DETAILED ACTION 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. Priority The applicant’s claim to priority EP22178327.7 on 10 June 2022 is acknowledged. Information Disclosure Statement The information disclosure statement (IDS) submitted on 09 June 2023 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 102 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claim(s) 1-2, 6 -7, and 9 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Horton et al. (20210080334; hereinafter Horton) . Regarding claim 1 , Horton teaches i nertial measurement unit for providing output sensor data according to a force or motion applied on the inertial measurement unit (Horton: Abstract) , comprising: - a sensor unit including one or more sensor elements which is configured to detect motion and to provide sensor data depending on the detected motion (Horton: “ an inertial measurement apparatus, including: a plurality of inertial sensors each configured to output an inertial sensing signal ” ¶ 9) ; - a filter unit comprising one or more filter elements wherein the filter unit is configured to apply one or more filter elements on the sensor data according to filter parameters (Horton: “ a filter module configured to separate the inertial sensing signal of at least one of the plurality of inertial sensors ” ¶ 9) ; - a filter parameter unit including a data-driven filter parameter model which is configured to provide filter parameters to the filter unit in response to the sensor data obtained from the sensor elements (Horton: “ the processing unit 320 includes several logic units which can perform digital signal filtering, sensing data enhancement processing, and the like ” ¶ 26, see also ¶ 9) . Regarding claim 2 , Horton teaches inertial measurement unit according to claim 1, wherein the sensor unit further comprises one or more operating sensor elements, which particularly include at least one of a temperature sensor (Horton: “ The processing unit 320 includes a filter module 321, a temperature calibration module 322 ” ¶ 28) , an ambient pressure sensor, a mechanical stress sensor, a magnetic sensor, and an electromagnetic field sensor, wherein the one or more operating sensor elements provide at least one operating parameter (Horton: “ the temperature calibration module 322 includes a plurality of temperature calibration units 3221. Each of the temperature calibration units 3221 is configured to multiply one low-frequency component ” ¶ 31, see also ¶ 32, 33) ; wherein the filter parameter unit is configured to receive the at least one operating parameter and to provide filter parameters depending on the at least one operating parameter (Horton: “ Each of the temperature calibration units 3221 is configured to multiply one low-frequency component ... of the inertial sensing signal of one inertial sensor by corresponding temperature calibration coefficient ... to obtain the calibrated low-frequency component ” ¶ 31 ) . Regarding claim 6 , Horton teaches Inertial measurement unit according to claim 1 , wherein the filter parameters include filter type selection parameters (S) for selecting one of the multiple filter elements of the filter unit so that only the selected filter element provides the output sensor data (Horton: “ the noise reduction module 323 includes a plurality of noise reduction units 3231. Each noise reduction unit is configured to multiply the high-frequency component ... of the inertial sensing signal of one inertial sensor by a corresponding noise reduction coefficient ” ¶ 45) . Regarding claim 7 , Horton teaches Inertial measurement unit according to claim 6 , wherein the filter parameters of each filter element are predefined (Horton: “ The filter module 321 separates the inertial sensing signal of each inertial sensor 310 into a low-frequency component and a high-frequency component ... The LPF and the HPF need to use same cut-off frequency (e.g. 0.1 Hz) to ensure that no scale factor or amplitude response distortion is introduced ” ¶ 30) . Regarding claim 9 , Horton teaches Inertial measurement unit according t o claim 1 , wherein the filter parameters include setting parameters for providing a setting for each filter element (Horton: “ The filter module 321 separates the inertial sensing signal of each inertial sensor 310 into a low-frequency component and a high-frequency component ... The LPF and the HPF need to use same cut-off frequency (e.g. 0.1 Hz) to ensure that no scale factor or amplitude response distortion is introduced ” ¶ 30) . 