Alexander S Kern

US Patent:

20200334674, Oct 22, 2020

Filed:

Apr 20, 2020

Appl. No.:

16/853462

Inventors:

- San Francisco CA, US
Brian Bland - San Francisco CA, US
Robert John Kearney - San Francisco CA, US
Nikhil Srinivasan - San Francisco CA, US
Paul Hamera - San Francisco CA, US
Alexander Kern - San Francisco CA, US

International Classification:

G06Q 20/38
G06Q 20/36

Abstract:

Systems and methods for participation in blockchain administration.

US Patent:

20200195433, Jun 18, 2020

Filed:

Jun 25, 2019

Appl. No.:

16/452195

Inventors:

- San Francisco CA, US
Alexander Kern - San Francisco CA, US
Peter Jihoon Kim - San Francisco CA, US
Sahil Amoli - San Francisco CA, US
Rohith Varanasi - San Francisco CA, US
Andrew Gold - San Francisco CA, US

International Classification:

H04L 9/08
H04L 9/06
H04L 9/32

Abstract:

A method for managing sensitive data, including: receiving an encryption key from a third party recovery agent; at a user agent executing on a user device, encrypting the sensitive data with the encryption key; and storing the encrypted sensitive data at a third party storage provider system. The method can optionally include, at the user agent: requesting the encryption key from the third party recovery agent using a set of recovery agent authentication credentials; requesting the encrypted sensitive data from the third party storage provider system using a set of storage provider authentication credentials; and decrypting the encrypted sensitive data using the encryption key.

US Patent:

20180300653, Oct 18, 2018

Filed:

Apr 18, 2018

Appl. No.:

15/955918

Inventors:

- San Francisco CA, US
Alexander Simon Kern - San Francisco CA, US

International Classification:

G06N 99/00
G06F 9/48
H04L 29/08

Abstract:

A distributed machine learning system and method are disclosed. According to some implementations of this disclosure, the method includes identifying one or more available computing resources and receiving a task object that indicates a training job to perform. The method includes retrieving a container image based on the type of model architecture. The container image includes the model architecture and a filesystem. The method includes retrieving and mounting a base model to the filesystem of the container image. The method further includes retrieving and mounting a volume of training data to the filesystem of the container image to obtain a training container. In some implementations, the method further includes executing the training container on at least one of the one or more available computing resources and receiving a trained model from the container after the container completes the training job. The method further includes storing the trained model.

Author:

Alexander Kern

ISBN #:

0230525555