Processing dynamic data within an adaptive oracle-trained learning system using curated training data for incremental re-training of a predictive model

A computer program product, stored on a non-transitory computer readable medium, comprising instructions that when executed on one or more computers cause the one or more computers to implement an adaptive learning framework for automatically building and maintaining a predictive model for processing dynamic data, wherein the adaptive learning framework is configured to include:

• the predictive model, wherein the model is configured to generate model output from processing an input data instance received by the adaptive learning framework, and wherein the model output includes a judgment and a confidence value representing certainty of the judgment;
• a training data set from which the predictive model is derived using machine learning;
• and
• a training data manager, wherein the training data manager is configured for curating the training data set;
• and
• a labeled data reservoir configured to store labeled data instances that have been processed by the predictive model, wherein the labeled data reservoir includes a pool of possible training data, wherein the set of labeled data instances are not included in the training data;
• and
• wherein each labeled data instance is associated with a true label representing the instance;
• and
• wherein the training data manager is configured to perform operations comprising:
• determining whether to update the training data set;
• in an instance in which the training data set is to be updated,
• selecting a set of labeled data instances from a labeled data reservoir;
• and
• updating the training data using the set of labeled data instances.