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公开(公告)号：US11687352B2
公开(公告)日：2023-06-27
申请号：US17350889
申请日：2021-06-17
Applicant: Adobe Inc.
Inventor： Nikhil Sheoran , Nayan Raju Vysyaraju , Varun Srivastava , Nisheeth Golakiya , Dhruv Singal , Deepali Jain , Atanu Sinha
CPC classification number: G06F9/451 , G06F3/048 , G06F11/3438 , G06F18/23 , G06N20/00
Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences 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.
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公开(公告)号：US20210311751A1
公开(公告)日：2021-10-07
申请号：US17350889
申请日：2021-06-17
Applicant: Adobe Inc.
Inventor： Nikhil Sheoran , Nayan Raju Vysyaraju , Varun Srivastava , Nisheeth Golakiya , Dhruv Singal , Deepali Jain , Atanu Sinha
Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences 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.
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公开(公告)号：US20210089331A1
公开(公告)日：2021-03-25
申请号：US16576310
申请日：2019-09-19
Applicant: Adobe Inc.
Inventor： Nikhil Sheoran , Nayan Raju Vysyaraju , Varun Srivastava , Nisheeth Golakiya , Dhruv Singal , Deepali Jain , Atanu Sinha
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.
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公开(公告)号：US11068285B2
公开(公告)日：2021-07-20
申请号：US16576310
申请日：2019-09-19
Applicant: Adobe Inc.
Inventor： Nikhil Sheoran , Nayan Raju Vysyaraju , Varun Srivastava , Nisheeth Golakiya , Dhruv Singal , Deepali Jain , Atanu Sinha
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.
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