Brian E Townsend

US Patent:

20130033486, Feb 7, 2013

Filed:

Oct 28, 2011

Appl. No.:

13/284412

Inventors:

Jeffrey McCartney - Irvine CA, US
Claus W. Jensen - Silkeborg, DK
Thomas Tegar - Orange CA, US
Brian Townsend - Irvine CA, US
Henrik W. Jensen - Solana Beach CA, US

International Classification:

G06T 13/20

US Classification:

345419

Abstract:

The present invention relates to a novel 3D animation system for animating models. The system uses offset transforms to make it possible to make animation components that can be added individually to the animation system. The animation components simplify the process of animating 3D models performed conventionally. For example, the user can apply various animation components to the 3D object to make it move for a specific time, rather than the conventional process of specifying the precise position of a 3D object at specific frames.

US Patent:

20200167437, May 28, 2020

Filed:

Nov 27, 2018

Appl. No.:

16/201872

Inventors:

- Seattle WA, US
Sahika Genc - Mercer Island WA, US
Leo Parker Dirac - Seattle WA, US
Bharathan Balaji - Seattle WA, US
Eric Li Sun - Milpitas CA, US
Marthinus Coenraad De Clercq Wentzel - Seattle WA, US
Brian James Townsend - San Francisco CA, US
Pramod Ravikumar Kumar - Santa Clara CA, US

International Classification:

G06F 17/50
G06N 20/00

Abstract:

A simulation workflow manager obtains a set of parameters for simulation of a system and training of a reinforcement learning model for optimizing an application of the system. In response to obtaining the set of parameters, the simulation workflow manager configures a first compute node that includes a training application for training the reinforcement learning model. The simulation workflow manager also configures a second compute note with a simulation application to perform the simulation of the system in a simulation environment. Data is generated through execution of the simulation in the second compute node that is provided to the first compute node to cause the training application to use the data to train the reinforcement learning model.