Server-side rate-limiting algorithms for piggybacking social updates for mobile devices
First Claim
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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Abstract
In one embodiment, device status information is received from a mobile device of a first user. The device status information may include: information related to power for the device, information related to network connectivity of the device, or a notification mode of the device. Outgoing messages may be identified to be sent to the first user. A value may be computed for each of the outgoing messages. The computed value may be based on an affinity score determined with respect to an originator of the message and the first user. Based on the computed value, at least one of the outgoing messages may be selected for placement into an outgoing message queue. Upon the detection of a triggering condition, the selected messages may be sent to the mobile device. The triggering condition may be based on the device status information and the respective values of the outgoing messages.
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Citations
20 Claims
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1. A method comprising:
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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, and wherein 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; andby 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. One or more computer-readable non-transitory storage media embodying software that is operable when executed by a computing device to:
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receive, from a mobile device of a first user, information related to activity of the first user; identify one or more outgoing messages to be sent to the first user; compute 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, and wherein 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; andsend the outgoing messages to the mobile device, wherein the sending is based on at least the respective affinity scores of the outgoing messages. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A system comprising:
- one or more processors; and
a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to;receive, from a mobile device of a first user, information related to activity of the first user; identify one or more outgoing messages to be sent to the first user; compute 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, and wherein 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; andsend the outgoing messages to the mobile device, wherein the sending is based on at least the respective affinity scores of the outgoing messages. - View Dependent Claims (16, 17, 18, 19, 20)
- one or more processors; and
Specification