DETAILED ACTION
Final Rejection
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 Amendment
Applicant’s amendments, filed 11/24/2025 to claims are accepted. In this amendment, Claims 1 and 5 has been amended. Regarding claims 2-4: Cancelled.
Claim Rejections - 35 USC § 101
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.
Claims 1 and 5 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims1and 5 falls within one of the four statutory categories. See MPEP § 2106.03. For example, each of claims 1-5 fall within category of process.
Regarding Claims 1 and 5
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of localizing vehicle emergency stops.
A computer-implemented method for detecting and localizing vehicle emergency stops using distributed fiber optic sensing (DFOS), the method comprising:
providing the DFOS system including:
a length of optical sensing fiber located adjacent to a highway carrying vehicle traffic such that it senses highway vibrational activity;
an optical interrogator configured to generate optical pulses, introduce the generated optical pulses into the optical sensing fiber and receive Rayleigh reflected signals from the optical sensing fiber;
an artificial intelligence (Al) analyzer comprising a processor configured to analyze the Rayleigh reflected signals received by the optical interrogator and determine emergency stops made by vehicles traveling on the highway;
detecting, by the Al analyzer, an emergency stop event determined by an event detection procedure selected from the group consisting of: template matching, and generalized model training;
localizing, by the Al analyzer, a location of the emergency stop event along the length of the optical sensing fiber; and
reporting, by the Al analyzer, the occurrence of and the location of the emergency stop event;
wherein the received Rayleigh reflected signals are organized by the Al analyzer into an input waterfall image and the template matching includes reshaping designed filters to a same dimension and stacked as kernels of a larger filter which is then convolved with the input waterfall image, activating existing events of interest in the input waterfall image.
The italicized limitations above represent a combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental steps (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitation “analyze the Rayleigh reflected signals [..]; determine emergency stops [..]; detecting, an emergency stop [..];localizing, a location of the emergency stop[..] ” are a combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental steps (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “receive Rayleigh reflected signals from the optical sensing fiber; the Rayleigh reflected signals received by the optical interrogator; wherein the received Rayleigh reflected signals are organized by the Al analyzer into an input waterfall image and the template matching includes reshaping designed filters to a same dimension and stacked as kernels of a larger filter which is then convolved with the input waterfall image, activating existing events of interest in the input waterfall image; reporting, by the Al analyzer, the occurrence of and the location of the” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g)
The 2nd additional element is “using distributed fiber optic sensing (DFOS), the method comprising: providing the DFOS system including: a length of optical sensing fiber located adjacent to a highway carrying vehicle traffic such that it senses highway vibrational activity; an optical interrogator configured to generate optical pulses, introduce the generated optical pulses into the optical sensing fiber ”. This element amounts to mere use of a generic components of distributed fiber optic sensing, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
The 3rd additional element of “an artificial intelligence (AI) analyzer configured to analyze” in limitations are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept and is recited a computer component at a high level of generality See MPEP 2106.05(f).
In view of the above, the three “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a plurality of generic DFOS system with computer software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea.
.
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
.
Dependent Claim 5
Dependent claim 5 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claim 5 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For example, the limitations of Claim 5 is a mental step.
