Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
Claims 1- 11 have been examined in this application. This communication is the first action on the merits. Information Disclosure Statement (IDS) filed 12/05/2023 is acknowledged.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Masad et al., US Publication No. 20190277655A1, [hereinafter Masad], in view of Avantaggiati, US 20220397395A1, [hereinafter Avantaggiati].
Regarding Claim 1,
Masad teaches
A method for operating a microelectromechanical inertial sensor, wherein the microelectromechanical inertial sensor is configured to …(Masad Par. 19-“ The disclosed technique provides a self-calibrating inertial measurement system (INS) and method that minimizes the influence of stray measurements outputted from particular inertial sensors (e.g., micro-inertial sensors) in a sensor cluster. Particularly, the INS employs microelectromechanical system (MEMS) sensors (e.g., accelerometers, gyroscopes, magnetometers, pressure sensors, and thermometers, etc.) for measuring physical properties (e.g., acceleration, angular velocity and orientation, magnetic field strength, pressure, temperature, etc.)”
provide a measurement signal with respect to a measurement direction and at least one first transverse signal with respect to a first transverse direction that extends transversely to the measurement direction (Masad Par. 21- According to a more typical and general sensor configuration of the disclosed technique, each sensor includes multiple sensing elements (e.g., a sensor triad) operative to measure a physical property with respect to multiple axes. For example, according to a typical implementation of the disclosed technique, each sensor includes at least three sensor elements operative to measure a physical property with respect to at least three respective axes. The description hereinbelow first details the simple sensor configuration, and thereafter reference will be made to the general sensor configuration with regard to the differences between the two configurations, as for the principles of operation of the disclosed technique are substantially the same for both sensor configurations.”; Par. 22- Reference frame 722 includes a plurality of axes X, Y, and Z. Without loss of generality, reference frame 722 shown in FIG. 1 is represented by a three-dimensional (3-D) Cartesian coordinate system, whose axes X, Y, and Z are orthogonal to one another. Alternatively, other types of coordinate systems may be used with the principles of the disclosed technique (e.g., non-orthogonal or skewed coordinate system, etc.). Each one of the accelerometers and gyroscopes shown in FIG. 1 are sensor elements or constituents that form separate units of multi-dimensional sensors. For example, a typical configuration of the disclosed technique, there are at least three sensor elements, which make up an individual physical sensor.”; Par. 31)
the method comprising the following method steps: filtering a first crosstalk signal occurring due to a coupling between the measurement signal and the first transverse signal from the measurement signal and a second crosstalk signal occurring due to a coupling from the first transverse signal (Masad Par. 19-20- The processing engine is configured during operation of the INS to dynamically self-calibrate at least one of: individual scale factor of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual gain corrections; individual cross-axis sensitivity of inertial sensors whose individual outputs were detected to diverge, by respectively applying individual cross-axis corrections; and individual bias of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual bias corrections. The principles of the disclosed technique apply to different kinds of inertial sensors, such as MEMS (“micro-inertial”), accelerometers, gyroscopes, fiber-optic gyroscopes (FOGs), and the like.; Par. 34- According to another implementation, combining involves fusing data from individual outputs acquired from sensors of different type (e.g., between at least two sensor types such as acceleration sensors, gyroscopic sensors, and magnetic field sensors) by using for example, Kalman filtering (i.e., executed by processing engine 702). Par. 53);
ascertaining a correction signal by fusing the filtered measurement signal and the filtered first transverse signal(Masad Par. 3- “The processing engine is configured during operation and during at least one of acceleration and angular velocity to dynamically self-calibrate at least one parameter. The at least one parameter includes individual scale factor of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual gain corrections; individual cross-axis sensitivity of the inertial sensors whose individual outputs were detected to diverge, by respectively applying individual cross-axis corrections; and individual bias of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual bias corrections.”; Par. 20; Par. 27; Par. 34);
Masad teaches the fusing of signals and the feature is expounded upon by Avantaggiati:
fusing the correction signal with the measurement signal ( Avantaggiati Par. 28- “In some embodiments, certain types of information may be determined based on data from multiple MEMS inertial sensors 102 and other sensors 108, in a process that may be referred to as sensor fusion. By combining information from a variety of sensors it may be possible to accurately determine information that is useful in a variety of applications.; Par 55; Par. 60- Proceeding to block 720, an output of the MEMS sensor 102, e.g., the sense signal, may be modified based on the compensation terms. In example approaches directed to corrections based upon temperature, the sense signal may be adjusted based upon the temperature signal received at block 710. In the example of FIGS. 2 and 3 , compensation terms may be employed by an inverse non-linear GOS 124 and applied based upon temperature measurements at the MEMS sensor 102. Accordingly, adjustments based upon the compensation terms and measured temperature may be facilitated by the gain-output sensor 116. )
Masad and Avantaggiati are directed to sensor correction analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Masad, as taught by Avantaggiati, by utilizing additional combination/recalibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Masad with the motivation of improved capability of correcting non-linear and temperature variations for both offset and sensitivity (Avantaggiati Par. 77).
