Ramon R Gonzalez

US Patent:

46797939, Jul 14, 1987

Filed:

Mar 10, 1986

Appl. No.:

6/838308

Inventors:

Ramon Gonzalez - Chicago IL

International Classification:

A63F 724

US Classification:

273 85R

Abstract:

A table game as provided to be played by up to four players and to simulate the action of a boxing contest, or the like. The game includes a flat board with players and markers and an adjustable pendulum used by the players to attempt to knock over the opponents' game pieces.

US Patent:

20210098127, Apr 1, 2021

Filed:

Sep 30, 2019

Appl. No.:

16/588080

Inventors:

- Milwaukee WI, US
- Boston MA, US
Neil Tenenholtz - Boston MA, US
Ramon Gilberto Gonzalez - Boston MA, US

International Classification:

G16H 50/20
G16H 30/40
G16H 15/00
G16H 30/20
G06N 3/04
G06N 3/08
G06T 7/00

Abstract:

Systems and techniques for generating and/or employing a medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate output data regarding a brain anatomical region based on diffusion-weighted imaging (DWI) data associated with the brain anatomical region and apparent diffusion coefficient (ADC) data associated with the brain anatomical region. The system also detects presence or absence of a medical stroke condition associated with the brain anatomical region based on the output data.

US Patent:

20200294241, Sep 17, 2020

Filed:

Mar 12, 2020

Appl. No.:

16/817551

Inventors:

- Boston MA, US
Ramon Gilberto Gonzalez - Boston MA, US

International Classification:

G06T 7/10
G06T 7/00
G06N 3/08
G06N 20/00
G06K 9/62
G16H 30/40

Abstract:

Lesions associated with acute ischemic stroke are automatically segmented in images acquired with computed tomography (“CT”) using a trained machine learning algorithm (e.g., a neural network). The machine learning algorithm is trained on labeled data and associated CT data (e.g., non-contrast CT data and CT angiography source image (“CTA-SI”) data). The labeled data can include segmented data indicating lesions, which are generated by segmenting diffusion-weighted magnetic resonance images acquired within a specified time window from when the associated CT data were acquired. CT data (e.g., non-contrast CT data and CTA-SI data) acquired from a subject are then acquired and input to the trained machine learning algorithm to generate output as segmented CT data, which indicate lesions in the subject.