公开(公告)号：US20250104011A1

公开(公告)日：2025-03-27

申请号：US18680364

申请日：2024-05-31

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Vikas Agrawal ,  Jagdish Chand ,  Krishnan Ramanathan

IPC: G06Q10/087 ,  G06Q10/0631 ,  G06Q10/067 ,  G06Q30/0202

Abstract: In accordance with an embodiment, described herein are systems and methods for providing a supply chain command center for intelligent procurement assistance, based on an assessment of inventory trends, demand, or other inputs related to the procurement or management of an inventory of items. In accordance with an embodiment, the system can simultaneously optimize for a set of variables related to procurement, by creating time series forecasts of leaf-level independent variables, and performing a simulation within the boundary conditions of historical or expected distributions of each variable, to determine an optimal timing, quantity, location and/or vendor for each order of items that are to be placed in the inventory.

公开(公告)号：US20240242162A1

公开(公告)日：2024-07-18

申请号：US18096358

申请日：2023-01-12

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Dipawesh Pawar ,  Krishnan Ramanathan ,  Jagdish Chand

IPC: G06Q10/0639 ,  G06F40/30 ,  G06Q10/0631

CPC classification number: G06Q10/06398 ,  G06F40/30 ,  G06Q10/063112

Abstract: Embodiments described herein are generally related to computer data analytics, and computer-based methods of providing business intelligence data, and are particularly related to systems and methods for use with enterprise data for profile matching and generating gap scores and upskilling recommendations. In accordance with an embodiment, the system can operate to match a set of position requirements with candidate attributes or skillsets, ranking them on the basis of match scores. The system can be used, for example, to determine a skill gap between the position requirements and candidate attributes, and recommend which skills might be augmented to better address the position requirements.

公开(公告)号：US20210049183A1

公开(公告)日：2021-02-18

申请号：US17076164

申请日：2020-10-21

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Krishnan Ramanathan ,  Aman Madaan ,  Somashekhar Pammar

IPC: G06F16/25 ,  G06F16/28 ,  G06F16/22 ,  G06F16/2457

Abstract: In accordance with various embodiments, described herein are systems and methods for use with an analytic applications environment, for ranking of database tables for use in controlling extract, transform, load (ETL) processes. In accordance with an embodiment, the system uses a ranking algorithm or process to rank database tables and/or table columns associated with a set of data. The table/column rankings can then be used to prioritize ETL processing of a customer's data for use with a data warehouse or other data analytics environment. In accordance with an embodiment, the method includes determining a global rank; a business rank; and a tenant or customer-specific rank, for a plurality of tables and columns in a customer's database; and aggregating or otherwise using the determined rankings to control the ETL process for a particular customer (tenant), to load their data into the data warehouse.

公开(公告)号：US20250013911A1

公开(公告)日：2025-01-09

申请号：US18233975

申请日：2023-08-15

Applicant: Oracle International Corporation

Inventor： Vikas AGRAWAL ,  Karthik Bangalore Mani ,  Krishnan Ramanathan

IPC: G06N20/00

Abstract: Embodiments generate a machine learning (“ML”) model. Embodiments receive training data, the training data including time dependent data and a plurality of dates corresponding to the time dependent data. Embodiments date split the training data by two or more of the plurality of dates to generate a plurality of date split training data. For each of the plurality of date split training data, embodiments split the date split training data into a training dataset and a corresponding testing dataset using one or more different ratios to generate a plurality of train/test splits. For each of the train/test splits, embodiments determine a difference of distribution between the training dataset and the corresponding testing dataset. Embodiments then select the train/test split with a smallest difference of distribution and train and test the ML model using the selected train/test split.

公开(公告)号：US20240257019A1

公开(公告)日：2024-08-01

申请号：US18632114

申请日：2024-04-10

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Krishnan Ramanathan ,  Jagdish Chand ,  Aman Madaan

IPC: G06Q10/0637 ,  G06F16/21 ,  G06F16/25 ,  G06F16/27 ,  G06F16/28 ,  G06F17/18 ,  G06N5/04 ,  G06Q30/0201

CPC classification number: G06Q10/06375 ,  G06F16/211 ,  G06F16/254 ,  G06F16/27 ,  G06F16/283 ,  G06F17/18 ,  G06N5/04 ,  G06Q30/0201 ,  G06Q30/0206

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for determination of recommendations and alerts in such environments. A data pipeline or process can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. When provided as part of a software-as-a-service (SaaS) or cloud environment, the data sourced from a plurality of organizations can be aggregated, to leverage information gleaned from the collective or shared data. The system can be used to generate semantic alerts, including obtaining permission from; and analyzing the collective data of; the plurality of organizations, to determine operational advantages indicated by the data, and providing alerts associated with those operational advantages.

