DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information Disclosure Statement (IDS) filed on 12/23/2022 has been considered.
Preliminary Amendment
3. Preliminary Amendments filed 12/23/2022 to the specification and claims are accepted and entered. In this amendment:
Claims 1-15 have been canceled.
Claims 16-31 have been added.
Claim 16-31 have been examined.
Specification Objection
4. The Application's specification filed on 12/23/2022 is objected to because of the following informalities:
The content of specification does not include Cross-References to related applications. See 37 CFR 1.78 MPEP § 211 et seq.
Applicant is advised to correct the specification in compliance with 37 CFR 1.121(b) as required.
Claim Objections
5. Claims 16, 18-20, 22, and 24-29 are objected to because of the following informalities:
Claim 16, line 2 recites “the characterization” should read “a characterization”, and further: in line 6: “the construction” should read “a construction”,
In lines 8-9: “the leak type and the leak flow rate with at least one vibro-acoustic signal” should read “a leak type and a leak flow rate with the at least one vibro-acoustic signal”,
In line 11: “on the thus constructed” should read “on the constructed”.
Claim 18 recites “at least the geometry of the fluid network and the location” should read “at least a geometry of the fluid network and a location”.
Claim 19 recites “from the actually measured vibro-acoustic signals from the real vibro-acoustic sensors and the geometric data” should read “from an actually measured vibro-acoustic signals from a real vibro-acoustic sensors and a geometric data”.
Claim 20 line 2 recites “the leak from the vibro-acoustic signals” and line 5 “at the level of the leak” should read “a leak from the vibro-acoustic signals” and line 5 “at a level of the leak”.
Claim 22 recites “the raw vibro-acoustic signal from at least one vibro-acoustic sensor” should read “a raw vibro-acoustic signal from the at least one vibro-acoustic
sensor”.
Claim 24 is a method depends on claim 16, thus, the limitation refers to claim 16 should be “the” not “a”, i.e., “with the plurality of vibro-acoustic sensors configured to provide the vibro-acoustic signals…. receiving, by the statistical learning model, and so on. Claims 25-29 depends on claim 24 should be corrected all antecedent basis as well.
Examiner note: It is suggested claims 24-27 should be included in claim 1 and should state as “the method further comprising …”.
Due to the number of claim objections, the examiner has provided a number of examples of the claim deficiencies in the above objections, however, the list of deficiencies may not be all inclusion. Applicant should refer to theses as examples of deficiencies and should make all the necessary correction to eliminate the claim objections in the claim amendments.
Appropriate correction is required.
Claim Interpretation
6. 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.
7. 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), 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), 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), 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), except as otherwise indicated in an Office action.
8. 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 limitations use 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 is:
In Claim 28:
"a module for characterizing a leak”, because the claim limitation is being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (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).
Claim Rejections - 35 USC § 101
9. 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.
10. Claims 28-31 are rejected under 35 U.S.C. 101 because the claims are directed toward non-statutory subject matter.
Claim 28 recites “A module for…” that is directed toward non-statutory subject matter. The claim recites a “module”, however, it is not clear whether this module refers to a hardware or software. Thus, the claim is considered non-statutory.
Dependent claim 29 is rejected for the same reason as respective parent claim.
Claims 30-31 recite “A computer program comprising instructions” that is directed toward non-statutory subject matter. The claims recite “computer program”, however, the computer program is not identifiable to one of the four categories of permissible subject matter. Thus claims 30-31 are considered non-statutory.
11. Claims 16-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a method (claim 16) which is statutory category.
However, evaluating claim 16, under Step 2A, Prong One, the claim is directed to the judicial exception of an abstract idea using the groupings of mathematical concepts and mental process including “associating, with the construction of a database, at least for a plurality of documented leaks, at least one leak characterization data actually determined among the leak type and the leak flow rate with at least one
vibro-acoustic signal obtained directly or indirectly from at least one vibroacoustic
sensor”, where associating leaks “mental process” to construct a database “mathematical construct and building” requires mental process (includes an observation, evaluation, judgment, opinion).
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional element of a fluid network includes a plurality of vibro-acoustic sensors is recited in the pre-amble which is a field of use. Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. A vibro-acoustic sensor recited in a body of the claim is a conventional device which is insignificantly more than the abstract idea and does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there is no additional element that make the claim significantly more than the abstract idea.
The additional elements as recited above in step 2A prong Two, are considered generic, conventional equipment which are insignificant and not sufficient to integrate the claim into a particular practical application.
The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself.
Dependent claims 17-27 do not disclose limitations considered to be significantly more which would render the claimed invention a patent eligible application of the abstract idea. The claims merely extend (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above.
