David J Eigen

US Patent:

7783932, Aug 24, 2010

Filed:

Apr 13, 2007

Appl. No.:

11/735299

Inventors:

David J. Eigen - San Francisco CA, US
David A. Grunwald - San Francisco CA, US

Assignee:

Network Appliance, Inc. - Sunnyvale CA

International Classification:

G06F 11/00

US Classification:

714 45, 714 38

Abstract:

A method of generating a microcore file, which is a subset of a full core dump, for a networked storage system includes generating a microcore file according to a microcore specification, and dynamically defining the microcore specification at runtime of the network storage system. The microcore specification is dynamically defined with information provided by results of an event that triggers the generation of the microcore file. After the microcore specification is defined, a region of a system memory is identified according to the microcore specification. The method further includes dumping the data from the region of the system memory into the microcore file.

US Patent:

20220292333, Sep 15, 2022

Filed:

Feb 25, 2022

Appl. No.:

17/680543

Inventors:

- Wilmington DE, US
David Eigen - New York NY, US
Ryan Compton - New York NY, US
Christopher Fox - New York NY, US

International Classification:

G06N 3/04
G06N 3/08

Abstract:

In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

US Patent:

20180089556, Mar 29, 2018

Filed:

Sep 27, 2017

Appl. No.:

15/717133

Inventors:

- New York NY, US
David EIGEN - New York NY, US
Ryan COMPTON - New York NY, US
Christopher FOX - New York NY, US

International Classification:

G06N 3/04
G06N 3/08

Abstract:

In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

US Patent:

20160300333, Oct 13, 2016

Filed:

Jun 20, 2016

Appl. No.:

15/187224

Inventors:

- New York NY, US
DAVID EIGEN - Brooklyn NY, US
DILIP KRISHNAN - Jersey City NJ, US

International Classification:

G06T 5/00
G06K 9/46
G06K 9/66

Abstract:

Systems, methods and computer-accessible mediums for modifying an image(s) can be provided. For example, first image information for the image(s) can be received. Second image information can be generated by separating the first image information into at least two overlapping images. The image(s) can be modified using a prediction procedure based on the second image information.

US Patent:

20150178591, Jun 25, 2015

Filed:

Dec 18, 2014

Appl. No.:

14/575179

Inventors:

- New York NY, US
DAVID EIGEN - Brooklyn NY, US
DILIP KRISHNAN - Jersey City NJ, US

International Classification:

G06K 9/52
G06K 9/66

Abstract:

Systems, methods and computer-accessible mediums for modifying an image(s) can be provided. For example, first image information for the image(s) can be received. Second image information can be generated by separating the first image information into at least two overlapping images. The image(s) can be modified using a prediction procedure based on the second image information.