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    Deep Hybrid Graph-Based Forecasting Systems
    11.
    发明申请
    Deep Hybrid Graph-Based Forecasting Systems 有权

    公开(公告)号：US20220138557A1

    公开(公告)日：2022-05-05

    申请号：US17089157

    申请日：2020-11-04

    Applicant: Adobe Inc.

    Inventor： Ryan A. Rossi ,  Hongjie Chen ,  Kanak Vivek Mahadik ,  Sungchul Kim

    IPC: G06N3/08 ,  G06N3/04 ,  G06F16/2458

    Abstract: In implementations of deep hybrid graph-based forecasting systems, a computing device implements a forecast system to receive time-series data describing historic computing metric values for a plurality of processing devices. The forecast system determines dependency relationships between processing devices of the plurality of processing devices based on time-series data of the processing devices. Time-series data of each processing device is represented as a node of a graph and the nodes are connected based on the dependency relationships. The forecast system generates an indication of a future computing metric value for a particular processing device by processing a first set of the time-series data using a relational global model and processing a second set of the time-series data using a relational local model. The first and second sets of the time-series data are determined based on a structure of the graph.

    System for identifying typed graphlets
    12.
    发明授权
    System for identifying typed graphlets 有权

    公开(公告)号：US11170048B2

    公开(公告)日：2021-11-09

    申请号：US16451956

    申请日：2019-06-25

    Applicant: Adobe Inc.

    Inventor： Ryan Rossi ,  Aldo Gael Carranza ,  David Arbour ,  Anup Rao ,  Sungchul Kim ,  Eunyee Koh

    IPC: G06F16/00 ,  G06F16/901 ,  G06F17/18

    Abstract: A system is disclosed for identifying and counting typed graphlets in a heterogeneous network. A methodology implementing techniques for the disclosed system according to an embodiment includes identifying typed k-node graphlets occurring between any two selected nodes of a heterogeneous network, wherein the nodes are connected by one or more edges. The identification is based on combinatorial relationships between (k−1)-node typed graphlets occurring between the two selected nodes of the heterogeneous network. Identification of 3-node typed graphlets is based on computation of typed triangles, typed 3-node stars, and typed 3-paths associated with each edge connecting the selected nodes. The method further includes maintaining a count of the identified k-node typed graphlets and storing those graphlets with non-zero counts. The identified graphlets are employed for applications including visitor stitching, user profiling, outlier detection, and link prediction.

    DYNAMICALLY DETERMINING SCHEMA LABELS USING A HYBRID NEURAL NETWORK ENCODER
    14.
    发明申请
    DYNAMICALLY DETERMINING SCHEMA LABELS USING A HYBRID NEURAL NETWORK ENCODER 有权

    公开(公告)号：US20210232908A1

    公开(公告)日：2021-07-29

    申请号：US16751755

    申请日：2020-01-24

    Applicant: Adobe Inc.

    Inventor： Yikun Xian ,  Tak Yeon Lee ,  Sungchul Kim ,  Ryan Rossi ,  Handong Zhao

    IPC: G06N3/08

    Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for dynamically determining schema labels for columns regardless of information availability within the columns. For example, the disclosed systems can identify a column that contains an arbitrary amount of information (e.g., a header-only column, a cell-only column, or a whole column). Additionally, the disclosed systems can generate a vector embedding for an arbitrary input column by selectively using a header neural network and/or a cell neural network based on whether the column includes a header label and/or whether the column includes a populated column cell. Furthermore, the disclosed systems can compare the column vector embedding to schema vector embeddings of candidate schema labels in a d-dimensional space to determine a schema label for the column.

    FIGURE CAPTIONING SYSTEM AND RELATED METHODS
    16.
    发明申请
    FIGURE CAPTIONING SYSTEM AND RELATED METHODS 审中-公开

    公开(公告)号：US20200285951A1

    公开(公告)日：2020-09-10

    申请号：US16296076

    申请日：2019-03-07

    Applicant: ADOBE INC.

