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1.发明申请公开(公告)号：US20190073592A1
公开(公告)日：2019-03-07
申请号：US15694321
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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Patent Agency Ranking
公开(公告)号：US10853696B1
公开(公告)日：2020-12-01
申请号：US16382162
申请日：2019-04-11
Applicant: Facebook, Inc.
Inventor： Enming Luo , Emanuel Alexandre Strauss
Abstract: An online system uses a model to detect violations of policies enforced by the online system for content uploaded to the online system by users for viewing by other users. The online system trains the model in multiple stages. To train the model, the online system obtains a set of training content items, with each content item of the set labeled with both a policy violated by the content item and a source of the content item, which acts as a proxy for a sub-category identifying a way in which the content item violated the policy. In the first stage, the online system trains the model using the set of training content items. In a second stage, the model of trained to predict policy violations from content items that are not labeled with a source. For example, the second stage is performed by freezing earlier layers in the model.
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公开(公告)号：US11195099B2
公开(公告)日：2021-12-07
申请号：US15694321
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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公开(公告)号：US10599774B1
公开(公告)日：2020-03-24
申请号：US15905709
申请日：2018-02-26
Applicant: Facebook, Inc.
Inventor： Enming Luo , Emanuel Alexandre Strauss
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users contain text that violates a policy of the online system. The content review system generates a semantic vector representing semantic features of text extracted from the content item, for example, using a neural network. By comparing the semantic vector for the extracted text with stored semantic vectors of extracted text previously determined to violate one or more policies, the content review system determines whether the content item contains text that also violates one or more policies. The content review system also reviews stored semantic vectors previously determined to be unsuitable, in order to remove false positives, as well as unsuitable semantic vectors that are sufficiently similar to known suitable semantic vectors and as such may cause content items having suitable text to be erroneously rejected.
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公开(公告)号：US10936952B2
公开(公告)日：2021-03-02
申请号：US15694339
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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公开(公告)号：US20190073593A1
公开(公告)日：2019-03-07
申请号：US15694339
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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