Jeffrey M Young

20200409670, Dec 31, 2020

Jun 28, 2019

16/457210

- New York NY, US
Jeffrey James Young - Hillsborough NJ, US
Nicolas Antomarioni - Ciudad Autonoma de Buenos Aires, AR
Geoffrey Kendall Abbott - Bloomfield NJ, US
Satchuthananthavale Rasiah Kuhan Branavan - London, GB
Michael Hoa Thai - Astoria NY, US

G06F 8/36
G06F 8/33
G06Q 30/00
G06N 3/08

Software for a computer system may be automatically generated to reduce costs. Software may be automatically generated using a set of software components where each component may have one or more input properties and one or more output properties. A property may correspond to a type of data (e.g., a customer ID) used by the computer system. A graph may be created from the components where the graph includes component nodes for the components and property nodes for the input and output properties. To automatically generate software for a task, a task specification may be received that includes a task input property and a task output property. A path on the graph may be determined from the task input property and the task output property, and software for accomplishing the task may be generated using components on the path. The software may then be executed or stored for later execution.

20190037019, Jan 31, 2019

Oct 4, 2018

16/152082

- Salt Lake City UT, US
Michael R. Donnelly - Salt Lake City UT, US
Orland Kip Williams - Sandy UT, US
Cary J. Martin - Kaysville UT, US
Ming-Chieh Tu - Salt Lake City UT, US
Charlton Park - Holladay UT, US
Jeffrey Todd Young - West Valley City UT, US
Andrew Mark Hansen - Taylorsville UT, US
Jonathan Paul Godfrey - Salt Lake City UT, US
Nic Holbrook - Brigham City UT, US
Timothy John Morris - Bountiful UT, US
Tony Drake - West Jordan UT, US
Michael Houston - Kaysville UT, US

H04L 29/08
G06F 19/00
G16H 10/60
G16H 15/00
H04W 4/02

In an example implementation, the method includes automatically determining whether a computer system is located on-premises of a health service provider or on a multi-tenant cloud. The method includes communicatively coupling with health service provider data sources via a local network and extracting health service provider data from them. The health service provider data may include protected health information (PHI) and non-PHI data. The method includes storing the PHI data and the non-PHI data in an on-premises operational data store that is located on-premises of the health service provider. The method includes obtaining data analytics based on the PHI data and the non-PHI data stored in the on-premises operational data store. The method also includes communicatively coupling with a multi-tenant cloud via a global network and synchronizing the non-PHI data in the on-premises operational data store with the multi-tenant cloud via the global network.

20160350481, Dec 1, 2016

May 27, 2015

14/723294

- Salt Lake City UT, US
Michael R. DONNELLY - Salt Lake City UT, US
Orland Kip WILLIAMS - Salt Lake City UT, US
Cary J. MARTIN - Salt Lake City UT, US
Ming-Chieh TU - Salt Lake City UT, US
Charlton PARK - Salt Lake City UT, US
Jeffrey Todd YOUNG - Salt Lake City UT, US
Andrew Mark HANSEN - Salt Lake City UT, US
Jonathan Paul GODFREY - Salt Lake City UT, US
Nic HOLBROOK - Salt Lake City UT, US
Timothy John MORRIS - Salt Lake City UT, US
Tony DRAKE - Salt Lake City UT, US
Michael HOUSTON - Salt Lake City UT, US

G06F 19/00
H04L 29/08

In an example implementation, a method includes communicatively coupling a tenant instance of a multi-tenant cloud with a corresponding agent that is located on-premises of a health service provider via a global network. The method includes synchronizing the tenant instance with the agent including receiving non-Protected Health Information (non-PHI) data absent Protected Health Information (PHI) data from the agent at the tenant instance via the global network. The method includes extracting the non-PHI data from the agent into a cloud-based operational data store of the tenant instance that corresponds to the agent. The method includes storing the non-PHI data in the cloud-based operational data store. The method includes analyzing the non-PHI data in the cloud-based operational data store by load-balanced services.

20160350482, Dec 1, 2016

May 27, 2015

14/723364

- Salt Lake City UT, US
Michael R. DONNELLY - Salt Lake City UT, US
Orland Kip WILLIAMS - Sandy UT, US
Cary J. MARTIN - Kaysville UT, US
Ming-Chieh TU - Salt Lake City UT, US
Charlton PARK - Holladay UT, US
Jeffrey Todd YOUNG - West Valley City UT, US
Andrew Mark HANSEN - Taylorsville UT, US
Jonathan Paul GODFREY - Salt Lake City UT, US
Nic HOLBROOK - Salt Lake City UT, US
Timothy John MORRIS - Salt Lake City UT, US
Tony DRAKE - Salt Lake City UT, US
Michael HOUSTON - Salt Lake City UT, US

G06F 19/00
H04L 29/08

In an example implementation, the method includes automatically determining whether a computer system is located on-premises of a health service provider or on a multi-tenant cloud. The method includes communicatively coupling with health service provider data sources via a local network and extracting health service provider data from them. The health service provider data may include protected health information (PHI) and non-PHI data. The method includes storing the PHI data and the non-PHI data in an on-premises operational data store that is located on-premises of the health service provider. The method includes obtaining data analytics based on the PHI data and the non-PHI data stored in the on-premises operational data store. The method also includes communicatively coupling with a multi-tenant cloud via a global network and synchronizing the non-PHI data in the on-premises operational data store with the multi-tenant cloud via the global network.