Machine-learning models applied to interaction data for determining interaction goals and facilitating experience-based modifications to interface elements in online environments

US11068285B2 · kind B2 · utility

Assignee

• Adobe Inc. · US

Inventors

• Nikhil Sheoran · Chandigarh, IN
• Nayan Raju Vysyaraju · Dashapalla, IN
• Varun Srivastava · Sunnyvale, US
• Nisheeth Golakiya · Sayla, IN
• Dhruv Singal · Bengaluru, IN
• Deepali Jain · Savanuru, IN
• Atanu R. Sinha · Bengaluru, IN

Key dates

• Filing date: Sep 19, 2019
• Grant date: Jul 20, 2021
• Expiry date: Sep 19, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

In some embodiments, interaction data associated with user interactions with a user interface of an interactive computing environment is identified, and goal clusters of the interaction data are computed based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, likelihood values of additional sequences of user interactions falling within the goal clusters are computed based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Computing interface experience metrics of the additional sequences are computed using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.