Removing biases within a distributed model
AssigneeQomplx LLC
InventorsJason Crabtree, Andrew Sellers
Life of this patent
Estimating the term…
Abstract
Improving a distributed model with distributed data receives an instance of the distributed model from the network-connected computing system, create a cleansed dataset from data stored in the memory with at least biases within the data stored in memory corrected, train the instance of the distributed model with the cleansed dataset, and generate an update report based at least in part by updates to the instance of the distributed model.
Claims43
A system for improving a distributed model, comprising one or more computers with executable instructions that, when executed, cause the system to:
- receive an instance of a distributed model via a computer network;
- train a machine learning algorithm on the instance of the distributed model to identify a plurality of data biases within the instance of the distributed model;
- create a modified dataset based on the distributed model and the identified biases;
- wherein the identified biases comprise trends exhibited within the original distributed model data;
- and
- generate an update report based upon which remote instances of the distributed model may be adjusted to use the cleansed dataset.
A method for improving a distributed model with distributed data, comprising the steps of:
- receiving an instance of a distributed model via a computer network;
- training a machine learning algorithm on the instance of the distributed model to identify a plurality of data biases within the instance of the distributed model;
- creating a modified dataset based on the distributed model and the identified biases;
- wherein the identified biases comprise trends exhibited within the original distributed model data;
- and
- generating an update report based upon which remote instances of the distributed model may be adjusted to use the cleansed dataset.
A computing system for improving a distributed model with distributed data, the computing system comprising:
- one or more hardware processors configured for:
- receiving an instance of a distributed model via a computer network;
- training a machine learning algorithm on the instance of the distributed model to identify a plurality of data biases within the instance of the distributed model;
- creating a modified dataset based on the distributed model and the identified biases;
- wherein the identified biases comprise trends exhibited within the original distributed model data;
- and
- generating an update report based upon which remote instances of the distributed model may be adjusted to use the modified dataset.
Non-transitory, computer-readable storage media having computer-executable instructions embodied thereon that, when executed by one or more processors of a computing system for improving a distributed model with distributed data, cause the computing system to:
- receive an instance of a distributed model via a computer network;
- train a machine learning algorithm on the instance of the distributed model to identify a plurality of data biases within the instance of the distributed model;
- create a modified dataset based on the distributed model and the identified biases;
- wherein the identified biases comprise trends exhibited within the original distributed model data;
- and
- generate an update report based upon which remote instances of the distributed model may be adjusted to use the modified dataset.
Dependencies are inferred from the claim text.