CPC G06F 16/353 (2019.01) [G06F 16/316 (2019.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 5/025 (2013.01)] | 19 Claims |
1. A computer-implemented method, comprising:
receiving master data, transaction data, and one or more existing process models of a domain;
aggregating, based on domain knowledge ontology of the domain, the master data and the transaction data to generate a fact table;
converting entries in the fact table into vectors;
inputting the vectors into one or more machine learning algorithms to generate one or more algorithm outputs, at least one of the algorithm outputs corresponding to an improved process model, wherein the inputting the vectors into the one or more machine learning algorithms to generate the one or more algorithm outputs comprises:
inputting the vectors into a particular machine learning algorithm to generate one or more event sequences that comprise a plurality of events, and
filtering the one or more event sequences to identify an association with a particular action; and
providing the improved process model to the domain to replace one of the one or more existing process models.
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