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 .
This action is in response to the application and claims filed 03/14/2023. Claims 1-17 are pending and have been examined. Claims 1-17 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/07/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 17 objected to because of the following informalities: "code instructions to receive pressure data and flow rate data and flow rate data measured…" should be “code instructions to receive pressure data and flow rate data measured…" Appropriate correction is required.
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.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “typical” in claim 7 is a relative term which renders the claim indefinite. The term “typical” 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 “typical” renders the limitations of “demand of compressed air” indefinite. Since the claim relies on “typical demand” to define the “reference pressure and/or reference air flow”, the scope of these reference values becomes ambiguous. The claim fails to provide a definite standard to measure what constitutes “typical”, making it impossible to determine the boundaries of the reference vales.
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 1-17 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: The claim recites a method; therefore, it is directed to the statutory category of process.
Step 2A Prong 1: The claim recites:
“…detecting leaks in a compressed air system…” and “…and detecting at least one compressed air leak in the compressed air system…” (a person can visually or audibly inspect a compressed air system and find a leak)
identifying at least one shutdown event in a compressed air system… during the at least one shutdown event demand of compressed air by the at least one compressed air client is at least partially reduced; (a person can look at a compressed air system or the system interface and identify if the compressed air to a client is reduced or stopped.)
identify a plurality of compressed air leak patterns based on pressure and flow rate data. (a person can visually monitor data (pressure and flow rate) and can intuitively identify a trend of leakage patterns.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
A computer implemented method… (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
using machine learning (ML)… (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning applied to an abstract idea.)
comprising at least one compressed air client consuming compressed air delivered by at least one air compressor, (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This additional element does not integrate the judicial exception into a practical application (MPEP 2106.05(h)).)
receiving pressure data and flow rate data measured in the compressed air system during the at least one shutdown event; (Data Gather- Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).)
using at least one trained ML model applied to the pressure data and the flow rate data, the at least one trained ML model is trained to (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). )). – EN: Claim recites a generic machine learning and training applied to an abstract idea.)
Step 2B:
A computer implemented method… (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
using machine learning (ML)… (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
comprising at least one compressed air client consuming compressed air delivered by at least one air compressor, (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).)
receiving pressure data and flow rate data measured in the compressed air system during the at least one shutdown event; (MPEP 2106.05(d)(II) indicates that merely gathering data is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
using at least one trained ML model applied to the pressure data and the flow rate data, the at least one trained ML model is trained to (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Claim 2
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 2 depends on. Claim 2 further recites:
detect the at least one compressed air leak based on analysis of the pressure data and the flow rate data accumulated during a predefined time period. (a person can mentally or with a pen and paper analyze pressure and flow data from a certain time period and perform evaluation and judgement to detect a leak.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
wherein the at least one trained ML model is trained to (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Step 2B:
wherein the at least one trained ML model is trained to (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Claim 3:
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 3 depends on.
Step 2A prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
wherein the pressure data and the flow rate data are measured by at least one pressure sensor and at least one flow sensor respectively which are deployed in the compressed air system. (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
Step 2B:
wherein the pressure data and the flow rate data are measured by at least one pressure sensor and at least one flow sensor respectively which are deployed in the compressed air system. (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
Claim 4
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 3 above, which claim 4 depends on. Claim 4 further recites:
wherein the pressure data used for detecting the at least one compressed air leak is measured by at least one selected closest pressure sensor which is located at a shortest distance from the at least one flow sensor among a plurality of pressure sensors deployed in the compressed air system. (A person can measure the distance between sensors and choose the one that is the closest.)
Step 2A Prong 2: The claim does not recite any additional elements that integrate the judicial exception
into a practical application.
Step 2B: The claim does not recite any additional elements that amount to significantly more than the
judicial exception. Therefore, the claim is not patent eligible.
