Patents by Inventor Somdeb Sarkhel

Somdeb Sarkhel 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).

  • Publication number: 20240134918
    Abstract: Systems and methods for dynamic user profile projection are provided. One or more aspects of the systems and methods includes computing, by a prediction component, a predicted number of lookups for a future time period based on a lookup history of a user profile using a lookup prediction model; comparing, by the prediction component, the predicted number of lookups to a lookup threshold; and transmitting, by a projection component, the user profile to an edge server based on the comparison.
    Type: Application
    Filed: October 23, 2022
    Publication date: April 25, 2024
    Inventors: Nathan Ng, Tung Mai, Thomas Greger, Kelly Quinn Nicholes, Antonio Cuevas, Saayan Mitra, Somdeb Sarkhel, Anup Bandigadi Rao, Ryan A. Rossi, Viswanathan Swaminathan, Shivakumar Vaithyanathan
  • Publication number: 20240134919
    Abstract: Systems and methods for dynamic user profile management are provided. One aspect of the systems and methods includes receiving, by a lookup component, a request for a user profile; computing, by a profile component, a time-to-live (TTL) refresh value for the user profile based on a lookup history of the user profile; updating, by the profile component, a TTL value of the user profile based on the request and the TTL refresh value; storing, by the profile component, the user profile and the updated TTL value in the edge database; and removing, by the edge database, the user profile from the edge database based on the updated TTL value.
    Type: Application
    Filed: October 23, 2022
    Publication date: April 25, 2024
    Inventors: Nathan Ng, Tung Mai, Thomas Greger, Kelly Quinn Nicholes, Antonio Cuevas, Saayan Mitra, Somdeb Sarkhel, Anup Bandigadi Rao, Ryan A. Rossi, Viswanathan Swaminathan, Shivakumar Vaithyanathan
  • Publication number: 20240037149
    Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.
    Type: Application
    Filed: July 29, 2022
    Publication date: February 1, 2024
    Inventors: Somdeb Sarkhel, Xiang Chen, Viswanathan Swaminathan, Swapneel Mehta, Saayan Mitra, Ryan Rossi, Han Guo, Ali Aminian, Kshitiz Garg
  • Publication number: 20240029107
    Abstract: Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.
    Type: Application
    Filed: September 29, 2023
    Publication date: January 25, 2024
    Applicant: Adobe Inc.
    Inventors: Xiang Chen, Viswanathan Swaminathan, Somdeb Sarkhel
  • Patent number: 11810152
    Abstract: Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.
    Type: Grant
    Filed: October 10, 2019
    Date of Patent: November 7, 2023
    Assignee: Adobe Inc.
    Inventors: Xiang Chen, Viswanathan Swaminathan, Somdeb Sarkhel
  • Publication number: 20230139824
    Abstract: Various disclosed embodiments are directed to using one or more algorithms or models to select a suitable or optimal variation, among multiple variations, of a given content item based on feedback. Such feedback guides the algorithm or model to arrive at suitable variation result such that the variation result is produced as the output for consumption by users. Further, various embodiments resolve tedious manual user input requirements and reduce computing resource consumption, among other things, as described in more detail below.
    Type: Application
    Filed: November 4, 2021
    Publication date: May 4, 2023
    Inventors: Trisha Mittal, Viswanathan Swaminathan, Ritwik Sinha, Saayan Mitra, David Arbour, Somdeb Sarkhel
  • Publication number: 20220398230
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.
    Type: Application
    Filed: June 14, 2021
    Publication date: December 15, 2022
    Inventors: Ritwik Sinha, Saayan Mitra, Handong Zhao, Somdeb Sarkhel, Trevor Paulsen, William Brandon George
  • Patent number: 11430219
    Abstract: Systems and methods predict a performance metric for a video and identify key portions of the video that contribute to the performance metric, which can be used to edit the video to improve the ultimate viewer response to the video. An initial performance metric is computed for an initial video (e.g., using a neural network). A perturbed video is generated by perturbing a video portion of the initial video. A modified performance metric is computed for the perturbed video. Based on a difference between the initial and modified performance metrics, the system determines that the video portion contributed to a predicted user viewer response to the initial video. An indication of the video portion that contributed to the predicted user viewer response is provided as output, which can be used to edit the video to improve the predicted viewer response.
