公开(公告)号：US20230113287A1

公开(公告)日：2023-04-13

申请号：US17694323

申请日：2022-03-14

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Anku Kumar Pandey ,  Ravijeet Ranjit Kumar ,  Samik Raychaudhuri

IPC: G06K9/62 ,  G06N20/20

Abstract: A time series forecasting service system is disclosed. The system identifies a set of cross-validation parameters to be used for cross-validating a model to be used for generating a requested forecast. The requested forecast includes a time series dataset and a forecast horizon identifying a number of time steps for which a forecast is to be made using the time series dataset. The system identifies an objective function to be minimized for determining optimal values for the set of cross-validation parameters and identifies constraints for the cross-validation parameters. The system uses an optimization technique to determine the optimal values for the cross-validation parameters. The optimization technique performs processing that determines the optimal values by minimizing the objective function while satisfying the set of constraints. The system uses the optimal values for the cross-validation parameters to perform cross-validation of the model to be used for making the requested forecast.

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：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：US20240005201A1

公开(公告)日：2024-01-04

申请号：US17854487

申请日：2022-06-30

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Lakshmi Sirisha Chodisetty ,  Samik Raychaudhuri

IPC: G06N20/00 ,  G06K9/62 ,  G06F16/2458

CPC classification number: G06N20/00 ,  G06F16/2477 ,  G06K9/6248 ,  G06K9/6242

Abstract: Aspects if the disclosure are directed towards multi-step forecasting via temporal aggregation. An example embodiment includes a method the includes receiving a time series including a first time step value and a second time step value. The method can further include generating a temporally aggregated time series by summing the first time step value and the second time step value to create a third time step value. The method can further include calculating a first set of input values and a second set of input values from the temporally aggregated time series. The method can further include forecasting a fourth time step value using the first set of input values and the second set of input values, and a fifth time step using the second set of input values from the temporally aggregated time series.

公开(公告)号：US20230274195A1

公开(公告)日：2023-08-31

申请号：US17854482

申请日：2022-06-30

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Vikas Pandey ,  Praneet Pabolu ,  Samik Raychaudhuri

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: The present embodiments relate to using feature engineering to generate time-varying features via metadata. A first exemplary embodiment provides a method for performing feature engineering to generate time-varying features. The method can include receiving a first value and a second value of the time-series data. The method can further include receiving metadata that describes a relationship between the first value and the second value. The method can further include detecting the relationship between the first value and the second value based on the metadata. The method can further include generating, a time-varying feature from a combination of the first value and the second value based on the relationship detected from the metadata. The method can further include generating, by implementing the machine learning forecasting model, a forecasted value for the time-series data based on the time-varying feature.