Claim 5
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 3 above, which claim 5 depends on. Claim 5 further recites:
“…detect the at least one compressed air leak based on analysis of aggregated flow rate data aggregating flow rate data measured… at different locations in the compressed air system.” (A person can take readings from multiple meters and use a pen and paper to sum them up to get a total flow number, and determine if that total is higher or lower than it should be and judge if there is an air leak.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
“…by a plurality of flow sensors deployed to measure the air flow rate…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
Step 2B:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
“…by a plurality of flow sensors deployed to measure the air flow rate…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
Claim 6
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 6 depends on. Claim 6 further recites:
“...detect the at least one compressed air leak based on analysis of an averaged air flow rate averaging the measured air flow rate to compensate for compressor load and unload periods.” (A person can record flow rates during the load and unload periods of a compressor, add them together, and divide by the time to find an average value and use that average value to judge if there is an air leak.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Step 2B:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Claim 7
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 7 depends on. Claim 7 further recites:
detect the at least one compressed air leak based on reference pressure and/or reference air flow defining typical demand of compressed air in the compressed air system having no compressed air leaks. (A person can compare current pressure and air flow readings against a “standard” or “expected” value to see if the current numbers look wrong and judge if there is an air leak.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Step 2B:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Claim 8
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 8 depends on. Claim 8 further recites:
detect the at least one compressed air leak based on normalized flow rate data in which the air flow rate is normalized according to the pressure. (A person can take the current pressure reading and use a standard normalization formula to adjust (normalize) the flow number to account for pressure changes, and judging if this adjusted number indicates a leak.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Step 2B:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
Claim 9
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 9 depends on. Claim 9 further recites:
computing a flow to pressure relation based on the plurality of measured air flow rates and the plurality of corresponding pressure levels, the flow to pressure relation is indicative of the at least one compressed air leak which induces a pressure dependent air flow rate, and inferring the amount of leaked compressed air based on the flow to pressure relation which is indicative of a pressure dependent air flow rate induced by the at least one air leak. (A person can plot data points on graph paper (flow vs pressure) to draw a curve (relation) and then use that curve to estimate/judge how much air is escaping.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
receiving a plurality of air flow rates measured in the compressed air system for a plurality of corresponding pressure levels, (Data Gather- Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).)
Step 2B:
receiving a plurality of air flow rates measured in the compressed air system for a plurality of corresponding pressure levels, (MPEP 2106.05(d)(II) indicates that merely gathering data is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
Claim 10
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 9 above, which claim 10 depends on. Claim 10 further recites:
further comprising actively controlling the at least one air compressor to deliver compressed air to induce the plurality of pressure levels in the compressed air system. (A person working at the manufacturing site can manually adjust the compressor to achieve variable pressure levels.)
Step 2A Prong 2: The claim does not recite any additional elements that integrate the judicial exception
into a practical application.
Step 2B: The claim does not recite any additional elements that amount to significantly more than the
judicial exception. Therefore, the claim is not patent eligible.
Claim 11
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 11 depends on. Claim 11 further recites:
wherein the at least one shutdown event is identified (a person can look at a compressed air system or the system interface and identify if the compressed air to a client is reduced or stopped.)
detect at least one shutdown event based on measured pressure data and flow rate data. (A person can look at the flow gauges and decide that because the flow is very low, the machines must be in a shutdown state.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“by the at least one trained ML model which is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Step 2B:
“by the at least one trained ML model which is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Claim 12
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 11 above, which claim 12 depends on. Claim 12 further recites:
…filter out at least one potential shutdown event detected while the air flow rate in the compressed air system exceeds a certain threshold. (A person can review a list of possible shutdown times and can cross out any instances where the flow number is too high (above a threshold), deciding those weren’t real shutdowns.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Step 2B:
“wherein the at least one trained ML model is further trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Claim 13
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 13 depends on. Claim 13 further recites:
“wherein the at least one shutdown event is identified according to an indication received…” (A person can wait for signal to tell someone (indicate) that the system experiencing a shutdown event.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“…from at least one control unit of the compressed air system.” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
Step 2B:
“…from at least one control unit of the compressed air system.” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
Claim 14
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 14 depends on. Claim 14 further recites:
identify at least one of the plurality of compressed air leak patterns using a plurality of training samples comprising pressure data and flow rate data measured in at least one another compressed air system having at least one compressed air leak and not having compressed air leaks. (A person can mentally judge and identify an air leak pattern given a list of pressure and flow rate data having leaks and not having leaks from other systems.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one trained ML model is trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Step 2B:
“wherein the at least one trained ML model is trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Claim 15
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 15 depends on. Claim 15 further recites:
identify at least one of the plurality of compressed air leak patterns using a plurality of training samples comprising pressure data and flow rate data measured in at least one another compressed air system having at least one compressed air leak and not having compressed air leaks. (A person can mentally judge and identify an air leak pattern given a list of pressure and flow rate data having leaks and not having leaks from its own system.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“wherein the at least one trained ML model is trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Step 2B:
“wherein the at least one trained ML model is trained to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
Claim 16
Step 1: A process, as above.
Step 2A prong 1: See the rejection of Claim 1 above, which claim 16 depends on. Claim 16 further recites:
“… reporting the at least one detected compressed air leak.” (This falls under Certain Methods Of Organizing Human Activity - A person can notify someone if they find an air leak.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
“transmitting at least one alert…” (amounts to adding insignificant extra solution activity to the judicial exception – see MPEP 2106.05(g))
Step 2B:
“transmitting at least one alert…” (MPEP 2106.05(d)(II) indicate that merely "receiving and transmitting data over a network" is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer).