公开(公告)号：US12045215B2

公开(公告)日：2024-07-23

申请号：US17971832

申请日：2022-10-24

Applicant: Oracle International Corporation

Inventor： Akash Baviskar ,  Krishnan Ramanathan

IPC: G06F16/215 ,  G06F3/06 ,  G06N20/20

CPC classification number: G06F16/215 ,  G06F3/0641 ,  G06N20/20

Abstract: Embodiments detect duplicate invoices, each invoice including a plurality of fields. Embodiments generate synthetic training data using a plurality of training invoices and generating one or more modified fields for each of the plurality of training invoices. Embodiments train a machine learning model using the synthetic training data and generate a plurality of candidate invoice pairs. Embodiments input the plurality of candidate invoice pairs to the trained machine learning model and generate, by the trained machine learning model, a prediction of whether each of the candidate invoices pairs is a duplicate invoice pair.

公开(公告)号：US20200334089A1

公开(公告)日：2020-10-22

申请号：US16852509

申请日：2020-04-19

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Krishnan Ramanathan ,  Jagan Narayanareddy ,  Gunaranjan Vasireddy ,  Aman Madaan

IPC: G06F9/50 ,  G06F9/455 ,  G06F16/25

Abstract: In accordance with an embodiment, described herein are systems and methods for determining or allocating an amount, quantity, or number of compute instances or virtual machines for use with extract, transform, load (ETL) processes. In an example embodiment, a particular (e.g., optimal) number of virtual machines (VM's) can be determined by predicting ETL completion times for customers, using historical data. ETL processes can be simulated with an initial/particular number of virtual machines. If the predicted duration is greater than the desired duration, the number of virtual machines can be incremented, and the simulation repeated. Actual completion times from ETL processes can be fed back, to update a determined number of compute instances or virtual machines. In accordance with an embodiment, the system can be used, for example, to generate alerts associated with customer service level agreements (SLA's).

公开(公告)号：US20250104152A1

公开(公告)日：2025-03-27

申请号：US18680384

申请日：2024-05-31

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Vikas Agrawal ,  Krishnan Ramanathan ,  Jagdish Chand

IPC: G06Q40/06 ,  G06Q10/04

Abstract: In accordance with an embodiment, described herein are systems and methods for generating enterprise forecasts based on an analysis of input variables and direct forecasting. In accordance with an embodiment, the system can use linear regression or other mathematical models or modeling techniques to assess a set of variables related to an enterprise forecast, and their values and rate of change of such values, within a particular forecast window. Based on such assessment, the system can generate an enterprise forecast for that time period, or for a subsequent time period.

公开(公告)号：US11966870B2

公开(公告)日：2024-04-23

申请号：US16851872

申请日：2020-04-17

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Krishnan Ramanathan ,  Jagdish Chand ,  Aman Madaan

IPC: G06Q10/0637 ,  G06F16/21 ,  G06F16/25 ,  G06F16/27 ,  G06F16/28 ,  G06F17/18 ,  G06N5/04 ,  G06Q30/0201

CPC classification number: G06Q10/06375 ,  G06F16/211 ,  G06F16/254 ,  G06F16/27 ,  G06F16/283 ,  G06F17/18 ,  G06N5/04 ,  G06Q30/0201 ,  G06Q30/0206

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for determination of recommendations and alerts in such environments. A data pipeline or process can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. When provided as part of a software-as-a-service (SaaS) or cloud environment, the data sourced from a plurality of organizations can be aggregated, to leverage information gleaned from the collective or shared data. The system can be used to generate semantic alerts, including obtaining permission from; and analyzing the collective data of; the plurality of organizations, to determine operational advantages indicated by the data, and providing alerts associated with those operational advantages.

公开(公告)号：US20200334267A1

公开(公告)日：2020-10-22

申请号：US16851869

申请日：2020-04-17

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Krishnan Ramanathan ,  Gangadhar Ronanki ,  Aman Madaan

IPC: G06F16/25 ,  G06F16/28 ,  G06F16/21 ,  G06F17/18

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for automatic generation of asserts in such environments. A data pipeline or process, such as, for example an extract, transform, load (ETL) process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema. During the process of populating a data warehouse instance, the system can automatically generate dynamic data-driven ETL asserts, including determining a list of columns for tables in the data warehouse; determining a data type for each column; generating an assert for each determined data type; validating the generated assert; and maintaining the generated assert.