Abstract: The present invention discloses method and virtual assistance system for determining response to user queries. The virtual assistance system receives data comprising plurality of interaction between one or more users and one or more real agents for resolving query, where data is classified into user and real agent data. Entities and intent identified from each of the classified user data and real agent data are classified using predefined domain model. The entities and intent are combined to identify plurality of sequence of resolution data. Based on classification of entities and intent, virtual assistance system determines first set of resolution data and second set of resolution data. Thereafter, each of plurality of sequence of resolution data is clustered into one or more category associated with type of users, based on first set and second set of resolution data, parameters associated with users and historical resolution data used for responding to query.

Abstract: This disclosure relates to method and system for of dynamically annotating data or validating annotated data. The method may include receiving input data comprising a plurality of input data points. The method may further include one of: a) generating a plurality of annotations for each of the plurality of input data points using at least one of a state-label mapping model and a comparative ANN model, or b) receiving the plurality of annotations for each of the plurality of input data points from an external device or from a user, and validating the plurality of annotations using at least one of the state-label mapping model and the comparative artificial neural network (ANN) model.

Abstract: Methods and content rendering systems for rendering Augmented Reality (AR) content for textureless objects are disclosed. The content rendering system captures one or more image frames associated with objects and transmits to a cloud server to detect one or more textureless objects along with one or more associated buffer Regions of Interest (ROI) based on a pre-trained object detection model. A grid index associated with each buffer ROI defines a position of the one or more textureless objects in each buffer ROI. The content rendering system obtains AR script from the cloud server based on the detected one or more textureless objects. Thereafter, based on the AR script, AR content associated with the one or more textureless objects is rendered in real time at the position of the one or more textureless objects.

Abstract: The present invention discloses method and virtual assistance system for determining response to user queries. The virtual assistance system receives data comprising plurality of interaction between one or more users and one or more real agents for resolving query, where data is classified into user and real agent data. Entities and intent identified from each of the classified user data and real agent data are classified using predefined domain model. The entities and intent are combined to identify plurality of sequence of resolution data. Based on classification of entities and intent, virtual assistance system determines first set of resolution data and second set of resolution data. Thereafter, each of plurality of sequence of resolution data is clustered into one or more category associated with type of users, based on first set and second set of resolution data, parameters associated with users and historical resolution data used for responding to query.

Abstract: This disclosure relates to method and system for of dynamically annotating data or validating annotated data. The method may include receiving input data comprising a plurality of input data points. The method may further include one of: a) generating a plurality of annotations for each of the plurality of input data points using at least one of a state-label mapping model and a comparative ANN model, or b) receiving the plurality of annotations for each of the plurality of input data points from an external device or from a user, and validating the plurality of annotations using at least one of the state-label mapping model and the comparative artificial neural network (ANN) model.

Abstract: Methods and content rendering systems for rendering Augmented Reality (AR) content for textureless objects are disclosed. The content rendering system captures one or more image frames associated with objects and transmits to a cloud server to detect one or more textureless objects along with one or more associated buffer Regions of Interest (ROI) based on a pre-trained object detection model. A grid index associated with each buffer ROI defines a position of the one or more textureless objects in each buffer ROI. The content rendering system obtains AR script from the cloud server based on the detected one or more textureless objects. Thereafter, based on the AR script, AR content associated with the one or more textureless objects is rendered in real time at the position of the one or more textureless objects.