Machine learned system for predicting item package quantity relationship between item descriptions
US11461829B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 27, 2019 |
| Grant date | Oct 4, 2022 |
| Priority date | — |
| Expiry date | Jul 3, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0625
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods are disclosed to implement a machine learned system to determine the comparative relationship between item package quantity (IPQ) information indicated in two item descriptions. In embodiments, the system employs a neural network that includes a token encoding layer, an attribute summarizing layer, and a comparison layer. The token encoding layer accepts an item description as a token sequence and encodes the tokens with token attributes that are relevant to IPQ extraction. The attribute summarizing layer uses a convolutional neural network to generate a set of fixed-size feature vectors for each encoded token sequence. All feature vectors for both item descriptions are then provided to the comparison layer to generate the IPQ comparison result. Advantageously, the disclosed neural network model can be trained to make accurate predictions about the IPQ relationship of the two item descriptions using a small set of token-level attributes as input signals.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.