US 12,314,905 B2
Predictive computing and data analytics for project management
Gabriele Picco, Dublin (IE); Natalia Mulligan, Dublin (IE); Thanh Lam Hoang, Maynooth (IE); and Marco Luca Sbodio, Castaheany (IE)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Apr. 11, 2022, as Appl. No. 17/658,806.
Prior Publication US 2023/0325775 A1, Oct. 12, 2023
Int. Cl. G06Q 10/30 (2023.01); G06Q 10/10 (2023.01)
CPC G06Q 10/103 (2013.01) [G06Q 10/30 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method of facilitating predictive computing and data analytics within a computing system in a computing environment, the computer-implemented method, comprising:
training, via a machine learning component, a machine learning model using data collected over time from one or more Internet of Things (IoT) sensors associated with at least one container to contain objects of a plurality of objects, the at least one container comprising at least one smart bin for a respective material class and the plurality of objects being a plurality of objects of the respective material class, the machine learning model to provide predictive analytics for the plurality of objects related to predicting percentage composition of objects within the at least one container based on object type, the objects within the at least one container including different types of objects, wherein the machine learning model is associated with one or more application programming interfaces (“API”) to collect the data from the one or more IoT sensors;
receiving, across a network of the computing environment, data from a plurality of data sources to electronically monitor, based on the data received from the plurality of data sources, a lifecycle of one or more objects of the plurality of objects, the plurality of data sources including the at least one smart bin and the at least one smart bin including at least one Internet of Things (IoT) device associated therewith generating, at least in part, the data from the plurality of sources; and
executing a predictive component to provide predictive analytics related to the plurality of objects, based on the monitored lifecycle of the one or more objects of the plurality of objects, wherein the predictive analytics includes a predicted percentage composition of the objects within the at least one container based on object type.