Abstract: This disclosure relates generally to method and system for predicting distance of gazed objects using IR camera. Eye tracking technology is widely used to study human behavior and patterns in eye movements. Existing gaze trackers focus on predicting gaze point and hardly analyzes distance of the gazed object from the gazer or directly classify region of focus. The method of the present disclosure predicts gazed objects distance using a pair of IR cameras placed on either side of a smart glass. The gaze predictor ML model predicts distance at least one gazed object positioned from eye of each subject during systematic execution of a set of tasks. From each pupillary information of each pupil a set of features are extracted which are utilized to classify the gazed object of the subject based on the distance into at least one of a near class, an intermediate class, and a far class.

Abstract: Provided is a method for masking scuff and/or scratch marks that develop over time on containers. More specifically, the method includes coating the scuff and/or scratch marks on the container with a coating composition that includes renewable and sustainable oils and biodegradable surfactants, and optionally a diluting solvent. The coating composition is disposed on the outer surface of the container, thus masking or covering the scuff and/or scratch marks on the container.

Abstract: This disclosure relates generally to method and system for predicting distance of gazed objects using IR camera. Eye tracking technology is widely used to study human behavior and patterns in eye movements. Existing gaze trackers focus on predicting gaze point and hardly analyzes distance of the gazed object from the gazer or directly classify region of focus. The method of the present disclosure predicts gazed objects distance using a pair of IR cameras placed on either side of a smart glass. The gaze predictor ML model predicts distance at least one gazed object positioned from eye of each subject during systematic execution of a set of tasks. From each pupillary information of each pupil a set of features are extracted which are utilized to classify the gazed object of the subject based on the distance into at least one of a near class, an intermediate class, and a far class.

Abstract: A method for creating machine learning model performance alerts showing the drifting of functions is described herein. The method starts by creating the initial machine learning model using a training data set. This initial machine learning model is then used in production, and the model is updated to account for the production data. To assure the quality of the updated machine learning model, test data results from the initial machine learning model is compared to the results from the updated machine learning model. Each feature is checked to see if the difference is within a p-value and whether the confidence intervals overlap. If not, an alert is generated to take action on the model.