中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

93 records (took 0.023 seconds)

    Filtering and clustering crowd-sourced data for determining beacon positions
    62.
    发明授权
    Filtering and clustering crowd-sourced data for determining beacon positions 有权

    公开(公告)号：US08577389B2

    公开(公告)日：2013-11-05

    申请号：US13185520

    申请日：2011-07-19

    Applicant: Jyh-Han Lin ,  Sindhura Bandhakavi ,  Pradipta Kumar Basu

    Inventor： Jyh-Han Lin ,  Sindhura Bandhakavi ,  Pradipta Kumar Basu

    IPC: H04W68/00

    CPC classification number: H04W64/003 ,  H04W24/10 ,  H04W64/00

    Abstract: Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data involving a particular beacon is filtered based on a cluster start time associated with the beacon. A clustering analysis groups the filtered crowd-sourced data for the beacon into a plurality of clusters based on spatial distance. Timestamps associated with the crowd-sourced data in the clusters are compared to select one of the clusters. The crowd-sourced data associated with the selected cluster is used to determine position information for the moved beacon. The cluster start time for the beacon is adjusted based on the earliest timestamp associated with the positioned observations corresponding to the selected cluster. Adjusting the cluster start time removes from a subsequent analysis the positioned observations associated with one or more prior positions of the beacon.

    Network latency estimation for mobile devices
    63.
    发明授权
    Network latency estimation for mobile devices 有权
    移动设备的网络延迟估计

    公开(公告)号：US08566441B2

    公开(公告)日：2013-10-22

    申请号：US12951781

    申请日：2010-11-22

    Applicant: Sharad Agarwal ,  Jyh-Han Lin ,  Weili Liu

    Inventor： Sharad Agarwal ,  Jyh-Han Lin ,  Weili Liu

    IPC: G06F15/173

    CPC classification number: H04L43/0852 ,  H04L43/04 ,  H04L43/0864

    Abstract: Embodiments calculate an estimated latency between computing devices. A latency service aggregates latency records defining latency measurements and corresponding latency factors from a plurality of computing devices. From the aggregated latency records, the latency service defines relationships between the latency measurements and the corresponding latency factors. Responsive to a request for an estimated latency from a mobile computing device, the latency service applies the defined relationships to estimate the latency based on the latency factors associated with the received request. In some embodiments, the estimated latency includes three portions: a first latency value representing the latency from the mobile computing device to a cell site, a second latency value representing the latency from the cell site to an access point, and a third latency value representing the latency from the access point to a destination computing device.

    Abstract translation: 实施例计算计算设备之间的估计等待时间。 延迟服务聚合从多个计算设备定义延迟测量和相应的延迟因子的延迟记录。 延迟服务从汇总的延迟记录中定义延迟测量与相应延迟因子之间的关系。 响应于来自移动计算设备的估计等待时间的请求，等待时间服务应用所定义的关系，以基于与接收到的请求相关联的等待时间因素来估计等待时间。 在一些实施例中，估计的等待时间包括三个部分：表示从移动计算设备到小区站点的等待时间的第一等待时间值，表示从小区站点到接入点的等待时间的第二等待时间值，以及表示 从接入点到目的地计算设备的延迟。

    Location determination based on weighted received signal strengths
    64.
    发明授权
    Location determination based on weighted received signal strengths 有权
    基于加权接收信号强度的位置确定

    公开(公告)号：US08559975B2

    公开(公告)日：2013-10-15

    申请号：US13252605

    申请日：2011-10-04

    Applicant: Jyh-Han Lin ,  Pradipta Kumar Basu

    Inventor： Jyh-Han Lin ,  Pradipta Kumar Basu

    IPC: H04W24/00

    CPC classification number: G01S5/0252 ,  G01S5/021

    Abstract: Training datasets and test datasets consisting of observations (i.e., RSS measurements) partitioned per a mapping tile system are used to evaluate possible RSS weighting functions for each such tile. The observations from the training dataset are used to determine an optimal weighting function based on the training dataset that minimizes the error for the test data, wherein the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data. The accuracy of the optimal weighted function for each tile is characterized to determine whether to use the weighted function or an alternative (such as a non-weighted function) for subsequent inquiries.

    Abstract translation: 训练数据集和测试数据集被用于评估每个这样的瓦片可能的RSS加权函数，每个测绘数据集和测试数据集由每个映射瓦片系统划分的观测（即RSS测量）组成。 训练数据集的观测值用于确定基于最小化测试数据误差的训练数据集的最优加权函数，其中误差可以是测试数据集中观测值的GPS位置与预测位置之间的差值的函数 从RSS加权函数应用于测试数据。 每个瓦片的最佳加权函数的准确性的特征在于确定是否使用加权函数或替代（例如非加权函数）用于随后的查询。

    LOCATION DETERMINATION BASED ON WEIGHTED RECEIVED SIGNAL STRENGTHS
    66.
    发明申请
    LOCATION DETERMINATION BASED ON WEIGHTED RECEIVED SIGNAL STRENGTHS 有权
    基于加权信号强度的位置确定

    公开(公告)号：US20130023282A1

    公开(公告)日：2013-01-24

    申请号：US13252605

    申请日：2011-10-04

    Applicant: Jyh-Han Lin ,  Pradipta Kumar Basu

    Inventor： Jyh-Han Lin ,  Pradipta Kumar Basu

    IPC: H04W24/00

    CPC classification number: G01S5/0252 ,  G01S5/021

    Abstract: Training datasets and test datasets consisting of observations (i.e., RSS measurements) partitioned per a mapping tile system are used to evaluate possible RSS weighting functions for each such tile. The observations from the training dataset are used to determine an optimal weighting function based on the training dataset that minimizes the error for the test data, wherein the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data. The accuracy of the optimal weighted function for each tile is characterized to determine whether to use the weighted function or an alternative (such as a non-weighted function) for subsequent inquiries.

