Correlating parallelized data from disparate data sources to aggregate graph data portions to predictively identify entity data
US11755602B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 30, 2021 |
| Grant date | Sep 12, 2023 |
| Priority date | — |
| Expiry date | Oct 5, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to receive or analyze datasets in parallel by implementing, for example, parallel computing processor systems to correlate subsets of parallelized data from disparately-formatted data sources to identify entity data and to aggregate graph data portions. In some examples, a method may include classifying data parallelized data to identify a class of observation data, constructing one or more content graphs in a graph data format, correlating parallelized data to other subsets of parallelized data associated with a class of observation data; and aggregating observation data to represent an individual entity.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.