Robert N Power

US Patent:

8369556, Feb 5, 2013

Filed:

Jul 20, 2010

Appl. No.:

12/839825

Inventors:

Robert Power - New York NY, US

International Classification:

H04R 9/08
H04R 11/04
H04R 17/02
H04R 19/04
H04R 21/02

US Classification:

381359, 381355, 381356

Abstract:

A microphone pop filter for attenuating plosive artifacts utilizes a substantially acoustically transparent material configured to define multiple airfoil surfaces that are oriented non-orthogonally relative to an axis defined between an audio source and a microphone diaphragm, with the substantially acoustically transparent material disposed intermediate the audio source and the microphone diaphragm and separated from the microphone diaphragm by an airspace. Plosive artifacts from the audio source may be deflected away from the microphone diaphragm by the airfoil surfaces to reduce the impact of such artifacts on the microphone diaphragm and the resulting electronic signal output therefrom.

US Patent:

20230115039, Apr 13, 2023

Filed:

Dec 13, 2022

Appl. No.:

18/065410

Inventors:

- Menlo Park CA, US
Sean Michael BOYLE - Menlo Park CA, US
Rachel Marty PYKE - Menlo Park CA, US
Eric LEVY - Menlo Park CA, US
Dattatreya MELLACHERUVU - Menlo Park CA, US
Rena MCCLORY - Menlo Park CA, US
Richard CHEN - Menlo Park CA, US
Robert POWER - Menlo Park CA, US
Gabor BARTHA - Menlo Park CA, US
Jason HARRIS - Menlo Park CA, US
Pamela MILANI - Menlo Park CA, US
Prateek TANDON - Menlo Park CA, US
Paul MCNITT - Menlo Park CA, US
Massimo MORRA - Menlo Park CA, US
Sejal DESAI - Menlo Park CA, US
Michael CLARK - Menlo Park CA, US
Christian HAUDENSCHILD - Menlo Park CA, US
John WEST - Menlo Park CA, US
Nick PHILLIPS - Menlo Park CA, US
Simo V. ZHANG - Menlo Park CA, US

Assignee:

Personalis, Inc. - Menlo Park CA

International Classification:

G16B 40/20
G16B 25/10
G06N 20/20

Abstract:

The disclosure provides methods for predicting surface-presenting peptides using binding and surface-presentation characteristics. The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression levels and the one or more peptide-presentation metrics are related in accordance with a population-level relationship between expression and presentation. For each peptide of the set of peptides for a tissue sample, a score can be determined using the machine-learning model and genomic and transcriptomic data corresponding to the peptide. The score is predictive of whether a corresponding peptide is a surface-presenting peptide that binds to an MHC molecule and is presented on a cell surface.

US Patent:

20230050395, Feb 16, 2023

Filed:

Oct 13, 2022

Appl. No.:

17/965719

Inventors:

- Menlo Park CA, US
Sean Michael BOYLE - Menlo Park CA, US
Rachel Marty PYKE - Menlo Park CA, US
Eric LEVY - Menlo Park CA, US
Dattatreya MELLACHERUVU - Menlo Park CA, US
Rena MCCLORY - Menlo Park CA, US
Richard CHEN - Menlo Park CA, US
Robert POWER - Menlo Park CA, US
Gabor BARTHA - Menlo Park CA, US
Jason HARRIS - Menlo Park CA, US
Pamela MILANI - Menlo Park CA, US
Prateek TANDON - Menlo Park CA, US
Paul MCNITT - Menlo Park CA, US
Massimo MORRA - Menlo Park CA, US
Sejal DESAI - Menlo Park CA, US
Michael CLARK - Menlo Park CA, US
Christian HAUDENSCHILD - Menlo Park CA, US
John WEST - Menlo Park CA, US
Nick PHILLIPS - Menlo Park CA, US
Simo V. ZHANG - Menlo Park CA, US

Assignee:

Personalis, Inc. - Menlo Park CA

International Classification:

G16B 20/00
G16B 5/00
G16B 40/20

Abstract:

Methods for generating a composite biomarker that identifies a predicted level of responsiveness of a subject to a particular type of an immunotherapy treatment is provided. The method can include generating genomic metrics that represent one or more characteristics corresponding to one or more DNA sequences. The method can also include generating transcriptomic metrics represent one or more characteristics corresponding to a set of peptides that are translated from a corresponding RNA sequence of the one or more RNA sequences. The method can also include generating a composite biomarker score derived from the set of genomic metrics and the set of transcriptomic metrics. The method can also include determining, based on the composite biomarker score, a predicted level of responsiveness of the subject to a particular type of an immunotherapy treatment.