Training and utilizing multi-phase learning models to provide digital content to client devices in a real-time digital bidding environment

US11288709B2 · kind B2 · utility

Assignee

• Adobe Inc. · US

Inventors

• Zhenyu Yan · San Francisco, US
• Chen Dong · Beijing, CN
• Abhishek Pani · San Francisco, US
• Yuan Yuan · Sunnyvale, US

Key dates

• Filing date: Mar 28, 2018
• Grant date: Mar 29, 2022
• Expiry date: Jan 1, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0242
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

The present disclosure includes systems, methods, and non-transitory computer readable media that train and utilize multi-phase learning models to predict performance during digital content campaigns and provide digital content to client devices in a real-time bidding environment. In particular, one or more embodiments leverage organizational structure of digital content campaigns to train two learning models, utilizing different data sources, to predict performance, generate bid responses, and provide digital content to client devices. For example, the disclosed systems can train a first performance learning model in an offline mode utilizing parent-level historical data. Then, in an online mode, the disclosed systems can train a second performance learning model utilizing child-level historical data and utilize the first performance learning model and the second performance learning model to generate bid responses and bid amounts in a real-time bidding environment.