Regarding Claim 2, Masad in view Avantaggiati teach The method according to claim 1,…
wherein the microelectromechanical inertial sensor is configured to provide a second transverse signal with respect to a second transverse direction that extends transversely to the measurement direction and transversely to the first transverse direction, (Masad Par. 31- In a preliminary (processing) phase 752, processing engine 702 collects, calibrates, synchronizes, and combines data (e.g., in the form of signals) from individual sensor outputs x1, x2, x3, x4, . . . ,xN, xN so as to yield combined outputs corresponding to each axis X, Y, Z: Xa, Ya, Za and Xg, Yg, Zg (where suffixes ‘a’ and ‘g’ respectively denote accelerometers and gyroscopes). Each sensor output (i.e., in the form of a signal, data) includes a unique identifier (not shown) with which processing engine 702 can identify the sensor corresponding thereto.)
wherein a crosstalk signal occurring due to a coupling between the measurement signal and/or the first transverse signal and/or the second transverse signal is filtered in each case from the measurement signal, from the first transverse signal, and from the second transverse signal (Masad Par. 19; Par. 34; Par. 53)
and wherein the correction signal is ascertained by fusing the filtered measurement signal, the filtered first transverse signal, and the filtered second transverse signal (Masad Par. 20-The processing engine is configured during operation of the INS to dynamically self-calibrate at least one of: individual scale factor of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual gain corrections; individual cross-axis sensitivity of inertial sensors whose individual outputs were detected to diverge, by respectively applying individual cross-axis corrections; and individual bias of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual bias corrections. The principles of the disclosed technique apply to different kinds of inertial sensors, such as MEMS (“micro-inertial”), accelerometers, gyroscopes, fiber-optic gyroscopes (FOGs), and the like.; Par. 40-“ In a phase 758, processing engine 702 is configured and operative to determine respective re-calibration parameters and re-synchronization parameters for those individual outputs detected to diverge (from at least one of: (a) their inter-comparison; and (b) their respective combined output). Specifically, processing engine 702 determines re-calibration parameters (corrections) and re-synchronization parameters (if applicable) for those sensors whose individual sensor outputs in each sensor group of a particular type were detected as diverging from their respective combined output (phase 754).”).
Regarding Claim 3, Masad in view Avantaggiati teach The method according to claim 1,…
wherein the filtering of the first and second crosstalk signals is performed in each case using a filter that is configured as a filter with a finite impulse response or as a filter with an infinite impulse response. (Avantaggiati Abstract Pe. 30; Par. 72- “While not shown in the example of FIG. 11 , a finite impulse response (FIR) non-linear equalizer may be provided to shape the overall transfer function by way of a reference transfer function or reference model. In such examples, the FIR non-linear equalizer may be included as part of the processing of higher-order terms of the polynomial compensation function 132 illustrated in FIG. 11”)
Masad and Avantaggiati are directed to sensor correction analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Masad, as taught by Avantaggiati, by utilizing additional combination/recalibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Masad with the motivation of improved capability of correcting non-linear and temperature variations for both offset and sensitivity (Avantaggiati Par. 77).
