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 .
Detailed Action
The following non-final action is in response to application 18685767 filed on 02/22/2024. The communication is the first action on the merits.
Status of Claims
Claims 1-16 and 18 are currently pending and have been rejected as follows.
Drawings
The drawings filed on 02/22/2024 are accepted.
Foreign Priority
Applicant’s claim to foreign priority has been acknowledged and the corresponding documents have been received.
Domestic Benefit/National Stage
Applicant’s claim to domestic benefit/national stage has been acknowledged and the corresponding documents have been received.
IDS
The IDS has been received, and the documents within it have been considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. - An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word "means" (or "step") in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word "means" (or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or pre- AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Apparatus for training…comprising: a geospatial calibration unit arranged to obtain…; a training data segmentor arranged to receive…; and a model training unit arranged to use…;” (claim 11)
“A calibration vibration source for generating…” (claim 12) and “A calibration position detector collocated with the calibration vibration source for detecting…” (claim 12)
“wherein the positions of the implemented events of interest are measured using the one or more event position detectors” (claim 15)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Examiner has identified the corresponding structure as including, for the apparatus specifically, one or more of: a geospatial calibration unit arranged to obtain a calibration which defines a mapping between the distributed acoustic sensing signal and positions along the sensing optical fibre; a training data segmentor [p.4].
As for the geospatial calibration unit, the training data segmentor and the model training unit, they may be implemented in software in such a manner using one or more computer systems which may be collocated or in different locations. Each such computer system may typically each comprise one or more computer processors or microprocessors arranged to execute the computer program code, the processors being coupled to suitable volatile and/or non-volatile memory [p.19].
As for the calibration vibration source, as shown in figure 3 (element 150), it could be provided by a human agent 156 carrying the calibration position detector 152, and thumping periodically on the ground with a tool acting as the calibration vibration source or periodically operating a sounds generation device of characteristic acoustic frequency, or the calibration vibration source could similarly be provided by a wheeled device pushed or pulled by the human agent 154. Alternatively, the vibration source could be implemented using a driven or autonomous vehicle carrying the calibration position detector [p.14].
As for the calibration position detector, it might typically be a satellite based navigation system detector such as a GPS receiver [p.3].
As for the event position detector, it may be a satellite based navigation system detector such as a GPS receiver [p.4].
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Claim 1 recites:
A method of training one or more event models for use in identifying, from a distributed acoustic sensing signal representing acoustic vibration at positions along a sensing optical fibre, events of interest proximal to the sensing optical fibre, the method comprising:
providing a distributed acoustic sensor arranged to form a distributed acoustic sensing signal;
obtaining a calibration defining a mapping between the distributed acoustic sensing signal and positions along the sensing optical fibre;
implementing events of interest of one or more different event categories, at measured positions along the sensing optical fibre;
forming the distributed acoustic sensing signal during the implemented events of interest;
for each implemented event of interest, defining one or more training data subsets of the distributed acoustic sensing signal which are contemporary with the implemented event, and spatially positioned within the signal using the measured positions and the calibration so as to include the implemented event;
and using the training data subsets to train one or more event models to detect the events of interest.
The bolded language in the claim limitations indicate abstract ideas, and the remaining limitations are considered to be additional elements.
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim. Claim 11 is a machine claim. Claim 18 is a machine claim.
Under Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Under Step 2A, Prong One, the broadest reasonable interpretation consistent with the specification of the limitations recited in Claim 1 recite at least one judicial exception, that being a mental process (observations/evaluation/judgement/ or opinion). and a mathematical concept (mathematical calculations/relationships/formulas/ or equations).
According to the specification, “obtaining a calibration defining a mapping between the distributed acoustic sensing signal and positions along the sensing optical fibre” involves obtainment of a calibration in various ways…an installed path 130 of the sensing optical fibre 110 through the environment 112 may be sufficiently well known, for example from existing geographical mapping, and the properties of the interrogator 114 also sufficiently well known, that the calibration can be calculated or derived from that information without further measurements being needed [p.13]. This claim limitation recites a mathematical concept given that the calibration can be calculated or derived using mathematical calculations.
