Patents by Inventor Charles Rowe
Charles Rowe 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: 20250049572Abstract: This application relates to methods, systems, and apparatus for replacing native heart valves with prosthetic heart valves and treating valvular insufficiency. In some implementations, a heart valve leaflet clipping mechanism includes a delivery apparatus and clipping arms. The clipping arms are coupled to the delivery apparatus. The clipping arms are movable between an open position and a closed position. The two clipping arms are biased to the closed position. The delivery apparatus is configured to hold the clipping arms in the open position. The delivery apparatus is configured to allow the clipping arms to move to the closed position to engage native heart valve leaflet tissue.Type: ApplicationFiled: October 30, 2024Publication date: February 13, 2025Inventors: Emil Karapetian, Maria Charles Vija Stanislaus, Gregory Bak-Boychuk, Christopher J. Olson, Cristobal R. Hernandez, William C. Brunnett, Netanel Benichou, Lauren R. Freschauf, Alexander J. Siegel, Stanton J. Rowe, Alison S. Curtis
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Publication number: 20250013884Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.Type: ApplicationFiled: September 13, 2024Publication date: January 9, 2025Applicant: Oracle International CorporationInventors: Alberto Polleri, Larissa Cristina Dos Santos Romualdo Suzuki, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 12190254Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.Type: GrantFiled: November 3, 2023Date of Patent: January 7, 2025Assignee: Oracle International CorporationInventors: Alberto Polleri, Sergio Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Publication number: 20240430139Abstract: Techniques for smoothing a signal are disclosed. The system partitions the portion of the data sequence into a stable subsequence and an unstable subsequence of data points. The system applies a rate of change exhibited by the stable subsequence to the unstable subsequence to create a smoothed, more stable subsequence.Type: ApplicationFiled: June 21, 2023Publication date: December 26, 2024Applicant: Oracle International CorporationInventors: Matthew Charles Rowe, Sahil Malhotra, Sergio Aldea Lopez, Oleg Gennadievich Shevelev, Alberto Polleri
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Publication number: 20240420343Abstract: Techniques for generating high-precision localization of a moving object on a trajectory are provided. In one technique, a particular image that is associated with a moving object is identified. A set of candidate images is selected from a plurality of images that were used to train a neural network. For each candidate image in the set of candidate images: (1) output from the neural network is generated based on inputting the particular image and said each candidate image to the neural network; (2) a predicted position of the particular image is determined based on the output and a position that is associated with said each candidate image; and (3) the predicted position is added to a set of predicted positions. The set of predicted positions is aggregated to generate an aggregated position for the particular image.Type: ApplicationFiled: June 15, 2023Publication date: December 19, 2024Inventors: Oleg Gennadievich Shevelev, Sahil Malhotra, Sergio Aldea Lopez, Matthew Charles Rowe, Alberto Polleri
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Patent number: 12118474Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.Type: GrantFiled: April 10, 2023Date of Patent: October 15, 2024Assignee: Oracle International CorporationInventors: Alberto Polleri, Larissa Cristina Dos Santos Romualdo Suzuki, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Xiaoxue Zhao, Matthew Charles Rowe
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Publication number: 20240320303Abstract: A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.Type: ApplicationFiled: May 31, 2024Publication date: September 26, 2024Applicant: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria Del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 12039004Abstract: A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.Type: GrantFiled: September 12, 2020Date of Patent: July 16, 2024Assignee: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 11921815Abstract: A server system can receive an input identifying a problem to generate a solution using a machine-learning application. The method selects a machine-learning model template from a plurality of templates based at least in part on the input. The method analyzes one or more formats of the customer data to generate a customer data schema based at least in part a data ontology that applies to the identified problem. The method determines whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template. Based on this determination, the method analyzes the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations. The method can include gathering the addition information required to re-align the customer data with the data expectations of the selected machine-learning model template.Type: GrantFiled: September 13, 2020Date of Patent: March 5, 2024Assignee: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Publication number: 20240070494Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.Type: ApplicationFiled: November 3, 2023Publication date: February 29, 2024Applicant: Oracle International CorporationInventors: Alberto Polleri, Sergio Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 11847578Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.Type: GrantFiled: January 23, 2023Date of Patent: December 19, 2023Assignee: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 11811925Abstract: The present disclosure relates to systems and methods for a machine-learning platform for the safe serialization of a machine-learning application. Individual library components (e.g., a pipeline, a microservice routine, a software module, and an infrastructure model) can be encrypted using one or more keys. The keys can be stored in a location different from the storage location of the machine-learning application. Prior to incorporation of the library component into a machine-learning model, one or more keys can be retrieved from the remote storage location to authenticate that the one or more encrypted library components are authentic. The process can reject any of the one or more component, when the encrypted library component fails authentication. If a component is rejected, the system can roll back to a previous, authenticated version of the library component. The authenticated library components can be compiled into machine-learning software.Type: GrantFiled: September 12, 2020Date of Patent: November 7, 2023Assignee: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Publication number: 20230336340Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.Type: ApplicationFiled: April 10, 2023Publication date: October 19, 2023Applicant: Oracle International CorporationInventors: Alberto Polleri, Larissa Cristina Dos Santos Romualdo Suzuki, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander loannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Xiaoxue Zhao, Matthew Charles Rowe
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Publication number: 20230267374Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.Type: ApplicationFiled: April 19, 2023Publication date: August 24, 2023Applicant: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Loannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Publication number: 20230237348Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.Type: ApplicationFiled: January 23, 2023Publication date: July 27, 2023Applicant: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 11663523Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.Type: GrantFiled: June 4, 2020Date of Patent: May 30, 2023Assignee: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 11625648Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.Type: GrantFiled: June 4, 2020Date of Patent: April 11, 2023Assignee: Oracle International CorporationInventors: Alberto Polleri, Larissa Cristina Dos Santos Romualdo Suzuki, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 11562267Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.Type: GrantFiled: June 4, 2020Date of Patent: January 24, 2023Assignee: Oracle International CorporationInventors: Alberto Polleri, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Larissa Cristina Dos Santos Romualdo Suzuki, Xiaoxue Zhao, Matthew Charles Rowe
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Patent number: 11556862Abstract: The present disclosure relates to systems and methods for using existing data ontologies for generating machine learning solutions for a high-precision search of relevant services to compose pipelines with minimal human intervention. Data ontologies can be used to create a combination of non-logic based and logic-based sematic services that can significantly outperform both kinds of selection in terms of precision. Quality of Service (QoS) and product Key Performance Indicator (KPI) constraints can be used as part of architecture selection in developing, training, validating, and improving machine learning models. For data sets without existing ontologies, one or more ontologies be generated and stored for future use.Type: GrantFiled: June 4, 2020Date of Patent: January 17, 2023Assignee: Oracle International CorporationInventors: Alberto Polleri, Larissa Cristina Dos Santos Romualdo Suzuki, Sergio Aldea Lopez, Marc Michiel Bron, Dan David Golding, Alexander Ioannides, Maria del Rosario Mestre, Hugo Alexandre Pereira Monteiro, Oleg Gennadievich Shevelev, Xiaoxue Zhao, Matthew Charles Rowe
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Publication number: 20220366280Abstract: Techniques for generating confidence scores for machine learning predictions are disclosed. The confidence score for a predicted label corresponding to a target data point is based at least in part on how well the machine learning model predicts labels for other data points that are similar to the target data point. The system uses k data points, closest to the target data point, with known labels to compute the confidence score of a predicted label for the target data point. The accuracy of the predictions and the distance of each of the k data points from the target data point are used to compute a confidence score for a label predicted for the target data point.Type: ApplicationFiled: September 15, 2021Publication date: November 17, 2022Applicant: Oracle International CorporationInventors: Matthew Charles Rowe, Alberto Polleri, Rhys David Green