Patents by Inventor Sundeep Parsa
Sundeep Parsa 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: 11816696Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.Type: GrantFiled: June 23, 2021Date of Patent: November 14, 2023Assignee: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nupur Kumari, Nikaash Puri, Mayank Singh, Eshita Shah, Balaji Krishnamurthy, Akash Rupela
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Publication number: 20210319473Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.Type: ApplicationFiled: June 23, 2021Publication date: October 14, 2021Applicant: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nupur Kumari, Nikaash Puri, Mayank Singh, Eshita Shah, Balaji Krishnamurthy, Akash Rupela
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Patent number: 11109084Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).Type: GrantFiled: November 25, 2019Date of Patent: August 31, 2021Assignee: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nikaash Puri, Eshita Shah, Balaji Krishnamurthy, Nupur Kumari, Mayank Singh, Akash Rupela
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Patent number: 11107115Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.Type: GrantFiled: August 7, 2018Date of Patent: August 31, 2021Assignee: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nupur Kumari, Nikaash Puri, Mayank Singh, Eshita Shah, Balaji Krishnamurthy, Akash Rupela
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Patent number: 10609434Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).Type: GrantFiled: August 7, 2018Date of Patent: March 31, 2020Assignee: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nikaash Puri, Eshita Shah, Balaji Krishnamurthy, Nupur Kumari, Mayank Singh, Akash Rupela
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Publication number: 20200092593Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).Type: ApplicationFiled: November 25, 2019Publication date: March 19, 2020Applicant: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nikaash Puri, Eshita Shah, Balaji Krishnamurthy, Nupur Kumari, Mayank Singh, Akash Rupela
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Publication number: 20200053403Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).Type: ApplicationFiled: August 7, 2018Publication date: February 13, 2020Applicant: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nikaash Puri, Eshita Shah, Balaji Krishnamurthy, Nupur Kumari, Mayank Singh, Akash Rupela
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Publication number: 20200051118Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.Type: ApplicationFiled: August 7, 2018Publication date: February 13, 2020Applicant: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nupur Kumari, Nikaash Puri, Mayank Singh, Eshita Shah, Balaji Krishnamurthy, Akash Rupela
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Publication number: 20140278620Abstract: Disclosed is an improved approach for performing opportunity association and attribution analysis. An advanced rules framework is provided to automatically infer associations between a set of marketing data and a set of sales data, allowing advanced revenue funnel analytics to be performed to measure marketing campaigns.Type: ApplicationFiled: March 14, 2013Publication date: September 18, 2014Applicant: ORACLE INTERNATIONAL CORPORATIONInventors: Arif Rashid Khan, Justin Thomas Anderson, Sundeep Parsa