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 .
Status of Claims
This action is in reply to the amendments filed on 11/21/2025.
Claims 1-16 and 18-21 are currently pending and have been examined.
Claims 1, 14, 16 and 18 are amended.
Claim 17 is cancelled.
Claim 21 is added.
Claims 1-16 and 18-21 are currently rejected.
This action is made FINAL.
Response to Arguments
Applicant’s arguments filed 11/21/2025 have been fully considered but they are not fully persuasive.
In response to the amendments, the 101 rejections have been withdrawn.
Applicant’s arguments with regards to the art rejections have been considered and are not persuasive. Applicant argues that Tseng fails to filter noise from a wheel speed sensor. While Tseng was not clear if the filtering was being performed to each value of the ratio individually or to the ratio as a whole, the previous claims stated “filtering noise from the sensor data” which is being done in either scenario since the wheels speed data is what the ratio is based off of. The amended claims now require the output of the filtering process is specifically a filtered wheel speed. The updated rejections below bring in additional art to explicitly map that the output is specifically a filtered wheel speed. Applicant argues Tseng fails to generate a filtered wheel speed. As discussed supra, Tseng is not explicit if the filtering is performed on the wheel speed values individually or only to the ratio as a whole, however Gustafsson as shown below explicitly teaches this amended limitation. Applicant argues Tseng does not teach a steady interval and non-steady interval of rotation. Applicants arguments do not seem to be commensurate with the claim language. Tseng determines if there is excessive noise above a threshold that designates between a steady state and non-steady state. It appears that the applicant is arguing that “Steady interval” and “non-steady interval” are meant to mean non-noise variations in the wheel speed, however the specification does appear to define that special definition as argued. Therefore examiner is not persuaded that the total value of the signal, including noise, cannot be used to determine a steady and non-steady state. The applicant is invited to amend the claims to limit the interval to periods of non-acceleration of the vehicle if that is the intent of the invention. Applicant argues that in claim 10, Tseng does not teach the variability process as claimed. The “speed variance” of the sensor data includes variances due to noise as well as “real” speed of the vehicle since the variance is of the sensor data not in the true speed of the vehicle. Paragraph [0041] of applicant’s specification states “The variability process of the longer window aggregation assessment may include a variability calculation for characterizing an amount of noise within the unfiltered sensor data 64 according to a standard deviation or other statistical modeling thereof, optionally with a self-learning aspect whereby the variability process 90 may learn over time differing nuances of noise differences between the wheel sensors.” Which appears to indicate the variability can also include noise. As mentioned above, if the intent is for a steady interval to be one where the vehicle is in a non-accelerating state, the claims can be amended to match the intended scope. The remaining arguments appear to be due to the dependence upon the claims argued supra. Therefore as explained above, applicants arguments are not persuasive and the rejections are maintained in the updated rejections below.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 10-14, 18, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tseng et. al. (GB 2430990), herein Tseng in view of Gustafsson et. al. (US 2003/0172728), herein Gustafsson and Bande et. al. (US 2022/0230019), herein Bande.
Regarding claim 1:
Tseng teaches:
A method of filtering noise (determine a filtered wheel speed [page 1]) for a wheel speed sensor (fig. 1, wheel speed sensors 32a, 32b, 32c, and 32d), the wheel speed sensor configured for sensing rotational speed of a wheel included onboard a vehicle (determining a wheel speed [page 1]), the method comprising:
determining sensor data generated with the wheel speed sensor (The wheel speeds of the driven wheels are also determined [page 7]), the sensor data representing rotational speed of the wheel (determining a wheel speed [page 1]);
determining a steady interval of wheel rotation (fig. 2, step 58 No) and a non-steady interval of wheel rotation (fig. 2, step 58 Yes), the steady interval corresponding with the wheel rotating at a steady state (if the signals are not noisy then step 60 determines an instant ratio of driven to non-driven wheels [abstract]), the non-steady interval corresponding with the wheel rotating at a non-steady state (step 58 determines whether the signals are noisy, i.e. if the high frequency content of the signals is significant or above a noise threshold. If the signals are noisy, a proper signal to noise ratio for an accurate determination may not be present. Therefore, step 52 is executed when the signals are noisy. [page 7]);
filtering noise from the sensor data associated with the steady interval (fig. 2, steps 66 and 68 only occur if step 58 is “NO”) according to an adaptive filtering process (In step 66, fast adaptation filter constant is used when the amount is greater than the threshold [page 8]; step 68 uses a slow adaptation and thus a slow adaptation filter constant is used [page 8]) to generate a filtered wheel speed (while Tseng is not explicit if the filtering is being performed on the wheels speed data individually or the ratio of the wheel speed values, Gustafsson as mapped below explicitly teaches filtering individual wheel speed data which can be used in the ratio as taught by Tseng above.); and
