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    • 6. 发明申请
    • Multi-modal handwriting recognition correction
    • 多模式手写识别校正
    • US20050128181A1
    • 2005-06-16
    • US10734305
    • 2003-12-15
    • Jian WangJian-Lai ZhouJiang WuHongyun YangXianfang WangWenli Zhu
    • Jian WangJian-Lai ZhouJiang WuHongyun YangXianfang WangWenli Zhu
    • G06F17/00G06K9/00G10L15/22G10L15/24
    • G10L15/24G06K9/00436G06K9/00872
    • Systems, methods, and computer-readable media for processing electronic ink receive an electronic ink input; convert the electronic ink input to a first machine-generated object using handwriting recognition; display the first machine-generated object on a display; receive speech input; convert the speech input to a second machine-generated object using speech recognition; generate a list of machine-generated objects based on the electronic ink input, the list including the first machine-generated object and alternative machine-generated objects and functioning as a dictionary for converting the speech input; and replace the first machine-generated object with the second machine-generated object. The machine-generated objects may correspond to words, lines, and/or other groupings of machine-generated text. A user may confirm that the second machine-generated object should replace the first machine-generated object and the system will perform the replacement. The systems and methods may generate a list of alternative machine-generated object candidates to the first machine-generated object based on handwriting recognition of the electronic ink input alone or in combination with a statistical language model.
    • 用于处理电子墨水的系统,方法和计算机可读介质接收电子墨水输入; 使用手写识别将电子墨水输入转换为第一机器生成对象; 在显示器上显示第一台机器生成的对象; 接收语音输入; 使用语音识别将语音输入转换为第二机器生成对象; 基于电子墨水输入生成机器生成对象的列表,该列表包括第一机器生成的对象和替代的机器生成的对象,并且用作用于转换语音输入的字典; 并用第二个机器生成的对象替换第一个机器生成的对象。 机器生成的对象可以对应于机器生成的文本的单词,行和/或其他分组。 用户可以确认第二机器生成的对象应该替换第一个机器生成的对象,并且系统将执行替换。 系统和方法可以基于单独的电子墨水输入的手写识别或者与统计语言模型结合来生成针对第一机器生成对象的备选的机器生成对象候选的列表。
    • 7. 发明申请
    • Extracting Patterns from Sequential Data
    • 从顺序数据提取模式
    • US20100191753A1
    • 2010-07-29
    • US12359343
    • 2009-01-26
    • Jie SuMin ChuWenli ZhuJian Wang
    • Jie SuMin ChuWenli ZhuJian Wang
    • G06F17/30
    • G06F17/30985G06F3/038
    • Described is a technology in which sequential data, such as application program command sequences, are processed into patterns, such as for use in analyzing program usage. In one aspect, sequential data may be first transformed via state machines that remove repeated data, group similar data into sub-sequences, and/or remove noisy data. The transformed data is then segmented into units. A pattern extraction mechanism extracts patterns from the units into a pattern set, by calculating a stability score (e.g., a mutual information score) between succeeding units, selecting the pair of units having the most stability (e.g., the highest score), and adding corresponding information for that pair into the pattern set. Pattern extraction is iteratively repeated until a stopping criterion is met, e.g., the pattern set reaches a defined size, or when the stability score is smaller than a pre-set threshold.
    • 描述了一种技术,其中诸如应用程序命令序列的顺序数据被处理成模式,例如用于分析程序使用。 在一个方面,顺序数据可以首先通过去除重复数据,将相似数据分组成子序列和/或去除噪声数据的状态机进行变换。 然后将转换后的数据分割成单位。 模式提取机制通过计算后续单元之间的稳定性分数(例如,相互信息得分),选择具有最稳定性的单元对(例如,最高分数),将模型从单元提取到模式集中,并且添加 该对的对应信息进入模式集。 迭代重复模式提取,直到满足停止标准,例如,模式集达到定义的大小,或当稳定性分数小于预设阈值时。
    • 8. 发明申请
    • Software feature modeling and recognition
    • 软件特征建模与识别
    • US20080312899A1
    • 2008-12-18
    • US11818596
    • 2007-06-15
    • Yantao LiBing SunShuguang YeGuowei LiuWenli ZhuHaidong ZhangMin WangJian Wang
    • Yantao LiBing SunShuguang YeGuowei LiuWenli ZhuHaidong ZhangMin WangJian Wang
    • G06F9/45G06F3/048
    • G06F11/28
    • Described is a technology by which software program feature usage is located within a sequence of commands collected during program usage sessions. For example, feature generally corresponds to a series of commands, such as copy and paste. A visual modeling component is controlled via drag-and-drop operations to describe a feature model, which is then compiled by a compiler into a finite state machine. Noise models may be used to exclude any command in the sequence that is irrelevant to the feature usage. A recognition process uses the finite state machine to locate program feature usage within the sequence of recorded commands by matching command sub-sequences corresponding to the feature model via the state machine. An analyzer may then use the located matches to provide an analysis report on feature usage.
    • 描述了软件程序特征使用位于在程序使用会话期间收集的一系列命令中的技术。 例如,特征通常对应于一系列命令,例如复制和粘贴。 视觉建模组件通过拖放操作进行控制,以描述特征模型,然后将其由编译器编译成有限状态机。 噪声模型可用于排除序列中与特征使用无关的任何命令。 识别过程使用有限状态机通过状态机匹配与特征模型对应的命令子序列来定位记录命令序列内的节目特征使用。 然后,分析仪可以使用定位的匹配来提供关于特征使用的分析报告。
    • 10. 发明授权
    • Extracting patterns from sequential data
    • 从顺序数据提取模式
    • US08335757B2
    • 2012-12-18
    • US12359343
    • 2009-01-26
    • Jie SuMin ChuWenli ZhuJian Wang
    • Jie SuMin ChuWenli ZhuJian Wang
    • G06F7/00G06F17/30
    • G06F17/30985G06F3/038
    • Described is a technology in which sequential data, such as application program command sequences, are processed into patterns, such as for use in analyzing program usage. In one aspect, sequential data may be first transformed via state machines that remove repeated data, group similar data into sub-sequences, and/or remove noisy data. The transformed data is then segmented into units. A pattern extraction mechanism extracts patterns from the units into a pattern set, by calculating a stability score (e.g., a mutual information score) between succeeding units, selecting the pair of units having the most stability (e.g., the highest score), and adding corresponding information for that pair into the pattern set. Pattern extraction is iteratively repeated until a stopping criterion is met, e.g., the pattern set reaches a defined size, or when the stability score is smaller than a pre-set threshold.
    • 描述了一种技术,其中诸如应用程序命令序列的顺序数据被处理成模式,例如用于分析程序使用。 在一个方面,顺序数据可以首先通过去除重复数据,将相似数据分组成子序列和/或去除噪声数据的状态机进行变换。 然后将转换后的数据分割成单位。 模式提取机制通过计算后续单元之间的稳定性分数(例如,相互信息得分),选择具有最稳定性的单元对(例如,最高分数),将模型从单元提取到模式集中,并且添加 该对的对应信息进入模式集。 迭代重复模式提取,直到满足停止标准,例如,模式集达到定义的大小,或当稳定性分数小于预设阈值时。