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Russell C Salsbury

from Owego, NY
Age ~80
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Also known as:
Russel Salsbury
Related to:
Sandra K Salsbury, 40Vannessa Elainenakahara Salsbury, 42Anne HolmanAnne Holman, 45Ruslan Ponomarenko
Connected to:
Alan E Salsburg, 76Alexander J Salsbury, 33Elizabeth S Salsbury, 59Eva E Salsbury, 54Fred R Salsbury, 92Judith K Salsbury, 59Lynn R Salsbury, 62Marissa J Salsbury, 56Ronald F Salsbury, 74Russell S Salsbury, 52

Russell Salsbury Phones & Addresses

  • Owego, NY
  • 4900 Grove St, Cambria, CA 93428 (805) 927-5997
  • Mountain View, CA
  • Menlo Park, CA
  • Redwood City, CA
  • Cupertino, CA
  • 4900 Grove St, Cambria, CA 93428

Work

Company: Mypoints.com 2010 to 2011 Position: Senior developer

Skills

Agile Methodologies • C# • Java • Javascript • Software Development • Sql • Xml

Languages

English

Industries

Computer Software

Resumes

Resumes

Russell Salsbury Photo 1

Russell Salsbury

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Location:
4900 Grove St, Cambria, CA 93428
Industry:
Computer Software
Work:
Mypoints.com 2010 - 2011
Senior Developer
Coremetrics 2005 - 2007
Senior Software Engineer
Skills:
Agile Methodologies
C#
Java
Javascript
Software Development
Sql
Xml
Languages:
English

Publications

Us Patents

Method, Medium And System For Determining Whether A Target Item Is Related To A Candidate Affinity Item

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US Patent:
20100121777, May 13, 2010
Filed:
Nov 10, 2009
Appl. No.:
12/615476
Inventors:
Charles McGonigal - Austin TX, US
Russell Salsbury - Cambria CA, US
Saritha Guntumadugu - Cupertino CA, US
Mike Niemann - Austin TX, US
Hemanth Puttaswamy - Fremont CA, US
Srinivasa Ogireddy - San Mateo CA, US
Assignee:
Coremetrics, Inc. - San Mateo CA
International Classification:
G06Q 99/00
G06F 15/173
US Classification:
705347, 709224
Abstract:
Recommendations for purchase are made based on customer behavior across multiple sessions. Correlations used for recommendations include: buy-to-buy (cross-session), view-to-view (same-session), view-to-buy (same-session), and abandon-to-buy (same-session) actions. A preview display allows a merchant to adjust recommendation algorithm weightings to achieve a desired result. A closed-loop system is provided with real-time feedback. The recommendations can be based on various segments of other users, including users of the same search engine.

Method, Medium, And System For Determining Whether A Target Item Is Related To A Candidate Affinity Item

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US Patent:
7636677, Dec 22, 2009
Filed:
May 14, 2007
Appl. No.:
11/748391
Inventors:
Charles McGonigal - Austin TX, US
Russell Salsbury - Cambria CA, US
Saritha Guntumadugu - Cupertino CA, US
Mike Niemann - Austin TX, US
Hemanth Puttaswamy - Fremont CA, US
Srinivasa Ogireddy - San Mateo CA, US
Assignee:
Coremetrics, Inc. - San Mateo CA
International Classification:
G06F 17/30
G06Q 30/00
US Classification:
705 26, 705 27, 705 14
Abstract:
Recommendations for purchase are made based on customer behavior across multiple sessions. Correlations used for recommendations include: buy-to-buy (cross-session), view-to-view (same-session), view-to-buy (same-session), and abandon-to-buy (same-session) actions. A preview display allows a merchant to adjust recommendation algorithm weightings to achieve a desired result. A closed-loop system is provided with real-time feedback. The recommendations can be based on various segments of other users, including users of the same search engine.

Intelligent Offer For Related Products With Preview And Real Time Feedback

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US Patent:
20190279274, Sep 12, 2019
Filed:
May 24, 2019
Appl. No.:
16/422605
Inventors:
- Armonk NY, US
Russell Salsbury - Cambria CA, US
Saritha Guntumadugu - Cupertino CA, US
Mike Niemann - Austin TX, US
Hemanth Puttaswamy - Fremont CA, US
Srinivasa Ogireddy - San Mateo CA, US
International Classification:
G06Q 30/06
G06F 16/9535
G06Q 30/02
Abstract:
Recommendations for purchase are made based on customer behavior across multiple sessions. Correlations used for recommendations include: buy-to-buy (cross-session), view-to-view (same-session), view-to-buy (same-session), and abandon-to-buy (same-session) actions. A preview display allows a merchant to adjust recommendation algorithm weightings to achieve a desired result. A closed-loop system is provided with real-time feedback. The recommendations can be based on various segments of other users, including users of the same search engine.
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Russell C Salsbury from Owego, NY, age ~80 Get Report