According to the specification, “implementing events of interest of one or more different event categories, at measured positions along the sensing optical fibre” involves implementing the events of interest by agents 164, for example a human agent 164-1 walking approximately along the optical fibre to implement walking events of interest, and an excavator agent 164-2 digging proximally to the optical fibre to implement mechanical digging events of interest [p.15]. This claim limitation falls into the category of certain methods of organizing human activity due to the presence of the idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). For example, an event of interest may be implemented by an agent (person) 164-1 [Fig. 3] by instructing the agent to walk approximately along the optical fibre to implement walking events of interest.
According to the specification, “for each implemented event of interest, defining one or more training data subsets of the distributed acoustic sensing signal” involves involves a mental process given that one of ordinary skill in the art would be capable of performing the limitation within the human mind. For example, one would be capable of defining a subset for a walking event by evaluating a series of steps by observing the amount of time in seconds elapsed in the walking event, observing the distance traveled in meters, observing the exact position of each step creating a trajectory of steps in the walking event, and then evaluating this data to define a rectangle with a distance length of, say, 2 to 10 meters, and durations in time of perhaps 10 to 100 seconds.
According to the specification, “using the training data subsets to train one or
more event models to detect the events of interest” involves the event models being statistical and/or one or more machine learning event models [p.19] where the training data segmentor 200 then outputs a plurality of such training data subsets S which are passed to the model training unit 300. The model training unit 300 trains, or refines, one or more event models 50 for operational use in the arrangement of FIG. 1 to identify and locate events of interest in a DAS signal A [p.16] where the event models 50 may be implemented using a variety of statistical and/or artificial intelligence or automated machine learning tools, e.g. [p.19]. The claim limitation under the broadest reasonable interpretation recites mathematical calculations and thus falls into the category of mathematical concept.
Claims 11 and 18 recite similar abstract ideas.
Step 2A, Prong Two of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
The additional elements in the preambles of all independent claims are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
Claim 1 recites the following additional elements:
forming the distributed acoustic sensing signal
Claim 11 recites the following additional elements:
obtaining a calibration
receiving the distributed acoustic sensing signal
Claim18 does not recite similar additional elements
These claim limitations generically recite collecting/outputting by sensors/devices measurement data (all independent claims), which represents the insignificant extra-solution activity of mere data gathering/outputting results. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Claim 1 recites the following additional elements:
a sensing optical fibre
a distributed acoustic sensor
Claim 11 recites similar additional elements:
a geospatial calibration unit
a training data segmentor
a model training unit
Claim 18 recites similar additional elements:
One or more non-transitory computer readable media comprising computer program code
These additional elements are computer components and devices recited in generality and are not meaningful and, therefore, are not qualified as particular machines to indicate a practical application.
Claim 1 also recites the additional elements:
Using the training data subsets to train one or more event models to detect the events of interest
In this case, the event models are one or more machine learning event models
[p.7] where the training data segmentor 200 then outputs a plurality of such training data subsets S which are passed to the model training unit 300. The model training unit 300 trains, or refines, one or more event models 50 for operational use in the arrangement of FIG. 1 to identify and locate events of interest in a DAS signal A [p.16] where the event models 50 may be implemented using a variety of statistical and/or artificial intelligence or automated machine learning tools [p.19].
The limitation reciting “using the training data subsets to train one or more event models to detect the events of interest” provides nothing more than mere instructions to implement an abstract idea on a generic computer. 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 “using the training data subsets to train one or more event models to detect the events of interest” is performed using the Ai algorithm. The trained Ai algorithm is used to generally apply the abstract idea without placing any limits on how the trained Ai algorithm functions. Rather, these limitations only recite the outcome of “detecting the events of interest” and do not include any details about how “using the training data subsets to train” is accomplished. See MPEP 2106.05(f).
The recitation of using an “Ai algorithm” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional elements “training data subsets and event models” limits the identified judicial exception “training one or more event models to detect the events of interest”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. When re-evaluated under Step 2B, the claim limitations are found to be well-understood, routine, and conventional as explained by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a communication network) as referenced by Englund and Thiruvenkatanathan.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claims 1, 11 and 18 amount to significantly more than the abstract idea.
