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    SYSTEMS AND METHODS FOR DOCUMENT GENERATION
    1.
    发明公开
    SYSTEMS AND METHODS FOR DOCUMENT GENERATION 审中-公开

    公开(公告)号：US20230418881A1

    公开(公告)日：2023-12-28

    申请号：US17809371

    申请日：2022-06-28

    Applicant: ADOBE INC.

    Inventor： Shunan Guo ,  Yeuk-Yin Chan ,  Eunyee Koh ,  Caroline Jiwon Kim ,  Cole Edward Connelly ,  Andrew Douglas Thomson

    IPC: G06F16/93 ,  G06F40/103 ,  G06F40/14 ,  G06F40/166

    CPC classification number: G06F16/93 ,  G06F40/103 ,  G06F40/14 ,  G06F40/166

    Abstract: Systems and methods for document generation are provided. One aspect of the systems and methods includes identifying, by a style extractor, a document fragment comprising a first style element of a first style category; computing, by a style generator, a reward function based on a correlation value between the first style element and a second style element of a second style category different from the first style category, wherein the correlation value is based on correlations between style elements in a plurality of historical document fragments; selecting, by the style generator, the second style element based on the reward function; and generating, by a document generator, a modified document fragment that includes the first style element of the first style category and the second style element of the second style category.

    HYPERGRAPH REPRESENTATION LEARNING
    3.
    发明申请
    HYPERGRAPH REPRESENTATION LEARNING 有权

    公开(公告)号：US20250036936A1

    公开(公告)日：2025-01-30

    申请号：US18358502

    申请日：2023-07-25

    Applicant: ADOBE INC.

    Inventor： Ryan A. Rossi ,  Ryan Aponte ,  Shunan Guo ,  Jane Elizabeth Hoffswell ,  Nedim Lipka ,  Chang Xiao ,  Yeuk-yin Chan ,  Eunyee Koh

    IPC: G06N3/08

    Abstract: A method, apparatus, and non-transitory computer readable medium for hypergraph processing are described. Embodiments of the present disclosure obtain, by a hypergraph component, a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes; perform, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph to obtain an updated node embedding for a node of the plurality of nodes; and generate, by the hypergraph component, an augmented hypergraph based on the updated node embedding.

    BUILDING TIME-DECAYED LINE GRAPHS FOR DIRECT EMBEDDING OF CONTINUOUS-TIMED INTERACTIONS IN GENERATING TIME-AWARE RECOMMENDATIONS
    4.
    发明公开
    BUILDING TIME-DECAYED LINE GRAPHS FOR DIRECT EMBEDDING OF CONTINUOUS-TIMED INTERACTIONS IN GENERATING TIME-AWARE RECOMMENDATIONS 审中-公开

    公开(公告)号：US20240311623A1

    公开(公告)日：2024-09-19

    申请号：US18183387

    申请日：2023-03-14

    Applicant: Adobe Inc.

    Inventor： Ryan Rossi ,  Eunyee Koh ,  Jane Hoffswell ,  Nedim Lipka ,  Shunan Guo ,  Sudhanshu Chanpuriya ,  Sungchul Kim ,  Tong Yu

    IPC: G06N3/049

    CPC classification number: G06N3/049

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.

    Utilizing a graph neural network to generate visualization and attribute recommendations
    5.
    发明授权
    Utilizing a graph neural network to generate visualization and attribute recommendations 有权

    公开(公告)号：US12093322B2

    公开(公告)日：2024-09-17

    申请号：US17654933

    申请日：2022-03-15

    Applicant: Adobe Inc.

    Inventor： Fayokemi Ojo ,  Ryan Rossi ,  Jane Hoffswell ,  Shunan Guo ,  Fan Du ,  Sungchul Kim ,  Chang Xiao ,  Eunyee Koh

    IPC: G06F16/904 ,  G06N3/02

    CPC classification number: G06F16/904 ,  G06N3/02

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.

    GENERATING DATA INSIGHTS
    7.
    发明申请
    GENERATING DATA INSIGHTS 有权

    公开(公告)号：US20240403313A1

    公开(公告)日：2024-12-05

    申请号：US18328980

    申请日：2023-06-05

    Applicant: ADOBE INC.

    Inventor： Chen Chen ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Fan Du ,  Nathan Carl Ross ,  Ryan A. Rossi ,  Yeuk Yin Chan ,  Eunyee Koh

    IPC: G06F16/26 ,  G06F16/248 ,  G06T11/20

    Abstract: Systems and methods for data analysis are described. Embodiments of the present disclosure data analysis include displaying, via a data analysis interface, a data visualization in a first region of the data analysis interface; and displaying, via the data analysis interface, an analysis thread visualization in a second region of the data analysis interface. The analysis thread visualization depicts an analysis thread graph including a first node corresponding to the data visualization and an edge corresponding to an analysis path between the first node and a second node.

    GENERATING EDITABLE EMAIL COMPONENTS UTILIZING A CONSTRAINT-BASED KNOWLEDGE REPRESENTATION
    8.
    发明公开
    GENERATING EDITABLE EMAIL COMPONENTS UTILIZING A CONSTRAINT-BASED KNOWLEDGE REPRESENTATION 审中-公开

    公开(公告)号：US20240163238A1

    公开(公告)日：2024-05-16

    申请号：US18055238

    申请日：2022-11-14

    Applicant: Adobe Inc.

    Inventor： Yeuk-yin Chan ,  Andrew Thomson ,  Caroline Kim ,  Cole Connelly ,  Eunyee Koh ,  Michelle Lee ,  Shunan Guo

    IPC: H04L51/07 ,  G06F3/04842

    CPC classification number: H04L51/07 ,  G06F3/04842

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates editable email components by utilizing an Answer Set Programming (ASP) model with hard and soft constraints. For instance, in one or more embodiments, the disclosed systems generate editable email components from email fragments of an email file utilizing an Answer Set Programming (ASP) model. In particular, the disclosed systems extract facts for the ASP model from the email file. In addition, the disclosed systems determine rows or columns defining cells of the email file utilizing ASP hard constraints defined by a first set of ASP atoms corresponding to the facts. Moreover, the disclosed systems determine editable email component classes for the email fragments utilizing ASP soft constraints defined by ASP classification weights and a second set of ASP atoms corresponding to the facts.

    Predicting and visualizing outcomes using a time-aware recurrent neural network
    9.
    发明授权
    Predicting and visualizing outcomes using a time-aware recurrent neural network 有权

    公开(公告)号：US11475295B2

    公开(公告)日：2022-10-18

    申请号：US16394227

    申请日：2019-04-25

    Applicant: Adobe Inc.

    Inventor： Fan Du ,  Eunyee Koh ,  Sungchul Kim ,  Shunan Guo ,  Sana Malik Lee

    IPC: G06N3/04 ,  G06N3/08 ,  G06N5/02 ,  G06N7/00 ,  G06N20/10

    Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

    PREDICTING AND VISUALIZING OUTCOMES USING A TIME-AWARE RECURRENT NEURAL NETWORK
    10.
    发明申请
    PREDICTING AND VISUALIZING OUTCOMES USING A TIME-AWARE RECURRENT NEURAL NETWORK 审中-公开

    公开(公告)号：US20200342305A1

    公开(公告)日：2020-10-29

    申请号：US16394227

    申请日：2019-04-25

    Applicant: Adobe Inc.

    Inventor： Fan Du ,  Eunyee Koh ,  Sungchul Kim ,  Shunan Guo ,  Sana Malik Lee

    IPC: G06N3/08 ,  G06N5/02 ,  G06N20/10 ,  G06N7/00

    Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

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