Infosys Limited

Venugopal Subbarao

Swaminathan Natarajan

Radha Krishna Pisipati

Sandeep Kumar Dewangan

Balaji Raghunathan

Rajesh Balakrishnan

Amit Kumar

Krishnamurthy Sai Deepak

Vishal Krishna Saxena

Prasanna Nagesh Teli

Veera Venkata Ravi Kumar Geddam

Sambasiva Rao Maddali

Devaraja Holla Vaderahobli

Narendhar Rao Soma

Janupalli Pranay

Vishal Manchanda

This disclosure relates to method and system for determination of personality traits of agents in a contact center. The method includes retrieving textual data corresponding to a conversation between a first agent and a first customer. The method further includes generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model. The natural language justification may include one or more sentences. The one or more sentences may include a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits. The method further includes determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.

Method and system for determination of personality traits of agents in a contact center

Systems and methods for improved management of sentiments over conventional approaches are disclosed. Supervised approach is used to augment the rule-based approach for classification. Initially, sentiment evaluation is performed by the system using a rule based approach and an interface is provided to the user to give feedback on the correctness of evaluated sentiment. This feedback is used by the sentiment evaluation system to update the set of rule-based and also apply the supervised approach to train the classifier for evaluating complex posts.

System for management of sentiments and methods thereof

System and methods for anonymizing data is disclosed. The method includes intercepting a communication, identifying sensitive data in the communication and transmitting data to an anonymization engine for masking. Masked data is received from the anonymization engine and transmitted to a client device.

Methods and systems for runtime data anonymization

A framework for supporting one or more repair processes of one or more aircraft. The repair processes are based on repair information or repair solution corresponding to the damages to one or more structural components of the aircraft. The framework includes a knowledge engine, and a deployment engine. The knowledge engine automatically generates one or more knowledge interpretation systems based on the user inputs. The knowledge interpretation systems provide the repair information corresponding to the various structural components of the aircraft based on the user inputs. The deployment engine fulfills the deployment requirements corresponding to the one or more knowledge interpretation systems.

Framework for supporting repair processes of aircraft

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Bengaluru, India

Venugopal Subbarao