Regarding Claim 4, Masad in view Avantaggiati teach The method according to claim 3,…
wherein a transformation function is determined in each case for filtering the first and second crosstalk signals from the measurement signal and the at least one first transverse signal, wherein the transformation functions are determined based on a channel model that models crosstalk due to the coupling between the measurement signal and the at least one first transverse signal. (Avantaggiati Par62-63 “As discussed above, e.g., regarding the inverse non-linear GOS 124 of FIG. 3 , some example approaches to compensation terms may employ a bi-dimensional non-linear function. A bi-dimensional non-linear function may be configured to correct for a non-linear temperature-based variation of the MEMS electromechanical structure 160 as seen in the non-linear capacitance 122..”)
Masad and Avantaggiati are directed to sensor correction analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Masad, as taught by Avantaggiati, by utilizing additional combination/recalibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Masad with the motivation of improved capability of correcting non-linear and temperature variations for both offset and sensitivity (Avantaggiati Par. 77).
Regarding Claim 5, Masad in view Avantaggiati teach The method according to claim 1,…
further comprising: fusing the at least one first transverse signal with the correction signal. (Masad Par. 33- Combining generally involves at least one of merging, averaging, filtering (e.g., Kalman filter), fusing, intermixing, and uniting the individual sensor outputs into at least one combined output. According to one implementation, each combined output Xa, Ya, Za, Xg, Yg, Zg is a weighted average of the individual sensor outputs of a particular sensor type (e.g., accelerometer, gyroscope, magnetometer, etc.) that is associated with a particular axis. Thus, Xa denotes a combined sensor output of accelerometers 710 1, . . . ,710 J in sensor group 720 1 (FIG. 1) that are operative to measure acceleration along the X-axis. Likewise, Xg denotes a combined sensor output of gyroscopes 712 1, . . . ,712 J′ in sensor group 720 1 that are operative to measure angle rate about the X-axis. Similar logic applies to Ya, Za, Xg, Yg and Zg. The combined outputs Xa, Ya, Za, Xg, Yg, Zg are produced,”; Par. 40-41)
Regarding Claim 6, Masad in view Avantaggiati teach The method according to claim 1,…
further comprising: fusing the at least one first crosstalk signal with a further correction signal based on the first and second crosstalk signals (Masad Par. 3; Par. 20; Par. 40-41- cording to the disclosed technique, apart from random errors, several systematic errors may transpire during operation of INS 700 that may affect the measurement outputs of the sensors. Systematic errors include (and not limited to) namely, scale factor (gain), cross-axis sensitivity, constant bias (e.g., zero-bias), mutual alignment of the sensors, thermal bias, thermal gain, non-linearity, gyroscopic inertial bias, and the like. To determine the re-calibration parameters for a particular sensor whose individual output was determined to diverge (e.g., sensor output x.sub.3) processing engine 702 assesses whether the aforementioned errors can correct for the divergence. Without loss of generality, the example given in FIG. 5 relates to the detected divergence of individual sensor output x.sub.3, in phase 754). Determination of the re-calibration parameters involves determining of a calibration function for each sensor (i.e., multi-sensor, e.g., a 3-D sensor) separately (described in greater detail hereinbelow) that generally relates an input with a single output. According to a particular implementation of the disclosed technique, the input is a combined output of a particular sensor group of particular type, i.e., one of: Xa, Ya, Za, Xg, Yg, Zg (e.g., Xa, represented by combined output graph 824), whilst the output is its corresponding raw individual sensor output, i.e., an x.sub.i (e.g., x.sub.3, represented by sensor output graph 782.sub.3), as shown diagrammatically in block 822 of FIG. 5. )
Regarding Claim 7, Masad in view Avantaggiati teach The method according to claim 1,…
wherein the filtered measurement signal and the filtered first transverse signal are weighted when ascertaining the correction signal.(Masad Par. 33- Combining generally involves at least one of merging, averaging, filtering (e.g., Kalman filter), fusing, intermixing, and uniting the individual sensor outputs into at least one combined output. According to one implementation, each combined output Xa, Ya, Za, Xg, Yg, Zg is a weighted average of the individual sensor outputs of a particular sensor type (e.g., accelerometer, gyroscope, magnetometer, etc.) that is associated with a particular axis. Thus, Xa denotes a combined sensor output of accelerometers 710 1, . . . ,710 J in sensor group 720 1 (FIG. 1) that are operative to measure acceleration along the X-axis.)
