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    Dynamic Address Negotiation for Shared Memory Regions in Heterogeneous Muliprocessor Systems
    1.
    发明申请
    Dynamic Address Negotiation for Shared Memory Regions in Heterogeneous Muliprocessor Systems 有权
    非均匀多处理器系统中共享内存区域的动态地址协商

    公开(公告)号：US20150046661A1

    公开(公告)日：2015-02-12

    申请号：US13961085

    申请日：2013-08-07

    Applicant: QUALCOMM Incorporated

    Inventor： Anil Gathala ,  Andrey Ermolinskiy ,  Christopher A. Vick

    IPC: G06F3/06

    CPC classification number: G06F3/0638 ,  G06F3/0613 ,  G06F3/0671 ,  G06F9/544

    Abstract: Mobile computing devices may be configured to compile and execute portions of a general purpose software application in an auxiliary processor (e.g., a DSP) of a multiprocessor system by reading and writing information to a shared memory. A first process (P1) on the applications processor may request address negotiation with a second process (P2) on the auxiliary processor, obtain a first address map from a first operating system, and send the first address map to the auxiliary processor. The second process (P2) may receive the first address map, obtain a second address map from a second operating system, identify matching addresses in the first and second address maps, store the matching addresses as common virtual addresses, and send the common virtual addresses back to the applications processor. The first and second processes (i.e., P1 and P2) may each use the common virtual addresses to map physical pages to the memory.

    Abstract translation: 移动计算设备可以被配置为通过将信息读取和写入到共享存储器来编译和执行多处理器系统的辅助处理器（例如，DSP）中的通用软件应用的部分。 应用处理器上的第一进程（P1）可以在辅助处理器上请求与第二进程（P2）的地址协商，从第一操作系统获得第一地址映射，并将第一地址映射发送到辅助处理器。 第二进程（P2）可以接收第一地址映射，从第二操作系统获得第二地址映射，识别第一和第二地址映射中的匹配地址，将匹配地址存储为公共虚拟地址，并发送公共虚拟地址 回到应用处理器。 第一和第二进程（即P1和P2）可以各自使用公共虚拟地址将物理页面映射到存储器。

    Collaborative learning for efficient behavioral analysis in networked mobile device
    3.
    发明授权
    Collaborative learning for efficient behavioral analysis in networked mobile device 有权
    网络化移动设备中有效行为分析的协同学习

    公开(公告)号：US09298494B2

    公开(公告)日：2016-03-29

    申请号：US13804518

    申请日：2013-03-14

    Applicant: QUALCOMM Incorporated

    Inventor： Anil Gathala ,  Rajarshi Gupta

    IPC: H04W24/00 ,  G06F9/46 ,  G06F11/34 ,  G06F21/50 ,  G06F21/55 ,  H04L12/24 ,  H04W24/02 ,  H04W52/02 ,  G06N5/04

    CPC classification number: G06F9/46 ,  G06F11/3013 ,  G06F11/3062 ,  G06F11/3082 ,  G06F11/3409 ,  G06F21/50 ,  G06F21/552 ,  G06F2201/86 ,  G06N5/043 ,  H04L41/06 ,  H04W24/00 ,  H04W24/02 ,  H04W52/0209

    Abstract: Methods, systems and devices for classifying mobile device behaviors of a first mobile device may include the first mobile device monitoring mobile device behaviors to generate a behavior vector, and applying the behavior vector to a first classifier model to obtain a first determination of whether a mobile device behavior is benign or not benign. The first mobile device may also send the behavior vector to a second mobile device, which may receive and apply the behavior vector to a second classifier model to obtain a second determination of whether the mobile device behavior is benign or not benign. The second mobile device may send the second determination to the first mobile device, which may receive the second determination, collate the first determination and the second determination to generate collated results, and determine whether the mobile device behavior is benign or not benign based on the collated results.

    Abstract translation: 用于分类第一移动设备的移动设备行为的方法，系统和设备可以包括第一移动设备监视移动设备行为以生成行为向量，以及将行为向量应用于第一分类器模型，以获得移动设备 设备行为是良性还是不良。 第一移动设备还可以将行为向量发送到第二移动设备，第二移动设备可以将行为向量接收并应用于第二分类器模型，以获得移动设备行为是否良性或良性的第二确定。 第二移动设备可以向可能接收第二确定的第一移动设备发送第二确定，对第一确定和第二确定进行整理以生成整理的结果，并且基于该第二确定来确定移动设备的行为是良性还是不良 整理结果。

    Adaptive observation of behavioral features on a mobile device
    5.
    发明授权
    Adaptive observation of behavioral features on a mobile device 有权
    自适应观察移动设备上的行为特征

    公开(公告)号：US09495537B2

    公开(公告)日：2016-11-15

    申请号：US13923547

    申请日：2013-06-21

    Applicant: QUALCOMM Incorporated

    Inventor： Rajarshi Gupta ,  Vinay Sridhara ,  Anil Gathala ,  Xuetao Wei

    IPC: G06F21/00 ,  G06F21/50 ,  G06F21/31 ,  G06F21/55

    CPC classification number: G06F21/50 ,  G06F21/316 ,  G06F21/552

    Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.

