US 11,537,880 B2 · System and methods for generation of synthetic data cluster vectors and refinement of machine learning models

Abstract

Embodiments of the present invention provide an improvement to conventional machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. The proposed invention involves generating synthetic data clusters to be stored and used for retraining the main model as well as other models. In addition, the invention includes using one or more (subset) of the synthetic data clusters to train or retrain machine learning models, developing and training machine learning models that are trained with emerging synthetic data clusters, and ensembling machine learning models trained with emerging synthetic data clusters.

Claims20

Dependent claims

A system for generation of synthetic data cluster vectors, the system comprising:

a module containing a memory storage device, a communication device, and a processor, with computer-readable program code stored thereon, wherein executing the computer-readable code is configured to cause the processor to:
receive input data for analysis and expansion;
analyze the input data using a machine learning model to identify an emerging pattern in the input data;
extract common data characteristics from the identified emerging pattern in order to determine a data scenario;
expand the data scenario via general adversarial neural network encoding to produce expanded data scenarios;
batch the expanded data scenarios to create multiple synthetic data sets containing varied subsets of the expanded data scenarios;
generate one or more synthetic data vectors comprising one or more of the multiple synthetic data sets;
and
transmit the one or more synthetic data vectors to one or more machine learning models in order to train the models based on varied subsets of the expanded data scenarios.

6 more elements

A computer program product for iterative synthetic data generation, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:

an executable portion configured for receiving input data for analysis and expansion;
an executable portion configured for analyzing the input data using a machine learning model to identify an emerging pattern in the input data;
an executable portion configured for extracting common data characteristics from the identified emerging pattern in order to determine a data scenario;
an executable portion configured for expanding the data scenario via general adversarial neural network encoding to produce expanded data scenarios;
an executable portion configured for batching the expanded data scenarios to create multiple synthetic data sets containing varied subsets of the expanded data scenarios;
an executable portion configured for generating one or more synthetic data vectors comprising one or more of the multiple synthetic data sets;
and
an executable portion configured for transmitting the one or more synthetic data vectors to one or more machine learning models in order to train the models based on varied subsets of the expanded data scenarios.

5 more elements

Dependencies are inferred from the claim text.

System and methods for generation of synthetic data cluster vectors and refinement of machine learning models

Life of this patent

System and methods for generation of synthetic data cluster vectors and refinement of machine learning models

Life of this patent

Abstract

Claims20