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    LIFELONG SCHEMA MATCHING
    12.
    发明申请
    LIFELONG SCHEMA MATCHING 有权

    公开(公告)号：US20220100714A1

    公开(公告)日：2022-03-31

    申请号：US17036453

    申请日：2020-09-29

    Applicant: ADOBE INC.

    Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T. Dinh ,  Caroline Jiwon Kim

    IPC: G06F16/21 ,  G06K9/62 ,  G06N3/08 ,  G06K9/72

    Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.

    GENERATING EXPLANATORY PATHS FOR PREDICTED COLUMN ANNOTATIONS
    13.
    发明申请
    GENERATING EXPLANATORY PATHS FOR PREDICTED COLUMN ANNOTATIONS 有权

    公开(公告)号：US20210264244A1

    公开(公告)日：2021-08-26

    申请号：US16796681

    申请日：2020-02-20

    Applicant: Adobe Inc.

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

    IPC: G06N3/08 ,  G06F16/22 ,  G06F16/901 ,  G06F16/248 ,  G06F16/2457 ,  G06N5/02

    Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating generate explanatory paths for column annotations determined using a knowledge graph and a deep representation learning model. For instance, the disclosed systems can utilize a knowledge graph to generate an explanatory path for a column label determination from a deep representation learning model. For example, the disclosed systems can identify a column and determine a label for the column using a knowledge graph (e.g., a representation of a knowledge graph) that includes encodings of columns, column features, relational edges, and candidate labels. Then, the disclosed systems can determine a set of candidate paths between the column and the determined label for the column within the knowledge graph. Moreover, the disclosed systems can generate an explanatory path by ranking and selecting paths from the set of candidate paths using a greedy ranking and/or diversified ranking approach.

    Multi-task equidistant embedding
    14.
    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
    15.
    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.

    Digital content query-aware sequential search
    16.
    发明授权
    Digital content query-aware sequential search 有权

    公开(公告)号：US12124439B2

    公开(公告)日：2024-10-22

    申请号：US17513127

    申请日：2021-10-28

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhe Lin ,  Zhaowen Wang ,  Zhankui He ,  Ajinkya Gorakhnath Kale

    IPC: G06F16/245 ,  G06F16/248 ,  G06N20/00

    CPC classification number: G06F16/245 ,  G06F16/248 ,  G06N20/00

    Abstract: Digital content search techniques are described that overcome the challenges found in conventional sequence-based techniques through use of a query-aware sequential search. In one example, a search query is received and sequence input data is obtained based on the search query. The sequence input data describes a sequence of digital content and respective search queries. Embedding data is generated based on the sequence input data using an embedding module of a machine-learning model. The embedding module includes a query-aware embedding layer that generates embeddings of the sequence of digital content and respective search queries. A search result is generated referencing at least one item of digital content by processing the embedding data using at least one layer of the machine-learning model.

    Locally constrained self-attentive sequential recommendation
    17.
    发明授权
    Locally constrained self-attentive sequential recommendation 有权

    公开(公告)号：US12019671B2

    公开(公告)日：2024-06-25

    申请号：US17501191

    申请日：2021-10-14

    Applicant: Adobe Inc.

    Inventor： Handong Zhao ,  Zhankui He ,  Zhaowen Wang ,  Ajinkya Gorakhnath Kale ,  Zhe Lin

    IPC: G06F16/43 ,  G06F16/438 ,  G06F16/44 ,  G06N3/045

    CPC classification number: G06F16/438 ,  G06F16/447 ,  G06N3/045

    Abstract: Digital content search techniques are described. In one example, the techniques are incorporated as part of a multi-head self-attention module of a transformer using machine learning. A localized self-attention module, for instance, is incorporated as part of the multi-head self-attention module that applies local constraints to the sequence. This is performable in a variety of ways. In a first instance, a model-based local encoder is used, examples of which include a fixed-depth recurrent neural network (RNN) and a convolutional network. In a second instance, a masking-based local encoder is used, examples of which include use of a fixed window, Gaussian initialization, and an adaptive predictor.

    PREDICTIVE AGENTS FOR MULTI-ROUND CONVERSATIONAL RECOMMENDATIONS OF BUNDLED ITEMS
    18.
    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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