Hybrid machine learning model for code classification
US12299586B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 11, 2019 |
| Grant date | May 13, 2025 |
| Priority date | — |
| Expiry date | Jul 12, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An embodiment involves a hybrid machine learning classifier that uses a random forest of decision tree classifiers to predict a tariff code prefix, and uses a plurality of expert trees to predict a tariff code suffix from properties related to chemical components associated with the respective tariff code prefixes. The embodiment also involves: determining a proportion of a dominant chemical component in comparison to other chemical components in a new set of chemical components; calculating similarity scores for the new set of chemical components and words associated with the tariff code prefixes; generating a feature vector from the proportion and the similarity scores; and obtaining a predicted tariff code including a predicted tariff code prefix determined by applying the random forest to the feature vector, and a predicted tariff code suffix determined by traversing a particular expert tree in accordance with properties related to the new set of chemical components.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.