With regards to dependent claims 2-10 and 12-17, they provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
For example, claim 2 further limits the abstract idea.
Claim 4 provides additional elements recited in generality and therefore not meaningful.
Claim 5 provides additional elements recited in generality and therefore not meaningful.
Claim 9 provides additional elements recited in generality and therefore not meaningful.
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 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 1-16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Englund (US 20200202687 A1) in view of Thiruvenkatanathan (US 20200291772 A1).
Regarding claim 1, Englund teaches
a method…for identifying, from a distributed acoustic sensing signal representing acoustic vibration at positions along a sensing optical fibre, events of interest proximal to the sensing optical fibre, (methods and systems of distributed acoustic sensing which may be useful in the detection of acoustic events near or within the selected geographical area [Abstract] and FIG. 1b illustrates an example density plot combining electrical signals 112 generated by the DAS unit 100 of over time. The horizontal axis (labelled “Channel”) represents position along the fibre [0043]) the method comprising:
providing a distributed acoustic sensor arranged to form a distributed acoustic sensing signal (a unit 100 for using distributed acoustic sensing (DAS) is illustrated in FIG. 1a where an optical interrogation signal 106 is formed along with a corresponding electrical signal 112 [0043]);
obtaining a calibration (acoustic calibration [0055]) defining a mapping (Fig. 4) between the distributed acoustic sensing signal and positions along the sensing optical fibre (the step 204 includes spatially calibrating between a position along the optical fibre and a location in the geographical area. The spatial calibration may include generating an acoustic calibration signal at specific locations of the geographical area to cause fluctuations for detection along the length of the optical fibre [0061]);
implementing events of interest of one or more different event categories, at measured positions along the sensing optical fibre (If an acoustic event is detected at a position along the fibre between two calibration points, an interpolation (e.g. linear or nonlinear) may be used to estimate the location of the corresponding occurrence within the geographical area. If an acoustic event is detected at a position along the fibre beyond the first and the last calibration points, an extrapolation (e.g. linear or nonlinear) may be used to estimate the location of the corresponding occurrence within the geographical area [0064] where acoustic events-including objects—being determined may be indicative of specific stationary or moving occurrences [0068] where the disclosed method 200 may include the step of determining whether an acoustic event is stationary or moving. This determination may include whether a moving acoustic event relates to a slow moving noise source (e.g. drilling, excavating, bore tunnelling, etc.) or a fast moving noise source (e.g. cars, trains, etc) [0068] where the epicentre of acoustic events may be triangulated based a measurement of direction of propagation and time of flight calculations [0073]);
forming the distributed acoustic sensing signal during the implemented events of interest (acoustic events-including objects—being determined may be indicative of specific stationary or moving occurrences. For example, as illustrated in FIG. 1b, features such as straight lines with relatively constant gradients are associated with the moving objects (with the gradients being indicative of speed) that cause the relevant acoustic events detected by the DAS unit 100 [0068]);
Englund teaches the implemented events of interest as disclosed above as well as data subsets of the distributed acoustic sensing signal which are contemporary with the implemented event, and spatially positioned within the signal using the measured positions and the calibration so as to include the implemented event (Table 1: Geospatial Calibration Look-Up Table).
Englund does not explicitly teach defining one or more training data subsets and using the training data subsets to train one or more event models to detect the events of interest.
Thiruvenkatanathan teaches defining one or more training data subsets and using the training data subsets to train one or more event models to detect the events of interest (The model(s) can be developed by determining one or more frequency domain features from the acoustic signal for at least a portion of the reference data. The training of the model(s) can use machine learning, including any supervised or unsupervised learning approach [0075]…where the plurality of the reference data sets used for training the model(s) can be a subset of the plurality of reference data sets, and the tests used to validate the models can be another subset of the reference data sets [0079]… and acoustic signals can be obtained from the optical fiber 116 and computations can be performed to determine the frequency domain features associated with different events…an analysis of the different frequency domain features associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302 may be used to determine the acoustic signatures that are used to determine if a specific signal is present. For example, the thresholds or references that are used to determine if a specific signal is present are based on a comparison of the different acoustic signatures associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302. Various methods can be used to develop the acoustic signatures such as various supervised learning techniques [0070]…where within the processing unit 404, the acoustic data can be analyzed, for example, by being compared to one or more acoustic signatures to determine if an event of interest is present. In some embodiments, the acoustic signatures can define thresholds or ranges of frequencies and/or frequency domain features [0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Englund with the teachings of Thiruvenkatanathan to use one or more training data subsets and use the training data subsets to train one or more event models to detect the events of interest to enable handling high-volume data, reducing false alarms, and enhancing detection speed and accuracy for events of interest.
