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    • 3. 发明申请
    • COMMUNITY MODEL BASED POINT OF INTEREST LOCAL SEARCH
    • 基于社区模型的兴趣点本地搜索
    • US20110313954A1
    • 2011-12-22
    • US12819080
    • 2010-06-18
    • Feng ZhaoNicholas D. LaneDimitrios LymperopoulosJing Zhao
    • Feng ZhaoNicholas D. LaneDimitrios LymperopoulosJing Zhao
    • G06F17/30G06F15/18
    • G06F16/35G06F16/335G06F16/9535G06F16/9537
    • The present disclosure describes a community model based point of interest local search platform. Specifically, logs of users that store selections while accessing a point of interest application are loaded into a database. The logs are of users that have similar demographic or other community attributes. The logs are then mined for contextual parameters, including, but not limited to time of day, day of week, distance, activity, environment, popularity, and personal preferences. The point of interest selections are then mapped to a multi-dimensional map where each dimension corresponds to a contextual parameter. Clusters are evaluated by a classifier and classes of users of the community are identified. When a user then queries the community model based point of interest local search platform, contextual parameters are submitted with the query, relevant classes identified, and the corresponding point of interest information is displayed to the user.
    • 本公开描述了基于社区模型的兴趣点本地搜索平台。 具体来说,在访问兴趣点应用程序时存储选择的用户的日志被加载到数据库中。 日志是具有类似人口统计或其他社区属性的用户。 然后为日志参数挖掘日志,包括但不限于一天中的时间,星期几,距离,活动,环境,人气和个人喜好。 然后将兴趣点选择映射到多维映射,其中每个维度对应于上下文参数。 集群由分类器评估,并且识别社区的用户类。 当用户随后查询基于社区模型的兴趣点本地搜索平台时,上下文参数与查询一起提交,识别相关类,并将相应的兴趣点信息显示给用户。
    • 4. 发明授权
    • Distance measurements between computing devices
    • 计算设备之间的距离测量
    • US09170325B2
    • 2015-10-27
    • US13599823
    • 2012-08-30
    • Zengbin ZhangDavid Chiyuan ChuThomas MoscibrodaXiaomeng ChenFeng Zhao
    • Zengbin ZhangDavid Chiyuan ChuThomas MoscibrodaXiaomeng ChenFeng Zhao
    • G01S3/80G01S11/14
    • G01S11/14
    • Some implementations provide techniques and arrangements for distance measurements between computing devices. Some examples determine a distance between devices based at least in part on a propagation time of audio tones between the devices. Further, some examples determine the arrival time of the audio tones by performing autocorrelation on streaming data corresponding to recorded sound to determine a timing of an autocorrelation peak indicative of a detection of an audio tone in the streaming data. In some cases, cross correlation may be performed on the streaming data in a search window to determine a timing of a cross correlation peak indicative of the detection of the audio tone in the streaming data. The location of the search window in time may be determined based at least in part on the timing of the detected autocorrelation peak.
    • 一些实现提供了用于计算设备之间的距离测量的技术和布置。 一些示例至少部分地基于设备之间的音频音调的传播时间来确定设备之间的距离。 此外,一些示例通过对与记录的声音相对应的流数据执行自相关来确定音频音调的到达时间,以确定指示流数据中的音频音调的检测的自相关峰值的定时。 在一些情况下,可以在搜索窗口中对流数据执行互相关,以确定指示流数据中的音频音调的检测的互相关峰值的定时。 可以至少部分地基于检测到的自相关峰值的定时来确定搜索窗口在时间上的位置。
    • 5. 发明申请
    • DISTANCE MEASUREMENTS BETWEEN COMPUTING DEVICES
    • 计算设备之间的距离测量
    • US20140064034A1
    • 2014-03-06
    • US13599823
    • 2012-08-30
    • Zengbin ZhangDavid Chiyuan ChuThomas MoscibrodaXiaomeng ChenFeng Zhao
    • Zengbin ZhangDavid Chiyuan ChuThomas MoscibrodaXiaomeng ChenFeng Zhao
    • G01S3/80
    • G01S11/14
    • Some implementations provide techniques and arrangements for distance measurements between computing devices. Some examples determine a distance between devices based at least in part on a propagation time of audio tones between the devices. Further, some examples determine the arrival time of the audio tones by performing autocorrelation on streaming data corresponding to recorded sound to determine a timing of an autocorrelation peak indicative of a detection of an audio tone in the streaming data. In some cases, cross correlation may be performed on the streaming data in a search window to determine a timing of a cross correlation peak indicative of the detection of the audio tone in the streaming data. The location of the search window in time may be determined based at least in part on the timing of the detected autocorrelation peak.
    • 一些实现提供了用于计算设备之间的距离测量的技术和布置。 一些示例至少部分地基于设备之间的音频音调的传播时间来确定设备之间的距离。 此外,一些示例通过对与记录的声音相对应的流数据执行自相关来确定音频音调的到达时间,以确定指示流数据中的音频音调的检测的自相关峰值的定时。 在一些情况下,可以在搜索窗口中对流数据执行互相关,以确定指示流数据中的音频音调的检测的互相关峰值的定时。 可以至少部分地基于检测到的自相关峰值的定时来确定搜索窗口在时间上的位置。
    • 8. 发明授权
    • Methods and apparatuses for facilitating content-based image retrieval
    • 用于促进基于内容的图像检索的方法和装置
    • US08571358B2
    • 2013-10-29
    • US12982698
    • 2010-12-30
    • Feng ZhaoHao Wang
    • Feng ZhaoHao Wang
    • G06K9/54G06F7/00
    • G06F17/30256
    • Methods and apparatuses are provided for facilitating content-based image retrieval. A method may include determining a selected target image. The method may further include generating a candidate region of interest set including one or more regions of interest within the target image. The method may additionally include determining a recommended region of interest set including one or more recommended regions of interest selected from the candidate region of interest set based at least in part upon evaluation criteria. The evaluation criteria may be determined based at least in part upon analysis of maintained region of interest-based searching history. The method may also include providing the recommended region of interest set for user selection of one or more target regions of interest from the recommended region of interest set as query criteria for searching an image library for one or more result images. Corresponding apparatuses are also provided.
    • 提供了用于促进基于内容的图像检索的方法和装置。 方法可以包括确定所选择的目标图像。 该方法还可以包括在目标图像内生成包括一个或多个感兴趣区域的关注集合候选区域。 该方法可以另外包括至少部分地基于评估标准来确定包括一个或多个推荐兴趣区域的兴趣区域,所述推荐区域选自所述候选区域集合。 评估标准可以至少部分地基于对维护的基于兴趣的搜索历史的区域的分析来确定。 该方法还可以包括为推荐的感兴趣区域设置用户选择一个或多个感兴趣的目标区域作为用于搜索图像库的一个或多个结果图像的查询准则的建议的兴趣区域。 还提供了相应的装置。