Patents by Inventor Karthik Kumara
Karthik Kumara 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).
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Patent number: 11042898Abstract: Technology for predicting online user shopping behavior, such as whether a user will purchase a product, is described. An example method includes receiving current session data describing a current session for a current user, extracting a current clickstream from the current session data classifying the current clickstream as a purchase clickstream or a non-purchase clickstream by processing the current clickstream using one or more sets of Hidden Markov Model parameters produced by one or more Hidden Markov Models, and computing, using the one or more computing devices, a purchase probability that the current user will purchase a product during the current session based on the classifying.Type: GrantFiled: December 23, 2014Date of Patent: June 22, 2021Assignee: Staples, Inc.Inventors: Courosh Mehanian, Tchavdar Dangaltchev, Karthik Kumara, Jing Pan, Timothy Wee
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Patent number: 10796337Abstract: The disclosure relates in some cases to a technology for selecting one or more promotions to be presented to online customers using Bayesian bandits and affinity-based dynamic user clustering In some embodiments, a computer-implemented method determines a set of offers is determined, and computes affinity scores measuring affinities of users to items included in the offers. The method builds an affinity score distribution for the offers and identifies clusters of affinity scores for the offers using the corresponding affinity score distribution.Type: GrantFiled: December 28, 2015Date of Patent: October 6, 2020Assignee: STAPLES, INC.Inventors: Timothy Wee, Karthik Kumara, Ryan Applegate, Majid Hosseini
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Patent number: 10713560Abstract: A computer-implemented method and system are described for learning a vector representation for unique identification codes. An example method may include generating a unique identification code list using one or more virtual interaction contexts, the unique identification code list being a list of unique identification codes, selecting a target unique identification code in the unique identification code list, and determining, from the unique identification code list, an input set of unique identification codes using the target unique identification code, the input set including the target unique identification code and one or more context unique identification codes. Some implementations may further include inputting the input set of unique identification codes into a semantic neural network model, the semantic neural network model including one or more weight matrices, and modifying the one or more weight matrices using the input set of unique identification codes.Type: GrantFiled: July 7, 2016Date of Patent: July 14, 2020Assignee: Staples, Inc.Inventors: Ryan Applegate, Majid Hosseini, Karthik Kumara, Timothy Wee
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Patent number: 10580035Abstract: Technology for selecting promotion(s) to display in a page of an application for display to a user is described. An example method includes determining a promotion for a product; calculating for the promotion a posterior distribution of a user-action probability reflecting estimates for a user response to a display of the promotion for the product on a computing device of the user; determining the posterior distribution as collapsing beyond a certain threshold; responsive thereto, calculating an uncollapsed posterior distribution of the user-action probability reflecting modified estimates for the user response to the display of the promotion for the product on a computing device of the user; storing the uncollapsed posterior distribution of the user-action probability in a response database; and determining whether to select the promotion from the promotion database for display on a computing device of the user based on the modified estimates.Type: GrantFiled: May 27, 2015Date of Patent: March 3, 2020Assignee: Staples, Inc.Inventors: Courosh Mehanian, Timothy Wee, Karthik Kumara
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Patent number: 10198762Abstract: Technology for determining the order of the search results to maximize a financial goal is described. In an example embodiment, a method, implemented using the one or more computing devices, such as client and/or server devices, may receive a product search request from a user device associated with a user and retrieve a set of products from a product database based on the product search request. Based on a purchase probability and one or more of a margin and a price for that product, the method determines an expected financial gain for each of the products of the set and sorts the set of products into an ordered set of products having an order based on the expected financial gain associated with each of the products. The method may then provide the ordered set of products for display to the user on the user device.Type: GrantFiled: December 23, 2014Date of Patent: February 5, 2019Assignee: STAPLES, INC.Inventors: Tchavdar Dangaltchev, Timothy Wee, Karthik Kumara
