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 .
Response to Arguments
Applicant's arguments filed 02/07/2026 have been fully considered are addressed below:
Applicant’s amendments overcome the objection to claim 6. The objection has been withdrawn.
Applicant’s arguments with respect to the 103 rejections claim(s) 6-8 and 10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues that the cited prior art does not teach or suggest at least using a Short-Term Fourier Transform to generate a spectrogram or analyzing the spectrogram using a Convolutional Neural Network to determine utility pole integrity (remarks page 5). However, newly cited CN 108645498 A by Zeng et al. is relied upon to teach these limitations. Also, as further addressed below, there does not appear to be support in the original disclosure for at least “a Convolutional Neural Network”, nor has the applicant pointed out support in the remarks. Claims 7-9 have been cancelled. Claims 6 and 10-14 are rejected in view of Yoda and Zeng.
Claim Objections
Claim 12 is objected to because of the following informalities:
Regarding claim 12, in line 2, “a central office” should read “the central office” since claim 1 already recites the term.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6 and 10-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 6, the limitation requiring the “machine learning model to comprise a Convolutional Neural Network (CNN) configured to extract features from the spectrogram” does not have support in the original disclosure. "MPEP 2163 I(B) states, 'To comply with the written description requirement of 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, or to be entitled to an earlier priority date or filing date under 35 U.S.C. 119, 120, 365, or 386, each claim limitation must be expressly, implicitly, or inherently supported in the originally filed disclosure.'” Although the applicant’s specification describes training a machine learning model ([0008]-[0009]) and a distributed fiber optic sensing system that may advantageously include artificial intelligence / machine learning (AI/ML) analysis is shown illustratively in FIG. 1(A) ([00025]), no basis has been found in the original disclosure for a Convolutional Neural Network or any other type of neural network.
Regarding claim 11, the limitation requiring “determining the utility pole integrity condition classes comprises distinguishing between at least two of: a healthy condition, a rot condition, woodpecker damage, or loose hardware” does not have support in the original disclosure. "MPEP 2163 I(B) states, 'To comply with the written description requirement of 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, or to be entitled to an earlier priority date or filing date under 35 U.S.C. 119, 120, 365, or 386, each claim limitation must be expressly, implicitly, or inherently supported in the originally filed disclosure.'” Although the applicant’s specification describes known integrity conditions ([0038]) and different pole integrity condition classes ([0039]), no basis has been found in the original disclosure for a healthy condition, a rot condition, woodpecker damage, or loose hardware.
Claims 10 and 12-14 are rejected based on their dependencies.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 6 and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Yoda (WO2020044648A1; previously cited) in view of CN 108645498 A by Zeng et al. (hereinafter “Zeng”; translation provided).
Regarding claim 6, Yoda teaches a method for determining utility pole integrity, the method comprising ([0025]; [0028]; [0031]):
receiving, from an aerial optical fiber suspended from a plurality of utility poles ([0016]), backscattered light containing a vibration pattern resulting from environmental noise imposed on one or more of the utility poles ([0032] poles 10 vibrate due to conditions such as wind; [0025] vibration data obtained from backscattered light; Fig. 1 and 12 shows optical fiber 20 suspended from poles 10);
analyzing, based on the vibration pattern, vibrations of the optical fiber due to vibrations of one or more of the utility poles, wherein the analyzing utilizes a machine learning model trained on vibration patterns resulting from environmental noise (specifying unit 332; [0018] “vibrations patterns”; [0024]; [0038] vibration data can be analyzed using a machine learning model; [0032] environmental noise); and
simultaneously determining ([0052]), for each of the plurality of utility poles and remotely from a central office (communication carrier station building 30 is the remote central office where determinations are made [0062]), based only on the vibrations of the optical fiber due to the vibrations of one or more of the utility poles, the utility pole integrity of one or more of the plurality of utility poles ([0018]-[0020] the state of the utility pole 10 (e.g., the deterioration state of the utility pole 10) is the integrity of the pole; [0060] machine learning model can determine "types or states of utility poles"; [0058] correspondence table can be used to identify states of utility poles);
wherein the determining comprises classifying, utilizing the machine learning model, the vibration pattern into corresponding utility pole integrity condition classes ([0060] perform machine learning for the characteristic patterns of the utility poles 10 according to the states of the utility poles 10, and specify the types or the states of the utility poles 10 by using the learning result of the machine learning; where states of the utility poles are integrity condition classes);
wherein the backscattered light is received from a plurality of aerial optical fibers geographically separated from one another (Fig. 1 and 12 shows optical fiber 20 suspended from poles 10 that are geographically separated from one another), and wherein the aerial optical fiber simultaneously carries live telecommunications traffic ([0016] optical fiber cable 20 is a cable containing at least one communication optical fiber).
Although Yoda does teach methods which include manual hammering (second method [0029]) and thus does not teach environmental noise in every embodiment, Yoda does teach methods which use non-artificially generated vibrations from the surrounding environment ([0026]). Therefore, it would have been well known and obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to use a vibration pattern resulting from environmental noise to perform a non-destructive and efficient remote measurement.
Further, even if Yoda does not explicitly teach simultaneously determining based only on the vibrations of the optical fiber due to the vibrations of one or more of the utility poles, the utility pole integrity of one or more of the plurality of utility poles, Yoda teaches simultaneously determining locations of utility poles. Further, Yoda teaches an embodiment which can determine the location of the poles using a characteristic pattern which changes due to the deterioration state (an integrity) of the pole ([0055]). Thus, if the deterioration state is used to determine location, then the deterioration state is also determined simultaneously and remotely using the vibration pattern. Therefore, it would have been well known and obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to include simultaneously determining based only on the vibrations of the optical fiber due to the vibrations of one or more of the utility poles, the utility pole integrity of one or more of the plurality of utility poles in order to safely and efficiently evaluate the condition of the pole.