Regarding Claim 8, Masad in view Avantaggiati teach The method according to claim 2,…
wherein a signal history of the measurement signal and/or the first transverse signal and/or a second transverse signal is taken into account when ascertaining the correction signal. (Masad Par.46- Further additionally, processing engine 702 assess whether or not there exists a thermal gain error λtherm. (diagrammatically represented by subblock 836), as well as a thermal bias error btherm. (diagrammatically represented by subblock 838) by evaluating if there exists a statistically significant correlation (over time) between an individual sensor output and its corresponding temperature measurement (not shown) that is outputted by its respective temperature sensor. According to one implementation, statistical analysis is performed by searching (in recent past sensor data, e.g., stored in memory 706) for statistically significant correlation(s) of rapid changes in gain, bias and non-orthogonality that coincide with temperature changes. An example of such data is represented in Table 1.)
Regarding Claim 9, Masad in view Avantaggiati teach The method according to claim 8,…
wherein in each case, a post-oscillation signal in the measurement signal and/or in the first transverse signal and/or in the second transverse signal is taken into account when ascertaining the correction signal. (Avantaggiati Par77 “In one example application of the foregoing GOS architecture, an improved capability of correcting non-linear and temperature variations for both offset and sensitivity was demonstrated. Non-linear compensation functions such as those described in the foregoing examples demonstrated improved performance, e.g., by reducing MEMS measurement error spread. In one example, a ratio of 1.74 on the x-axis, 1.8 on the y-axis, and 2.17 on the z-axis resulted, with respect to an uncompensated device. Residual offset due to high frequency vibration resulting from a high acceleration profile was reduced over 80% from a worst case at high temperature of 100 milligees (mgee) to less than 20 mgee. These example GOS architectures were calibrated using the above-described temperature-input signal pairs. Additionally, offset (i.e., a magnitude of sensitivity and offset error) caused by vibrations was reduced 1.7 to 2.1 times after completion of the compensation process, in comparison to a piecewise linear approach.”)
Masad and Avantaggiati are directed to sensor correction analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Masad, as taught by Avantaggiati, by utilizing additional combination/recalibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Masad with the motivation of improved capability of correcting non-linear and temperature variations for both offset and sensitivity (Avantaggiati Par. 77).
Regarding Claim 10, Masad in view Avantaggiati teach The method according to claim 1,…
wherein the microelectromechanical inertial sensor is an acceleration sensor or a rotation rate sensor. (Masad Par. 19- The disclosed technique provides a self-calibrating inertial measurement system (INS) and method that minimizes the influence of stray measurements outputted from particular inertial sensors (e.g., micro-inertial sensors) in a sensor cluster. Particularly, the INS employs microelectromechanical system (MEMS) sensors (e.g., accelerometers, gyroscopes, magnetometers, pressure sensors, and thermometers, etc.) for measuring physical properties (e.g., acceleration, angular velocity and orientation, magnetic field strength, pressure, temperature, etc.).)
Regarding Claim 11,
Masad teaches
A system for operating a microelectromechanical inertial sensor, wherein the microelectromechanical inertial sensor is configured to…(Masad Par. 19-“ The disclosed technique provides a self-calibrating inertial measurement system (INS) and method that minimizes the influence of stray measurements outputted from particular inertial sensors (e.g., micro-inertial sensors) in a sensor cluster. Particularly, the INS employs microelectromechanical system (MEMS) sensors (e.g., accelerometers, gyroscopes, magnetometers, pressure sensors, and thermometers, etc.) for measuring physical properties (e.g., acceleration, angular velocity and orientation, magnetic field strength, pressure, temperature, etc.)”
