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发明公开 CN103597424A 用于对多个装置状态分类的方法和设备_+++A_C_E_F_b_d+++.docx
2025-12-24 00:57
发明公开 JP2011525414A Adjustment of the program set based on the identification of the activity_+++A_b_c+++.docx
2025-12-24 00:57
发明公开 JP2014531816A モバイルデバイスが車両に乗っていることの検出_+++A_C_F_b_d_e_g+++.docx
2025-12-24 00:57
发明公开 US8983412B2 Monitoring mobile device context_+++C_E_F_a+++.docx
2025-12-24 00:57
发明公开 US2009319221A1 Program Setting Adjustments Based on Activity Identification_+++A_B_C_F_d_e_g+++.docx
2025-12-24 00:57
发明公开 US2012299828A1 METHOD AND APPARATUS FOR CLASSIFYING MULTIPLE DEVICE STATES_+++A_b+++.docx
2025-12-24 00:57
发明公开 US2013245986A1 DETECTING THAT A MOBILE DEVICE IS RIDING WITH A VEHICLE_+++A_b_c_d_e_f_g+++.docx
2025-12-24 00:57
发明公开 US2014167973A1 PHYSICAL ACTIVITY INFERENCE FROM ENVIRONMENTAL METRICS_+++A_C_F_b_d_e+++.docx
2025-12-24 00:57
发明公开 US2014247206A1 ADAPTIVE SENSOR SAMPLING FOR POWER EFFICIENT CONTEXT AWARE INFERENCES_+++A_B_c_d_e_f+++.docx
2025-12-24 00:57
发明公开 WO2011083572A1 移動状態推定装置、方法およびプログラム_+++A_C_b_d+++.docx
2025-12-24 00:57
发明公开 WO2013040493A1 DETECTING THAT A MOBILE DEVICE IS RIDING WITH A VEHICLE_+++A_C_F_b_d_e_g+++.docx
2025-12-24 00:57
发明公开 WO2013157332A1 行動識別装置、行動識別システム及び行動識別プログラム_+++A_B_c_d_e_f+++.docx
2025-12-24 00:57
发明公开 WO2014147785A1 動作検知装置,動作検知方法,プログラム及び記録媒体_+++A_B_C_E_F_d+++.docx
2025-12-24 00:57
发明公开 WO2014209697A1 USER ACTIVITY TRACKING SYSTEM AND DEVICE_+++A_C_F_d_e+++.docx
2025-12-24 00:57
发明公开 WO2013040493A1 DETECTING THAT A MOBILE DEVICE IS RIDING WITH A VEHICLE_+++A_C_F_b_d_e_g+++.docx

**对比文件名称**:发明公开 WO2013040493A1 DETECTING THAT A MOBILE DEVICE IS RIDING WITH A VEHICLE

**特征比对表格**

技术特征描述及公开性判断对比文件原文引用公开性论述
**A:一种用于分类移动装置的运动状态的方法**<br>**《直接公开》**第[0016]段:“Techniques are described herein for classifying the motion and/or position state of a mobile device, e.g., to determine that a mobile device is located in a vehicle (e.g., car, bus, train, etc.) using a hidden Markov model (HMM) and associated state machine.”<br>第[0066]-[0069]段(结合图10):“a process 1000 of classifying the motion state of a mobile device includes the stages shown. ... At stage 1006, likelihoods are computed for the one or more pedestrian motion states and the one or more vehicular motion states for respective time intervals based on the acceleration data. ... At stage 1008, the computed likelihoods are filtered to obtain present motion states for the respective time intervals.”对比文件明确公开了用于分类移动装置运动状态的技术和方法,其核心是利用传感器数据(如加速度计)计算各运动状态的概率(似然度),并通过模型(如HMM)进行过滤以确定当前运动状态。这与目标专利权利要求1所要求保护的“用于分类移动装置的运动状态的方法”属于相同类型的技术主题,因此该技术特征被直接公开。