Tseng does not explicitly teach, however Gustafsson teaches:
an adaptive filtering process to generate a filtered wheel speed (The signal from each wheel speed sensor is individually pre-processed by sensor signal pre-processing means 111, 112, 113, 114 and is then filtered by an adaptive or recursive filtering means 121, 122, 123, 124 adapted for a frequency model estimation and calculation of parameter values upon which the tire pressure depends [0074])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng to include the teachings as taught by Gustafsson with a reasonable expectation of success. Both arts are in the same field of endeavor of processing vehicle data. Gustafsson teaches the benefit of “the invention estimates parameter values of a predetermined or pre-selected tire pressure calculation model such that the model is adapted to the current specific situation. The inventor has realised that by estimating tire pressure model parameter values in a recursive filtering process, a large number of error sources are taken into consideration and their influence on the parameter estimate value, and in the next stage the error influence on the tire pressure indication value, is suppressed [Gustafsson, 0019]”.
Tseng in view of Gustafsson does not explicitly teach, however Bande teaches:
controlling a vehicle through an advanced driving assistant system (The results of this lane mapping and localization performed by the filter 230 can then be provided to any of a variety of systems within the vehicle 110, including ADAS systems 255 [0028]) based on the filtered wheel speed (vehicle sensors 205 may include one or more cameras 210, IMUs 215, wheel speed sensors 220, GNSS receivers 225, and/or other sensors capable of indicating vehicle movement and/or tracking lane boundaries on a road on which the vehicle 110 is traveling. The sensors 205 provide inputs to a filter [0026]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng and Gustafsson to include the teachings as taught by Bande with a reasonable expectation of success. All of the arts are in the same field of endeavor of processing vehicle data. Bande teaches the benefit of “Vehicle systems, such as autonomous driving and ADAS, often need to track vehicle position and lane boundaries of a road on which the vehicle is traveling. To do so, ADAS systems may utilize information from a variety of sources. These sources may include, for example, a Global Navigation Satellite Systems (GNSS) receiver, inertial measurement unit (IMU), and one or more cameras. [Bande, 0002]”.
Regarding claim 10:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 1, upon which this claim is dependent.
Tseng further teaches:
determining the steady and non-steady intervals according to a variability process (fig. 2, step 58), the variability processing determining the steady interval to coincide with the sensor data indicating speed variances in the wheel rotation to be within a steady range (step 58 determines whether the signals are noisy, i.e. if the high frequency content of the signals is significant or above a noise threshold. If the signals are noisy, a proper signal to noise ratio for an accurate determination may not be present. Therefore, step 52 is executed when the signals are noisy. [page 7]).
Regarding claim 11:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 10, upon which this claim is dependent.
Tseng further teaches:
determining the wheel rotation to be within the steady range based on a two-factor authentication process, the two-factor authentication process assessing the speed variances to be within the steady range in response to the sensor data associated therewith separately passing both of a shorter window aggregation assessment and a longer window aggregation assessment.
Regarding claim 12:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 11, upon which this claim is dependent.
Tseng further teaches:
determining the shorter window aggregation assessment to be passed in response to the sensor data indicating the speed variances occurring throughout a shorter sampling window (In step 56, if the wheel speeds are above a threshold, step 58 determines whether the signals are noisy, i.e. if the high frequency content of the signals is significant or above a noise threshold. If the signals are noisy, a proper signal to noise ratio for an accurate determination may not be present. [page 7]) to be less than a first threshold (“a noise threshold” [page 7]).
Regarding claim 13:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 12, upon which this claim is dependent.
Tseng further teaches:
determining the longer window aggregation assessment to be passed in response to the sensor data indicating the speed variances occurring throughout a longer sampling window to be less than a second threshold (fig. 2, step 64), wherein the longer sampling window is larger than the shorter sampling window (examiner notes that the noise in step 58 is instantaneous noise and therefore in an instantaneous window and step 64 is comparing an instant and long term ratio which inherently requires a longer window of data to compare instant values to a long term average value in order to determine if a change is caused by a transient change or a long term change.).
