Identifying contributors that explain differences between subsets of a data set
US9135286B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 27, 2015 |
| Grant date | Sep 15, 2015 |
| Priority date | — |
| Expiry date | Mar 27, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q40/08
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution. Similarly, differences in observations between two groups can be decomposed into multiple contributing sub-groups for each of the groups and pairwise differences among sub-groups can be quantified and analyzed.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.