Further, although Yoda does not teach wherein the analyzing step comprises pre-processing the vibration pattern using a Short-Time Fourier Transform (STFT) to generate a spectrogram, and the machine learning model analyzes the spectrogram to determine the utility pole integrity and wherein the machine learning model comprises a Convolutional Neural Network (CNN) configured to extract features from the spectrogram, Yoda does teach the use a Fast Fourier Transform to obtain frequency characteristics of vibration data ([0055]) and Fig. 10 shows frequency and magnitude/intensity, while Fig. 11 shows time and magnitude ([0055]).
However, Zeng does address these limitations. Zeng and Yoda are considered to be analogous to the present invention as they are in the same field of Optical Time-Domain Reflectometry.
Zeng teaches structural health monitoring ([001]) using phase sensitive optical time domain reflection ([003]) and includes an impact load position identification method, where an impact is creates a vibration signal or pattern ([012]) and the method includes wherein the analyzing step comprises pre-processing the vibration pattern using a Short-Time Fourier Transform (STFT) to generate a spectrogram ([032] the one-dimensional signal can be Converted into a time-frequency map and used for input of CNN; time-frequency analysis methods include Wavelet transform, Short-time Fourier transform (STFT)), and the machine learning model analyzes the spectrogram to determine the impact load ([032]); and wherein the machine learning model comprises a Convolutional Neural Network (CNN) configured to extract features from the spectrogram ([032] CNN used for image classification, CNN can be used to discriminate impact loads).
It would have been well known to someone of ordinary skill in the art before the effective filing date of the claimed invention to use Short-time Fourier transform and a Convolutional Neural Network for processing OTDR data for structural health monitoring. Therefore, it would have been obvious to modify Yoda to include wherein the analyzing step comprises pre-processing the vibration pattern using a Short-Time Fourier Transform (STFT) to generate a spectrogram, and the machine learning model analyzes the spectrogram to determine the utility pole integrity and wherein the machine learning model comprises a Convolutional Neural Network (CNN) configured to extract features from the spectrogram as suggested by Zeng in order to implement well-known and effective data processing and image classification methods which can handle processing and learning of massive data, thus increasing efficiency ([073]).
Regarding claim 10, Yoda modified by Zeng teaches the method of claim 6 and Yoda further teaches wherein the determining step is performed without a visual inspection, hammer test, digging, or drilling into any of the plurality of utility poles ([0026]).
Further, although Yoda does teach methods which include manual hammering (second method [0029]), Yoda does also teach methods which use non-artificially generated vibrations from the surrounding environment ([0026]) and either method of generating vibrations can be used to determine the state of the utility poles. Therefore, it would have been well known and obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the determining step is performed without a visual inspection, hammer test, digging, or drilling into any of the plurality of utility poles in order to perform a non-destructive and efficient remote measurement.
Regarding claim 11, Yoda modified by Zeng teaches the method of claim 6 and Yoda further teaches wherein determining the utility pole integrity condition classes comprises distinguishing between at least two of: a healthy condition, a rot condition, woodpecker damage, or loose hardware ([0060] perform machine learning for the characteristic patterns of the utility poles 10 according to the states of the utility poles 10, and specify the types or the states of the utility poles 10 by using the learning result of the machine learning; where states of the utility poles are integrity condition classes; deterioration state of the pole ([0055]; deterioration state would refer to either a healthy condition (not deteriorated) or rot condition (deteriorated)).
Regarding claim 12, Yoda modified by Zeng teaches the method of claim 6 and Yoda further teaches wherein receiving the backscattered light comprises interrogating the aerial optical fiber with a Distributed Acoustic Sensing (DAS) interrogator located at a central office, and wherein the analyzing is performed by a processor located at the central office (communication carrier station building 30 is the remote central office where determinations are made [0062]; [0025] fiber sensing unit 331 inside building 30 uses a Distributed Acoustic Sensing (DAS)).
Regarding claim 13, Yoda modified by Zeng teaches the method of claim 6 and Yoda further teaches comprising training the machine learning model using a training dataset comprising vibration patterns resulting from environmental noise collected from a plurality of training utility poles, wherein each of the plurality of training utility poles has a known utility pole integrity condition ([0060] the specifying unit 332 may perform machine learning for the characteristic patterns of the utility poles 10 according to the types of the utility poles 10 or perform machine learning for the characteristic patterns of the utility poles 10 according to the states of the utility poles 10, and specify the types or the states of the utility poles 10 by using the learning result of the machine learning; in order to perform machine learning, the state or integrity must be known). Yoda also teaches in reference to a different method, that a correspondence table in which the characteristic patterns of utility poles 10 are associated with the states of the utility poles 10 can also be used to specify states using a known state ([0058]).
Regarding claim 14, Yoda modified by Zeng teaches the method of claim 6 and Yoda further teaches comprising identifying a location of a fiber coil (simultaneously and remotely specify the locations of a plurality of utility poles 10 by using an existing communication optical fiber ([0052]; determine the location of the poles using a characteristic pattern which changes due to the deterioration state of the pole ([0055]).)
Conclusion
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 KAITLYN E KIDWELL whose telephone number is (703)756-1719. The examiner can normally be reached Monday - Friday 8 a.m. - 5 p.m. ET.
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/KAITLYN E KIDWELL/Examiner, Art Unit 2877
/TARIFUR R CHOWDHURY/Supervisory Patent Examiner, Art Unit 2877