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 .
Response to Arguments
Applicant’s arguments, see "Remarks", page 7, filed July 15, 2026, with respect to the objections to the specification and the claims and the rejection(s) of claims 18-34 under 35 U.S.C. § 112(b) have been fully considered and are persuasive. The above rejection(s) and objection(s) of April 16, 2026 have been withdrawn.
Applicant's arguments filed July 15, 2026, with respect to the rejection(s) of claims 18 have been fully considered but they are not persuasive. Although Examiner agrees that the paragraphs selected by Applicant show an embodiment where Snyder “determines the wheel diameter from the vibration data and from the ground speed from the GNSS circuitry, then determines a predicted or estimated ground speed based on the diameter” [Applicant’s “Remarks” filed July 15, 2026, page 9, para. 4] – Examiner would respectfully draw Applicant’s attention to the Non-Final Rejection of April 16, 2026, page 5, para. 2, where Examiner states that Snyder’s “Fig. 9 shows determined speed correlated to GPS speed, para. [0106] describes using GPS as a ground truth speed and calculating running dimensions from GPS speed”. However, in the interest of compact prosecution, the relevant paras. have been included below. Snyder para. [0106] recites:
The processor 760 may determine or facilitate determination of the running dimension of the wheelset 321, such as wheelset running diameter, circumference, and/or radius. For example, the wheelset running diameter may be calculated from the ground speed (e.g. 32.00 mph) (determined by the GPS circuitry) divided by the rotational velocity (e.g. 5 revolutions per second) (determined using an accelerometer) and converted to inches…
Additionally, Snyder para. [0108] recites:
The rotational velocity in the above example may be calculated by the following: In exactly the same time frame as above, across a five second length, data may have been captured from an arrangement of accelerometers and microphones. By using various digital filters, deconvolution algorithms, and autocorrelation (serial correlation or time series) algorithms, the rotational velocity of the wheelset 321 may be determined (in this example, exactly 5.000 revolutions per second). The above works because of the repeating nature of the wheelset vibration data due to imperfections or non-uniformities in the wheels or axle or bearings.
Examiner reads Snyder paras. [0106]-[0108] to mean that the a wheel diameter [wheelset running diameter] can be determined based on the differences between the predicted speed [rotational velocity] and a detected corresponding ground truth speed [ground speed determined by GPS circuitry]. For at least the above reasons, the rejection(s) of claims 18 and 28 under 35 U.S.C. § 102 in view of Snyder on April 16, 2026, stand.
In regard to Applicant’s arguments (“Remarks” filed July 15, 2026,, page 10) that the dependent claim 19 is not disclosed by Snyder, namely that Snyder does not describe using time-resolved Fourier transforms, Examiner respectfully disagrees, and would draw Applicant’s attention to para. [0109] of Snyder, which recites:
The acceleration signature may align from time-to-time, but due to the difference in the frequency of the vibration signatures, the wheelsets 321 move out of phase with one another. This enables the vibration signatures associated with each wheelset to be determined and monitored. Deconvolution or other methods may be used to compensate for this time misalignment. Graph 804 is the result of an autocorrelation calculation applied to the data shown in graph 802. [Emphasis added].
As described on page 5 of the Non-Final Rejection mailed on April 16, 2026, deconvolution is commonly known to include Deconvolutive Short-Time Fourier Transform(s) (DSTFT(s)), and DSTFTs are a type of time-resolved Fourier Transform – thus, Snyder discloses the use of time-resolved Fourier Transforms [DTSFT] on vibration data [vibration signatures associated with each wheelset] to generate a raw spectrogram [graphs 802 and 804 are spectrograms] at least in para. [0109]. Therefore, the prior art rejection of claim 19 under 35 U.S.C. § 102 in view of Snyder on April 16, 2026, stands.
As Applicant did not provide further arguments against the prior art rejections of claims 20-27 and 29-34, those rejections under 35 U.S.C. § 102 and §103 similarly stand.
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 (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 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.
Claims 18-25 and 28-32 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Snyder.
In regard to claims 18-25 and 28-32, Snyder discloses:
[claims 18 and 28] a device and method for estimating a current wheel diameter of a wheel of a rail-based vehicle on a predetermined network of routes [mobile railway asset monitoring apparatus, abstract], comprising: an interface {railway asset node, para. [0009]} for collecting vibration data {described throughout, at least paras. [0002] are explicit in measuring vibration} corresponding to vibrations of at least one wheel {para. [0004] describes a vibration of a wheelset}, the vibrations acting on the rail-based vehicle as an acceleration of the rail-based vehicle {vibrations acting as at least an acceleration, para. [0011]}; at least one wireless sensor arranged proximate the at least one wheel and configured for detecting the vibrations {wireless sensor nodes, described at least in para. 0048]}; at least one computing unit {server computer, para. [0009]} configured for generating a predicted speed based on the vibration data {para. [0007] describes ground speed as a parameter (running dimension), para. [0008] describes predicting future parameters} and for estimating a wheel diameter {para. [0007] describes wheelset diameter as a parameter (running diameter), para. [0008] describes predicting future parameters} based on differences between the predicted speed and a detected corresponding ground truth speed {Fig. 9 shows determined speed correlated to GPS speed, para. [0106] describes using GPS as a ground truth speed and calculating running dimensions from GPS speed};
[claims 19 and 29] that the at least one computing unit is configured for: applying a time-resolved Fourier transform to the vibration data {para. [0076] describes using a Fourier transform to compute data, para. [0109] describes using deconvolution to assess vibration/acceleration data, [deconvolution is commonly known to include DSTFT, a type of time-resolved Fourier transform]} to generate a raw spectrogram [spectrograms shown at least in Figs. 8 and 10]; applying a filter to the raw spectrogram {para. [0076] describes using digital filtering, para. [0109] describes using low pass filtering}; and applying a normalization to generate an acceleration spectrogram {Fig. 10 shows the left and right wheel normalized to compare vibration amplitude, Fig. 8 shows the vibration data normalized in graph 804 to address crosstalk, described in para. [0109]} based on the time-resolved, normalized vibration data [shown in Fig. 10 and Fig. 8 graph 804] in order to generate the predicted speed from the acceleration spectrogram {para. [0116] describes using vibration data to determine shifts in the wheels and their location, which would affect the predicted speed at the location of the wheel};
