Accenture Global Solutions Limited

Accenture Global Services Gmbh

Kaushik Sanyal

Arnab Chakraborty

Sanjay Sharma

Gurpreet Singh Bawa

Kaustav Pakira

Souvik Chakraborty

Vinoth Venkataraman

Ruchika Sachdeva

Vikas Kumar

Mayank Kant

Rajan Shingari

Jonathan Saginaw

Shoban Babu Balasubramani

Saket Bhardawaj

Kumar Saurabh

Michael Baldino, Iii

Juan Morlanes Montesinos

Fernando Rex Lopez

Arnab D Chakraborty

Deepthi Adimulam

Jonathan Stribley

Juhi Munjal

Youssef D Tuma

Laura Chittick

Muthumari Shanmugasundaram

Ketaki Deshpande

Ruchi Chaudhary

Meet Navinchandra Jivani

Michael Baldino Iii

Embodiments provide for prediction and mitigation of network faults. Information associated network nodes may be compiled and used to generate network analytical records (NARs). A first model may be executed against the NARs to predict faults associated with one or more nodes of the network. Tickets are generated for predicted faults and stored in a ticket database. The tickets may be analyzed to predict executable actions to mitigate the faults associated with each ticket. To analyze the tickets, ticket data may be compiled and used to generate ticket analytical records (TARs). A second model may be executed against the TARs predict actions to resolve the predicted faults. The predicted actions may be executed to mitigate the impact that the faults have on the network, which may include preventing the faults entirely (e.g., via preventative maintenance) or minimizing the impact of the faults via use of the predicted actions.

Intelligent node failure prediction and ticket triage solution

In some implementations, a device may receive unstructured interaction data identifying an interaction of a user with a user device. The device may receive historical unstructured interaction data identifying historical interactions of users and historical unstructured resolution data identifying historical resolutions to the historical interactions. The device may process the historical unstructured interaction data and the historical unstructured resolution data to determine historical structured interaction data and historical structured resolution data. The device may process the unstructured interaction data and the historical structured interaction data to determine pretext identifiers for the interaction of the user. The device may process the pretext identifiers and the historical structured resolution data to generate a resolution network identifying possible resolutions to the interaction of the user. The device may process the pretext identifiers and the resolution network to determine a resolution path identifying a resolution to the interaction of the user.

Utilizing machine learning models for determining an optimized resolution path for an interaction

A device may communicate with a group of devices to obtain data regarding a set of events occurring for the group of devices. The device may process the data regarding the set of events to remove a subset of data entries, from the data, that is associated with an anomalous event. A first layer of analysis may relate to the group of devices, a second layer of analysis relating to a set of sessions of operating a user interface via the group of devices, and a third layer of analysis relating to information provided via the user interface. The device may perform the multiple layers of analysis via a machine learning technique to identify an alteration relating to the information provided via the user interface. The device may alter the information provided via the user interface based on performing the multiple layers of analysis.

Video analytics device

A method may include determining first network data corresponding to a first time period, including alarm data and incident data collected by a set of network devices of a core network during the first time period. The method may include creating, based on the first network data, an incident prediction model. The method may include receiving second network data, associated with the core network, corresponding to a second time period. The second network data may include alarm data and incident data collected by a network device, of the set of network devices, during the second time period. The method may include generating, based on the second network data and the incident prediction model, an incident prediction that includes a prediction whether the network device will experience an incident during a third time period.

Core network analytics system

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Bengaluru, India

Kaushik Sanyal