Patents by Inventor Deepa Madhavan
Deepa Madhavan 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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Publication number: 20240138697Abstract: A method for generating an image of a subject with a magnetic resonance imaging (MRI) system is presented. The method includes first performing a localizer scan of the subject to acquire localizer scan data. A machine learning (ML) module is then used to detect the presence of metal regions in the localizer scan data based on magnitude and phase information of the localizer scan data. Based on the detected metal regions in the localizer scan data, the MRI workflow is adjusted for diagnostic scan of the subject. The image of the subject is then generated using the adjusted workflow.Type: ApplicationFiled: October 26, 2022Publication date: May 2, 2024Inventors: Dattesh Dayanand Shanbhag, Chitresh Bhushan, Deepa Anand, Kavitha Manickam, Dawei Gui, Radhika Madhavan
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Publication number: 20240045991Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.Type: ApplicationFiled: September 6, 2023Publication date: February 8, 2024Inventors: Deepa Madhavan, Sudheer Kilari, Meena Nagarajan, Alejandro Picos, Vladimir Bacvanski, Arunkumar Kannimar Ponnaiah, Srinivasabharathi Selvaraj
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Patent number: 11893130Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.Type: GrantFiled: December 18, 2020Date of Patent: February 6, 2024Assignee: PayPal, Inc.Inventors: Deepa Madhavan, Sudheer Kilari, Meena Nagarajan, Alejandro Picos, Vladimir Bacvanski, Arunkumar Kannimar Ponnaiah, Srinivasabharathi Selvaraj
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Publication number: 20220198054Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.Type: ApplicationFiled: December 18, 2020Publication date: June 23, 2022Inventors: Alejandro Picos, Vladimir Bacvanski, Meena Nagarajan, Sudheer Kilari, Arunkumar Kannimar Ponnaiah, Srinivasabharathi Selvaraj, Deepa Madhavan
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Publication number: 20220198044Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.Type: ApplicationFiled: December 18, 2020Publication date: June 23, 2022Inventors: Deepa Madhavan, Srinivasabharathi Selvaraj, Sudheer Kilari, Meena Nagarajan, Alejandro Picos, Vladimir Bacvanski, Arunkumar Kannimar Ponnaiah
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Publication number: 20220198053Abstract: Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.Type: ApplicationFiled: December 18, 2020Publication date: June 23, 2022Inventors: Deepa Madhavan, Sudheer Kilari, Meena Nagarajan, Alejandro Picos, Vladimir Bacvanski, Arunkumar Kannimar Ponnaiah, Srinivasabharathi Selvaraj
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Publication number: 20210326457Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.Type: ApplicationFiled: July 1, 2021Publication date: October 21, 2021Inventors: AMIR HOSSEIN YOUSSEFI, Ravi Retineni, Alejandro Picos, Gaoyuan Wang, Li Cao, Deepa Madhavan, Srinivasabharathi Selvaraj
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Patent number: 11062036Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.Type: GrantFiled: June 29, 2018Date of Patent: July 13, 2021Assignee: PAYPAL, INC.Inventors: Amir Hossein Youssefi, Ravi Retineni, Alejandro Picos, Gaoyuan Wang, Li Cao, Deepa Madhavan, Srinivasabharathi Selvaraj
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Publication number: 20190347428Abstract: Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.Type: ApplicationFiled: June 29, 2018Publication date: November 14, 2019Inventors: Amir Hossein Youssefi, Ravi Retineni, Alejandro Picos, Gaoyuan Wang, Li Cao, Deepa Madhavan, Srinivasabharathi Selvaraj