- Patent Title: Evaluating content for compliance with a content policy enforced by an online system using a machine learning model determining compliance with another content policy
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Application No.: US15449448Application Date: 2017-03-03
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Publication No.: US11023823B2Publication Date: 2021-06-01
- Inventor: Emanuel Alexandre Strauss
- Applicant: Facebook, Inc.
- Applicant Address: US CA Menlo Park
- Assignee: Facebook, Inc.
- Current Assignee: Facebook, Inc.
- Current Assignee Address: US CA Menlo Park
- Agency: Fenwick & West LLP
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06Q30/02 ; G06Q50/00 ; G06N7/00

Abstract:
An online system maintains machine learning models that determine risk scores for content items indicating likelihoods of content items violating content policies associated with the machine learning models. When the online system obtains an additional content policy, the online system applies a maintained machine learning model to a set including content items previously identified as violating or not violating the additional content policy. The online system maps the risk scores determined for content items of the set to likelihoods of violating the additional content policy based on the identifications of content times in the set violating or not violating the additional content policy. Subsequently, the online system applies the maintained machine learning model to content items and determines likelihoods of the content items violating the additional content policy based on the mapping of risk scores to likelihood of violating the additional content policy.
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