**B:确定与最高概率值且与大于第一阈值的第一置信度水平相关联的第一运动状态**<br>**《隐含公开》**第[0026]段:“These models may apply the data from the feature extraction module to compute a set of likelihoods reflecting probabilities of what state the user is in, at module 204. ... The filtered states are then provided to a confidence test block 210 which removes low confidence decisions, as a result of which the device state is output as the most likely state identified by the filtering block 208.”<br>第[0051]段:“... the driving detection decision may be discarded if the posterior probabilities of the two most probable states are comparable to one another (corresponding to a high degree of uncertainty in the decision). Discarding of decisions is based on a confidence threshold...”对比文件公开了计算一组反映用户处于各状态的概率(似然度)(第[0026]段),并输出过滤模块识别出的“最可能状态”(most likely state)。同时,系统包含置信度测试(confidence test),会基于置信度阈值(confidence threshold)剔除低置信度的决策(第[0026]、[0051]段)。这意味着,最终被输出/确定的“最可能状态”,必然是已经通过了置信度测试(即其置信度水平高于某个阈值)的状态。虽然对比文件未明确使用“与最高概率值且与大于第一阈值的第一置信度水平相关联的第一运动状态”这一表述,但其技术方案必然包含了“确定一个最可能状态,且该状态的置信度高于某个阈值”这一逻辑步骤。因此,该技术特征被隐含公开。在对比文件中,该特征的作用是确保状态分类结果的可靠性,避免输出不确定性高的状态,这与目标专利中为解决准确分类技术问题所起的作用相同。
**C:进入第一运动状态**<br>**《直接公开》**第[0026]段:“... the device state is output as the most likely state identified by the filtering block 208.”<br>第[0069]段:“... the present motion states for the respective time intervals are not limited to a present state at the time the likelihoods are calculated; rather, the present motion states may refer to the motion state that was present during the respective time interval...”对比文件明确记载了将设备状态输出(或报告)为识别出的最可能状态(第[0026]段),并且该状态被称为“当前运动状态”(present motion state)(第[0069]段)。将某个状态确定为并输出为“当前状态”,即意味着系统进入了该状态。因此,“进入第一运动状态”这一特征被直接公开。
**D:当第一运动状态处于作用中时,确定与最高概率值且与大于第一阈值的第二置信度水平相关联的第二运动状态,第二运动状态不同于第一运动状态**<br>**《隐含公开》**第[0037]段:“The HMM of some embodiments may facilitate the disambiguation of sit and autoStop states by remembering the previous state. Thus, if the user is driving and comes to a stop at a traffic light, the HMM will report autoStop rather than sit, as the previous state was autoMove and it is not possible to transition from autoMove to sit.”<br>第[0040]段:“... the only way to enter the autoMove state is by transitioning from walk to autoStop, and the only way to exit the autoMove state is by transitioning from autoStop to walk.”对比文件描述了基于历史状态和状态转移规则来确定当前状态的过程。例如,当用户驾车并在红灯处停下时,系统会根据之前的状态(autoMove)和状态机规则(不能从autoMove直接转到sit),确定当前应报告autoStop状态(第[0037]段)。这个过程发生在“autoMove”状态作为当前作用中状态期间,系统基于新数据计算概率,并确定了一个新的、不同的最可能状态(autoStop)。同时,系统包含置信度测试(见特征B论述),因此这个新确定的“最可能状态”也必然关联于一个大于阈值的置信度水平。虽然对比文件未以完全相同的措辞描述此过程,但其状态分类机制在运行时,必然隐含了“在当前状态作用期间,持续计算并确定新的最可能状态(若不同)”这一技术特征。因此,该特征被隐含公开。在对比文件中,该特征是实现状态间准确转换、避免误判(如将停车误判为静坐)的核心机制,与目标专利中为实现平滑、准确状态转换所起的作用相同。
