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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119
(a)-(d). The certified copy has been filed in parent Application No. GR20220100042, filed on
01/17/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/16/2024 is in compliance
with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being
considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The term “period of time” in claim 2 is a relative term which renders the claim indefinite. The term “period of time” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term period of time renders the cause of a leak indefinite because it is not clear as to when the leak can appear.
Claim 11 recites the limitation "the first algorithm and second algorithm" in the first line. There is insufficient antecedent basis for this limitation in the claim.
The following is a quotation of 35 U.S.C. 112(e):
(e) REFERENCE IN MULTIPLE DEPENDENT FORM.—A claim in multiple dependent form shall contain a reference, in the alternative only, to more than one claim previously set forth and then specify a further limitation of the subject matter claimed. A multiple dependent claim shall not serve as a basis for any other multiple dependent claim. A multiple dependent claim shall be construed to incorporate by reference all the limitations of the particular claim in relation to which it is being considered.
Claim 13 is rejected under 35 U.S.C. 112(e) as it is a dependent claim which refers to two claims in the conjunctive (claim 12 AND claim 1) rather than the alternative (claim 12 OR claim 1). This form is improper under 35 U.S.C. 112 and 37 CFR 1.75(C). The claim cannot be further examined on its merits based on the improper claim dependency.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1, 3, 8, 10, 18, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Abbas Pub. No.: US 20180300639 A1.
Regarding Claim 1 Abbas teaches A method of training a computer implemented leak prediction algorithm to predict leaks from pipework carrying a liquid, comprising: performing supervised training of the computer implemented leak prediction algorithm
that receives, as an input, training measurement data from sensors monitoring an environment in proximity to the pipework and provides, (Abbas, paragraph 0067, 0111, teaches the training of a random forest algorithm that is used to predict leaks in pipe systems, that receives data either in real time or from a pipe database, that comes from sensors that are monitoring the pipe and the environment.)
as an output, a prediction of whether a leak is likely to occur in future, (Abbas, paragraphs 0026, 0066, and 0076, teaches the prediction of if a pipe is going to leak in the future without prior knowledge of if the pipe has leaked in the past.)
wherein the supervised training comprises adjusting parameters of the computer implemented leak prediction algorithm to improve the accuracy of the prediction, (Abbas, paragraphs 0036, 0037, and 0040, teaches the predictive model to be updated overtime as new data is added to the datasets and the training and updating of model parameters using the updated datasets to improve the prediction.)
based on labels indicating which periods of the training measurement data correspond with one or more fault scenarios selected to cause leaks in future. (Abbas, paragraphs 0037, 0040, teaches data containing information about pipes that have leaked and the causes of the leaks in order to train the leak detection algorithm.)
Regarding Claim 3 Abbas teaches the method of claim 1, wherein the method produces a computer implemented leak prediction algorithm that predicts leaks prior to them becoming significant (Abbas, paragraph 0032, teaches the ability of the pipe leak prediction algorithm to predict leaks before they happen or become significant.)
Regarding Claim 8 Abbas teaches The method of claim 1, wherein the method further comprises training a first algorithm to classify measurement data as including a pattern. (Abbas, paragraph 0037, teaches a leak detection algorithm that classifies patterns related to leak detection using measurement data.)
Regarding Claim 10 Abbas teaches The method of claim 1, wherein the leak prediction algorithm comprises an artificial neural network. (Abbas, paragraph 0030 - 0031, teaches a leak detection algorithm that can be a neural network.)
Regarding Claim 18 Abbas teaches A system for predicting leaks from water carrying pipework, comprising:
a plurality of environmental sensors disposed in proximity to the pipework; (Abbas, paragraph 0067, 0111, teaches the running of a random forest algorithm that is used to predict leaks in pipe systems, that receives data either in real time or from a pipe database, that comes from sensors that are monitoring the pipe and the environment.)
and a computer, receiving environmental data measured by the plurality of environmental sensors and configured with a leak prediction algorithm that has been trained to predict leaks based on experimental data obtained during fault scenarios that will cause a leak in future. (Abbas, paragraphs 0026, 0066, and 0076, teaches the prediction of if a pipe is going to leak in the future without prior knowledge of if the pipe has leaked in the past.)
