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Michael A Perrone

from Seaford, NY
Age ~38
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Also known as:
Mike A Perrone, Michaela Perrone
Phone and address:
1259 Ide Ave, Seaford, NY 11783
(516) 837-0549
Related to:
Andrew G Pirrone, 35Ann E Pirrone, 96Joseph J Perrone, 67John R Townsend, 66Michael G Pirrone, 68Jennifer C Pirrone, 40John T Pirrone, 70Diann M Townsend, 65Michael Pirrone, 37Frank DeluciaJohn M Pirrone, 44John J PirroneSalvatore PerroneFrank DeluciaJoann Perrone, 66Jjae Pirrone, 69Joanne M Corrao, 64
Connected to:
Aubrey Perrone, 31Carolyn E Perrone, 64Celeste D Perrone, 89Ella Perrone, 99Florry Perrone, 101Gerard M Perrone, 63Immacolat Perrone, 75Jacquelyn M Perrone, 46Joanne M Perrone, 63Linda A Perrone, 68

Michael Perrone Phones & Addresses

  • 1259 Ide Ave, Seaford, NY 11783 (516) 837-0549
  • Lynbrook, NY
  • Keuka Park, NY
  • 33 Saint Johns Ave, Valley Stream, NY 11580 (516) 285-9573
  • Schenectady, NY
  • Mineola, NY
  • East Rockaway, NY

Public records

Vehicle Records

Michael Perrone

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Address:
674 Wyngate Dr W, Valley Stream, NY 11580
Phone:
(516) 270-3670
VIN:
1HGCM56887A173010
Make:
HONDA
Model:
ACCORD
Year:
2007

Resumes

Resumes

Michael Perrone Photo 1

Michael Perrone

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Location:
United States
Michael Perrone Photo 2

Michael Perrone

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Location:
Yorktown Heights, New York
Industry:
Computer Hardware
Michael Perrone Photo 3

Michael Perrone

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Location:
United States
Michael Perrone Photo 4

Experienced Retail Executive

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Position:
DSA ZEGNA SPORTSWEAR at Saks Fifth Avenue
Location:
Greater New York City Area
Industry:
Retail
Work:
Saks Fifth Avenue
DSA ZEGNA SPORTSWEAR
Hugo Boss Sep 2004 - May 2006
Regional Manager
Michael Perrone Photo 5

Customer Service Manager At Canteen Corporation

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Position:
Customer Service Manager at Canteen Corporation
Location:
Greater New York City Area
Industry:
Food & Beverages
Work:
Canteen Corporation
Customer Service Manager

Business Records

Name / Title
Company / Classification
Phones & Addresses
Michael Perrone
Chairman
Pepsi-Cola
Air Transportation, Scheduled
700 Anderson Hill Rd, Purchase, NY 10577
Michael Perrone
Vice President - Sales
Osnet, Inc.
Computer Integrated Systems Design
6930 Manse St, Flushing, NY 11375
Michael Perrone
President
In Social Sign Inc
Custom Computer Programing · Custom Computer Programming Services, Nsk
26 Vly Rd, Cos Cob, CT 06807
PO Box 7793, Greenwich, CT 06836
Michael Perrone
President
FIREHOUSE INNOVATIONS, CORP
Mfg Firefighting Training Equip
3795 Martin Ct, Seaford, NY 11783
(516) 749-6390, (516) 221-2226
Michael Perrone
Principal
Reigncloud Advisors Inc
Business Services at Non-Commercial Site
96 Scudder Pl, Northport, NY 11768
Michael Perrone
Principal
MKMX INTERACTIVE DESIGNS, INC
Business Services
Michael Perrone 674 Wyngate Dr W, Valley Stream, NY 11580
1225 Franklin Ave / SUITE 325, Garden City, NY 11530
674 Wyngate Dr W, Valley Stream, NY 11580
Michael Perrone
Sales Staff, Vice-President, VP Sales, Vice President - Sales
Osnet Inc
Computer Hardware · Computer Systems Design · Custom Computer Programming Svcs · Computers-System Designers & C
6930 Manse St, Forest Hills, NY 11375
(718) 520-2900
Michael Perrone
Manager
Alpine Chem Dry
Consumer Services · Carpet/Upholstery Cleaning · Carpet Cleaners · Upholstery Cleaning
4053 Maywood Dr, Seaford, NY 11783
(516) 735-3653
Michael Perrone
Chairman
Pepsi-Cola
Air Transportation, Scheduled
700 Anderson Hill Rd, Purchase, NY 10577
Michael Perrone
Vice President - Sales
Osnet, Inc.
Computer Integrated Systems Design
6930 Manse St, Flushing, NY 11375

Publications

Us Patents

Handwriting Recognition System And Method Using Compound Characters For Improved Recognition Accuracy

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US Patent:
6567548, May 20, 2003
Filed:
Jan 29, 1999
Appl. No.:
09/240362
Inventors:
Krishna S. Nathan - New York NY
Michael P. Perrone - Yorktown NY
John F. Pitrelli - Danbury CT
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06K 918
US Classification:
382186, 382159, 382179, 382187
Abstract:
A handwriting recognition system and method whereby various character sequences (which are typically âslurredâ together when handwritten) are each modelled as a single character (âcompound character modelâ) so as to provide increased decoding accuracy for slurred handwritten character sequences. In one aspect of the present invention, a method for generating a handwriting recognition system having compound character models comprises the steps of: providing an initial handwriting recognition system having individual character models; collecting and labelling a set of handwriting data; aligning the labelled set of handwriting data; generating compound character data using the aligned handwriting data; and retraining the initial recognition system with the compound character data to generate a new recognition system having compound character models. Once these compound character models are trained, they may be used to accurately decode slurred handwritten character sequences for which compound character models were previously generated. Once recognized, the compound characters are expanded into the constituent individual characters comprising the compound character.

