Michael J Sannella, 651737 89Th St, Seattle, WA 98115

8087002, Dec 27, 2011

Apr 4, 2007

11/784209

Brian Fjeldstad - Seattle WA, US
Michael J Sannella - Seattle WA, US
Charles B Roosen - Seattle WA, US

Tibco Software Inc. - Palo Alto CA

G06F 9/44

717124

Methods and systems for visual debugging of an interpreted language in, for example, an Interactive Development Environment are provide. Example embodiments provide an S-PLUS Visual Debugging System (“SPVDS”), which includes an S-PLUS Workbench Debugger (“SPWD”) that provides “step-based” visual debugging, enabling programmers to step through execution of expressions by setting and otherwise managing breakpoints, examining variables and expressions, and controlling execution such as by step, step-in, step-out, step-over, continue, stop commands. In addition, the SPWD provides a profiler which tracks the number and duration of calls to functions and the amount of memory allocated to variables. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

8286142, Oct 9, 2012

Dec 21, 2011

13/333993

Brian Fjeldstad - Seattle WA, US
Michael J. Sannella - Seattle WA, US
Charles B. Roosen - Seattle WA, US

Tibco Software Inc. - Palo Alto CA

G06F 9/44

717129

Methods and systems for visual debugging of an interpreted language in, for example, an Interactive Development Environment are provide. Example embodiments provide an S-PLUS Visual Debugging System (“SPVDS”), which includes an S-PLUS Workbench Debugger (“SPWD”) that provides “step-based” visual debugging, enabling programmers to step through execution of expressions by setting and otherwise managing breakpoints, examining variables and expressions, and controlling execution such as by step, step-in, step-out, step-over, continue, stop commands. In addition, the SPWD provides a profiler which tracks the number and duration of calls to functions and the amount of memory allocated to variables. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.

2003008, May 8, 2003

Oct 15, 2002

10/272504

Thomas Walter - Issaquah WA, US
Stephen Kaluzny - Seattle WA, US
Douglas Martin - Seattle WA, US
Charles Roosen - Seattle WA, US
Michael Sannella - Seattle WA, US

Insightful Corporation - Seattle WA

G06F007/00

707/006000

Methods and systems for mining large data sets using block model averaging techniques are provided. Example embodiments provide a Block Model Averaging System (“BMAS”), which enables users to build/train, test, deploy, and maintain predictive statistical models that can be used to gain knowledge from both static and dynamic data. In one embodiment, the BMAS incrementally builds predictive models from portions (blocks) of input data using block model averaging techniques, determines a voting population of the predictive models to use as components of an ensemble model, generates an ensemble model with these determined components, and deploys the generated ensemble model to input data to derive answers. One technique for determining the voting population is correctness; another is diversity of response. When the BMA ensemble model is deployed, it incorporates a voting protocol, appropriate to the component predictive models, to derive a single response from the outputs of the component predictive models. In one embodiment, the BMAS comprises an ensemble generator, one or more predictive model generators, and a voting and model data repository. These components cooperate to generate predictive models using BMA and to combine appropriate subsets of these models to generate an ensemble model.

2007004, Feb 22, 2007

Apr 7, 2006

11/400070

David Smith - Seattle WA, US
Michael Sannella - Seattle WA, US
Charles Roosen - Seattle WA, US
William Dunlap - Mount Vernon WA, US

G05B 1/00

250202000

Methods and systems for providing support for large data sets are provided. Example embodiments provide a Big Data Object Library “BDOL,” which defines data structures and routines for handling big data objects using out of memory techniques. In one embodiment, the BDOL defines a bdFrame object which stores the data in binary form in a cache on an external storage medium, such as a file on a disk. The example BDOL provides support for user defined block processing a bdFrames using a pipeline engine. Also, the BDOL provides for Trellis plots, and other charts, of big data objects using hexagonal binning. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.