Utilizing intelligent sectioning and selective document reflow for section-based printing

    公开(公告)号:US11283964B2

    公开(公告)日:2022-03-22

    申请号:US16879019

    申请日:2020-05-20

    Applicant: Adobe Inc.

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing intelligent sectioning and selective document reflow for section-based printing. For example, the disclosed systems can intelligently identify document objects (e.g., document structures and sections) within a digital document by utilizing a machine-learning model. In so doing, the disclosed systems can identify document-object types and document-object locations for the document objects in the digital document. In turn, the disclosed systems can provide, for display within a dynamic printing interface, selectable document sections comprising the identified document objects. In response to a user selection of one or more of the selectable document sections, the disclosed system can generate a modified digital document for printing by reflowing the identified document objects in accordance with the user selection. In some cases, reflowing comprises removing unselected document objects and/or repositioning one or more of the selected document objects.

    Facilitating implementation of machine learning models in embedded software

    公开(公告)号:US12204964B2

    公开(公告)日:2025-01-21

    申请号:US18177636

    申请日:2023-03-02

    Applicant: Adobe Inc.

    Abstract: Methods and systems are provided for facilitating implementation of machine learning models in embedded software. In embodiments, a lean machine learning model, having a limited number of layers, is trained in association with a complex machine learning model, having a greater number of layers. To this end, a complex machine learning model, having a first number of layers, can be trained based on an output generated from a lean machine learning model used as input to the complex machine learning model. Further, the lean machine learning model, having a second number of layers less than the first number of layers, is trained using a loss value generated in association with training the complex machine learning model. Thereafter, the trained lean machine learning model can be provided for implementation in an embedded software.

    UTILIZING INTELLIGENT SECTIONING AND SELECTIVE DOCUMENT REFLOW FOR SECTION-BASED PRINTING

    公开(公告)号:US20210368064A1

    公开(公告)日:2021-11-25

    申请号:US16879019

    申请日:2020-05-20

    Applicant: Adobe Inc.

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing intelligent sectioning and selective document reflow for section-based printing. For example, the disclosed systems can intelligently identify document objects (e.g., document structures and sections) within a digital document by utilizing a machine-learning model. In so doing, the disclosed systems can identify document-object types and document-object locations for the document objects in the digital document. In turn, the disclosed systems can provide, for display within a dynamic printing interface, selectable document sections comprising the identified document objects. In response to a user selection of one or more of the selectable document sections, the disclosed system can generate a modified digital document for printing by reflowing the identified document objects in accordance with the user selection. In some cases, reflowing comprises removing unselected document objects and/or repositioning one or more of the selected document objects.

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