Machine-learning based multi-step engagement strategy generation and visualization

US11109084B2 · kind B2 · utility

Assignee

• Adobe Inc. · US

Inventors

• Pankhri Singhai · Rohini, IN
• Sundeep Parsa · San Jose, US
• Piyush Gupta · Bound Brook Junction, US
• Nikaash Puri · New Delhi, IN
• Eshita Shah · San Francisco, US
• Balaji Krishnamurthy · Noida, IN
• Nupur Kumari · Noida, IN
• Mayank Singh · Survanagiri, IN
• Akash Rupela · Delhi, IN

Key dates

• Filing date: Nov 25, 2019
• Grant date: Aug 31, 2021
• Expiry date: Dec 5, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/10
• WIPO field: Audio-visual technology
• WIPO sector: Electrical engineering

Abstract

Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.