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.

公开(公告)号：US11475214B1

公开(公告)日：2022-10-18

申请号：US17341886

申请日：2021-06-08

Applicant: Oracle International Corporation

Inventor： Ranjit Joseph Chacko ,  Hugo Alexandre Pereira Monteiro ,  Beat Nuolf ,  Alberto Polleri ,  Oleg Gennadievich Shevelev

IPC: G06F40/174 ,  G06N20/00 ,  G06F3/0482 ,  G06F3/04847

Abstract: Systems and methods described herein relate to determining whether to provide auto-completed values for fields in a digital form. More specifically, for a given field in the digital form, a machine-learning model can be trained to transform an input data set into a predicted field value and can further generate a corresponding confidence metric. A relative-loss parameter can be determined for the field, where the relative-loss parameter represents a loss of responding to an inaccurate predicted field value for the field relative to a loss corresponding to a human user providing a field value for the field. A confidence-metric threshold can be determined for the field based on the relative-loss parameter. For a given usage of the digital form, it can then be determined whether to auto-complete the field with a predicted field value generated by the model by determining whether the corresponding confidence metric exceeds the confidence-metric threshold.

公开(公告)号：US20220261660A1

公开(公告)日：2022-08-18

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

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：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

IPC: G06N20/20 ,  G06N5/00

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：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：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

IPC: 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：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.