Abstract: Non-homogeneous products on a shelf are tracked using weighing assemblies jointly operable to measure the weight of the shelf and of the products. Weight measurement data is monitored and transmitted as streams of weight measurement data points. Responsively to a change in the values of the data, a set of weight-event parameters is determined including a product identification and an action taken with respect to the product. The determining includes aggregating, across all of the streams, changes in the weight measurement data corresponding to a specific time, mapping a change in weight distribution on the shelf, using the aggregated changes in weight measurement data, and assigning a set of weight-event parameters for resolving the mapped change in weight distribution. The product identification is based at least in part on per-product weight data retrieved from a product database.

Abstract: A method for tracking non-homogeneous products on a shelf comprises monitoring weight measurement data points corresponding to the weight of the shelf and the products arranged thereupon, determining a set of weight-event parameters of a weight event, such as product identification and an action taken with respect to the product, and at least one of recording information about the results and displaying information about the results. A system for tracking non-homogeneous products on a shelf comprises processors for carrying out stored program instructions for carrying out the steps of the method.

Abstract: Systems, methods, logic, and devices may support machine learning-based anomaly detections for embedded software applications. In a learning phase, an anomaly model training engine may construct an anomaly detection model, and the anomaly detection model configured to provide a determination of whether the embedded software application exhibits abnormal behavior based on activity measure and application parameter inputs. In a run-time phase, an anomaly detection engine may sample the embedded software application to obtain an activity measure and application parameters during the run-time execution and provide, as inputs to the anomaly detection model, the activity measure and the application parameters sampled during the run-time execution. The anomaly detection engine may further determine whether the embedded software application exhibits abnormal behavior based on an output from the anomaly detection model for the provided inputs.

Abstract: A method for tracking non-homogeneous products on a shelf comprises monitoring weight measurement data points corresponding to the weight of the shelf and the products arranged thereupon, determining a set of weight-event parameters of a weight event, such as product identification and an action taken with respect to the product, and at least one of recording information about the results and displaying information about the results. A system for tracking non-homogeneous products on a shelf comprises processors for carrying out stored program instructions for carrying out the steps of the method.