Edward Allen Green

20140067363, Mar 6, 2014

May 21, 2013

13/899383

Luis Rivas - Denver CO, US
Edward A. Green - Englewood CO, US

G06F 17/28

704 2

Computer-based tools and methods for conversion of data from a first form to a second form without reference to the context of data to be converted. The conversion may be facilitated by matching source data with external information (e.g., public and/or private schema) that contain rules (e.g., context specific rules) for conversion of the data. The matching may be performed based on an optimized index string matching technique that may be operable to match source data to external information that is context dependent without specific identification of the context of either the source data or the external information identified. Accordingly, the conversion of data may be performed in an unsupervised machine learning environment.

20140067728, Mar 6, 2014

Aug 28, 2012

13/596844

Philip Ogren - Boulder CO, US
Luis Rivas - Denver CO, US
Edward A. Green - Englewood CO, US

ORACLE INTERNATIONAL CORPORATION - Redwood CA

G06F 15/18
G06F 17/00

706 12, 706 54

A string analysis tool for calculating a similarity metric between a source string and a plurality of target strings. The string analysis tool may include optimizations that may reduce the number of calculations to be carried out when calculating the similarity metric for large volumes of data. In this regard, the string analysis tool may represent strings as features. As such, analysis may be performed relative to features (e.g., of either the source string or plurality of target strings) such that features from the strings may be eliminated from consideration when identifying target strings for which a similarity metric is to be calculated. The elimination of features may be based on a minimum similarity metric threshold, wherein features that are incapable of contributing to a similarity metric above the minimum similarity metric threshold are eliminated from consideration.

58052810, Sep 8, 1998

Apr 21, 1997

8/845223

Dennis J. Knowlton - Boulder CO
Edward R. Green - Boulder CO

Particle Measuring Systems - Boulder CO

G01N 1514

356336

Noise reduction is effected by multiplying together electrical signals that include pulses embedded in noise, with the pulses being indicative of the same event, such as light scattered by particles, occurring at a viewing area, and with the signals being processed prior to multiplication so that the pulses and the variance of the noise in the electrical signals are multiplied by one another. A detector in each of a plurality of channels detects the events occurring at the common viewing area and provides pulses indicative thereof, and the output from each detector is amplified, passed through matched low-pass filters to enhance pulse detection, and then passed through a baseline restorer to remove DC components from the noise then present in the signal, prior to coupling of the signals to a multiplier. At the multiplier, peak values of the pulses included in the signals from each of the channels multiply, as do the variances of the uncorrelated noise included in the signals from each of the channels, resulting in multiplication of the signal-to-noise ratio, and hence improvement in signal-to-noise.