Graph data structure for using inter-feature dependencies in machine-learning
US11861464B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 31, 2019 |
| Grant date | Jan 2, 2024 |
| Priority date | — |
| Expiry date | Nov 27, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.