With respect to claims 28-31, the examiner suggests applicant to review the entire eligibility test (i.e., see rejection of claims 16-27 under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more) after appropriate corrections are made for the rejection under 35 U.S.C. 101 because the claims are directed toward non-statutory subject matter.
Claim Rejections - 35 USC § 102
12. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
13. Claims 16 and 18-31 are rejected under AIA 35 U.S.C. 102(a)(2) as being anticipated over Solomon et al “Solomon”, US 2021/0388950.
As per Claim 16, Solomon teaches a method for training a statistical learning model intended for the characterization of a leak in a fluid network including a plurality of pipes (a fluid flow into a pipe network considered “a fluid network” [0101]. [0108]), wherein the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals (pipe network part of related leak intensity based on “vibration/acoustic sensors” considered “vibro-acoustic sensors” [0066], [0103], [0061]-[0062]), the method comprising:
associating, with the construction of a database, at least for a plurality of documented leaks (Fig 2B shows pipe network parts databases 122 considered representing part of a construction database for a pipe network, includes failed part characteristic, fail part prior events, etc. considered “documented leaks” [0094], [0138]), at least one leak characterization data actually determined among the leak type and the leak flow rate with at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor (the leak is determined by acoustic and/or flow rate sensors [0078], [0093], [0142], [0198]-[0199]), and
training of the statistical learning model (machine learning model classifies and detects failure represents training statistical learning model [0166]-[0169], [0081]) on the thus constructed database (pipe network parts database 122 which stores a precise and/or general locations of the monitored pipe network part 104 [0202], [0198]).
As per Claim 18, Solomon teaches the training method according to claim 16, wherein the fluid network and the location of said vibro-acoustic sensors is provided with a digital mapping comprising at least the geometry of the fluid network and the location of said vibro-acoustic sensors (functions stored digital versatile disc [0230]. It is noted a digital versatile disc “DVD” can include digital mapping. Fig 2B shows monitored pipe network 102 and failure alert and location 206 - It is noted a pipe network is a form of fluid network geometry, Fig 3A shows two acoustic sensors attached on sections pipe network).
As per Claim 19, Solomon teaches the training method according to claim 18, comprising, for at least one documented leak, a step of simulating at least one virtual vibro-acoustic sensor having a virtual location recorded in the digital mapping of the fluid network and a simulated vibro-acoustic signal from the actually measured vibro-acoustic signals from the real vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network (simulating detectable acoustic reflections [0057], simulate a high frequency in section of the pipe network [0206], [0093]. It is noted
simulating high-frequency wave “vibration" propagation in a pipe network is a fundamental part of simulating acoustic sensors for applications such as leak detection and pipeline condition monitoring).
As per Claim 20, Solomon teaches the training method according to claim 18, comprising a step of locating the leak from the vibro-acoustic signals from the vibro- acoustic sensors and the geometric data from the digital mapping of the fluid network and, for at least one documented leak, a step of reconstructing the vibro-acoustic signal at the level of the leak from the vibro-acoustic signals from the vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network (measure vibration/ acoustic signals corresponding to level of pipe leak changes [0123] reading at multiple locations [0124], estimating leak intensity of pipe section includes installing two acoustic sensors as shown in Fig 3A, and predetermined a distance between two acoustic sensors and calculate leak intensity of a pipe section based on acoustic power [0018], [0108], [0196], [0216]. Thus, measuring acoustic power “intensity” considered for reconstructing acoustic signals in pipe network, i.e., readjust or predetermine a distance between two acoustic sensors).
As per Claim 21, Solomon teaches the training method according to claim 16, wherein the database comprises, for at least one documented leak, structural data of the pipe at level of the leak (pipe network parts database 122 is used to characterize the leak considered determining its levels or severity of a leak [0198]-[0200]. It is noted database contains structural data).
As per Claim 22, Solomon teaches the training method according to claim 16, comprising a standardization step resulting in converting the raw vibro-acoustic signal from at least one vibro-acoustic sensor into a standardized vibro-acoustic signal having a predetermined format (It is noted converting a raw vibro-acoustic signal into a standardized signal with a predetermined format which refers to “signal processing”, Fig 2A-B of Solomon shows monitored pipe network 102 includes signal processing circuits 200, see [0062], [0085], [0195]).
As per Claim 23, Solomon teaches the training method according to claim 16, wherein the statistical learning model is a neural network, see [0168].
