公开(公告)号：US20240420343A1

公开(公告)日：2024-12-19

申请号：US18210506

申请日：2023-06-15

Applicant: Oracle International Corporation

Inventor： Oleg Gennadievich Shevelev ,  Sahil Malhotra ,  Sergio Aldea Lopez ,  Matthew Charles Rowe ,  Alberto Polleri

IPC: G06T7/246 ,  G06T7/70

Abstract: 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.

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

IPC: G06N5/04 ,  G06F40/40 ,  G06N20/00 ,  G06N20/20 ,  H04L51/02

CPC classification number: G06N5/04 ,  G06F40/40 ,  G06N20/00 ,  G06N20/20 ,  H04L51/02

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.

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.

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.

公开(公告)号：US11775843B2

公开(公告)日：2023-10-03

申请号：US17661316

申请日：2022-04-29

Applicant: Oracle International Corporation

Inventor： Tara U. Roberts ,  Alberto Polleri ,  Rajiv Kumar ,  Ranjit Joseph Chacko ,  Jonathan Stanesby ,  Kevin Yordy

IPC: G06N5/02 ,  G06N20/00

CPC classification number: G06N5/02 ,  G06N20/00

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.

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.

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.

公开(公告)号：US20210083855A1

公开(公告)日：2021-03-18

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 ,  G06N20/00 ,  H04L9/32

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.

公开(公告)号：US20210081196A1

公开(公告)日：2021-03-18

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: G06F8/75 ,  G06F8/77 ,  G06N5/02 ,  G06N20/00 ,  G06F16/907 ,  G06F16/9035

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.