Accenture Global Solutions Limited

Javier Miguel Sastre-Martinez

Paul M Walsh

Md Faisal Zaman

Nóirín Duggan

Aisling Nugent

Caitlin McDonagh

Daire Corley-Carmody

In some implementations, a system may receive non-deterministic finite state automata (NFSA) to represent a set of dialog flows associated with a human-machine interface. The system may generate a deterministic finite state automaton (DFSA) that includes a minimum set of states that represents all dialog flows included in the set of dialog flows represented in the NFSA and does not represent any dialog flows that are not included in the set of dialog flows represented in the NFSA. The system may traverse the DFSA to identify a set of K paths that have a highest total weight based on a weight assigned to each transition in the DFSA. The system may prune the DFSA to remove any states and any transitions that do not belong to the set of K paths. The system may generate an output related to one or more subsets of the set of K paths.

Dialog flow inference based on weighted finite state automata

A device may generate, from a subset of historical ontology data and a substance description of a substance, a knowledge base. The subset of historical ontology data may be associated with historical substances. The device may generate, based on the knowledge base, a substance knowledge graph embedding (KGE) that is representative of the substance; compare the substance KGE and a historical KGE associated with the knowledge base; determine, based on comparing the substance KGE and the historical KGE, a similarity score associated with the substance KGE and the historical KGE; determine, based on the similarity score, whether substance data associated with a related substance is similarly represented in the substance KGE and the historical KGE; and perform, based on whether the substance data is similarly represented in the substance KGE and the historical KGE, an action associated with the related substance relative to the substance description or the knowledge base.

Substance description management based on substance information analysis using machine learning techniques

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Dublin, Ireland

Javier Miguel Sastre-Martinez