Regarding claim 2, Englund teaches distinguishing between events of two or more event categories (acoustic events-including objects—being determined may be indicative of specific stationary or moving occurrences [0068] where the disclosed method 200 may include the step of determining whether an acoustic event is stationary or moving. This determination may include whether a moving acoustic event relates to a slow moving noise source (e.g. drilling, excavating, bore tunnelling, etc.) or a fast moving noise source (e.g. cars, trains, etc) [0068]).
Englund does not explicitly teach the trained event models.
Thiruvenkatanathan teaches the trained event models (The model(s) can be developed by determining one or more frequency domain features from the acoustic signal for at least a portion of the reference data. The training of the model(s) can use machine learning, including any supervised or unsupervised learning approach [0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Englund with the teachings of Thiruvenkatanathan to use the trained event models to enable handling high-volume data, reducing false alarms, and enhancing detection speed and accuracy for events of interest.
Regarding claim 3, Englund teaches wherein the one or more different event categories comprise one or more of: a person walking, manual digging, mechanical digging, and a vehicle driving (the disclosed method 200 may include the step of determining whether an acoustic event is stationary or moving. This determination may include whether a moving acoustic event relates to a slow moving noise source (e.g. drilling, excavating, bore tunnelling, etc.) or a fast moving noise source (e.g. cars, trains, etc) [0068]).
Regarding claim 4, Englund teaches using a calibration vibration source to generate calibration acoustic signals…at multiple different locations along the sensing optical fibre while using the distributed acoustic sensor to form a distributed acoustic sensing signal, detecting the calibration acoustic signals within the distributed acoustic sensing signal (The spatial calibration may include generating an acoustic calibration signal (e.g. a single-frequency tone at 420 Hz+/−5 Hz selected to be distinct to typical noise sources in urban centres) at specific locations of the geographical area to cause fluctuations for detection along the length of the optical fibre [0061]);
separately detecting locations of the calibration vibration source as the calibration acoustic signals are being generated (An optical fluctuation corresponding to the calibration acoustic signal is expected to be detected at a specific position along the optical fibre. The corresponding pair of coordinates corresponding to a location within a geographical region and the position along the optical fibre where the fluctuation is detected forms a geospatial calibration reference point, which then forms part of a look-up table of the type indicated below, which includes further spatial calibration points along the fibre and within the geographical area [0062, Table 1: GEOSPATIAL CALIBRATION LOOK-UP TABLE]);
and using the detected calibration acoustic signals with the separately detected locations to obtain the calibration (If an acoustic event is detected at a position along the fibre between two calibration points, an interpolation (e.g. linear or nonlinear) may be used to estimate the location of the corresponding occurrence within the geographical area. If an acoustic event is detected at a position along the fibre beyond the first and the last calibration points, an extrapolation (e.g. linear or nonlinear) may be used to estimate the location of the corresponding occurrence within the geographical area [0064]).
Englund does not explicitly teach using a calibration vibration source to generate calibration acoustic signals sequentially.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Englund to use a calibration vibration source to generate calibration acoustic signals sequentially at multiple different locations along the sensing optical fibre while using the distributed acoustic sensor to form a distributed acoustic sensing signal to optimize calibration accuracy by overcoming field noise in dynamic environments.
