DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-2 are pending and presented for examination.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-2 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The representative claim 1 recites:
A method of determining utility pole locations, the method comprising:
operating a distributed fiber optic sensing (DFOS) system configured to monitor ambient vibrational events affecting utility poles;
analyzing two-dimensional spatiotemporal time-series data received from the monitoring and separating the data into a training, validation, and testing sets according to labeled utility pole geographical locations;
transforming the time-series data into frequency domain data using a Fourier transform;
separating the transformed frequency domain data into low frequency data sequences and high frequency data sequences for feature extraction;
measure similarities between features extracted from the high frequency data sequences and low frequency data sequences and fusing learned features into a ResNet for pole detection;
applying further monitored two-dimensional spatiotemporal time-series data to the ResNet for determination of utility pole location; and
outputting an indicium of utility pole locations.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process).
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and/or mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because the additional limitations in the claim are only: operating a distributed fiber optic sensing (DFOS) system configured to monitor ambient vibrational events affecting utility poles;… a ResNet… and outputting an indicium of utility pole locations. The limitation “operating a distributed fiber optic sensing (DFOS) system configured to monitor ambient vibrational events affecting utility poles” is recited at a high level of generality (i.e., gathering data using a distributed sensor system) such that it amounts no more than mere instructions to apply the exception using a generic sensor.
Further, the claim recites the additional element(s) of using generic AI/ML technology, i.e. “ResNet,” to perform data evaluations or calculations, as identified under Prong 1 above. The claim does not recites any details regarding how the “ResNet” algorithm is trained. Instead, the claim is found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “ResNet” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “ResNet” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
Furthermore, the claim limitation “outputting an indicium of utility pole locations”, is recited at a high level of generality (i.e., as a generic computer structures performing a generic computer function of outputting information) such that it amounts no more than mere instructions to apply the exception using a generic computer components.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional limitations recited at a high level of generality (i.e., as a generic sensor and storing data outputting information using a computer components). Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Yoda et al. US 2021/0172767 (hereinafter, Yoda), ([0061], [0078], and Figs. 4, 6), and Lu et al. “Automatic Fine-Grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear Resnets” (hereinafter, Lu), (Abstract, page 4677, and Fig. 1). Therefore, claim 1 is directed to an abstract idea without significantly more.
The claim is not patent eligible.
Dependent claim 2, adds further details of the identified abstract idea. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
5. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
6. Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Yoda et al. US 2021/0172767 (hereinafter, Yoda), in view of Lu et al. “Automatic Fine-Grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear Resnets”, June 2021 (hereinafter, Lu).
7. Regarding claim 1, Yoda discloses a method of determining utility pole locations, the method comprising:
operating a distributed fiber optic sensing (DFOS) system configured to monitor ambient vibrational events affecting utility poles ([0020], [0052], [0061], Figs. 1-2);
analyzing time-series data received from the monitoring ([0051], [0068], Fig. 2); and separating the data into a training according to labeled utility pole geographical locations ([0061], [0080]-[0081], Figs. 6-7);
transforming the time-series data into frequency domain data using a Fourier transform ([0105]);
separating the transformed frequency domain data into low frequency data sequences and high frequency data sequences for feature extraction ([0075], [0105]-[0106]);
measure similarities between features extracted from the high frequency data sequences and low frequency data sequences and fusing learned features into a ResNet for pole detection ([0075], [0078], [0086]: the specifying unit 332 performs machine learning (e.g., deep learning) for vibration patterns at locations where utility poles 10 are present, and specifies the locations of utility poles 10 by using the learning result of the machine learning (an initial training model)….[0105]-[0106], [0111]: Figs. 10 and 11 show frequency characteristics (a horizontal axis indicates frequencies and a vertical axis indicates magnitudes (amplitudes)) of vibration data (a horizontal axis indicates time and a vertical axis indicates magnitudes (amplitudes)) of a utility pole 10 after an FFT (Fast Fourier Transform) is performed for the vibration data…Further, in the case where the specifying unit 332 performs machine learning for the characteristic patterns of the utility poles 10 by the above-described fourth method or the like, it is considered that the characteristic patterns of the utility poles 10 also change depending on the region. For example, the characteristic patterns in a temperate region are different from those in a cold region. Therefore, the specifying unit 332 may perform machine learning for each region by using teacher data corresponding to that region); wherein the machine learning (e.g., deep learning) is interpreted as equivalent to ResNet within the claim,
applying further monitored time-series data to the ResNet for determination of utility pole location ([0077]-[0078]); and
outputting an indicium of utility pole locations ([0081]-[0083], Figs. 1, 4, 6).
Yoda does not disclose:
analyzing two-dimensional spatiotemporal time-series data; and separating the data into a training, validation, and testing sets.
However, Lu discloses:
analyzing two-dimensional spatiotemporal time-series data (page 4676, section 2.1 Data Preprocessing, and Fig. 1c); and
separating the data into a training, validation, and testing sets (page 4676, section 2.1 Data Preprocessing, and page 4677, section 4. Experiments).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Yoda to use analyzing two-dimensional spatiotemporal time-series data; and separating the data into a training, validation, and testing sets as taught by Lu. The motivation for doing so would have been in order to determine the location of the utility pole accurately (Lu, page 4676).
8. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Yoda, in view of Lu, in further view of Wu et al. “Vibration Detection in Distributed Acoustic Sensor With Threshold-Based Technique: A Statistical View and Analysis” June 2021 (hereinafter, Wu).
9. Regarding claim 2, Yoda in view of Lu disclose the method of claim 1, as disclosed above.
Yoda in view of Lu further discloses measuring similarities as disclosed above.
Yoda in view of Lu does not disclose:
applying a Gaussian distribution to the measured similarities.
However, Wu discloses:
applying a Gaussian distribution to the measured similarities (page 4685, and page 4089, 2nd col., Par. 3).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Yoda in view of Lu to use applying a Gaussian distribution to the measured similarities as taught by Wu. One would have been motivated to do so in order apply the Gaussian distribution methodology of a distributed optical fiber sensor system as known in the art and as taught by Wu in a utility pole detection distributed optical fiber sensor system such as that of Yoda and Lu, thereby identifying vibration event from background noise (Wu, Abstract).
Conclusion
10. Examiner has cited particular columns and line numbers, and/or paragraphs, and/or pages 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.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EYOB HAGOS whose telephone number is (571)272-3508. The examiner can normally be reached on 8:30-5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Eyob Hagos/
Primary Examiner, Art Unit 2857