Patents by Inventor James Osial

James Osial 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: 8924265
    Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.
    Type: Grant
    Filed: January 14, 2013
    Date of Patent: December 30, 2014
    Assignee: RichRelevance, Inc.
    Inventors: David Lee Selinger, Tyler David Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Patent number: 8583524
    Abstract: A method of improving user set recommendations for product advertising including receiving a request for user set recommendations from any of a set of retailers where such request is related to one or more products, receiving from a plurality of user sets from one or more automated user recommendation systems, wherein the plurality of user sets are generated using different user models and using ensemble learning to select one or more most relevant user sets from the plurality of user sets.
    Type: Grant
    Filed: May 6, 2008
    Date of Patent: November 12, 2013
    Assignee: RichRelevance, Inc.
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Patent number: 8364528
    Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.
    Type: Grant
    Filed: May 6, 2008
    Date of Patent: January 29, 2013
    Assignee: RichRelevance, Inc.
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Patent number: 8108329
    Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes receiving recommendation boost instructions, receiving a request for one or more recommendations, receiving a set of recommendations from one or more automated recommendation systems, with each recommendation system utilizing selection models or user models and modifying the set of product recommendations according to the recommendation boost instructions.
    Type: Grant
    Filed: May 6, 2008
    Date of Patent: January 31, 2012
    Assignee: RichRelevance, Inc.
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Patent number: 8019642
    Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes presenting a user with a visual electronic interface adapted to receive recommendation boost instructions regarding a boost subject, receiving recommendation boost instructions via the visual electronic interface, wherein the recommendation boost instructions indicate how strongly the boost subject should be recommended or suppressed from being recommended, receiving a set of recommendations from one or more automated product recommendation systems, wherein each recommendation system utilizes one or more selection models or user models and modifying the set of recommendations according to the recommendation boost instructions.
    Type: Grant
    Filed: May 6, 2008
    Date of Patent: September 13, 2011
    Assignee: RichRelevance, Inc.
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090281884
    Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes presenting a user with a visual electronic interface adapted to receive recommendation boost instructions regarding a boost subject, receiving recommendation boost instructions via the visual electronic interface, wherein the recommendation boost instructions indicate how strongly the boost subject should be recommended or suppressed from being recommended, receiving a set of recommendations from one or more automated product recommendation systems, wherein each recommendation system utilizes one or more selection models or user models and modifying the set of recommendations according to the recommendation boost instructions.
    Type: Application
    Filed: May 6, 2008
    Publication date: November 12, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090281895
    Abstract: A method of improving user set recommendations for product advertising including receiving a request for user set recommendations from any of a set of retailers where such request is related to one or more products, receiving from a plurality of user sets from one or more automated user recommendation systems, wherein the plurality of user sets are generated using different user models and using ensemble learning to select one or more most relevant user sets from the plurality of user sets.
    Type: Application
    Filed: May 6, 2008
    Publication date: November 12, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090281923
    Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.
    Type: Application
    Filed: May 6, 2008
    Publication date: November 12, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090281973
    Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes receiving recommendation boost instructions, receiving a request for one or more recommendations, receiving a set of recommendations from one or more automated recommendation systems, with each recommendation system utilizing selection models or user models and modifying the set of product recommendations according to the recommendation boost instructions.
    Type: Application
    Filed: May 6, 2008
    Publication date: November 12, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090198554
    Abstract: A process for identifying a subset of users for which a non-competitive advertisement is relevant includes the steps of generating a plurality of user models including user-specific data, identifying a subset of the plurality of user models by applying an advertisement-specific selection model to identify users for which a specific advertisement is relevant and applying a non-competitive rule set to the identified user models to identify which user models are associated with one or more non-competitive originating retailers.
    Type: Application
    Filed: February 1, 2008
    Publication date: August 6, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090198555
    Abstract: A process for providing cooperative electronic advertising includes the steps of generating a user model associated with an originating retailer, receiving requests to advertise products sold by originating retailers, identifying products by applying advertisement-specific selection models to the user model to identify which of the products is relevant and communicating an advertisement for the products to the user such that the user receives a communication that appears to have been sent by the originating retailer.
    Type: Application
    Filed: February 1, 2008
    Publication date: August 6, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090198556
    Abstract: A process for selecting personalized non-competitive electronic advertising from a plurality of competitive and non-competitive advertisements includes the steps of generating a selection model based on product data and user-specific data, generating a user model using the user-specific data and selecting non-competitive personalized electronic advertising from the plurality of advertisements using the selection model and user model to identify relevant advertisements and using a rule set for identifying advertisements not competitive to the specific retailer.
    Type: Application
    Filed: February 1, 2008
    Publication date: August 6, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090198551
    Abstract: A process of selecting personalized non-competitive electronic advertising from a plurality of competitive and non-competitive advertisements for electronic display including the steps of generating a selection model, generating a user model, selecting personalized non-competitive electronic advertising from the plurality of advertisements using the selection model and user model to identify relevant advertisements and using a rule set for identifying non-competitive advertisements and providing in an electronic format identified relevant and non-competitive advertisements. An arrangement for the same includes memory for storing a selection model and a user and a controller that selects personalized non-competitive electronic advertising from the plurality of advertisements using the selection model and user model to identify relevant advertisements and using a rule set for identifying non-competitive advertisements.
    Type: Application
    Filed: February 1, 2008
    Publication date: August 6, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090199233
    Abstract: A process for generating a selection model to be used in providing personalized non-competitive advertising including the steps of collecting data from advertising retail websites regarding product data, collecting data from the originating retail websites regarding user behavior, generating a selection model based on the product data and the transactional data and using the selection model to generate personalized non-competitive advertisements for presentation. An arrangement for the same includes memory for storing product data collected from advertising retail websites and data colleted from originating retail websites regarding user behavior and a controller that generates a selection model based on the product data and the transactional data, wherein the selection model includes data sets identifying similar and popular products, and uses the selection model to generate personalized non-competitive advertisements for presentation to one or more of the users for which user behavior has been collected.
    Type: Application
    Filed: February 1, 2008
    Publication date: August 6, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090198552
    Abstract: A process for identifying a subset of users for which a cooperative electronic advertising is relevant includes the steps of generating user models associated with originating retailers, receiving a request to advertise products related to products sold by the retailers, identifying user models by applying an advertisement specific selection model and communicating the advertising to the identified users such that each user receives a communication apparently sent by the originating retailer.
    Type: Application
    Filed: February 1, 2008
    Publication date: August 6, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo
  • Publication number: 20090198553
    Abstract: A process for generating a user model to be used in providing personalized advertisements to a specific user includes the steps of collecting user-specific data while the specific user is interacting with any of a plurality of retailers. The process further includes generating a user model for the specific user utilizing the user-specific data and using the model to generate a personalized advertisement for presentation to the user. An arrangement for generating a user model to be used in providing personalized advertisements to a specific user includes memory for storing user-specific data collected from user interactions with any of a plurality of retailers and a controller that generates a user model for the specific user utilizing the user-specific data and uses the model to generate a personalized advertisement for presentation to one of the users for which user-specific data has been collected.
    Type: Application
    Filed: February 1, 2008
    Publication date: August 6, 2009
    Inventors: David Selinger, Tyler Kohn, Michael DeCourcey, Sundeep Ahuja, James Osial, Albert Sunwoo