Jonathan T Rubin

US Patent:

7021785, Apr 4, 2006

Filed:

Aug 8, 2003

Appl. No.:

10/637826

Inventors:

Jonathan Rubin - Portchester NY, US

International Classification:

F21S 6/00

US Classification:

362123, 362 86, 362397

Abstract:

The present invention is a miniature illuminating Christmas tree intended to create a decorative display in the window of a home, car, or office. In the preferred mode, the tree-shaped ornament is made from plastic and artificial green Christmas tree material and contains an illumination source, such as blinking LEDs, fiberoptics, or bulbs configured according to a previously determined pattern. A power source, such as at least one battery, is housed in the base or trunk of the ornament. The device is removably attached to a window through usage of one or more suction cups, and may be reversed, allowing the user to display the object on the outside or the inside of the window in question.

US Patent:

D676079, Feb 12, 2013

Filed:

Jul 26, 2012

Appl. No.:

29/421430

Inventors:

Jonathan Rubin - Brooklyn NY, US

International Classification:

1801

US Classification:

D18 12, D18 11

US Patent:

D677715, Mar 12, 2013

Filed:

Jul 26, 2012

Appl. No.:

29/421431

Inventors:

Jonathan Rubin - Brooklyn NY, US

International Classification:

1801

US Classification:

D18 12, D18 11

US Patent:

D689922, Sep 17, 2013

Filed:

Oct 9, 2012

Appl. No.:

29/421869

Inventors:

Jonathan Rubin - Brooklyn NY, US

International Classification:

1801

US Classification:

D18 12, D18 11

US Patent:

D689923, Sep 17, 2013

Filed:

Nov 13, 2012

Appl. No.:

29/442011

Inventors:

Jonathan Rubin - Brooklyn NY, US

International Classification:

1801

US Classification:

D18 12, D18 11

US Patent:

20210012897, Jan 14, 2021

Filed:

May 21, 2020

Appl. No.:

16/880250

Inventors:

- EINDHOVEN, NL
Bryan Conroy - Garden City NY, US
Jonathan Rubin - Cambridge MA, US

International Classification:

G16H 50/20
G06N 20/00
G06N 5/00
G06F 17/16

Abstract:

A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for each disease state based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models .

US Patent:

20200160201, May 21, 2020

Filed:

Nov 11, 2019

Appl. No.:

16/679842

Inventors:

- Eindhoven, NL
Bryan Conroy - Garden City South NY, US
Jack He - Cambridge MA, US
Jonathan Rubin - Cambridge MA, US

International Classification:

G06N 7/00
G06N 3/08
G06N 3/04
G06N 20/20
G06F 17/18
G06F 17/16

Abstract:

A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.