Accenture Global Solutions Limited

Sumeet Sawarkar

Madhura Shivaram

Chinnappa Guggilla

Suraj Govind Jadhav

Chung-Sheng Li

Prakash Ghatage

Guanglei Xiong

Emmanuel Munguia Tapia

Nirav Sampat

Swati Tata

Colin Connors

Nagendra B Kumar

Mohammad Jawad Ghorbani

Pratip Samanta

Kyle Patrick Johnson

Saurabh Mahadik

Kaushal Mody

Himani Shukla

Praneeth Medhatithi Shishtla

Christopher Cole

Samatha Kottha

Praveen Maniyan

Nagendra Kumar M R

Naveen Bansal

Karthik Meenakshisundaram

Manash Jyoti Konwar

Keshav Bohra

Nagendra Kumar Karamala

Amit Sharma

Karthik Lakshminarayanan

Harinarayan Ojha

Madhu Sudhan H V

Anirudh Murthy

Sachin Aul

Sudhanshu A Dwivedi

Ankit Pandey

Hariram Krishnamurth

Examples of a content alignment system are provided. The system may receive a content record and a content creation requirement. The system may implement an artificial intelligence component to sort the content record into a plurality of objects and for identifying an object boundary for each of the plurality of objects. The system may identify a plurality of images and implement a first cognitive learning operation to identify an image boundary for each of the plurality of images. The system may identify a plurality of exhibits and implement a second cognitive learning operation to identify a data pattern associated with each of the plurality of exhibits. The system may implement a third cognitive learning operation for determining a content creation model by evaluating the plurality of objects, the plurality of images, and the plurality of exhibits. The system may generate a content creation output to resolve the content creation requirement.

Content alignment

Examples of a record generation system are provided. The system may receive a record generation requirement from a user. The system may obtain record data, a plurality of user documents, and identify a record corpus from the record data. The system may sort the record data into a plurality of data domains. The system may determine at least one record mapping context including a record value from the plurality of user documents. The system may determine a selection rule from the plurality of data domains for each of the record mapping context. The system may create a record index corresponding to the plurality of user documents. The system may create a record generation model corresponding to the record generation requirement based on the record index. The system may generate a record generation result corresponding to the record generation requirement comprising the relevant record generation model.

Intelligent record generation

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

A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.

Integrated service centre support

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Thane, India

Sumeet Sawarkar