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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting user touch events, the method comprising: receiving, by one or more computer processors, a detected electromagnetic noise signal of an object; comparing, by the one or more computer processors, 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, determining, by the one or more computer processors, the identity of the object; responsive to determining the identity of the object, storing, by the one or more computer processors, metadata corresponding to an electromagnetic noise signal detection event associated with the object; determining, by the one or more computer processors, whether a quantity and a frequency of recorded metadata corresponding to the electromagnetic signal detection event associated with the object meets a learning threshold; and responsive to determining the quantity and the frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the object meets the learning threshold, predicting, by the one or more computer processors, a subsequent electromagnetic noise signal detection event associated with the object. 2. The method of claim 1, further comprising: based, at least in part, on the subsequent electromagnetic noise signal detection event associated with the object, determining, by the one or more computer processors, an action; and performing, by the one or more computer processors, the action. 3. The method of claim 2, wherein the 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. 4. The method of claim 1, further comprising: responsive to determining the quantity and the frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the 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 object; and storing, by the one or more computer processors, additional metadata associated with the one or more additional detected electromagnetic noise signals. 5. The method of claim 1, further comprising prompting, by the one or more computer processors, a user to input metadata associated with the object. 6. The method of claim 1, wherein predicting the subsequent electromagnetic noise signal detection event further comprises utilizing, by the one or more computer processors, a predictive analytics algorithm selected from the group consisting of: a time series model and a supervised learning classifier. 7. The method of claim 6, wherein the supervised learning classifier is a regression analysis. 8. A computer program product for predicting user touch events, the computer program product comprising: one or more computer readable storage devices and program instructions stored on the one or more computer readable storage devices, the stored program instructions comprising: program instructions to receive a detected electromagnetic noise signal of an object; program instructions to 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, program instructions to determine the identity of the object; responsive to determining the identity of the object, program instructions to store metadata corresponding to an electromagnetic noise signal detection event associated with the object; program instructions to determine whether a quantity and a frequency of recorded metadata corresponding to the electromagnetic signal detection event associated with the object meets a learning threshold; and responsive to determining the quantity and the frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the object meets the learning threshold, program instructions to predict a subsequent electromagnetic noise signal detection event associated with the object. 9. The computer program product of claim 8, the stored program instructions further comprising: based, at least in part, on the subsequent electromagnetic noise signal detection event associated with the object, program instructions to determine an applicable action; and program instructions to perform the applicable action. 10. The computer program product of claim 9, wherein the applicable action is selected from the group consisting of displaying an advertisement on one or more computing devices, 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. 11. The computer program product of claim 8, the stored program instructions further comprising: responsive to determining the quantity and the frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the object does not meet the learning threshold, program instructions to receive one or more additional detected electromagnetic noise signals associated with the object; and program instructions to store additional metadata associated with the one or more additional detected electromagnetic noise signals. 12. The computer program product of claim 8, the stored program instructions further comprising program instructions to prompt a user to input metadata associated with the object. 13. The computer program product of claim 8, wherein predicting the subsequent electromagnetic noise signal detection event comprises program instructions to utilize a predictive analytics algorithm selected from the group consisting of a time series model and a supervised learning classifier. 14. The computer program product of claim 13, wherein the supervised learning classifier is a regression analysis. 15. A computer system for predicting user touch events, the computer system comprising: one or more computer processors; one or more computer readable storage devices; program instructions stored on the one or more computer readable storage devices for execution by at least one of the one or more computer processors, the stored program instructions comprising: program instructions to receive a detected electromagnetic noise signal of an object; program instructions to 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, program instructions to determine the identity of the object; responsive to determining the identity of the object, program instructions to store metadata corresponding to an electromagnetic noise signal detection event associated with the object; program instructions to determine whether a quantity and a frequency of recorded metadata corresponding to the electromagnetic signal detection event associated with the object meets a learning threshold; and responsive to determining the quantity and the frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the object meets the learning threshold, program instructions to predict a subsequent electromagnetic noise signal detection event associated with the object. 16. The computer system of claim 15, the stored program instructions further comprising: based, at least in part, on the subsequent electromagnetic noise signal detection event associated with the object, program instructions to determine an applicable action; and program instructions to perform the applicable action. 17. The computer system of claim 16, wherein the applicable action is selected from the group consisting of displaying an advertisement on one or more computing devices, 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. 18. The computer system of claim 15, the stored program instructions further comprising: responsive to determining the quantity and the frequency of the recorded metadata corresponding to the electromagnetic signal detection event associated with the object does not meet the learning threshold, program instructions to receive one or more additional detected electromagnetic noise signals associated with the object; and program instructions to store additional metadata associated with the one or more additional detected electromagnetic noise signals. 19. The computer system of claim 15, the stored program instructions further comprising program instructions to prompt a user to input metadata associated with the object. 20. The computer system of claim 15, wherein predicting the subsequent electromagnetic noise signal detection event comprises program instructions to utilize a predictive analytics algorithm selected from the group consisting of a time series model and a supervised learning classifier.