Pattern identification in time-series social media data, and output-dynamics engineering for a dynamic system having one or more multi-scale time-series data sets
US11367149B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 13, 2017 |
| Grant date | Jun 21, 2022 |
| Priority date | — |
| Expiry date | Mar 12, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L67/535
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.