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    Image-based item reviews
    1.
    发明授权
    Image-based item reviews 有权

    公开(公告)号：US11100544B1

    公开(公告)日：2021-08-24

    申请号：US14980556

    申请日：2015-12-28

    Applicant: Amazon Technologies, Inc.

    Inventor： Adam James Finkelstein ,  Ho Nam Ho ,  Markus Wai-Keen Kwok ,  Siqi Zhao

    IPC: G06Q30/02 ,  G06K9/62 ,  G06K9/46 ,  G06F16/51 ,  G06F16/583

    Abstract: A service may create image-based reviews, which include minimal or no text, to assist customers in researching products. The reviews may include images sorted or grouped (e.g., by sentiment, by product review rating, by age of item, by number of uses, etc.). Images of items may be obtained by the service from user reviews and/or other sources. The images may be associated with text, such as at least some text from associated reviews, commentary, and/or other metadata. The images may be analyzed by a classifier to identify features in the visual image, such as a location of a particular item. The images may be categorized for use in one or more user interfaces that provide image-based item reviews. In some embodiments, the images may be arranged based on a number of uses of the item in the image or by an item age of the item in the image.

    Identification of item attributes using artificial intelligence
    3.
    发明授权
    Identification of item attributes using artificial intelligence 有权
    使用人工智能识别项目属性

    公开(公告)号：US09569700B1

    公开(公告)日：2017-02-14

    申请号：US14573892

    申请日：2014-12-17

    Applicant: Amazon Technologies, Inc.

    Inventor： Anthony Alexander Santos ,  Adam James Finkelstein ,  David Akira Gingrich ,  David Michael Hurley ,  Siqi Zhao

    IPC: G06K9/62 ,  G06K9/66 ,  G06N99/00 ,  G06N5/04 ,  G06F17/30

    CPC classification number: G06K9/66 ,  G06F17/30247 ,  G06F17/30256 ,  G06F17/3028 ,  G06K9/46 ,  G06K9/4628 ,  G06K9/6202 ,  G06K9/623 ,  G06K9/6282 ,  G06N3/04 ,  G06N3/0427 ,  G06N3/0454 ,  G06N3/08

    Abstract: A system that identifies attributes of an item depicted in an image using artificial intelligence is provided. For example, the system may use one or more deep belief networks (DBNs) or convolution neural networks (CNNs) trained to analyze images and identify attributes in items depicted in the images. A first artificial intelligence module may analyze an image to determine a type of item depicted in the image. The system may then select a second artificial intelligence module that is associated with the type of item and use the second artificial intelligence module to identify attributes in the item depicted in the image. Identified attributes, if associated with a confidence level over a threshold value, may be provided to a user. The user may provide feedback on the accuracy of the identified attributes, which can be used to further train the first and/or second artificial intelligence modules.

    Abstract translation: 提供了一种使用人工智能识别图像中描绘的项目的属性的系统。 例如，该系统可以使用经过训练的一个或多个深层信念网络（DBN）或卷积神经网络（CNN）来分析图像并识别图像中描绘的项目的属性。 第一人造智能模块可以分析图像以确定图像中描绘的项目的类型。 系统然后可以选择与项目类型相关联的第二人造智能模块，并使用第二人造智能模块来识别图像中描绘的项目中的属性。 如果与置信水平超过阈值相关联的标识属性可以被提供给用户。 用户可以提供关于所识别的属性的准确性的反馈，其可以用于进一步训练第一和/或第二人工智能模块。

    Artificial intelligence based identification of item attributes associated with negative user sentiment
    5.
    发明授权
    Artificial intelligence based identification of item attributes associated with negative user sentiment 有权

    公开(公告)号：US10410273B1

    公开(公告)日：2019-09-10

    申请号：US14562451

    申请日：2014-12-05

    Applicant: Amazon Technologies, Inc.

    Inventor： Adam James Finkelstein ,  David Akira Gingrich ,  David Michael Hurley ,  Stephen Brent Ivie ,  Siu Nam Wong ,  Siqi Zhao

    IPC: G06N5/04 ,  G06N20/00 ,  G06Q30/06

    Abstract: A recommendation system uses artificial intelligence to identify, based on negative sentiment cues from users, item attributes, such as keywords, that users may find offensive or undesirable. The negative sentiment cues may be explicit (e.g., a user selects an option not to view a particular recommendation again), implicit (e.g., a user does not interact with recommendations relating to an attribute), or both. The system may use a computer model generated based on these identified attributes to filter or modify recommendations to a user or group of users. For instance, if a particular keyword is identified as highly offensive to a group of users, items associated with the keyword may be filtered from item recommendations presented to the group of users. If an attribute is identified as moderately offensive to a user, items associated with the attribute may be down-weighted in item recommendations presented to the user.

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