Regarding claim 14:
Tseng teaches:
A computer-readable storage medium having a plurality of non-transitory instructions stored thereon, which, when executed with one or more processors, are operable for filtering noise (determine a filtered wheel speed [page 1]) of a wheel speed sensor (fig. 1, wheel speed sensors 32a, 32b, 32c, and 32d) configured for sensing rotational speed of a wheel included onboard a vehicle (determining a wheel speed [page 1]), wherein the non-transitory instructions are operable for:
determining sensor data generated with the wheel speed sensor (The wheel speeds of the driven wheels are also determined [page 7]), the sensor data representing rotational speed of the wheel (determining a wheel speed [page 1]);
determining a steady interval of wheel rotation (fig. 2, step 58 No) and a non-steady interval of wheel rotation (fig. 2, step 58 Yes), the steady interval corresponding with the wheel rotating at a steady state (if the signals are not noisy then step 60 determines an instant ratio of driven to non-driven wheels [abstract]), the non-steady interval corresponding with the wheel rotating at a non-steady state (step 58 determines whether the signals are noisy, i.e. if the high frequency content of the signals is significant or above a noise threshold. If the signals are noisy, a proper signal to noise ratio for an accurate determination may not be present. Therefore, step 52 is executed when the signals are noisy. [page 7]);
characterizing the sensor data associated with the steady interval as steady-state sensor data (fig. 2, step 58, No) and the sensor data associated with the non-steady interval as non-steady-state sensor data (fig. 2, step 58, yes; step 58 determines whether the signals are noisy, i.e. if the high frequency content of the signals is significant or above a noise threshold. If the signals are noisy, a proper signal to noise ratio for an accurate determination may not be present. Therefore, step 52 is executed when the signals are noisy. [page 7]);
determining the steady and non-steady intervals according to a variability process (fig. 2, step 58), the variability processing determining the steady interval to coincide with the sensor data indicating speed variances in the wheel rotation to be within a steady range (if the signals are not noisy then step 60 determines an instant ratio of driven to non-driven wheels, in step 62 a difference between the instant ratio and a dual rate filtered ratio is established [abstract]).
generating a steady-state noise characterization for the steady-state sensor data (fig. 2, step 64);
selecting a noise filter from a plurality of available filters (fig. 2, steps 66 and 68) based on the steady-state noise characterization (fig. 2, step 64, yes or no outcome); and
filtering noise from the sensor data associated with the steady interval (fig. 2, steps 66 and 68 only occur if step 58 is “NO”) according to an adaptive filtering process (In step 66, fast adaptation filter constant is used when the amount is greater than the threshold [page 8]; step 68 uses a slow adaptation and thus a slow adaptation filter constant is used [page 8]) to generate a filtered wheel speed (while Tseng is not explicit if the filtering is being performed on the wheels speed data individually or the ratio of the wheel speed values, Gustafsson as mapped below explicitly teaches filtering individual wheel speed data which can be used in the ratio as taught by Tseng above.); and
Tseng does not explicitly teach, however Gustafsson teaches:
an adaptive filtering process to generate a filtered wheel speed (The signal from each wheel speed sensor is individually pre-processed by sensor signal pre-processing means 111, 112, 113, 114 and is then filtered by an adaptive or recursive filtering means 121, 122, 123, 124 adapted for a frequency model estimation and calculation of parameter values upon which the tire pressure depends [0074])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng to include the teachings as taught by Gustafsson with a reasonable expectation of success. Both arts are in the same field of endeavor of processing vehicle data. Gustafsson teaches the benefit of “the invention estimates parameter values of a predetermined or pre-selected tire pressure calculation model such that the model is adapted to the current specific situation. The inventor has realised that by estimating tire pressure model parameter values in a recursive filtering process, a large number of error sources are taken into consideration and their influence on the parameter estimate value, and in the next stage the error influence on the tire pressure indication value, is suppressed [Gustafsson, 0019]”.
