公开(公告)号：US20230186667A1

公开(公告)日：2023-06-15

申请号：US17549270

申请日：2021-12-13

Applicant: ADOBE INC.

Inventor： Navita Goyal ,  Ani Nenkova Nenkova ,  Natwar Modani ,  Ayush Maheshwari ,  Inderjeet Jayakumar Nair

IPC: G06V30/413 ,  G06V30/416 ,  G06V10/26 ,  G06V10/74 ,  G06N20/00

CPC classification number: G06V30/413 ,  G06N20/00 ,  G06V10/26 ,  G06V10/761 ,  G06V30/416

Abstract: Techniques described herein are directed to assisting review of documents. In one embodiment, one or more text segments and one or more subjects in a document are identified. A text segment in the document is associated with a corresponding subject identified in the document. The text segment is classified with a content type value corresponding to a relation of the text segment to the corresponding subject. Thereafter, information is provided for the text segment associated with the corresponding subject for display on a user interface. Such information can include a representation of the content type value for the text segment.

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：US20220414400A1

公开(公告)日：2022-12-29

申请号：US17902586

申请日：2022-09-02

Applicant: Adobe Inc.

Inventor： Navita Goyal ,  Balaji Vasan Srinivasan ,  Anandha velu Natarajan ,  Abhilasha Sancheti

IPC: G06K9/62 ,  G06F40/40 ,  G06K9/00 ,  G06V30/414 ,  G06F40/205

Abstract: In some embodiments, a style transfer computing system receives, from a computing device, an input text and a request to transfer the input text to a target style combination including a set of target styles. The system applies a style transfer language model associated with the target style combination to the input text to generate a transferred text in the target style combination. The style transfer language model comprises a cascaded language model configured to generate the transferred text. The cascaded language model is trained using a set of discriminator models corresponding to the set of target styles. The system provides, to the computing device, the transferred text.

公开(公告)号：US20220121879A1

公开(公告)日：2022-04-21

申请号：US17073258

申请日：2020-10-16

Applicant: Adobe Inc.

Inventor： Navita Goyal ,  Balaji Vasan Srinivasan ,  Anandha velu Natarajan ,  Abhilasha Sancheti

IPC: G06K9/62 ,  G06K9/00 ,  G06F40/205

Abstract: In some embodiments, a style transfer computing system generates a set of discriminator models corresponding to a set of styles based on a set of training datasets labeled for respective styles. The style transfer computing system further generates a style transfer language model for a target style combination that includes multiple target styles from the set of styles. The style transfer language model includes a cascaded language model and multiple discriminator models selected from the set of discriminator models. The style transfer computing system trains the style transfer language model to minimize a loss function containing a loss term for the cascaded language model and multiple loss terms for the multiple discriminator models. For a source sentence and a given target style combination, the style transfer computing system applies the style transfer language model on the source sentence to generate a target sentence in the given target style combination.