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. Claim(s) 3- 5 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Horton in view of Kulik (20130106697) . Regarding claim 3 , Horton teaches i nertial measurement unit according to claim 1, wherein the filter unit comprises as filter elements at least one of a complementary filter (Horton: “ The filter module 321 separates the inertial sensing signal of each inertial sensor 310 into a low-frequency component and a high-frequency component. Specifically, the filter module 321 includes a plurality of low-pass filters (LPF) and a plurality of high-pass filters (HPF) complementary to the plurality of LPFs ” ¶ 30) . Although Horton discloses of various filtering processing, including a complementary filter, Horton fails to teach of a Kalman filter . However, in a similar field of endeavor, Kulik teaches a Kalman filter (Kulik: “ One way of determining orientation may be by using gyroscope 1660, accelerometer 1655 and magnetometer 1665 output in Kalman filter equations ” ¶ 59). As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the filtering system of Horton so that it also includes the element of a Kalman filter , as taught by Kulik , in order to remove inaccuracies (Kulik: ¶ 61) . Regarding claim 4 , Horton fails to teach i nertial measurement unit according to claim 1, wherein the filter unit is continuously operated in succeeding time steps . However, in a similar field of endeavor, Kulik teaches wherein the filter unit is continuously operated in succeeding time steps (Kulik: “ A Kalman filter may be used for removing noise and other inaccuracies to produce values that tend to be closer to the true values of the measurements by observing measurements over a time period (determined by a time constant) ” ¶ 61, see also ¶ 63). As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the filtering system of Horton so that it also includes the element of filtering in time steps , as taught by Kulik , in order to remove inaccuracies in data over time (Kulik: ¶ 61) . Regarding claim 5 , Horton teaches Inertial measurement unit according to claim 4, wherein the sensor data applied on the filter parameter unit includes actual values of the sensor data for each of the one or more sensor elements and at least one delayed value of the sensor data (Horton: “ The obtained set of temperature calibration coefficients of each of the inertial sensors is stored in a memory. In the temperature calibration performed subsequently, the stored temperature calibration coefficients may be used for the temperature calibration ” ¶ 40) . Regarding claim 8 , Horton fails to teach i nertial measurement unit according to claim 1 , wherein the filter parameters include filter type selection parameters for providing a classification vector (CL) each element of which is associated with a respective filter element, wherein the filter output sensor data of each filter element is weighted depending on the value of the element of the classification vector (CL) wherein the output sensor data is provided by adding the weighted filter output sensor data . However, in a similar field of endeavor, Kulik teaches wherein the filter parameters include filter type selection parameters for providing a classification vector (CL) each element of which is associated with a respective filter element (Kulik: “ a system for filtering the output of an accelerometer 1655 and a magnetometer 1665 along with a gyroscope 1660 using separate filters ... process the accelerometer data and the gyroscope data together at a gravity vector filter 202 for determining the gravity vector ... a component of gravity vector filter 202 may be used in processing the sensor data. ... process the magnetometer data and the gyroscope data together at a magnetic vector filter 204 to determine the magnetic vector ... a component of magnetic vector filter 204 may be used in processing the sensor data ” ¶ 62) , wherein the filter output sensor data of each filter element is weighted depending on the value of the element of the classification vector (CL) wherein the output sensor data is provided by adding the weighted filter output sensor data (Kulik: “ an adaptive filter is used that allows dynamically adjusting of one or more parameters of the adaptive filter using the processor 1610 ” ¶ 62, see also ¶ 63, 65, 66). As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the filtering system of Horton so that it also includes the element of classifying and weighing sensor data , as taught by Kulik , in order to remove inaccuracies within various sensor outputs (Kulik: ¶ 61 , 63 ) . Claim(s) 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Horton in view of Vissiere et al. (20210247206; hereinafter Vissiere ) Regarding claim 10 , Horton fails to teach training method for training a filter parameter model, particular to be applied in the filter parameter unit of the inertial measurement unit according to claim 1 , wherein the filter parameter model is trained using training data items, wherein each training data item associates an input data item comprising at least one raw sensor data item to a desired output sensor data of a predefined motion according to a motion profile, wherein the raw sensor data ( Xraw ) is obtained by moving the inertial measurement unit according to the motion profile and recording the raw sensor data ( Xraw ) . However, in a similar field of endeavor, Vissiere teaches training method for training a filter parameter model ( Vissiere : “ In the case of a recursive filter, the normal can be used ... of the innovation of the filter over a given period of time. Note that in the case of use of residues for step (b), it is possible to use any set of calibration parameters and any set of acquired data ” ¶ 101) , particular to be