Regarding claim 5, Englund teaches wherein separately detecting locations of the calibration vibration source as the calibration acoustic signals are being generated comprises using a calibration position detector collocated with the calibration vibration source to detect the locations (In the geospatial calibration process the calibration acoustic signal is generated at each successive pit location and GPS coordinates of latitude and longitude in decimal degrees are taken at successive cable pits. Alternatively other locations along the cable path may be used, where the calibration acoustic signal is easily detectable, provided the GPS coordinates are recorded or noted [0062] where in the table above each point is identified with a date and time stamp and an name or reference which is similarly associated with the GPS co-ordinates captured by the data logger on an appropriate GPS enabled device [0063]).
Regarding claim 6, Englund teaches wherein the calibration position detector is a satellite based navigation system detector such as a GPS receiver As explained above with respect to parent claim 5.
Regarding claim 7, Englund teaches wherein implementing events of interest of one or more different event categories, at measured positions along the sensing optical fibre, comprises using one or more agents to implement the events of interest, (The acoustic event being determined may be indicative of specific stationary or moving occurrences, such as excavation, drilling, digging, traffic flows, trains passing by and pedestrian flows [0043] where the disclosed method 200 may include the step of determining whether an acoustic event is stationary or moving. This determination may include whether a moving acoustic event relates to a slow moving noise source (e.g. drilling, excavating, bore tunnelling, etc.) or a fast moving noise source (e.g. cars, trains, etc) [0068]) and measuring the positions of the implemented events using an event position detector collocated with each such agent (In the geospatial calibration process the calibration acoustic signal is generated at each successive pit location and GPS coordinates of latitude and longitude in decimal degrees are taken at successive cable pits. Alternatively other locations along the cable path may be used, where the calibration acoustic signal is easily detectable, provided the GPS coordinates are recorded or noted [0062] where in the table above each point is identified with a date and time stamp and an name or reference which is similarly associated with the GPS co-ordinates captured by the data logger on an appropriate GPS enabled device [0063]).
Regarding claim 8, Englund teaches measuring the positions along the sensing optical fibre at the same times as the implementation of the events of interest (The DAS unit 100 includes an optical time-domain reflectometer (OTDR) 102 where an acoustic event is determined based on the measured fluctuations 116 in intensity as compared between two different times (t.sub.1 and t.sub.2). FIG. 1b illustrates an example density plot combining electrical signals 112 generated by the DAS unit 100 of over time where the horizontal axis represents position along the fibre
and the vertical axis represents time [0043]); (In TABLE 1: GEOSPATIAL CALIBRATION LOOK-UP TABLE, each point is identified with a date and time stamp and an name or reference which is similarly associated with the GPS co-ordinates captured by the data logger on an appropriate GPS [0063]).
Regarding claim 9, Englund teaches wherein the event position detectors are satellite based navigation system detectors As explained above with respect to parent claim 7.
Regarding claim 10, Englund teaches receiving a distributed acoustic sensing signal from the same or a different distributed acoustic sensor (a unit 100 for using distributed acoustic sensing (DAS) is illustrated in FIG. 1a where an optical interrogation signal 106 is formed along with a corresponding electrical signal 112 [0043]) and detecting one or more events of interest (The acoustic event being determined may be indicative of specific stationary or moving occurrences, such as excavation, drilling, digging, traffic flows, trains passing by and pedestrian flows [0043]).
Englund does not explicitly teach detecting one or more events of interest using the one or more trained event models.
Thiruvenkatanathan teaches detecting one or more events of interest using the one or more trained event models (acoustic signals can be obtained from the optical fiber 116 and computations can be performed to determine the frequency domain features associated with different events…an analysis of the different frequency domain features associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302 may be used to determine the acoustic signatures that are used to determine if a specific signal is present. For example, the thresholds or references that are used to determine if a specific signal is present are based on a comparison of the different acoustic signatures associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302. Various methods can be used to develop the acoustic signatures such as various supervised learning techniques [0070]…where within the processing unit 404, the acoustic data can be analyzed, for example, by being compared to one or more acoustic signatures to determine if an event of interest is present. In some embodiments, the acoustic signatures can define thresholds or ranges of frequencies and/or frequency domain features [0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Englund with the teachings of Thiruvenkatanathan to detect one or more events of interest using the one or more trained event models to enable handling high-volume data, reducing false alarms, and enhancing detection speed and accuracy for events of interest.
