Electromagnetic noise signal based predictive analytics

Abstract

In an approach to predicting user touch events, one or more computer processors receive a detected electromagnetic noise signal of an object. The one or more computer processors compare the detected electromagnetic noise signal of the object to one or more stored electromagnetic noise signals associated with one or more objects. Based, at least in part, on the comparison, the one or more computer processors determine the identity of the object. Responsive to determining the identity of the object, the one or more computer processors store metadata associated with at least one of the objects and an electromagnetic noise signal detection event. The one or more computer processors determine whether an amount of the metadata associated with the object meets a learning threshold. If the amount of metadata meets the learning threshold, the one or more computer processors predict a subsequent electromagnetic noise signal detection event associated with the object.

What is claimed is: 
     
         1 . A method for predicting user touch events, the method comprising:
 receiving, by one or more computer processors, a detected electromagnetic noise signal of a first object;   comparing, by the one or more computer processors, the detected electromagnetic noise signal of the first object to one or more stored electromagnetic noise signals associated with one or more objects;   based, at least in part, on the comparison, determining, by the one or more computer processors, the identity of the first object;   responsive to determining the identity of the first object, storing, by the one or more computer processors, metadata corresponding to an electromagnetic noise signal detection event associated with the first object;   determining, by the one or more computer processors, whether a first quantity and a first frequency of recorded metadata corresponding to the electromagnetic signal detection event associated with the first object meets a learning threshold;   responsive to determining the first quantity and the first frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the first object meets the learning threshold, predicting, by the one or more computer processors, a first subsequent electromagnetic noise signal detection event associated with the first object utilizing a predictive analytics algorithm selected from the group consisting of a time series model and a supervised learning classifier, and wherein the supervised learning classifier is a regression analysis;   based, at least in part, on the subsequent electromagnetic noise signal detection event associated with the first object, determining, by the one or more computer processors, a first action;   performing, by the one or more computer processors, the first action, wherein the first action is selected from the group consisting of: displaying an advertisement on one or more computing devices associated with a user, sending an executable machine readable instruction to the one or more computing devices, and a combination of displaying an advertisement and sending an executable machine readable instruction to the one or more computing devices;   receiving, by one or more computer processors, a detected electromagnetic noise signal of a second object;   comparing, by the one or more computer processors, the detected electromagnetic noise signal of the second object to the one or more stored electromagnetic noise signals associated with one or more objects;   in response to determining, by the one or more computer processors, that the detected electromagnetic noise signal of the second object cannot be identified among the one or more stored electromagnetic noise signals, prompting, by the one or more computer processors, the user to input metadata associated with the second object;   determining, by the one or more computer processors, whether a second quantity and a second frequency of recorded metadata corresponding to the electromagnetic signal detection event associated with the second object meets the learning threshold;   responsive to determining that the second quantity and the second frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the second object does not meet the learning threshold, receiving, by the one or more computer processors, one or more additional detected electromagnetic noise signals associated with the second object; and   storing, by the one or more computer processors, additional metadata associated with the one or more additional detected electromagnetic noise signals.