Machine-learning based multi-step engagement strategy modification

US11107115B2 · kind B2 · utility

Assignee

• Adobe Inc. · US

Inventors

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

Key dates

• Filing date: Aug 7, 2018
• Grant date: Aug 31, 2021
• Expiry date: Feb 9, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/0264
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.