Regarding claim 11, Englund, as modified, would teach an apparatus (Fig. 1a, DAS unit 100)…for identifying, from a distributed acoustic sensing signal representing acoustic vibration at positions along a sensing optical fibre, events of interest proximal to the sensing optical fibre (methods and systems of distributed acoustic sensing which may be useful in the detection of acoustic events near or within the selected geographical area [Abstract] and FIG. 1b illustrates an example density plot combining electrical signals 112 generated by the DAS unit 100 of over time. The horizontal axis (labelled “Channel”) represents position along the fibre [0043]) the apparatus comprising:
a geospatial calibration unit (DAS unit 100, [Fig. 1a]) arranged to obtain a calibration which defines a mapping between the distributed acoustic sensing signal and positions along the sensing optical fibre (as disclosed in the claim 1 analysis);
a training data segmentor arranged to receive the distributed acoustic sensing signal formed during implementation of events of interest of one or more different event categories, at measured positions along the sensing optical fibre,
and for each implemented event of interest, to define…data subsets of the distributed acoustic sensing signal which are contemporary with the implemented event, and which are spatially positioned within the signal using the measured positions and the calibration so to include the implemented event (Table 1: Geospatial Calibration Look-Up Table).
Englund does not explicitly teach an apparatus for training one or more event models as well as defining one or more training data subsets and a model training unit arranged to use the training data subsets to train one or more event models to detect the events of interest.
Thiruvenkatanathan teaches an apparatus for training one or more event models (Any of the systems and methods disclosed herein can be carried out on a computer or other device comprising a processor, such as the acquisition device 118 of FIG. 1. FIG. 7 illustrates a computer system 780 suitable for implementing one or more embodiments disclosed herein such as the acquisition device or any portion thereof. The computer system 780 includes a processor 782 (which may be referred to as a central processor unit or CPU) that is in communication with memory devices including secondary storage 784, read only memory (ROM) 786, random access memory (RAM) 788, input/output (I/O) devices 790, and network connectivity devices 792 [0143]) as well as defining one or more training data subsets (The model(s) can be developed by determining one or more frequency domain features from the acoustic signal for at least a portion of the reference data. The training of the model(s) can use machine learning, including any supervised or unsupervised learning approach [0075]…where the plurality of the reference data sets used for training the model(s) can be a subset of the plurality of reference data sets, and the tests used to validate the models can be another subset of the reference data sets [0079]) and a model training unit (wherein the processor unit is adapted for (training the model(s)) [See the capabilities of this unit in 0170]) arranged to use the training data subsets to train one or more event models to detect the events of interest (acoustic signals can be obtained from the optical fiber 116 and computations can be performed to determine the frequency domain features associated with different events…an analysis of the different frequency domain features associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302 may be used to determine the acoustic signatures that are used to determine if a specific signal is present. For example, the thresholds or references that are used to determine if a specific signal is present are based on a comparison of the different acoustic signatures associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302. Various methods can be used to develop the acoustic signatures such as various supervised learning techniques [0070]…where within the processing unit 404, the acoustic data can be analyzed, for example, by being compared to one or more acoustic signatures to determine if an event of interest is present. In some embodiments, the acoustic signatures can define thresholds or ranges of frequencies and/or frequency domain features [0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Englund with the teachings of Thiruvenkatanathan to use an apparatus for training one or more event models as well as define one or more training data subsets and use a model training unit arranged to use the training data subsets to train one or more event models to detect the events of interest to enable handling high-volume data, reducing false alarms, and enhancing detection speed and accuracy for events of interest.
Regarding claim 12, Englund, as modified, would teach a calibration vibration source for generating calibration acoustic signals sequentially at multiple different locations along the sensing optical fibre, (The spatial calibration may include generating an acoustic calibration signal (e.g. a single-frequency tone at 420 Hz+/−5 Hz selected to be distinct to typical noise sources in urban centres) at specific locations of the geographical area to cause fluctuations for detection along the length of the optical fibre [0061]) as explained above for similar claim 4 regarding the sequential element of this method.
Englund further teaches a calibration position detector collocated with the calibration vibration source for detecting locations of the calibration vibration source as it generates calibration acoustic signals as explained above for similar claim 5.
