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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：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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公开(公告)号：US20220366280A1
公开(公告)日：2022-11-17
申请号：US17475557
申请日：2021-09-15
Applicant: Oracle International Corporation
Inventor： Matthew Charles Rowe , Alberto Polleri , Rhys David Green
Abstract: 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.
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公开(公告)号：US11238377B2
公开(公告)日：2022-02-01
申请号：US17019255
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: G06F8/77 , G06N20/20 , G06N5/00 , G06F16/36 , G06N20/00 , G06F16/901 , G06F11/34 , G06F16/907 , G06F16/9035 , G06F8/75 , G06N5/02 , G06F16/28 , G06F16/21 , G06F16/2457 , H04L9/08 , H04L9/32 , G06K9/62 , G06F16/23
Abstract: A server system may match a segment of code for a code integration request to metadata about similar segments of code, wherein the metadata qualifies one or more outcomes of previous integration requests. The server may determine usage rights and rules based on the metadata, wherein some of the usage rights and rules have previously have been approved by a multi-approval workflow that enforces a predetermined process to authorize use of the segment of code for code segment integrations. The server may analyze the metadata to predict an integration score based at least in part on the usage rights and rules of the segments of code. If the integration score of the segment of code exceeds a threshold, the system may automatically generate a data structure for deploying the segment of code, wherein the automatically generating the data structure is performed without the multi-approval workflow.
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公开(公告)号：US20210081819A1
公开(公告)日：2021-03-18
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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Patent Agency Ranking
公开(公告)号：US20240320303A1
公开(公告)日：2024-09-26
申请号：US18680987
申请日：2024-05-31
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 , H04L9/0894 , 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：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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9.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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10.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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