    Abstract translation: 训练数据集和测试数据集被用于评估每个这样的瓦片可能的RSS加权函数，每个测绘数据集和测试数据集由每个映射瓦片系统划分的观测（即RSS测量）组成。 训练数据集的观测值用于确定基于最小化测试数据误差的训练数据集的最优加权函数，其中误差可以是测试数据集中观测值的GPS位置与预测位置之间的差值的函数 从RSS加权函数应用于测试数据。 每个瓦片的最佳加权函数的准确性的特征在于确定是否使用加权函数或替代（例如非加权函数）用于随后的查询。

    NETWORK LATENCY ESTIMATION FOR MOBILE DEVICES
    67.
    发明申请
    NETWORK LATENCY ESTIMATION FOR MOBILE DEVICES 有权
    移动设备的网络延迟估计

    公开(公告)号：US20120131129A1

    公开(公告)日：2012-05-24

    申请号：US12951781

    申请日：2010-11-22

    Applicant: Sharad Agarwal ,  Jyh-Han Lin ,  Weili Liu

    Inventor： Sharad Agarwal ,  Jyh-Han Lin ,  Weili Liu

    IPC: G06F15/167 ,  G06F9/44

    CPC classification number: H04L43/0852 ,  H04L43/04 ,  H04L43/0864

    Abstract: Embodiments calculate an estimated latency between computing devices. A latency service aggregates latency records defining latency measurements and corresponding latency factors from a plurality of computing devices. From the aggregated latency records, the latency service defines relationships between the latency measurements and the corresponding latency factors. Responsive to a request for an estimated latency from a mobile computing device, the latency service applies the defined relationships to estimate the latency based on the latency factors associated with the received request. In some embodiments, the estimated latency includes three portions: a first latency value representing the latency from the mobile computing device to a cell site, a second latency value representing the latency from the cell site to an access point, and a third latency value representing the latency from the access point to a destination computing device.

    Abstract translation: 实施例计算计算设备之间的估计等待时间。 延迟服务聚合从多个计算设备定义延迟测量和相应的延迟因子的延迟记录。 延迟服务从汇总的延迟记录中定义延迟测量与相应延迟因子之间的关系。 响应于来自移动计算设备的估计等待时间的请求，等待时间服务应用所定义的关系，以基于与接收到的请求相关联的等待时间因素来估计等待时间。 在一些实施例中，估计的等待时间包括三个部分：表示从移动计算设备到小区站点的等待时间的第一等待时间值，表示从小区站点到接入点的等待时间的第二等待时间值，以及表示 从接入点到目的地计算设备的延迟。

    SELECTING BEACONS FOR LOCATION INFERENCE
    68.
    发明申请
    SELECTING BEACONS FOR LOCATION INFERENCE 有权
    选择竞争对手的位置

    公开(公告)号：US20110227791A1

    公开(公告)日：2011-09-22

    申请号：US12727901

    申请日：2010-03-19

    Applicant: Jyh-Han Lin ,  Lon-Chan Chu ,  Aravind Krishnamachari Seshadri ,  Prasanta Ghosal ,  Christopher Russell Rice ,  Anup Kashinath Pachlag

    Inventor： Jyh-Han Lin ,  Lon-Chan Chu ,  Aravind Krishnamachari Seshadri ,  Prasanta Ghosal ,  Christopher Russell Rice ,  Anup Kashinath Pachlag

    IPC: G01S1/08 ,  G01S3/02

    CPC classification number: G01S5/0252 ,  G01S5/02 ,  G01S5/0278 ,  H04W8/26 ,  H04W48/08 ,  H04W48/16 ,  H04W64/00

    Abstract: Location inference using selected beacons. Data is received representing a set of beacons observed by a computing device. The beacons are located within a first geographic area. A subset (e.g., a clique) of the beacons is selected based on a coverage area of each of the beacons, where each of the beacons in the selected subset has a coverage area that overlaps with the coverage area of each of the other beacons in the selected subset. Using known or estimated positions of the beacons, a second geographic area is defined based on the selected subset of beacons and the beacon reference data and the coverage areas associated therewith. The second geographic area, smaller than the first geographic area, represents an approximate location of the computing device. In some embodiments, the computing device is calculated to be within the second geographic area with 95% probability.

    Abstract translation: 使用选定信标的位置推理。 接收的数据表示由计算设备观察到的一组信标。 信标位于第一个地理区域内。 基于每个信标的覆盖区域选择信标的子集（例如，集团），其中所选择的子集中的每个信标具有与每个其他信标的覆盖区域重叠的覆盖区域 所选子集。 使用信标的已知或估计位置，基于所选择的信标子集和信标参考数据及与其相关联的覆盖区域来定义第二地理区域。 小于第一地理区域的第二地理区域代表计算设备的大致位置。 在一些实施例中，计算设备被计算为具有95％概率的第二地理区域内。

Patent Agency Ranking