Abstract: Systems and methods for managing supply chain of products and services are disclosed herein. A system generates supply chain data based on historical data received from data sources corresponding to supply chain of product or service. Further, system extracts data entity and set of attributes from supply chain data, to determine semantically related data entities. Furthermore, system determines use case corresponding to management of supply chain, based on semantically related data entities. Additionally, system predicts, risk or priority associated with product or service in the supply chain, to generate risks and alerts, based on prediction. Further, system assigns critical and high-priority use case to one or more agents based on a performance score of the one or more agents. Furthermore, system provides insights and suggestions for managing the supply chain of product or service at regional level and global level of supply chain.

Abstract: Systems and methods for detecting, classifying, and managing impediments are disclosed. For example, embodiments may be related to impediments in project management. The proposed systems and methods are configured to evaluate data harvested from multiple different sources (in different formats), identify potential impediments that may be described or present in the data, and classify said impediments based on whether the impediment is non-technical or technical. In addition, the proposed systems implement a technical solution of active learning combined with reinforcement learning to produce a feedback loop that, over each iteration, improves the accuracy of the impediment classification. The impediment management assistant is configured to identify impediments from various inputs sources across industries with an AI-based self-learning capability, providing a robust and accurate model even with only a limited training dataset.

Abstract: A domain-specific question answering system receives an end-user's question, identifies relevant information from domain-specific documents, composes a natural-language based response including the relevant information to be provided to the end-user. A knowledge base includes a domain-specific ontology and is built from data structures configured to preserve the hierarchy of the informational elements within the domain-specific documents. The question received from the end-user is analyzed to extract the features and the key phrases which are used to identify the relevant data structures. A pre-trained model that is further trained on domain-specific data is used to identify the sentences that include information responsive to the question. A response to the end-user's question is composed from the sentences. The question answering system also enables validation of the answers provided to the users' questions.

Abstract: A domain-specific question answering system receives an end-user's question, identifies relevant information from domain-specific documents, composes a natural-language based response including the relevant information to be provided to the end-user. A knowledge base includes a domain-specific ontology and is built from data structures configured to preserve the hierarchy of the informational elements within the domain-specific documents. The question received from the end-user is analyzed to extract the features and the key phrases which are used to identify the relevant data structures. A pre-trained model that is further trained on domain-specific data is used to identify the sentences that include information responsive to the question. A response to the end-user's question is composed from the sentences. The question answering system also enables validation of the answers provided to the users' questions.

Abstract: Disclosed herein is a method and system for generating an ontology based on plurality of tickets in an enterprise system 100. The method includes processing, by the enterprise system 100, input data associated with the plurality of tickets to obtain a structured data 228 associated with each of the plurality of tickets. Also, the method comprises performing multi-level clustering on the structured data 228 to obtain a plurality of clusters and corresponding error indicators, based on one or more parameters associated with the plurality of tickets. Further, the method comprises mapping each of the plurality of dusters with each of the error indicators, to obtain a mapped data at each cluster of the plurality of clusters and generating the ontology using the mapped data at each duster of the plurality of clusters, corresponding to the plurality of tickets.

Abstract: This disclosure relates to predicting and managing supply chain network risks. In one embodiment, a processor-implemented method obtains identifiers for supply chain contributors and parameters; and a query. The method performs a natural language processing algorithm on the query to extract text components, which it analyzes to identify supply chain component clusters and risk identifiers. It also includes executing a machine learning technique for learning of the risk identifiers and generating co-occurrence rules between the risk identifiers, as well as associated rule support and rule confidence parameters. It further includes sorting the co-occurrence rules to generate a prioritized rules list, and generating a risk prediction model for the supply chain using the prioritized rules list, using a classifier algorithm.

Abstract: The present disclosure relates to method and system for generation an ontology for one or more tickets by an ontology generation system. The ontology generation system comprises receiving Standard Operating Procedures (SOP) from ticket management system, the SOP comprises issue description of each of one or more tickets along with corresponding one or more resolutions. The ontology generation system identifies data properties associated with one or more tickets using natural language processing and text analysis and generate ontology for each of one or more tickets by building relationship between data properties, application name, instance, issue description and one or more resolutions corresponding to each of one or more tickets. The application name and instance associated with each of one or more tickets are identified based on SOP. The present disclosure provides quick response time for user query and saves memory and time.

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 rendering a resolution for an incident ticket. The method includes receiving the incident ticket, analyzing the incident ticket to determine at least one error symptom, determining the resolution for the incident ticket based on the at least one error symptom using an ontology based prediction model derived from a past ticket repository, and rendering the resolution to resolve the incident ticket.

Abstract: A method and device for performing risk analysis using risk relationship mapping in a supply chain network is disclosed. The method includes creating a plurality of risk categories within the supply chain network. The method further includes creating a multi-dimensional risk relationship map for the plurality of risk categories that includes at least one first-dimension including a first set of risk relationships and at least one second dimension including a second set of risk relationships. The method includes assigning an impact priority to each of the first set of risk relationships and each of the second set of risk relationships. The method further includes optimizing the first set of risk relationships and the second set of risk relationships in response to the assigning.

Abstract: A method of identifying root cause associated with risks in a supply chain network is disclosed. In one embodiment, the method includes performing natural language processing and text analysis on a user query to derive keywords, entities involved, and relationships between the keywords and the entities. The method further includes categorizing the user query into a risk category selected from a plurality of risk categories. The method includes creating a relationship mapping amongst at least one of a plurality of attributes associated with the risk category, in response to the categorization. The method further includes identifying, via the root cause identification device, a risk in the supply chain network based on the risk category and the relationship mapping. The method includes detecting a root cause from amongst a plurality of root causes associated with the risk.

