Patents by Inventor Pavel Zelinsky

Pavel Zelinsky 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: 10956859
    Abstract: A method, system and computer program product for fulfilling an online order. An online order to purchase an item(s) is received. The “candidate locations” that stock the item(s) of the online order and that can be used to fulfill at least a portion of the online order are determined. A stockout cost for each of these candidate locations for fulfilling an item of the online order may be calculated, where the stockout cost is a cost of a potential lost sale of the item of the online order by the candidate location if the candidate location fulfills the item of the online order. A shipping location among the candidate locations to fulfill the item is then determined based at least in part on the stockout cost for each of the candidate locations for fulfilling the item. The item is then shipped to the customer from the determined shipping location.
    Type: Grant
    Filed: June 1, 2018
    Date of Patent: March 23, 2021
    Assignee: International Business Machines Corporation
    Inventors: France Savard, Xiaowei Bao, Emrah Zarifoglu, Pavel Zelinsky
  • Publication number: 20190370734
    Abstract: A method, system and computer program product for fulfilling an online order. An online order to purchase an item(s) is received. The “candidate locations” that stock the item(s) of the online order and that can be used to fulfill at least a portion of the online order are determined. A stockout cost for each of these candidate locations for fulfilling an item of the online order may be calculated, where the stockout cost is a cost of a potential lost sale of the item of the online order by the candidate location if the candidate location fulfills the item of the online order. A shipping location among the candidate locations to fulfill the item is then determined based at least in part on the stockout cost for each of the candidate locations for fulfilling the item. The item is then shipped to the customer from the determined shipping location.
    Type: Application
    Filed: June 1, 2018
    Publication date: December 5, 2019
    Inventors: France Savard, Xiaowei Bao, Emrah Zarifoglu, Pavel Zelinsky
  • Publication number: 20150058087
    Abstract: A computer-implemented method and computer program product for identifying similar stores and determining store parameters based on the similar stores. The one or more computer programs identify key items by selecting a subset of all items. The one or more computer programs assign store feature vectors each including values of a store behavior for the key items. The one or more computer programs determine a similarity distance between each pair of the vectors. The one or more computer programs identify similar stores of a given store based on the similarity distance. The one or more computer programs determine one or more parameters for the given stores, based on the similar stores.
    Type: Application
    Filed: August 20, 2013
    Publication date: February 26, 2015
    Applicant: International Business Machines Corporation
    Inventors: Dmitry A. Kulagin, Oleg Sidorkin, Egor Zakharov, Pavel Zelinsky
  • Publication number: 20090089241
    Abstract: An expert decision-making method is emulated based on a history of behaviors by experts in a variety of observed situations. The history of behaviors is built up from observations of actions taken by experts in analyzing a plurality of situations. Situation data representative of a situation to be processed is received, and situation features are extracted from the situation data. Each situation feature is associated with an expert behavior method used to process the situation. A behavior method is recognized from a pattern of situation features. Recognizing a behavior method is based on feature/method separation data in multidimensional space of features into so areas with each area associated with a method used by experts. Parameter values for parameters in the recognized behavior method are calculated based on the situation features. The calculation of parameter values is accomplished by recognizing parameter calculation rules and calculating the parameter values using the rules.
    Type: Application
    Filed: December 9, 2008
    Publication date: April 2, 2009
    Applicant: Applied Intelligence Solutions
    Inventors: Pavel Zelinsky, Grigory Baytsur, Ivan Kopiev, Vitaly Grechko, Oleg Sidorkin, Andrey Belousov
  • Publication number: 20070094194
    Abstract: An expert behavior-emulation system that assists a operator-controlled-decision system, is managed based on group performance results of operators using the systems. The performance results achieved by an expert group of operators and a non-expert group of operators is evaluated. Both groups are using the operator-controlled-decision system assisted by the expert-behavior emulation system to take action on a situation task to produce the performance results. The performance results of the actions taken are grouped according to expert group and non-expert group. A gap is measured which indicates one or more changes in group performance results as a measure of the extent to which the expert behavior-emulation system is contributing to performance results achieved by operators using the operator-controlled-decision system assisted by the expert-behavior emulation system. The expert behavior-emulation may be adjusted, and the gap may be measured again.
    Type: Application
    Filed: August 3, 2005
    Publication date: April 26, 2007
    Inventors: Pavel Zelinsky, James Dixon
  • Publication number: 20060112047
    Abstract: Expert decision-making operations are trained to emulate expert behavior based on an history of behaviors by experts in a variety of observed situations. A history of behaviors is built up from observations of actions taken by experts in analyzing a plurality of situations. The observations are captured, and behaviors from the observations are constructed. The behaviors indicate an association between situation features and methods with parameter for solving the situations. The training operations capture observations of behavior by experts. The observations include situation data about multiple situations and actions by the experts. The actions are associated with the situations. Subject knowledge information is loaded from the observations; the subject knowledge information has a features library, a method library and a parameters library.
    Type: Application
    Filed: October 26, 2004
    Publication date: May 25, 2006
    Applicant: Parascript LLC
    Inventors: Pavel Zelinsky, Grigory Baytsur, Ivan Kopiev, Vitaly Grechko, Oleg Sidorkin, Andrey Belousov
  • Publication number: 20060089830
    Abstract: An expert decision-making method is emulated based on a history of behaviors by experts in a variety of observed situations. The history of behaviors is built up from observations of actions taken by experts in analyzing a plurality of situations. Situation data representative of a situation to be processed is received, and situation features are extracted from the situation data. Each situation feature is associated with an expert behavior method used to process the situation. A behavior method is recognized from a pattern of situation features. Recognizing a behavior method is based on feature/method separation data in multidimensional space of features into areas with each area associated with a method used by experts. Parameter values for parameters in the recognized behavior method are calculated based on the situation features. The calculation of parameter values is accomplished by recognizing parameter calculation rules and calculating the parameter values using the rules.
    Type: Application
    Filed: October 26, 2004
    Publication date: April 27, 2006
    Applicant: Parascript LLC
    Inventors: Pavel Zelinsky, Grigory Baytsur, Ivan Kopiev, Vitaly Grechko, Oleg Sidorkin, Andrey Belousov