Patents by Inventor Shiva Prasad Kasiviswanathan
Shiva Prasad Kasiviswanathan 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: 11983085Abstract: Systems and methods are provided for dynamic segmentation of users during an experiment based on changes to application data collected during the experiment. Data regarding application interactions and associated application metadata may be collected from users during application experiments that involve testing different variants of a feature or otherwise different user experiences. The data regarding application interactions and associated application metadata may be evaluated to discover segments of users and/or usage patterns (e.g., “cohorts”). During the experiment, the users may be dynamically re-segmented into new/different cohorts based on new application data being collected.Type: GrantFiled: March 31, 2022Date of Patent: May 14, 2024Assignee: Amazon Technologies, Inc.Inventors: Sudhir Kumar, Xiaoshan Wang, Shiva Prasad Kasiviswanathan, Adel Lahlou, Varsha Velagapudi
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Patent number: 11853912Abstract: Described are systems and methods for determining causal connections between various metrics collected by wearable devices and using those causal connections to provide causal insights to other users. For example, some users may elect to perform one or more self-experiments to explore the impact certain changes in their behavior may have on metrics measured by the user's wearable device. Causal connections determined from those experiments may be used to provide causal insights relating to those metrics to other users who have not performed the same or similar experiments.Type: GrantFiled: January 30, 2020Date of Patent: December 26, 2023Assignee: Amazon Technologies, Inc.Inventors: Shiva Prasad Kasiviswanathan, Nina Mishra, Yonatan Naamad
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Patent number: 11797572Abstract: Techniques for hotspot detection in a dataset are described. A hotspot being a region (or a collection of points) where the value of a function of given any region in the space measures the concentration of points in that region is significantly higher than its other regions of the dataspace. As such, a region that has a denser concentration of points than other regions of the dataspace may be considered a hotspot. In some implementations, hotspot detection includes finding two or more regions to evaluate for high-density in the dataset, a high-density region indicating a potential hotspot and extending a size of the manipulated found two or more regions to determine borders for these regions.Type: GrantFiled: August 7, 2018Date of Patent: October 24, 2023Assignee: Amazon Technologies, Inc.Inventors: Yonatan Naamad, Shiva Prasad Kasiviswanathan, Nina Mishra, Morteza Monemizadeh, Lauren Anne Moos, Joshua M. Tokle
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Patent number: 11734567Abstract: A method includes deploying a neural network (NN) model on an electronic device. The NN model is generated by training a first NN architecture on a first dataset. A first function defines a first layer of the first NN architecture. The first function is constructed based on approximating a second function applied by a second layer of a second NN architecture. Retraining of the NN model is enabled on the electronic device using a second data set.Type: GrantFiled: February 13, 2018Date of Patent: August 22, 2023Assignee: SAMSUNG ELECTRONICS CO., LTD.Inventors: Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin
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Patent number: 11308407Abstract: Examples of techniques for anomaly detection with feedback are described. An instance includes a technique is receiving a plurality of unlabeled data points from an input stream; performing anomaly detection on a point of the unlabeled data points using an anomaly detection engine; pre-processing the unlabeled data point that was subjected to anomaly detection; classifying the pre-processed unlabeled data point; determining the anomaly detection was not proper based on a comparison of a result of the anomaly detection and a result of the classifying of the pre-processed unlabeled data point; and in response to determining the anomaly detection was not proper, providing feedback to the anomaly detection engine to change at least one emphasis used in anomaly detection.Type: GrantFiled: December 14, 2017Date of Patent: April 19, 2022Assignee: Amazon Technologies, Inc.Inventors: Sudipto Guha, Tal Wagner, Shiva Prasad Kasiviswanathan, Nina Mishra
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Publication number: 20190251440Abstract: A method includes deploying a neural network (NN) model on an electronic device. The NN model being generated by training a first NN architecture on a first dataset. A first function defines a first layer of the first NN architecture. The first function is constructed based on approximating a second function applied by a second layer of a second NN architecture. Retraining of the NN model is enabled on the electronic device using a second data set.Type: ApplicationFiled: February 13, 2018Publication date: August 15, 2019Inventors: Shiva Prasad Kasiviswanathan, Nina Narodytska, Hongxia Jin
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Patent number: 10346616Abstract: One method for developing a data loss prevention model includes receiving, at a processing device, an event record corresponding to an operation performed on a computing device. The event record includes an event type and event data. The method also includes transforming, using the processing device, the event type to an event number corresponding to the event type. The method includes transforming, using the processing device, the event data to a numerical representation of the event data. The method includes associating an indication of whether the event type and the event data correspond to a data loss event with the event number and the numerical representation. The method also includes determining the data loss prevention model using the indication, the event number, and the numerical representation.Type: GrantFiled: July 15, 2013Date of Patent: July 9, 2019Assignee: GENERAL ELECTRIC COMPANYInventors: Shiva Prasad Kasiviswanathan, Lei Wu, Daniel Edward Marthaler, Scott Charles Evans, Varian Paul Powles, Philip Paul Beauchamp
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Publication number: 20180039927Abstract: A system, medium, and method including receiving input data relating to an employee, the input data including a plurality of sentences of descriptive language regarding the employee's performance; processing the input data to determine sentences of refined textual data; determining a category for each of the sentences of the refined textual data from a plurality of categories, each of the plurality of categories being different from each other and relating to a particular type of performance evaluation characteristic; generating, based on the refined textual data and the determined category for the sentences of the refined textual data, a plurality of summary sentences reflective of the input data; and generating a summarization of the employee's performance, the summarization including an ordered listing of the plurality of summary sentences.Type: ApplicationFiled: August 5, 2016Publication date: February 8, 2018Inventors: Abhay HARPALE, James JOBIN, Shiva Prasad KASIVISWANATHAN, Anuj TEWARI
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Publication number: 20150020207Abstract: One method for developing a data loss prevention model includes receiving, at a processing device, an event record corresponding to an operation performed on a computing device. The event record includes an event type and event data. The method also includes transforming, using the processing device, the event type to an event number corresponding to the event type. The method includes transforming, using the processing device, the event data to a numerical representation of the event data. The method includes associating an indication of whether the event type and the event data correspond to a data loss event with the event number and the numerical representation. The method also includes determining the data loss prevention model using the indication, the event number, and the numerical representation.Type: ApplicationFiled: July 15, 2013Publication date: January 15, 2015Inventors: Shiva Prasad Kasiviswanathan, Lei Wu, Daniel Edward Marthaler, Scott Charles Evans, Varian Paul Powles, Philip Paul Beauchamp