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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.
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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.
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公开(公告)号：US20240403693A1
公开(公告)日：2024-12-05
申请号：US18203568
申请日：2023-05-30
Applicant: Oracle International Corporation
Inventor： Uday Bhaskar Yalamanchi , FNU Akshat , Prashanth Ramanathan , Abhiram Jagarlapudi , Ye Zhang , Aditya Banerjee , Varaprasad Ballingam , Athinder Patlola , Beiwen Guo , Varun Ketanbhai Shah , Safia Rahmat , Shreyas Vinayakumar , Jigar Mody , Elad Ziklik , Senthilkumar Ponnappan , Pranav Varia , Denesh Krishnan Rajaram , Hariharan Balasubramanian
IPC: G06N20/00
Abstract: Techniques for providing machine-learned (ML)-based artificial intelligence (AI) capabilities are described. In one technique, multiple AI capabilities are stored in a cloud environment. While the AI capabilities are stored, a request for a particular AI capability is received from a computing device of a user. Also, in response to receiving training data based on input from the user, the training data is stored in a tenancy, associated with the user, in the cloud environment. In response to receiving the request, the particular AI capability is accessed, a ML model is trained based on the particular AI capability and the training data to produce a trained ML model, and an endpoint, in the cloud environment, is generated that is associated with the trained ML model. The endpoint is provided to the tenancy associated with the user.
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公开(公告)号：US20230297861A1
公开(公告)日：2023-09-21
申请号：US17696685
申请日：2022-03-16
Applicant: Oracle International Corporation
Inventor： Chirag Ahuja , Vikas Rakesh Upadhyay , Syed Fahad Allam Shah , Samik Raychaudhuri , Hariharan Balasubramanian , Michal Piotr Prussak , Shwan Ashrafi
IPC: G06N5/04 , G06F16/901
CPC classification number: G06N5/046 , G06F16/9024 , G06N20/00
Abstract: A computing device may access a graph comprising one or more model nodes, one or more dataset nodes, and one or more edges, the model nodes having a plurality of features. The device may add one or more test dataset nodes and test edges to the graph. The device may perform a series of iterative steps until a threshold is reached. For each iterative step: a selection probability is determined, the selection probability being based at least in part on a plurality of selection criteria; a particular model node is selected, the particular model node being selected based at least in part on the selection probability; the selection criteria is updated based at least in part on the particular model; and the plurality of features are updated based at least in part on the particular model. The device may provide the particular model node selected in the last iterative step.
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公开(公告)号：US12242332B2
公开(公告)日：2025-03-04
申请号：US17962869
申请日：2022-10-10
Applicant: Oracle International Corporation
Inventor： Shwan Ashrafi , Michal Piotr Prussak , Hariharan Balasubramanian , Vijayalakshmi Krishnamurthy
IPC: G06F11/07
Abstract: Techniques are described for identifying root cause anomalies in time series. Information to be used for root cause analysis (RCA) is obtained from a graph neural network (GNN) and is used to construct a dependency graph having nodes corresponding to each time series and directed edges corresponding to dependencies between the time series. Nodes corresponding to time series that do not contain anomalies may be removed from this dependency graph, as well as edges connected to these nodes. This edge and node removal may result in the creation of one or more sub-graphs from the dependency graph. A root cause analysis algorithm may be run on these one or more sub-graphs to create a root cause graph for each sub-graph. These root cause graphs may then be used to identify root cause anomalies within the multiple time series, as well as sequences of anomalies within the multiple time series.
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Patent Agency Ranking
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
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.
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公开(公告)号：US20240112065A1
公开(公告)日：2024-04-04
申请号：US17934299
申请日：2022-09-22
Applicant: Oracle International Corporation
Inventor： Amir Hossein Rezaeian , Hariharan Balasubramanian
CPC classification number: G06N20/00 , G06K9/6262 , G06N7/005
Abstract: The present disclosure generally relates to systems and methods for operation research optimization. The systems and methods include receiving, at a data processing system, a payload including a request for optimizing a service and processing the payload using a meta learning classifier. The processing includes extracting a problem and use case characteristics from the payload, predicting at least one machine learning model capable of solving the problem having the use case characteristics, and executing the at least one machine learning model to solve the problem. The systems and methods also include outputting a solution to the problem for optimizing the service from the at least one machine learning model, and providing the solution to a computing device.
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公开(公告)号：US20230121897A1
公开(公告)日：2023-04-20
申请号：US17506200
申请日：2021-10-20
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Yixiu Liu , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross , Hariharan Balasubramanian
Abstract: Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes partitioning a frequency spectrum of output into a plurality of discrete bins, wherein the output is collected from vibration sensors monitoring a reference device; generating a representative time series signal for each bin while the device is operated in a deterministic stress load; generating a PSD for each bin by converting each signal from the time domain to the frequency domain; determining a maximum power spectral density value and a peak frequency value for each bin; selecting a subset of the bins that have maximum PSD values exceeding a threshold; assigning the representative time series signals from the selected subset of bins as operation vibration signals indicative of operational load on the reference device; and configuring a machine learning model based on at least the operation vibration signals.
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公开(公告)号：US11740122B2
公开(公告)日：2023-08-29
申请号：US17506200
申请日：2021-10-20
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Yixiu Liu , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross , Hariharan Balasubramanian
Abstract: Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes partitioning a frequency spectrum of output into a plurality of discrete bins, wherein the output is collected from vibration sensors monitoring a reference device; generating a representative time series signal for each bin while the device is operated in a deterministic stress load; generating a PSD for each bin by converting each signal from the time domain to the frequency domain; determining a maximum power spectral density value and a peak frequency value for each bin; selecting a subset of the bins that have maximum PSD values exceeding a threshold; assigning the representative time series signals from the selected subset of bins as operation vibration signals indicative of operational load on the reference device; and configuring a machine learning model based on at least the operation vibration signals.
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10.发明申请公开(公告)号：US20230035541A1
公开(公告)日：2023-02-02
申请号：US17386965
申请日：2021-07-28
Applicant: Oracle International Corporation
Inventor： Menglin Liu , Richard P. Sonderegger , Kenneth P. Baclawski , Dieter Gawlick , Anna Chystiakova , Guang C. Wang , Zhen Hua Liu , Hariharan Balasubramanian , Kenny C. Gross
Abstract: The disclosed embodiments relate to a system that optimizes a prognostic-surveillance system to achieve a user-selectable functional objective. During operation, the system allows a user to select a functional objective to be optimized from a set of functional objectives for the prognostic-surveillance system. Next, the system optimizes the selected functional objective by performing Monte Carlo simulations, which vary operational parameters for the prognostic-surveillance system while the prognostic-surveillance system operates on synthesized signals, to determine optimal values for the operational parameters that optimize the selected functional objective.
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