Response to Argument
Applicant’s arguments with respect 101 rejection, specially claim 1, the applicant did not agree with it, see pages 4-7.The Applicant argus that “the claim is not directed to abstract idea and also integrated the operation into practical operation”
In response, the Examiner respectfully disagree because current 101 rejection based on 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (examples 47-49). outputting the result from the AI analyzer” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Furthermore, reciting “using the AI analyzer; herein the AI analyzer is specifically configured for receive Rayleigh reflected signals from the optical sensing fiber; the Rayleigh reflected signals received by the optical interrogator; wherein the received Rayleigh reflected signals are organized by the Al analyzer into an input waterfall image and the template matching includes reshaping designed filters to a same dimension and stacked as kernels of a larger filter which is then convolved with the input waterfall image, activating existing events of interest in the input waterfall image; ” provide nothing more than mere instructions to implement an abstract idea on a generic computer component with high level of generality and “reporting, by the Al analyzer, the occurrence of and the location of the”, this element amounts to mere use of data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. furthermore, “using distributed fiber optic sensing (DFOS), the method comprising: providing the DFOS system including: a length of optical sensing fiber located adjacent to a highway carrying vehicle traffic such that it senses highway vibrational activity; an optical interrogator configured to generate optical pulses, introduce the generated optical pulses into the optical sensing fiber ”. This element amounts to mere use of a generic components of distributed fiber optic sensing, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).In view of the above “additional elements” individually does not provide a practical application of the abstract idea. See, MPEP §§2106.05(a) . See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of “analyze the Rayleigh reflected signals received by the optical interrogator and determine emergency stops made by vehicles traveling on the highway; detecting, by the Al analyzer, an emergency stop event determined by an event detection procedure selected from the group consisting of: template matching, and generalized model training; localizing, by the Al analyzer, a location of the emergency stop event along the length of the optical sensing fiber; and reporting, by the Al analyzer, the occurrence of and the location of the emergency stop event” is performed “using the AI analyzer.” The AI analyzer is used to generally apply the abstract idea without placing any limits on how the AI analyzer functions. Rather, these limitations only recite the outcome of “determining” and “analyzing it” and do not include any details about how the “detecting” and “analyzing” are accomplished. See MPEP 2106.05(f). As such 101 rejection is maintained.
Applicant’s arguments and amendments with respect to claim(s) 1has been considered and withdrawn..
Examiner Notes
Three is no prior art rejection over claim 1, however there is 101 rejection. Closes prior arts fail to teach the limitations of “received Rayleigh reflected signals are organized into an input image and the template matching includes reshaping designed filters to a same dimension and stacked as kernels of a larger filter which is then convolved with input the waterfall image, activating existing events of interest in the input waterfall image”
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Wang et al. (US 20200313763) disclose Aspects of the present disclosure describe optical fiber sensing systems, methods and structures disclosing a distributed fiber sensor network constructed on an existing, live network, data carrying, optical fiber telecommunications infrastructure to detect temperatures, acoustic effects, and vehicle traffic—among others. Of particular significance, sensing systems, methods, and structures according to aspects of the present disclosure may advantageously identify specific network locations relative to manholes/handholes and environmental conditions within those manholes/handholes namely, normal, flooded, frozen/iced, etc.
b) Sciegg et al. (US 2022/0180249) disclose a computer-implemented method for training a generative machine learning model for modelling operation profiles of a vehicle or a robot, comprising adversarially training a generator model and a discriminator model is provided. In accordance with an example embodiment of the present invention, the method comprises: [0006] obtaining a plurality of data series from a data space, wherein each data series describes at least one operation profile of a vehicle or robot; [0007] during at least one generation phase when training the generative machine learning model, generating a plurality of synthetic data series by sampling from a distribution; [0008] during at least one discrimination phase when training the generative machine learning model, inputting into the discriminator model either (i) a data series from the plurality of obtained data series, or (ii) a synthetic data series from the plurality of synthetic data series; [0009] during the at least one discrimination phase when training the generative machine learning model, classifying the input to the discriminator model as either a data series from the data space, or a synthetic data series using at least a first and a second function of the discriminator model; According to a third aspect of the present invention, there is provided a computer-implemented method for training a further machine learning model for modelling operation profiles of a vehicle. In accordance with an example embodiment of the present invention, the method comprises: obtaining a further plurality of synthetic data series representing synthetic operation profiles of a vehicle as generated according to the computer-implemented method of the second aspect; [0026] inputting the further plurality of synthetic data series representing synthetic operation profiles of a vehicle into a further machine learning model; iteratively training the further machine learning model; and [0028] outputting a further plurality of model parameters of the further machine learning model for modelling operation profiles of a vehicle. a distribution resulting from real or synthetic data series. In other examples, an operation profile may encompass an integer number of vehicle stops per unit time.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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/MOHAMMAD K ISLAM/Primary Examiner, Art Unit 2857