Servicenow Canada Inc.

Element Ai Inc.

Alexei Nordell-Markovits

Andrej Todosic

Marie-Claude Côté

Thomas Fortier

Pegah Kamousi

Fanny Lalonde Lévesque

Adam Salvail-Bérard

Systems and methods for adding process actions to the design of a robotic software process. A context-recognition module recognizes a current state of a process being designed, and passes information on that current state to a recommendation module. The recommendation module evaluates the current state and identifies at least one suitable process action to recommend in response to that current state. The recommendation module then recommends the at least one process action to the human designer. If the designer accepts the recommendation, a design module adds the process action to the process design. The recommendation module may also use information about previous actions in the process and in other processes when identifying suitable process actions. The context-recognition module and the recommendation module may each comprise at least one machine learning module, which may or may not be neural network based.

Context-based recommendations for robotic process automation design

Methods and systems for managing network resources enabling network services over a network and for managing maintenance of network resources. The method comprises determining a desired performance level for the network services, the desired performance level being associated with service metrics that establish compliance with a service level agreement; accessing internal data relating to operations of the network and external data not relating to operations of the network; generating a prediction of a network service pattern based on the desired performance level of the network services, the internal data and the external data; identifying a decision scenario for orchestration of the network resources, the decision scenario establishing a configuration of the network resources, the decision scenario being generated based on a correlation of the prediction of the network service pattern and availability of the network resources; and causing to adapt the network resources based on the decision scenario.

System and method for managing network resources

Systems and methods for routing a document based on the contents of this document. The content of this document is first subjected to a recognition process and then the result is subjected to multiple types of analysis. Based on the results of the analysis (including contextual analysis), a destination is determined along with any timelines detailed in the document. As well, a severity of the document, indicating the severity of consequences if the document is not handled quickly, is determined. Based on these, an urgency tag and/or a severity tag are assigned to the document. A final destination is determined based on the output of the analysis of the severity, the urgency, and of the destination.

Document routing based on document contents

Systems and method for use in assisting a user in data aggregation tasks. A system determines the type of data needed by the user to complete the data aggregation task and, based on an indication of the data needed, queries multiple data sources. The results from the multiple data sources are then collated and aligned as necessary. Inconsistencies in the data are resolved or flagged to the user for attention. A completed form or a presentation set of data is then presented to the user for validation.

Machine assisted data aggregation

Systems and methods relating to enhancing capabilities of robotic process automation systems. A system and method includes recognizing and analyzing the components of a user interface on which at least one task is to be executed. The task can be executed regardless of changes to the user interface as the components of the task are based on the presence and function of areas of the user interface and not on the location of the components necessary to execute the task.

Robust user interface related robotic process automation

A system for anomaly estimation for a telephonic call is described, receiving a call object including an identifier field associating the call object to a purported user; retrieving a user data object associated with the purported user, and processing the user data object to retrieve one or more vectorized user features associated with the purported user. A neural network processes the one or more vectorized call features and the one or more vectorized user features through a machine learning model.

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Montreal, Canada

Alexei Nordell-Markovits