Handwritten Word Recognition Using Nearest Neighbor Techniques That Allow Adaptive Learning

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US Patent:
7343041, Mar 11, 2008
Filed:
Feb 19, 2002
Appl. No.:
10/079763
Inventors:
Thomas Yu-Kiu Kwok - Washington Township NJ, US
Michael Peter Perrone - Yorktown Heights NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06K 9/00
G06K 9/62
US Classification:
382187, 382224
Abstract:
A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word. Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.

Retrieving Handwritten Documents Using Multiple Document Recognizers And Techniques Allowing Both Typed And Handwritten Queries

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US Patent:
7627596, Dec 1, 2009
Filed:
Feb 19, 2002
Appl. No.:
10/079741
Inventors:
Thomas Yu-Kiu Kwok - Washington Township NJ, US
James Randal Moulic - Poughkeepsie NY, US
Kenneth Blair Ocheltree - Ossining NY, US
Michael Peter Perrone - Yorktown Heights NY, US
John Ferdinand Pitrelli - Danbury CT, US
Eugene Henry Ratzlaff - Hopewell Junction NY, US
Gregory Fraser Russell - Yorktown Heights NY, US
Jayashree Subrahmonia - White Plains NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 17/00
US Classification:
707102, 715268
Abstract:
The techniques in the present invention allow both text and handwritten queries, and the queries can be single-word or multiword. Generally, each handwritten word in a handwritten document is converted to a document stack of words, where each document stack contains a list of text words and a word score of some type for each text word in the list. The query is also converted to one or more stacks of words. A measure is determined from each query and document stack. Documents that meet search criteria in the query are then selected based on the query and the values of the measures. The present invention also performs multiple recognitions, with multiple recognizers, on a handwritten document to create multiple recognized transcriptions of the document. The multiple transcriptions are used for document retrieval. In another embodiment, a single transcription is created from the multiple transcriptions, and the single transcription is used for document retrieval.

Handwritten Word Recognition Using Nearest Neighbor Techniques That Allow Adaptive Learning

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US Patent:
7697760, Apr 13, 2010
Filed:
Jan 11, 2008
Appl. No.:
11/972913
Inventors:
Thomas Yu-Kiu Kwok - Washington Township NJ, US
Michael Peter Perrone - Yorktown Heights NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06K 9/00
G06K 9/18
G06K 9/72
US Classification:
382186, 382187, 382229
Abstract:
A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.

Forcible Entry Training Door System

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US Patent:
8408917, Apr 2, 2013
Filed:
Mar 3, 2011
Appl. No.:
13/040213
Inventors:
Michael Perrone - Seaford NY, US
International Classification:
G09B 19/00
US Classification:
434226
Abstract:
The present invention relates generally to apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training prop adapted to simulate a locked door and allow individuals to conduct training in forcible entry techniques for different door types and mounting configurations (e. g. , opening inward or outward, left hand opening or right hand opening, steel jam or wood jams).

Forcible Entry Training Door System

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US Patent:
8419435, Apr 16, 2013
Filed:
Oct 6, 2011
Appl. No.:
13/267822
Inventors:
Michael Perrone - Seaford NY, US
International Classification:
G09B 19/00
US Classification:
434226
Abstract:
Apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training apparatus that allows individuals to simulate forcible entry of outward opening metal doors mounted in metal frames.

Forcible Entry Training Door System

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US Patent:
20130224700, Aug 29, 2013
Filed:
Mar 29, 2013
Appl. No.:
13/853085
Inventors:
Michael Perrone - Seaford NY, US
International Classification:
A62C 99/00
US Classification:
434226
Abstract:
The present invention relates generally to apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training prop adapted to simulate a locked door and allow individuals to conduct training in forcible entry techniques for different door types and mounting configurations (e.g., opening inward or outward, left hand opening or right band opening, steel jam or wood jams).

Forcible Entry Training Door System

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US Patent:
20130224701, Aug 29, 2013
Filed:
Mar 29, 2013
Appl. No.:
13/853086
Inventors:
Michael Perrone - Seaford NY, US
International Classification:
G09B 25/00
US Classification:
434226
Abstract:
Apparatus for training the art of forcible door entry for emergency personnel such as firefighters and, in particular, a reusable forcible entry door training apparatus that allows individuals to simulate forcible entry of outward opening metal doors mounted in metal frames.
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Michael A Perrone from Seaford, NY, age ~38 Get Report