    Abstract translation: 用于智能地，动态地和/或自适应地检测待观察的计算设备行为，要观察的行为的数量以及细节或粒度的级别来检测可疑或降级性能的移动设备行为的方法，设备和系统 在那里要观察移动设备的行为。 各个方面有效地识别可疑或降低性能的移动设备行为，而不需要过多的处理，存储器或能量资源。

    On-device real-time behavior analyzer
    6.
    发明授权
    On-device real-time behavior analyzer 有权
    在设备上的实时行为分析仪

    公开(公告)号：US09324034B2

    公开(公告)日：2016-04-26

    申请号：US13773247

    申请日：2013-02-21

    Applicant: QUALCOMM Incorporated

    Inventor： Rajarshi Gupta ,  Xuetao Wei ,  Anil Gathala ,  Vinay Sridhara

    IPC: G06N99/00 ,  G06N5/04

    CPC classification number: G06N99/005 ,  G06N5/043

    Abstract: Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.

    Abstract translation: 用于在通信系统中生成数据模型的方法，系统和设备可以包括应用机器学习技术来生成使用加强的决策树来描述行为矢量语料库的分类器模型的第一族。 可以使用这样的行为矢量来计算升压决策树的一个或多个节点的权重值。 可以识别分类器模型的因素，其可能基于所计算的权重值来确定移动设备行为是良性还是不良性。 用于升压的决策树节点的计算权重值可以包括计算生成的升压决策树节点的独占应答比率。 识别的因素可以应用于行为矢量语料库以产生第二类分类器模型，其识别与使移动设备能够确定行为是良性还是不良性相关的较少因素和数据点。

    COLLABORATIVE LEARNING FOR EFFICIENT BEHAVIORAL ANALYSIS IN NETWORKED MOBILE DEVICE
    8.
    发明申请
    COLLABORATIVE LEARNING FOR EFFICIENT BEHAVIORAL ANALYSIS IN NETWORKED MOBILE DEVICE 有权
    网络移动设备高效行为分析的协同学习

    公开(公告)号：US20130303159A1

    公开(公告)日：2013-11-14

    申请号：US13804518

    申请日：2013-03-14

    Applicant: QUALCOMM INCORPORATED

    Inventor： Anil Gathala ,  Rajarshi Gupta

    IPC: H04W24/00

    CPC classification number: G06F9/46 ,  G06F11/3013 ,  G06F11/3062 ,  G06F11/3082 ,  G06F11/3409 ,  G06F21/50 ,  G06F21/552 ,  G06F2201/86 ,  G06N5/043 ,  H04L41/06 ,  H04W24/00 ,  H04W24/02 ,  H04W52/0209

    Abstract: Methods, systems and devices for classifying mobile device behaviors of a first mobile device may include the first mobile device monitoring mobile device behaviors to generate a behavior vector, and applying the behavior vector to a first classifier model to obtain a first determination of whether a mobile device behavior is benign or not benign. The first mobile device may also send the behavior vector to a second mobile device, which may receive and apply the behavior vector to a second classifier model to obtain a second determination of whether the mobile device behavior is benign or not benign. The second mobile device may send the second determination to the first mobile device, which may receive the second determination, collate the first determination and the second determination to generate collated results, and determine whether the mobile device behavior is benign or not benign based on the collated results.

    Abstract translation: 用于分类第一移动设备的移动设备行为的方法，系统和设备可以包括第一移动设备监视移动设备行为以生成行为向量，以及将行为向量应用于第一分类器模型，以获得移动设备 设备行为是良性还是不良。 第一移动设备还可以将行为向量发送到第二移动设备，第二移动设备可以将行为向量接收并应用于第二分类器模型，以获得移动设备行为是否良性或良性的第二确定。 第二移动设备可以向可能接收第二确定的第一移动设备发送第二确定，对第一确定和第二确定进行整理以生成整理的结果，并且基于该第二确定来确定移动设备的行为是良性还是不良 整理结果。

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