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Mark S Teflian

from Parker, CO
Age ~68

Mark Teflian Phones & Addresses

  • 9819 Bluestar Dr, Parker, CO 80138 (303) 484-8333
  • Redstone, CO
  • 43 Tower Ln, Cohasset, MA 02025 (781) 383-8856 (781) 383-9317
  • Boston, MA
  • Phoenix, AZ
  • Aurora, CO
  • Eagle, CO

Business Records

Name / Title
Company / Classification
Phones & Addresses
Mark Teflian
Chairman
Aha! Software
Computer Software · Prepackaged Software Services
9137 E Mineral Cir SUITE 140, Englewood, CO 80112
6025 S Quebec St, Englewood, CO 80111
9137 E Mineral Ave, Englewood, CO 80112
(303) 945-3318, (303) 945-3317

Publications

Us Patents

Performance Modeling For Information Systems

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US Patent:
20040220792, Nov 4, 2004
Filed:
Apr 30, 2003
Appl. No.:
10/427121
Inventors:
Peter Gallanis - Highlands Ranch CO, US
Thomas Holloran - Parker CO, US
Mark Teflian - Parker CO, US
Scott Sleeper - Franktown CO, US
Bruce Bacon - Lone Tree CO, US
International Classification:
G06F017/50
G06G007/62
US Classification:
703/013000
Abstract:
A method for monitoring performance of an IT-based computing system over the life cycle of the system is disclosed. The life cycle includes a conception phase, an analysis phase, an architecture design phase, a system design phase, a construction phase, a testing phase, a deployment phase, and finally, an operational phase. Performance requirements for the IT-based computing system are defined during the analysis phase. In the architecture design phase, the appropriate hardware and software infrastructure are selected for use in designing the base architecture for the system. In the system design phase, application specific system components are defined for the system. In the construction phase, the hardware platform is assembled and the software design specifications are turned into executable code. The constructed system is then tested for functionality and performance prior to being deployed for subsequent operation. A performance model is created during the analysis phase using the performance requirements. The model, which is refined during each phase in the life cycle to more accurately reflect the system being constructed, may be executed at any phase in the life cycle to render optimal levels of performance for the system at that phase. Furthermore, the refined model may be used during the operational phase of the life cycle to test the effect of enhancing a pre-existing IT-based computing system.

System And Method Of Analyzing Cmts Data Streams

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US Patent:
20170230211, Aug 10, 2017
Filed:
Feb 6, 2017
Appl. No.:
15/426017
Inventors:
- St. Louis MO, US
Mark Teflian - Parker CO, US
Assignee:
CHARTER COMMUNICATIONS OPERATING, LLC - St. Louis MO
International Classification:
H04L 27/01
H04L 27/00
H04Q 9/02
Abstract:
Systems, methods, architectures, mechanisms or apparatus for analyzing cable modem termination system (CMTS) streams by correlating anomalies found in full spectrum CMTS upstream data to changes in cable modem operational settings to identify and correct network fault conditions, model CMTS behavior, improve network performance and the like.

Dynamic Model Data Facility And Automated Operational Model Building And Usage

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US Patent:
20160210554, Jul 21, 2016
Filed:
Mar 28, 2016
Appl. No.:
15/082988
Inventors:
Robert W. Lange - Orange CA, US
Bruce Allen Bacon - Castle Rock CO, US
Peter T. Gallanis - Highlands Ranch CO, US
Mark Samuel Teflian - Parker CO, US
International Classification:
G06N 5/02
Abstract:
A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.

Dynamic Model Data Facility And Automated Operational Model Building And Usage

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US Patent:
20160210555, Jul 21, 2016
Filed:
Mar 28, 2016
Appl. No.:
15/083014
Inventors:
Robert W. Lange - Orange CA, US
Bruce Allen Bacon - Castle Rock CO, US
Peter T. Gallanis - Highlands Ranch CO, US
Mark Samuel Teflian - Parker CO, US
International Classification:
G06N 5/02
G06N 99/00
Abstract:
A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.

Dynamic Model Data Facility And Automated Operational Model Building And Usage

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US Patent:
20140180982, Jun 26, 2014
Filed:
Oct 23, 2013
Appl. No.:
14/060820
Inventors:
- Centennial CO, US
Bruce Allen Bacon - Castle Rock CO, US
Peter T. Gallanis - Highlands Ranch CO, US
Mark Samuel Teflian - Parker CO, US
Assignee:
Aha! Software LLC - Centennial CO
International Classification:
G06N 99/00
US Classification:
706 14, 707609
Abstract:
A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.
Mark S Teflian from Parker, CO, age ~68 Get Report