provide a measurement signal with respect to a measurement direction and at least one first transverse signal with respect to a first transverse direction that extends transversely to the measurement direction (Masad Par. 21- According to a more typical and general sensor configuration of the disclosed technique, each sensor includes multiple sensing elements (e.g., a sensor triad) operative to measure a physical property with respect to multiple axes. For example, according to a typical implementation of the disclosed technique, each sensor includes at least three sensor elements operative to measure a physical property with respect to at least three respective axes. The description hereinbelow first details the simple sensor configuration, and thereafter reference will be made to the general sensor configuration with regard to the differences between the two configurations, as for the principles of operation of the disclosed technique are substantially the same for both sensor configurations.”; Par. 22- Reference frame 722 includes a plurality of axes X, Y, and Z. Without loss of generality, reference frame 722 shown in FIG. 1 is represented by a three-dimensional (3-D) Cartesian coordinate system, whose axes X, Y, and Z are orthogonal to one another. Alternatively, other types of coordinate systems may be used with the principles of the disclosed technique (e.g., non-orthogonal or skewed coordinate system, etc.). Each one of the accelerometers and gyroscopes shown in FIG. 1 are sensor elements or constituents that form separate units of multi-dimensional sensors. For example, a typical configuration of the disclosed technique, there are at least three sensor elements, which make up an individual physical sensor.”; Par. 31)
the system comprising: at least two filters and two fusion modules, wherein the filters are configured to receive the measurement signal and the at least one first transverse signal and to filter crosstalk signals from the measurement signal and the at least one first transverse signal (Masad Par. 19-20- The processing engine is configured during operation of the INS to dynamically self-calibrate at least one of: individual scale factor of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual gain corrections; individual cross-axis sensitivity of inertial sensors whose individual outputs were detected to diverge, by respectively applying individual cross-axis corrections; and individual bias of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual bias corrections. The principles of the disclosed technique apply to different kinds of inertial sensors, such as MEMS (“micro-inertial”), accelerometers, gyroscopes, fiber-optic gyroscopes (FOGs), and the like.; Par. 34- According to another implementation, combining involves fusing data from individual outputs acquired from sensors of different type (e.g., between at least two sensor types such as acceleration sensors, gyroscopic sensors, and magnetic field sensors) by using for example, Kalman filtering (i.e., executed by processing engine 702). Par. 53);
wherein a first fusion module of the fusion modules is configured to ascertain the correction signal by fusing measurement signal unfiltered with the at least one filtered first transverse signal (Masad Par. 3- “The processing engine is configured during operation and during at least one of acceleration and angular velocity to dynamically self-calibrate at least one parameter. The at least one parameter includes individual scale factor of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual gain corrections; individual cross-axis sensitivity of the inertial sensors whose individual outputs were detected to diverge, by respectively applying individual cross-axis corrections; and individual bias of those inertial sensors whose individual outputs were detected to diverge, by respectively applying individual bias corrections.”; Par. 20; Par. 27; Par. 34);
Masad teaches the fusing of signals and the feature is expounded upon by Avantaggiati:
wherein a second fusion module of the fusion modules is configured to fuse the correction signal with the measurement signal and to output a corrected measurement signal ( Avantaggiati Par. 28- “In some embodiments, certain types of information may be determined based on data from multiple MEMS inertial sensors 102 and other sensors 108, in a process that may be referred to as sensor fusion. By combining information from a variety of sensors it may be possible to accurately determine information that is useful in a variety of applications.; Par 55; Par. 60- Proceeding to block 720, an output of the MEMS sensor 102, e.g., the sense signal, may be modified based on the compensation terms. In example approaches directed to corrections based upon temperature, the sense signal may be adjusted based upon the temperature signal received at block 710. In the example of FIGS. 2 and 3 , compensation terms may be employed by an inverse non-linear GOS 124 and applied based upon temperature measurements at the MEMS sensor 102. Accordingly, adjustments based upon the compensation terms and measured temperature may be facilitated by the gain-output sensor 116. )
Masad and Avantaggiati are directed to sensor correction analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon analysis of Masad, as taught by Avantaggiati, by utilizing additional combination/recalibration analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Masad with the motivation of improved capability of correcting non-linear and temperature variations for both offset and sensitivity (Avantaggiati Par. 77).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20210389344 A1 to Hiyoshi et al.- Abstract-“ An inertia sensor apparatus includes a first sensor module including a first inertia sensor that outputs a first signal relating to a plurality of first detection axes and a first correction circuit that generates a first correction signal by correcting the first signal in such a way that the plurality of first detection axes are perpendicular to each other, a second sensor module including a second inertia sensor that outputs a second signal relating to a plurality of second detection axes and a second correction circuit that generates a second correction signal by correcting the second signal in such a way that the plurality of second detection axes are perpendicular to each other, a matching processor that generates a first matching signal by applying a first correction coefficient that causes the plurality of first detection axes to match with the plurality of second detection axes to the first correction signal, and a combining processor that combines the first matching signal with the second correction signal and outputs the combined signal.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624