Automated hybrid propensity decision vector generation using artificial intelligence
US11321762B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 30, 2020 |
| Grant date | May 3, 2022 |
| Priority date | — |
| Expiry date | Jun 30, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer generates an optimized decision distribution vector for a plurality of related, demand-correlated products. The computer receives data indexed by product, with each entry including several entry attributes. The computer receives decision context data for the products. The computer determines a set of primary attributes and trains a first machine learning model based upon those attributes. The computer receives a decision optimization request that includes an associated set of attributes corresponding to the primary attributes. The computer scores the associated set of attributes, using the first machine learning model, to generate a baseline purchase propensity. The computer trains a second machine learning model, based upon the baseline purchase propensity and the decision context data, to generate own-product and cross-product elasticity data. The computer, using the own-product and cross-product elasticity data, generates a decision distribution vector for the group of related, demand-correlated products.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.