MICHAEL RICHARD KEENAN, SR
Pilots at Sunset Cyn Pl, Albuquerque, NM

6584413, Jun 24, 2003

Jun 1, 2001

09/872740

Michael R. Keenan - Albuquerque NM
Paul G. Kotula - Albuquerque NM

Sandia Corporation - Albuquerque NM

G06F 1900

702 28, 702194, 702196

An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S , by performing a constrained alternating least-squares analysis of D=CS , where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

6675106, Jan 6, 2004

Jun 1, 2001

09/873078

Michael R. Keenan - Albuquerque NM
Paul G. Kotula - Albuquerque NM

Sandia Corporation - Albuquerque NM

G06F 1900

702 28, 702194, 702196

A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S , by performing a constrained alternating least-squares analysis of D=CS , where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

7472153, Dec 30, 2008

Mar 4, 2004

10/794538

Michael R. Keenan - Albuquerque NM, US

Sandia Corporation - Albuquerque NM

G06F 7/38, G01N 31/00

708446, 702 23

Bias plays an important role in factor analysis and is often implicitly made use of, for example, to constrain solutions to factors that conform to physical reality. However, when components are collinear, a large range of solutions may exist that satisfy the basic constraints and fit the data equally well. In such cases, the introduction of mathematical bias through the application of constraints may select solutions that are less than optimal. The biased alternating least squares algorithm of the present invention can offset mathematical bias introduced by constraints in the standard alternating least squares analysis to achieve factor solutions that are most consistent with physical reality. In addition, these methods can be used to explicitly exploit bias to provide alternative views and provide additional insights into spectral data sets.

7840626, Nov 23, 2010

Feb 9, 2010

12/702934

Michael R. Keenan - Albuquerque NM, US

Sandia Corporation - Albuquerque NM

G06F 7/38, G01N 31/00

708446, 708520, 702 23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are “simple” in the sense that they consist of relatively discrete chemical phases.

7725517, May 25, 2010

Sep 22, 2005

11/233223

Michael R. Keenan - Albuquerque NM, US

Sandia Corporation - Albuquerque NM

G06F 7/38, G01N 31/00

708446, 702 23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are “simple” in the sense that they consist of relatively discrete chemical phases.

7400772, Jul 15, 2008

Feb 4, 2004

10/772805

Michael R. Keenan - Albuquerque NM, US

Sandia Corporation - Albuquerque NM

G06K 9/36, G06K 9/46, H04B 1/66

382232, 375240

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

7451173, Nov 11, 2008

Sep 9, 2004

10/938444

Mark H. Van Benthem - Middletown DE, US
Michael R. Keenan - Albuquerque NM, US

Sandia Corporation - Albuquerque NM

G06F 17/16

708607

A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in large-scale problems in order to minimize the number of arithmetic operations required to obtain a solution.

7283684, Oct 16, 2007

Feb 4, 2004

10/772548

Michael R. Keenan - Albuquerque NM, US

Sandia Corporation - Albuquerque NM

G06K 9/36

382276, 382235, 382243, 382277, 345644

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.