Accenture Global Solutions Limited

Accenture Global Services Gmbh

Prakash Ghatage

Nirav Sampat

Naveen Kumar Thangaraj

Kumar Viswanathan

Kaustubh Kurhekar

Sattish Sundarakrishnan

Vinu Varghese

Sameer Keshkamat

Suraj Govind Jadhav

Prabir GhoushalKumar

Suhail Hagalwadi

Gautam Choudhury

Rahul Kotnala

Sreevidya Prasad

Kunal Chaubey

Sebastian Fernandes

Ramesh Jonnavithula

Santhosh Kumar Rajavelu Balachandar

Harsh Kumar Katiyar

Chung-Sheng Li

Sanjay Podder

Madhura Shivaram

Guanglei Xiong

Emmanuel Munguia Tapia

Colin Connors

Raghavan Tinniyam Iyer

Nagendra B Kumar

Guruprasad Dasappa

Benjamin Nathan Grosof

Mohammad Jawad Ghorbani

Ullas Balan Nambiar

Sunil Balugari

Sumeet Sawarkar

Kyle Patrick Johnson

Siddesha Swamy

Ashish Jain

Saurabh Mahadik

Kaushal Mody

Satyendra Shinde

Samatha Kottha

Karthik Meenakshisundaram

Amit Porwal

Yakob Raja R

Shishir Kumar Roy

Bhushan Ananda Patil

Earnest Paul Wesley

Prakash Patil

Sriram Sankaranarayanan

Richard Stephen Vincent Price

Billy Hart

Indrajeet

Kavitha Subramanian

Anil Mistry

Peter Dillon

Kameshkumar Lakshminarayanan

Aravind Krishnan

Rekha Mani

Srikanth Narayanan

Suvendu Kumar Mahapatra

Prabhat Kumar Singh

Pavel Sem

Tomas Chaloupka

Madhu Sudhan H V

Ashwin D'Silva

Prabir Ghoushalkumar

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 process data associated with a first entity and a second entity, using one or more machine learning techniques, to identify a set of trends associated with a set of documents. The device may receive new documents associated with the first entity and the second entity. The device may generate, based on the set of trends, a first set of exceptions indicating that a new document is associated with a first type of error. The device may generate, using a similarity analysis technique, a second set of exceptions indicating that a new document is associated with a second type of error. The device may communicate with one or more systems to perform one or more actions associated with correction or prevention of processing errors relating to the new document based a claim relating to the first set of exceptions or the second set of exceptions.

End-to-end identification of erroneous data using machine learning and similarity analysis

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

Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.

Process orchestration

A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.

Collaborative virtual environment

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 may receive, from a user device, a request to access a virtual reality (VR) learning environment that includes an identifier associated with a program that supports the VR learning environment. The device may identify a set of objects to use within the VR learning environment by searching a data structure using the identifier associated with the program that supports the VR learning environment. The device may provide the VR learning environment to the user device. The device may receive, from the user device, information associated with interactions within the VR learning environment. The device may identify one or more additional objects to use within the VR learning environment by using one or more natural language processing techniques to analyze the information associated with the interactions within the VR learning environment. The device may provide the one or more additional objects to the user device.

Customized virtual reality learning environment

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

Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.

Learning based routing of service requests

An Artificial Intelligence (AI) based virtual automated assistance system provides services pertaining to component processes of a task that is to be automatically executed. The virtual automated assistance system includes a pipeline studio that enables generating the services. Historical data pertaining to a service is accessed for training and validating various ML models. The ML models are scored and a selected ML model is registered as a service on the virtual automated assistance system. The services thus registered are represented as process blocks within the pipeline studio wherein the process blocks pertaining to the component processes of the task are arranged in order to form a pipeline. The pipeline thus constructed enables automatic execution of the task by receiving and processing a request pertaining to the task via the services that form the pipeline.

Artificial intelligence based virtual automated assistance

A method may include receiving, from multiple, different sources, documents. The documents may be received in multiple, different file formats. The method may include performing operations on the documents to prepare the documents for processing, to obtain data included in the documents, or to determine information describing the documents. The method may include storing or providing, for storage, status information corresponding to the documents. The status information for a document may identify results of performing the operations with regard to the document. The method may include receiving an instruction that identifies an action to perform with regard to the document. The method may include performing the action. The method may include updating the status information to generate updated status information based on a result of performing the action. The method may include providing the updated status information.

Digital document processing based on document source or document type

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Bengaluru, India

Prakash Ghatage