Tseng in view of Gustafsson does not explicitly teach, however Bande teaches:
controlling a vehicle through an advanced driving assistant system (The results of this lane mapping and localization performed by the filter 230 can then be provided to any of a variety of systems within the vehicle 110, including ADAS systems 255 [0028]) based on the filtered wheel speed (vehicle sensors 205 may include one or more cameras 210, IMUs 215, wheel speed sensors 220, GNSS receivers 225, and/or other sensors capable of indicating vehicle movement and/or tracking lane boundaries on a road on which the vehicle 110 is traveling. The sensors 205 provide inputs to a filter [0026]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng and Gustafsson to include the teachings as taught by Bande with a reasonable expectation of success. All of the arts are in the same field of endeavor of processing vehicle data. Bande teaches the benefit of “Vehicle systems, such as autonomous driving and ADAS, often need to track vehicle position and lane boundaries of a road on which the vehicle is traveling. To do so, ADAS systems may utilize information from a variety of sources. These sources may include, for example, a Global Navigation Satellite Systems (GNSS) receiver, inertial measurement unit (IMU), and one or more cameras. [Bande, 0002]”.
Regarding claim 18:
Tseng teaches:
A vehicle (a motor vehicle [page 2]), comprising:
a plurality of wheels operable to facilitate movement of the vehicle (fig. 1, wheels 12a-d);
a powertrain fig. 1, drivetrain 16) operable to rotate one or more of the wheels (fig. 1, wheels 12a-d) in response to mechanical power generated with an internal combustion engine and/or an electric motor (fig. 1, drive system 14);
a plurality of wheel sensors (fig. 1, wheel speed sensors 32a, 32b, 32c, and 32d) operable for sensing rotational speed of a corresponding one of the wheels (determining a wheel speed [page 1]), the wheel sensors configured for generating sensor data to represent the rotational speed of the wheel associated therewith (The wheel speeds of the driven wheels are also determined [page 7]); and
a noise filter controller configured for adaptively filtering noise from the sensor data using a noise filter individually selected for each of the wheel sensors from a plurality of available filters (In step 66, fast adaptation filter constant is used when the amount is greater than the threshold [page 8]; step 68 uses a slow adaptation and thus a slow adaptation filter constant is used [page 8]), wherein the noise filter controller is configured for selecting the noise filters based on a noise characterization for the wheel sensor associated therewith (fig. 2, step 64) to generate a filtered wheel speed (while Tseng is not explicit if the filtering is being performed on the wheels speed data individually or the ratio of the wheel speed values, Gustafsson as mapped below explicitly teaches filtering individual wheel speed data which can be used in the ratio as taught by Tseng above.); and
Tseng does not explicitly teach, however Gustafsson teaches:
generate a filtered wheel speed (The signal from each wheel speed sensor is individually pre-processed by sensor signal pre-processing means 111, 112, 113, 114 and is then filtered by an adaptive or recursive filtering means 121, 122, 123, 124 adapted for a frequency model estimation and calculation of parameter values upon which the tire pressure depends [0074])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng to include the teachings as taught by Gustafsson with a reasonable expectation of success. Both arts are in the same field of endeavor of processing vehicle data. Gustafsson teaches the benefit of “the invention estimates parameter values of a predetermined or pre-selected tire pressure calculation model such that the model is adapted to the current specific situation. The inventor has realised that by estimating tire pressure model parameter values in a recursive filtering process, a large number of error sources are taken into consideration and their influence on the parameter estimate value, and in the next stage the error influence on the tire pressure indication value, is suppressed [Gustafsson, 0019]”.
Tseng in view of Gustafsson does not explicitly teach, however Bande teaches:
an advanced driving assistant system configured for controlling the vehicle (The results of this lane mapping and localization performed by the filter 230 can then be provided to any of a variety of systems within the vehicle 110, including ADAS systems 255 [0028]) based on the filtered wheel speed (vehicle sensors 205 may include one or more cameras 210, IMUs 215, wheel speed sensors 220, GNSS receivers 225, and/or other sensors capable of indicating vehicle movement and/or tracking lane boundaries on a road on which the vehicle 110 is traveling. The sensors 205 provide inputs to a filter [0026]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng and Gustafsson to include the teachings as taught by Bande with a reasonable expectation of success. All of the arts are in the same field of endeavor of processing vehicle data. Bande teaches the benefit of “Vehicle systems, such as autonomous driving and ADAS, often need to track vehicle position and lane boundaries of a road on which the vehicle is traveling. To do so, ADAS systems may utilize information from a variety of sources. These sources may include, for example, a Global Navigation Satellite Systems (GNSS) receiver, inertial measurement unit (IMU), and one or more cameras. [Bande, 0002]”.
