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    TEXT EDITING OF DIGITAL IMAGES
    1.
    发明公开
    TEXT EDITING OF DIGITAL IMAGES 审中-公开

    公开(公告)号：US20240119646A1

    公开(公告)日：2024-04-11

    申请号：US18541377

    申请日：2023-12-15

    Applicant: Adobe Inc.

    Inventor： Paridhi Maheshwari ,  Vishwa Vinay ,  Shraiysh Vaishay ,  Praneetha Vaddamanu ,  Nihal Jain ,  Dhananjay Bhausaheb Raut

    IPC: G06T11/00 ,  G06F40/30 ,  G06V10/40

    CPC classification number: G06T11/001 ,  G06F40/30 ,  G06V10/40 ,  G06T2207/10024 ,  G06T2207/20081

    Abstract: Digital image text editing techniques as implemented by an image processing system are described that support increased user interaction in the creation and editing of digital images through understanding a content creator's intent as expressed using text. In one example, a text user input is received by a text input module. The text user input describes a visual object and a visual attribute, in which the visual object specifies a visual context of the visual attribute. A feature representation generated by a text-to-feature system using a machine-learning module based on the text user input. The feature representation is passed to an image editing system to edit a digital object in a digital image, e.g., by applying a texture to an outline of the digital object within the digital image.

    Predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks
    3.
    发明授权
    Predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks 有权

    公开(公告)号：US11521221B2

    公开(公告)日：2022-12-06

    申请号：US15909723

    申请日：2018-03-01

    Applicant: Adobe Inc.

    Inventor： Shiv Kumar Saini ,  Vishwa Vinay ,  Vaibhav Nagar ,  Aishwarya Mittal

    IPC: G06Q30/02 ,  G06N3/04 ,  G06N7/00 ,  G06F16/9535

    Abstract: This disclosure involves predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks. For example, a method includes a processing device performing operations including accessing input data for an entity and transforming the input data into a dense vector entity representation representing the entity. Transforming the input data includes applying, to the input data, a neural network including simultaneously trained propensity models. Each propensity model predicts a different task based on the input data. Transforming the input data also includes extracting the dense vector entity representation from a common layer of the neural network to which the propensity models are connected. The operations performed by the processing device include computing a predicted behavior by applying a predictive model to the dense vector entity representation and transmitting the predicted behavior to a computing device that customizes a presentation of electronic content at a remote user device.

    Systems for Generating Sequential Supporting Answer Reports
    4.
    发明申请
    Systems for Generating Sequential Supporting Answer Reports 有权

    公开(公告)号：US20210382607A1

    公开(公告)日：2021-12-09

    申请号：US16896820

    申请日：2020-06-09

    Applicant: Adobe Inc.

    Inventor： Kevin Gary Smith ,  William Brandon George ,  Vishwa Vinay ,  Iftikhar Ahamath Burhanuddin

    IPC: G06F3/0484 ,  G06F40/30

    Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

    UTILIZING ITEM-LEVEL IMPORTANCE SAMPLING MODELS FOR DIGITAL CONTENT SELECTION POLICIES
    5.
    发明申请
    UTILIZING ITEM-LEVEL IMPORTANCE SAMPLING MODELS FOR DIGITAL CONTENT SELECTION POLICIES 审中-公开

    公开(公告)号：US20200286154A1

    公开(公告)日：2020-09-10

    申请号：US16880168

    申请日：2020-05-21

    Applicant: ADOBE INC.

    Inventor： Shuai Li ,  Zheng Wen ,  Yasin Abbasi Yadkori ,  Vishwa Vinay ,  Branislav Kveton

    IPC: G06Q30/06 ,  G06Q30/02 ,  G06N20/00

    Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy. In addition, the systems described herein can apply the item-level importance weights to training data to train item-level importance sampling model.

    Method, medium, and system for training and utilizing item-level importance sampling models
    6.
    发明授权
    Method, medium, and system for training and utilizing item-level importance sampling models 有权

    公开(公告)号：US10706454B2

    公开(公告)日：2020-07-07

    申请号：US15943807

    申请日：2018-04-03

    Applicant: Adobe Inc.

    Inventor： Shuai Li ,  Zheng Wen ,  Yasin Abbasi Yadkori ,  Vishwa Vinay ,  Branislav Kveton

    IPC: G06Q30/00 ,  G06Q30/06 ,  G06Q30/02 ,  G06N20/00

    Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy. In addition, the systems described herein can apply the item-level importance weights to training data to train item-level importance sampling model.

    Digital Document Update
    7.
    发明申请
    Digital Document Update 审中-公开

    公开(公告)号：US20190251150A1

    公开(公告)日：2019-08-15

    申请号：US15897059

    申请日：2018-02-14

    Applicant: Adobe Inc.

    Inventor： Vishwa Vinay ,  Sopan Khosla ,  Sanket Vaibhav Mehta ,  Sahith Thallapally ,  Gaurav Verma

    IPC: G06F17/24 ,  G06F17/30 ,  G06F17/27 ,  G06F15/18 ,  G06F9/451

    CPC classification number: G06F17/24 ,  G06F9/451 ,  G06F16/3323 ,  G06F16/93 ,  G06F17/278 ,  G06N20/00

    Abstract: Techniques and systems are described in which a document management system is configured to update content of document portions of digital documents. In one example, an update to the digital document is initially triggered by a document management system by detecting a triggering change applied to an initial portion of the digital document. The document management system, in response to the triggering change, then determines whether trailing changes are to be made to other document portions, such as to other document portions in the same digital document or another digital document. To do so, triggering and trailing change representations are generated and compared to determine similarity of candidate document portions with an initial document portion.

    Text-based color palette searches utilizing text-to-color models
    9.
    发明授权
    Text-based color palette searches utilizing text-to-color models 有权

    公开(公告)号：US11934452B1

    公开(公告)日：2024-03-19

    申请号：US18051417

    申请日：2022-10-31

    Applicant: Adobe Inc.

    Inventor： Rohit Jain ,  Syed Mohd Mehadi ,  Vishwa Vinay ,  Jose Ignacio Echevarria Vallespi

    IPC: G06F16/583 ,  G06F16/532 ,  G06F16/538 ,  G06F40/40 ,  G06T11/00

    CPC classification number: G06F16/5838 ,  G06F16/532 ,  G06F16/538 ,  G06F40/40 ,  G06T11/001

    Abstract: The present disclosure relates to systems that perform text-based palette searches that convert a text query into a color distribution and utilize the color distribution to identify relevant color palettes. More specifically, the disclosed systems receive a textual color palette search query and convert, utilizing a text-to-color model, the textual color palette search query into a color distribution. The disclosed systems determine, utilizing a palette scoring model, distance metrics between the color distribution and a plurality of color palettes in a color database by: identifying swatch matches between colors of the color distribution and unmatched swatches of the plurality of color palettes and determining distances between the colors of the color distribution and matched swatches of the plurality of color palettes. The disclosed systems return one or more color palettes of the plurality of color palettes in response to the textual color palette search query based on the distance metrics.

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