**E:确定是否将进入第二运动状态**<br>**《隐含公开》**第[0040]段:“... the only way to enter the autoMove state is by transitioning from walk to autoStop, and the only way to exit the autoMove state is by transitioning from autoStop to walk.”<br>第[0042]段:“This forces the HMM to pass through several intermediate sub-state transitions before changing state, which has the net effect of reducing or eliminating rapid fluctuations.”对比文件公开的状态机对状态转移施加了明确的限制(如只有walk能进入autoStop,只有autoStop能进入walk或autoMove)(第[0040]段),并且通过扩展状态机引入了“粘性”,要求系统在真正改变状态前必须通过一系列子状态(第[0042]段)。这些机制本质上都是在“确定是否将进入一个新状态”时进行的逻辑判断。系统需要评估当前状态、候选状态以及它们之间的转移规则(包括时间持续性要求),才能决定是否执行状态转换。因此,“确定是否将进入第二运动状态”这一决策步骤是隐含在对比文件所公开的状态转换逻辑中的。在对比文件中,该特征的作用是控制状态转换的合理性和稳定性,防止状态频繁或不合理的跳变,这与目标专利中为解决状态切换频繁的技术问题所起的作用相同。
**F:响应于确定将进入第二运动状态,进入第二运动状态**<br>**《直接公开》**第[0037]段:“... the HMM will report autoStop rather than sit...”<br>第[0069]段:“... the present motion states for the respective time intervals ... may refer to the motion state that was present during the respective time interval...”对比文件明确记载了系统在做出状态判断后,会“报告”(report)新的状态(第[0037]段),该状态即成为“当前运动状态”(第[0069]段)。这直接对应于“进入第二运动状态”这一动作。因此,该技术特征被直接公开。
**G:其中当出现以下情况时,将进入第二运动状态:1)第二置信度水平大于第二阈值,第二阈值大于第一阈值,或2)当第一运动状态处于作用中时,第二运动状态在预定时间周期内连续地与最高概率值且与大于第一阈值但小于第二阈值的第二置信度水平相关联。**<br>**《隐含公开》****对于条件1:** 第[0040]段:“... self-transition probabilities are set to relatively high values to discourage rapid state oscillations... The autoMove state is given the highest self-transition probability in Table 1 since it is anticipated that the user will remain in the autoMove state for long periods of time.”<br>**对于条件2:** 第[0042]段:“The extended state machine breaks one or more existing states into a set of consecutive sub-states which the system must pass through before exiting that state. ... This forces the HMM to pass through several intermediate sub-state transitions before changing state, which has the net effect of reducing or eliminating rapid fluctuations.”<br>第[0043]段:“... a transition from one state to another state can only occur within the final sub-state of a given state. Thus, to move from one state to another state, the state transition must be indicated N times in a row...”对比文件虽然没有明确设定两个置信度阈值,但其技术方案隐含了与条件1和条件2实质相同的两种状态转换触发机制。<br>**条件1对应高置信度立即转换:** 对比文件通过设置高自转移概率来“阻止快速状态振荡”(第[0040]段)。这意味着,当新状态的置信度(或证据强度)非常高,足以克服这种高自转移概率的“惯性”时,状态转换会立即发生。这隐含了存在一个较高的“阈值”(第二阈值),当置信度超过它时,无需延迟即可转换。<br>**条件2对应中等置信度延迟转换:** 对比文件公开的扩展状态机要求系统在改变主状态前,必须连续通过N个子状态(第[0042]、[0043]段)。这等效于要求新状态必须在“预定时间周期内”(即通过N个子状态所需的时间)连续被指示为最可能状态。只有当这种连续性得以维持,系统才会最终转换到新状态。这隐含了在置信度水平不足以立即触发转换(即小于上述高阈值但大于某个基础阈值)时,需要满足时间持续性条件。<br>因此,对比文件隐含公开了基于两种不同置信度水平(对应不同阈值)和时间持续性条件来决定是否进入新状态的机制。该机制在对比文件中的作用是平衡状态分类的响应速度和稳定性,减少误报和抖动,这与目标专利中为解决相同技术问题所起的作用相同。

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