Patent ID: 11915113
Assignee: VERINT AMERICAS INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method of scaling active learning across a distributed active learning system, the system comprising a first processor and a plurality of distributed processing devices, the method including the first processor performing operations comprising:
selecting a distributed query strategy for application to a pool of unlabeled data and partitioning the pool into a predetermined number of subsets of unlabeled training data;
allocating each of the predetermined subsets to a respective one of the plurality of distributed processing devices;
applying the selected distributed query strategy, wherein the selected distributed query strategy comprises a predetermined number of tasks, wherein the predetermined number of tasks equals the predetermined number of subsets and wherein the distributed query strategy is adapted to work in parallel across the plurality of distributed processing devices;
receiving, from each of the plurality of distributed processing devices, a predetermined number (n) of resulting unlabeled samples after the selected query strategy is performed by the plurality of distributing processing devices, wherein confidence in the resulting unlabeled samples, determined by a machine learned model, is lowest;
providing the resulting unlabeled samples for labeling to generate labeled selected samples;
receiving the labeled selected samples; and
retraining the machine learned model using the labeled selected samples.