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    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS
    26.
    发明申请
    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS 有权

    公开(公告)号：US20220146988A1

    公开(公告)日：2022-05-12

    申请号：US17438677

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： Brian E. Brooks ,  Gilles J. Benoit ,  Peter O. Olson ,  Tyler W. Olson ,  Himanshu Nayar ,  Frederick J. Arsenault ,  Nicholas A. Johnson

    IPC: G05B13/02

    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; determining a temporal extent for the procedural instance based on temporal extent parameters for the one or more entities in the procedural instance; selecting control settings for the procedural instance; monitoring environment responses to the control settings that are received for the one or more entities; determining which of the environment responses to attribute to the procedural instance in a causal model; and adjusting, based at least in part on the environment responses that are attributed to the procedural instance, the temporal extent parameters for the one or more entities.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS
    27.
    发明申请
    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS 有权

    公开(公告)号：US20220128955A1

    公开(公告)日：2022-04-28

    申请号：US17438725

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： Gilles J. Benoit ,  Brian E. Brooks ,  Peter O. Olson ,  Tyler W. Olson ,  Himanshu Nayar ,  Frederick J. Arsenault ,  Nicholas A. Johnson

    IPC: G05B13/02 ,  G05B23/02 ,  G06N5/04

    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, by a control system for the environment, control settings for the environment based on internal parameters of the control system, wherein: at least some of the control settings for the environment are selected based on a causal model, and the internal parameters include a first set of internal parameters that define a number of previously received performance metric values that are used to generate the causal model for a particular controllable element; obtaining, for each selected control setting, a performance metric value; determining that generating the causal model for the particular controllable element would result in higher system performance; and adjusting, based on the determining, the first set of internal parameters.

    DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION
    28.
    发明申请
    DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION 有权

    公开(公告)号：US20220121971A1

    公开(公告)日：2022-04-21

    申请号：US17431533

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： Gilles J. Benoit ,  Brian E. Brooks ,  Peter O. Olson ,  Tyler W. Olson

    IPC: G06N5/04 ,  G06K9/62

    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.

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