Jerzy Wojciech Bala

US Patent:

20220358324, Nov 10, 2022

Filed:

Jul 14, 2022

Appl. No.:

17/864704

Inventors:

- Portsmouth NH, US
Jerzy Bala - Potomac Falls VA, US

Assignee:

Bottomline Technologies, Inc. - Portsmouth NH

International Classification:

G06K 9/62
G06N 20/00

Abstract:

A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

US Patent:

20150363801, Dec 17, 2015

Filed:

Jun 16, 2014

Appl. No.:

14/305246

Inventors:

- Portsmouth NH, US
Jerzy Bala - Potomac Falls VA, US

International Classification:

G06Q 30/02

Abstract:

A method and apparatus are presented for predicting the behavior or state of a negative occurrence class by scoring histories of members of the negative occurrence class against pasts of members of a positive occurrence class. The method and apparatus predicts the members of the negative occurrence class that are most likely to next transition to members of the positive occurrence class.

US Patent:

20140143186, May 22, 2014

Filed:

Jul 1, 2013

Appl. No.:

13/932299

Inventors:

- Scarsdale NY, US
Jerzy W. Bala - Potomac Falls VA, US

International Classification:

G06N 99/00

US Classification:

706 12

Abstract:

The invention includes methods and systems for analyzing data to determine trends in the data and to identify outliers. The methods and systems include a learning algorithm whereby a data space is co-populated with artificial, evenly distributed data, and then the data space is carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. Additionally, a final quality control measurement is used to merge clusters that are too similar to be meaningful. The invention is widely applicable to data analytics, generally, including financial transactions, retail sales, elections, and sports.