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    Automatic generation of content using multimedia
    6.
    发明授权
    Automatic generation of content using multimedia 有权

    公开(公告)号：US11170270B2

    公开(公告)日：2021-11-09

    申请号：US16656389

    申请日：2019-10-17

    Applicant: International Business Machines Corporation

    Inventor： Michele Merler ,  Mauro Martino ,  Cicero Nogueira Dos Santos ,  Alfio Massimiliano Gliozzo ,  John R. Smith

    IPC: G06K9/62 ,  G06N5/04 ,  G06N3/08 ,  G06N3/04 ,  G06N20/00

    Abstract: Techniques for content generation are provided. A plurality of discriminative terms is determined based at least in part on a first plurality of documents that are related to a first concept, and a plurality of positive exemplars and a plurality of negative exemplars are identified using the plurality of discriminative terms. A first machine learning (ML) model is trained to classify images into concepts, based on the plurality of positive exemplars and the plurality of negative exemplars. A second concept related to the first concept is then determined, based on the first ML model. A second ML model is trained to generate images based on the second concept, and a first image is generated using the second ML model. The first image is then refined using a style transfer ML model that was trained using a plurality of style images.

    ENTITY STANDARDIZATION FOR APPLICATION MODERNIZATION
    9.
    发明公开
    ENTITY STANDARDIZATION FOR APPLICATION MODERNIZATION 审中-公开

    公开(公告)号：US20240256852A1

    公开(公告)日：2024-08-01

    申请号：US18160301

    申请日：2023-01-26

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Jiaqing Yuan ,  Michele Merler ,  Mihir Choudhury ,  Venkata Nagaraju Pavuluri ,  Maja Vukovic

    IPC: G06N3/08 ,  G06N5/022

    CPC classification number: G06N3/08 ,  G06N5/022

    Abstract: Standardizing a mention of an application component in a free-form text describing the technology stack of the application includes extracting the mention and encoding the mention with an embedding space encoder. The encoding creates an encoded representation of the mention in a multi-dimensional embedding space. The embedding space encoder implements a machine learning model trained using contrastive learning. The encoded representation of the mention is mapped to an encoded representation of an entity in the multi-dimensional embedding space, the entity extracted from a knowledge base of computer components. The entity whose encoded representation maps to the encoded representation of the mention can be output responsive to the mapping.

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