Patents by Inventor Deepali Jain
Deepali Jain 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: 11954307Abstract: Visually selecting application activities can include superimposing a visual selector overlay on a page displayed on a device, the page corresponding to a foreground activity. The superimposing can be responsive to receiving user input invoking the foreground activity. Contextual information corresponding to the foreground activity can be detected. The contextual information can be presented to the user visually within the visual selector overlay. The contextual information can be automatically added to a list and the list stored electronically on the device in response to received user input. The list can be configured to contain contextual information selected from page displays corresponding to a plurality of activities relating to one or more apps stored on the device.Type: GrantFiled: August 2, 2021Date of Patent: April 9, 2024Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Deepali Vinay, Shivangi Jain Mehra, Savan Kiran
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Patent number: 11687352Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform.Type: GrantFiled: June 17, 2021Date of Patent: June 27, 2023Assignee: Adobe Inc.Inventors: Nikhil Sheoran, Nayan Raju Vysyaraju, Varun Srivastava, Nisheeth Golakiya, Dhruv Singal, Deepali Jain, Atanu Sinha
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Patent number: 11663497Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.Type: GrantFiled: April 19, 2019Date of Patent: May 30, 2023Assignee: ADOBE INC.Inventors: Atanu Sinha, Prakhar Gupta, Manoj Kilaru, Madhav Goel, Deepanshu Bansal, Deepali Jain, Aniket Raj
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Patent number: 11551239Abstract: There is described a method and system in an interactive computing environment modified with user experience values based on behavior logs. An experience valuation system determines an experience value and an estimated experience value. The experience value is based on a current state of interaction data from a user session, based on a history of past events, and an estimation function defined by parameters to model the user experience values. The estimated experience value is determined based on, in addition to the current state and the estimation function, next states associated with the current state, and a reward function. The parameters of the estimation function are updated based on a comparison of the expected experience value and the estimated experience value. For another aspect, the method and system may further include a state prediction system to determine probabilities of transitioning that may be applied to determine the estimated experience value.Type: GrantFiled: October 16, 2018Date of Patent: January 10, 2023Assignee: Adobe Inc.Inventors: Deepali Jain, Atanu R. Sinha, Deepali Gupta, Nikhil Sheoran, Sopan Khosla, Reshmi Naduparambil Sasidharan
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Publication number: 20210311751Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform.Type: ApplicationFiled: June 17, 2021Publication date: October 7, 2021Inventors: Nikhil Sheoran, Nayan Raju Vysyaraju, Varun Srivastava, Nisheeth Golakiya, Dhruv Singal, Deepali Jain, Atanu Sinha
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Patent number: 11113475Abstract: An example chatbot generation platform may receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain custom chatbot information according to the chatbot template; generate a chatbot corpus for the chatbot using the custom chatbot information and the chatbot template; generate a set of question and answer (QnA) pairs based on the chatbot corpus; configure a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation. The chatbot may be built to engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.Type: GrantFiled: April 15, 2019Date of Patent: September 7, 2021Assignee: Accenture Global Solutions LimitedInventors: Nirav Jagdish Sampat, Saran Prasad, Manish Jain, Sriram Lakshminarasimhan, Dharmesh Dhirajlal Barochia, Purnanga Prema Borah, Deepali Jain, Suhas Vinod Sane
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Publication number: 20210241158Abstract: 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: ApplicationFiled: April 21, 2021Publication date: August 5, 2021Inventors: Atanu R. Sinha, Deepali Jain, Nikhil Sheoran, Deepali Gupta, Sopan Khosla
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Patent number: 11068285Abstract: In some embodiments, interaction data associated with user interactions with a user interface of an interactive computing environment is identified, and goal clusters of the interaction data are computed based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, likelihood values of additional sequences of user interactions falling within the goal clusters are computed based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Computing interface experience metrics of the additional sequences are computed using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.Type: GrantFiled: September 19, 2019Date of Patent: July 20, 2021Assignee: Adobe Inc.Inventors: Nikhil Sheoran, Nayan Raju Vysyaraju, Varun Srivastava, Nisheeth Golakiya, Dhruv Singal, Deepali Jain, Atanu Sinha
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Patent number: 11023819Abstract: 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 6, 2018Date of Patent: June 1, 2021Assignee: ADOBE INC.Inventors: Atanu R. Sinha, Deepali Jain, Nikhil Sheoran, Deepali Gupta, Sopan Khosla
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Publication number: 20210089331Abstract: In some embodiments, interaction data associated with user interactions with a user interface of an interactive computing environment is identified, and goal clusters of the interaction data are computed based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, likelihood values of additional sequences of user interactions falling within the goal clusters are computed based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Computing interface experience metrics of the additional sequences are computed using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.Type: ApplicationFiled: September 19, 2019Publication date: March 25, 2021Inventors: Nikhil Sheoran, Nayan Raju Vysyaraju, Varun Srivastava, Nisheeth Golakiya, Dhruv Singal, Deepali Jain, Atanu Sinha
