公开(公告)号：US20240364606A1

公开(公告)日：2024-10-31

申请号：US18639799

申请日：2024-04-18

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Ronald Sielinski ,  Hong Wu ,  Imran Ali ,  Jie Xing ,  Sowjanya Yaddanapudi ,  Todd Himple ,  Muhammad Suhail

IPC: H04L43/065 ,  H04L43/028

CPC classification number: H04L43/065 ,  H04L43/028

Abstract: In accordance with an embodiment, systems and methods are provided for two-tier reporting for cloud computing realms. An exemplary method can deploy a central instance of an analytics service in a central cloud realm. The method can further deploy a respective instance of the analytics service in each of a plurality of cloud realms. The method can implement a respective different data pipeline for each deployed respective instance of the analytics service, wherein a respective different data pipeline is configured to perform at least one of ingest or transform data for the deployed respective instance of the analytics service, said data being descriptive of use of services associated with a respective cloud realm of the plurality of cloud realms.

公开(公告)号：US20240386047A1

公开(公告)日：2024-11-21

申请号：US18198975

申请日：2023-05-18

Applicant: Oracle International Corporation

Inventor： Ankit Aggarwal ,  Chirag Ahuja ,  Vikas Pandey ,  Sharmily Sidhartha ,  Hariharan Balasubramanian ,  Jie Xing

IPC: G06F16/38 ,  G06F16/31

Abstract: Techniques are described herein for cold-start forecasting datasets using backcasting and composite embedding. An example method can include a system receiving a set of time series and metadata text comprising a first subset of metadata text and a second subset of metadata text. The system can generate a plurality of embeddings, each embedding comprising a numerical representation of a metadata text of the set of metadata text. The system can generate a plurality of vectors, each vector comprising a time series of the set of time series each time series associated with a metadata text of the first subset of metadata text. The system can generate a plurality of composite embeddings based at least in part on combining each embedding with a respective vector of the plurality of vectors. The system can determine a forecasted value associated with the second subset of metadata text based on the composite embeddings.

公开(公告)号：US20250077901A1

公开(公告)日：2025-03-06

申请号：US18238708

申请日：2023-08-28

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Navya Sree Yadavalli ,  Ravijeet Ranjit Kumar ,  Hariharan Balasubramanian ,  Jie Xing

IPC: G06N5/022

Abstract: Techniques for multi-output model forecasting are provided herein. An example method can include a computing system receiving a request to forecast a value for a variable at a future time point based upon a time series, the time series comprising a sequence of data points, each data point in the sequence of data points identifying a time point and at least one value associated with the time point. The computing system can predict, using a first trained machine learning model and based upon the times series, a plurality of forecast values for the future time point, the plurality of forecast values including: a first forecast value forecasted for the variable at the future time point; and a set of one or more forecast attribute values for one or more attributes of the time series, each of the set of one or more forecast attribute values predicted for the future time point.

公开(公告)号：US20250094688A1

公开(公告)日：2025-03-20

申请号：US18780384

申请日：2024-07-22

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Jie Xing ,  Haad kahn

IPC: G06F40/166 ,  G06F40/289 ,  G16H50/70

Abstract: A summary generation system is disclosed that is configured to generate a summary for content to be summarized by identifying relevant chunks of information from the content to be summarized using a large language model (LLM) and a set of questions. The set of questions enable the system to identify and retrieve relevant chunks of information. Each question undergoes a translation or transformation process to generate multiple question variants for each question. The multiple question variants are used by the system to optimize the search to obtain relevant chunks of information. Then, using the multiple question variants and an LLM, the system extracts information (i.e., answers) from the relevant chunks of information. The summary generation system then collates the answers to create an accurate and comprehensive summary for the content to be summarized.

公开(公告)号：US20240362210A1

公开(公告)日：2024-10-31

申请号：US18139492

申请日：2023-04-26

Applicant: Oracle International Corporation

Inventor： Ankit Aggarwal ,  Jie Xing ,  Chirag Ahuja ,  Vikas Pandey ,  Hariharan Balasubramanian

IPC: G06F16/242 ,  G06F16/2455

CPC classification number: G06F16/244 ,  G06F16/24553

Abstract: Techniques are described herein for forecasting datasets using blend of temporal aggregation and grouped aggregation. An example method can include a device accessing a first and second time series, comprising a first data point associated with a first time step and a first value and a second data point associated with a second time step and a second value. The method can further include the device determining a grouped aggregated data point using the first and second time series by aligning the first and second data point. The method can further include the device determining the grouped aggregated data point by summing the first and second value. The method can further include determining a grouped aggregated time series. The method can further include the device determining a first set of input values for a machine learning model. The method can further include the device determining a first forecasted future value.

公开(公告)号：US20250094861A1

公开(公告)日：2025-03-20

申请号：US18470220

申请日：2023-09-19

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Vikas Pandey ,  Chirag Ahuja ,  Jie Xing ,  Hariharan Balasubramanian

IPC: G06N20/00

Abstract: Techniques for time-bound hyperparameter tuning are disclosed. The techniques enable the determination of optimized hyperparameters for a machine learning (ML) model given a specified time bound using a three-stage approach. A series of trials are executed, during each of which the ML model is trained using a distinct set of hyperparameters. In the first stage, a small number of trials are executed to initialize the algorithm. In the second and third stages, a certain number of trials are executed in each stage. The number of trials to run in each stage are determined using one or more computer-implemented techniques. The computer-implemented techniques can also be used to narrow the hyperparameter search space and the feature space. Following the third stage, a set of optimized hyperparameters is adopted based a predefined optimization criterion like minimization of an error function.

公开(公告)号：US20250094732A1

公开(公告)日：2025-03-20

申请号：US18663988

申请日：2024-05-14

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Haad Khan ,  Liyu Gong ,  Jie Xing ,  Pramir Sarkar

IPC: G06F40/40

Abstract: A summary generation and summary selection system is disclosed that is capable of automatically evaluating multiple summaries generated for content and selecting a single summary that is deemed to be the “best” among the multiple generated summaries. The system includes capabilities to use multiple different selection techniques to select the best summary from multiple generated summaries. A first selection technique involves identifying entities and entity relationships from the content to be summarized and selecting a summary from multiple summaries generated for the content based on the entities and entity relationships identified in the content. A second selection technique involves determining a set of questions that are answered by each summary. The technique then selects a summary based upon the set of questions answered by each summary. The system then outputs the selected summary as the summary for the content.

公开(公告)号：US20240362517A1

公开(公告)日：2024-10-31

申请号：US18138930

申请日：2023-04-25

Applicant: Oracle International Corporation

Inventor： Ankit Aggarwal ,  Chirag Ahuja ,  Jie Xing ,  Michal Piotr Prussak

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Techniques described herein are directed toward univariate series truncation policy using change point detection. An example method can include a device determining a first time series comprising a first set of data points indexed over time. The device can determine a first and second change point of the first time series based on a relative position and a category of the change points. The device can generate a first and second truncated time series based on the change points. The device can generate a first and second forecasted value using a first forecasting technique. The device can compare the first forecasted value and the second forecasted value using a second time series. The device can select one of the forecasting techniques to generate a final forecasted value based on the comparison. The device can generate, using the selected first forecasting technique, the final forecasted value.