Abstract: Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

Abstract: An imaging apparatus to which an attachable and detachable device attachable to and detachable from the imaging apparatus can be attached includes a control unit configured to cause the attachable and detachable device attached to the imaging apparatus to start an image analysis of a predetermined image, a determination unit configured to determine whether the image analysis of the predetermined image is completed, and a determining unit configured to, based on a result of the determination by the determination unit, determine a waiting time from when the imaging apparatus causes the attachable and detachable device to start an image analysis of a captured image captured by the imaging apparatus to when the imaging apparatus requests the attachable and detachable device to acquire a result of the image analysis.