Patents by Inventor Lawrence Lee Wai
Lawrence Lee Wai 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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Publication number: 20210166277Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers.Type: ApplicationFiled: December 9, 2020Publication date: June 3, 2021Inventor: Lawrence Lee Wai
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Patent number: 10977694Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers.Type: GrantFiled: January 2, 2019Date of Patent: April 13, 2021Assignee: Groupon, Inc.Inventor: Lawrence Lee Wai
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Publication number: 20210090127Abstract: A computer-executable method, a computer system and a non-transitory computer-readable medium are provided for causing electronic marketing communications of one or more promotions to be generated on a mobile computing device associated with a consumer. A method includes programmatically retrieving promotion data indicative of a plurality of promotions from a computer memory. The method includes determining, using processing circuitry, a promotion score for each of the plurality of promotions. Each promotion score is determined based on consumer profile data, stored consumer activity data, and at least one of: current consumer activity data, current local context data, or predicted consumer activity data. The method further includes outputting indications configured to generate electronic marketing communications associated with the plurality of promotions based on the promotion scores of the plurality of promotions.Type: ApplicationFiled: October 2, 2020Publication date: March 25, 2021Inventors: Don Albert CHENNAVASIN, Lawrence Lee WAI, Hamish BARNEY, Devdatta GANGAL, Daniel BEARD, Valampuri LAKSHMINARAYANAN, Michael BURTON
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Publication number: 20210090119Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system based on an analysis of previous consumer behavior. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving data representing a user, the data including user identification and historical data; receiving a set of promotions recommended for the user; assigning the user to a consumer lifecycle model state based in part on the historical data and the user identification; selecting a ranking algorithm associated with the consumer lifecycle model state; and ranking the received set of promotions based on a predicted promotion relevance value associated with each promotion, the predicted promotion value being calculated using the ranking algorithm.Type: ApplicationFiled: October 2, 2020Publication date: March 25, 2021Inventor: Lawrence Lee Wai
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Patent number: 10902477Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers.Type: GrantFiled: January 2, 2019Date of Patent: January 26, 2021Assignee: Groupon, Inc.Inventor: Lawrence Lee Wai
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Patent number: 10891658Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers.Type: GrantFiled: January 2, 2019Date of Patent: January 12, 2021Assignee: Groupon, Inc.Inventor: Lawrence Lee Wai
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Publication number: 20200364743Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.Type: ApplicationFiled: May 27, 2020Publication date: November 19, 2020Inventors: Boris Lerner, Sunil Ramnik Raiyani, Lawrence Lee Wai
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Patent number: 10832290Abstract: A computer-executable method, a computer system and a non-transitory computer-readable medium are provided for causing electronic marketing communications of one or more promotions to be generated on a mobile computing device associated with a consumer. A method includes programmatically retrieving promotion data indicative of a plurality of promotions from a computer memory. The method includes determining, using processing circuitry, a promotion score for each of the plurality of promotions. Each promotion score is determined based on consumer profile data, stored consumer activity data, and at least one of: current consumer activity data, current local context data, or predicted consumer activity data. The method further includes outputting indications configured to generate electronic marketing communications associated with the plurality of promotions based on the promotion scores of the plurality of promotions.Type: GrantFiled: December 19, 2014Date of Patent: November 10, 2020Assignee: GROUPON, INC.Inventors: Don Albert Chennavasin, Lawrence Lee Wai, Hamish Barney, Devdatta Gangal, Daniel Beard, Valampuri Lakshminarayanan, Michael Burton
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Patent number: 10825046Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system based on an analysis of previous consumer behavior. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving data representing a user, the data including user identification and historical data; receiving a set of promotions recommended for the user; assigning the user to a consumer lifecycle model state based in part on the historical data and the user identification; selecting a ranking algorithm associated with the consumer lifecycle model state; and ranking the received set of promotions based on a predicted promotion relevance value associated with each promotion, the predicted promotion value being calculated using the ranking algorithm.Type: GrantFiled: May 3, 2019Date of Patent: November 3, 2020Assignee: GROUPON, INC.Inventor: Lawrence Lee Wai
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Publication number: 20200250700Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.Type: ApplicationFiled: February 6, 2020Publication date: August 6, 2020Inventor: Lawrence Lee Wai
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Patent number: 10706439Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.Type: GrantFiled: November 29, 2017Date of Patent: July 7, 2020Assignee: GROUPON, INC.Inventors: Boris Lerner, Sunil Ramnik Raiyani, Lawrence Lee Wai