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Publication number: 20170185585Abstract: A computer-implemented method and system are described for learning a vector representation for unique identification codes. An example method may include generating a unique identification code list using one or more virtual interaction contexts, the unique identification code list being a list of unique identification codes, selecting a target unique identification code in the unique identification code list, and determining, from the unique identification code list, an input set of unique identification codes using the target unique identification code, the input set including the target unique identification code and one or more context unique identification codes. Some implementations may further include inputting the input set of unique identification codes into a semantic neural network model, the semantic neural network model including one or more weight matrices, and modifying the one or more weight matrices using the input set of unique identification codes.Type: ApplicationFiled: July 7, 2016Publication date: June 29, 2017Inventors: Ryan Applegate, Majid Hosseini, Karthik Kumara, Timothy Wee
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Publication number: 20170061481Abstract: The disclosure relates in some cases to a technology for selecting one or more promotions to be presented to online customers using Bayesian bandits and affinity-based dynamic user clustering In some embodiments, a computer-implemented method determines a set of offers is determined, and computes affinity scores measuring affinities of users to items included in the offers. The method builds an affinity score distribution for the offers and identifies clusters of affinity scores for the offers using the corresponding affinity score distribution.Type: ApplicationFiled: December 28, 2015Publication date: March 2, 2017Inventors: Timothy Wee, Karthik Kumara, Ryan Applegate, Majid Hosseini
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Publication number: 20160350802Abstract: Technology for selecting promotion(s) to display in a page of an application for display to a user is described. An example method includes determining a promotion for a product; calculating for the promotion a posterior distribution of a user-action probability reflecting estimates for a user response to a display of the promotion for the product on a computing device of the user; determining the posterior distribution as collapsing beyond a certain threshold; responsive thereto, calculating an uncollapsed posterior distribution of the user-action probability reflecting modified estimates for the user response to the display of the promotion for the product on a computing device of the user; storing the uncollapsed posterior distribution of the user-action probability in a response database; and determining whether to select the promotion from the promotion database for display on a computing device of the user based on the modified estimates.Type: ApplicationFiled: May 27, 2015Publication date: December 1, 2016Inventors: Courosh Mehanian, Timothy Wee, Karthik Kumara
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Publication number: 20160267377Abstract: Technology for semantically processing user-submitted text and determining probabilities using computer learning model(s) is described. In an embodiment, a method, implemented using a computing device, may include receiving data including user-submitted product review(s) for a product. A product review includes review text and the method determines attributes of the product review text and feed the attributes of the product review text into a first hidden layer of an artificial neural network based on attribute type, feeding the first output of the first hidden layer of the neural network into a second hidden layer of the artificial neural network based on an association of the attributes of the product review with one or more of a story, a function, and a sentiment, and determining a predicted probability of recommendation of the review based on the second output of the second layer.Type: ApplicationFiled: March 11, 2016Publication date: September 15, 2016Inventors: Jing Pan, Karthik Kumara
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Publication number: 20160148233Abstract: Technology for determining an optimal discounted price for a customer for a particular product is described. In an example embodiment, a method comprises determining a number of visits to a product page of a particular product by customers, calculating a purchase probability of a customer to purchase the particular product associated with the product page as a function of a price discount, determining a discount-corrected margin specific to the customer for the particular product based on the purchase probability of the customer, and calculating a predicted profit or a predicted revenue for the particular product resulting from the number of visits to the product page and based on the purchase probability and the discount-corrected margin of the particular product.Type: ApplicationFiled: November 21, 2014Publication date: May 26, 2016Inventors: Tchavdar Dangaltchev, Courosh Mehanian, Karthik Kumara, Timothy Wee
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Publication number: 20150269609Abstract: Technology for predicting online user shopping behavior, such as whether a user will purchase a product, is described. An example method includes receiving current session data describing a current session for a current user, extracting a current clickstream from the current session data classifying the current clickstream as a purchase clickstream or a non-purchase clickstream by processing the current clickstream using one or more sets of Hidden Markov Model parameters produced by one or more Hidden Markov Models, and computing, using the one or more computing devices, a purchase probability that the current user will purchase a product during the current session based on the classifying.Type: ApplicationFiled: December 23, 2014Publication date: September 24, 2015Inventors: Courosh Mehanian, Tchavdar Dangaltchev, Karthik Kumara, Jing Pan, Timothy Wee