Michael Ray Palmer

Address:

1115 Harwall Rd, Gwynn Oak, MD 21207

VIN:

4T1BE46K17U633437

Make:

TOYOTA

Model:

CAMRY

Year:

2007

US Patent:

7533398, May 12, 2009

Filed:

Jul 24, 2003

Appl. No.:

10/625774

Inventors:

Sean Cullinan - Kingshill, VG
Michael Palmer - Alexandria VA, US

Assignee:

The Associated Press - Washington DC

International Classification:

G06F 3/00

US Classification:

725 37, 709219, 709247

Abstract:

A system, method and computer program product for automatic selection of encoding parameters for transmission of media objects is provided. The method and computer program product involve the automatic selection of media encoding parameters based on editorial and technical contexts. Such contexts include time constraints, duration, size, quality, processing time and compression time. The media objects are then automatically encoded using these automatically selected parameters, and then transmitted from a remote site to one or more additional sites via a communications network.

US Patent:

8485294, Jul 16, 2013

Filed:

Apr 6, 2011

Appl. No.:

13/066152

Inventors:

Michael William Palmer - Virginia Beach VA, US

International Classification:

B60K 1/00

US Classification:

180 658, 180 651

Abstract:

An electric automobile includes an electric motor coupled to a first axle that propels the automobile in a desired direction. An alternator or generator is coupled to the second axle and includes an output that powers the electric motor or recharges a power source that powers the electric motor. A gearbox is arranged to harness the reciprocating motion of one of the axles and provide angular motion to the shaft of the generator or alternator.

US Patent:

20020108115, Aug 8, 2002

Filed:

Dec 10, 2001

Appl. No.:

10/006236

Inventors:

Michael Palmer - Alexandria VA, US

Assignee:

The Associated Press - Washington DC

International Classification:

H04N005/445
H04N007/16

US Classification:

725/050000, 725/138000

Abstract:

A news information content list may be updated dynamically by first altering the content list at a feed station server. More specifically, the content list may be altered by implementing one or more revisions thereto. The content list is comprised of an ordered sequence of stories, with each story being comprised of any one or combination of text elements, metadata, and one or more references to media objects. Next, the revisions implemented to the content list are packaged into a message and transmitted to one or more field stations. These revisions are used in updating copies of the content list at the field stations. Thus, media objects and the like are transmitted only when they are modified, not when they are included or moved in multiple other content lists. A content list at a field station is processed in a similar manner. Specifically, one or more revisions to a content list at a feed station are received via a message at a field station. The content list is comprised of an ordered sequence of stories, with each story including text elements and one or more references to media objects. Subsequently, a copy of the content list at the field station is updated by utilizing only the revisions to replace original content.

US Patent:

20220245335, Aug 4, 2022

Filed:

Apr 20, 2022

Appl. No.:

17/724934

Inventors:

- Kirkland WA, US
Steven DeRose - Silver Spring MD, US
Taqi Jaffri - Kirkland WA, US
Luis Marti Orosa - Las Condes, CL
Michael B. Palmer - Edmonds WA, US
Jean Paoli - Kirkland WA, US
Christina Pavlopoulou - Emeryville CA, US
Elena Pricoiu - Issaquah WA, US
Swagatika Sarangi - Bellevue WA, US
Marcin Sawicki - Kirkland WA, US
Manar Shehadeh - Kirkland WA, US
Michael Taron - Seattle WA, US
Bhaven Toprani - Cupertino CA, US
Zubin Rustom Wadia - Chappaqua NY, US
David Watson - Seattle WA, US
Eric White - San Luis Obispo CA, US
Joshua Yongshin Fan - Bellevue WA, US
Kush Gupta - Seattle WA, US
Andrew Minh Hoang - Olympia WA, US
Zhanlin Liu - Seattle WA, US
Jerome George Paliakkara - Seattle WA, US
Zhaofeng Wu - Seattle WA, US
Yue Zhang - St Paul MN, US
Xiaoquan Zhou - Bellevue WA, US

International Classification:

G06F 40/186
G06N 20/00
G06F 40/30
G06F 40/169
G06F 40/117
G06F 40/106
G06F 40/289
G06F 40/295
G06F 16/93
G06F 16/2457
G06F 16/248
G06V 30/414
G06V 30/416

Abstract:

Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

US Patent:

20210081411, Mar 18, 2021

Filed:

Aug 5, 2020

Appl. No.:

16/986146

Inventors:

- Kirkland WA, US
Steven DeRose - Silver Spring MD, US
Taqi Jaffri - Kirkland WA, US
Luis Marti Orosa - Las Condes, CL
Michael Palmer - Edmonds WA, US
Jean Paoli - Kirkland WA, US
Christina Pavlopoulou - Emeryville CA, US
Elena Pricoiu - Issaquah WA, US
Swagatika Sarangi - Bellevue WA, US
Marcin Sawicki - Kirkland WA, US
Manar Shehadeh - Kirkland WA, US
Michael Taron - Seattle WA, US
Bhaven Toprani - Cupertino CA, US
Zubin Rustom Wadia - Chappaqua NY, US
David Watson - Seattle WA, US
Eric White - San Luis Obispo CA, US
Joshua Yongshin Fan - Bellevue WA, US
Kush Gupta - Seattle WA, US
Andrew Minh Hoang - Olympia WA, US
Zhanlin Liu - Seattle WA, US
Jerome George Paliakkara - Seattle WA, US
Zhaofeng Wu - Seattle WA, US
Yue Zhang - St Paul MN, US
Xiaoquan Zhou - Bellevue WA, US

International Classification:

G06F 16/2457
G06F 16/93
G06F 16/248
G06N 20/00
G06F 40/186
G06F 40/30

Abstract:

Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

US Patent:

20210081601, Mar 18, 2021

Filed:

Aug 5, 2020

Appl. No.:

16/986136

Inventors:

- Kirkland WA, US
Steven DeRose - Silver Spring MD, US
Taqi Jaffri - Kirkland WA, US
Luis Marti Orosa - Las Condes, CL
Michael Palmer - Edmonds WA, US
Jean Paoli - Kirkland WA, US
Christina Pavlopoulou - Emeryville CA, US
Elena Pricoiu - Issaquah WA, US
Swagatika Sarangi - Bellevue WA, US
Marcin Sawicki - Kirkland WA, US
Manar Shehadeh - Kirkland WA, US
Michael Taron - Seattle WA, US
Bhaven Toprani - Cupertino CA, US
Zubin Rustom Wadia - Chappaqua NY, US
David Watson - Seattle WA, US
Eric White - San Luis Obispo CA, US
Joshua Yongshin Fan - Bellevue WA, US
Kush Gupta - Seattle WA, US
Andrew Minh Hoang - Olympia WA, US
Zhanlin Liu - Seattle WA, US
Jerome George Paliakkara - Seattle WA, US
Zhaofeng Wu - Seattle WA, US
Yue Zhang - St Paul MN, US
Xiaoquan Zhou - Bellevue WA, US

International Classification:

G06F 40/169
G06F 40/106
G06F 40/117

Abstract:

Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

US Patent:

20210081602, Mar 18, 2021

Filed:

Aug 5, 2020

Appl. No.:

16/986139

Inventors:

- Kirkland WA, US
Steven DeRose - Silver Spring MD, US
Taqi Jaffri - Kirkland WA, US
Luis Marti Orosa - Las Condes, CL
Michael Palmer - Edmonds WA, US
Jean Paoli - Kirkland WA, US
Christina Pavlopoulou - Emeryville CA, US
Elena Pricoiu - Issaquah WA, US
Swagatika Sarangi - Bellevue WA, US
Marcin Sawicki - Kirkland WA, US
Manar Shehadeh - Kirkland WA, US
Michael Taron - Seattle WA, US
Bhaven Toprani - Cupertino CA, US
Zubin Rustom Wadia - Chappaqua NY, US
David Watson - Seattle WA, US
Eric White - San Luis Obispo CA, US
Joshua Yongshin Fan - Bellevue WA, US
Kush Gupta - Seattle WA, US
Andrew Minh Hoang - Olympia WA, US
Zhanlin Liu - Seattle WA, US
Jerome George Paliakkara - Seattle WA, US
Zhaofeng Wu - Seattle WA, US
Yue Zhang - St Paul MN, US
Xiaoquan Zhou - Bellevue WA, US

International Classification:

G06F 40/169
G06F 40/106
G06F 40/30
G06F 40/295

Abstract:

Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

US Patent:

20210081608, Mar 18, 2021

Filed:

Aug 5, 2020

Appl. No.:

16/986151

Inventors:

- Kirkland WA, US
Steven DeRose - Silver Spring MD, US
Taqi Jaffri - Kirkland WA, US
Luis Marti Orosa - Las Condes, CL
Michael Palmer - Edmonds WA, US
Jean Paoli - Kirkland WA, US
Christina Pavlopoulou - Emeryville CA, US
Elena Pricoiu - Issaquah WA, US
Swagatika Sarangi - Bellevue WA, US
Marcin Sawicki - Kirkland WA, US
Manar Shehadeh - Kirkland WA, US
Michael Taron - Seattle WA, US
Bhaven Toprani - Cupertino CA, US
Zubin Rustom Wadia - Chappaqua NY, US
David Watson - Seattle WA, US
Eric White - San Luis Obispo CA, US
Joshua Yongshin Fan - Bellevue WA, US
Kush Gupta - Seattle WA, US
Andrew Minh Hoang - Olympia WA, US
Zhanlin Liu - Seattle WA, US
Jerome George Paliakkara - Seattle WA, US
Zhaofeng Wu - Seattle WA, US
Yue Zhang - St Paul MN, US
Xiaoquan Zhou - Bellevue WA, US

International Classification:

G06F 40/186
G06K 9/00
G06F 40/30
G06F 40/169
G06N 20/00

Abstract:

Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.