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Publication number: 20200334545Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.Type: ApplicationFiled: April 19, 2019Publication date: October 22, 2020Inventors: Atanu Sinha, Prakhar Gupta, Manoj Kilaru, Madhav Goel, Deepanshu Bansal, Deepali Jain, Aniket Raj
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Publication number: 20200327196Abstract: An example chatbot generation platform may receive a request to generate a chatbot; determine a chatbot template for the chatbot based on the request; obtain custom chatbot information according to the chatbot template; generate a chatbot corpus for the chatbot using the custom chatbot information and the chatbot template; generate a set of question and answer (QnA) pairs based on the chatbot corpus; configure a language analysis model for the chatbot; build the chatbot according to the set of QnA pairs and the language analysis model; and deploy the chatbot to a chatbot host platform for operation. The chatbot may be built to engage in an interaction with a user via the chatbot host platform, use the language analysis model to select one or more QnA pairs from the set of QnA pairs during the interaction, and train the language analysis model based on the interaction.Type: ApplicationFiled: April 15, 2019Publication date: October 15, 2020Inventors: Nirav Jagdish SAMPAT, Saran PRASAD, Manish JAIN, Sriram LAKSHMINARASIMHAN, Dharmesh DHIRAJLAL BAROCHIA, Purnanga Prema BORAH, Deepali JAIN, Suhas Vinod SANE
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Patent number: 10783361Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.Type: GrantFiled: December 20, 2019Date of Patent: September 22, 2020Assignee: ADOBE INC.Inventors: Sungchul Kim, Deepali Jain, Deepali Gupta, Eunyee Koh, Branislav Kveton, Nikhil Sheoran, Atanu Sinha, Hung Hai Bui, Charles Li Chen
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Publication number: 20200134300Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.Type: ApplicationFiled: December 20, 2019Publication date: April 30, 2020Inventors: SUNGCHUL KIM, DEEPALI JAIN, DEEPALI GUPTA, EUNYEE KOH, BRANISLAV KVETON, NIKHIL SHEORAN, ATANU SINHA, HUNG HAI BUI, CHARLES LI CHEN
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Publication number: 20200118145Abstract: There is described a method and system in an interactive computing environment modified with user experience values based on behavior logs. An experience valuation system determines an experience value and an estimated experience value. The experience value is based on a current state of interaction data from a user session, based on a history of past events, and an estimation function defined by parameters to model the user experience values. The estimated experience value is determined based on, in addition to the current state and the estimation function, next states associated with the current state, and a reward function. The parameters of the estimation function are updated based on a comparison of the expected experience value and the estimated experience value. For another aspect, the method and system may further include a state prediction system to determine probabilities of transitioning that may be applied to determine the estimated experience value.Type: ApplicationFiled: October 16, 2018Publication date: April 16, 2020Applicant: Adobe Inc.Inventors: Deepali Jain, Atanu R. Sinha, Deepali Gupta, Nikhil Sheoran, Sopan Khosla, Reshmi Naduparambil Sasidharan
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Patent number: 10558852Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.Type: GrantFiled: November 16, 2017Date of Patent: February 11, 2020Assignee: ADOBE INC.Inventors: Sungchul Kim, Deepali Jain, Deepali Gupta, Eunyee Koh, Branislav Kveton, Nikhil Sheoran, Atanu Sinha, Hung Hai Bui, Charles Li Chen
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Publication number: 20190311279Abstract: 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: ApplicationFiled: April 6, 2018Publication date: October 10, 2019Inventors: Atanu R. Sinha, Deepali Jain, Nikhil Sheoran, Deepali Gupta, Sopan Khosla
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Publication number: 20190147231Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.Type: ApplicationFiled: November 16, 2017Publication date: May 16, 2019Inventors: SUNGCHUL KIM, DEEPALI JAIN, DEEPALI GUPTA, EUNYEE KOH, BRANISLAV KVETON, NIKHIL SHEORAN, ATANU SINHA, HUNG HAI BUI, CHARLES LI CHEN
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Publication number: 20180365709Abstract: Techniques are disclosed for generating personalized creator recommendations to viewers interested in viewing and interacting with creative works, in the context of a creative platform for publishing and viewing creative works. For each creator, a vector is generated indicating that creator's creative output with respect to a set of one or more creative fields. For each viewer, a vector is generated indicating that viewer's affinity with respect to the same set of creative fields. For a given viewer, a respective creator score is calculated based upon the vector associated with the viewer and the vector associated with that creator (e.g., based on a vector similarity computation). A ranking of each creator for the given viewer is then performed using the respective score, and a set of one or more personalized recommendations is then provided to the viewer based upon the ranking.Type: ApplicationFiled: June 16, 2017Publication date: December 20, 2018Applicant: Adobe Systems IncorporatedInventors: Natwar Modani, Palak Agarwal, Gaurav Kumar Gupta, Deepali Jain, Ujjawal Soni
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Publication number: 20180336281Abstract: Techniques for creator aware and diverse recommendations of digital content are described. In one example, a digital medium environment is configured to allocate an amount of content creator access as part of a service. Based on this content creator access, recommendations of content are generated that prioritize content for recommendations based in part the amount of content creator access. Recommendations are generated further based on a representative diversity preference value that captures a level of interest of a consumer in different categories, resulting in a recommendation that is representatively diverse.Type: ApplicationFiled: May 17, 2017Publication date: November 22, 2018Applicant: Adobe Systems IncorporatedInventors: Natwar Modani, Ujjawal Soni, Gaurav Kumar Gupta, Palak Agarwal, Deepali Jain