INTELLIGENTLY GENERATING CLIENT DEVICE APPLICATION RECOMMENDATIONS BASED ON DYNAMIC DIGITAL USER CONTEXT STATES

    公开(公告)号:US20220413881A1

    公开(公告)日:2022-12-29

    申请号:US17823811

    申请日:2022-08-31

    Applicant: Adobe Inc.

    Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods that intelligently sense digital user context across client devices applications utilizing a dynamic sensor graph framework and then utilize a persistent context store to generate flexible digital recommendations across digital applications. In one or more embodiments, the disclosed systems utilize triggers to select and activate one or more sensor graphs. These sensor graphs can include software sensors arranged according to an architecture of dependencies and subject to various constraints. The underlying architecture of dependencies and constraints in each sensor graph allows the disclosed systems to avoid race-conditions in persisting actionable user-context based signals, verify the validity of sensor output through the sensor graph, generate user-context based recommendations across multiple related applications, and accommodate a specific latency/refresh rate of context values.

    CREATION AND PERSONALIZATION OF COMPOSITE FONTS

    公开(公告)号:US20250068829A1

    公开(公告)日:2025-02-27

    申请号:US18238123

    申请日:2023-08-25

    Applicant: Adobe Inc.

    Abstract: Techniques for creation and personalization of composite fonts are described. In one embodiment, a method includes receiving an input font sequence comprising font embeddings for a first font and sequence information for the first font, the font embeddings comprising numerical vectors, predicting a second font based on the font embeddings of the first font and the sequence information for the first font using a transformer-based machine learning model, selecting a character from the second font, and adding the character of the second font to a character of the first font to generate a set of characters for a composite font. Other embodiments are described and claimed.

    IMAGE GENERATION WITH LEGIBLE SCENE TEXT

    公开(公告)号:US20250061610A1

    公开(公告)日:2025-02-20

    申请号:US18449286

    申请日:2023-08-14

    Applicant: ADOBE INC.

    Abstract: Systems and methods for generating images with legible scene text are described. Embodiments are configured to obtain a prompt describing a scene, where the prompt includes scene text indicating text that is intended to be shown in a generated image; encode, using a prompt encoder, the prompt to generate a prompt embedding; encode, using a character-level encoder, the scene text to generate a character-level embedding; and generate, using an image generation network, an image that includes the scene text based on the prompt embedding and the character-level embedding.

    Intelligently generating client device application recommendations based on dynamic digital user context states

    公开(公告)号:US12159151B2

    公开(公告)日:2024-12-03

    申请号:US17823811

    申请日:2022-08-31

    Applicant: Adobe Inc.

    Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods that intelligently sense digital user context across client devices applications utilizing a dynamic sensor graph framework and then utilize a persistent context store to generate flexible digital recommendations across digital applications. In one or more embodiments, the disclosed systems utilize triggers to select and activate one or more sensor graphs. These sensor graphs can include software sensors arranged according to an architecture of dependencies and subject to various constraints. The underlying architecture of dependencies and constraints in each sensor graph allows the disclosed systems to avoid race-conditions in persisting actionable user-context based signals, verify the validity of sensor output through the sensor graph, generate user-context based recommendations across multiple related applications, and accommodate a specific latency/refresh rate of context values.

    GENERATING DIGITAL DESIGN RECOMMENDATIONS
    25.
    发明公开

    公开(公告)号:US20240273285A1

    公开(公告)日:2024-08-15

    申请号:US18638275

    申请日:2024-04-17

    Applicant: Adobe Inc.

    CPC classification number: G06F40/186 G06F40/30 G06N3/08

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that provides to a user a subset of digital design templates as recommendations based on a creative segment classification and template classifications. For instance, in one or more embodiments, the disclosed systems generate the creative segment classification for the user and determines geo-seasonal intent data. Furthermore, the disclosed system generates template classifications using a machine learning model based on geo-seasonality and creative intent. In doing so, the disclosed system identifies a subset of digital design templates based on the template classifications, geo-seasonal intent data, and the creative segment classification of the user.

    Generating personalized in-application recommendations utilizing in-application behavior and intent

    公开(公告)号:US12061916B2

    公开(公告)日:2024-08-13

    申请号:US17657477

    申请日:2022-03-31

    Applicant: Adobe Inc.

    CPC classification number: G06F9/451

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that recommends application features of software applications based on in-application behavior and provides the recommendations within a dynamically updating graphical user interface. For instance, in one or more embodiments, the disclosed systems utilize behavioral signals reflecting the behavior of a user with respect to one or more software applications to recommend application features of the software application(s). For instance, in some cases, the disclosed systems recommend an application feature related to recent activity user, an application feature from a curated recommendation list that has yet to be viewed, and/or an application feature determined via machine learning. In some embodiments, the disclosed systems dynamically update a graphical user interface of a client device in real time as the user utilizes the client device to access and navigate the software application(s) to display these recommendations.

    GENERATING TEMPLATES USING STRUCTURE-BASED MATCHING

    公开(公告)号:US20240127577A1

    公开(公告)日:2024-04-18

    申请号:US17965291

    申请日:2022-10-13

    Applicant: Adobe Inc.

    CPC classification number: G06V10/761 G06T11/60

    Abstract: In implementations of systems for generating templates using structure-based matching, a computing device implements a template system to receive input data describing a set of digital design elements. The template system represents the input data as a sentence in a design structure language that describes structural relationships between design elements included in the set of digital design elements. An input template embedding is generated based on the sentence in the design structure language. The template system generates a digital template that includes the set of digital design elements for display in a user interface based on the input template embedding.

    GENERATING PERSONALIZED IN-APPLICATION RECOMMENDATIONS UTILIZING IN-APPLICATION BEHAVIOR AND INTENT

    公开(公告)号:US20230315491A1

    公开(公告)日:2023-10-05

    申请号:US17657477

    申请日:2022-03-31

    Applicant: Adobe Inc.

    CPC classification number: G06F9/451

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that recommends application features of software applications based on in-application behavior and provides the recommendations within a dynamically updating graphical user interface. For instance, in one or more embodiments, the disclosed systems utilize behavioral signals reflecting the behavior of a user with respect to one or more software applications to recommend application features of the software application(s). For instance, in some cases, the disclosed systems recommend an application feature related to recent activity user, an application feature from a curated recommendation list that has yet to be viewed, and/or an application feature determined via machine learning. In some embodiments, the disclosed systems dynamically update a graphical user interface of a client device in real time as the user utilizes the client device to access and navigate the software application(s) to display these recommendations.

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