Patents by Inventor Tathagata Sengupta
Tathagata Sengupta 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: 11861664Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.Type: GrantFiled: September 29, 2022Date of Patent: January 2, 2024Assignee: Adobe Inc.Inventors: Anirban Basu, Tathagata Sengupta, Kunal Kumar Jain, Ashish Kumar
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Publication number: 20230021653Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.Type: ApplicationFiled: September 29, 2022Publication date: January 26, 2023Applicant: Adobe Inc.Inventors: Anirban Basu, Tathagata Sengupta, Kunal Kumar Jain, Ashish Kumar
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Patent number: 11494810Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.Type: GrantFiled: August 29, 2019Date of Patent: November 8, 2022Assignee: Adobe Inc.Inventors: Anirban Basu, Tathagata Sengupta, Kunal Kumar Jain, Ashish Kumar
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Patent number: 11295310Abstract: A method, system, and computer program product for fraud detection receive transaction data associated with a plurality of transactions; determine, based on the transaction data, that two or more consecutive transactions associated with a same account identifier include a value for a same at least one transaction parameter; in response to determining that each of the two or more consecutive transactions associated with the same account identifier include the value for the same at least one transaction parameter, determine a difference between the value for the at least one transaction parameter associated with a first transaction of the two or more consecutive transactions and the value for the at least one transaction parameter associated with a second transaction of the two or more consecutive transactions; and determine, based on the difference, that the two or more consecutive transactions are fraudulent transactions.Type: GrantFiled: February 4, 2020Date of Patent: April 5, 2022Assignee: Visa International Service AssociationInventors: Durga Kala, Tathagata Sengupta, Debabrata Chowdhury, Juharasha Shaik
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Publication number: 20210241278Abstract: A method, system, and computer program product for fraud detection receive transaction data associated with a plurality of transactions; determine, based on the transaction data, that two or more consecutive transactions associated with a same account identifier include a value for a same at least one transaction parameter; in response to determining that each of the two or more consecutive transactions associated with the same account identifier include the value for the same at least one transaction parameter, determine a difference between the value for the at least one transaction parameter associated with a first transaction of the two or more consecutive transactions and the value for the at least one transaction parameter associated with a second transaction of the two or more consecutive transactions; and determine, based on the difference, that the two or more consecutive transactions are fraudulent transactions.Type: ApplicationFiled: February 4, 2020Publication date: August 5, 2021Inventors: Durga Kala, Tathagata Sengupta, Debabrata Chowdhury, Juharasha Shaik
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Publication number: 20210065250Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.Type: ApplicationFiled: August 29, 2019Publication date: March 4, 2021Applicant: Adobe Inc.Inventors: Anirban Basu, Tathagata Sengupta, Kunal Kumar Jain, Ashish Kumar
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Patent number: 10783549Abstract: The present disclosure is directed towards methods and systems for determining a persuasiveness of a content item. The systems and methods receive a content item from a client device and analyze the content item. Analyzing the content item includes analyzing at least one textual element, at least one image element, and at least one layout element of the content item to determine a first persuasion score, a second persuasion score, and a third persuasion score of the elements the content item. The systems and methods also generate a persuasion score of the content item and provide the persuasion score of the content item to the client device.Type: GrantFiled: November 18, 2016Date of Patent: September 22, 2020Assignee: ADOBE INC.Inventors: Moumita Sinha, Varun Gupta, Tathagata Sengupta, Niloy Ganguly, Faran Ahmad
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Publication number: 20180143986Abstract: The present disclosure is directed towards methods and systems for determining a persuasiveness of a content item. The systems and methods receive a content item from a client device and analyze the content item. Analyzing the content item includes analyzing at least one textual element, at least one image element, and at least one layout element of the content item to determine a first persuasion score, a second persuasion score, and a third persuasion score of the elements the content item. The systems and methods also generate a persuasion score of the content item and provide the persuasion score of the content item to the client device.Type: ApplicationFiled: November 18, 2016Publication date: May 24, 2018Inventors: Moumita Sinha, Varun Gupta, Tathagata Sengupta, Niloy Ganguly, Faran Ahmad