David Leroy Knowles

US Patent:

7011897, Mar 14, 2006

Filed:

Aug 18, 2003

Appl. No.:

10/643413

Inventors:

Mark E. Thompson - Anaheim Hills CA, US
Peter Djurovich - Long Beach CA, US
Raymond Kwong - Plainsboro NJ, US
David B. Knowles - Apollo PA, US
Jason Brooks - Lambertville NJ, US
Robert W. Walters - Export PA, US
Bin Ma - Monroeville PA, US

Assignee:

The University of Southern California - Los Angeles CA
Universal Display Corporation - Ewing NJ

International Classification:

C09K 11/06
H05B 33/14

US Classification:

428690, 428917, 313504, 546 4, 25230116, 257 40, 257102

Abstract:

An organic light emitting device is provided. The device has an anode, a cathode, and an emissive layer disposed between the anode and the cathode, the emissive layer further comprising an emissive material having the structure: wherein each of the variables are defined herein.

US Patent:

8183498, May 22, 2012

Filed:

Feb 12, 2007

Appl. No.:

11/673980

Inventors:

Brandon A. Turk - Saratoga CA, US
David S. Knowles - San Diego CA, US

Assignee:

TCZ, LLC - San Diego CA

International Classification:

B23K 26/00

US Classification:

21912165, 21912166, 21912162

Abstract:

A thin beam directional crystallization system configured to process a substrate comprises a laser configured to produce laser light, the laser configured to have a high energy mode and a low energy mode. The high energy mode is configured to produce light energy sufficient to completely melt a substrate coated with amorphous silicon film, while the low energy mode is configured to produce light energy that is not sufficient to completely melt a substrate coated with amorphous silicon film. The system further comprises beam shaping optics coupled to the laser and configured to convert the laser light emitted from the laser into a long thin beam with a short axis and a long axis, a stage configured to support the substrate and film, and a translator coupled with the stage, the translator configured to advance the substrate and film so as to produce a step size in conjunction with the firing of the laser.

US Patent:

20220189016, Jun 16, 2022

Filed:

Mar 2, 2022

Appl. No.:

17/685115

Inventors:

- Tucson AZ, US
- Palo Alto CA, US
David Knowles - Menlo Park CA, US

International Classification:

G06T 7/00
C12Q 1/6886
G06K 9/62
G06V 20/69

Abstract:

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

US Patent:

20200126222, Apr 23, 2020

Filed:

Oct 21, 2019

Appl. No.:

16/659545

Inventors:

- Tucson AZ, US
- Palo Alto CA, US
David Knowles - Menlo Park CA, US

International Classification:

G06T 7/00
G06K 9/00
G06K 9/62
C12Q 1/6886

Abstract:

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

US Patent:

20170091937, Mar 30, 2017

Filed:

Dec 9, 2016

Appl. No.:

15/374998

Inventors:

- Tucson AZ, US
- Palo Alto CA, US
David Knowles - Menlo Park CA, US

International Classification:

G06T 7/00
G06K 9/00

Abstract:

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.