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    • 1. 发明申请
    • TARGET REPLICATION DISTRIBUTION
    • 目标复制分配
    • WO2014209320A1
    • 2014-12-31
    • PCT/US2013/048198
    • 2013-06-27
    • THOMSON LICENSINGIOANNIDIS, EstratiosMASSOULIE, LaurentPICCONI, FabioJIANG, Wenjie
    • IOANNIDIS, EstratiosMASSOULIE, LaurentPICCONI, FabioJIANG, Wenjie
    • H04W28/02
    • H04L67/1008H04L65/4069H04L65/4084H04L65/80H04L67/1097
    • A content distribution network, including: a content server that stores a set of content items; a plurality of gateways for storing and serving content requests to a subset of the content items, the plurality of gateways being grouped into a plurality of classes of gateways; and a plurality of class trackers corresponding to the plurality of classes; wherein each class tracker manages a placement of content items and an assignment of content requests for its class of gateways; wherein the plurality of class trackers exchange congestion signals among themselves; wherein, for each content item, each class tracker determines a fraction of gateways in its class of gateways that store the content item and a rate of content requests that are forwarded to the content server or other class trackers, based on requests for the content item entering its class and the congestion signals received from other class trackers.
    • 一种内容分发网络,包括:存储一组内容项的内容服务器; 多个网关,用于存储和向内容项的子集服务内容请求,所述多个网关被分组成多个类别的网关; 以及对应于所述多个类的多个类跟踪器; 其中每个类跟踪器管理内容项的放置和对其类别的网关的内容请求的分配; 其中所述多个类跟踪器在它们之间交换拥塞信号; 其中,对于每个内容项目,每个类别跟踪器基于对所述内容项目的请求来确定存储所述内容项目的网关类别的网关的一小部分以及被转发到所述内容服务器或其他类别跟踪器的内容请求的速率 进入其类别和从其他类跟踪器收到的拥塞信号。
    • 10. 发明申请
    • COMPARISON-BASED ACTIVE SEARCHING/LEARNING
    • 基于比较的主动搜索/学习
    • WO2013169968A1
    • 2013-11-14
    • PCT/US2013/040248
    • 2013-05-09
    • THOMSON LICENSINGIOANNIDIS, EfstratiosMASSOULIE, Laurent
    • IOANNIDIS, EfstratiosMASSOULIE, Laurent
    • G06F17/30
    • G06F17/30451G06F17/3002G06F17/30023G06N99/005
    • A method is provided for performing a content search through comparisons, where a user is presented with two candidate objects and reveals which is closer to the user's intended target object. The disclosed principles provide active strategies for finding the user's target with few comparisons. The so-called rank-net strategy for noiseless user feedback is described. For target distributions with a bounded doubling constant, rank-net finds the target in a number of steps close to the entropy of the target distribution and hence of the optimum. The case of noisy user feedback is also considered. In that context a variant of rank-nets is also described, for which performance bounds within a slowly growing function (doubly logarithmic) of the optimum are found. Nu-merical evaluations on movie datasets show that rank-net matches the search efficiency of generalized binary search while incurring a smaller computational cost.
    • 提供了一种用于通过比较来执行内容搜索的方法,其中向用户呈现两个候选对象,并且揭示哪个更靠近用户的预期目标对象。 所公开的原则提供了用于通过少量比较来查找用户的目标的主动策略。 描述了所谓的无噪声用户反馈的排序策略。 对于具有有限倍增常数的目标分布,等级网在接近目标分布熵的几个步骤中找到目标,因此找到目标。 还会考虑用户反馈异常的情况。 在这种情况下,还描述了排序网格的变体,其中找到了最优的缓慢增长函数(双对数)的性能界限。 电影数据集的动词评估表明,排序网匹配广义二叉搜索的搜索效率,同时导致较小的计算成本。