Abstract: Disclosed herein is a method and response generation system for providing contextual responses to user interaction. In an embodiment, input data related to user interaction, which may be received from a plurality of input channels in real-time, may be processed using processing models corresponding to each of the input channels for extracting interaction parameters. Thereafter, the interaction parameters may be combined for computing a contextual variable, which in turn may be analyzed to determine a context of the user interaction. Finally, responses corresponding to the context of the user interaction may be generated and provided to the user for completing the user interaction. In some embodiments, the method of present disclosure accurately detects context of the user interaction and provides meaningful contextual responses to the user interaction.

Abstract: Disclosed herein is method and system for detecting pose of a subject in real-time. In an embodiment, nodal points corresponding to the subject may be identified and used for identifying skeleton pose of the subject. Thereafter, a feature descriptor for the skeleton pose may be computed based on the nodal points. Further, the feature descriptor of the skeleton pose may be compared with predetermined feature descriptors for detecting the pose of the subject as predefined pose corresponding to one of the predetermined feature descriptors used for the comparison. The method of present disclosure makes accurate pose detection from a two-dimensional image of the subject, using a pose detection model, which is pre-trained with predetermined feature descriptors and deep learning techniques.

Abstract: Disclosed herein is a method and response generation system for providing contextual responses to user interaction. In an embodiment, input data related to user interaction, which may be received from a plurality of input channels in real-time, may be processed using processing models corresponding to each of the input channels for extracting interaction parameters. Thereafter, the interaction parameters may be combined for computing a contextual variable, which in turn may be analyzed to determine a context of the user interaction. Finally, responses corresponding to the context of the user interaction may be generated and provided to the user for completing the user interaction. In some embodiments, the method of present disclosure accurately detects context of the user interaction and provides meaningful contextual responses to the user interaction.

Abstract: Disclosed herein is method and system for detecting pose of a subject in real-time. In an embodiment, nodal points corresponding to the subject may be identified and used for identifying skeleton pose of the subject. Thereafter, a feature descriptor for the skeleton pose may be computed based on the nodal points. Further, the feature descriptor of the skeleton pose may be compared with predetermined feature descriptors for detecting the pose of the subject as predefined pose corresponding to one of the predetermined feature descriptors used for the comparison. The method of present disclosure makes accurate pose detection from a two-dimensional image of the subject, using a pose detection model, which is pre-trained with predetermined feature descriptors and deep learning techniques.