Abstract: Conversational systems are intelligent machines that can understand language and conversing with a customer in writing or verbally. Embodiments herein provide a method for generating a universal conversational system using an ensemble of chatbots and a universal conversational system that adopts wisdom of crowd manifesting as an ensemble of chatbots. The ensemble of chatbots takes responses from NER and rule based conversational models. The knowledge based conversation models where complex queries that require question and answer, and the ensemble of generative knowledge chatbots are relying on a pre-trained models. The pre-trained models are complemented by domain specific training to answer queries that fall outside rule-based chatbot or knowledge graph-based conversation bot capability. The universal conversational system capable of building online virtuous automated learning loop where the models learn from each other and also from human response as wisdom of crowd.

Abstract: This disclosure relates generally to digital telecommunications, and more particularly to systems and methods for generating and managing composite digital identities. In one embodiment, a system and method for generating and managing digital identities is disclosed. The method includes receiving, from an identity seeker, a request having an identifier associated with an entity, the request includes a context associated with a transaction. An encrypted token corresponding to the identifier is identified from encrypted tokens stored in a database and is further decrypted to obtain context matrix on which analysis is performed with respect to the request. An activity status associated with the transaction is determined based on analysis to generate composite provisional digital identity (CPDI) specific to the transaction.

Abstract: The present disclosure discloses system and method for generating hyper-personalized recommendations from the enterprise and its partner/(s) product offerings space for the users based on dynamic persona creation, influenced by a dynamic context capture and context-chaining mechanism triggered by the past and current activities of a user or the time of occurrence of an event. The User data may be processed to generate a user persona, which may further vary dynamically based on his current and historic subscription/consumption/association data. The context trigger enables the system to generate recommendations even before the need relevant to the context is realized by the user. Thus, the system is capable of generating real-time dynamic, context-driven, hyper-personalized recommendations, based on the enterprise requirements.

Abstract: This disclosure relates generally to digital telecommunications, and more particularly to systems and methods for generating and managing composite digital identities. In one embodiment, a system and method for generating and managing digital identities is disclosed. The method includes receiving, from an identity seeker, a request having an identifier associated with an entity, the request includes a context associated with a transaction. An encrypted token corresponding to the identifier is identified from encrypted tokens stored in a database and is further decrypted to obtain context matrix on which analysis is performed with respect to the request. An activity status associated with the transaction is determined based on analysis to generate composite provisional digital identity (CPDI) specific to the transaction.

Abstract: The present disclosure discloses system and method for generating hyper-personalized recommendations from the enterprise and its partner/(s) product offerings space for the users based on dynamic persona creation, influenced by a dynamic context capture and context-chaining mechanism triggered by the past and current activities of a user or the time of occurrence of an event. The User data may be processed to generate a user persona, which may further vary dynamically based on his current and historic subscription/consumption/association data. The context trigger enables the system to generate recommendations even before the need relevant to the context is realized by the user. Thus, the system is capable of generating real-time dynamic, context-driven, hyper-personalized recommendations, based on the enterprise requirements.