Abstract: Disclosed herein is a method and system for generating an ontology based on plurality of tickets in an enterprise system 100. The method includes processing, by the enterprise system 100, input data associated with the plurality of tickets to obtain a structured data 228 associated with each of the plurality of tickets. Also, the method comprises performing multi-level clustering on the structured data 228 to obtain a plurality of clusters and corresponding error indicators, based on one or more parameters associated with the plurality of tickets. Further, the method comprises mapping each of the plurality of dusters with each of the error indicators, to obtain a mapped data at each cluster of the plurality of clusters and generating the ontology using the mapped data at each duster of the plurality of clusters, corresponding to the plurality of tickets.

Abstract: A technique is provided for providing resolution to tickets in an incident management system. The technique includes dynamically creating, by an analytics module, a taxonomy based on at least a database comprising one or more historical tickets, a description of incidents corresponding to each of the one or more historical tickets, and a corresponding resolution of each of the incidents. The technique further includes receiving, by an input module, one or more current tickets corresponding to an incident encountered by a user. The technique further includes, determining, by a pre-processing module, incident data corresponding to the received one or more current tickets based on pre-processing each of the one or more current tickets. Furthermore, a learning module determines one or more resolution steps corresponding to the received one or more current tickets based on at least the dynamically created taxonomy and the determined incident data.

Abstract: The present disclosure relates to a method for dynamically prioritizing electronic messages in an electronic device. In an embodiment an electronic message prioritization unit configured in an electronic device receives one or more electronic messages from other electronic device. The prioritization unit analyzes each of the received electronic messages using language and text processing logic to identify prioritization parameters. The prioritization unit assigns a value in a predetermined order to each of the prioritization parameters based on a predefined prioritization order of the prioritization parameters. The electronic message prioritization unit prioritizes the electronic messages based on the assigned values.

Abstract: This disclosure relates generally to incident ticket management, and more particularly to system and method for predicting relevant resolution for an incident ticket. In one embodiment, a method is provided for predicting a relevant resolution for an incident ticket. The method comprises receiving the incident ticket, analyzing the incident ticket to determine at least one query Ngram and at least one category, determining a similar past incident ticket based on a comparison of the at least one query Ngram and at least one Ngram derived from each of a plurality of past incident tickets belonging to the at least one category, and predicting the relevant resolution based on one or more resolution mapped to the similar past incident ticket.

Abstract: This disclosure relates to predicting and managing supply chain network risks. In one embodiment, a processor-implemented method obtains identifiers for supply chain contributors and parameters; and a query. The method performs a natural language processing algorithm on the query to extract text components, which it analyzes to identify supply chain component clusters and risk identifiers. It also includes executing a machine learning technique for learning of the risk identifiers and generating co-occurrence rules between the risk identifiers, as well as associated rule support and rule confidence parameters. It further includes sorting the co-occurrence rules to generate a prioritized rules list, and generating a risk prediction model for the supply chain using the prioritized rules list, using a classifier algorithm.

Abstract: A method for optimizing risks in supply chain networks is disclosed. The method includes categorizing, via a risk optimizing device, contextually relevant keywords derived from a user query into a risk category selected from a plurality of risk categories; identifying, via the risk optimizing device, a risk in the supply chain network based on the contextually relevant keywords and the risk category; creating, via the risk optimizing device, a plurality of risk association rules representative of interdependencies of the risk with at least one associated risk; assigning, via the risk optimizing device, priority to each of the plurality of risk association rules based on impact of interdependent risks within corresponding risk association rules; and optimizing a risk association rule assigned high priority within the plurality of risk association rules by removing the risk or one of the at least one associated risk from the risk association rule.

Abstract: This disclosure relates generally to supply chain networks and more particularly to methods and systems for identifying risks and associated root causes in supply chain networks. In one embodiment, the method includes receiving, via a risk analyzing device, a user query; performing, via the risk analyzing device, natural language processing and text analysis on the user query to derive contextually relevant keywords from the user query; categorizing, via the risk analyzing device, the contextually relevant keywords into a risk category selected from a plurality of risk categories; identifying, via the risk analyzing device, a risk in the supply chain network based on the contextually relevant keywords and the risk category; and detecting, via the risk analyzing device, a root cause from amongst a plurality of root causes associated with the risk using a causal analysis.

Abstract: The present disclosure relates to a method for dynamically prioritizing electronic messages in an electronic device. In an embodiment an electronic message prioritization unit configured in an electronic device receives one or more electronic messages from other electronic device. The prioritization unit analyzes each of the received electronic messages using language and text processing logic to identify prioritization parameters. The prioritization unit assigns a value in a predetermined order to each of the prioritization parameters based on a predefined prioritization order of the prioritization parameters. The electronic message prioritization unit prioritizes the electronic messages based on the assigned values.

Abstract: Described herein are methods and systems implementing a web page classification system for automatically generating at least one context feature for a web page and classifying the web page based on the at least one context feature. In one implementation, a context feature generating module of the web page classification system is configured to automatically generate at least one context feature based on at least two of uniform resource locator (URL) features, title features, and meta tags features of a web page and a classifying module is configured to classify the web page based on the at least one context feature.

Abstract: Described herein are methods and systems implementing a web page classification system for automatically generating at least one context feature for a web page and classifying the web page based on the at least one context feature. In one implementation, a context feature generating module of the web page classification system is configured to automatically generate at least one context feature based on at least two of uniform resource locator (URL) features, title features, and meta tags features of a web page and a classifying module is configured to classify the web page based on the at least one context feature.