Regarding claim 21:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 18, upon which this claims is dependent.
Tseng further teaches:
wherein the noise filter controller is configured to:
determine sensor data generated from at least one of the plurality of wheel speed sensors (The wheel speeds of the driven wheels are also determined [page 7]), the sensor data representing the rotational speed of the wheel (determining a wheel speed [page 1]);
determine a steady interval of wheel rotation (fig. 2, step 58 No) and a non-steady interval of wheel rotation (fig. 2, step 58 Yes), the steady interval corresponding with the wheel rotating at a steady state (if the signals are not noisy then step 60 determines an instant ratio of driven to non-driven wheels [abstract]), the non-steady interval corresponding with the wheel rotating at a non-steady state (step 58 determines whether the signals are noisy, i.e. if the high frequency content of the signals is significant or above a noise threshold. If the signals are noisy, a proper signal to noise ratio for an accurate determination may not be present. Therefore, step 52 is executed when the signals are noisy. [page 7]);
characterize the sensor data associated with the steady interval as steady-state sensor data (fig. 2, step 58 - No) and the sensor data associated with the non-steady interval as non- steady-state sensor data (fig. 2, step 58 - YES);
generate a steady-state noise characterization for the steady-state sensor data (the above process may be performed continuously so that the ratio is constantly adapted when the conditions of steps 50-58 are met [page 9]);
select a noise filter from a plurality of available filters (fig. 2, steps 66 or 68) based on the steady-state noise characterization (the above process may be performed continuously so that the ratio is constantly adapted when the conditions of steps 50-58 are met [page 9]); and
filter noise from the steady-state sensor data (fig. 2, steps 66 and 68 only occur if step 58 is “NO”) according to an adaptive filtering process (In step 66, fast adaptation filter constant is used when the amount is greater than the threshold [page 8]; step 68 uses a slow adaptation and thus a slow adaptation filter constant is used [page 8]) to generate the filtered wheel speed (while Tseng is not explicit if the filtering is being performed on the wheels speed data individually or the ratio of the wheel speed values, Gustafsson as mapped below explicitly teaches filtering individual wheel speed data which can be used in the ratio as taught by Tseng above.).
Gustafsson further teaches:
an adaptive filtering process to generate the filtered wheel speed (The signal from each wheel speed sensor is individually pre-processed by sensor signal pre-processing means 111, 112, 113, 114 and is then filtered by an adaptive or recursive filtering means 121, 122, 123, 124 adapted for a frequency model estimation and calculation of parameter values upon which the tire pressure depends [0074])
Claim(s) 2-3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tseng et. al. (GB 2430990), herein Tseng in view of Gustafsson et. al. (US 2003/0172728), herein Gustafsson and Bande et. al. (US 2022/0230019), herein Bande in further view of Adams et. al. (US 11,897,486), herein Adams.
Regarding claim 2:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 1, upon which this claim is dependent.
Tseng in view of Gustafsson and Bande does not explicitly teach, however Adams teaches:
performing the adaptive filtering process based on a variability calculation (comparing the filtered sensor data to assess variability [col 2, lines 41-44]), the variability calculation generating a noise value to represent an amount of noise within the sensor data associated therewith (filtering sensor data (e.g., from multiple sensors) over time to reduce the noise and bias and comparing the filtered sensor data to assess variability [col 2, lines 41-44]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng in view of Gustafsson and Bande to include the teachings as taught by Adams with a reasonable expectation of success. Adams teaches the benefit of “IMU data (e.g., raw IMU data) from the multiple IMUs is passed through a filter (e.g., low-pass filter, high-pass filter, bandpass filter, etc.) to remove values that might be associated with sensor bias and/or noise. The filtered IMU data from the IMUs may then be compared for consistency, and discrepancies can be analyzed to detect an IMU sensor error. In examples, by modeling the sensor noise and bias with filter parameters, techniques described in this disclosure may detect errors in a manner conductive to satisfying safety considerations [Adams, col 1, lines 58-67]”.
Regarding claim 3:
Tseng in view of Gustafsson, Bande, and Adams teaches all the limitations of claim 2, upon which this claim is dependent.
