In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system. A result of the solution is an influence indicator corresponding to intervention dynamics that can be applied to the dynamic system to influence outcome dynamics of the system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of determining patterns with a time-series social-media data set, the computer-implemented method comprising: receiving, by a social-media-data pattern-identification system, the time-series social-media data set; applying, by a social-media-data pattern-identification system, a deep-learning algorithm to the time-series social-media data set, wherein the deep-learning algorithm is designed and configured to analyze the time-series social-media data set for patterns across multiple time scales and to output pattern-identification data containing information on patterns in a plurality of the multiple time scales and across a plurality of the multiple time scales; and providing the output pattern-identification data to an output-interface of the social-media-data pattern-identification system. 2. The computer-implemented method according to claim 1, wherein the deep-learning algorithm comprises a convolutional Bayesian model (CBM). 3. The computer-implemented method according to claim 2, wherein the RCBM uses a convolutional operator that carries out a scale-and-copy task. 4. The computer-implemented method according to claim 2, wherein the deep-learning algorithm comprises a recursive CBM in which a CBM is applied to the time-series social-media data set at each of multiple differing time scales. 5. The computer-implemented method according to claim 3, wherein the deep-learning algorithm comprises a CBM applied to CBM results obtained at a plurality of the multiple differing time scales. 6. The computer-implemented method according to claim 1, wherein the deep-learning algorithm comprises a set of filter matrices and a corresponding set of activation vectors applied to the time-series social-media data set, wherein solution for the filter matrices results in the output-identification data. 7. The computer-implemented method according to claim 1, wherein the deep-learning algorithm is applied to a particular application, and the applying of a deep-learning algorithm includes: setting an initial temporal resolution to be a finest resolution for the particular application; and using updating rules, learning relevant patterns, starting with the initial temporal resolution and iterating the learning using at least one increased temporal resolution. 8. The computer-implemented method according to claim 7, wherein each of the at least one increased temporal resolution is determined by max-pooling activation strength vectors. 9. The computer-implemented method according to claim 7, further comprising: determining whether a most recent one of the at least one temporal resolution is still relevant to the particular application; terminating the iterating when the most recent one of the at least one temporal resolution is not relevant to the application; and output patterns identified in all relevant ones of the initial temporal resolution and the at least one increased temporal resolution. 10. A computer-implemented method of engineering outcome dynamics of a dynamic system that includes one or more multi-scale time-series data sets, the computer-implemented method comprising: training a generative model using each of the one or more multi-scale time-series data sets; providing an optimization problem composed of a likelihood function of the generative model and a score function that reflects a utility of the dynamic system; solving the optimization problem so as to determine an influence indicator indicating an influence scheme for influencing the outcome dynamics; and providing the influence indicator to an outcome-dynamics influencing system. 11. The computer-implemented method according to claim 10, wherein the generative model comprises a recursive convolutional Bayesian model (RCBM). 12. The computer-implemented method according to claim 11, wherein the RCBM uses a convolutional operator that carries out a scale-and-copy task. 13. The computer-implemented method according to claim 10, wherein the computer-implemented method is configured for a particular application, and the score function is selected based on the particular application. 14. The computer-implemented method according to claim 13, wherein the score function is a pattern-matching score function. 15. The computer-implemented method according to claim 13, wherein the score function is a profit-maximization score function. 16. The computer-implemented method according to claim 13, wherein the particular application comprises a social-media marketing campaign. 17. The computer-implemented method according to claim 10, wherein the particular application comprises event-alert notification. 18. The computer-implemented method according to claim 10, wherein the one or more multi-scale time-series data sets includes a social-media data set. 19. The computer-implemented method according to claim 18, wherein the social-media data set comprises a social-media data stream. 20. The computer-implemented method according to claim 10, wherein the one or more multi-scale time-series data sets includes video data.