Regarding Claim 20 Abbas teaches The system of claim 18, wherein the leak prediction algorithm has been trained by performing supervised training of the leak prediction algorithm that receives, as an input, training measurement data from sensors monitoring an environment in proximity to the pipework and provides, (Abbas, paragraph 0067, 0111, teaches the training of a random forest algorithm that is used to predict leaks in pipe systems, that receives data either in real time or from a pipe database, that comes from sensors that are monitoring the pipe and the environment.) as an output, a prediction of whether a leak is likely to occur in future, (Abbas, paragraphs 0026, 0066, and 0076, teaches the prediction of if a pipe is going to leak in the future without prior knowledge of if the pipe has leaked in the past.) and wherein the supervised training comprises adjusting parameters of a machine learning algorithm to improve the accuracy of the prediction, (Abbas, paragraphs 0036, 0037, and 0040, teaches the predictive model to be updated overtime as new data is added to the datasets and the training and updating of model parameters using the updated datasets to improve the prediction.) based on labels indicating which periods of the training measurement data correspond with one or more fault scenarios selected to cause leaks in future. (Abbas, paragraphs 0037, 0040, teaches the data containing information about pipes that have leaked and the causes of the leaks in order to train the leak detection algorithm.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Abbas Pub. No.: US 20180300639 A1 in view of Wang et al. “Experimental study on water pipeline leak using In-Pipe acoustic signal analysis and artificial neural network prediction”.
Regarding Claim 2 Abbas teaches The method of claim 1,
Abbas does not teach wherein the training measurement data is obtained during controlled experiments in which the one or more fault scenarios are introduced to a system of pipework carrying a liquid so as to cause leaks at a period of time after the introduction of the one or more fault scenarios. However, Wang in analogous art teaches this limitation (Wang, page 3, section 2, teaches the use of an experimental setup where faults are introduced to a controlled experimental pipeline platform in which faults were introduced and the data from these faults were used to train a prediction model in section 5.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Wangs teaching of using simulated experiment data to train a leak detection algorithm with Abbas’ teaching of a leak detection algorithm. The motivation to do so would be to be able to train the algorithm on data for leaks that might not have a lot of data associated with them.
Claims 4, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Abbas Pub. No.: US 20180300639 A1 in view of Mezghani et al. Pub. No.: US 20110227721 A1.
Regarding Claim 4 Abbas teaches The method of claim 1,
Abbas does not teach wherein the one or more fault scenarios comprises progressive decompression of a compression fitting. However, Mezghani in analogous art teaches this limitation (Mezghani, paragraph 0016, teaches the use of and detecting pipe leaks at the point of a fitting, that is compressed between two pipes, caused by the seal deterioration. (i.e. progressive decompression))
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Mezghani teaching of detecting leaks caused by fittings decompressing with Abbas’ teaching of a leak detection algorithm. The motivation to do so would be to be able to train the algorithm to predict leaks that are caused by the joining of two pipes together and not just single pipes.
Regarding Claim 12 Abbas teaches A method of predicting leaks from water carrying pipework, comprising: receiving measurement data from sensors monitoring an environment in proximity to the pipework; (Abbas, paragraph 0067, 0111, teaches the running of a random forest algorithm that is used to predict leaks in pipe systems, that receives data either in real time or from a pipe database, that comes from sensors that are monitoring the pipe and the environment.)
providing the measurement data to a computer; and (Abbas, paragraph 0167-0168, teaches the ability of the system to transmit the measurement data to the computer system.)
running a leak prediction algorithm on computer to process the measurement data and, responsive to an output from the leak prediction algorithm, (Abbas, paragraphs 0026, 0066, and 0076, teaches the prediction of if a pipe is going to leak in the future without prior knowledge of if the pipe has leaked in the past.)
Abbas does not teach providing an alert in the event a leak is predicted. However, Mezghani in analogous art teaches this limitation (Mezghani, paragraph 0016, teaches the sending of an alert when a leak is detected.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Mezghani teaching sending an alert when a leak is detected with Abbas’ teaching of a leak detection algorithm. The motivation to be able to alert users of the system when a leak is about to or has already occurred in order to prevent or fix the leak.
Regarding Claim 14 the combination of Abbas and Mezghani teaches The method of claim 12, wherein providing the measurement data to the computer comprises transmitting the measurement data via a network. (Abbas, paragraph 0167-0168, teaches the ability of the system to transmit the measurement data to the computer system.)
Claims 5, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Abbas Pub. No.: US 20180300639 A1 in view of Kamkalow et al. Pub. No.: WO9941580A1.
Regarding Claim 5 Abbas teaches The method of claim 1,
Abbas does not teach wherein the sensors monitoring the environment comprise at least one of: a humidity sensor, a temperature sensor, and an atmospheric pressure sensor. However, Kamkalow in analogous art teaches this limitation (Kamkalow, page 3, paragraph 4, teaches a pipe leak detection system that utilizes temperature, humidity and pressure sensors.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Kamkalow teaching of specific sensors for leak detection with Abbas’ teaching of a leak detection algorithm. The motivation to be able to detect and track changes in common factors that lead to the leaking of pipes in pipe systems
Regarding Claim 16 Abbas teaches The method of claim 1,
Abbas does not teach wherein the sensors comprise sensor units, each configured to sense environments comprising: temperature, humidity and atmospheric pressure. However, Kamkalow in analogous art teaches this limitation (Kamkalow, page 3, paragraph 4, teaches a pipe leak detection system that utilizes temperature, humidity and pressure sensors.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Kamkalow teaching of specific sensors for leak detection with Abbas’ teaching of a leak detection algorithm. The motivation to be able to detect and track changes in common factors that lead to the leaking of pipes in pipe systems
Regarding Claim 19 Abbas teaches The system of claim 18,
Abbas does not teach wherein the environmental sensors comprise at least one of: a humidity sensor, a temperature sensor, and an atmospheric pressure sensor. However, Kamkalow in analogous art teaches this limitation (Kamkalow, page 3, paragraph 4, teaches a pipe leak detection system that utilizes temperature, humidity and pressure sensors.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Kamkalow teaching of specific sensors for leak detection with Abbas’ teaching of a leak detection algorithm. The motivation to be able to detect and track changes in common factors that lead to the leaking of pipes in pipe systems
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Abbas Pub. No.: US 20180300639 A1 in view of Davis et al. Pub. No.: US 20160284193 A1.
