Determining feature contributions to data metrics utilizing a causal dependency model
US11797515B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 9, 2020 |
| Grant date | Oct 24, 2023 |
| Priority date | — |
| Expiry date | Jan 16, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/9024
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.