Accenture Global Solutions Limited

Suraj Govind Jadhav

Madhura Shivaram

Chung-Sheng Li

Saurabh Mahadik

Prakash Ghatage

Guanglei Xiong

Emmanuel Munguia Tapia

Colin Connors

Mohammad Jawad Ghorbani

Kyle Patrick Johnson

Sumeet Sawarkar

Amioy Kumar

Kaushal Mody

Nagendra K Kumar

Nirav Sampat

Chinnappa Guggilla

Deepak Kumar

Abhishek Datta Sharma

Nagendra B Kumar

Guruprasad Dasappa

Benjamin Nathan Grosof

Arvind Maheswaran

Christopher Cole

Samatha Kottha

Nagendra Kumar M R

Praveen Maniyan

Naveen Bansal

Karthik Meenakshisundaram

Hariram Krishnamurth

Karthik Lakshminarayanan

Madhu Sudhan H V

Sachin Aul

Sudhanshu A Dwivedi

Ankit Pandey

In some examples, image content moderation may include classifying, based on a learning model, an object displayed in an image into a category. Further, image content moderation may include detecting, based on another learning model, the object, refining the detected object based on a label, and determining, based on the another learning model, a category for the refined detected object. Further, image content moderation may include identifying, based on the label, a keyword associated with the object, and determining, based on the identified keyword, a category for the object. Further, image content moderation may include categorizing, based on a set of rules, the object into a category, and moderating image content by categorizing, based on aforementioned analysis the object into a category. Yet further, image content moderation may include tagging, based on fusion-based tagging, the object with a category and a color associated with the object.

Image content moderation

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 receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.

Identifying attributes associated with an entity using natural language processing

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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Navi Mumbai, India

Suraj Govind Jadhav