Various examples are provided for dynamically updating or adapting predictive modeling for prediction of outcomes of interest for operating systems and processes. Embodiments of the disclosure may provide systems, apparatus, processes, and methods for generating and deploying dynamically updated predictive models. In some embodiments, the predictive model may be deployed for the purpose of predicting operational outcomes of interests in operating systems, hardware devices, machines and/or processes associated therewith prior to the operational outcomes of interest occurring. The predictions can, for example, provide sufficient time for maintenance or repairs to be scheduled and carried out to avoid the predicted operational outcome. Autonomous evaluation of features allows the predictive models to be dynamically updated in response to changes in the environment or monitored data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for predicting at least one operational outcome of interest associated with an operating system, hardware device, or machine comprising: a. at least one memory that stores computer-executable instructions; and b. at least one processor that, when executing the computer-executable instructions, configures the system to: i. collect, by the computer, source data collected from over a period of time, wherein the source data is derived information obtained from operation of an operating system, hardware device, or machine; ii. create, by the computer, feature data context values comprising feature data having at least one contextual relationship, wherein the feature data context values comprise information derived from the source data, and wherein the feature data context values are derived from a plurality of operational parameters associated with operation of the operating system, hardware device, or machine during the period of time in which the source data was generated; iii. analyze, by the computer, the feature data context values by application of a plurality of statistical models, independently, to each feature data context value, thereby providing a correlation between the feature data in each feature data context value and each of the applied statistical models, wherein each correlation generates a probability model associated with the likelihood of occurrence of an operational outcome of interest during operation of the operating system, hardware device, or machine; iv. validate, by the computer, each of the plurality of probability models, by either or both of: 1. testing each of the probability models, independently, against at least some of the source data selected from the data source; or 2. combining some of all of the plurality of the probability models to generate a supermodel and testing the supermodel against at least some of the source data selected from the data source; and v. deploy, by the computer, at least one of the validated probability models for the prediction of the operational outcome of interest based on results of the validation. 2. The system of claim 1, wherein the system is further configured to test, by the computer, at least one of the validated probability models against updated source data, wherein the updated source data is derived from information obtained during continued operation of the operating system, hardware device, or machine. 3. The system of claim 1, wherein the system is configured to, by the computer, remove from use at least one predictive model that has been superseded by one or more subsequently generated probability models, wherein the one or more subsequently generated probability models have an increased correlation with the likelihood of occurrence of the operational outcome of interest, and wherein the increased correlation is determined, at least in part, by source data derived from information generated from operation of the operating system, hardware device, or machine that occurred after generation of the source data used to generate the at least one probability model that was removed from use. 4. The system of claim 1, wherein the system is, optionally, configured to remove at least some of the source data or feature data context values prior to analysis with the plurality of statistical models and to re-incorporate at least some of the removed data or feature data context values at a later time. 5. The system of claim 1, wherein the plurality of operational parameters is derived from information comprising one or more of at least times, locations, or environmental conditions associated with operation of the operating system, hardware device, or machine. 6. The system of claim 1, wherein the system is further configured to generate a signal receivable by a computer or a user related to the probability that the operational outcome of interest is likely to occur within a time period of interest. 7. The system of claim 1, wherein the at least one operational condition of interest includes a fault, failure, energy use, resource use, production quantity, production quality, compliance with required operational parameters, or operational cost metrics. 8. The system of claim 1, wherein the operating system, hardware device, or machine is associated with a vehicle, industrial machine or industrial process. 9. The system of claim 1, wherein the source data comprises information derived from: a. one or more sensors associated with the operating system, hardware device, or machine; and b. recorded information associated with operation of the operating system, hardware device, or machine. 10. The system of claim 8, wherein the one or more sensors comprise thermometers, hygrometers, pressure sensors, flow meters, vibration sensors, odometers, accelerometers, ammeters, voltmeters, power meters, digital multi-meters, digital imagers, microscopes, distance measurement devices, tackiness detectors, rigidity detectors, stiffness detectors, alignment detectors, pH detectors, GPS detectors, or combinations thereof. 11. A method for predicting at least one operational outcome of interest associated with an operating system, hardware device, or machine comprising: a. providing a computing device comprising at least one memory that stores computer-executable instructions and at least one processor for reading the computer-executable instructions; b. collecting, by the computing device, source data collected from over a period of time, wherein the source data is derived from information obtained from operation of an operating system, hardware device, or machine; c. creating, by the computing device, feature data context values comprising feature data having at least one contextual relationship, wherein the feature data context values comprise information derived from the source data, and wherein the feature data context values are derived from a plurality of operational parameters associated with operation of the operating system, hardware device, or machine during the period of time in which the source data was generated; d. analyzing, by the computing device, the feature data context values by application of a plurality of statistical models, independently, to each feature data context value, thereby providing a correlation between the feature data in each feature data context value and each of the applied statistical models, wherein each correlation generates a probability model associated with the likelihood of occurrence of an operational outcome of interest during a operation of the operating system, hardware device, or machine; e. validating, by the computing device, each of the plurality of probability models, by either or both of: i. testing each of the probability models, independently, against at least some of the source data selected from the data source; or ii. combining some of all of the plurality of the probability models to generate a supermodel and testing the supermodel against at least some of the source data selected from the data source; and f. deploying, by the computing device, at least one of the validated probability models for the prediction of the operational outcome of interest based on results of the validation. 12. The method of claim 11, wherein the method further comprises: a. testing, by the computer, at least one of the validated probability models against updated source data, wherein the updated source data is derived from information obtained during continued operation of the operating system, hardware device, or machine. 13. The method of claim 11, wherein the method further comprises removing from use, by the computer, at least one predictive model that has been superseded by one or more subsequently generated probability models, wherein the subsequently generated probability models have an increased correlation with the likelihood of occurrence of the operational outcome of interest, and wherein the increased correlation is determined, at least in part, by source data derived from information generated from operation of the operating system, hardware device, or machine that occurred after generation of the source data used to generate the at least one probability model that was removed from use. 14. The method of claim 11, wherein the method comprises, optionally, removing at least some of the source data or feature data context values prior to analysis with the plurality of statistical models and re-incorporating at least some of the removed data or feature data context values at a later time. 15. The methods of claim 11, wherein the plurality of operational parameters are derived from information comprising one or more of at least times, locations, or environmental conditions associated with operation of the operating system, hardware device, or machine. 16. The method of claim 11, wherein the method further comprises generating a signal receivable by a computer or a user related to the probability that the operational outcome of interest is likely to occur within a time period of interest. 17. The method of claim 11, wherein the at least one operational condition of interest includes a fault, failure, energy use, resource use, production quantity, production quality, compliance with required operational parameters, or operational cost metrics. 18. The method of claim 11, wherein the operating system, hardware device, or machine is associated with a vehicle, industrial machine or industrial process. 19. The method of claim 11, wherein the source data comprises information derived from: a. one or more sensors associated with the operating system, hardware device, or machine; and b. recorded information associated with operation of the operating system, hardware device, or machine. 20. The method of claim 19, wherein the recorded information comprises one or more of: a. maintenance records; b. temperature reports; c. environmental conditions; d. reports of the condition of devices or equipment associated with the operation of the operating system, hardware device, or machine; or e. records incorporating information regarding personnel associated with the operation of the operating system, hardware device, or machine.