As per Claim 24, Solomon teaches a method for characterizing a leak in a fluid network including a plurality of pipes, wherein the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals (see claim 16), the method comprising:
receiving, by a statistical learning model, as input at least one vibro- acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor (a failure classification 118 in Fig 1A considered a type of “statistical model” which receives vibro-acoustic signal, see [0087]-[0088]) and providing, from the statistical learning model, as output at least one leak characterization data among the leak type and the leak flow rate (Figs 1A shows the failure classification 118 outputs a failure detection and localization data considered “leak characterization data [0086], Fig 1B shows monitor vibro-acoustic/vibration and flow rate [0090], [0093], [0199), and wherein the statistical learning model has been trained using a training method according to claim 16 (see claim 16)
Claim 25 is rejected for the same rational as in claim 18.
As per Claim 26, Solomon teaches the characterization method according to claim 25, comprising a step of locating the leak from the vibro-acoustic signals from the vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network and a step of reconstructing the vibro-acoustic signal at the level of the leak from the vibro-acoustic signals from the vibro-acoustic sensors and the geometric data from the digital mapping of the fluid network (measure vibration/ acoustic signals corresponding to level of pipe leak changes [0123] reading at multiple locations [0124], estimating leak intensity of pipe section includes installing two acoustic sensors as shown in Fig 3A, and predetermined a distance between two acoustic sensors and calculate leak intensity of a pipe section based on acoustic power [0018], [0108], [0196], [0216]. Thus, measuring acoustic power “intensity” considered for reconstructing acoustic signals in pipe network, i.e., readjust or predetermine a distance between two acoustic sensors), wherein the statistical learning model receives as input at least the vibro- acoustic signal reconstructed at the level of the leak (a failure classification 118 in Fig 1A considered a type of “statistical model” which receives acoustic/vibration “vibro-acoustic” signal, see [0087]-[0088]).
As per Claim 27, Solomon teaches the characterization method according to claim 25, wherein the digital mapping of the fluid network comprises structural data of the fluid network (pipe network parts database 122 is used to characterize the leak [0198]-[0200]. It is noted database contains structural data and recorded characteristics of monitoring parts and indicating failure/leak [0063], [0230], thus, a digital versatile disc considered digital mapping of the pipe network [0230] including structural data).
Claim 28 is rejected for the same rational as in claim 24.
As per Claim 29, Solomon teaches a fluid network, comprising: a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals (“vibration/acoustic sensors” considered “vibro-acoustic sensors” [0066], [0103], [0061]-[0062]), and a characterization module according to claim 28 (pipe network parts database 122 is used to characterize the leak [0198]-[0200]).
As per Claim 30, Solomon teaches a computer program comprising instructions for executing the steps of the training method of claim 16 when the program is executed by a computer, see [0230].
As per Claim 31, Solomon teaches a computer program comprising instructions for executing the steps of the characterization method of claim 24 when the program is executed by a computer, see [0230].
Claim Rejections - 35 USC § 103
14. The following is a quotation under AIA of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action.
A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
15. Claim 17 is rejected under 35 U.S.C. 103 as being obvious over Solomon in view of Howitt, US 2016/0252422.
As per Claim 17, Solomon teaches the training method according to claim 16, wherein the fluid network is equipped with at least one flow rate sensor (Fig 2B block 106) proving sectorization data (estimated based on flow meters reading [0143], i.e., estimating leak intensity of a pipe section [0016], [0214]. It is noted measured leak data of a pipe section of the pipe considered sectorization data), and wherein the training method comprises, for at least one documented leak (database stored [0138], [0175]), Solomon does not explicitly teach a step of determining the leak flow rate using sectorization data. Howitt teaches a step of determining the leak flow rate using sectorization data (estimating the location of leaks within pipes based on” measuring flow rate at multiple points within a pipe network” considered estimating location of leaks based on multiple points within a pipe network considered sectorization data [0021]).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to modify the teaching of Solomon to estimate location of leaks at multiple points within a pipe network based on flow rate as taught by Howitt that would facilitate for detecting and locating leaks in a pipeline network, flow models are used to characterize both the steady and unsteady state flow behavior corresponding to absence and presence of modeled leaks, respectively, i.e., per Abhulimen and Susu' s method.
Conclusion
16. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20130332397 of Scolnicov et al (Method for locating a leak in a fluid network).
US20110093220 of Yang et al (Integrated acoustic leak detection system using intrusive and non-intrusive sensors).
US20210148781 of Karnachev et al (Method for Fluid Measurement for a Discrete Area of a Fluid Supply Network).
CN 113076617B of Yao et al (Method, system and equipment for visualizing urban water supply pipe network structure and function).
CN 114542997A of Xia (Water supply pipe network abnormal leakage detection method based on digital twinning).
17. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to LYNDA DINH whose telephone number is (571) 270-
7150. The examiner can normally be reached on M-F 10 AM-6 PM ET.
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/LYNDA DINH/Examiner, Art Unit 2857
/LINA CORDERO/Primary Examiner, Art Unit 2857