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21.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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公开(公告)号：US20250013884A1
公开(公告)日：2025-01-09
申请号：US18885502
申请日：2024-09-13
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：US20240070494A1
公开(公告)日：2024-02-29
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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Patent Agency Ranking
公开(公告)号：US11811925B2
公开(公告)日：2023-11-07
申请号：US17019256
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: H04L9/08 , G06N20/20 , G06F16/36 , G06N20/00 , G06F16/901 , G06F11/34 , G06F16/907 , G06F16/9035 , G06F8/75 , G06F8/77 , G06N5/025 , G06F16/28 , G06F16/21 , G06F16/2457 , H04L9/32 , G06F16/23 , G06F11/30 , G06F18/10 , G06F18/213 , G06F18/2115 , G06F18/214 , G06N5/01
CPC classification number: H04L9/0894 , 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 , G06F16/907 , G06F16/9024 , G06F16/9035 , G06F18/10 , G06F18/213 , G06F18/2115 , G06F18/2155 , G06N5/01 , G06N5/025 , G06N20/00 , G06N20/20 , H04L9/088 , H04L9/3236
Abstract: 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.
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公开(公告)号：US20230336340A1
公开(公告)日：2023-10-19
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 loannides , Maria del Rosario Mestre , Hugo Alexandre Pereira Monteiro , Oleg Gennadievich Shevelev , Xiaoxue Zhao , Matthew Charles Rowe
IPC: H04L9/08 , G06N20/20 , G06F16/36 , G06N20/00 , G06F16/901 , G06F11/34 , G06F16/907 , G06F16/9035 , G06F8/75 , G06F8/77 , G06N5/025 , G06F16/28 , G06F16/21 , G06F16/2457 , H04L9/32 , G06F16/23 , G06F11/30 , G06F18/10 , G06F18/213 , G06F18/2115 , G06F18/214 , G06N5/01
CPC classification number: H04L9/0894 , G06N20/20 , G06F16/367 , G06N20/00 , G06F16/9024 , G06F11/3466 , G06F16/907 , G06F16/9035 , G06F8/75 , G06F8/77 , G06N5/025 , G06F16/285 , G06F16/211 , G06F16/24578 , H04L9/088 , H04L9/3236 , G06F11/3409 , G06F16/24573 , G06F16/2365 , G06F11/3433 , G06F11/3452 , G06F11/3003 , G06F18/10 , G06F18/213 , G06F18/2115 , G06F18/2155 , G06N5/01
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：US11625648B2
公开(公告)日：2023-04-11
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
IPC: G06N20/20 , G06N5/00 , G06F16/36 , G06N20/00 , G06F16/901 , G06F11/34 , G06F16/907 , G06F16/9035 , G06F8/75 , G06F8/77 , G06N5/025 , G06F16/28 , G06F16/21 , G06F16/2457 , H04L9/08 , H04L9/32 , G06K9/62 , G06F16/23 , G06F11/30
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：US20230066143A1
公开(公告)日：2023-03-02
申请号：US17464534
申请日：2021-09-01
Applicant: Oracle International Corporation
Inventor： Liviu Sebastian Matei , Filip Trojan , Marc Michiel Bron , Andrew Kenneth Hind , Yingzhao Zhou , Maria-Monica Petrica , Rajesh Ashwinbhai Shah
Abstract: A document may be received as part of a request to identify similar documents in a collection of documents. However, the received document and the documents in the collection may have different schemas or formats. To provide semantic context to the search and allow similarity scores to be generated between different document types, a configuration may be accessed that defines how to generate queries from one schema into another schema. The configuration may map queries between different fields in both schemas. Results of the multiple queries can be combined to generate a weighted combination for each document that can be used as a similarity score between different document types.
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30.发明授权公开(公告)号：US11556862B2
公开(公告)日：2023-01-17
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: G06N20/20 , G06N5/00 , G06F16/36 , G06N20/00 , G06F16/901 , G06F11/34 , G06F16/907 , G06F16/9035 , G06F8/75 , G06F8/77 , G06N5/02 , G06F16/28 , G06F16/21 , G06F16/2457 , H04L9/08 , H04L9/32 , G06K9/62 , G06F16/23 , G06F11/30
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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