Patents by Inventor Gaurush Hiranandani
Gaurush Hiranandani 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: 11886971Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.Type: GrantFiled: August 15, 2019Date of Patent: January 30, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua
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Patent number: 11816120Abstract: Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.Type: GrantFiled: March 17, 2020Date of Patent: November 14, 2023Assignee: Adobe Inc.Inventors: Shiv Kumar Saini, Sunav Choudhary, Gaurush Hiranandani
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Patent number: 11704714Abstract: Technologies are provided for reformulation of a tail query to a head query with the same purchase intent by mapping the tail query to the head query. In some of the technologies, a reasonable embedding can be learned on historical head queries. The embedding can then be refined by leveraging rewards generated from a persistently noisy oracle that compensates for the lack of historical behavioral signal for tail queries. Further, a contextual sampling technique that uses text-based rewards or oracle-based rewards, or both, can be implemented in order to avoid biases introduced by persistent noise in the oracle. Numerical experiments on large scale e-commerce datasets demonstrate that the provided technologies can outperform several conventional approaches to query reformulation.Type: GrantFiled: May 20, 2020Date of Patent: July 18, 2023Assignee: Amazon Technologies, Inc.Inventors: Gaurush Hiranandani, Sumeet Katariya, Nikhil S. Rao, Karthik Subbian
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Patent number: 11443389Abstract: Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.Type: GrantFiled: August 25, 2020Date of Patent: September 13, 2022Assignee: Adobe Inc.Inventors: Gaurush Hiranandani, Tanya Goyal, Sumit Shekhar, Payal Bajaj
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Patent number: 11205111Abstract: Techniques of forecasting web metrics involve generating, prior to the end of a period of time, a probability of a metric taking on an anomalous value, e.g., a value indicative of an anomaly with respect to web traffic, at the end of the period based on previous values of the metric. Such a probability is based on a distribution of predicted values of the metric at some previous period of time. For example, a web server may use actual values of the number of bounces collected at hourly intervals in the middle of a day to predict a number of bounces at the end of the current day. Further, the web server may also compute a confidence interval to determine whether a predicted end-of-day number of bounces may be considered anomalous. The width of the confidence interval indicates the probability that a predicted end-of-day number of bounces has an anomalous value.Type: GrantFiled: May 31, 2017Date of Patent: December 21, 2021Assignee: ADOBE INC.Inventors: Shiv Kumar Saini, Prakhar Gupta, Harvineet Singh, Gaurush Hiranandani
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Publication number: 20210366016Abstract: Technologies are provided for reformulation of a tail query to a head query with the same purchase intent by mapping the tail query to the head query. In some of the technologies, a reasonable embedding can be learned on historical head queries. The embedding can then be refined by leveraging rewards generated from a persistently noisy oracle that compensates for the lack of historical behavioral signal for tail queries. Further, a contextual sampling technique that uses text-based rewards or oracle-based rewards, or both, can be implemented in order to avoid biases introduced by persistent noise in the oracle. Numerical experiments on large scale e-commerce datasets demonstrate that the provided technologies can outperform several conventional approaches to query reformulation.Type: ApplicationFiled: May 20, 2020Publication date: November 25, 2021Applicant: A9.com, Inc.Inventors: Gaurush Hiranandani, Sumeet Katariya, Nikhil S. Rao, Karthik Subbian
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Patent number: 11184301Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.Type: GrantFiled: August 15, 2019Date of Patent: November 23, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua
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Patent number: 10984058Abstract: A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.Type: GrantFiled: February 8, 2018Date of Patent: April 20, 2021Assignee: ADOBE INC.Inventors: Branislav Kveton, Zheng Wen, Prakhar Gupta, Iftikhar Ahamath Burhanuddin, Harvineet Singh, Gaurush Hiranandani
