Proactively predicting transaction dates based on sparse transaction data
US11854022B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 5, 2020 |
| Grant date | Dec 26, 2023 |
| Priority date | — |
| Expiry date | Jun 27, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0605
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction dates for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over, starting at a lowest level. For each current level in the iteration, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.