized in an analytically meaningful way by simply counting the instances of particular values of the operational parameters of interest in the form of a histogram and stored as a single row of data. The reduction in number of rows of data allows acceleration of processing of later queries by a factor of one thousand (assuming an average trip length of ten minutes) or more.

Abstract: Large amounts of data resulting from periodic sampling and capture of data corresponding to variable operational parameters of one or more systems, such as a plurality of vehicles, and stored in a large number of rows of data corresponding to the number of captured samples are summarized in an analytically meaningful way by simply counting the instances of particular values of the operational parameters of interest in the form of a histogram and stored as a single row of data. The reduction in number of rows of data allows acceleration of processing of later queries by a factor of one thousand (assuming an average trip length of ten minutes) or more.