Proactively predicting transaction quantity based on sparse transaction data
US11875368B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 5, 2020 |
| Grant date | Jan 16, 2024 |
| Priority date | — |
| Expiry date | Mar 5, 2040 |
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 quantities 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, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features.Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.