Utilizing a touchpoint attribution attention neural network to identify significant touchpoints and measure touchpoint contribution in multichannel, multi-touch digital content campaigns
US11287894B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 9, 2018 |
| Grant date | Mar 29, 2022 |
| Priority date | — |
| Expiry date | Aug 9, 2038 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L51/23
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.