Brian A Gilmore

US Patent:

20220318308, Oct 6, 2022

Filed:

Apr 18, 2022

Appl. No.:

17/723440

Inventors:

- San Francisco CA, US
Brian Matthew Gilmore - Alameda CA, US

International Classification:

G06F 16/901
H04L 67/10
G05B 19/4063
G06F 16/26
G06F 16/248
G06F 16/9535

Abstract:

A method to provide an interface for asset tee determination includes performing a search of data, the search including user-supplied criteria information, causing display of results of the search, receiving user input providing classifications for the results of the search, the classifications indicating asset identifier and asset parent identifier fields in the results of the search, identifying, based on the user input and the results of the search, a plurality of unique assets identifiers and corresponding asset parent identifiers, and automatically generating a computer representation of an asset hierarchy comprising an asset node for each asset identifier, an asset parent node for each asset parent identifier, and a representation of hierarchical relationships between asset nodes and asset parent nodes.

US Patent:

20200012966, Jan 9, 2020

Filed:

Sep 17, 2019

Appl. No.:

16/573745

Inventors:

- San Francisco CA, US
Adam Jamison Oliner - San Francisco CA, US
Brian Matthew Gilmore - Alameda CA, US
Erick Anthony Dean - Piedmont CA, US
Jiahan Wang - San Francisco CA, US

International Classification:

G06N 20/00

Abstract:

Disclosed is a technique that can be performed by an electronic device. The electronic device can generate time-stamped events, extract training data from the time-stamped events, and sending the training data over a network to a remote computer. The electronic device can receive model data generated by the remote computer from the training data by use of a machine learning process, update a local model of the electronic device based on the received model data, and generate an output by processing locally sourced data of the electronic device with the updated local model.

US Patent:

20180032908, Feb 1, 2018

Filed:

Jul 29, 2016

Appl. No.:

15/224439

Inventors:

- San Francisco CA, US
Adam Jamison Oliner - San Francisco CA, US
Brian Matthew Gilmore - Alameda CA, US
Erick Anthony Dean - Piedmont CA, US
Jiahan Wang - San Francisco CA, US

International Classification:

G06N 99/00
G06F 17/30

Abstract:

Disclosed is a technique that can be performed by an electronic device. The technique can include generating raw data based on inputs to the electronic device, and sending the raw data or data items over a network to a server computer system. The sent raw data or the data items can include training data. The technique can further include receiving global model data from the server computer system over the network. The global model data may have been derived from the training data in accordance with a machine learning process. The technique can further include generating an updated local model by updating a local model associated with the electronic device based on the received global model data, and processing local data based on the updated local model to generate output data. The local data can include raw data or data items generated based on inputs to the electronic device.

US Patent:

20180034715, Feb 1, 2018

Filed:

Jul 29, 2016

Appl. No.:

15/224440

Inventors:

- San Francisco CA, US
Adam Jamison Oliner - San Francisco CA, US
Brian Matthew Gilmore - Alameda CA, US
Erick Anthony Dean - Piedmont CA, US
Jiahan Wang - San Francisco CA, US

International Classification:

H04L 12/26
H04L 12/24
G06F 17/30
G06N 99/00

Abstract:

Disclosed is a technique that can be performed by an electronic device. The technique can include generating timestamped events, where the timestamped events include raw data generated by electronic device. The technique can further include obtaining results by performing a operation on the timestamped events, in accordance with instructions. The technique can further include sending the results or indicia thereof over a network to a server computer system, and receiving back new instructions generated by the server computer system based on the sent results. Lastly, the technique can include performing a new operation on timestamped events including raw data generated based by the electronic device, where the new operation can be performed in accordance with the new instructions to obtain new results.