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公开(公告)号：US12039004B2
公开(公告)日：2024-07-16
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
IPC: G06N5/00 , G06F8/75 , G06F8/77 , G06F11/30 , G06F11/34 , G06F16/21 , G06F16/23 , G06F16/2457 , G06F16/28 , G06F16/36 , G06F16/901 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/213 , G06F18/214 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/08 , H04L9/32
CPC classification number: G06F18/213 , G06F8/75 , G06F8/77 , G06F11/3003 , G06F11/3409 , G06F11/3433 , G06F11/3452 , G06F11/3466 , G06F16/211 , G06F16/2365 , G06F16/24573 , G06F16/24578 , G06F16/285 , G06F16/367 , G06F16/9024 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/2155 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/088 , H04L9/0894 , H04L9/3236
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：US11663523B2
公开(公告)日：2023-05-30
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
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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13.发明申请公开(公告)号：US20210081836A1
公开(公告)日：2021-03-18
申请号：US16892724
申请日：2020-06-04
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
IPC: G06N20/00 , G06F16/901 , G06F11/34 , G06F16/36
Abstract: 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.
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14.发明申请公开(公告)号：US20210081377A1
公开(公告)日：2021-03-18
申请号：US16893073
申请日：2020-06-04
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
IPC: G06F16/21 , G06N20/00 , G06F16/2457 , G06F16/28
Abstract: The present disclosure relates to systems and methods for a self-adjusting corporation-wide discovery and integration feature of a machine learning system that can review a client's data store, review the labels for the various data schema, and effectively map the client's data schema to classifications used by the machine learning model. The various techniques can automatically select the features that are predictive for each individual use case (i.e., one client), effectively making a machine learning solution client-agnostic for the application developer. A weighted list of common representations of each feature for a particular machine learning solution can be generated and stored. When new data is added to the data store, a matching service can automatically detect which features should be fed into the machine-learning solution based at least in part on the weighted list. The weighted list can be updated as new data is made available to the model.
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15.发明授权公开(公告)号：US11921815B2
公开(公告)日：2024-03-05
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: G06F18/213 , G06F8/75 , G06F8/77 , G06F11/30 , G06F11/34 , G06F16/21 , G06F16/23 , G06F16/2457 , G06F16/28 , G06F16/36 , G06F16/901 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/214 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/08 , H04L9/32
CPC classification number: G06F18/213 , G06F8/75 , G06F8/77 , G06F11/3003 , G06F11/3409 , G06F11/3433 , G06F11/3452 , G06F11/3466 , G06F16/211 , G06F16/2365 , G06F16/24573 , G06F16/24578 , G06F16/285 , G06F16/367 , G06F16/9024 , G06F16/9035 , G06F16/907 , G06F18/10 , G06F18/2115 , G06F18/2155 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/088 , H04L9/3236
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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Patent Agency Ranking
公开(公告)号：US11847578B2
公开(公告)日：2023-12-19
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：US11562267B2
公开(公告)日：2023-01-24
申请号：US16893193
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
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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18.发明授权公开(公告)号：US11475374B2
公开(公告)日：2022-10-18
申请号：US16893073
申请日：2020-06-04
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
IPC: G06F16/28 , G06N20/20 , G06N5/00 , G06F16/36 , G06N20/00 , G06F16/901 , G06F11/34 , G06F16/907 , G06F16/9035 , G06F8/75 , G06F8/77 , G06N5/02 , G06F16/21 , G06F16/2457 , H04L9/08 , H04L9/32 , G06K9/62 , G06F16/23
Abstract: The present disclosure relates to systems and methods for a self-adjusting corporation-wide discovery and integration feature of a machine learning system that can review a client's data store, review the labels for the various data schema, and effectively map the client's data schema to classifications used by the machine learning model. The various techniques can automatically select the features that are predictive for each individual use case (i.e., one client), effectively making a machine learning solution client-agnostic for the application developer. A weighted list of common representations of each feature for a particular machine learning solution can be generated and stored. When new data is added to the data store, a matching service can automatically detect which features should be fed into the machine-learning solution based at least in part on the weighted list. The weighted list can be updated as new data is made available to the model.
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公开(公告)号：US20210081848A1
公开(公告)日：2021-03-18
申请号：US16892935
申请日：2020-06-04
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
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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公开(公告)号：US20250077915A1
公开(公告)日：2025-03-06
申请号：US18954220
申请日：2024-11-20
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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