Patent ID: 11960975
Assignee: QUALCOMM INCORPORATED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. An electronic device configured for multi-instance learning (MIL)-based classification of a streaming input, comprising:
a memory; and
a processor in communication with the memory, the processor configured to:
receive a threshold quantity of instances of the streaming input over a period of time, wherein each instance comprises a portion of the streaming input, the streaming input comprises a webpage and the instances comprise executable streaming input blocks within the webpage, and wherein the threshold quantity of instances comprises less than a total quantity of instances of the streaming input;
generate after receiving the threshold number of instances, and prior to executing the streaming input, a first classification result by applying feature vectors that are dynamically extracted from the received threshold number of instances to a first biased MIL model that is biased toward a first classification;
generate, prior to executing the streaming input, a second classification result by applying the feature vectors to a second biased MIL model that is biased toward a second classification, wherein the first biased MIL model is biased opposite the second biased MIL model;
wherein the extracted feature vectors are applied to the first biased MIL model and the second biased MIL model simultaneously for classification;
use the generated first classification result and the generated second classification result to generate a classification value for the streaming input based on the subset of the received instances of the streaming input and before all of the instances of the streaming input are available at the electronic device;
determine that the generated classification value is not accurate;
in response to determining that the generated classification value is not accurate and prior to both executing the streaming input and receiving the total quantity of instances of the streaming input:
update at least one of the generated first classification result and the generated second classification result using additional instances of the streaming input; and
update the generated classification value using at least one of the updated first classification result and the updated second classification result;

determine whether the streaming input is benign based on the updated classification value being accurate; and
execute the benign streaming input in response to determining the updated classification value is accurate.