Akhileswar G Vaidyanathan from 202 Dangina Dr, Landenberg, PA 19350, age 70, Phone: 610 274-1311

Us Patents

Process Of Preparing Textured Fluoropolymer Films
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US Patent:

6348117, Feb 19, 2002
Filed:

Oct 6, 1997
Appl. No.:

08/944660
Inventors:

Mark Joseph Tribo - East Amherst NY
Robert G. Pembleton - Wilmington DE
Michael James Merrill - New Castle DE
Akhileswar Ganesh Vaidyanathan - Hockessin DE
Assignee:

E. I. du Pont de Nemours and Company - Wilmington DE
International Classification:

B29C 3902
US Classification:

156245, 156246, 264544, 264316, 264320, 264322, 26433114, 264294
Abstract:

A method of texturing fluoropolymer film and the textured product produced, which product retains the texture imparted after further processing, such as in thermoforming or molding processes.

Distributed Hierarchical Evolutionary Modeling And Visualization Of Empirical Data
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US Patent:

6941287, Sep 6, 2005
Filed:

Dec 17, 1999
Appl. No.:

09/466041
Inventors:

Akhileswar Ganesh Vaidyanathan - Hockessin DE, US
Aaron J. Owens - Newark DE, US
James Arthur Whitcomb - Brevard NC, US
Assignee:

E. I. du Pont de Nemours and Company - Wilmington DE
International Classification:

G06F015/18
US Classification:

706 12, 706 14, 706 46
Abstract:

A distributed hierarchical evolutionary modeling and visualization of empirical data method and machine readable storage medium for creating an empirical modeling system based upon previously acquired data. The data represents inputs to the systems and corresponding outputs from the system. The method and machine readable storage medium utilize an entropy function based upon information theory and the principles of thermodynamics to accurately predict system outputs from subsequently acquired inputs. The method and machine readable storage medium identify the most information-rich (i. e. , optimum) representation of a data set in order to reveal the underlying order, or structure, of what appears to be a disordered system. Evolutionary programming is one method utilized for identifying the optimum representation of data.

Method Of Discovering Patterns In Symbol Sequences
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US Patent:

7467047, Dec 16, 2008
Filed:

May 9, 2001
Appl. No.:

09/851674
Inventors:

Akhileswar Ganesh Vaidyanathan - Hockessin DE, US
David Reuben Argentar - Bear DE, US
Karen Marie Bloch - Wilmington DE, US
Herbert Alan Holyst - Morton PA, US
Allan Robert Moser - Swarthmore PA, US
Wade Thomas Rogers - West Chester PA, US
Assignee:

E.I. Du Pont de Nemours & Company - Wilmington DE
International Classification:

G01N 33/48
US Classification:

702 20, 702 19
Abstract:

A method of discovering one or more patterns in two sequences of symbols Sand Sincludes the formation, for each sequence, of a master offset table that groups for each symbol the position in the sequence occupied by each occurrence of that symbol. The difference in position between each occurrence of a symbol in one of the sequences and each occurrence of that same symbol in the other sequence is determined and a Pattern Map is formed. For each given value of a difference in position the Pattern Map lists the position in the first sequence of each symbol therein that appears in the second sequence at that difference in position. The collection of the symbols tabulated for each value of difference in position thereby defines a parent pattern in the first sequence that is repeated in the second sequence. A computer readable medium having instructions for controlling a computer system to perform the method and a computer readable medium containing a data structure used in the practice of the method are also disclosed.

Flexscape: Data Driven Hypothesis Testing And Generation System
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US Patent:

20110231356, Sep 22, 2011
Filed:

Jul 1, 2010
Appl. No.:

12/829241
Inventors:

Akhileswar Ganesh Vaidyanathan - Landenberg PA, US
Eric N. Jean - Denver CO, US
Mani Thomas - Parsippany NJ, US
David Louis Hample - Newark DE, US
Michael Thomas McGowan - Newark DE, US
Jijun Wang - Hockessin DE, US
Eli T. Faulkner - Wilmington DE, US
Jay Dee Askren - Bear DE, US
Albert Josef Boehmler - Newark DE, US
Durban A. Frazer - Kentfield CA, US
Assignee:

QUANTUM LEAP RESEARCH, INC. - Claymont DE
International Classification:

G06N 5/02
US Classification:

706 52
Abstract:

The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.

