Patents by Inventor Atanu R. Sinha
Atanu R. Sinha 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: 12340333Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality”—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle.Type: GrantFiled: March 14, 2022Date of Patent: June 24, 2025Assignee: Adobe Inc.Inventors: Arpit Ajay Narechania, Fan Du, Atanu R. Sinha, Ryan A. Rossi, Jane Elizabeth Hoffswell, Shunan Guo, Eunyee Koh, John Anderson, Sonali Surange, Saurabh Mahapatra, Vasanthi Holtcamp
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Publication number: 20250200608Abstract: Techniques for joint optimization of user segments and delivery channels are described. In one aspect, a method, includes obtaining activity data from a user device associated with a user, selecting, using a selector of a machine learning model, a user segment for the user based on the activity data, mapping, using a mapping function of the machine learning model, activity data for the user segment to features defined by multiple media channels, each media channel assigned a resource component, generating, using an objective predictor of the machine learning model, an objective prediction for the user segment based on the features and resource components of the media channels, the objective prediction identifying a media channel from the multiple media channels with a composite scalar metric above a defined threshold, and providing content to the user device via the media channel. Other embodiments are described and claimed.Type: ApplicationFiled: December 18, 2023Publication date: June 19, 2025Applicant: Adobe Inc.Inventors: Sunav Choudhary, Atanu R. Sinha, Harshita Chopra, Ryan A. Rossi, Veda Pranav Parwatala, Paavan Kumar Indela, Srinjayee Paul, Shunan Guo
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Patent number: 12294755Abstract: Systems and methods for identifying key moments, such as key moments within a livestream, are described. Embodiments of the present disclosure obtain video data and text data. In some cases, the text data is aligned with a timeline of the video data. The system then computes a moment importance score for a time of the video data using a machine learning model based on the video data and the text data, and presents content to a user at the time of the video data based on the moment importance score.Type: GrantFiled: February 28, 2023Date of Patent: May 6, 2025Assignee: ADOBE INC.Inventors: Sunav Choudhary, Atanu R. Sinha, Sarthak Chakraborty, Sai Shashank Kalakonda, Liza Dahiya, Purnima Grover, Kartavya Jain
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Publication number: 20250061488Abstract: Systems and methods for delivery aware audience segmentation and subsequent delivery of content are described. Embodiments are configured to obtain activity data for a user, assign the user to a user segment based on the activity data using a machine learning model, generate a reach prediction for the user segment, select a media channel for communicating with the user based on the user segment and the reach prediction, and provide targeted content to the user via the selected media channel. According to some aspects, the machine learning model is trained based on content reach data.Type: ApplicationFiled: August 17, 2023Publication date: February 20, 2025Inventors: Atanu R. Sinha, Ryan A. Rossi, Sunav Choudhary, Harshita Chopra, Paavan Indela, Veda Pranav Parwatala, Srinjayee Paul, Saurabh Mahapatra, Aurghya Maiti
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Patent number: 12206925Abstract: Systems and methods for content customization are provided. One aspect of the systems and methods includes receiving dynamic characteristics for a plurality of users, wherein the dynamic characteristics include interactions between the plurality of users and a digital content channel; clustering the plurality of users in a plurality of segments based on the dynamic characteristics using a machine learning model; assigning a user to a segment of the plurality of segments based on static characteristics of the user; and providing customized digital content for the user based on the segment.Type: GrantFiled: July 20, 2022Date of Patent: January 21, 2025Assignee: ADOBE INC.Inventors: Atanu R. Sinha, Aurghya Maiti, Atishay Ganesh, Saili Myana, Harshita Chopra, Sarthak Kapoor, Saurabh Mahapatra
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Patent number: 12182829Abstract: A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.Type: GrantFiled: June 24, 2022Date of Patent: December 31, 2024Assignee: Adobe Inc.Inventors: Sarthak Chakraborty, Sunav Choudhary, Atanu R. Sinha, Sapthotharan Krishnan Nair, Manoj Ghuhan Arivazhagan, Yuvraj, Atharva Anand Joshi, Atharv Tyagi, Shivi Gupta
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Patent number: 12124948Abstract: In some embodiments, a computing system computes, with a state prediction model, probabilities of transitioning from a click state represented by interaction data to various predicted next states. The computing system computes an interface experience metric for the click with an experience valuation model. To do so, the computing system identifies base values for the click state and the predicted next states. The computing system computes value differentials for between the click state's base value and each predicted next state's base value. Value differentials indicate qualities of interface experience. The computing system determines the interface experience metric from a summation that includes the current click state's base value and the value differentials weighted with the predicted next states' probabilities.Type: GrantFiled: April 21, 2021Date of Patent: October 22, 2024Assignee: ADOBE INC.Inventors: Atanu R. Sinha, Deepali Jain, Nikhil Sheoran, Deepali Gupta, Sopan Khosla