Regarding claim 13, Englund, as modified, would teach wherein the geospatial calibration unit is arranged to obtain the calibration by detecting the calibration acoustic signals in the distributed acoustic sensing signal (The spatial calibration may include generating an acoustic calibration signal (e.g. a single-frequency tone at 420 Hz+/−5 Hz selected to be distinct to typical noise sources in urban centres) at specific locations of the geographical area to cause fluctuations for detection along the length of the optical fibre [0061]);
(An optical fluctuation corresponding to the calibration acoustic signal is expected to be detected at a specific position along the optical fibre. The corresponding pair of coordinates corresponding to a location within a geographical region and the position along the optical fibre where the fluctuation is detected forms a geospatial calibration reference point, which then forms part of a look-up table of the type indicated below, which includes further spatial calibration points along the fibre and within the geographical area [0062, Table 1: GEOSPATIAL CALIBRATION LOOK-UP TABLE]);
(If an acoustic event is detected at a position along the fibre between two calibration points, an interpolation (e.g. linear or nonlinear) may be used to estimate the location of the corresponding occurrence within the geographical area. If an acoustic event is detected at a position along the fibre beyond the first and the last calibration points, an extrapolation (e.g. linear or nonlinear) may be used to estimate the location of the corresponding occurrence within the geographical area [0064]) and combining the detected acoustic calibration signals with the detected locations of the calibration vibration source (the step 204 includes spatially calibrating between a position along the optical fibre and a location in the geographical area. The spatial calibration may include generating an acoustic calibration signal (e.g. a single-frequency tone at 420 Hz+/−5 Hz selected to be distinct to typical noise sources in urban centres) at specific locations of the geographical area to cause fluctuations for detection along the length of the optical fibre [0061] and in the geospatial calibration process the calibration acoustic signal is generated at each successive pit location and GPS coordinates of latitude and longitude in decimal degrees are taken at successive cable pits. Alternatively other locations along the cable path may be used, where the calibration acoustic signal is easily detectable, provided the GPS coordinates are recorded or noted. An optical fluctuation corresponding to the calibration acoustic signal is expected to be detected at a specific position along the optical fibre. The corresponding pair of coordinates corresponding to a location within a geographical region and the position along the optical fibre where the fluctuation is detected forms a geospatial calibration reference point, which then forms part of a look-up table of the type indicated below, which includes further spatial calibration points along the fibre and within the geographical area [0062, Table 1: Geospatial Calibration Look-Up Table]).
Regarding claim 14, Englund, as modified, would teach wherein the calibration position detector is a satellite based navigation system detector such as a GPS receiver as explained above for similar claim 6.
Regarding claim 15, Englund, as modified, would teach one or more event position detectors collocated with one or more agents implementing the events of interest, wherein the positions of the implemented events of interest are measured using the one or more event position detectors as explained above for similar claim 7.
Regarding claim 16, Englund, as modified, would teach wherein the event position detectors are satellite based navigation system detectors as explained above for similar claim 9.
Regarding claim 18, Englund, as modified, would teach identifying, from a distributed acoustic sensing signal representing acoustic vibration at positions along a sensing optical fibre, events of interest proximal to the sensing optical fibre (as explained above for similar claim 1), the processor (processing unit 114 [Fig. 1a]) being arranged to:
determine a calibration which defines a mapping between the distributed acoustic sensing signal and positions along the sensing optical fibre;
receive the distributed acoustic sensing signal formed during implementation of events of interest of one or more different event categories, and measured positions of the implemented events of interest along the sensing optical fibre, and for each implemented event of interest, to define one or more data subsets of the distributed acoustic sensing signal which are contemporary with the implemented event, and which are spatially positioned within the signal using the measured positions and the calibration so to include the implemented event (as explained above for similar claim 1).
Englund does not explicitly teach one or more non-transitory computer readable media comprising computer program code executed by a processor arranged to train one or more event models as well as defining one or more
training data subsets and using the training data subsets to train one or more event models to detect the events of interest.
