Steven J Welch

US Patent:

8402553, Mar 19, 2013

Filed:

Oct 30, 2009

Appl. No.:

12/609186

Inventors:

Jon E. Graham - San Jose CA, US
Anurag Sharma - Mountain View CA, US
Steven Welch - Gilroy CA, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 7/04

US Classification:

726 27, 713100, 717168

Abstract:

The present invention provides a processor-implemented method and system of updating an operating system of a computer system, where the operating system is subject to a system lockdown that does not allow changes to a list of approved executables of the operating system and that does not allow changes to a base system configuration of the operating system. In an exemplary embodiment, the method and system include, (1) identifying at least one trusted updater process in the operating system and (2) allowing the trusted updater process to make at least one change to the list of approved executables. In an exemplary embodiment, the method and system include, (1) identifying at least one trusted updater process in the operating system and (2) allowing the trusted updater process to make at least one change to the base system configuration.

US Patent:

20020062338, May 23, 2002

Filed:

Sep 30, 1998

Appl. No.:

09/163498

Inventors:

KEVIN SNOW MCCURLEY - SAN JOSE CA, US
FLORIAN PESTONI - SAN JOSE CA, US
BENJAMIN CLAY REED - SAN JOSE CA, US
STEVEN RAY WELCH - GILROY CA, US
JASON YEONG ZIEN - PALO ALTO CA, US

International Classification:

G06F015/16

US Classification:

709/203000, 709/201000

Abstract:

A network computing device, known as a CyberHub, based on low-cost hardware and Java programming provides an architecture for extensible and inexpensive network connectivity and can be thought of as a combination of router and server in a box. The CyberHub provides all necessary functions with a small footprint and lightweight components, so that it can perform as an embedded device or thin server. The CyberHub can be employed in many different applications, ranging from an “instant office” to embedded network connectivity for remote devices.

US Patent:

6061739, May 9, 2000

Filed:

Nov 26, 1997

Appl. No.:

8/979141

Inventors:

Benjamin Clay Reed - San Jose CA
Steven R. Welch - Gilroy CA

Assignee:

International Business Machines Corp. - Armonk NY

International Classification:

H04L 1200

US Classification:

709245

Abstract:

A method, apparatus, and article of manufacture for initial network address configuration using physical address resolution protocol. A device attempts a connection to the network, which causes address resolution (ARP) packets to be generated. The device monitors communications on the network for a specified number of unanswered ARP packets. Thereafter, the device adopt the network address in the unanswered ARP packets and responds to the unanswered ARP packets with the its' physical address.

US Patent:

20220390847, Dec 8, 2022

Filed:

Jun 8, 2021

Appl. No.:

17/342176

Inventors:

- Santa Clara CA, US
Mangesh Ashok BANGAR - San Jose CA, US
Srinivas D. NEMANI - Sunnyvale CA, US
Steven Hiloong WELCH - Milpitas CA, US
Ellie Y. YIEH - San Jose CA, US
Dmitry LUBOMIRSKY - Cupertino CA, US

International Classification:

G03F 7/11
G03F 7/16
G03F 7/004
G03F 7/38
G03F 7/40
G03F 7/038
G03F 7/32
C23C 16/06

Abstract:

A method for processing a substrate is described. The method includes forming a metal containing resist layer onto a substrate, patterning the metal containing resist layer, and performing a post exposure bake on the metal containing resist layer. The post exposure bake on the metal containing resist layer is a field guided post exposure bake operation and includes the use of an electric field to guide the ions or charged species within the metal containing resist layer. The field guided post exposure bake operation may be paired with a post development field guided bake operation.

US Patent:

20220300709, Sep 22, 2022

Filed:

Mar 18, 2021

Appl. No.:

17/205755

Inventors:

- Armonk NY, US
Chad Eric DeLuca - Morgan HIll CA, US
Petar Ristoski - San Jose CA, US
Linda Ha Kato - San Jose CA, US
Alfredo Alba - Morgan Hill CA, US
Daniel Gruhl - San Jose CA, US
Steven R. Welch - Gilroy CA, US

International Classification:

G06F 40/284
G06F 40/151
G06F 16/33
G06N 3/04

Abstract:

Systems, computer-implemented methods, and computer program products to transform a lexicon that describes an information asset are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a term validation component that can determine from a subject matter expert, a validated term that can indicate validation of a candidate term that describes an information asset. The computer executable components can further comprise a lexicon transforming component that, based on the validated term, can transform a lexicon that describes the information asset, by incorporating the validated term into the lexicon.

US Patent:

20210406314, Dec 30, 2021

Filed:

Jun 30, 2020

Appl. No.:

16/917638

Inventors:

- Armonk NY, US
Linda Ha Kato - San Jose CA, US
Petar Ristoski - San Jose CA, US
Steven R. Welch - Gilroy CA, US
Chad Eric DeLuca - Morgan Hill CA, US
Anna Lisa Gentile - San Jose CA, US
Alfredo Alba - Morgan Hill CA, US
Dmitry Zubarev - San Jose CA, US
Chandrasekhar Narayan - San Jose CA, US
Nathaniel H. Park - San Jose CA, US

International Classification:

G06F 16/9038
G06F 16/9032
G06F 16/906
G06F 16/93
G06F 16/903
G06N 3/08

Abstract:

A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.

US Patent:

20210303762, Sep 30, 2021

Filed:

Mar 31, 2020

Appl. No.:

16/835892

Inventors:

- Armonk NY, US
Dmitry Zubarev - San Jose CA, US
Linda Ha Kato - San Jose CA, US
Anna Lisa Gentile - San Jose CA, US
Nathaniel H. Park - San Jose CA, US
Daniel Gruhl - San Jose CA, US
Steven R. Welch - Gilroy CA, US
Daniel Paul Sanders - San Jose CA, US
James L. Hedrick - Pleasanton CA, US
Chandrasekhar Narayan - San Jose CA, US
Chad Eric DeLuca - Morgan Hill CA, US
Alfredo Alba - Morgan Hill CA, US

International Classification:

G06F 30/27
G06N 5/04
G06N 3/04
G06N 20/10

Abstract:

A set of material candidates expected to yield materials with target properties can be generated. A subject matter expert's decision indicating accepted and rejected material candidates from the set of material candidates can be received. Based on the subject matter expert's input, a machine learning model can be trained to replicate the subject matter expert's decision.

US Patent:

20210304852, Sep 30, 2021

Filed:

Mar 31, 2020

Appl. No.:

16/835958

Inventors:

- Armonk NY, US
Dmitry Zubarev - San Jose CA, US
Linda Ha Kato - San Jose CA, US
Anna Lisa Gentile - San Jose CA, US
Nathaniel H. Park - San Jose CA, US
Daniel Gruhl - San Jose CA, US
Steven R. Welch - Gilroy CA, US
Daniel Paul Sanders - San Jose CA, US
James L. Hedrick - Pleasanton CA, US
Chandrasekhar Narayan - San Jose CA, US
Chad Eric DeLuca - Morgan Hill CA, US
Alfredo Alba - Morgan Hill CA, US

International Classification:

G16C 20/70
G06N 3/08
G06N 3/04
G06N 20/20
G06N 5/00

Abstract:

Candidate material for polymerization can be received. One or more desired features in the candidate material can be identified. A machine learning model can be trained to generate a new material having one or more of the desired features. Permissively, the candidate material can be determined from running a machine learning classification model that ranks a plurality of material as candidates. Permissively, the generated new material can be input to the machine learning classification model, for the machine learning classification model to include in ranking the plurality of material as candidates.