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Publication number: 20240303176Abstract: A computing resource allocation system receives entity resource usage data describing computing resource usage of an executable service platform by an entity as part of a first allocation generated using a first allocation mechanism. A computing resource allocation system generates an entity resource model based on the entity resource usage data of the computing resource usage of the executable service platform as part of the first allocation mechanism. A computing resource allocation system simulates computing resource usage of the executable service platform by the entity as part of a second allocation mechanism based on the entity resource model and the entity resource usage data. A computing resource allocation system estimates a second allocation to provide to the entity based on the simulating.Type: ApplicationFiled: March 6, 2023Publication date: September 12, 2024Applicant: Adobe Inc.Inventors: Raunak Shah, Shiv Kumar Saini, Atanu R. Sinha
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Patent number: 12086646Abstract: In implementations of systems for cloud-based resource allocation using meters, a computing device implements a resource system to receive resource data describing an amount of cloud-based resources reserved for consumption by client devices during a period of time and a total amount of cloud-based resources consumed by the client devices during the period of time. The resource system determines a consumption distribution using each meter included in a set of meters. Each of the consumption distributions allocates a portion of the total amount of the cloud-based resources consumed to each client device of the client devices. A particular meter used to determine a particular consumption distribution is selected based on a Kendall Tau coefficient of the particular consumption distribution. An amount of cloud-based resources to allocate for a future period of time is estimated using the particular meter and an approximate Shapley value.Type: GrantFiled: February 17, 2022Date of Patent: September 10, 2024Assignee: Adobe Inc.Inventors: Atanu R. Sinha, Shiv Kumar Saini, Sapthotharan Krishnan Nair, Saarthak Sandip Marathe, Manupriya Gupta, Brahmbhatt Paresh Anand, Ayush Chauhan
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Publication number: 20240292046Abstract: Systems and methods for identifying key moments, such as key moments within a livestream, are described. Embodiments of the present disclosure obtain video data and text data. In some cases, the text data is aligned with a timeline of the video data. The system then computes a moment importance score for a time of the video data using a machine learning model based on the video data and the text data, and presents content to a user at the time of the video data based on the moment importance score.Type: ApplicationFiled: February 28, 2023Publication date: August 29, 2024Inventors: Sunav Choudhary, Atanu R. Sinha, Sarthak Chakraborty, Sai Shashank Kalakonda, Liza Dahiya, Purnima Grover, Kartavya Jain
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Publication number: 20240232702Abstract: One aspect of a method for data processing includes identifying target time series data for a target metric and candidate time series data for a plurality of indicators predictive of the target metric; training a machine learning model to predict the target time series data based on the candidate time series data; computing first through third predictivity values based on the machine learning model, wherein the first predictivity value indicates that a source indicator from the plurality of indicators is predictive of the target metric, the second predictivity value indicates that an intermediate indicator from the plurality of indicators is predictive of the target metric, and the third predictivity value indicates that the source indicator is predictive of the intermediate indicator; and displaying a portion of the candidate time series data corresponding to the intermediate indicator and the source indicator based on the first through third predictivity values.Type: ApplicationFiled: January 11, 2023Publication date: July 11, 2024Inventors: Aurghya Maiti, Iftikhar Ahamath Burhanuddin, Atanu R. Sinha, Saurabh Mahapatra, Fan Du
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Publication number: 20240232775Abstract: In some examples, an environment evaluation system accesses interaction data recording interactions by users with an online platform hosted by a host system and computes, based on the interaction data, interface experience metrics. The interface experience metrics includes an individual experience metric for each user and a transition experience metric for each transition in the interactions by the users with the online platform. The environment evaluation system identifies a user with the individual experience metric below a pre-determined threshold, identifies a transition performed by the user that has a transition experience metric below a second threshold, and analyzes the transition to determine users who have performed the transition. The environment evaluation system updates the host system with the individual experience metrics and the transition metrics, based on which the host system can perform modifications of interface elements of the online platform to improve the experience.Type: ApplicationFiled: October 19, 2022Publication date: July 11, 2024Inventors: Atanu R. Sinha, Shiv Kumar Saini, Prithvi Bhutani, Nikhil Sheoran, Kevin Cobourn, Jeff D. Chasin, Fan Du, Eric Matisoff
