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    SEQUENTIAL FEATURE COMPUTATION FOR POWER EFFICIENT CLASSIFICATION
    2.
    发明申请
    SEQUENTIAL FEATURE COMPUTATION FOR POWER EFFICIENT CLASSIFICATION 审中-公开
    用于功率有效分类的顺序特征计算

    公开(公告)号：US20140143579A1

    公开(公告)日：2014-05-22

    申请号：US13841960

    申请日：2013-03-15

    Applicant: QUALCOMM INCORPORATED

    Inventor： Leonard Henry GROKOP ,  Disha AHUJA ,  Rashmi KULKARNI ,  Shankar SADASIVAM ,  Edward Harrison TEAGUE ,  Sanjiv NANDA

    IPC: G06F1/28

    CPC classification number: G06F1/28 ,  G06F9/4893 ,  Y02D10/24

    Abstract: Disclosed is an apparatus and method for power efficient processor scheduling of features. In one embodiment, features may be scheduled for sequential computing, and each scheduled feature may receive a sensor data sample as input. In one embodiment, scheduling may be based at least in part on each respective feature's estimated power usage. In one embodiment, a first feature in the sequential schedule of features may be computed and before computing a second feature in the sequential schedule of features, a termination condition may be evaluated.

    Abstract translation: 公开了一种用于功率效率处理器调度功能的装置和方法。 在一个实施例中，特征可以被调度用于顺序计算，并且每个调度的特征可以接收传感器数据样本作为输入。 在一个实施例中，调度可以至少部分地基于每个相应特征的估计功率使用。 在一个实施例中，可以计算特征的顺序调度中的第一特征，并且在计算特征的顺序调度表中的第二特征之前，可以评估终止条件。

    CONTEXT LABELS FOR DATA CLUSTERS
    4.
    发明申请
    CONTEXT LABELS FOR DATA CLUSTERS 有权
    数据集的语境标签

    公开(公告)号：US20140129560A1

    公开(公告)日：2014-05-08

    申请号：US13831393

    申请日：2013-03-14

    Applicant: QUALCOMM INCORPORATED

    Inventor： Leonard Henry GROKOP ,  Shankar SADASIVAM

    IPC: G06F17/30

    CPC classification number: G06F17/30705 ,  G05B13/00 ,  G06F15/18 ,  G06F17/30265 ,  G06F17/30598 ,  G06F17/30702 ,  G06F17/3071 ,  G06F17/30864 ,  G06N5/043 ,  H04M1/72569

    Abstract: Systems and methods for applying and using context labels for data clusters are provided herein. A method described herein for managing a context model associated with a mobile device includes obtaining first data points associated with a first data stream assigned to one or more first data sources; assigning ones of the first data points to respective clusters of a set of clusters such that each cluster is respectively assigned ones of the first data points that exhibit a threshold amount of similarity and are associated with times within a threshold amount of time of each other; compiling statistical features and inferences corresponding to the first data stream or one or more other data streams assigned to respective other data sources; assigning context labels to each of the set of clusters based on the statistical features and inferences.

    Abstract translation: 本文提供了应用和使用数据集群上下文标签的系统和方法。 本文描述的用于管理与移动设备相关联的上下文模型的方法包括：获得与分配给一个或多个第一数据源的第一数据流相关联的第一数据点; 将所述第一数据点中的一个分配给一组集群的相应集群，使得每个集群分别被分配出呈现阈值相似度的第一数据点中的一个并且与彼此的阈值时间量内的时间相关联; 编译对应于分配给各个其他数据源的第一数据流或一个或多个其他数据流的统计特征和推论; 基于统计特征和推论将上下文标签分配给每组集群。

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