Mutual information resolution recommendations and graphical visualizations using probabilistic graphical models
US12254431B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 18, 2022 |
| Grant date | Mar 18, 2025 |
| Priority date | — |
| Expiry date | Sep 25, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/087
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
A system and method are disclosed for training a probabilistic graphical model based on historical attributes of a supply chain to represent supply chain performance, selecting supply chain entity target variables, collating with the use of machine learning models, a list of features and classes pertaining to selected supply chain entity target variables, calculating first and second level features associated with the list of features and classes, generating supply chain predictions based on the trained probabilistic graphical model, where the supply chain predictions are based on test data, comparing the supply chain predictions to desired supply chain outputs to determine a delta distance, and generating resolution actions, to decrease the delta distance between the supply chain output predictions and the desired supply chain outputs.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.