Benjamin L Edwards

US Patent:

57786070, Jul 14, 1998

Filed:

Jul 11, 1996

Appl. No.:

8/680247

Inventors:

Benjamin J. Edwards - Dearborn MI

International Classification:

E04H 100

US Classification:

52 791

Abstract:

A smoker's booth, for use within an enclosed area, including sidewalls having a door providing access to a single user compartment, wherein the sidewalls define upper and lower open ends. The lower end is closed by a base panel supporting the sidewalls, while the upper end is closed by a modular cap assembly, adapted for treating tobacco smoke-laden air drawn upwardly from the closed compartment. The modular cap assembly, which includes a closed space divided into a plurality of chambers, provides at least one plenum chamber and an electrical equipment chamber. An air treating unit is located in the plenum chamber, intermediate intake and return air vent openings communicating with the compartment. A blower draws tobacco smoke-laden air upwardly from the compartment, through the intake vent opening and plenum chamber, and returns only treated air to the compartment, via its return vent. As the compartment air is self contained, the booth obviates exchange of ambient air with tobacco smoke-laden air during its operation.

US Patent:

20190318099, Oct 17, 2019

Filed:

Apr 16, 2018

Appl. No.:

15/953956

Inventors:

- Armonk NY, US
Bryant Chen - San Jose CA, US
Benjamin J. Edwards - Ossining NY, US
Taesung Lee - Ridgefield CT, US
Ian M. Molloy - Chappaqua NY, US
Jialong Zhang - White Plains NY, US

International Classification:

G06F 21/57
G06N 3/08
G06N 99/00

Abstract:

Mechanisms are provided for evaluating a trained machine learning model to determine whether the machine learning model has a backdoor trigger. The mechanisms process a test dataset to generate output classifications for the test dataset, and generate, for the test dataset, gradient data indicating a degree of change of elements within the test dataset based on the output generated by processing the test dataset. The mechanisms analyze the gradient data to identify a pattern of elements within the test dataset indicative of a backdoor trigger. The mechanisms generate, in response to the analysis identifying the pattern of elements indicative of a backdoor trigger, an output indicating the existence of the backdoor trigger in the trained machine learning model.