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    METHOD, MEDIUM, AND SYSTEM FOR INTELLIGENT ONLINE PERSONAL ASSISTANT WITH IMAGE TEXT LOCALIZATION
    32.
    发明申请
    METHOD, MEDIUM, AND SYSTEM FOR INTELLIGENT ONLINE PERSONAL ASSISTANT WITH IMAGE TEXT LOCALIZATION 有权

    公开(公告)号：US20250117839A1

    公开(公告)日：2025-04-10

    申请号：US18986611

    申请日：2024-12-18

    Applicant: eBay Inc.

    Inventor： Shuai Zheng ,  Robinson Piramuthu

    IPC: G06Q30/0601 ,  G06F16/532

    Abstract: Systems, methods, and computer program products for identifying a candidate product in an electronic marketplace based on a visual comparison between candidate product image visual text content and input query image visual text content. Unlike conventional optical character recognition (OCR) based systems, embodiments automatically localize and isolate portions of a candidate product image and an input query image that each contain visual text content, and calculate a visual similarity measure between the respective portions. A trained neural network may be re-trained to more effectively find visual text content by using the localized and isolated visual text content portions as additional ground truths. The visual similarity measure serves as a visual search result score for the candidate product. Any number of images of any number of candidate products may be compared to an input query image to enable text-in-image based product searching without resorting to conventional OCR techniques.

    Generating personalized banner images using machine learning
    36.
    发明授权
    Generating personalized banner images using machine learning 有权

    公开(公告)号：US10885558B2

    公开(公告)日：2021-01-05

    申请号：US16155255

    申请日：2018-10-09

    Applicant: eBay Inc.

    Inventor： Shuai Zheng ,  Mohammadhadi Kiapour ,  Nandini Ramakrishnan ,  Christophe Boudet ,  Fred Aye Zaw, Jr.

    IPC: G06Q30/00 ,  G06Q30/02 ,  G06N20/00 ,  G06F40/186 ,  G06F3/0484

    Abstract: A machine is configured to generate in real time personalized online banner images for users based on data pertaining to user behavior in relation to an image of a product. For example, the machine receives a user selection indicating one or more data features associated with the user. The one or more data features include a data feature pertaining to user behavior in relation to an image of a product. The machine generates, using a machine learning algorithm, a data representation of the machine learning algorithm based on the one or more data features including the data feature pertaining to user behavior in relation to the image of the product. The data representation includes one or more data features pertaining to one or more characteristics of online banner images. The machine generates an online banner image for the user based on the data representation.

    GENERATING A DIGITAL IMAGE USING A GENERATIVE ADVERSARIAL NETWORK
    37.
    发明申请
    GENERATING A DIGITAL IMAGE USING A GENERATIVE ADVERSARIAL NETWORK 审中-公开

    公开(公告)号：US20200218947A1

    公开(公告)日：2020-07-09

    申请号：US16733766

    申请日：2020-01-03

    Applicant: eBay Inc.

    Inventor： Mohammadhadi Kiapour ,  Shuai Zheng ,  Robinson Piramuthu ,  Omid Poursaeed

    IPC: G06K9/66 ,  G06Q30/06 ,  G06K9/00 ,  G06K9/32 ,  G06K9/62 ,  G06F16/9535 ,  G06F16/583 ,  G06F16/532

    Abstract: Various embodiments described herein utilize multiple levels of generative adversarial networks (GANs) to facilitate generation of digital images based on user-provided images. Some embodiments comprise a first generative adversarial network (GAN) and a second GAN coupled to the first GAN, where the first GAN includes an image generator and at least two discriminators, and the second GAN includes an image generator and at least one discriminator. According to some embodiments, the (first) image generator of the first GAN is trained by processing a user-provided image using the first GAN. For some embodiments, the user-provided image and the first generated image, generated by processing the user-provided image using the first GAN, are combined to produce a combined image. For some embodiments, the (second) image generator of the second GAN is trained by processing the combined image using the second GAN.

    GENERATING A DIGITAL IMAGE USING A GENERATIVE ADVERSARIAL NETWORK
    39.
    发明申请
    GENERATING A DIGITAL IMAGE USING A GENERATIVE ADVERSARIAL NETWORK 审中-公开

    公开(公告)号：US20190286950A1

    公开(公告)日：2019-09-19

    申请号：US15923347

    申请日：2018-03-16

    Applicant: eBay Inc.

    Inventor： Mohammadhadi Kiapour ,  Shuai Zheng ,  Robinson Piramuthu ,  Omid Poursaeed

    IPC: G06K9/66 ,  G06K9/62 ,  G06K9/32 ,  G06K9/00 ,  G06F17/30 ,  G06Q30/06

    Abstract: Various embodiments described herein utilize multiple levels of generative adversarial networks (GANs) to facilitate generation of digital images based on user-provided images. Some embodiments comprise a first generative adversarial network (GAN) and a second GAN coupled to the first GAN, where the first GAN includes an image generator and at least two discriminators, and the second GAN includes an image generator and at least one discriminator. According to some embodiments, the (first) image generator of the first GAN is trained by processing a user-provided image using the first GAN. For some embodiments, the user-provided image and the first generated image, generated by processing the user-provided image using the first GAN, are combined to produce a combined image. For some embodiments, the (second) image generator of the second GAN is trained by processing the combined image using the second GAN.

    Camera Platform incorporating Schedule and Stature
    40.
    发明申请
    Camera Platform incorporating Schedule and Stature 审中-公开

    公开(公告)号：US20190080172A1

    公开(公告)日：2019-03-14

    申请号：US15859056

    申请日：2017-12-29

    Applicant: eBay Inc.

    Inventor： Shuai Zheng ,  Fan Yang ,  Mohammadhadi Kiapour ,  Qiaosong Wang ,  Japjit S. Tulsi ,  Robinson Piramuthu

    IPC: G06K9/00 ,  G06K9/62 ,  G06T7/60 ,  G06K9/78

    Abstract: Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.

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