公开(公告)号：US20230342799A1

公开(公告)日：2023-10-26

申请号：US17660544

申请日：2022-04-25

Applicant: Adobe Inc.

Inventor： Aurghya Maiti ,  Atanu R Sinha ,  Harshita Chopra ,  Sarthak Kapoor ,  Atishay Ganesh ,  Saili Myana ,  Saurabh Mahapatra

IPC: G06N3/08 ,  G06Q30/02 ,  G06N3/04

CPC classification number: G06Q30/0204 ,  G06N3/0454 ,  G06N3/08 ,  G06Q30/0202

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for incorporating unobserved behaviors when generating user segments or predictions of future user actions. In particular, in one or more embodiments, the disclosed systems utilize a deep learning-based clustering algorithm that segments the behavioral history of users based on a future outcome. Further, the disclosed systems recognize that users may exhibit behaviors that represent two or more segments and allow for targeted marketing to users based on the user’s inclusion in multiple segments.

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.

公开(公告)号：US10963627B2

公开(公告)日：2021-03-30

申请号：US16005217

申请日：2018-06-11

Applicant: Adobe Inc.

Inventor： Anandhavelu N ,  Padmanabhan Anandan ,  Niyati Chhaya ,  Cedric Huesler ,  Balaji Vasan Srinivasan ,  Atanu R Sinha

IPC: G06F40/166 ,  G06F3/0482 ,  G06N20/00 ,  G06F40/20

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that, based on a sparse textual segment, can use machine learning models to generate document variants that are both conforming to digital content guidelines and uniquely tailored for distribution to client devices of specific audiences via specific delivery channels. To create such variants, in some embodiments, the methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of content-guideline-conforming documents. Additionally, or alternatively, in certain implementations, the disclosed methods, non-transitory computer readable media, and systems generate suggested modifications to a draft document that correspond to features of audience-channel-specific documents.