公开(公告)号：US12301388B2

公开(公告)日：2025-05-13

申请号：US18339065

申请日：2023-06-21

Applicant: Oracle International Corporation

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

IPC: H04L25/03

Abstract: Techniques for smoothing a signal are disclosed. The system partitions the portion of the data sequence into a stable subsequence and an unstable subsequence of data points. The system applies a rate of change exhibited by the stable subsequence to the unstable subsequence to create a smoothed, more stable subsequence.

公开(公告)号：US20250086810A1

公开(公告)日：2025-03-13

申请号：US18367415

申请日：2023-09-12

Applicant: Oracle International Corporation

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

IPC: G06T7/292 ,  G06F17/12

Abstract: Techniques for preparing data for high-precision absolute localization of a moving object along a trajectory are provided. In one technique, a sliding window of a set of adjacent points along a trajectory of a moving object is identified, along with a midpoint in the sliding window. Based on the set of adjacent points, a first polynomial equation is generated for a first dimension and a second polynomial equation is generated for a second dimension. A first derivative at a particular timestamp associated with the midpoint is a first velocity along the first dimension, while a particular first derivative at the particular timestamp is a second velocity along the second dimension. A velocity in direction of yaw is generated based on the first velocity, the second velocity, and a slip angle associated with the midpoint. A yaw angle is generated based on the velocity in direction of yaw.

公开(公告)号：US20250085434A1

公开(公告)日：2025-03-13

申请号：US18367400

申请日：2023-09-12

Applicant: Oracle International Corporation

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

IPC: G01S19/13

Abstract: Techniques for preparing data for high-precision absolute localization of a moving object along a trajectory are provided. In one technique, a sequence of points is stored, where each point corresponds to a different set of Cartesian coordinates. A curve is generated that approximates a line that passes through the sequence of points. Based on the curve, a set of points is generated on the curve, where the set of points is different than the sequence of points. New Cartesian coordinates are generated for each point in the set of points. After generating the new Cartesian coordinates, Cartesian coordinates of a position of a moving object are determined.

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

IPC: G06N5/02 ,  G06F16/907 ,  G06F18/20 ,  G06F18/21 ,  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.

公开(公告)号：US20240430139A1

公开(公告)日：2024-12-26

申请号：US18339065

申请日：2023-06-21

Applicant: Oracle International Corporation

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

IPC: H04L25/03

Abstract: Techniques for smoothing a signal are disclosed. The system partitions the portion of the data sequence into a stable subsequence and an unstable subsequence of data points. The system applies a rate of change exhibited by the stable subsequence to the unstable subsequence to create a smoothed, more stable subsequence.

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：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：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.