US 11,743,679 B2
Systems and methods for pacing information delivery to mobile devices
Can Liang, Sunnyvale, CA (US); Yilin Chen, Sunnyvale, CA (US); Jingqi Huang, Santa Clara, CA (US); Shun Jiang, Sunnyvale, CA (US); and Amit Goswami, Campbell, CA (US)
Assigned to xAd, Inc., New York, NY (US)
Filed by xAd, Inc., New York, NY (US)
Filed on Nov. 2, 2021, as Appl. No. 17/517,650.
Application 17/517,650 is a continuation of application No. 16/780,802, filed on Feb. 3, 2020, granted, now 11,172,324.
Application 16/780,802 is a continuation in part of application No. 16/749,746, filed on Jan. 22, 2020, granted, now 11,146,911, issued on Oct. 12, 2021.
Application 16/749,746 is a continuation in part of application No. 16/726,056, filed on Dec. 23, 2019, granted, now 11,134,359, issued on Sep. 28, 2021.
Application 16/726,056 is a continuation in part of application No. 16/506,940, filed on Jul. 9, 2019, granted, now 10,939,233, issued on Mar. 2, 2021.
Application 16/506,940 is a continuation of application No. 15/999,331, filed on Aug. 17, 2018, granted, now 10,349,208, issued on Jul. 9, 2019.
Prior Publication US 2022/0060847 A1, Feb. 24, 2022
Int. Cl. H04W 4/021 (2018.01); G06N 20/00 (2019.01); G06F 16/29 (2019.01); H04L 67/52 (2022.01)
CPC H04W 4/021 (2013.01) [G06F 16/29 (2019.01); G06N 20/00 (2019.01); H04L 67/52 (2022.05)] 18 Claims
OG exemplary drawing
 
1. A method, comprising,
at one or more computer systems coupled to a packet-based network and including, or having access to, one or more databases storing therein datasets associated with mobile devices, a respective dataset including data related to an associated mobile device, a respective time stamp, and at least one respective event involving the associated mobile device at a time indicated by the respective time stamp:
for each respective request of a first plurality of requests received from the packet-based network during a first time unit:
determining whether the respective request qualifies for information delivery based on respective request data included in the respective request and a set of information delivery parameters;
in response to the respective request qualifying for information delivery, predicting a respective conversion probability for the respective request, the respective conversion probability corresponding to a predicted probability of a mobile device associated with the respective request having at least one location event at any of one or more POIs during a first time frame corresponding to the first time unit;
inputting the respective conversion probability to a bidding model to determine a respective bid for fulfilling the respective request; and
transmitting the respective bid to the packet-based network;
receiving feedbacks from the package-based network, the feedbacks indicating a first set of requests having been fulfilled among the first plurality of requests;
determining a projected number of conversions using predicted probabilities of the first set of requests;
determining a predicted number of conversions using a win rate profile and predicted conversion probabilities of qualified requests, the win rate profile for estimating a rate for wining a bid on a request having a predicted conversion probability in any of a plurality of ranges of predicted conversion probabilities; and
adjusting the bidding model based at least on the predicted number of conversions and the projected number of conversions;
wherein predicting a respective conversion probability for the respective request includes constructing a respective feature set for the respective request using at least the respective request data, and applying a machine-trained location prediction model to the respective feature set to obtain the respective conversion probability; and
wherein the method further comprises machine-training the location prediction model using at least a training feature space and a set of training labels, the training feature space being constructed using datasets having time stamps in a training time period, the set of training labels being determined using datasets having time stamps in a training time frame.