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 .
Claim Objections
Claims 1-10 are objected to because of the following informalities:
Claim 1 recites at step (4) “features” which needs to be replaced by --the leakage identification features- - as the leakage identification features recited at line 2 of step (3) appears to be the features that the recited “features” at step (4) refers to.
Appropriate correction is required.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1: Yes.
Claim 1 recites “a method of constructing a water supply pipeline leakage identification model based on deep learning, comprising: (1) acquiring leakage sound signals…(2) preprocessing leakage sound signals…(3) calculating Mel-frequency cepstral coefficient…(4) learning features…to construct a leakage identification model”, which is a process.
Step 2A, Prong One: Judicial exception? Yes.
The recited steps (1)-(4) which falls into mathematical concepts and/or mental processes, that may be carried out in human mind or with the aid of pencil and paper in simple situations. The claim does not recite a particular equation or algorithm for making the recited calculating and learning features steps, this just means that the abstract idea is being recited broadly enough to monopolize all possible equations or algorithms that might be used (Please also see MPEP 2106.04(a)(2)(III)(A), (B), (C), and (D). The steps may be carried out as a mental process if the algorithm is simple enough, and as a mathematical process if the algorithm is more complicated.
The broadest reasonable interpretation of the steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.
Limitation “calculating…” under BRI covers a mathematical calculation if it is simple
Step 2A, Prong Two: Practical application? No.
The recited steps (1)-(4) are not performed by any particular machine.
Claim 1 when viewed as a whole or in ordered combination does not integrate the judicial exception into a practical application.
Limitation (1) acquiring leakage sound signals encompasses data gathering.
Limitation (2) preprocessing…encompasses data gathering which can be a mental process and a mathematical concept.
Limitation (3) calculating…encompasses a mathematical calculation.
Limitation “(4) learning features of the leakage sound signals based on learning algorithm to construct a leakage identification model” encompasses a mathematical calculation and a mental process.
The limitation (4) encompasses an insignificant extra solution. The “(constructed) leakage identification model” is considered insignificant extra solution. The usage of the constructed model is unlimited.
The limitations recite only the idea of learning features of the leakage sound signals based on a deep learning algorithm to construct a leakage identification model without details on how this is accomplished. Likewise, the recited steps (1)-(3) omits details as how to perform each step but only outcomes.
Further, when viewed as a whole or ordered combination does not provide an improvement in detecting and positioning of leakage of urban water supply pipelines in municipal engineering (see MPEP 2106.05(a)).
The recited steps in claim 1 are not performed by any particular machine. In light of specification, no machine was shown to perform the recited limitations in claim 1.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, for reasons that are analogous to the discussion of additional elements at Prong 2.
Dependent claim 2 adds two limitations which are data gathering. The recited “sound sensor, pipeline, nozzle” are not particular machines. They are used as a tool to perform the abstract idea.
Dependent claims 3 and 4 add limitations which are mathematical calculation merely extending the abstract idea without adding any additional elements.
Dependent claim 5 adds a limitation which is mathematical concept and data gathering merely extending the abstract idea without adding any additional elements.
Dependent claim 6 recites a leakage detection method based on deep learning by a water supply pipeline leakage identification model which is constructed according to claim 1. Claim 1’s explanation is herein incorporated by reference. Dependent claim 6 adds limitations “selecting…”, “collecting sound signal”, “performing…”, “performing…” and “inputting the static feature…and determining whether a leakage occurs at this point by an outputted leakage probability” which are data gathering merely extending the abstract idea without adding any elements.
The limitation “inputting the static feature…and determining whether a leakage occurs at this point by an outputted leakage probability” encompasses an insignificant extra solution and data gathering, which is equivalent to the words “apply it.”
Dependent claim 7 adds limitations which are data gathering merely extending the abstract idea without adding any elements.
Dependent claims 8 and 9 add limitations which are mathematical calculation merely extending the abstract idea without adding any elements.
Dependent claim 10 adds a limitation which is data gathering merely extending the abstract idea without adding any additional elements.
Claim Rejections - 35 USC § 102
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.
Claim 1 is rejected under 35 U.S.C. (a)(2) being anticipated by Ge et al. (hereinafter “Ge”)(CN 111022941)(English Translation submitted by Examiner).
Regarding claim 1, Ge discloses a method of constructing a water supply pipeline leakage identification model based on deep learning, comprising:
(1) acquiring leakage sound signals (Abstract step 1, collecting pipeline audio data; see summary of the invention, Page 1);
(2) preprocessing the leakage sound signals to obtain processed leakage sound signals (Abstract; step 2; summary of the invention Page 1, using improved MFCC method for feature extraction to the audio data);
(3) calculating Mel-frequency cepstral coefficients of the processed leakage sound signals, as leakage identification features (Abstract; steps 2 and 3; see Page 2, line 2); and (4) learning features of the leakage sound signals based on a deep learning algorithm to construct a leakage identification model (Abstract step 4).
