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    System for Information Extraction from Form-Like Documents
    1.
    发明申请
    System for Information Extraction from Form-Like Documents 有权

    公开(公告)号：US20210374395A1

    公开(公告)日：2021-12-02

    申请号：US16890287

    申请日：2020-06-02

    Applicant: Google LLC

    Inventor： Sandeep Tata ,  Bodhisattwa Prasad Majumder ,  Qi Zhao ,  James Bradley Wendt ,  Marc Najork ,  Navneet Potti

    IPC: G06K9/00 ,  G06K9/20 ,  G06K9/62 ,  G06T7/70 ,  G06K9/72 ,  G06N20/00 ,  G06N5/04

    Abstract: The present disclosure is directed to extracting text from form-like documents. In particular, a computing system can obtain an image of a document that contains a plurality of portions of text. The computing system can extract one or more candidate text portions for each field type included in a target schema. The computing system can generate a respective input feature vector for each candidate for the field type. The computing system can generate a respective candidate embedding for the candidate text portion. The computing system can determine a respective score for each candidate text portion for the field type based at least in part on the respective candidate embedding for the candidate text portion. The computing system can assign one or more of the candidate text portions to the field type based on the respective scores.

    RECOMMENDING A DOCUMENT FOR A USER TO ACCESS
    2.
    发明申请
    RECOMMENDING A DOCUMENT FOR A USER TO ACCESS 有权

    公开(公告)号：US20210019622A1

    公开(公告)日：2021-01-21

    申请号：US16948888

    申请日：2020-10-05

    Applicant: GOOGLE LLC

    Inventor： Alan Green ,  Cayden Meyer ,  Julian Gibbons ,  Alexandre Mah ,  Divanshu Garg ,  Reuben Kan ,  Michael Smith ,  Sandeep Tata ,  Alexandrin Popescul

    IPC: G06N3/08 ,  G06N3/04 ,  G06F16/9535

    Abstract: A user device can send, to a server, a request for a set of documents likely to be opened by a user, determine a client-suggested document to present to the user and a potential motive for the user to open the client-suggested document, receive a suggestion message from the server, the suggestion message including a set of documents likely to be opened by the user and potential motives for the user to open documents in the set of documents, and present, on a display of the user device, visual representations of the client-suggested document, the potential motive for the user to open the client-suggested document, multiple documents included in the set of documents, and the potential motives for the user to open the multiple documents in the set of documents.

    Systems and Methods for Active Learning
    3.
    发明申请
    Systems and Methods for Active Learning 审中-公开

    公开(公告)号：US20200250527A1

    公开(公告)日：2020-08-06

    申请号：US16750053

    申请日：2020-01-23

    Applicant: Google LLC

    Inventor： Qi Zhao ,  Abbas Kazerouni ,  Sandeep Tata ,  Jing Xie ,  Marc Najork

    IPC: G06N3/08 ,  G06N3/04

    Abstract: The present disclosure provides computing systems and methods directed to active learning and may provide advantages or improvements to active learning applications for skewed data sets. A challenge in training and developing high-quality models for many supervised learning scenarios is obtaining labeled training examples. This disclosure provides systems and methods for active learning on a training dataset that includes both labeled and unlabeled datapoints. In particular, the systems and methods described herein can select (e.g., at each of a number of iterations) a number of the unlabeled datapoints for which labels should be obtained to gain additional labeled datapoints on which to train a machine-learned model (e.g., machine-learned classifier model). Generally, the disclosure provides cost-effective methods and systems for selecting data to improve machine-learned models in applications such as the identification of content items in text, images, and/or audio.

    A Transferable Neural Architecture for Structured Data Extraction From Web Documents
    9.
    发明申请
    A Transferable Neural Architecture for Structured Data Extraction From Web Documents 有权

    公开(公告)号：US20230014465A1

    公开(公告)日：2023-01-19

    申请号：US17792788

    申请日：2020-01-29

    Applicant: Google LLC

    Inventor： Ying Sheng ,  Yuchen Lin ,  Sandeep Tata ,  Nguyen Vo

    IPC: G06F16/958 ,  G06F16/957 ,  G06F40/14

    Abstract: Systems and methods for efficiently identifying and extracting machine-actionable structured data from web documents are provided. The technology employs neural network architectures which process the raw HTML content of a set of seed websites to create transferable models regarding information of interest. These models can then be applied to the raw HTML of other websites to identify similar information of interest. Data can thus be extracted across multiple websites in a functional, structured form that allows it to be used further by a processing system.

    Systems and methods for active learning
    10.
    发明授权
    Systems and methods for active learning 有权

    公开(公告)号：US11526752B2

    公开(公告)日：2022-12-13

    申请号：US16750053

    申请日：2020-01-23

    Applicant: Google LLC

    Inventor： Qi Zhao ,  Abbas Kazerouni ,  Sandeep Tata ,  Jing Xie ,  Marc Najork

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Provided are computing systems and methods directed to active learning and may provide advantages or improvements to active learning applications for skewed data sets. A challenge in training and developing high-quality models for many supervised learning scenarios is obtaining labeled training examples. Provided are systems and methods for active learning on a training dataset that includes both labeled and unlabeled datapoints. In particular, the systems and methods described herein can select (e.g., at each of a number of iterations) a number of the unlabeled datapoints for which labels should be obtained to gain additional labeled datapoints on which to train a machine-learned model (e.g., machine-learned classifier model). Generally, provided are cost-effective methods and systems for selecting data to improve machine-learned models in applications such as the identification of content items in text, images, and/or audio.

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