Abstract: This disclosure relates generally to incident ticket management, and more particularly to system and method for resolving incident tickets. In one embodiment, a method is provided for determining a resolution for an incident ticket. The method comprises dynamically determining a signature of the incident ticket based on a description of the incident ticket, and determining a resolution code based on the signature using a knowledge graph model. The knowledge graph model is derived, from a plurality of past incident tickets and a plurality of corresponding resolutions, by determining a set of unique signatures and a set of corresponding resolution codes.

Abstract: The present disclosure relates to method and system for automatically identifying one or more issues in one or more tickets of an organization. An issue identification system retrieves a sequence pattern from ticket data received from one or more data sources. The issue identification system generates one or more first sub-sequence patterns of the n-grams from the sequence pattern. Further, frequency of occurrence and Part-of-Speech (POS) weightage of each of the one or more first sub-sequence patterns of the n-grams are determined by the issue identification system. A first score is determined for each of the one or more first sub-sequence patterns of the n-grams based on both the frequency and the POS weightage. Upon determining the first score, the issue identification system identifies one or more issues in the one or more tickets automatically based on the first sub-sequence pattern of the n-grams associated with a highest first score.

Abstract: The present disclosure relates to method and system for automatically identifying one or more issues in one or more tickets of an organization. An issue identification system retrieves a sequence pattern from ticket data received from one or more data sources. The issue identification system generates one or more first sub-sequence patterns of the n-grams from the sequence pattern. Further, frequency of occurrence and Part-of-Speech (POS) weightage of each of the one or more first sub-sequence patterns of the n-grams are determined by the issue identification system. A first score is determined for each of the one or more first sub-sequence patterns of the n-grams based on both the frequency and the POS weightage. Upon determining the first score, the issue identification system identifies one or more issues in the one or more tickets automatically based on the first sub-sequence pattern of the n-grams associated with a highest first score.

Abstract: A method and production surveillance device to perform oil and/or gas upstream surveillance that generates one or more real-time patterns from sensor data received from one or more sensors. The one or more real-time patterns are compared with one or more pre-defined patterns. A confidence prediction score is determined based on the comparison of the one or more real-time patterns with the one or more pre-defined patterns. One or more alerts are generated based on the confidence prediction score to perform oil and/or gas upstream surveillance.

Abstract: A method, non-transitory computer readable medium and utility management computing device for obtaining data associated with one or more electric utilities from a utility monitoring system. An asset reliability score and a consumption score is determined from the obtained data associated with the one or more electric utilities. Next, a customer satisfaction score is determined for the one or more electric utilities based on the determined asset reliability score and the consumption score. The determined customer satisfaction score for the one or more electric utilities is provided.

Abstract: Disclosed herein are a method and a system for optimizing the process of debt collection.

Abstract: This disclosure relates to methods and systems for estimating customer experience, the method comprising: receiving one or more interaction parameters associated with an interaction between the utility providing entity and a customer entity; determining, by a hardware processor in communication with the utility providing entity, a first interaction score based on the received one or more interaction parameters; determining a second interaction score based on the first interaction score and a feedback rating, provided by the customer entity, associated with the interaction; determining a third interaction score based on the second interaction score and a time interval between the interaction and a previous interaction between the utility providing entity and the customer entity; determining a customer experience score associated with the interaction based on the third interaction score and a stored customer experience score associated with the previous interaction; and providing the customer experience score.