Stephen J Green

US Patent:

7823272, Nov 2, 2010

Filed:

Nov 14, 2006

Appl. No.:

11/598877

Inventors:

Matthew J. Guay - North Attleboro MA, US
Robert Genovisi - Rehoboth MA, US
Stephen A. Green - Franklin MA, US
Norbert Binkowski - Franklin MA, US
Craig Stephen Vickery - Reading MA, US
Jon Fraser Ewing - Reading MA, US
Ingomar JK Summerauer - Arbon, CH

Assignee:

The Gillette Company - Boston MA

International Classification:

B23P 19/00

US Classification:

29820, 298966, 295641, 72 71, 72293, 30 401

Abstract:

This invention relates to methods and systems for attaching components to strips of material to form assemblies, e. g. , a system for fastening pre-sharpened razor blades to individual support strips on a continuous band of material, such as a metal, to form discrete blade assemblies.

US Patent:

20110010929, Jan 20, 2011

Filed:

Sep 23, 2010

Appl. No.:

12/888710

Inventors:

Matthew J. Guay - North Attleboro MA, US
Robert Genovisi - Rehoboth MA, US
Stephen A. Green - Franklin MA, US
Norbert Binkowski - Franklin MA, US
Craig Stephen Vickery - Reading MA, US
Jon Fraser Ewing - Reading MA, US
Ingomar JK Summerauer - Arbon, CH

International Classification:

B23P 23/04
B23P 19/04
B23K 26/22

US Classification:

29705, 29782, 29787, 21912163

Abstract:

This invention relates to methods and systems for attaching components to strips of material to form assemblies, e.g., a system for fastening pre-sharpened razor blades to individual support strips on a continuous band of material, such as a metal, to form discrete blade assemblies.

US Patent:

20220245339, Aug 4, 2022

Filed:

Jan 31, 2022

Appl. No.:

17/589662

Inventors:

- Redwood City CA, US
Ariel Kobren - Cambridge MA, US
Michael Louis Wick - Lexington MA, US
Stephen Green - Burlington MA, US

International Classification:

G06F 40/279
G06N 20/00

Abstract:

Debiasing pre-trained sentence encoders with probabilistic dropouts may be performed by various systems, services, or applications. A sentence may be received, where the words of the sentence may be provided as tokens to an encoder of a machine learning model. A token-wise correlation using semantic orientation may be determined to determine a bias score for the tokens in the input sentence. A probability of dropout that for tokens in the input sentence may be determined from the bias scores. The machine learning model may be trained or tuned based on the probabilities of dropout for the tokens in the input sentence.

US Patent:

20190114319, Apr 18, 2019

Filed:

Mar 23, 2018

Appl. No.:

15/934262

Inventors:

- Redwood Shores CA, US
Michael Wick - Medford MA, US
Stephen Green - Burlington MA, US

International Classification:

G06F 17/27
G06N 5/04
G06F 17/22
G06F 17/30

Abstract:

Embodiments make novel use of random data structures to facilitate streaming inference for a Latent Dirichlet Allocation (LDA) model. Utilizing random data structures facilitates streaming inference by entirely avoiding the need for pre-computation, which is generally an obstacle to many current “streaming” variants of LDA as described above. Specifically, streaming inference—based on an inference algorithm such as Stochastic Cellular Automata (SCA), Gibbs sampling, and/or Stochastic Expectation Maximization (SEM)—is implemented using a count-min sketch to track sufficient statistics for the inference procedure. Use of a count-min sketch avoids the need to know the vocabulary size V a priori. Also, use of a count-min sketch directly enables feature hashing, which addresses the problem of effectively encoding words into indices without the need of pre-computation. Approximate counters are also used within the count-min sketch to avoid bit overflow issues with the counts in the sketch.