[claim 20] that the at least one computing unit is configured to form a short-time Fourier transform (STFT) as an acoustic analysis of the raw spectrogram and/or the acceleration spectrogram {para. [0109] describes using deconvolution to assess vibration/acceleration data, [deconvolution is commonly known to include DSTFT, a type of STFT]};
[claims 21 and 30] that the at least one computing unit is configured for determining a computational frequency shift due to a changed wheel diameter {para. [0109] describes that the frequency of vibration signals may be unaligned and using deconvolution to assess running diameter of a wheel, thus the alignments are shifted as shown in Fig. 8 graph 804}, the computational frequency shift resulting when the predicted speed is adapted based on the vibration data and the ground truth speed {Fig. 8 shows that graph 804 has frequency shifts aligned for rotations of a wheelset and respective vibrations, described in para. [0109]; para. [0109] describes using the data;
[claim 22] that the at least one computing unit is configured for estimating the frequency shift based on at least a speed-dependent rotational speed parameter detected based on the vibration data {para. [0109] describes using vibration and wheel rotation frequencies to determine potential misalignment};
[claim 23] that the at least one computing unit is configured for utilizing one or both of toothing frequencies of a transmission and wheel frequencies of the at least one wheel as speed-dependent rotational speed parameters {para. [0019] describes using sensors to track the rotational speed of the wheels to perform calculations};
[claim 24] that the at least one computing unit is configured for utilizing one or more of a high-pass filter, a low-pass filter, a bandpass filter, and a median filter as filtering {para. [0109] describes using a low-pass filter};
[claims 25 and 32] that the interface is configured for receiving GPS positions of the rail-based vehicle, and the device is configured for determining the ground truth speed based on the GPS positions {GPS speed used for calculations described in para. [0106]}; and
[claim 31] that the acceleration is detected as vibration data from all wheels by the at least one wireless sensor [Fig. 10 shows vibration data from multiple wheels compared to each other].
Claim Rejections - 35 USC § 103
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 26-27 and 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Snyder as applied to claims 18-25 and 28-32 above, and further in view of Morita, (US 20210078619 A1; cited in prior PTO-892).
In regard to claims 26 and 33, Snyder does not teach applying a trained machine-learned model configured to ground truth speed and vibration data to determine a wheel diameter in order for the machine-learned model to estimate a wheel diameter based on the ground truth speed and vibration data.
However, Morita also teaches a device and method to monitor a rail-based vehicle [railway condition monitoring & device, abstract], such that the measurements include wheel diameter, vibration, speed, and acceleration [abstract], as well as a learning module configured to apply a trained machine-learned model to the ground truth speed and the vibration data to determine the wheel diameter {para. [0063] describes state information to include speed, acceleration, vibration, and wheel diameter}, wherein the trained machine-learned model is configured to estimate the wheel diameter based on the ground truth speed and the vibration data {at least para. [0080] describes a machine-learning module calculating future states based on information from previous and current states}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted Snyder's mobile railway asset monitoring apparatus and method for Morita's system of data delivery to train a machine-learned model in order to better speed up data processing and obtain higher determination accuracy, as taught by Morita {para. [0078]}.
In regard to claims 27 and 34, Snyder does not teach that the machine-learned model is configured to adapt the predicted speed based on the vibration data and the ground truth speed; determining a frequency shift based on the adapted predicted speed; and determining the changed wheel diameter using the trained machine-learned model based on the frequency shift.
However, Morita further teaches that the trained machine-learned model is configured to adapt the predicted speed based on the vibration data and the ground truth speed {at least paras. [0080] and [0118] describe calculating future speeds (future states, see para. [0063])}, a frequency shift determinable based on the adapted predicted speed {para. [0157] describes the use of shifting a frequency spectrum according to a vehicle speed, thus an adapted predicted speed}; and the trained machine-learned model is configured to determine the changed wheel diameter based on the frequency shift {at least paras. [0080] and [0118] describe calculating future wheel diameters (future states, see para. [0063])}; additionally, para. [0241]describes the machine model using Fourier transforms to analyze data}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted Snyder's mobile railway asset monitoring apparatus and method for Morita's system of data delivery to train a machine-learned model in order to better speed up data processing and obtain higher determination accuracy, as taught by Morita {para. [0078]}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Friesen (US 20120259487 A1) is directed to a method for monitoring the state of a railway vehicle (to include speeds, vibrational data/oscillations, etc.) through the use of electronics (computers, sensors, etc.) and data manipulation (fast Fourier transforms, signal filtering, etc.) and Wolf (US 20210277975 A1) is directed to damping torsional moments/vibrations on a drivetrain of a vehicle, as well as monitoring said torsional moments through the use of electronics (computers, sensors, etc.) and data manipulation (fast Fourier transforms, signal filtering, etc.)
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 DANIEL QUINN whose telephone number is (571)272-2690. The examiner can normally be reached T-R 07:00-19:00 PST, F 7:00-11:00 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN BREENE can be reached at (571)272-4107. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL M QUINN/
Examiner, Art Unit 2855
/NATALIE HULS/Primary Examiner, Art Unit 2855
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