Methods For Enabling A Scalable Transformation Of Diverse Data Into Hypotheses, Models And Dynamic Simulations To Drive The Discovery Of New Knowledge
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US Patent:

20120004893, Jan 5, 2012
Filed:

Sep 10, 2009
Appl. No.:

12/556591
Inventors:

Akhileswar Ganesh VAIDYANATHAN - Landenberg PA, US
Stephen D. PRIOR - Arlington VA, US
Jijun Wang - Newark DE, US
Bin Yu - Wilmington DE, US
Assignee:

QUANTUM LEAP RESEARCH, INC. - Claymont DE
International Classification:

G06G 7/58
G06G 7/48
G06F 17/30
US Classification:

703 11, 707739, 703 6, 707E17089
Abstract:

The present invention relates to a method for the automatic identification of at least one informative data filter from a data set that can be used to identify at least one relevant data subset against a target feature for subsequent hypothesis generation, model building and model testing. The present invention describes methods, and an initial implementation, for efficiently linking relevant data both within and across multiple domains and identifying informative statistical relationships across this data that can be integrated into agent-based models. The relationships, encoded by the agents, can then drive emergent behavior across the global system that is described in the integrated data environment.

Flexscape: Data Driven Hypothesis Testing And Generation System
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US Patent:

20120185424, Jul 19, 2012
Filed:

Aug 24, 2010
Appl. No.:

12/862657
Inventors:

Akhileswar Ganesh Vaidyanathan - Landenberg PA, US
Eric N. Jean - Denver CO, US
Mani Thomas - Hillsborough NJ, US
David Louis Hample - Newark DE, US
Michael Thomas McGowan - Newark DE, US
Jijun Wang - Hockessin DE, US
Eli T. Faulkner - Wilmington DE, US
Jay Dee Askren - Bear DE, US
Albert Josef Boehmler - West Grove PA, US
Durban A. Frazer - San Francisco CA, US
Assignee:

QUANTUM LEAP RESEARCH, INC. - Claymont DE
International Classification:

G06N 5/02
US Classification:

706 52
Abstract:

The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.

Method For Determining Quality Of Dispersion Of Glass Fibers In A Thermoplastic Resin Preform Layer And Preform Layer Characterized Thereby
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US Patent:

54369800, Jul 25, 1995
Filed:

Dec 22, 1992
Appl. No.:

7/995184
Inventors:

Gregory P. Weeks - Hockessin DE
Akhileswar G. Vaidyanathan - Hockessin DE
Michael J. Merrill - New Castle DE
Assignee:

E. I. Du Pont de Nemours and Company - Wilmington DE
International Classification:

G06K 900
US Classification:

382141
Abstract:

A method for determining the quality of dispersion of glass fibers in a thermoplastic resin preform layer compares the mean characteristic length of the glass fiber bundles in the preform layer to a predetermined value indicative of dispersion of the glass fibers. The thermoplastic resin preform layer characterized by this method comprises a plurality of individual glass fibers and some degree of undispersed glass fiber bundles intimately mixed with a plurality of discrete thermoplastic fibers. The glass fibers are introduced to the mixture having a length of about 1 cm. to about 8 cm. After mixing, some of the glass fiber bundles break up, and some residual glass fiber bundles remain. A preform layer which has acceptable dispersion has residual glass fiber bundles having a mean apparent length of less about 1. 365 mm.

Methods For Determining The Exterior Points Of An Object In A Background
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US Patent:

55794092, Nov 26, 1996
Filed:

Nov 30, 1993
Appl. No.:

8/159641
Inventors:

Akhileswar G. Vaidyanathan - Hockessin DE
Hooshmand M. Kalayeh - Hockessin DE
Assignee:

E. I. Du Pont de Nemours and Company - Wilmington DE
International Classification:

G06K 946
US Classification:

382203
Abstract:

Accessing the exterior points of an object is used in the area of object identification when it is important to use the information in the local exterior environment of an object in order to extract a feature which may be useful for object recognition. Examples of this type of feature which are important for recognizing and classifying objects occur in the area if bacterial detection. Adaptive classification is a way of making the analysis independent of variations in imaging conditions, such as lighting, positioning, electronic amplification, etc. Two general methods can be used to access the local exterior environment of an object, knowing the perimeter points of the object. In the first method, the shape of an annular exterior region around the object of interest can be made to follow the shape of the object itself. In the second method, an known exterior shape, such as a circle, is used to characterize the exterior contour region, approximating the shape of the object.