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公开(公告)号：US20250060220A1
公开(公告)日：2025-02-20
申请号：US18450003
申请日：2023-08-15
Applicant: Oracle International Corporation
Inventor： Matthew Charles Rowe , Sahil Malhotra , Sergio Aldea Lopez , Oleg Gennadievich Shevelev , Alberto Polleri
IPC: G01C21/34
Abstract: Techniques for deriving an optimal traversal path on a racetrack are disclosed. The system partitions a track into straight and curved segments. The system identifies optimal traversals through each segment from historical traversal data. The system stitches the optimal traversals together and smooths the optimal traversals at the transition points between track segments. The system verifies that the smoothed traversals meet one or more kinematic criteria before outputting the optimal traversal path.
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公开(公告)号：US20230267374A1
公开(公告)日：2023-08-24
申请号：US18136780
申请日：2023-04-19
Applicant: Oracle International Corporation
Inventor： 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
Abstract: 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.
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公开(公告)号：US20230237348A1
公开(公告)日：2023-07-27
申请号：US18100458
申请日：2023-01-23
Applicant: Oracle International Corporation
Inventor： 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
Abstract: 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.
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公开(公告)号：US20210081842A1
公开(公告)日：2021-03-18
申请号：US17019254
申请日：2020-09-12
Applicant: Oracle International Corporation
Inventor： 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
Abstract: 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.
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公开(公告)号：US20210081837A1
公开(公告)日：2021-03-18
申请号：US16893189
申请日：2020-06-04
Applicant: Oracle International Corporation
Inventor： 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
IPC: G06N20/00 , G06F8/41 , G06F9/54 , G06F16/9035 , G06F16/907 , G06F16/906
Abstract: 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.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20210081720A1
公开(公告)日：2021-03-18
申请号：US17019258
申请日：2020-09-13
Applicant: Oracle International Corporation
Inventor： 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
IPC: G06K9/62 , G06N20/00 , G06F16/21 , G06F16/2457 , G06F16/23
Abstract: 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.
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7.发明申请公开(公告)号：US20190102681A1
公开(公告)日：2019-04-04
申请号：US16146678
申请日：2018-09-28
Applicant: Oracle International Corporation
Inventor： Tara U. Roberts , Alberto Polleri , Rajiv Kumar , Ranjit Joesph Chacko , Jonathan Stanesby , Kevin Yordy
Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.
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公开(公告)号：US12190254B2
公开(公告)日：2025-01-07
申请号：US18501716
申请日：2023-11-03
Applicant: Oracle International Corporation
Inventor： 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
Abstract: 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.
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公开(公告)号：US12164877B2
公开(公告)日：2024-12-10
申请号：US17470179
申请日：2021-09-09
Applicant: Oracle International Corporation
Inventor： Oleg Gennadievich Shevelev , Alberto Polleri , Marc Michiel Bron
Abstract: Techniques for interacting with users in a discussion environment are disclosed. Upon identifying a question in the discussion environment, a system determines: (a) whether a stored answer has already been associated with the question, (b) whether an answer can be generated by the system using existing information accessible to the system, or (c) whether to contact an expert to answer the question. The system updates the knowledge base by storing the questions and answers, along with user feedback to the questions and answers. Based on the user feedback, the system determines whether to modify existing answers to user-generated questions or to seek answers from additional human experts.
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公开(公告)号：US12118474B2
公开(公告)日：2024-10-15
申请号：US18132859
申请日：2023-04-10
Applicant: Oracle International Corporation
Inventor： 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
CPC classification number: G06N5/02 , G06F16/907 , G06F18/217 , G06F18/2193 , G06F18/285 , G06N5/045 , G06N20/00 , G06N20/20
Abstract: 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.
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