Patent ID: 11907962
Assignee: PINTEREST, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 11:
12. A computer system, comprising:
one or more processors; and
a memory, storing program instructions that, when executed by the one or more processors, cause the one or more processors to at least:
capture online behaviors of a plurality of subscribers of an online service that were exposed to a content, each of the plurality of subscribers exposed to the content being an exposed subscriber, wherein the captured online behaviors include online actions, behaviors, and activities in response to the content;
train a machine learning model to predict conversion counts and corresponding error rates for subscribers based at least in part on the captured online behaviors of the plurality of subscribers in response to the content, wherein training the machine learning model includes:
generating a training dataset;
segmenting the training dataset into a plurality of batches of training data; and
training, using the plurality of batches of training data, the machine learning model by iteratively randomly disabling a processing node of the machine learning model, training the machine learning model with a respective batch of training data of the plurality of batches of training data while the processing node is disabled, and re-enabling the processing node;

for each exposed subscriber, determining whether the exposed subscriber is a measurable subscriber or a non-measurable subscriber, wherein a measurable subscriber is an exposed subscriber of the online service whose online behaviors outside of interaction with the online service are accessible and can be evaluated;
for each measurable subscriber:
determine, using the machine learning model, a first conversion count of the measurable subscriber in response to the content; and
add the first conversion count of the measurable subscriber to a total conversion count for the content;

for each non-measurable subscriber:
process, using the machine learning model, the captured online behaviors of the non-measurable subscriber to generate a second conversion count and an estimated error rate of the second conversion count in response to the content;

add the second conversion count to the total conversion count upon a determination that the estimated error rate is below a threshold; and
store the total conversion count in conjunction with the content.