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    METHOD OF PERFORMING A PROCESS AND OPTIMIZING CONTROL SIGNALS USED IN THE PROCESS
    2.
    发明申请
    METHOD OF PERFORMING A PROCESS AND OPTIMIZING CONTROL SIGNALS USED IN THE PROCESS 审中-公开

    公开(公告)号：WO2020188328A1

    公开(公告)日：2020-09-24

    申请号：PCT/IB2019/057648

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： BROOKS, Brian E. ,  BENOIT, Gilles J. ,  OLSON, Peter O. ,  OLSON, Tyler W. ,  NAYAR, Himanshu ,  ARSENAULT, Frederick J. ,  JOHNSON, Nicholas A.

    IPC: G05B19/042

    Abstract: A method of performing a process using a plurality of control signals and resulting in a plurality of measurable outcomes is described. The method includes optimizing the plurality of control signals by at least: receiving a plurality of process constraints; receiving, for each measurable outcome, an optimum range; receiving, for each control signal, a plurality of potential optimum values; iteratively performing the process, where for each process iteration, the value of each control signal is selected from among the plurality of potential optimum values received for the control signal; for each process iteration, measuring each outcome in the plurality of measurable outcomes; and generating confidence intervals for the control signals to determine a causal relationship between the control signals and the measurable outcomes. The method includes performing the process using at least the control signals determined by the causal relationship to causally affect at least one of the measurable outcomes.

    DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION
    3.
    发明申请
    DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION 审中-公开

    公开(公告)号：WO2020188331A1

    公开(公告)日：2020-09-24

    申请号：PCT/IB2019/057673

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： BENOIT, Gilles J. ,  BROOKS, Brian E. ,  OLSON, Peter O. ,  OLSON, Tyler W.

    IPC: G06N3/08

    Abstract: A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS
    5.
    发明申请
    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS 审中-公开

    公开(公告)号：WO2020190327A1

    公开(公告)日：2020-09-24

    申请号：PCT/US2019/050701

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： BROOKS, Brian E. ,  BENOIT, Gilles J. ,  OLSON, Peter O. ,  OLSON, Tyler W. ,  NAYAR, Himanshu ,  ARSENAULT, Frederick J. ,  JOHNSON, Nicholas A.

    IPC: G06N5/00 ,  G06N7/00 ,  G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting control settings for the environment based on (i) a causal model that identifies causal relationships between possible settings for controllable elements in the environment and environment responses that reflect a performance of the control system in controlling the environment and (ii) current values of a set of internal parameters; and during the repeatedly selecting: monitoring environment responses to the selected control settings; determining, based on the environment responses, an indication that one or more properties of the environment have changed; and in response, modifying the current values of one or more of the internal parameters.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS
    8.
    发明申请
    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS 审中-公开

    公开(公告)号：WO2020190328A1

    公开(公告)日：2020-09-24

    申请号：PCT/US2019/050703

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： NAYAR, Himanshu ,  ARSENAULT, Frederick J. ,  JOHNSON, Nicholas A. ,  BROOKS, Brian E. ,  BENOIT, Gilles J. ,  OLSON, Peter O. ,  OLSON, Tyler W.

    IPC: G06F17/30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes identifying a procedural instance; selecting control settings for the procedural instance, comprising, for a particular one of the controllable elements: assigning the procedural instance to a cluster for the particular controllable element in accordance with current values of a set of clustering parameters for the particular controllable element; and selecting a setting for the particular controllable element for the procedural instances based on a causal model that is specific to the cluster; obtaining environment responses to the selected control settings that define a value of the performance metric for the procedural instance; and updating, for the particular controllable element, the causal model for the cluster for the controllable element to which the procedural instance was assigned based on the value of the performance metric.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS
    9.
    发明申请
    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS 审中-公开

    公开(公告)号：WO2020190326A1

    公开(公告)日：2020-09-24

    申请号：PCT/US2019/050699

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： BROOKS, Brian E. ,  BENOIT, Gilles J. ,  OLSON, Peter O. ,  OLSON, Tyler W. ,  NAYAR, Himanshu ,  ARSENAULT, Frederick J. ,  JOHNSON, Nicholas A.

    IPC: G06F17/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

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