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    • 2. 发明申请
    • DISCOVERING AND AUTOMATICALLY SIZING A PLACE OF RELEVANCE
    • 发现和自动设置相关位置
    • US20130080457A1
    • 2013-03-28
    • US13627823
    • 2012-09-26
    • Vidya NarayananPawel LukowiczLukas D. KuhnJin Won LeeSanjiv Nanda
    • Vidya NarayananPawel LukowiczLukas D. KuhnJin Won LeeSanjiv Nanda
    • G06F17/30
    • H04W4/021G06F17/30241G06F17/30598H04W4/023H04W4/043H04W4/21H04W64/00Y10S707/921
    • One or more mobile devices make measurements while moving along a path. Each measurement may comprise a specific group of identifiers of wireless transmitters, and strengths of corresponding wireless signals. A set of measurements are made in a sequence along a path, and the subsets of the measurements are identified for satisfying a test on a value of a measure of similarity of measurements included in the subset. A new place of relevance is identified, by comparing the just-described subsets of the measurements with similar subsets of additional measurements (e.g. by clustering). Alternatively, a known place of relevance (e.g. having a label) is identified, by comparing the just-described subsets of the measurements with pre-computed model of measurements. Also, the just-described subsets of the measurements may be compared with corresponding subsets of measurements of another path, e.g. to identify common portions therein.
    • 一个或多个移动设备在沿着路径移动时进行测量。 每个测量可以包括无线发射机的特定组标识符和相应无线信号的强度。 在沿着路径的序列中进行一组测量,并且识别测量的子集以满足对包括在子集中的测量的相似度的测量值的测试。 通过将刚刚描述的测量子集与附加测量的类似子集(例如通过聚类)进行比较来识别新的相关地点。 或者,通过将刚刚描述的测量的子集与预先计算的测量模型进行比较来识别已知的相关位置(例如,具有标签)。 此外,可以将刚刚描述的测量的子集与另一路径的测量的相应子集进行比较,例如。 以识别其中的公共部分。
    • 6. 发明授权
    • Device position estimates from motion and ambient light classifiers
    • 来自运动和环境光分类器的装置位置估计
    • US09366749B2
    • 2016-06-14
    • US13348497
    • 2012-01-11
    • Leonard Henry GrokopVidya Narayanan
    • Leonard Henry GrokopVidya Narayanan
    • G06F15/00G01C19/00G01S5/16G01C21/16G06F1/16H04M1/725G01C19/34G01C19/44
    • G01S5/16G01C19/34G01C19/44G01C21/165G06F1/1686G06F1/1694H04M1/72522
    • A position estimate for a mobile device is generated using data from motion sensors, such as accelerometers, magnetometers, and/or gyroscopes, and data from light sensors, such as an ambient light sensor, proximity sensor and/or camera intensity sensor. A plurality of proposed positions with associated likelihoods is generated by analyzing information from the motion sensors and a list of candidate positions is produced based on information from the light sensors. At least one of the plurality of proposed positions is eliminated using the list of candidate positions and a position estimate for the mobile device is determined based on the remaining proposed positions and associated likelihoods. The proposed positions may be generated by extracting features from the information from the motion sensors and using models to generate likelihoods for the proposed positions. The likelihoods may be filtered over time. Additionally, a confidence metric may be generated for the estimated position.
    • 使用来自诸如加速度计,磁力计和/或陀螺仪的运动传感器的数据以及来自诸如环境光传感器,接近传感器和/或照相机强度传感器的光传感器的数据来生成移动设备的位置估计。 通过分析来自运动传感器的信息来产生具有相关似然性的多个提出的位置,并且基于来自光传感器的信息产生候选位置的列表。 使用候选位置的列表来消除多个提出的位置中的至少一个,并且基于剩余的建议位置和相关联的可能性来确定移动设备的位置估计。 可以通过从运动传感器的信息中提取特征并使用模型来产生所提出的位置的可能性来产生所提出的位置。 可能性可能会随时间过滤。 另外,可以为估计位置生成置信度量度。