Patents by Inventor Paul Deepakraj Retinraj
Paul Deepakraj Retinraj 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: 20250021641Abstract: A secure, modular multi-tenant machine learning platform is configured to: receive untrusted code supplied by a first tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies a set of one or more security requirements; responsive to determining that the untrusted code satisfies the security requirement(s): deploy the untrusted code to a runtime execution environment; deploy a machine learning model associated with the first tenant to the runtime execution environment, the untrusted code being configured to perform one or more functions using the machine learning model; receive a set of untrusted code supplied by a second tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies the security requirement(s); and responsive to determining that the untrusted code does not satisfy the security requirement(s): refraining from deploying the untrusted code to a runtime execution environment.Type: ApplicationFiled: September 26, 2024Publication date: January 16, 2025Applicant: Oracle International CorporationInventors: Madalasa Venkataraman, Paul Deepakraj Retinraj, Pradeep Sanchana, Rohit Sukumaran, Oleksandr Khimich
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Patent number: 12124564Abstract: A secure, modular multi-tenant machine learning platform is configured to: receive untrusted code supplied by a first tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies a set of one or more security requirements; responsive to determining that the untrusted code satisfies the security requirement(s): deploy the untrusted code to a runtime execution environment; deploy a machine learning model associated with the first tenant to the runtime execution environment, the untrusted code being configured to perform one or more functions using the machine learning model; receive a set of untrusted code supplied by a second tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies the security requirement(s); and responsive to determining that the untrusted code does not satisfy the security requirement(s): refraining from deploying the untrusted code to a runtime execution environment.Type: GrantFiled: July 21, 2022Date of Patent: October 22, 2024Assignee: Oracle International CorporationInventors: Madalasa Venkataraman, Paul Deepakraj Retinraj, Pradeep Sanchana, Rohit Sukumaran, Oleksandr Khimich
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Publication number: 20240160912Abstract: The present disclosure relates to systems and methods for automatic rule generation based on natural language input. Natural language input can be received. The natural language input can be tokenized. First tokens can be mapped to a first condition of a rule, and second tokens can be mapped to a second condition of the rule. A graph representation of the natural language input can be generated. A pre-generated, tenant-specific graph can be selected that corresponds to the graph representation of the natural language input. A rule can be generated based on the tenant-specific graph. The rule can be provided to facilitate implementation of the rule.Type: ApplicationFiled: November 10, 2022Publication date: May 16, 2024Applicant: Oracle International CorporationInventors: Paul Deepakraj Retinraj, Rajan Madhavan, Sandeep Datar
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Publication number: 20230410143Abstract: Techniques for detecting anomalies in live marketing campaign data are disclosed, including: obtaining baseline data associated with one or more digital marketing campaigns; configuring an anomaly detection model to detect anomalies in digital marketing data, based at least on the baseline data; receiving a live stream of a set of digital marketing data associated with a particular digital marketing campaign that is currently being executed; while the particular digital marketing campaign is being executed: applying the anomaly detection model to the set of digital marketing data, to determine if the set of digital marketing data includes an anomaly relative to the baseline data; prior to completion of the particular digital marketing campaign and responsive to determining that the set of digital marketing data includes the anomaly relative to the baseline data, executing an action to address the anomaly.Type: ApplicationFiled: June 2, 2022Publication date: December 21, 2023Applicant: Oracle International CorporationInventors: Paul Deepakraj Retinraj, Sanjana Arun, Roma Khimani, Saurabh Surendra Shastri
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Patent number: 11836120Abstract: Techniques are disclosed for generating a database schema using trained machine learning models that, in some embodiments, may include graph neural networks (GNN). A GNN may identify source to target database schema mappings using, among other features of the graph, context data associated with each node in a graph. Context data describes relationships between a particular node and some (or all) of the other nodes in the graph. The system may use this context data (and other graph data) in combination with a trained GNN model to identify a mapping between one or more source database entities to corresponding target database entities.Type: GrantFiled: July 23, 2021Date of Patent: December 5, 2023Assignee: Oracle International CorporationInventors: Paul Deepakraj Retinraj, Sravan Kumar Ananthula, Rajan Madhavan
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Publication number: 20230334145Abstract: A secure, modular multi-tenant machine learning platform is configured to: receive untrusted code supplied by a first tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies a set of one or more security requirements; responsive to determining that the untrusted code satisfies the security requirement(s): deploy the untrusted code to a runtime execution environment; deploy a machine learning model associated with the first tenant to the runtime execution environment, the untrusted code being configured to perform one or more functions using the machine learning model; receive a set of untrusted code supplied by a second tenant; perform a security scan of the untrusted code to determine whether the untrusted code satisfies the security requirement(s); and responsive to determining that the untrusted code does not satisfy the security requirement(s): refraining from deploying the untrusted code to a runtime execution environment.Type: ApplicationFiled: July 21, 2022Publication date: October 19, 2023Applicant: Oracle International CorporationInventors: Madalasa Venkataraman, Paul Deepakraj Retinraj, Pradeep Sanchana, Rohit Sukumaran, Oleksandr Khimich
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Publication number: 20230023645Abstract: Techniques are disclosed for generating a database schema using trained machine learning models that, in some embodiments, may include graph neural networks (GNN). A GNN may identify source to target database schema mappings using, among other features of the graph, context data associated with each node in a graph. Context data describes relationships between a particular node and some (or all) of the other nodes in the graph. The system may use this context data (and other graph data) in combination with a trained GNN model to identify a mapping between one or more source database entities to corresponding target database entities.Type: ApplicationFiled: July 23, 2021Publication date: January 26, 2023Applicant: Oracle International CorporationInventors: Paul Deepakraj Retinraj, Sravan Kumar Ananthula, Rajan Madhavan
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Publication number: 20230008904Abstract: For at least a selected class attribute of the multiple class attributes, one or more bias metrics are determined that estimate a degree to which a particular workflow (having a set of processing stages) is biased in association with the class attribute. Each user of a set of users is associated with a set of user data to be processed by the particular workflow. At least one of the set of processing stages includes executing a machine-learning model. It can be detected that a bias-mitigation option corresponding to a specific class attribute has been selected. For each of at least two of the set of processing stages: a de-biasing technique is selected; and the processing stage is modified by applying the de-biasing technique. A modified version of the particular workflow (which includes the modified processing stages) is applied to each of a set of input data sets.Type: ApplicationFiled: July 8, 2021Publication date: January 12, 2023Applicant: Oracle International CorporationInventors: Madalasa Venkataraman, Paul Deepakraj Retinraj, Dinesh Ghanta, Girish Nautiyal