Offering personalized and interactive decision support based on learned model to predict preferences from traits
US10628870B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 10, 2016 |
| Grant date | Apr 21, 2020 |
| Priority date | — |
| Expiry date | Jan 4, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a personalized interactive decision support system. A personalized product recommendation module executing within the personalized interactive decision support system correlates at least one customer to a set of consumption preferences using a machine learning model based on a set of traits of the at least one customer to form at least one customer-to-preference correlation. The personalized product recommendation module maps a set of products to the set of consumption preferences using a consumption preferences-to-product attribute mapping data structure based on a set of attributes of the set of products to form a set of product-to-preference correlations. The personalized product recommendation module matches the at least one customer to at least one product within a set of products based on the at least one customer-to-preference correlation and the set of product-to-preference correlations to form at least one product recommendation. …
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.