会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 73. 发明授权
    • Method of synthesizing radioisotopically labeled oligonucleotides by
direct solid-phase 5' phosphitylation
    • 通过直接固相5'磷酸化合成放射性同位素标记的寡核苷酸的方法
    • US5631361A
    • 1997-05-20
    • US447092
    • 1995-05-22
    • Weitian TanRadhakrishnan P. IyerZhiwei JiangDong YuSudhir Agrawal
    • Weitian TanRadhakrishnan P. IyerZhiwei JiangDong YuSudhir Agrawal
    • C07H21/00C07H1/02
    • C07H21/00
    • The present invention comprises a novel method of incorporating radiolabels and other type of labels at one or more predetermined sites within an oligonucleotide. In particular, the method comprises contacting a nascent, support-bound oligonucleotide having an unprotected 5' hydroxyl group with a suitable activating agent, followed by contacting the resulting activated nascent oligonucleotide with a labeled, Y-protected mononucleotide having an unprotected 3'-hydroxyl, thereby condensing the labeled mononucleotide and nascent oligonucleotide. Normal automated synthesis can then be continued to yield the oligonucleotide of desired length having the label in the desired location. This method advantageously yields oligonucleotides with high specific activity. The oligonucleotides thereby produced are useful for determining the pharmacokinetics and biodistribution of their non-radiolabeled counterparts, both in vitro and in vivo.
    • 本发明包括在寡核苷酸内的一个或多个预定位点掺入放射性标记和其他类型的标记的新方法。 特别地,该方法包括将具有未保护的5'羟基的新生支持结合的寡核苷酸与合适的活化剂接触,然后将所得活化的新生寡核苷酸与具有未保护的3'-羟基的标记的受Y保护的单核苷酸接触 从而使标记的单核苷酸和新生寡核苷酸缩合。 然后可以继续进行正常的自动合成,得到所需长度的寡核苷酸,其具有所需位置的标记。 该方法有利地产生具有高比活性的寡核苷酸。 由此产生的寡核苷酸可用于在体外和体内测定其非放射性标记的对应物的药代动力学和生物分布。
    • 76. 发明授权
    • Tensor deep stacked neural network
    • 张量深层神经网络
    • US09165243B2
    • 2015-10-20
    • US13397580
    • 2012-02-15
    • Dong YuLi DengBrian Hutchinson
    • Dong YuLi DengBrian Hutchinson
    • G06N3/04G06N3/08
    • G06N3/04G06N3/08
    • A tensor deep stacked neural (T-DSN) network for obtaining predictions for discriminative modeling problems. The T-DSN network and method use bilinear modeling with a tensor representation to map a hidden layer to the predication layer. The T-DSN network is constructed by stacking blocks of a single hidden layer tensor neural network (SHLTNN) on top of each other. The single hidden layer for each block then is separated or divided into a plurality of two or more sections. In some embodiments, the hidden layer is separated into a first hidden layer section and a second hidden layer section. These multiple sections of the hidden layer are combined using a product operator to obtain an implicit hidden layer having a single section. In some embodiments the product operator is a Khatri-Rao product. A prediction is made using the implicit hidden layer and weights, and the output prediction layer is consequently obtained.
    • 张量深层次神经(T-DSN)网络,用于获得鉴别建模问题的预测。 T-DSN网络和方法使用具有张量表示的双线性建模来将隐藏层映射到预测层。 T-DSN网络由单个隐层张量神经网络(SHLTNN)的堆叠堆叠构成。 然后,每个块的单个隐藏层被分离或分成多个两个或更多个部分。 在一些实施例中,隐藏层被分成第一隐藏层部分和第二隐藏层部分。 使用产品运算符组合隐藏层的这些多个部分以获得具有单个部分的隐式隐藏层。 在一些实施例中,产品操作者是Khatri-Rao产品。 使用隐式隐层和权重进行预测,从而获得输出预测层。
    • 77. 发明授权
    • Confidence measure generation for speech related searching
    • 语音相关搜索的置信度生成
    • US08793130B2
    • 2014-07-29
    • US13428917
    • 2012-03-23
    • Ye-Yi WangYun-Cheng JuDong Yu
    • Ye-Yi WangYun-Cheng JuDong Yu
    • G10L15/00
    • G10L15/1822
    • A method of generating a confidence measure generator is provided for use in a voice search system, the voice search system including voice search components comprising a speech recognition system, a dialog manager and a search system. The method includes selecting voice search features, from a plurality of the voice search components, to be considered by the confidence measure generator in generating a voice search confidence measure. The method includes training a model, using a computer processor, to generate the voice search confidence measure based on selected voice search features.
    • 提供了一种产生置信度量产生器的方法,用于语音搜索系统中,该语音搜索系统包括包括语音识别系统,对话管理器和搜索系统的语音搜索组件。 该方法包括从多个语音搜索组件中选择语音搜索特征,以由置信度量产生器在生成语音搜索置信度量时考虑。 该方法包括使用计算机处理器来训练模型,以基于所选择的语音搜索特征生成语音搜索置信度度量。