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Patent number: 10956967Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating augmented reality representations of recommended products based on style similarity with real-world surroundings. For example, the disclosed systems can identify a real-world object within a camera feed and can utilize a 2D-3D alignment algorithm to identify a three-dimensional model that matches the real-world object. In addition, the disclosed systems can utilize a style similarity algorithm to generate style similarity scores for products in relation to the identified three-dimensional model. The disclosed systems can also utilize a color compatibility algorithm to generate color compatibility scores for products, and the systems can determine overall scores for products based on a combination of style similarity scores and color compatibility scores. The disclosed systems can further generate AR representations of recommended products based on the overall scores.Type: GrantFiled: June 11, 2018Date of Patent: March 23, 2021Assignee: ADOBE INC.Inventors: Kumar Ayush, Gaurush Hiranandani
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Patent number: 10950060Abstract: Certain embodiments involve enhancing personalization of a virtual-commerce environment by identifying an augmented-reality visual of the virtual-commerce environment. For example, a system obtains a data set that indicates a plurality of augmented-reality visuals generated in a virtual-commerce environment and provided for view by a user. The system obtains data indicating a triggering user input that corresponds to a predetermined user input provideable by the user as the user views an augmented-reality visual of the plurality of augmented-reality visuals. The system obtains data indicating a user input provided by the user. The system compares the user input to the triggering user input to determine a correspondence (e.g., a similarity) between the user input and the triggering user input. The system identifies a particular augmented-reality visual of the plurality of augmented-reality visuals that is viewed by the user based on the correspondence and stores the identified augmented-reality visual.Type: GrantFiled: June 22, 2020Date of Patent: March 16, 2021Assignee: Adobe Inc.Inventors: Gaurush Hiranandani, Chinnaobireddy Varsha, Sai Varun Reddy Maram, Kumar Ayush, Atanu Ranjan Sinha
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Publication number: 20210051121Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.Type: ApplicationFiled: August 15, 2019Publication date: February 18, 2021Inventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua
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Publication number: 20210049442Abstract: Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as “complex entities” composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.Type: ApplicationFiled: August 15, 2019Publication date: February 18, 2021Inventors: Lekshmi Menon, Amar Budhiraja, Gaurush Hiranandani, Prateek Jain, Darshatkumar Anandji Shah, Ayush Choure, Navya Yarrabelly, Anurag Mishra, Mohammad Luqman, Shivangi Dhakad, Juhi Dua
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Patent number: 10922716Abstract: This disclosure generally covers systems and methods that identify objects within an augmented reality (“AR”) scene (received from a user) to gather information concerning the user's physical environment or physical features and to recommend products. In particular, the disclosed systems and methods detect characteristics of multiple objects shown within an AR scene received from a user and, based on the detected characteristics, select products to recommend to the user. When analyzing characteristics, in some embodiments, the disclosed systems and methods determine visual characteristics associated with the real object or virtual object, such as color or location of an object. The disclosed systems and methods, in some embodiments, then select an endorsed product to recommend for use with the real object—based on the determined visual characteristics—and create a product recommendation that recommends the endorsed product.Type: GrantFiled: March 9, 2017Date of Patent: February 16, 2021Assignee: ADOBE INC.Inventors: Gaurush Hiranandani, Kumar Ayush, Chinnaobireddy Varsha, Sai Varun Reddy Maram
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Publication number: 20200387979Abstract: Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.Type: ApplicationFiled: August 25, 2020Publication date: December 10, 2020Applicant: Adobe Inc.Inventors: Gaurush Hiranandani, Tanya Goyal, Sumit Shekhar, Payal Bajaj
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Patent number: 10853887Abstract: Techniques and systems for determining paywall metrics are described. In an implementation, a candidate paywall metric is created that corresponds to an increased propensity of users to engage in a paid transaction when exposed to a paywall. In this way, providers of digital content may increase the proportion of users that perform a transaction when exposed to the paywall.Type: GrantFiled: September 27, 2016Date of Patent: December 1, 2020Assignee: Adobe Inc.Inventors: Gaurush Hiranandani, Tanya Goyal, Sumit Shekhar, Payal Bajaj
