A machine-learning decision system includes an online decision system and an offline decision system. The online decision system produces a first time slice-specific decision output corresponding to a first time slice based on one or more situational inputs received in the first time slice. The offline decision system produces a second Lime slice-specific decision output corresponding to the first time slice based on one or more situational inputs received in the first time slice and in a plurality of subsequent time slices occurring after the first time slice. The system further includes an online training system that conducts negative-reinforcement training of the online decision system in response to a nonconvergence between the first and the second time slice-specific decision outputs.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine-learning decision system (MLDS), comprising: an online decision system to produce a first time slice-specific decision output corresponding to a first time slice based on one or more situational inputs received in the first time slice; and an offline decision system to produce a second time slice-specific decision output corresponding to the first time slice based on one or more situational inputs received in the first time slice and in a plurality of subsequent time slices occurring after the first time slice; and an online training engine to conduct negative-reinforcement training of the online decision system in response to a nonconvergence between the first and the second time slice-specific decision outputs. 2. The MLDS system of claim 1, further comprising: a training decision engine to compare the first time slice-specific decision output against the second time slice-specific decision output to determine determine the nonconvergence. 3. The MLDS system of claim 1, further comprising: an offline training engine to conduct unsupervised positive-reinforcement training of the offline decision system in response to a nonconvergence between the first and the second time slice-specific decision outputs. 4. The MLDS system of claim 1, wherein the online training engine is further to conduct positive-reinforcement training of the online decision system in response to a convergence between the first and the second time slice-specific decision outputs. 5. The MLDS system of claim 1, further comprising: a data adapter to receive input data from a plurality of data sources, and to transform the input data into a transformed representation. 6. The MLDS system of claim 5, wherein the input data consists of N data elements, and wherein the transformed representation includes M data elements, wherein M is greater than N. 7. The MLDS system of claim 1, further comprising: a data aggregator to receive input data from a plurality of data sources, and produce a time-ordered restructuring of the input data. 8. The MLDS system of claim 1, wherein the online decision system and the offline decision system are each an anomaly detection decision system. 9. The MLDS system of claim 1, wherein the online decision system and the offline decision system are each a deep neural network (DNN). 10. The MLDS system of claim 1, wherein the first time slice-specific decision output and the second Lime slice-specific decision output are each a binary value. 11. The MLDS system of claim 1, wherein the first time slice-specific decision output and the second time slice-specific decision output are each a graded-score value. 12. The MLDS system of claim 1, wherein the first time slice-specific decision output is a real-time assessment that is fed to a real-time control system as an input to the control system. 13. At least one machine-readable medium containing instructions that, when executed on a computing platform, cause the computing platform to: execute an online decision process to produce a first time slice-specific decision output corresponding to a first time slice based on one or more situational inputs received in the first time slice; and execute an offline decision process to produce a second time slice-specific decision output corresponding to the first time slice based on one or more situational inputs received in the first time slice and in a plurality of subsequent time slices occurring after the first time slice; and execute an online training process to conduct negative-reinforcement training of the online decision process in response to a nonconvergence between the first and the second time slice-specific decision outputs. 14. The at least one machine-readable medium of claim 13, further comprising: instructions that, when executed on a computing platform, cause the computing platform to compare the first time slice-specific decision output against the second time slice-specific decision output to determine the nonconvergence and a corresponding need to perform the negative-reinforcement training of the online decision process. 15. The at least one machine-readable medium of claim 13, further comprising: instructions that, when executed on a computing platform, cause the computing platform to execute an offline training process to conduct unsupervised positive-reinforcement training of the offline decision process in response to a nonconvergence between the first and the second time slice-specific decision outputs. 16. The at least one machine-readable medium of claim 13, wherein the online training process is to conduct positive-reinforcement training of the online decision process in response to a convergence between the first and the second time slice-specific decision outputs. 17. The at least one machine-readable medium of claim 13, further comprising: instructions that, when executed on a computing platform, cause the computing platform to execute a data adapter process to receive input data from a plurality of data sources, and to transform the input data into a transformed representation. 18. The at least one machine-readable medium of claim 17, wherein the input data consists of N data elements, and wherein the transformed representation includes M data elements, wherein M is greater than N. 19. The at least one machine-readable medium of claim 13, further comprising: instructions that, when executed on a computing platform, cause the computing platform to receive input data from a plurality of data sources, and produce a time-ordered restructuring of the input data. 20. A machine-implemented method for operating a machine-learning decision system (MLDS), the method comprising: executing an online decision system to produce a first time slice-specific decision output corresponding to a first time slice based on one or more situational inputs received in the first time slice; and executing an offline decision system to produce a second time slice-specific decision output corresponding to the first time slice based on one or more situational inputs received in the first time slice and in a plurality of subsequent time slices occurring after the first time slice; and executing an online training process to conduct negative-reinforcement training of the online decision system in response to a nonconvergence between the first and the second time slice-specific decision outputs. 21. The method of claim 20, further comprising: comparing the first time slice-specific decision output against the second time slice-specific decision output to determine the nonconvergence and a corresponding need to perform the negative-reinforcement training of the online decision system. 22. The method of claim 20, further comprising: executing an offline training process to conduct unsupervised positive-reinforcement training of the offline decision system in response to a nonconvergence between the first and the second time slice-specific decision outputs. 23. The method of claim 20, wherein the online training process is to conduct positive-reinforcement training of the online decision system in response to a convergence between the first and the second time slice-specific decision outputs. 24. The method of claim 20, further comprising: receiving input data from a plurality of data sources, and transforming the input data into a transformed representation. 25. The method of claim 20, further comprising: receiving input data from a plurality of data sources, and producing a time-ordered restructuring of the input data.