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Server-side rate-limiting algorithms for piggybacking social updates for mobile devices

  • US 9,801,032 B2
  • Filed: 12/10/2015
  • Issued: 10/24/2017
  • Est. Priority Date: 11/04/2011
  • Status: Active Grant
First Claim
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1. A method comprising:

  • by one or more computing devices, receiving, from a mobile device of a first user, information related to activity of the first user;

    by the one or more computing devices, identifying one or more outgoing messages to be sent to the first user;

    by the one or more computing devices, computing an affinity score for each of the outgoing messages with respect to an originator of the message and the first user, wherein the affinity score is based on the activity of the first user and further based on one or more predictor functions, wherein each predictor function is determined using one or more machine learning algorithms trained on the first user'"'"'s historical activity associated with one or more actions, andwherein each predictor function predicts one or more of the actions, the actions comprising one or more of;

    one or more types of a user'"'"'s communications, one or more types of a user'"'"'s observation actions, or one or more types of coincidence information about two or more users; and

    by the one or more computing devices, sending the outgoing messages to the mobile device, wherein the sending is based on at least the respective affinity scores of the outgoing messages.

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