Tseng further teaches:
performing a filter selection process (fig. 2, step 64) to select a noise filter for the adaptive filtering process from a plurality of available filters (In step 64, if the difference is less than the threshold, step 68 uses a slow adaptation and thus a slow adaptation filter constant is used. In this embodiment various filtering schemes such as averaging may be provided over the long term so that the values of the ratio slowly change [page 8]; In step 66, fast adaptation filter constant is used when the amount is greater than the threshold. [page 8]), including selecting the noise filter based on the noise value (In step 62, the instant ratio is compared to the dual rate filtered ratio that is constantly being adapted. In step 64, the instant and the long term or dual rate filtered ratios are compared. [page 8]).
Regarding claim 15:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 14, upon which this claim is dependent.
Tseng in view of Gustafsson and Bande does not explicitly teach, however Adams teaches:
determining the steady-state noise characterization based on a variability calculation (comparing the filtered sensor data to assess variability [col 2, lines 41-44]), configured for characterizing an amount of noise within the sensor data according to a standard deviation thereof (filtering sensor data (e.g., from multiple sensors) over time to reduce the noise and bias and comparing the filtered sensor data to assess variability [col 2, lines 41-44]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng in view of Gustafsson and Bande to include the teachings as taught by Adams with a reasonable expectation of success. Adams teaches the benefit of “IMU data (e.g., raw IMU data) from the multiple IMUs is passed through a filter (e.g., low-pass filter, high-pass filter, bandpass filter, etc.) to remove values that might be associated with sensor bias and/or noise. The filtered IMU data from the IMUs may then be compared for consistency, and discrepancies can be analyzed to detect an IMU sensor error. In examples, by modeling the sensor noise and bias with filter parameters, techniques described in this disclosure may detect errors in a manner conductive to satisfying safety considerations [Adams, col 1, lines 58-67]”.
Claim(s) 4-7, 16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tseng et. al. (GB 2430990), herein Tseng in view of Gustafsson et. al. (US 2003/0172728), herein Gustafsson, Bande et. al. (US 2022/0230019), herein Bande, and Adams et. al. (US 11,897,486), herein Adams in further view of Moncur (US 6,470,311), herein Moncur.
Regarding claim 4:
Tseng in view of Gustafsson, Bande, and Adams teaches all the limitations of claim 3, upon which this claim is dependent.
Tseng in view of Gustafsson, Bande, and Adams do not explicitly teach, however Moncur teaches:
the filter selection process including cross-referencing the noise value relative to a filter selection graph to determine the noise filter (FIG. 4 is a graph for illustrating the selection of an optimum filter by locating the first peak above average DeltaEnergy. DeltaEnergy is plotted along the Y axis while the X axis indicates the cutoff filter frequencies measured in hertz (hz). [col 4, lines 49-53]), the filter selection graph delineating the available filters relative to a plurality of possible noise values (The input signal is filtered using each of N cutoff frequencies and the energy of each output is calculated (stage 320) [col 4, lines 33-35]; The filter frequency that corresponds to the first peak in DeltaEnergy is the optimum filter cutoff frequency (stage 334). As shown in FIG. 4, point 412 is the first peak in DeltaEnergy that is above the average DeltaEnergy and corresponds to the optimum filter cutoff frequency of 130. [col 4, lines 59-64]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng in view of Gustafsson, Bande, Adams, and Moncur to include the teachings as taught by Moncur with a reasonable expectation of success. Moncur teaches the benefit of “the input signal is filtered at a frequency as low as possible to remove all other frequencies, but not so low as to degrade the fundamental frequency. [Moncur, col 4, lines 8-10]”.
Regarding claim 5:
Tseng in view of Gustafsson, Bande, Adams, and Moncur teaches all the limitations of claim 4, upon which this claim is dependent.
Moncur further teaches:
selecting the noise filter to correspond with a one of the available filters cross-referenced with a related one of the possible noise values most closely aligned with the noise value (FIG. 4 is a graph for illustrating the selection of an optimum filter by locating the first peak above average DeltaEnergy. DeltaEnergy is plotted along the Y axis while the X axis indicates the cutoff filter frequencies measured in hertz (hz). [col 4, lines 49-53]).
Regarding claim 6:
Tseng in view of Gustafsson, Bande, Adams, and Moncur teaches all the limitations of claim 5, upon which this claim is dependent.
