公开(公告)号：US11669755B2

公开(公告)日：2023-06-06

申请号：US16921202

申请日：2020-07-06

Applicant: Adobe Inc.

Inventor： Atanu R Sinha ,  Tanay Asija ,  Sunny Dhamnani ,  Raja Kumar Dubey ,  Navita Goyal ,  Kaarthik Raja Meenakshi Viswanathan ,  Georgios Theocharous

IPC: G06N5/04 ,  G06N20/00 ,  G06F9/451

CPC classification number: G06N5/04 ,  G06F9/451 ,  G06N20/00

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.

公开(公告)号：US20220004898A1

公开(公告)日：2022-01-06

申请号：US16921202

申请日：2020-07-06

Applicant: Adobe Inc.

Inventor： Atanu R Sinha ,  Tanay Asija ,  Sunny Dhamnani ,  Raja Kumar Dubey ,  Navita Goyal ,  Kaarthik Raja Meenakshi Viswanathan ,  Georgios Theocharous

IPC: G06N5/04 ,  G06F9/451 ,  G06N20/00

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.