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Michael Anthony Perrone

from Lawrenceville, GA
Age ~41
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Also known as:
Michael A Perrone, Michaela Perrone, Eerone P Micahel
Phone and address:
1650 Mckendree Church Rd, Lawrenceville, GA 30043
(770) 682-7470
Related to:
Raymond G Hicks, 51Richard HicksSuzanne Durham, 55Frank PerroneAnthony R Perrone, 39
Connected to:
Carolyn E Perrone, 63Celeste D Perrone, 88Gerard M Perrone, 62Linda A Perrone, 67Lorena S Perrone, 40Matt L Perrone, 41Ursula M Perrone, 51George T Perros, 75Howard L Perross, 77Jennifer Perrot, 51

Michael Perrone Phones & Addresses

  • 1650 Mckendree Church Rd, Lawrenceville, GA 30043 (770) 682-7470
  • Norcross, GA
  • Atlanta, GA
  • Suwanee, GA
  • Highland Falls, NY
  • Ft Montgomery, NY
  • Fort Myers, FL
  • 2915 Binghampton Ln, Lawrenceville, GA 30044

Work

Company: Pepsi-cola Address: 700 Anderson Hill Rd, Purchase, NY 10577 Phones: (914) 253-3392 Position: Chairman Industries: Air Transportation, Scheduled

Education

Degree: Bachelor's degree or higher

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

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
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 A. Perrone
Chief Financial Officer, Secretary
Gwinnett Refrigeration Services Inc
Plumbing/Heating/Air Cond Contractor
2101 Tropicana Dr, Suwanee, GA 30024
(770) 682-8161
Michael Perrone
Chairman
Pepsi-Cola
Air Transportation, Scheduled
700 Anderson Hill Rd, Purchase, NY 10577

Publications

Us Patents

Spatial Sorting And Formatting For Handwriting Recognition

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US Patent:
6603881, Aug 5, 2003
Filed:
Oct 23, 2001
Appl. No.:
10/047152
Inventors:
Michael P. Perrone - Yorktown NY
Eugene H. Ratzlaff - Hopewell Junction NY
Assignee:
International Business Machines Corporation - Armonk NJ
International Classification:
G06K 918
US Classification:
382186, 382181, 382187, 382189, 382202, 382225
Abstract:
Systems and methods for reordering unconstrained handwriting data using both spatial and temporal interrelationships prior to recognition, and for spatially organizing and formatting machine recognized transcription results. The present invention allows a machine recognizer to generate and present a full and accurate transcription of unconstrained handwriting in its correct spatial context such that the transcription output can appear to âmirrorâ the corresponding handwriting.

Methods And Apparatus For Automatic Page Break Detection

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US Patent:
7260779, Aug 21, 2007
Filed:
Sep 30, 2005
Appl. No.:
11/240605
Inventors:
Paul Turquand Keyser - Mount Kisco NY, US
Michael Peter Perrone - Yorktown NY, US
Eugene H. Ratzlaff - Hopewell Junction NY, US
Jayashree Subrahmonia - White Plains NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 15/00
US Classification:
715525, 382187
Abstract:
In one aspect of the present invention, page breaks are identified in the following manner. A set of ink data and a document description are processed by a variety of scoring methods, each of which generates a score for each possible insertion point in the ink. These scores are combined to produce a ranked list of hypothesized page breaks for the corresponding ink data. This ranked list is then used either to insert page breaks automatically using a predefined threshold to determine a cut-off in the list; or to present, on-line, to a human for verification/approval; or a mixture of the two based on two thresholds: one for automatic insertion and the other for human verification. It is to be understood not all scoring methods need be used, that is, one or more of the scoring methods may be used as needed.

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.

Method And System For The Compression Of Probability Tables

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US Patent:
7400277, Jul 15, 2008
Filed:
Apr 6, 2004
Appl. No.:
10/818567
Inventors:
Michael P. Perrone - Yorktown Heights NY, US
Eugene H. Ratzlaff - Hopewell Junction NY, US
Jianying Hu - Bronx NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H03M 7/00
US Classification:
341107, 341106, 341 65, 341 67
Abstract:
The present invention relates to a method, computer program product and system for the compression of a probability table and the reconstruction of one or more probability elements using the compressed data and method. After determining a probability table that is to be compressed, the probability table is compressed using a first probability table compression method, wherein the probability table compression method creates a first compressed probability table. The first compressed probability table contains a plurality of probability elements. Further, the probability table is compressed using a second probability table compression method, wherein the probability table compression method creates a second compressed probability table. The second compressed probability table containing a plurality of probability elements. A first probability element reconstructed using the first compressed probability table is thereafter merged with a second probability element reconstructed using the second compressed probability table in order to produce a merged probability element.

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.

Method And System For The Compression Of Probability Tables

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US Patent:
7705754, Apr 27, 2010
Filed:
Jun 25, 2008
Appl. No.:
12/146310
Inventors:
Michael P. Perrone - Yorktown Heights NY, US
Eugene H. Ratzlaff - Hopewell Junction NY, US
Jianying Hu - Bronx NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H03M 7/00
US Classification:
341107, 341 65, 341 67, 341106
Abstract:
The present invention relates to a method, computer program product and system for the compression of a probability table and the reconstruction of one or more probability elements using the compressed data and method. After determining a probability table that is to be compressed, the probability table is compressed using a first probability table compression method, wherein the probability table compression method creates a first compressed probability table. The first compressed probability table contains a plurality of probability elements. Further, the probability table is compressed using a second probability table compression method, wherein the probability table compression method creates a second compressed probability table. The second compressed probability table containing a plurality of probability elements. A first probability element reconstructed using the first compressed probability table is thereafter merged with a second probability element reconstructed using the second compressed probability table in order to produce a merged probability element.

Apparatus For Selecting Documents In Response To A Plurality Of Inquiries By A Plurality Of Clients By Estimating The Relevance Of Documents

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US Patent:
7743062, Jun 22, 2010
Filed:
Nov 30, 2007
Appl. No.:
11/948809
Inventors:
Michael P. Perrone - Yorktown Heights NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 17/30
US Classification:
707748
Abstract:
A system and method implemented by a computer for performing query based electronic document retrieval implementing a Markov process model adapted for determining a relationship or relevance between documents. The system ranks documents for retrieval based on their relevance measure. The model calculates the measure of relevance that a document from a given database is relevant to a given query. The method learns the Markov models mixture coefficients from the document database so as to maximize the relevance measure of the documents being retrieved. The method requires only that a similarity measure, D(d,d′), between two documents be specified. Any existing method may be used for generating a model that is at least as good as the chosen similarity measure.
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Michael Anthony Perrone from Lawrenceville, GA, age ~41 Get Report