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Publication number: 20240135296Abstract: In some examples, an environment evaluation system accesses interaction data recording interactions by users with an online platform hosted by a host system and computes, based on the interaction data, interface experience metrics. The interface experience metrics includes an individual experience metric for each user and a transition experience metric for each transition in the interactions by the users with the online platform. The environment evaluation system identifies a user with the individual experience metric below a pre-determined threshold, identifies a transition performed by the user that has a transition experience metric below a second threshold, and analyzes the transition to determine users who have performed the transition. The environment evaluation system updates the host system with the individual experience metrics and the transition metrics, based on which the host system can perform modifications of interface elements of the online platform to improve the experience.Type: ApplicationFiled: October 18, 2022Publication date: April 25, 2024Inventors: Atanu R. Sinha, Shiv Kumar Saini, Prithvi Bhutani, Nikhil Sheoran, Kevin Cobourn, Jeff D. Chasin, Fan Du, Eric Matisoff
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Patent number: 11954309Abstract: In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.Type: GrantFiled: May 4, 2020Date of Patent: April 9, 2024Assignee: Adobe Inc.Inventors: Amit Doda, Gaurav Sinha, Kai Yeung Lau, Akangsha Sunil Bedmutha, Shiv Kumar Saini, Ritwik Sinha, Vaidyanathan Venkatraman, Niranjan Shivanand Kumbi, Omar Rahman, Atanu R. Sinha
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Patent number: 11886964Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.Type: GrantFiled: May 17, 2021Date of Patent: January 30, 2024Assignee: ADOBE INC.Inventors: Atanu R. Sinha, Xiang Chen, Sungchul Kim, Omar Rahman, Jean Bernard Hishamunda, Goutham Srivatsav Arra, Shiv Kumar Saini
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Publication number: 20240031631Abstract: Systems and methods for content customization are provided. One aspect of the systems and methods includes receiving dynamic characteristics for a plurality of users, wherein the dynamic characteristics include interactions between the plurality of users and a digital content channel; clustering the plurality of users in a plurality of segments based on the dynamic characteristics using a machine learning model; assigning a user to a segment of the plurality of segments based on static characteristics of the user; and providing customized digital content for the user based on the segment.Type: ApplicationFiled: July 20, 2022Publication date: January 25, 2024Inventors: Atanu R. Sinha, Aurghya Maiti, Atishay Ganesh, Saili Myana, Harshita Chopra, Sarthak Kapoor, Saurabh Mahapatra
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Publication number: 20230419339Abstract: A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.Type: ApplicationFiled: June 24, 2022Publication date: December 28, 2023Inventors: Sarthak Chakraborty, Sunav Choudhary, Atanu R. Sinha, Sapthotharan Krishnan Nair, Manoj Ghuhan Arivazhagan, Yuvraj, Atharva Anand Joshi, Atharv Tyagi, Shivi Gupta
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Publication number: 20230342799Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for incorporating unobserved behaviors when generating user segments or predictions of future user actions. In particular, in one or more embodiments, the disclosed systems utilize a deep learning-based clustering algorithm that segments the behavioral history of users based on a future outcome. Further, the disclosed systems recognize that users may exhibit behaviors that represent two or more segments and allow for targeted marketing to users based on the user’s inclusion in multiple segments.Type: ApplicationFiled: April 25, 2022Publication date: October 26, 2023Inventors: Aurghya Maiti, Atanu R Sinha, Harshita Chopra, Sarthak Kapoor, Atishay Ganesh, Saili Myana, Saurabh Mahapatra
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Publication number: 20230306033Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality” - the condition of data (e.g., presence of incorrect or incomplete values), its “consumption” - the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility” - a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle.Type: ApplicationFiled: March 14, 2022Publication date: September 28, 2023Inventors: Arpit Ajay Narechania, Fan Du, Atanu R. Sinha, Ryan A. Rossi, Jane Elizabeth Hoffswell, Shunan Guo, Eunyee Koh, John Anderson, Sonali Surange, Saurabh Mahapatra, Vasanthi Holtcamp
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Publication number: 20230289696Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality”—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle.Type: ApplicationFiled: March 14, 2022Publication date: September 14, 2023Inventors: Arpit Ajay Narechania, Fan Du, Atanu R. Sinha, Ryan A. Rossi, Jane Elizabeth Hoffswell, Shunan Guo, Eunyee Koh, John Anderson, Sonali Surange, Saurabh Mahapatra, Vasanthi Holtcamp