Accenture Global Solutions Limited

Accenture Global Services Gmbh

Naveen Kumar Thangaraj

Prakash Ghatage

Nirav Sampat

Kumar Viswanathan

Kaustubh Kurhekar

Sattish Sundarakrishnan

Ramkumar Balasubramanian

Alan Sturtivant

Bikram Singh Negi

Pramod Chander Palkonda

Ramesh Jonnavithula

Chandra Shekar Chikkaveeraiah

Sreevidya Prasad

Sebastian Fernandes

Hema Raghunath

Sreenivasa Sekhar Josyula

Sanjay Podder

Guruprasad Dasappa

Ullas Balan Nambiar

Sunil Balugari

Rahul Kotnala

Karthik Meenakshisundaram

Satyendra Shinde

Richard Stephen Vincent Price

Bhushan Ananda Patil

Sriram Sankaranarayanan

Prabhat Kumar Singh

Peter Dillon

Anil Mistry

Kavitha Subramanian

Indrajeet

Prakash Patil

Earnest Paul Wesley

Shishir Kumar Roy

Yakob Raja R

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 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

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

Implementations directed to instantiating adapters for communication between a UAP and one or more RPA platforms, and include receiving, by an intelligent adapter service of the UAP, input representing an RPA platform of a plurality of RPA platforms, querying, by the intelligent adapter service, one or more files of the RPA platform to determine one or more identifiers of the RPA platform, selecting, by the intelligent adapter service, an adapter of a plurality of adapters based on the one or more identifiers of the RPA platform, installing, by the intelligent adapter service, the adapter, and configuring, by the intelligent adapter service, the adapter to be specific to the RPA platform, configuring including setting one or more parameters of the RPA platform.

Intelligent adaptor service in unified automation platforms for robotic process automation

A data narration generating system generates snippets that include representations of data in one of a plurality of formats for inclusion into a data narration. The narration generating system receives selected data to be included into the data narration and provides the selected data to a plurality of ML models. The plurality of ML models are trained in generating snippets in one of the plurality of formats which can include textual format and a tabular format. Snippets in graphical formats can also be generated by rule-based processes. A plurality of snippets are thus generated in one or more of the plurality of formats which can then be presented to a user for selection and inclusion into the data narration. Alternately, a subset of the plurality of snippets can also be selected automatically based on a quality and quantity of data and a voting mechanism. The data narration thus generated is further configured to present different views based on privileges associated with user profiles.

Artificial intelligence (AI) based generation of data presentations

A device receives documents from various sources, and processes the documents, with an optical character recognition engine, to generate digitized documents. The device processes the digitized documents, with a first machine learning model, to detect languages utilized in the digitized documents, and processes the digitized documents, in other languages that are different than a common language and with a second machine learning model, to translate the digitized documents, in the other languages, into the common language and to generate translated digitized documents. The device processes the translated digitized documents and untranslated digitized documents, with a classification model, to generate classified documents, and processes the classified documents, with a third machine learning model, to generate extracted information from the classified documents. The device validates the extracted information based on business rules and to generate validated extracted information, and generates a smart contract for a transaction based on the validated extracted information.

Automation and digitizalization of document processing systems

A device extracts data files from different data sources, and processes the data files to generate data from the data files. The device trains a first artificial intelligence model and a second artificial intelligence model based on historical data parameters, wherein the historical data parameters are associated with historical data previously processed by the device. The device utilizes the trained first artificial intelligence model and the data to generate data parameters for the data, and utilizes the trained second artificial intelligence model, the data, and the data parameters to generate a data structure configuration. The device generates a data structure for the data based on the data parameters and the data structure configuration, stores the data in the data structure, and provides a user interface for accessing the data structure.

Utilizing artificial intelligence to integrate data from multiple diverse sources into a data structure

Implementations directed to direct messaging between one or more robotic process automation (RPA) platforms, and an autonomic platform (AP), and include actions of receiving, by a platform-specific adapter of the AP, data from a RPA platform of a plurality of RPA platforms the AP interacts with, the data indicating occurrence of a scenario, providing, by an application programming interface (API) of the platform-specific adapter, an occurrence message responsive to the occurrence of the scenario, transmitting, by the API, the message to the AP through a first channel, and transmitting, by the platform-specific adapter, a set of messages to the AP through a second channel that is separate from the first channel, the set of messages communicating tagged data records.

Adapter extension for inbound messages from robotic automation platforms to unified automation platform

Electronically received data is validated based on a digital data image that is scanned from a paper document. Known paper document source entities, paper document types and associated paper document configuration information are stored in a database. The paper documents are converted to digital data images and optically processed to identify respective source entity and document type information represented within the digital data images. Appropriate document configuration information is retrieved based on association with the detected type of document. Validation target data is extracted from the digital data images based on the configuration information and processed. The electronically received data is validated based on the extracted and processed validation target data.

Intelligent data extraction

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reconciliation data. The methods include actions of determining to perform a reconciliation task between transactions of a primary type and transactions of a secondary type, determining a reconciliation profile associated with the reconciliation task, obtaining reconciliation rules associated with the reconciliation profile from a reconciliation rule database, obtaining primary records of the primary type and secondary records of the secondary type based on the reconciliation profile, identifying matching transactions in the primary records and the secondary records and identifying unmatched transactions in the primary records and secondary records based on the reconciliation rules, providing an indication of the matched transactions and the unmatched transactions, receiving input specifying a match between an unmatched primary record transaction and an unmatched secondary record transaction, and identifying the unmatched primary record transaction and the unmatched secondary record transaction as matching.

Data reconciliation

A device may obtain a document, of a document type, from which specific information is to be captured. The specific information to be captured may depend on a document class associated with the document. The device may identify the document class, associated with the document, based on a characteristic of the document. The document class may identify a category of document in which the document is included, and may be associated with multiple document types. The device may determine field information associated with the document class. The field information may include information that identifies a portion of the document where the specific information is located, or may include information that identifies a manner in which the specific information can be identified within the document. The device may analyze the document, based on the field information, in order to capture the specific information. The device may provide the captured specific information.

Capturing specific information based on field information associated with a document class

A device may obtain a set of documents, including at least two documents of different document types, for conversion to a uniform and an editable or a searchable format. The set of documents may include at least two documents of a different document type. The device may determine conversion information, associated with converting the set of documents, that includes an editable or a searchable document type to which the set of documents is to be converted. The device may determine zone information, associated with converting the set of documents, that includes information that identifies a portion of the set of documents that is to be converted. The device may generate a set of converted documents based on the conversion information and the zone information. Each document, of the set of converted documents, may be of the document type. The device may provide the set of converted documents.

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Salem, India

Naveen Kumar Thangaraj