中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

50 records (took 0.026 seconds)

    Capturing Ordinal Historical Dependence in Graphical Event Models with Tree Representations
    13.
    发明申请
    Capturing Ordinal Historical Dependence in Graphical Event Models with Tree Representations 有权

    公开(公告)号：US20230123421A1

    公开(公告)日：2023-04-20

    申请号：US17503557

    申请日：2021-10-18

    Applicant: International Business Machines Corporation

    Inventor： Debarun Bhattacharjya ,  Tian Gao ,  Dharmashankar Subramanian

    IPC: G06N20/00 ,  G06K9/62 ,  G06N7/00

    Abstract: A computer system, computer program product, and computer-implemented method are provided that includes learning a tree ordered graphical event model from an event dataset. Temporal relationships between one or more events in received temporal event data is modeled, and an ordered graphical event model (OGEM) graph is learned. The learned OGEM graph is configured to capture ordinal historical dependence. Leveraging the learned OGEM graph, a parameter sharing architecture is learned, including order dependent statistical and causal co-occurrence relationships among event types. A control signal to an operatively coupled event device that is associated with at least one event type reflected in the learned parameter sharing environment is dynamically issued. The control signal is configured to selectively control an event injection.

    PROCESS OPTIMIZATION WITH JOINT-LEVEL INFLOW MODEL
    14.
    发明申请
    PROCESS OPTIMIZATION WITH JOINT-LEVEL INFLOW MODEL 有权

    公开(公告)号：US20230119440A1

    公开(公告)日：2023-04-20

    申请号：US17505343

    申请日：2021-10-19

    Applicant: International Business Machines Corporation

    Inventor： Nianjun Zhou ,  Dharmashankar Subramanian

    IPC: G05B13/04 ,  G05B19/418 ,  G05B23/02

    Abstract: One or more systems, computer-implemented methods and/or computer program products to facilitate a process to monitor and/or facilitate a modification to a manufacturing process. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that identifies values of inflow data of one or more inflows of a set of inflows to a manufacturing process as control variables, and a computation optimization component that optimizes one or more intermediate flows, outflows or flow qualities of the manufacturing process using, for mode-specific regression models, decision variables that are based on a set of joint-levels of the control variables. An operation mode determination component can determine operation modes of the manufacturing process that are together defined by a set of joint-levels of the control variables.

    MODEL FIDELITY MONITORING AND REGENERATION FOR MANUFACTURING PROCESS DECISION SUPPORT
    15.
    发明申请
    MODEL FIDELITY MONITORING AND REGENERATION FOR MANUFACTURING PROCESS DECISION SUPPORT 有权

    公开(公告)号：US20220011760A1

    公开(公告)日：2022-01-13

    申请号：US16923148

    申请日：2020-07-08

    Applicant: International Business Machines Corporation

    Inventor： Nianjun Zhou ,  Dharmashankar Subramanian ,  Dhavalkumar C. Patel ,  Anuradha Bhamidipaty

    IPC: G05B23/02 ,  G05B13/04 ,  G05B13/02

    Abstract: Techniques for model fidelity monitoring and regeneration for manufacturing process decision support are described herein. Aspects of the invention include determining that an output of a regression model corresponding to a current time period of decision support for a manufacturing process is not within a predefined range of a historical process dataset, wherein the regression model was constructed based on the historical process dataset, and performing an accuracy and fidelity analysis on the regression model based on process data from the manufacturing process corresponding to a previous time period. Based on a result of the accuracy and fidelity analysis being below a threshold, a mismatch of the regression model as compared to the manufacturing process is determined. Based on determining the mismatch, a temporary regression model corresponding to the manufacturing process is generated, and decision support for the manufacturing process is performed based on the temporary regression model.

    USING NEGATIVE EVIDENCE TO PREDICT EVENT DATASETS
    16.
    发明申请
    USING NEGATIVE EVIDENCE TO PREDICT EVENT DATASETS 有权

    公开(公告)号：US20210383194A1

    公开(公告)日：2021-12-09

    申请号：US16894970

    申请日：2020-06-08

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Dharmashankar Subramanian ,  Tian Gao ,  Karthikeyan Shanmugam ,  Debarun Bhattacharjya

    IPC: G06N3/04 ,  G06N3/08

    Abstract: A computer-implemented method is presented for learning relationships between multiple event types by employing a multi-channel neural graphical event model (MCN-GEM). The method includes receiving, by a computing device, time-stamped, asynchronous, irregularly spaced event epochs, generating, by the computing device, at least one fake epoch between each inter-event interval, wherein fake epochs represent negative evidence, feeding the event epochs and the at least one fake epoch into long short term memory (LSTM) cells, computing hidden states for each of the event epochs and the at least one fake epoch, feeding the hidden states into spatial and temporal attention models, and employing an average attention across all event epochs to generate causal graphs representing causal relationships between all the event epochs.

    AUTOMATIC DECOMPOSITION METHOD FOR MDP
    18.
    发明申请
    AUTOMATIC DECOMPOSITION METHOD FOR MDP 有权

    公开(公告)号：US20250045608A1

    公开(公告)日：2025-02-06

    申请号：US18230138

    申请日：2023-08-03

    Applicant: International Business Machines Corporation

    Inventor： Alexander Zadorojniy ,  Long Vu ,  Dharmashankar Subramanian

    IPC: G06N7/01 ,  G06Q10/04 ,  G06Q50/04

    Abstract: A method for Markov Decision Process (“MDP”) decomposition includes receiving data elements for a problem that include finite state data for a set of state variables and a finite set of actions. A portion of the state data corresponding to state variables represents states. The method incudes creating two or more sub-MDPs. Each sub-MDP includes a portion of the set of state variables, the set of actions and a same reward function. The method includes executing each sub-MDP. Results include a policy and an expected reward from the reward function. The policy of the sub-MDP maps states of the sub-MDP to actions. The method includes aggregating, based on the expected rewards of the results, the actions of the policies of the sub-MDPs to create a resultant policy with resultant actions and generating, using state entries for the set of state variables, results to the problem based on the resultant policy.

Patent Agency Ranking