公开(公告)号：US11663497B2

公开(公告)日：2023-05-30

申请号：US16389628

申请日：2019-04-19

Applicant: Adobe Inc.

Inventor： Atanu Sinha ,  Prakhar Gupta ,  Manoj Kilaru ,  Madhav Goel ,  Deepanshu Bansal ,  Deepali Jain ,  Aniket Raj

IPC: G06N5/02 ,  G06F16/2457 ,  G06Q30/0204 ,  G06F16/901 ,  G06N3/049 ,  G06N5/043

CPC classification number: G06N5/02 ,  G06F16/24578 ,  G06F16/9024 ,  G06N3/049 ,  G06N5/043 ,  G06Q30/0204

Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

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

IPC: G06F9/451 ,  G06N20/00 ,  G06F11/34 ,  G06K9/62

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.

公开(公告)号：US10558852B2

公开(公告)日：2020-02-11

申请号：US15814979

申请日：2017-11-16

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Deepali Jain ,  Deepali Gupta ,  Eunyee Koh ,  Branislav Kveton ,  Nikhil Sheoran ,  Atanu Sinha ,  Hung Hai Bui ,  Charles Li Chen

IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06F16/954 ,  G06K9/62

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.