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    Generating in-app guided edits including concise instructions and coachmarks
    1.
    发明授权
    Generating in-app guided edits including concise instructions and coachmarks 有权

    公开(公告)号：US11709690B2

    公开(公告)日：2023-07-25

    申请号：US16812962

    申请日：2020-03-09

    Applicant: Adobe Inc.

    Inventor： Nedim Lipka ,  Doo Soon Kim

    IPC: G06F3/0484 ,  G06N3/04 ,  G06F40/279 ,  G06F9/451

    CPC classification number: G06F9/453 ,  G06F3/0484 ,  G06F40/279 ,  G06N3/04

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating coachmarks and concise instructions based on operation descriptions for performing application operations. For example, the disclosed systems can utilize a multi-task summarization neural network to analyze an operation description and generate a coachmark and a concise instruction corresponding to the operation description. In addition, the disclosed systems can provide a coachmark and a concise instruction for display within a user interface to, directly within a client application, guide a user to perform an operation by interacting with a particular user interface element.

    LEARNING TO FUSE SENTENCES WITH TRANSFORMERS FOR SUMMARIZATION
    3.
    发明申请
    LEARNING TO FUSE SENTENCES WITH TRANSFORMERS FOR SUMMARIZATION 有权

    公开(公告)号：US20220261555A1

    公开(公告)日：2022-08-18

    申请号：US17177372

    申请日：2021-02-17

    Applicant: ADOBE INC.

    Inventor： Logan Lebanoff ,  Franck Dernoncourt ,  Doo Soon Kim ,  Lidan Wang ,  Walter Chang

    IPC: G06F40/40 ,  G06F40/284 ,  G06F40/166 ,  G06N3/04 ,  G06N3/08

    Abstract: Systems and methods for sentence fusion are described. Embodiments receive coreference information for a first sentence and a second sentence, wherein the coreference information identifies entities associated with both a term of the first sentence and a term of the second sentence, apply an entity constraint to an attention head of a sentence fusion network, wherein the entity constraint limits attention weights of the attention head to terms that correspond to a same entity of the coreference information, and predict a fused sentence using the sentence fusion network based on the entity constraint, wherein the fused sentence combines information from the first sentence and the second sentence.

    Answer selection using a compare-aggregate model with language model and condensed similarity information from latent clustering
    4.
    发明授权
    Answer selection using a compare-aggregate model with language model and condensed similarity information from latent clustering 有权

    公开(公告)号：US11113323B2

    公开(公告)日：2021-09-07

    申请号：US16420764

    申请日：2019-05-23

    Applicant: ADOBE INC.

    Inventor： Seung-hyun Yoon ,  Franck Dernoncourt ,  Trung Huu Bui ,  Doo Soon Kim ,  Carl Iwan Dockhorn ,  Yu Gong

    IPC: G06F7/00 ,  G06F16/332 ,  G06N20/00 ,  G06F16/33

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for techniques for identifying textual similarity and performing answer selection. A textual-similarity computing model can use a pre-trained language model to generate vector representations of a question and a candidate answer from a target corpus. The target corpus can be clustered into latent topics (or other latent groupings), and probabilities of a question or candidate answer being in each of the latent topics can be calculated and condensed (e.g., downsampled) to improve performance and focus on the most relevant topics. The condensed probabilities can be aggregated and combined with a downstream vector representation of the question (or answer) so the model can use focused topical and other categorical information as auxiliary information in a similarity computation. In training, transfer learning may be applied from a large-scale corpus, and the conventional list-wise approach can be replaced with point-wise learning.

    Dialog system training using a simulated user system
    6.
    发明授权
    Dialog system training using a simulated user system 有权

    公开(公告)号：US11468880B2

    公开(公告)日：2022-10-11

    申请号：US16198302

    申请日：2018-11-21

    Applicant: Adobe Inc.

    Inventor： Tzu-Hsiang Lin ,  Trung Huu Bui ,  Doo Soon Kim

    IPC: G06N20/00 ,  G10L15/06 ,  G06N5/04

    Abstract: Dialog system training techniques using a simulated user system are described. In one example, a simulated user system supports multiple agents. The dialog system, for instance, may be configured for use with an application (e.g., digital image editing application). The simulated user system may therefore simulate user actions involving both the application and the dialog system which may be used to train the dialog system. Additionally, the simulated user system is not limited to simulation of user interactions by a single input mode (e.g., natural language inputs), but also supports multimodal inputs. Further, the simulated user system may also support use of multiple goals within a single dialog session

    CLASSIFYING TERMS FROM SOURCE TEXTS USING IMPLICIT AND EXPLICIT CLASS-RECOGNITION-MACHINE-LEARNING MODELS
    7.
    发明申请
    CLASSIFYING TERMS FROM SOURCE TEXTS USING IMPLICIT AND EXPLICIT CLASS-RECOGNITION-MACHINE-LEARNING MODELS 有权

    公开(公告)号：US20210027141A1

    公开(公告)日：2021-01-28

    申请号：US16518894

    申请日：2019-07-22

    Applicant: Adobe Inc.

    Inventor： Sean MacAvaney ,  Franck Dernoncourt ,  Walter Chang ,  Seokhwan Kim ,  Doo Soon Kim ,  Chen Fang

    IPC: G06N3/04 ,  G06K9/62 ,  G06F17/16 ,  G06F17/15

    Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.

    CONVERSATIONAL QUERY ANSWERING SYSTEM
    8.
    发明申请
    CONVERSATIONAL QUERY ANSWERING SYSTEM 审中-公开

    公开(公告)号：US20200004873A1

    公开(公告)日：2020-01-02

    申请号：US16020328

    申请日：2018-06-27

    Applicant: Adobe Inc.

    Inventor： Walter W. Chang ,  Jonathan Brandt ,  Doo Soon Kim

    IPC: G06F17/30

    Abstract: Techniques of directing a user to content based on a semantic interpretation of a query input by the user involves generating links to specific content in a collection of documents in response to user string query, the links being generated based on an answer suggestion lookahead index. The answer suggestion lookahead index references a mapping between a plurality of groups of semantically equivalent terms and a respective link to specific content of the collection of documents. These techniques are useful for the generalized task of natural language question answering.

    NATURAL LANGUAGE IMAGE EDITING ANNOTATION FRAMEWORK
    9.
    发明申请
    NATURAL LANGUAGE IMAGE EDITING ANNOTATION FRAMEWORK 审中-公开

    公开(公告)号：US20190278844A1

    公开(公告)日：2019-09-12

    申请号：US15913064

    申请日：2018-03-06

    Applicant: Adobe Inc.

    Inventor： Jacqueline Brixey ,  Walter W. Chang ,  Trung Bui ,  Doo Soon Kim ,  Ramesh Radhakrishna Manuvinakurike

    IPC: G06F17/27 ,  G06F3/0484 ,  G06F17/24

    Abstract: A framework for annotating image edit requests includes a structure for identifying natural language request as either comments or image edit requests and for identifying the text of a request that maps to an executable action in an image editing program, as well as to identify other entities from the text related to the action. The annotation framework can be used to aid in the creation of artificial intelligence networks that carry out the requested action. An example method includes displaying a test image, displaying a natural language input with selectable text, and providing a plurality of selectable action tag controls and entity tag controls. The method may also include receiving selection of the text, receiving selection of an action tag control for the selected text, generating a labeled pair, and storing the labeled pair with the natural language input as an annotated natural language image edit request.

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