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Publication number: 20200143429Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media configured to use a per-set level optimization of the rank order of promotions to be recommended to a consumer. In some embodiments, machine learning is used offline to generate a predictive diversity model that receives one or more similarity rank features associated with a promotion (e.g., category, price band) as input, and produces an output multiplier to be applied to the promotion's respective associated relevance score (e.g., a relevance score representing a prediction of the promotion's conversion rate without diversity features). At run time, per-set optimization of the ordering of a set of promotions is implemented by adjusting the respective associated relevance scores of the promotions using the diversity model and then re-ordering the set of promotions based on their respective adjusted relevance scores.Type: ApplicationFiled: October 31, 2019Publication date: May 7, 2020Inventor: Lawrence Lee Wai
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Patent number: 10592918Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.Type: GrantFiled: May 1, 2019Date of Patent: March 17, 2020Assignee: GROUPON, INC.Inventor: Lawrence Lee Wai
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Publication number: 20200034879Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system based on an analysis of previous consumer behavior. One aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving data representing a user, the data including user identification and historical data; receiving a set of promotions recommended for the user; assigning the user to a consumer lifecycle model state based in part on the historical data and the user identification; selecting a ranking algorithm associated with the consumer lifecycle model state; and ranking the received set of promotions based on a predicted promotion relevance value associated with each promotion, the predicted promotion value being calculated using the ranking algorithm.Type: ApplicationFiled: May 3, 2019Publication date: January 30, 2020Inventor: Lawrence Lee Wai
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Patent number: 10529000Abstract: Systems and methods for automatically tagging product for an e-commerce web application and providing product recommendations. Product information related to products is stored and the products are searchable via search queries. Results for the search queries are generated. Interactions of the users with the results for the search queries are monitored. Semantic tags are associated with the products based on the search queries and the results for the search queries. Weighted links between the products and the semantic tags are determined based on the interactions of the users with the results for the search queries. Users' interactions with the product information and/or the product are monitored and user links between the semantic tags and the users are determined based on the weighted links between the products and the semantic tags and the users' interactions. Product recommendations are determined based on the user links and the weighted links.Type: GrantFiled: February 22, 2017Date of Patent: January 7, 2020Assignee: Udemy, Inc.Inventors: Beliz Gokkaya, Lawrence Lee Wai
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Patent number: 10497025Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media configured to use a per-set level optimization of the rank order of promotions to be recommended to a consumer. In some embodiments, machine learning is used offline to generate a predictive diversity model that receives one or more similarity rank features associated with a promotion (e.g., category, price band) as input, and produces an output multiplier to be applied to the promotion's respective associated relevance score (e.g., a relevance score representing a prediction of the promotion's conversion rate without diversity features). At run time, per-set optimization of the ordering of a set of promotions is implemented by adjusting the respective associated relevance scores of the promotions using the diversity model and then re-ordering the set of promotions based on their respective adjusted relevance scores.Type: GrantFiled: May 11, 2015Date of Patent: December 3, 2019Assignee: Groupon, Inc.Inventor: Lawrence Lee Wai
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Publication number: 20190325475Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.Type: ApplicationFiled: May 1, 2019Publication date: October 24, 2019Inventor: Lawrence Lee Wai
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Publication number: 20190318390Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers.Type: ApplicationFiled: January 2, 2019Publication date: October 17, 2019Inventor: Lawrence Lee Wai
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Patent number: 10438229Abstract: Systems and related methods of providing promotional offers to consumers are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to provide promotional offers to consumers based on dimensions representing criteria by which promotions may be deemed relevant to a consumer. Some examples of dimensions may include location, time, environment, price, and/or consumer preference. Based on receiving signals from the consumer device, among other sources, indicating associated times, locations, and other characteristics of consumer activity, the apparatus may recognize patterns or trends in consumer behavior, and use such information to predict or influence future consumer behavior.Type: GrantFiled: June 30, 2015Date of Patent: October 8, 2019Assignee: GROUPON, INC.Inventors: Sridatta Viswanath, Amber Roy Chowdhury, Roger Henry Castillo, Sri Subramaniam, Lawrence Lee Wai, Bhupesh Bansal, Vijay Kumar
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Publication number: 20190279253Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers.Type: ApplicationFiled: January 2, 2019Publication date: September 12, 2019Inventor: Lawrence Lee Wai