    Type: Grant
    Filed: November 19, 2020
    Date of Patent: August 30, 2022
    Assignee: Adobe Inc.
    Inventors: Somdeb Sarkhel, Viswanathan Swaminathan, Stefano Petrangeli, Md Maminur Islam
  • Publication number: 20220156499
    Abstract: Systems and methods predict a performance metric for a video and identify key portions of the video that contribute to the performance metric, which can be used to edit the video to improve the ultimate viewer response to the video. An initial performance metric is computed for an initial video (e.g., using a neural network). A perturbed video is generated by perturbing a video portion of the initial video. A modified performance metric is computed for the perturbed video. Based on a difference between the initial and modified performance metrics, the system determines that the video portion contributed to a predicted user viewer response to the initial video. An indication of the video portion that contributed to the predicted user viewer response is provided as output, which can be used to edit the video to improve the predicted viewer response.
    Type: Application
    Filed: November 19, 2020
    Publication date: May 19, 2022
    Inventors: Somdeb Sarkhel, Viswanathan Swaminathan, Stefano Petrangeli, Md Maminur Islam
  • Publication number: 20210374809
    Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.
    Type: Application
    Filed: August 16, 2021
    Publication date: December 2, 2021
    Inventors: Somdeb Sarkhel, Saayan Mitra, Jiatong Xie, Alok Kothari
  • Patent number: 11127050
    Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.
    Type: Grant
    Filed: November 18, 2019
    Date of Patent: September 21, 2021
    Assignee: Adobe Inc.
    Inventors: Somdeb Sarkhel, Saayan Mitra, Jiatong Xie, Alok Kothari
  • Patent number: 11049041
    Abstract: Techniques are disclosed for training of factorization machines (FMs) using a streaming mode alternating least squares (ALS) optimization. A methodology implementing the techniques according to an embodiment includes receiving a datapoint that includes a feature vector and an associated target value. The feature vector includes user identification, subject matter identification, and a context. The target value identifies an opinion of the user relative to the subject matter. The method further includes applying an FM to the feature vector to generate an estimate of the target value, and updating parameters of the FM for training of the FM. The parameter update is based on application of a streaming mode ALS optimization to: the datapoint; the estimate of the target value; and to an updated summation of intermediate calculated terms generated by application of the streaming mode ALS optimization to previously received datapoints associated with prior parameter updates of the FM.
    Type: Grant
    Filed: April 26, 2018
    Date of Patent: June 29, 2021
    Assignee: Adobe Inc.
    Inventors: Saayan Mitra, Xueyu Mao, Viswanathan Swaminathan, Somdeb Sarkhel, Sheng Li
  • Publication number: 20210150585
    Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.
    Type: Application
    Filed: November 18, 2019
    Publication date: May 20, 2021
    Inventors: Somdeb Sarkhel, Saayan Mitra, Jiatong Xie, Alok Kothari
  • Publication number: 20210110432
    Abstract: Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.
    Type: Application
    Filed: October 10, 2019
    Publication date: April 15, 2021
    Applicant: Adobe Inc.
    Inventors: Xiang Chen, Viswanathan Swaminathan, Somdeb Sarkhel
  • Patent number: 10904599
    Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that determine multiple personas corresponding to a user account for digital content and train a persona classifier to predict a given persona (from among the multiple personas) for content requests associated with the user account. By using the persona classifier, the disclosed methods, non-transitory computer readable media, and systems accurately detect a given persona for a content request upon initiation of the request. Based on determining the given persona, in some implementations, the methods, non-transitory computer readable media, and systems generate a digital-content recommendation for presentation on a client device associated with the user account.
    Type: Grant
    Filed: May 31, 2018
    Date of Patent: January 26, 2021
    Assignee: ADOBE INC.
