Patents by Inventor Srinivas Palamarthy

Srinivas Palamarthy has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 7937282
    Abstract: Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.
    Type: Grant
    Filed: May 5, 2008
    Date of Patent: May 3, 2011
    Assignee: i2 Technologies US, Inc.
    Inventors: Joachim P. Walser, Vibhu Kalyan, Srinivas Palamarthy, James M. Crawford, Jr., Mukesh Dalal
  • Publication number: 20080208678
    Abstract: Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.
    Type: Application
    Filed: May 5, 2008
    Publication date: August 28, 2008
    Inventors: Joachim P. Walser, Vibhu Kalyan, Srinivas Palamarthy, James M. Crawford, Mukesh Dalal
  • Publication number: 20080040202
    Abstract: Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.
    Type: Application
    Filed: October 19, 2007
    Publication date: February 14, 2008
    Inventors: Joachim Walser, Vibhu Kalyan, Srinivas Palamarthy, James Crawford, Mukesh Dalal
  • Publication number: 20060161504
    Abstract: Generating a price schedule involves generating a graph (50) having paths that include states (52) with values (54, 56, 58). The graph (50) is generated by determining the values (56, 58) of a successor state (52) from the values (56, 58) of a predecessor state (52). An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. An best-fitting elasticity curve is selected. Adjusting a demand forecast value (56) includes estimating an inventory and a demand at a number of locations (24). An expected number of unrealized sales at each location (24) is calculated. An sales forecast value (56) is determined according to the expected number.
    Type: Application
    Filed: March 20, 2006
    Publication date: July 20, 2006
    Inventors: Joachim Walser, Vibhu Kalyan, Srinivas Palamarthy, James Crawford, Mukesh Dalal