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    • 2. 发明专利
    • Remote control with motion sensitive devices
    • 远程控制与运动敏感设备
    • JP2013008365A
    • 2013-01-10
    • JP2012137928
    • 2012-06-19
    • Ailive Incアイライブ インコーポレイテッド
    • REYNOLDS STUARTMUSICK CHARLES JRWILKINSON DANAWEI YENEUGENE CHEW
    • G06F3/0346
    • G06F3/0346
    • PROBLEM TO BE SOLVED: To enable an alternative device (e.g., a motion controller) or input mechanism to control applications on a controlled device originally designed to be working with a predefined device (e.g., a touch-screen device).SOLUTION: Output signals from a motion sensitive device may be in a different form from those of a pre-defined controlling device 101. According to one aspect of the present invention, a controlled device 102 is designed to respond to signals from a touch screen or touch screen-like signals. The output signals from the motion sensitive device include motion sensitive inputs to the controlled device 102 which are converted into touch-screen like signals that are coupled to the controlled device 102 or programs being executed in the controlled device 102 and subsequently cause the behavior of the controlled device 102 to change or respond thereto, without reconfiguration of the applications running on the controlled device 102.
    • 要解决的问题:使得替代设备(例如,运动控制器)或输入机构能够控制原始设计为与预定义设备(例如,触摸屏设备)一起工作的受控设备上的应用。 解决方案:来自运动敏感设备的输出信号可以与预定义的控制设备101的输出信号不同。根据本发明的一个方面,受控设备102被设计为响应来自 触摸屏或类似触摸屏的信号。 来自运动敏感设备的输出信号包括对受控设备102的运动敏感输入,其被转换成耦合到受控设备102的触摸屏状信号或在受控设备102中执行的程序,并且随后引起 受控设备102改变或响应于此,而不重新配置在受控设备102上运行的应用。(C)版权所有:(C)2013,JPO&INPIT
    • 9. 发明申请
    • REAL TIME CONTEXT LEARNING BY SOFTWARE AGENTS
    • 软件代理的实时语境学习
    • WO2006014560A3
    • 2009-04-09
    • PCT/US2005024139
    • 2005-07-06
    • AILIVE INC
    • FUNGE JOHNMUSICK RONDOBSON DANIELDUFFY NIGELMCNALLY MICHAELTU XIAOYUANWRIGHT IANYEN WEICABRAL BRIAN
    • G06N5/00
    • A63F13/67A63F13/10A63F2300/60A63F2300/6027G06N99/005
    • Providing dynamic learning for software agents (140) in a simulation. Software agents (140) with learners (165) are capable of learning from examples. When a non-player character (142) queries the learner (165), it can provide a next action similar to the player character (141). The game designer provides program code, from which compile-time steps determine a set of raw features (150). The code might identify a function (like computing distances). At compile-time steps, determining these raw features (150) in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features (160), responsive to the raw features (150), might be relatively simple, more complex, or determined in response to a learner (165). The set of such raw (150) and derived features (160) form a context for a learner (165). Learners (165) might be responsive to (more basic) learners, to results of state machines, to calculated derived features (160), or to raw features (150). The learner (165) includes a machine learning technique.
    • 在模拟中为软件代理(140)提供动态学习。 具有学习者(165)的软件代理(140)能够从示例中学习。 当非玩家角色(142)查询学习者(165)时,它可以提供类似于玩家角色的下一个动作(141)。 游戏设计师提供程序代码,从中编译时步骤确定一组原始特征(150)。 代码可能会识别一个函数(如计算距离)。 在编译时,通过脚本语言来确定这些原始特征(150),因此设计人员可以指定哪个代码应该被引用。 响应于原始特征(150)的一组派生特征(160)可能相对简单,更复杂或响应于学习者而确定(165)。 这种原始(150)和派生特征(160)的集合形成学习者的背景(165)。 学习者(165)可以对(更基础的)学习者,状态机的结果,计算的导出特征(160)或原始特征(150)做出响应。 学习者(165)包括机器学习技术。