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    • 2. 发明授权
    • System and methods for content-based financial decision making support
    • 基于内容的财务决策支持系统和方法
    • US08930247B1
    • 2015-01-06
    • US12023934
    • 2008-01-31
    • Xiaoping ZhangDavid KedmeyFang Wang
    • Xiaoping ZhangDavid KedmeyFang Wang
    • G06Q30/00G06Q10/06G06F17/18
    • G06Q10/0639G06F17/18G06Q40/04G06Q40/06
    • Robust content-based decision-making support is enabled by software with a customizable knowledge base. Utilizing proprietary information contained within a knowledge base, the software enables users to search the indexed database by feature, example firm, or pattern and update the knowledge base based on the results. The information contained in the knowledge base enables results to be ranked by relevance and enables other feedback to be provided. The system and methods provide process support by helping financial professionals identify, analyze, and construct data analysis patterns based on individual domain knowledge and preferences. The system and methods automatically detect abnormal patterns and automatically analyze their correlations to market events to provide further process support to financial professionals. Using the results of any searching, analysis, and processing, the system and methods provide a neural network or other learning algorithm to provide content-based decision-making support.
    • 基于内容的稳定的决策支持由具有可定制知识库的软件实现。 利用知识库中包含的专有信息,该软件使用户能够通过特征,示例公司或模式搜索索引数据库,并根据结果更新知识库。 知识库中包含的信息可使结果按照相关性进行排名,并提供其他反馈信息。 系统和方法通过帮助金融专业人员根据个人领域知识和偏好来识别,分析和构建数据分析模式来提供流程支持。 系统和方法自动检测异常模式,并自动分析其与市场事件的相关性,为财务专业人员提供进一步的流程支持。 使用任何搜索,分析和处理的结果,系统和方法提供神经网络或其他学习算法来提供基于内容的决策支持。
    • 4. 发明申请
    • SYSTEM AND METHOD FOR USING DATA INCIDENT BASED MODELING AND PREDICTION
    • 使用基于数据事件的建模和预测的系统和方法
    • US20160019218A1
    • 2016-01-21
    • US14750669
    • 2015-06-25
    • Xiaoping ZhangDavid KedmeyFang Wang
    • Xiaoping ZhangDavid KedmeyFang Wang
    • G06F17/30
    • G06F16/2457G06F16/2428G06F2216/03G06Q40/04
    • A system and method for enabling information extraction from large data sets (so-called “big data”) according to a new paradigm is disclosed. This system does not generate functions describing why certain inputs result in certain outputs. Instead, it creates incident mappings of inputs to outputs without regard to why inputs result in outputs. These mappings can be distributions or other data sets representative of different outcomes occurring. This enables several useful operations. For example, by providing a data set indicative of outputs that have historically occurred following a particular input, the disclosed system can be used to predict future outcomes with probabilities. For example, if a particular stock price pattern is provided as an input, the system generates an output data set indicating the probabilities of certain price behaviors following that input pattern. This data set can thus be used to predict future behavior. Other useful operations are disclosed herein.
    • 公开了一种用于根据新范例从大数据集(所谓的“大数据”)提取信息的系统和方法。 该系统不会产生描述为什么某些输入导致某些输出的功能。 相反,它会创建对输出的输入的事件映射,而不考虑为什么输入会导致输出。 这些映射可以是代表不同结果发生的分布或其他数据集。 这样可以进行几个有用的操作。 例如,通过提供指示在特定输入之后历史地发生的输出的数据集,所公开的系统可以用于以概率来预测未来的结果。 例如,如果提供特定股票价格模式作为输入,则系统生成指示在该输入模式之后的某些价格行为的概率的输出数据集。 因此,该数据集可用于预测将来的行为。 本文公开了其它有用的操作。
    • 9. 发明授权
    • Channel decoding method and decoder for tail-biting codes
    • 通道解码方法和解码器,用于尾码
    • US09083385B2
    • 2015-07-14
    • US13809932
    • 2012-03-19
    • Xiaotao WangHua QianJing XuHao HuangYang YangFang Wang
    • Xiaotao WangHua QianJing XuHao HuangYang YangFang Wang
    • H03M13/03H03M13/23H03M13/37H03M13/41H03M13/00H03M13/15
    • H03M13/23H03M13/1505H03M13/3738H03M13/413H03M13/6505H03M13/6525
    • A channel decoding method and decoder are disclosed. The decoding method is based on a Circular Viterbi Algorithm (CVA), rules out impossible initial states one by one through iterations according the received soft information sequence, and finally finds the global optimal tail-biting path. In the present invention, all impossible iterations are ruled out through multiple iterations, and only the initial state having most likelihood with the received sequence survives. The algorithm is finally convergent to an optimal tail-biting path to be output. In addition, the method also updates a metric of a maximum likelihood tail-biting path (MLTBP) or rules out impossible initial states through the obtained surviving tail-biting path, thereby effectively solving the problem that the algorithm is not convergent due to a circular trap, providing a practical optimal decoding algorithm for a tail-biting convolutional code, reducing the complexity of an existing decoding scheme, and saving the storage space.
    • 公开了一种信道解码方法和解码器。 解码方法基于循环维特比算法(CVA),根据接收到的软信息序列逐个排除不可能的初始状态,最终找到全局最优尾巴路径。 在本发明中,通过多次迭代排除所有不可能的迭代,并且只有具有接收序列的最可能性的初始状态才能存活。 该算法最终收敛到要输出的最佳尾巴路径。 另外,该方法还通过所获得的幸存尾巴路径来更新最大似然尾巴路径(MLTBP)的度量或者排除不可能的初始状态,从而有效地解决了算法由于循环而不会收敛的问题 陷阱,为尾部卷积码提供实用的最佳解码算法,降低了现有解码方案的复杂度,并节省了存储空间。