Michael David Warren

US Patent:

20210365596, Nov 25, 2021

Filed:

Dec 8, 2020

Appl. No.:

17/115770

Inventors:

- Malibu CA, US
Michael A. Warren - Northridge CA, US
Christopher Serrano - Whittier CA, US

International Classification:

G06F 21/88
G06N 3/04

Abstract:

Described is a system and method for generating safety conditions for a cyber-physical system with state space S, action space A and trajectory data labelled as either safe or unsafe. In operation, the system receives inputs and ten minimizes loss functions to cause a neural network to become a barrier function. Based on the barrier function, the system can then determine if the cyber-physical system is entering an usafe state, such that if the cyber-physical system is entering the usafe state, then the cyber-physical system is caused to initiate a maneuver to position the cyber-physical system into a safe state.

US Patent:

20210278854, Sep 9, 2021

Filed:

Nov 17, 2020

Appl. No.:

16/950803

Inventors:

- Malibu CA, US
Michael A. Warren - Northridge CA, US
Aleksey Nogin - Fresno CA, US

International Classification:

G05D 1/02
G06N 3/08

Abstract:

Described is a system for training a neural network for estimating surface normals for use in operating an autonomous platform. The system uses a parallelizable k-nearest neighbor sorting algorithm to provide a patch of points, sampled from the point cloud data, as input to the neural network model. The points are transformed from Euclidean coordinates in a Euclidean space to spherical coordinates. A polar angle of a surface normal of the point cloud data is estimated in the spherical coordinates. The trained neural network model is utilized on the autonomous platform, and the estimate of the polar angle of the surface normal is used to guide operation of the autonomous platform within the environment.

US Patent:

20200379486, Dec 3, 2020

Filed:

May 28, 2019

Appl. No.:

16/423892

Inventors:

- Chicago IL, US
Kevin R. Martin - Oak Park CA, US
Sean Soleyman - Calabasas CA, US
Ignacio M. Soriano - University City MO, US
Michael A. Warren - Northridge CA, US
Joshua G. Fadaie - Saint Louis MO, US
Charles Tullock - Saint Peters MO, US
Yang Chen - Westlake Village CA, US
Shawn Moffit - Glen Carbon IL, US
Calvin Chung - Saint Louis MO, US

International Classification:

G05D 1/10
G05B 13/02
G05D 1/00

Abstract:

Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.

US Patent:

20200034539, Jan 30, 2020

Filed:

Jul 12, 2018

Appl. No.:

16/034244

Inventors:

- Malibu CA, US
Michael A. Warren - Northridge CA, US
Gavin D. Holland - Newbury Park CA, US

International Classification:

G06F 21/57
G06F 8/30
G06F 8/10
H04L 12/24
H04L 29/08
H04L 12/66

Abstract:

Described is a high-assurance network gateway generator that generates and encodes network gateway code on a computer readable medium. In operation, the network gateway generator receives input artifacts, which are translated into corresponding formats as translated data. The translated data is distributed to an OS code generator, a glue code generator, and a communications code generator. The OS code generator then generates OS code based on the translated data. The communications code generator proceeds to generate deserialization and filtering code based on the translated data. Further, a glue code generator generates glue code based on the OS code and translated data. An executable network gateway code is then generated by combining the glue code, deserialization code, and filtering code. Finally, the executable network gateway code is then encoded on a computer readable medium.