Adams further teaches:
the available filters corresponding with a plurality of low-pass filters (operation 218 may include applying low-pass filtering parameters that reduce sensor noise (e.g., the values below a higher threshold are allowed to pass) [col 9, lines 47-49])
Moncur further teaches:
the available filters corresponding with a plurality of [low-pass] filters arranged in the filter selection graph such that a lowest frequency filter of the low-pass filters corresponds with a lowest one of the possible noise values and a highest frequency filter of the low-pass filters corresponds with a highest one of the possible noise values (see fig. 4 showing cutoff frequency on the X-axis going from low to high.).
Regarding claim 7:
Tseng in view of Gustafsson, Bande, Adams, and Moncur teaches all the limitations of claim 6, upon which this claim is dependent.
Moncur further teaches:
the available filters in the filter graph being dispersed in a linear manner between the lowest frequency filter and the highest frequency filter (see fig. 4 showing a linear relationship between each cutoff frequency and the filter output.).
Regarding claim 16:
Tseng in view of Gustafsson, Bande, and Adams teaches all the limitations of claim 15, upon which this claim is dependent.
Adams further teaches:
selecting the noise filter to correspond with one of a plurality of low-pass filters (operation 218 may include applying low-pass filtering parameters that reduce sensor noise (e.g., the values below a higher threshold are allowed to pass) [col 9, lines 47-49])
Tseng in view of Adams do not explicitly teach, however Moncur teaches:
selecting the noise filter from a filter selection graph configured for delineating the available filters relative to a plurality of possible steady-state noise characterizations (The input signal is filtered using each of N cutoff frequencies and the energy of each output is calculated (stage 320) [col 4, lines 33-35]; The filter frequency that corresponds to the first peak in DeltaEnergy is the optimum filter cutoff frequency (stage 334). As shown in FIG. 4, point 412 is the first peak in DeltaEnergy that is above the average DeltaEnergy and corresponds to the optimum filter cutoff frequency of 130. [col 4, lines 59-64]), including selecting the noise filter to correspond with the available filter cross-referenced with the steady-state noise characterization most closely aligned with the standard deviation (FIG. 4 is a graph for illustrating the selection of an optimum filter by locating the first peak above average DeltaEnergy. DeltaEnergy is plotted along the Y axis while the X axis indicates the cutoff filter frequencies measured in hertz (hz). [col 4, lines 49-53]);
selecting the noise filter to correspond with one of a plurality of low-pass filters arranged in the filter selection graph in a linear manner from a lowest frequency low-pass filter to a highest frequency low-pass filter with a plurality of intermediary low-pass filters therebetween (see fig. 4 showing cutoff frequency on the X-axis going from low to high.).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng in view of Gustafsson, Bande, and Adams to include the teachings as taught by Moncur with a reasonable expectation of success. Moncur teaches the benefit of “the input signal is filtered at a frequency as low as possible to remove all other frequencies, but not so low as to degrade the fundamental frequency. [Moncur, col 4, lines 8-10]”.
Regarding claim 19:
Tseng in view of Gustafsson, Bande, and Adams teaches all the limitations of claim 18, upon which this claim is dependent.
Adams further teaches:
the available filters include a plurality of low-pass filters configured for low-pass filtering according to differing ones of a plurality of filter frequencies (operation 218 may include applying low-pass filtering parameters that reduce sensor noise (e.g., the values below a higher threshold are allowed to pass) [col 9, lines 47-49])
Tseng in view of Gustafsson, Bande, and Adams do not explicitly teach, however Moncur teaches:
the noise filter control is configured for selecting the noise filters to correspond with the low-pass filter most closely aligned with the noise characterization of the wheel sensor associated therewith (The input signal is filtered using each of N cutoff frequencies and the energy of each output is calculated (stage 320) [col 4, lines 33-35]; The filter frequency that corresponds to the first peak in DeltaEnergy is the optimum filter cutoff frequency (stage 334). As shown in FIG. 4, point 412 is the first peak in DeltaEnergy that is above the average DeltaEnergy and corresponds to the optimum filter cutoff frequency of 130. [col 4, lines 59-64]; FIG. 4 is a graph for illustrating the selection of an optimum filter by locating the first peak above average DeltaEnergy. DeltaEnergy is plotted along the Y axis while the X axis indicates the cutoff filter frequencies measured in hertz (hz). [col 4, lines 49-53]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng in view of Gustafsson, Bande, and Adams to include the teachings as taught by Moncur with a reasonable expectation of success. Moncur teaches the benefit of “the input signal is filtered at a frequency as low as possible to remove all other frequencies, but not so low as to degrade the fundamental frequency. [Moncur, col 4, lines 8-10]”.
