Accenture Global Solutions Limited

Sattish Sundarakrishnan

Prakash Ghatage

Naveen Kumar Thangaraj

Nirav Sampat

Kumar Viswanathan

Kaustubh Kurhekar

Rahul Kotnala

Prakash Patil

Earnest Paul Wesley

Shishir Kumar Roy

Yakob Raja R

Kavitha Subramanian

Richard Stephen Vincent Price

Indrajeet

Nirav Jagdish Sampat

An Artificial Intelligence (AI) Driven multi-platform, multi-lingual translation system analyzes the speech context of each audio stream in the received audio input, selects one of a plurality translation engines based on the speech context, and provides translated audio output. If audio input from multiple speakers is provided in a single channel then it is diarized into multiple channels so that each speaker transmitted on a corresponding channel to improve audio quality. A translated textual output received from the selected translation engine is modified with sentiment data and converted into an audio format to be provided as the audio output.

Multi-platform voice analysis and translation

A device may receive log data from application logs associated with applications, service logs associated with services, and server logs associated with server devices. The device may store the log data. The device may perform natural language processing on the log data to convert the log data into event data identifying events associated with categories. The device may process the event data, with a first machine learning model, to identify patterns in the event data and to generate an alert based on the patterns. The device may process the event data, with a second machine learning model, to generate a correlation matrix for the event data and to predict an event based on the correlation matrix. The device may process the event data, with a third machine learning model, to classify the event data based on the categories and to generate a recommendation based on classifying the event data.

Utilizing machine learning models with a centralized repository of log data to predict events and generate alerts and recommendations

An Artificial Intelligence (AI)-based data transformation system receives an input package and enables automatic execution of one or more processes in a robotic process automation system (RPA). The input package includes a plurality of documents and metadata required for the execution of the automated processes. The plurality of documents are categorized into a domain. Entities with their corresponding name-value pairs and entity relationships are extracted from the plurality of documents. An ontology is selected based on the domain. The entities are mapped to output fields identified from the selected ontology. The mappings thus generated are transmitted to the RPA system which employs the mappings to automatically execute the one or more processes.

Data transformations for robotic process automation

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Agasthiar Patti, India

Sattish Sundarakrishnan