Conclusion
Claims 2-10 are patentably distinguishable over the prior art of record.
Regarding claim 2, Ge does not disclose “wherein the acquiring leakage sound signals in step (1) comprises: setting different working conditions to select an appropriate sound sensor, a sampling frequency, an appropriate detecting line and a distance between various detecting points on the detecting line, and collecting a large number of the leakage sound signals at the detecting points; wherein the sound sensor has the sampling frequency of at least 4000Hz; according to a position of a nozzle of a water supply pipeline, a position of an axis of the water supply pipeline is determined, and the axis of the pipeline is set as the detecting line.”
Kann et al. (USPAP. 2023/0111271) discloses a leak detection sensor may be capable of dynamically adjusting a sampling time for acoustic data based on monitored fluid flow. The leak detection sensor monitors leak detection information associated with a fluid in a fluid transporting medium over a predetermined time interval, where the leak detection information comprises flow and pressure of the fluid in the fluid transporting medium, and processing the leak detection data monitored at the sampling time in the predetermined time interval, where the sampling time is determined based at least in part on the monitored flow of the fluid (Abstract). a controller unit, or a headend device, may receive and analyze monitored water flow and pressure data from the group of leak detection sensors and determine, or coordinate, the individual sampling time for each leak detection sensor, the common sampling time for the group of leak detection sensors, or one or more sampling times for one or more subgroups of the group of leak detection sensors based on the topology and leak detection sensor locations (Par. 16). However, Kann et al. does not explicitly teach that “wherein the acquiring leakage sound signals in step (1) comprises: setting different working conditions to select an appropriate sound sensor, a sampling frequency, an appropriate detecting line and a distance between various detecting points on the detecting line, and collecting a large number of the leakage sound signals at the detecting points; wherein the sound sensor has the sampling frequency of at least 4000Hz; according to a position of a nozzle of a water supply pipeline, a position of an axis of the water supply pipeline is determined, and the axis of the pipeline is set as the detecting line.”
Regarding claim 2, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein the acquiring leakage sound signals in step (1) comprises: setting different working conditions to select an appropriate sound sensor, a sampling frequency, an appropriate detecting line and a distance between various detecting points on the detecting line, and collecting a large number of the leakage sound signals at the detecting points; wherein the sound sensor has the sampling frequency of at least 4000Hz; according to a position of a nozzle of a water supply pipeline, a position of an axis of the water supply pipeline is determined, and the axis of the pipeline is set as the detecting line.” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 3, Ge discloses preprocessing data including high pass filter and windowing (Pages 2 and 3. However, Ge does not explicitly disclose the claimed invention as recited in claim 3. Kann et al. does not teach the claimed invention as recited in claim 3. Therefore, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein the preprocessing in step (2) comprises pre-emphasizing, framing, and windowing…(23) windowing the leakage sound signal…detecting points” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 4, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein the step (3) comprises (31) performing fast Fourier transform on the processed leakage sound signal…(35) wherein the Mel-frequency cepstral coefficient…in D/3 dimension” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 5, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein the step (4) comprises adding a carry track on the basis…output of the model” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 6, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “a leakage detection method based on learning …inputting a static feature of the standard Mel-frequency cepstral coefficient…probability” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 7, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein acquiring the leakage sound signals in step (1) comprises: setting different working conditions to select…detecting line” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 8, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein the processing in the step (2) comprises…(23) windowing the leakage sound signal…detecting points” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 9, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein the step (3) comprises (31) performing fast Fourier transform…(35) wherein the Mel-frequency…D/3 dimension” in combination with other limitations in the claims as defined by Applicants.
Regarding claim 10, the closest prior art of record either alone or in combination fails to anticipate or render obvious the combination wherein “wherein the step (4) comprises: adding a carry track…the detecting point is an output of the model” in combination with other limitations in the claims as defined by Applicants.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shin et al. (USPAP. 20090277252) discloses a leakage detecting apparatus that installs a detection pipeline for detecting a leakage on a pipe through which a liquid flows and determines whether liquid leaks from the pipe based on a voltage change of the detection pipeline there is provided a leakage detection apparatus for detecting whether a leakage has occurred by using a detection pipeline including a plurality of wires, the leakage detection apparatus including: the detection pipeline including first through third electric wires in which end portions of the first and second electric wires are electrically connected through a diode; a pipeline measuring unit, after a constant current is applied to the first electric wire, measuring a pipeline voltage at specific point of place of each of the second and third electric wires; and a microprocessor determining whether the leakage has occurred based on the pipeline voltage of each of the second and third electric wires (Abstract; Pars. 30-34).
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/PHUONG HUYNH/ Primary Examiner, Art Unit 2857 September 25, 2026
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