US 12,118,473 B2Granted

Statistically-representative sample data generation

AssigneeClover Health

InventorsIan Blumenfeld, Brian Johnson

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Abstract

Systems and methods for statistically-representative sample data generation are disclosed. For example, a sample-data generator and/or a data discriminator may be received by a system, which may utilize the sample-data generator to generate sample data. The data discriminator may be utilized to train the sample-data generator until the data discriminator cannot discriminate between data received from the sample-data generator and data received by a database associated with the system. The trained sample-data generator may be sent to other systems, which may generate and utilize, such as for prediction model training, statistically-representative sample data generated by the trained sample-data generator.

Claims20

Dependent claims

A system comprising:

one or more processors;
and
computer-readable media storing first computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
sending, from a first component of a first system situated in a first geographic location to a second component of a second system situated in a second geographic location, first instructions to generate sample data from first data stored in association with the second system, the first geographic location being different then the second geographic location and the first component having a first component type that is a same type as a second component type of the second component;
sending, from the first system to the second system, second instructions to identify differences between the sample data and the first data, the second system further configured to train a model to minimize the differences;
receiving an indication that the model has been trained such that the differences are less than a threshold level of difference;
receiving, at the first system and from the second system, a trained model;
causing the trained model to generate second data that is statistically-representative of the first data;
generating a database associated with the first system;
storing the second data in a first portion of the database, the first portion of the database indicating that the second data is associated with the second system;
storing third data associated with the first system in a second portion of the database;
and
generating an interface configured to provide selective access to the database or at least one of the first portion or the second portion.

10 more elements

A method, comprising:

sending, from a first component of a first system located at a first geographic location to a second component of a second system located at a second geographic location, first instructions to generate sample data from first data stored in association with the second system, the first geographic location being different then the second geographic location and the first component having a first component type that is a same type as a second component type of the second component;
sending, from the first system to the second system, second instructions to train a model;
receiving, at the first system and from the second system, a trained model;
causing the trained model to generate second data that is statistically-representative of the first data;
generating a database associated with the first system;
storing the second data in a first portion of the database, the first portion of the database indicating that the second data is associated with the second system;
storing third data associated with the first system in a second portion of the database;
and
generating an interface configured to provide selective access to the database or at least one of the first portion or the second portion.

6 more elements

A system, comprising:

one or more processors;
and
computer-readable media storing first computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
sending, from a first component of a first system located at a first geographic location to a second component of a second system located at a second geographic location, first instructions to generate sample data from first data stored in association with the second system, the first geographic location being different then the second geographic location and the first component having a first component type that is a same type as a second component type of the second component;
sending, from the first system to the second system, second instructions to train a model;
receiving, at the first system and from the second system, a trained model;
causing the trained model to generate second data that is statistically-representative of the first data;
generating a database associated with the first system;
storing the second data in a first portion of the database, the first portion of the database indicating that the second data is associated with the second system;
storing third data associated with the first system in a second portion of the database;
and
generating an interface configured to provide selective access to the database or at least one of the first portion or the second portion.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/208,029Filed 3 Dec 2018
Published: US 2020/0175383 A14 Jun 2020
Granted: 15 Oct 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06N 3/088
Learning methods: non-supervised learning, e.g. competitive learning
G06N 5/02
Computing arrangements using knowledge-based models: knowledge representation; Symbolic representation
G06F 16/2379
Updating: updates performed during online database operations; commit processing
G06F 16/252
Integrating or interfacing systems involving database management systems: between a Database Management System and a front-end application

Family

US 2020/0175383 A1Pre-grant publication · published 4 Jun 2020
US 12,118,473 B2This patent

From the US filing history. Foreign family members aren’t listed.

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