Abstract: The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for automatic switching between visual responses, audio responses and textual responses in an omni-channel single view experience. Particularly, the system and method may empower a user to choose between any mode of interaction, the modes being provision of a visual interaction, audio interaction or a textual based interaction and a combination thereof based on a machine learning architecture and also provide seamless human agent handover. Thus, the system and method of the present disclosure may be beneficial for both entities and users.

Abstract: The present disclosure provides a system and method for utilizing an application programming interface (API) for handling multiple requests/models from various users. The API uses a machine learning (ML) model for processing the various requests and producing a plurality of trained models. The system is resilient to a race condition and incorporates an optimized random access memory (RAM) usage. Further, the system efficiently manages the limited available resources by loading and unloading the machine learning models based on their usage, thus maximizing the throughput of the API.

Abstract: The present disclosure relates to a system and method for generating an executable multi-faceted specific to an entity. In an exemplary implementation, the proposed system receives a knowledgebase comprising a set of potential queries associated with the entity, and receives responses that can be switched to a video form, an audio form or a textual form corresponding to the potential queries based on any or a combination of user preference, network conditions and user device features. The system processes, through a machine learning model, training data comprising the set of potential queries, the video frame responses, and the intent mapped to each potential query to generate a trained model, based on which a prediction engine is configured to process an end-user query and predict an intent associated with the end-user query, and facilitate response to the end-user query based on video frame response that is mapped with the predicted intent.

Abstract: The present disclosure provides a system and method for generating automatic video frame responses without getting help from an expert/professional assistance as well as ability to customize responses for a set of survey queries. The system includes a processor that executes a set of executable instructions stored in a memory, upon execution of which, the processor causes the system to receive video frame responses that are mapped to each survey query of the set of survey queries. The set of survey queries may be any or a combination of open ended, ratings related, satisfaction related, choice related queries but not limited to these. The system may be configured to link the set of queries to the video frame responses that have been recorded and also facilitate the user to initiate the survey at his/her convenience and resume the survey if there may be a break in the survey.

Abstract: The present disclosure relates to a system and method for enabling authentication on the bot application. The system may receive video stream along with query from a user computing device associated with a user. The query may be a generic or a privileged query and based on the nature of query, identification and verification of the user is carried out. For privileged services, the authentication procedure may include many levels of authentication processes that may include biometric authentication modules as well. Once the user is verified and authenticated, the response for the user query is provided to the user.

Abstract: The present disclosure relates to a system and method for generating an executable bot application specific to an entity. In an exemplary implementation, the proposed system receives a knowledgebase comprising a set of potential queries associated with the entity, and receives video frame responses corresponding to the potential queries, wherein each potential query is mapped to an intent. The system processes, through a machine learning model, training data comprising the set of potential queries, the video frame responses, and the intent mapped to each potential query to generate a trained model, based on which a prediction engine is configured to process an end-user query and predict an intent associated with the end-user query, and facilitate response to the end-user query based on video frame response that is mapped with the predicted intent. Using the prediction engine, the proposed system auto-generates executable bot application by the entity.