    Inventor： Sungchul Kim ,  Scott Cohen ,  Ryan A. Rossi ,  Charles Li Chen ,  Eunyee Koh

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06F17/28

    Abstract: Embodiments of the present invention are generally directed to generating figure captions for electronic figures, generating a training dataset to train a set of neural networks for generating figure captions, and training a set of neural networks employable to generate figure captions. A set of neural networks is trained with a training dataset having electronic figures and corresponding captions. Sequence-level training with reinforced learning techniques are employed to train the set of neural networks configured in an encoder-decoder with attention configuration. Provided with an electronic figure, the set of neural networks can encode the electronic figure based on various aspects detected from the electronic figure, resulting in the generation of associated label map(s), feature map(s), and relation map(s). The trained set of neural networks employs a set of attention mechanisms that facilitate the generation of accurate and meaningful figure captions corresponding to visible aspects of the electronic figure.

    Multi-task equidistant embedding
    18.
    发明授权
    Multi-task equidistant embedding 有权

    公开(公告)号：US12182713B2

    公开(公告)日：2024-12-31

    申请号：US16203263

    申请日：2018-11-28

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zheng Wen ,  Sungchul Kim ,  Sheng Li ,  Branislav Kveton

    IPC: G06N3/084 ,  G06F9/48 ,  G06F18/10 ,  G06F18/213 ,  G06N3/08 ,  G06N7/01 ,  G06N20/00

    Abstract: Systems and techniques for multi-task equidistant embedding are described that process categorical feature data to explore feature interactions. A digital analytics system enforces an equidistant relationship among features within a category while extracting high-order feature interactions by punishing both positive correlations and negative correlations among low-dimensional representations of different features. By enforcing an equidistant embedding, information is retained and accuracy is increased while higher order feature interactions are determined. Further, the digital analytics system shares knowledge among different tasks by connecting a shared network representation common to multiple tasks with exclusive network representations specific to particular tasks.

    CONTEXTUAL QUERY GENERATION
    19.
    发明申请
    CONTEXTUAL QUERY GENERATION 有权

    公开(公告)号：US20240427998A1

    公开(公告)日：2024-12-26

    申请号：US18339694

    申请日：2023-06-22

    Applicant: Adobe Inc.

    Inventor： Haoliang Wang ,  Tong Yu ,  Sungchul Kim ,  Ruiyi Zhang ,  Paiheng Xu ,  Junda Wu ,  Handong Zhao ,  Ani Nenkova

    IPC: G06F40/30 ,  G06N5/04

    Abstract: Contextual query generation techniques are described that enable generation of a contextual query for output to a question-answering (QA) model. A content processing system, for instance, configures a language model using in-context learning to generate queries based on semantic contexts of input documents, e.g., based on one or more linguistic cues from text of the input documents. The content processing system receives an input that includes a document having text and a reference query. The content processing system leverages the language model to generate a contextual query based on a semantic context of the text of the document and the reference query. The content processing system then outputs the contextual query and the document to a QA model. Using the QA model, the content processing system generates a response as an answer to the contextual query based on the contextual query and the document.

    PREDICTIVE AGENTS FOR MULTI-ROUND CONVERSATIONAL RECOMMENDATIONS OF BUNDLED ITEMS
    20.
    发明公开
    PREDICTIVE AGENTS FOR MULTI-ROUND CONVERSATIONAL RECOMMENDATIONS OF BUNDLED ITEMS 审中-公开

    公开(公告)号：US20240169410A1

    公开(公告)日：2024-05-23

    申请号：US17980790

    申请日：2022-11-04

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhankui He ,  Tong Yu ,  Fan Du ,  Sungchul Kim

    IPC: G06Q30/06

    CPC classification number: G06Q30/0631

    Abstract: Techniques for predicting and recommending item bundles in a multi-round conversation to discover a target item bundle that would be accepted by a client. An example method includes receiving an input response in reply to a first item bundle that includes one or more items. A state model is updated to reflect the input response to the first item bundle. A machine-learning (ML) conversation module is applied to the state model to determine an action type as a follow-up to the input response to the first item bundle. Based on selection of a recommendation action as the action type, an ML bundling module is applied to the state model to generate a second item bundle different than the first item bundle. The second item bundle is then recommended.

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