applied in the filter parameter unit of the inertial measurement unit according to claim 1, wherein the filter parameter model is trained using training data items ( Vissiere : “ use can be made of learning to improve the estimation of this error parameter and/or develop an approach for identifying favourable conditions ” ¶ 106) , wherein each training data item associates an input data item comprising at least one raw sensor data item to a desired output sensor data of a predefined motion according to a motion profile, wherein the raw sensor data ( Xraw ) is obtained by moving the inertial measurement unit according to the motion profile and recording the raw sensor data ( Xraw ) ( Vissiere : “ to implement learning mechanisms such as neural networks, machines with support vectors, nearest neighbour methods, decision trees, etc. Thus, at each occurrence of the steps (b) to (d), a learning base can be enriched wherein each dataset of measurements is “tagged” with the corresponding value of the parameter representative of an error, in such a way as to progressively (as the successive occurrences take place of steps (b) to (d)) and automatically learn to distinguish the acceptable calibrations from the unacceptable ones ” ¶ 107, “ the method advantageously comprises a step (f) of estimating by the data processing means 21 of the movement of said object 1 according to the angular velocity of the object 1 and/or of the components of the magnetic field, and/or of any measured linear velocity of the object 1 (by the means 10), and values of the calibration parameter ” ¶ 116, “ The step (f) can further comprise the calculation according to said parameter representative of an error on the calibration parameters, of a magnetometric or gyro- magnetometric orientation error ” ¶ 119) . As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the filtering system of Horton so that it also includes the element of training a filter model , as taught by Vissiere , in order to continuously improve data output ( Vissiere : ¶ 107) . Regarding claim 11 , Horton fails to teach training method according to claim 10 wherein the training is performed by means of a loss function depending on a residuum to the desired output sensor data to obtain filter setting for the filter elements, particularly on each filter element separately . However, in a similar field of endeavor, Vissiere teaches training method according to claim 10 wherein the training is performed by means of a loss function depending on a residuum to the desired output sensor data to obtain filter setting for the filter elements, particularly on each filter element separately ( Vissiere : “ estimation residues from step (c) are used, i.e. said parameter representative of an error is in particular the normal ... of the value of the first/second expression over a given time interval ... In the case of a recursive filter, the normal can be used ... of the innovation of the filter over a given period of time ” ¶ 101 , Note: Wherein the loss function is the errors over time function, based on residuum, in Vissiere to train (innovate) the filter ) . As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the filtering system of Horton so that it also includes the element of a loss functions based on residuum , as taught by Vissiere , in order to improve data output ( Vissiere : ¶ 107) . Regarding claim 12 , Horton fails to teach training method according to claim 10, wherein after the filter settings of the filter elements have been provided, the training is performed by means of a loss function depending on a performance of each of the filter elements on one or more training data items . However, in a similar field of endeavor, Vissiere teaches training method according to claim 10, wherein after the filter settings of the filter elements have been provided, the training is performed by means of a loss function depending on a performance of each of the filter elements on one or more training data items ( Vissiere : “ a learning base can be enriched wherein each dataset of measurements is “tagged” with the corresponding value of the parameter representative of an error, in such a way as to progressively (as the successive occurrences take place of steps (b) to (d)) and automatically learn to distinguish the acceptable calibrations from the unacceptable ones ” ¶ 107) . As such, it would have been obvious to one of ordinary skill in the art, at the time of effective filing and with a reasonable expectation for success, to have modified the filtering system of Horton so that it also includes the element of training with a loss function , as taught by Vissiere , in order to continuously improve data output based on error differences ( Vissiere : ¶ 107) . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Coatantiec et al. (20220178699) is in the similar field of endeavor as the claimed invention of inertial measuring units. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT CLINT V PHAM whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-4543 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 8-5 . 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