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    Systems and methods for determining an object type and an attribute for an observation based on fused sensor data
    63.
    发明授权
    Systems and methods for determining an object type and an attribute for an observation based on fused sensor data 有权

    公开(公告)号：US11841927B2

    公开(公告)日：2023-12-12

    申请号：US17659670

    申请日：2022-04-19

    申请人： FORD GLOBAL TECHNOLOGIES, LLC

    发明人： Kevin Lee Wyffels

    IPC分类号： G06V20/58 ,  G06F18/2415 ,  G06F18/25 ,  G05D1/02

    CPC分类号： G06F18/254 ,  G05D1/0248 ,  G06F18/24155 ,  G06V20/58

    摘要： This document discloses system, method, and computer program product embodiments for controlling a vehicle. For example, the method includes: receiving an observation probability distribution function associated with a target object that was detected by sensor(s) of an autonomous vehicle (AV); identifying a target attribute associated with the target object; detecting a target attribute value associated with the target attribute; and issuing vehicle control instruction(s) that cause AV to adjust driving operation(s) using a future behavior of the target object predicted based on an attribute probability distribution function that defines a probability that the target attribute is actually present for the target object based on probability distribution function(s), wherein the attribute probability distribution function comprises: a probability value associated with the target attribute being present for the target object; and a probability value associated with the target attribute not being present for the target object.

    TELEMETRY REPORTING IN VEHICLE SUPER RESOLUTION SYSTEMS
    64.
    发明公开
    TELEMETRY REPORTING IN VEHICLE SUPER RESOLUTION SYSTEMS 审中-公开

    公开(公告)号：US20230388150A1

    公开(公告)日：2023-11-30

    申请号：US18202680

    申请日：2023-05-26

    申请人： Cisco Technology, Inc.

    发明人： David A. Maluf ,  Shesha Bhushan Sreenivasamurthy

    IPC分类号： H04L12/40 ,  G06N3/08 ,  G06F18/2415

    CPC分类号： H04L12/40136 ,  G06N3/08 ,  H04L12/40 ,  G06F18/24155 ,  H04L2012/40273 ,  H04L2012/40215

    摘要： In one embodiment, a processor of a vehicle detects a difference between a physical characteristic of the vehicle predicted by a first machine learning-based model and a physical characteristic of the vehicle indicated by telemetry data generated by a sub-system of the vehicle. The processor forms a packet payload of an update packet indicative of the detected difference, based in part on a relevancy of the physical characteristic to the first machine learning-based model. The processor applies a synchronization strategy to the update packet, to synchronize the update packet with a second machine learning-based model executed by a receiver. The processor sends the update packet to the receiver via a network, to update the second machine learning-based model.

    SYSTEMS AND METHODS FOR DETERMINING A USER SPECIFIC MISSION OPERATIONAL PERFORMANCE METRIC, USING MACHINE-LEARNING PROCESSES
    65.
    发明公开
    SYSTEMS AND METHODS FOR DETERMINING A USER SPECIFIC MISSION OPERATIONAL PERFORMANCE METRIC, USING MACHINE-LEARNING PROCESSES 审中-公开

    公开(公告)号：US20230385172A1

    公开(公告)日：2023-11-30

    申请号：US18232676

    申请日：2023-08-10

    申请人： GMECI, LLC

    发明人： Bradford R. Everman ,  Brian Scott Bradke

    IPC分类号： G06F11/34 ,  G06N20/00 ,  G06F18/214 ,  G06F18/2431 ,  G06F18/2415

    CPC分类号： G06F11/3428 ,  G06N20/00 ,  G06F18/2155 ,  G06F18/2431 ,  G06F18/24155

    摘要： Aspects relate to system and methods for determining a user specific mission operational performance, using machine-learning processes. An exemplary system includes a computing device configured to perform operations including receiving user-input structured data from at least a user device, receiving observed structured data related to the user and a mission performance metric, inputting the user-input structured data and the observed structured data to a machine-learning model, generating a user performance metric as a function of the machine-learning model, receiving a deterministic mission operational performance metric, disaggregating a deterministic user performance metric as a function of the deterministic mission operation performance metric and the mission performance metric, inputting training data to a machine-learning algorithm, where the training data includes the user-input structured data and the observed structured data correlated to the deterministic user performance metric, and training the machine-learning model as a function of the machine-learning algorithm and the training data.

    Automatically determining storage system data breaches using machine learning techniques
    68.
    发明授权
    Automatically determining storage system data breaches using machine learning techniques 有权

    公开(公告)号：US11763039B2

    公开(公告)日：2023-09-19

    申请号：US17135274

    申请日：2020-12-28

    申请人： Dell Products L.P.

    发明人： Deepak Nagarajegowda ,  Bina K. Thakkar

    IPC分类号： G06F21/62 ,  G06F21/78 ,  G06F21/54 ,  G06N20/00 ,  G06F21/55 ,  G06F18/20 ,  G06F18/2415

    CPC分类号： G06F21/78 ,  G06F18/24155 ,  G06F18/295 ,  G06F21/54 ,  G06F21/554 ,  G06N20/00

    摘要： Methods, apparatus, and processor-readable storage media for automatically determining storage system data breaches using machine learning techniques are provided herein. An example computer-implemented method includes configuring a storage system by designating at least one storage object within the storage system for storing data identified as to be protected from breach; generating at least one multivariate data breach probability function using historical performance data of the designated storage object(s) and/or historical capacity data of the designated storage object(s); calculating at least one data breach score using the at least one multivariate data breach probability function, one or more machine learning techniques, and additional performance data of the designated storage object(s) and/or additional capacity data of the designated storage object(s); and performing one or more automated actions based at least in part on the at least one data breach score.