Regarding Claim 6 Abbas teaches The method of claim 1,
Abbas does not teach wherein the sensors are placed within 30 centimeters (cm) of the pipework. However, Davis in analogous art teaches this limitation (Davis. Paragraph 0321, teaches a sensor being affixed to a container that is attached to the pipe, wherein fig. 1 A-B show the sensor touching the pipe.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Davis teaching of sensors in proximity with the pipes with Abbas’ teaching of a leak detection algorithm. The motivation to be able to more accurately detect leaks with less interference from outside factors on the sensors.
Regarding Claim 7 Abbas teaches The method of claim 1,
Abbas does not teach wherein at least some of the sensors are placed in an enclosed cavity with the pipework. However, Davis in analogous art teaches this limitation (Davis. Paragraph 0321, teaches a sensor being affixed to an enclosed container (i.e. cavity) that is attached to the pipe.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Davis teaching of sensors in proximity with the pipes with Abbas’ teaching of a leak detection algorithm. The motivation to be able to more accurately detect leaks with less interference from outside factors on the sensors.
Claims 9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Abbas Pub. No.: US 20180300639 A1 in view of Reece et al. Pub. No.: US 20210216852 A1.
Regarding Claim 9 Abbas teaches The method of claim 8,
Abbas does not teach wherein the method further comprises training a second algorithm using measurement data that is classified as having a pattern by the first algorithm. However, Reece in analogous art teaches this limitation (Reece, paragraph 0064, teaches the use of a multiple classification models to be able to predict leaks and classify leaks based on measurement data.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Reece teaching of using multiple classifiers in the pipe detection process with Abbas’ teaching of a leak detection algorithm. The motivation to be able to not only predict if a leak will occur but also predict what kind of leak will occur.
Regarding Claim 11 Abbas teaches The method of claim 8,
Abbas does not teach wherein each of the first algorithm and second algorithm comprise a neural network. However, Reece in analogous art teaches this limitation (Reece, paragraph 0013, 0064, teaches the use of a multiple classification models, which can be neural networks, to be able to predict and classify leaks based on measurement data.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Reece teaching of using multiple classifiers in the pipe detection process with Abbas’ teaching of a leak detection algorithm. The motivation to be able to not only predict if a leak will occur but also predict what kind of leak will occur.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Abbas Pub. No.: US 20180300639 A1 in view of Mezghani et al. Pub. No.: US 20110227721 A1 in further view of Bond et al. Pub. No.: US 20020148294 A1.
Regarding Claim 15 the combination of Abbas and Mezghani teaches The method of claim 12,
The combination of Abbas and Mezghani does not teach wherein the sensors are distributed in different locations about the pipework. However, Bond in analogous art teaches this limitation (Bond, paragraph 0091, teaches the use of sensors for leak detection that are spaced out at least 1 meter apart, which are different locations.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Bond teaching of senors being placed at different locations within a pipe system with Abbas’ teaching of a leak detection algorithm. The motivation to be able to more accurately collect data on the length of the pipe system and detect leaks more accurately at different locations in the system.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Abbas Pub. No.: US 20180300639 A1 in view of Kamkalow et al. Pub. No.: WO9941580A1 in further view of Bond et al. Pub. No.: US 20020148294 A1.
Regarding Claim 17 the combination of Abbas and Kamkalow teaches The method of claim 16,
The combination of Abbas and Kamkalow does not teach wherein the sensor units are spaced apart by a distance of at least 50 centimeters (cm). However, Bond in analogous art teaches this limitation (Bond, paragraph 0091, teaches the use of sensors for leak detection that are spaced out at least 1 meter apart, which is a greater distance then 50cm.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Bond teaching of sensors being placed at different locations within a pipe system with Abbas’ teaching of a leak detection algorithm. The motivation to be able to more accurately collect data on the length of the pipe system and detect leaks more accurately at different locations in the system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7:20am-5:20pm; F: Out of Office.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MARIELA REYES can be reached at (571) 270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS BERNARD LANE/ Examiner, Art Unit 2142
/HAIMEI JIANG/ Primary Examiner, Art Unit 2142
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