Thiruvenkatanathan teaches one or more non-transitory computer readable media comprising computer program code executed by a processor (Any of the systems and methods disclosed herein can be carried out on a computer or other device comprising a processor, such as the acquisition device 118 of FIG. 1. FIG. 7 illustrates a computer system 780 suitable for implementing one or more embodiments disclosed herein such as the acquisition device or any portion thereof. The computer system 780 includes a processor 782 (which may be referred to as a central processor unit or CPU) that is in communication with memory devices including secondary storage 784, read only memory (ROM) 786, random access memory (RAM) 788, input/output (I/O) devices 790, and network connectivity devices 792 [0143] where the RAM 788, and/or the ROM 786 may be referred to in some contexts as computer readable storage media and/or non-transitory computer readable media [0146] where some or all of the functionality disclosed above may be provided as a computer program product. The computer program product may comprise one or more computer readable storage medium having computer usable program code embodied therein to implement the functionality disclosed above. The computer program product may comprise data structures, executable instructions, and other computer usable program code [0152] arranged to train one or more event models as well as defining one or more training data subsets and using the training data subsets to train one or more event models to detect the events of interest (acoustic signals can be obtained from the optical fiber 116 and computations can be performed to determine the frequency domain features associated with different events…an analysis of the different frequency domain features associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302 may be used to determine the acoustic signatures that are used to determine if a specific signal is present. For example, the thresholds or references that are used to determine if a specific signal is present are based on a comparison of the different acoustic signatures associated with the both the fluid 306 without any particulates 302 and the fluid 306 with particulates 302. Various methods can be used to develop the acoustic signatures such as various supervised learning techniques [0070]…where within the processing unit 404, the acoustic data can be analyzed, for example, by being compared to one or more acoustic signatures to determine if an event of interest is present. In some embodiments, the acoustic signatures can define thresholds or ranges of frequencies and/or frequency domain features [0068]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Englund with the teachings of Thiruvenkatanathan to use one or more non-transitory computer readable media comprising computer program code executed by a processor arranged to train one or more event models as well as defining one or more training data subsets and using the training data subsets to train one or more event models to detect the events of interest
to enable handling high-volume data, reducing false alarms, and enhancing detection speed and accuracy for events of interest.
Pertinent Prior Art
US 20180180451 A1: “Calibrating A Distributed Fibre Optic Sensing System”
A system and method for dynamically calibrating a distributed fibre optic sensing system is disclosed. The calibration system includes a light source for generating pulses of coherent light, an optical fibre arranged at least partly in a ground soil region to guide the light and a photo detector for detecting scattered light returning from the optical fibre in dependence of time. The method includes obtaining information from which a temporal change of an acoustic transfer characteristic of the ground soil region is derivable and calibrating a distributed acoustic sensing system based on the changed acoustic transfer characteristic.
US 20140362668 A1: “Indicating Locations”
This application describes methods and apparatus for remotely indicating a location of interest in an area, for instance the location of an event in the area. The method comprises positioning an acoustic source at the location of interest, activating the acoustic source to produce a predetermined acoustic output and performing distributed acoustic sensing on at least one optical fibre deployed at least partly in the area. The acoustic source thus acts as an acoustic marker which can be remotely detected by the distributed acoustic sensor. The acoustic signals detected by the distributed acoustic sensor are therefore analyzed to detect said predetermined acoustic output and determine the location of the acoustic source. The method is particularly applicable to transport networks already provided with optical fibre along the length of the network and can be used to indicate the location of work parties or emergencies.
US 20140092710 A1: “METHOD AND SYSTEM FOR LOCATING AN ACOUSTIC SOURCE”
Acoustic signals received by distributed acoustic sensors are processed in order to determine the position of a source or sources of the acoustic signals. The method and system are able to determine the position of several acoustic sources simultaneously, by measuring the corresponding several acoustic signals. Furthermore, the strength of the acoustic signal or signals can be determined. The location of the acoustic source may be overlaid on a map of an area being monitored, or be used to generate an alarm if perceived to correspond to a threat or an intrusion, for example in a pipeline monitoring application. Alternatively, the method and systems can be used to monitor a hydraulic fracturing process.
Conclusion
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/LOGAN D COONS/Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
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