Abstract: A ML-based method and system for optimizing industrial processes, is disclosed. The ML-based method includes: obtaining image data and video data associated with objects, from video capturing units installed on industrial floors; obtaining positioning information of the objects from positioning systems configured on the objects; computing trajectories for the objects, using a coordinate transformation module based on streams corresponding to the image data, the video data, and the positioning information; combining the trajectories associated with the objects, into a common coordinate frame, using the coordinate transformation module; determining data associated with tracks, and metadata, for the objects, using the coordinate transformation module; and combining information associated with the data associated with the tracks, and the metadata, to predict optimized industrial processes, using a combiner of a spatio-temporal reasoning engine with a machine learning (ML) model.

Abstract: A ML-based method and system for optimizing job efficiency based on generation of predetermined motion time system (PMTS) representations for tasks associated with jobs, is disclosed. The ML-based method includes: obtaining description data corresponding to the tasks associated with the jobs, in representations comprising at least one of: text representations, image representations, video representations, and CAD representations; generating the PMTS representations of the tasks associated with the jobs, based on the obtained description data corresponding to the tasks associated with the jobs, using a machine learning model; generating insights corresponding to the tasks associated with the jobs, based on PMTS representations of the tasks associated with the jobs, using the machine learning model; and converting one PMTS representation to another PMTS representation, using the machine learning model.

Abstract: A system and method for determining sub-activities in videos and segmenting the videos is disclosed. The method includes extracting one or more batches from one or more videos and extracting one or more features from set of frames associated with the one or more batches. The method further includes generating a set of predicted codes and determining a cross-entropy loss, temporal coherence loss and a final loss. Further, the method includes categorizing the set of frames into one or more predefined clusters and generating one or more segmented videos based on the categorized set of frames, the determined final loss, and the set of predicted codes by using s activity determination-based ML model. The method includes outputting the generated one or more segmented videos on user interface screen of one or more electronic devices associated with one or more users.

Abstract: A system and method for optimizing industrial assembly process in an industrial environment is disclosed. A system operates on artificial intelligence (AI) based conversational/GUI platform, where it receives user commands related to industrial assembly process improvement queries. By analyzing received user commands, system identifies type of industrial assembly process mentioned by extracting relevant keywords or other attributes. Using trained AI-based classification table, system determines performance attributes associated with identified type of process. The system leverages various sources such as domain knowledge, organization-specific knowledge bases, data from tools/internet-based services, and statistical measurements from industrial environment.

Abstract: A system and method for learning human activities from video demonstrations using video augmentation is disclosed. The method includes receiving original videos from one or more data sources. The method includes processing the received original videos using one or more video augmentation techniques to generate a set of augmented videos. Further, the method includes generating a set of training videos by combining the received original videos with the generated set of augmented videos. Also, the method includes generating a deep learning model for the received original videos based on the generated set of training videos. Further, the method includes learning the one or more human activities performed in the received original videos by deploying the generated deep learning model. The method includes outputting the learnt one or more human activities performed in the original videos.