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 .
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 1-5, 7-8, 10-12, 15-18, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gross et al. (US 2021/0081573 A1, Applicant’s submitted IDS filed 5/07/2024), hereinafter “Gross”.
As per claim 1, Gross teaches a method comprising:
“generating a set of time series signals from sensor readings of a reference device while the reference device is operated through multiple individual iterations of an exercise profile, wherein the reference device operates with a known state of degradation” at [0007], [0052] and Fig. 5;
(Gross teaches EMI signals (i.e., “time series signals”) is generated by a reference asset while the reference asset is executing a periodic workload, wherein the reference asset is certified not to contain unwanted electronic component (i.e., “operate with a known state of degradation”))
“separating the set of time series signals into segments that correspond to the individual iterations of the exercise profile” at [0007], [0052];
(Gross teaches the system divides the reference EMI signals into a set of profiles, which comprise EMI signals for non-overlapping time intervals of a fixed size)
“aligning and merging the segments to generate a merged reference fingerprint” at [0007], [0052];
(Gross teaches the system temporally aligns and merges profiles in the set of profiles to produce a reference profile. Next, the system generates the reference EMI fingerprint from the reference profile)
“training a machine learning model to detect anomalous departures from the original based on the merged reference fingerprint” at [0052]-[0053] and Figs. 5-6.
(Gross teaches the system trains an MSET model based on reference time-series signals in the reference EMI fingerprint and uses the trained MSET model to determine whether the target system contains any unwanted electronic component)
As per claim 2, Gross teaches the method of claim 1, wherein “aligning and merging the segments further comprises: coarsely aligning the segments to generate a coarse reference fingerprint, wherein the coarse reference fingerprint is a merge of the coarsely aligned segments; and finely realigning the segment to generate the merged reference fingerprint, wherein the merged reference fingerprint is a merge of the finely realigned segments” at [0032]-[0042].
As per claim 3, Gross teaches the computer-implemented method of claim 2, wherein “coarsely aligning the segments to generate the coarse reference fingerprint further comprises: selecting one segment from the segments to be an anchor segment; align an additional segment of the segments to the anchor segment based on a cross correlation coefficient between the anchor segment and the additional segment; merge the aligned additional segment with the anchor segment, wherein the aligning and merging are repeated for each additional segment of the segments; and providing the anchor segment merged with the aligned additional segments as the coarse reference fingerprint” at [0032]-[0042].
As per claim 4, Gross teaches the computer-implemented method of claim 2, wherein “finely aligning the segments to generate the merged reference fingerprint further comprises: extracting one segment from the coarse reference fingerprint; realign the one segment to the coarse reference fingerprint with the one segment removed based on a cross power spectral density between the one segment and the coarse reference fingerprint with the one segment removed, wherein the realignment increases alignment between the one segment and the coarse reference fingerprint with the one segment removed; merge the realigned one segment back into the coarse reference fingerprint, wherein the extracting, realigning, and merging are repeated for each of the segments in the coarse reference fingerprint; and providing the coarse reference fingerprint with the segments realigned as the merged reference fingerprint” at [0032]-[0042].
As per claim 5, Gross teaches the computer-implemented method of claim 1, further comprising, “before training the machine learning model: ensemble averaging the merged reference fingerprint in a moving window; and resampling the ensemble-averaged merged reference fingerprint at a uniform interval” at [0039]-[0042].
As per claim 7, Gross teaches the computer-implemented method of claim 1 discussed above. Gross also teaches: “wherein the operation of the reference device includes rest periods between the individual iterations of the exercise profile, wherein the rest periods provide stubs of flat noisy values at ends of the segments for the individual performances” at [0033].
As per claim 8, Gross teaches the computer-implemented method of claim 1, further comprising: “monitoring a field device with the trained machine learning model to detect an anomaly indicating degraded operation of the field device; and in response to detecting the anomaly, generating an electronic alert that the field device exhibits the degraded operation” at [0053] and Fig. 6.
Claims 10-20 recite similar limitations as in claims 1-9 and are therefore rejected by the same reasons.
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.
Claims 6, 13-14, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gross as applied to claims 1-5, 7-8, 10-12, 15-18, 20 above, and in view of Fischer et al. (US 2020/0186898 A1), hereinafter “Fischer”.
As per claims 6, Gross teaches the computer-implemented method of claim 1 discussed above. Gross does not teach: “wherein generating the set of time series signals further comprises: accepting the sensor readings of the reference device, wherein the sensor readings are sampled at a first sampling rate; divide a frequency spectrum of the sensor readings into a plurality of frequency bins; and sample the frequency bins at a second sampling rate to produce the set of time series signals, wherein the time series signals in the set of time series signals correspond to the frequency bins, and wherein the second sampling rate is lower than the first sampling rate” as claimed. However, Fischer teaches a sensor device which generates time series data and transforms data into spectrum data to be processed by a computer device, including the steps of “accepting the sensor readings of the reference device, wherein the sensor readings are sampled at a first sampling rate; divide a frequency spectrum of the sensor readings into a plurality of frequency bins; and sample the frequency bins at a second sampling rate to produce the set of time series signals, wherein the time series signals in the set of time series signals correspond to the frequency bins, and wherein the second sampling rate is lower than the first sampling rate” at [0019]-[0026] and Fig. 2. Thus, it would have been obvious to one of ordinary skill in the art to combine Fischer with Gross’s teaching because “using a relatively low sampling rate may lead to sampling sensor data 206 for a longer time period of time, which may decrease a response time for processor 120 to complete the transformation of sensor data into spectrum data”, as suggested by Fischer at [0022].
Claims 13-14, 19 recite similar limitations as in claim 6 and are therefore rejected by the same reasons.
Claim 9 are rejected under 35 U.S.C. 103 as being unpatentable over Gross as applied to claims 1-5, 7-8, 10-12, 15-18, 20 above, and in view of Nair et al. (US 2022/0214316 A1), hereinafter “Nair”.
As per claim 9, Gross teaches the computer-implemented method of claim 1 discussed above. Gross does not teach “cycling a motor of the reference device through a range of speeds a plurality of times over a time period to operate the reference device through the exercise profile; and generating a three-dimensional vibration fingerprint characterizing the motor, wherein the set of time series readings is the three- dimensional vibration fingerprint” as claimed. However, Nair teaches at [0099]-[0109] a failure detection apparatus include a signature derivation module configured to measure vibration information at the sensor for a plurality of machine speed to identify a vibration signature for each of the machine speeds, including the steps of: “cycling a motor of the reference device through a range of speeds a plurality of times over a time period to operate the reference device through the exercise profile; and generating a three-dimensional vibration fingerprint characterizing the motor, wherein the set of time series readings is the three- dimensional vibration fingerprint” at [0099]-[0109]. Thus, it would have been obvious to one of ordinary skill in the art to combine Nair with Gross’s teaching because “vibration data, rotating machine data, motor data, current data, voltage data, etc. is collected for various machine speeds and/or loading conditions to establish a baseline or envelope, such as a vibration signature, so that subsequent sensor readings that exceed the envelope for a particular machine speed/load to more particularly isolate and identify and predict failures at an early state before actual failures stop equipment and incur costly shutdowns”, as suggested by Nair at [0051].
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
November 25, 2025
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