Method, system, and computer program product for representational machine learning for product formulation
US12223530B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 24, 2020 |
| Grant date | Feb 11, 2025 |
| Priority date | — |
| Expiry date | Mar 18, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/025
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method, system, and computer program product for representational learning of product formulas are provided. The method accesses a set of product formulas. Each product formula includes a set of ingredient tuples. A directed graph is generated from the set of product formulas. The directed graph including a node for each ingredient of the sets of ingredient tuples of the set of formulas. The method generates a weighted graph from the directed graph. The weighted graph has a weight assigned to each edge in the directed graph. The method generates an embedding model based on the directed graph. A set of embeddings is determined for the weighted graph where each node is represented with low-dimensional numerical vectors.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.