    Inventors: Somdeb Sarkhel, Viswanathan Swaminathan, Shuo Yang, Saayan Mitra, Lakshmi Shivalingaiah, Jason Boyer, Dwight Rodgers
  • Patent number: 10887640
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing an artificial intelligence framework for generating enhanced digital content and improving digital content campaign design. In particular, the disclosed systems can utilize a metadata neural network, a summarizer neural network, and/or a performance neural network to generate metadata for digital content, predict future performance metrics, generate enhanced digital content, and provide recommended content changes to improve performance upon dissemination to one or more client devices.
    Type: Grant
    Filed: July 11, 2018
    Date of Patent: January 5, 2021
    Assignee: ADOBE INC.
    Inventors: Viswanathan Swaminathan, Somdeb Sarkhel, Saayan Mitra
  • Patent number: 10860858
    Abstract: The present disclosure relates to systems, methods, and computer readable media that utilize a trained multi-modal combination model for content and text-based evaluation and distribution of digital video content to client devices. For example, systems described herein include training and/or utilizing a combination of trained visual and text-based prediction models to determine predicted performance metrics for a digital video. The systems described herein can further utilize a multi-modal combination model to determine a combined performance metric that considers both visual and textual performance metrics of the digital video. The systems described herein can further select one or more digital videos for distribution to one or more client devices based on combined performance metrics associated with the digital videos.
    Type: Grant
    Filed: June 15, 2018
    Date of Patent: December 8, 2020
    Assignee: ADOBE INC.
    Inventors: Viswanathan Swaminathan, Saayan Mitra, Somdeb Sarkhel, Qi Lou
  • Patent number: 10789620
    Abstract: The present disclosure is directed toward systems and methods for identifying user segments. In particular, the systems and methods described herein evaluate user session logs to gather media content consumption history information associated with a plurality of users. Additionally, the systems and methods described herein analyze items of media content to identify keywords, genres, and other attributes, and further represent the items of media content as vectors. The systems and methods follow an algorithm to group items of media content into clusters and, based on the clusters of media content, further group users of media content into user clusters (e.g., user segments).
    Type: Grant
    Filed: February 3, 2017
    Date of Patent: September 29, 2020
    Assignee: ADOBE INC.
    Inventors: Wreetabrata Kar, Viswanathan Swaminathan, Somdeb Sarkhel
  • Publication number: 20200226675
    Abstract: The present disclosure relates to generating digital bids for providing digital content to remote client devices based on parametric bid distributions generated using a machine learning model (e.g., a mixture density network). For example, in response to identifying a digital bid request in a real-time bidding environment, the disclosed systems can utilize a trained parametric censored machine learning model to generate a parametric bid distribution. To illustrate, the disclosed systems can utilize a parametric censored, mixture density machine learning model to analyze bid request characteristics and generate a parametric, multi-modal distribution reflecting a plurality of parametric means, parametric variances, and combination weights. The disclosed systems can then utilize the parametric, multi-modal distribution to generate digital bids in response to the digital bid request in real-time (e.g., while a client device accesses digital assets corresponding to the bid request).
    Type: Application
    Filed: January 15, 2019
    Publication date: July 16, 2020
    Inventors: Saayan Mitra, Aritra Ghosh, Somdeb Sarkhel, Jiatong Xie
  • Patent number: 10685236
    Abstract: A metadata generation system utilizes machine learning techniques to accurately describe content of videos based on multi-model predictions. In some embodiments, multiple feature sets are extracted from a video, including feature sets showing correlations between additional features of the video. The feature sets are provided to a learnable pooling layer with multiple modeling techniques, which generates, for each of the feature sets, a multi-model content prediction. In some cases, the multi-model predictions are consolidated into a combined prediction. Keywords describing the content of the video are determined based on the multi-model predictions (or combined prediction). An augmented video is generated with metadata that is based on the keywords.
    Type: Grant
    Filed: July 5, 2018
    Date of Patent: June 16, 2020
    Assignee: Adobe Inc.
    Inventors: Saayan Mitra, Viswanathan Swaminathan, Somdeb Sarkhel, Julio Alvarez Martinez, Jr.