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Publication number: 20200320797Abstract: Certain embodiments involve enhancing personalization of a virtual-commerce environment by identifying an augmented-reality visual of the virtual-commerce environment. For example, a system obtains a data set that indicates a plurality of augmented-reality visuals generated in a virtual-commerce environment and provided for view by a user. The system obtains data indicating a triggering user input that corresponds to a predetermined user input provideable by the user as the user views an augmented-reality visual of the plurality of augmented-reality visuals. The system obtains data indicating a user input provided by the user. The system compares the user input to the triggering user input to determine a correspondence (e.g., a similarity) between the user input and the triggering user input. The system identifies a particular augmented-reality visual of the plurality of augmented-reality visuals that is viewed by the user based on the correspondence and stores the identified augmented-reality visual.Type: ApplicationFiled: June 22, 2020Publication date: October 8, 2020Inventors: Gaurush Hiranandani, Chinnaobireddy Varsha, Sai Varun Reddy Maram, Kumar Ayush, Atanu Ranjan Sinha
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Patent number: 10789622Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating augmented reality representations of recommended products based on style compatibility with real-world surroundings. For example, the disclosed systems can identify a real-world object within a camera feed and can utilize a 2D-3D alignment algorithm to identify a three-dimensional model that matches the real-world object. In addition, the disclosed systems can utilize a style compatibility algorithm to generate recommended products based on style compatibility in relation to the identified three-dimensional model. The disclosed systems can further utilize a color compatibility algorithm to determine product textures which are color compatible with the real-world surroundings and generate augmented reality representations of recommended products to provide as an overlay of the real-world environment of the camera feed.Type: GrantFiled: May 7, 2018Date of Patent: September 29, 2020Assignee: ADOBE INC.Inventors: Kumar Ayush, Gaurush Hiranandani
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Patent number: 10762283Abstract: Multimedia document summarization techniques are described. That is, given a document that includes text and a set of images, various implementations generate a summary by extracting relevant text segments in the document and relevant segments of images with constraints on the amount of text and number/size of images in the summary.Type: GrantFiled: November 20, 2015Date of Patent: September 1, 2020Assignee: Adobe Inc.Inventors: Natwar Modani, Vaishnavi Subramanian, . Utpal, Shivani Gupta, Pranav R. Maneriker, Gaurush Hiranandani, Atanu R. Sinha
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Patent number: 10755088Abstract: Systems and methods are disclosed herein for determining user behavior in an augmented reality environment. An augmented reality application executing on a computing system receives a video depicting a face of a person. The video includes a video frame. The augmented reality application augments the video frame with an image of an item selected via input from a user device associated with a user. The augmented reality application determines, from the video frame, a score representing an action unit. The action unit represents a muscle on the face of the person depicted by the video frame and the score represents an intensity of the action unit. The augmented reality application calculates, from a predictive model and based on the score, an indicator of intent of the person depicted by the video.Type: GrantFiled: January 11, 2018Date of Patent: August 25, 2020Assignee: ADOBE INC.Inventors: Kushal Chawla, Vaishnav Pawan Madandas, Moumita Sinha, Gaurush Hiranandani, Aditya Jain
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Patent number: 10726629Abstract: Certain embodiments involve enhancing personalization of a virtual-commerce environment by identifying an augmented-reality visual of the virtual-commerce environment. For example, a system obtains a data set that indicates a plurality of augmented-reality visuals generated in a virtual-commerce environment and provided for view by a user. The system obtains data indicating a triggering user input that corresponds to a predetermined user input provideable by the user as the user views an augmented-reality visual of the plurality of augmented-reality visuals. The system obtains data indicating a user input provided by the user. The system compares the user input to the triggering user input to determine a correspondence (e.g., a similarity) between the user input and the triggering user input. The system identifies a particular augmented-reality visual of the plurality of augmented-reality visuals that is viewed by the user based on the correspondence and stores the identified augmented-reality visual.Type: GrantFiled: November 13, 2018Date of Patent: July 28, 2020Assignee: Adobe Inc.Inventors: Gaurush Hiranandani, Chinnaobireddy Varsha, Sai Varun Reddy Maram, Kumar Ayush, Atanu Ranjan Sinha