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    DEEP CAUSAL LEARNING FOR DATA STORAGE AND PROCESSING POWER MANAGEMENT
    43.
    发明公开
    DEEP CAUSAL LEARNING FOR DATA STORAGE AND PROCESSING POWER MANAGEMENT 审中-公开

    公开(公告)号：US20230325077A1

    公开(公告)日：2023-10-12

    申请号：US18210216

    申请日：2023-06-15

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

    Inventor： Gilles J. Benoit ,  Nicholas A. Johnson

    IPC: G06F3/06

    CPC classification number: G06F3/0604 ,  G06F3/0658 ,  G06F3/0685

    Abstract: Method for active data storage management to optimize use of an electronic memory. The method includes providing signal injections for data storage. The signal injections can include various types of data and sizes of data files. Response signals corresponding with the signal injections are received, and a utility of those signals is measured. Based upon the utility of the response signals, parameters relating to storage of the data is modified to optimize use of long-term high latency passive data storage and short-term low latency active data storage.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS
    47.
    发明申请
    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS 有权

    公开(公告)号：US20220187774A1

    公开(公告)日：2022-06-16

    申请号：US17438096

    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: G05B13/04 ,  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 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.

    METHOD OF OPTIMIZING CONTROL SIGNALS USED IN OPERATING VEHICLE
    48.
    发明申请
    METHOD OF OPTIMIZING CONTROL SIGNALS USED IN OPERATING VEHICLE 有权

    公开(公告)号：US20220176968A1

    公开(公告)日：2022-06-09

    申请号：US17436751

    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 ,  John A. Wheatley

    IPC: B60W40/064 ,  B60W40/08 ,  B60W40/105 ,  G05B13/02

    Abstract: A method of optimizing a plurality of control signals used in operating a vehicle is described. The operation has a plurality of associated measurable parameters. The method includes: for each control signal, selecting a plurality of potential optimum values from a predetermined set; operating the vehicle in at least a first sequence of operation iterations, where for each pair of sequential first and second operation iterations in the first sequence of operation iterations, the potential optimum value of one control signal in the first operation iteration is replaced in the second operation iteration with a next potential optimum value of the control signal, while the potential optimum values of the remaining control signals are maintained; for each operation iteration, measuring each parameter in the plurality of measurable parameters; and generating confidence intervals for the control signals to determine causal relationships between the control signals and the measurable parameters.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS
    49.
    发明申请
    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS 有权

    公开(公告)号：US20220146995A1

    公开(公告)日：2022-05-12

    申请号：US17439105

    申请日：2019-09-11

    Applicant: 3M INNOVATIVE PROPERTIES COMPANY

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

    IPC: G05B13/04 ,  G05B13/02 ,  G06N7/00

    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 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.

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