Accenture Global Solutions Limited

Kaustubh Kurhekar

Prakash Ghatage

Naveen Kumar Thangaraj

Nirav Sampat

Kumar Viswanathan

Sreevidya Prasad

Ullas Balan Nambiar

Sattish Sundarakrishnan

Sriram Sankaranarayanan

Richard Stephen Vincent Price

Nirav Jagdish Sampat

A smart translation system that translates the input content received from an application based on translation metadata and the application is disclosed. It is initially determined if a translation of the input content exists in a user cache. If it is determined that the translation of the input content exists in the user cache, the translation is retrieved from the user cache. Else, if it is determined that the translation of the input content does not exist in the user cache, the domain and language contexts of the input content are determined and an automatic translation engine is selected based on the contexts and the translation metadata. The translated content is presented to the user via the application while maintaining the look and feel of the application.

Smart translation systems

An authentication and applications access system provides access to a plurality of backend applications via a social media application installed on a user device. Metadata associated with a request to access one of the plurality of backend applications is initially extracted. The metadata is used to authenticate the request via a plurality of validation steps. Upon authentication, the information from the request is provided to the backend application to receive the results responsive to the request. Any sensitive data included in the results is suppressed via data substitution steps from the transmission to the user device. The output from the applications access system with the sensitive data occluded is provided for display on a social media user interface (UI) on a user device.

Enabling secure access to applications via social media

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 document digitization, transformation and validation system extracts fields from digital documents via different document digitization processes. A document packet with a plurality of documents is initially accessed and any non-digital documents in the document packet are digitized. The errors in the digitized documents are corrected and non-English documents are translated into English. Each of the documents is provided to a plurality of digitization services for the extraction of fields by a plurality of field extraction models. If a field has multiple field instances extracted by more than one digitization service, then a field instance with the highest confidence score is selected for inclusion into the consolidated results. The consolidated results produced in different JavaScript Object Notation (JSON) formats are converted into a common JSON format which may be further validated and provided to downstream processes.

Document digitization, transformation and validation

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Pune, India

Kaustubh Kurhekar