Regarding claim 20:
Tseng in view of Gustafsson, Bande, Adams, and Moncur teaches all the limitations of claim 16, upon which this claim is dependent.
Moncur further teaches:
the low-pass filters are arranged in a filter selection graph in a linear manner relative to the noise characterizations from a lowest frequency low-pass filter to a highest frequency low-pass filter with a plurality of intermediary low-pass filters therebetween (see fig. 4 showing cutoff frequency on the X-axis going from low to high.).
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tseng et. al. (GB 2430990), herein Tseng in view of Gustafsson et. al. (US 2003/0172728), herein Gustafsson, Bande et. al. (US 2022/0230019), herein Bande in further view of Brimijoin et. al. (US 2022/0021996), herein Brimijoin.
Regarding claim 8:
Tseng in view of Gustafsson and Bande teaches all the limitations of claim 1, upon which this claim is dependent.
Tseng in view of Gustafsson and Bande does not explicitly teach, however Brimijoin teaches:
filtering noise from the sensor data associated with the non-steady interval according to a non-adaptive filtering process, the non-adaptive filtering process including filtering noise from the sensor data using a static filter (The stored data may include static filter parameter values [0047]; A static filter may be, e.g., a static high shelf filter, a static notch filter, some other type of filter, or some combination thereof. [0065]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Tseng in view of Gustafsson and Bande to include the teachings as taught by Brimijoin with a reasonable expectation of success. Providing a lookup table reduces the computational power required to dynamically filter data and can provide accurate results by creating a table the covers expected values that would be inherent or likely to occur in the system.
Regarding claim 9:
Tseng in view of Gustafsson, Bande, and Brimijoin teaches all the limitations of claim 8, upon which this claim is dependent.
Brimijoin further teaches:
selecting the static filter from a look-up table configured for cross-referencing (Model 420 represents the various models, such as look-up tables, functions, etc., used to obtain filter parameter values for static filters, dynamic filters, and delay in the audio TLDR 400. In some embodiments, the model 420 may be obtained from the data store 235. The model 420 may be any of the models described with respect to FIG. 3. Thus, in some embodiments, the model 420 may include one-dimensional and two-dimensional interpolating look-up tables that are used to obtain filter parameter values based on the input sound source angle values such as azimuth and/or elevation parameter values, as well as the delay values. [0073]) a plurality of speed based filters relative to a vehicle speed of the vehicle, the static filter corresponding with a one of the speed based filters most closely aligned with the vehicle speed associated with the sensor data being filtered (examiner notes that it would be obvious to one having ordinary skill in the art to apply the concept of a lookup table for a static filter to the specific parameters being filtered such as vehicle speed as is claimed and taught in the applied references supra.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yanase (US 2016/0131547) discloses A tire pressure decrease detection apparatus comprising a rotation speed information detection unit for detecting rotation speed information of wheels of a vehicle, a resonance frequency estimate unit for time-series estimating a torsional resonance frequency of the rotation speed information from the rotation speed information obtained by the rotation speed information detection unit, and a judgment unit for judging a decrease in pressure of tires installed in the wheels based on the estimated torsional resonance frequency. The resonance frequency estimate unit includes a noise removal unit for removing a noise superimposed on a wheel speed signal serving as the rotation speed information for each of the wheels with using an active noise control technology.
Singuru (US 2022/0153070) discloses A tire radius monitoring system for dynamically determining a tire effective radius for each of the wheels on a vehicle is described. The system includes a GPS sensor, a plurality of wheel speed sensors, and a controller. The controller determines, via the GPS sensor, a velocity vector related to longitudinal velocity of the vehicle. The controller determines wheel speeds for the plurality of vehicle wheels, and detects a no-wheel-slip state for the vehicle wheels and the velocity vector from the GPS sensor. The controller determines tire effective radii for the plurality of vehicle wheels based upon the velocity vector for the vehicle and the wheel speeds for the plurality of vehicle wheels during the no-wheel-slip state, and controls vehicle operation based upon the tire effective radii.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott R Jagolinzer whose telephone number is (571)272-4180. The examiner can normally be reached M-Th 8AM - 4PM Eastern.
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665