Claim 17
Step 1: The claim recites a system; therefore, it is directed to the statutory category of machine.
Step 2A prong 1: the claim recites:
“…detecting leaks in a compressed air system…” and “…and detect at least one compressed air leak in the compressed air system…” (a person can visually or audibly inspect a compressed air system and find a leak)
“…identify at least one shutdown event in a compressed air system… during the at least one shutdown event demand of compressed air by the at least one compressed air client is at least partially reduced;” (a person can look at a compressed air system or the system interface and identify if the compressed air to a client is reduced or stopped.)
“…identify a plurality of compressed air leak patterns based on pressure and flow rate data.” (a person can visually monitor data (pressure and flow rate) and can intuitively identify a trend of leakage patterns.)
Step 2A prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows:
using machine learning (ML), comprising: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
“at least one processor configured to execute a code, the code comprising: code instructions to…”, “code instructions to…” and “code instructions to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic processor and code instructions applied to an abstract idea.)
comprising at least one compressed air client consuming compressed air delivered by at least one air compressor, (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This additional element does not integrate the judicial exception into a practical application (MPEP 2106.05(h)).)
receiving pressure data and flow rate data measured in the compressed air system during the at least one shutdown event; (Data Gather- Mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity (MPEP 2106.05(g)).)
using at least one trained ML model applied to the pressure data and the flow rate data, the at least one trained ML model is trained to (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). )). – EN: Claim recites a generic machine learning and training applied to an abstract idea.)
Step 2B:
using machine learning (ML), comprising: (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning model and training applied to an abstract idea.)
“at least one processor configured to execute a code, the code comprising: code instructions to…”, “code instructions to…” and “code instructions to…” (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic processor and code instructions applied to an abstract idea.)
comprising at least one compressed air client consuming compressed air delivered by at least one air compressor, (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).)
receiving pressure data and flow rate data measured in the compressed air system during the at least one shutdown event; (MPEP 2106.05(d)(II) indicates that merely gathering data is a well- understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well- understood, routine, conventional activity is supported under Berkheimer.)
using at least one trained ML model applied to the pressure data and the flow rate data, the at least one trained ML model is trained to (Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). – EN: Claim recites a generic machine learning and training applied to an abstract idea.))
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-8, 11-13, and 16-17 are rejected under 35 U.S.C. 103 as being
unpatentable over patent application US 2023/0088241 A1 Streichert et al., hereinafter “Streichert” in view of non-patent literature Ma et al. (“Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries”, hereinafter “Ma”)
Claim 1
Streichert teaches:
A computer implemented method of detecting leaks in a compressed air system using machine learning (ML), comprising: (Abstract, “Continuous condition monitoring of a pneumatic system, and in particular for early fault detection, is provided. The condition monitoring unit is formed with an interface to a memory in which a trained normal condition model is stored as a one-class model, which has been trained in a training phase with normal condition data and represents a normal condition of the pneumatic system.” – Examiner’s Note (EN): this denotes a pneumatic system that is equivalent to the broadest reasonable interpretation of “Compressed air system” because the pneumatic system comprises actuators that function as the required “compressed air clients” consuming air delivered via values and supply lines.) “… receiving pressure data and flow rate data measured in the compressed air system during…” (Para 5, “The method comprises the following method steps… Continuous acquisition of sensor data of the pneumatic system by means of a set of sensors” Para 32, “For example, one or more of the following sensors may be used: Flow meter, Pressure sensor…”) “…and detecting at least one compressed air leak in the compressed air system using at least one trained ML model applied to the pressure data and the flow rate data,” (Para 49, “ In a further, preferred embodiment of the invention, the calculated and output anomaly score is used for anomaly detection, in particular for leakage detection… Furthermore, the normal state data may comprise pressure signals and/or flow signals” -- Examiner’s note (EN): The quote refers to a “calculated and output anomaly score” this denotes the use of a trained ML model. Earlier in the text, Streichert defines that this score comes from a trained normal state model. Para 6, “Provide a trained normal state model as a one-class model that has been trained in a training phase…” Para 9, “Determine deviations of extracted features from learned features of the normal state model…” Para 10, “Calculate an anomaly score from the determined deviations”) “the at least one trained ML model is trained to identify a plurality of compressed air leak patterns based on pressure and flow rate data.” (Para 6-10, “Provide a trained normal state model as a one-class model that has been trained in a training phase with normal state data representing a normal state of the pneumatic system; Continuous acquisition of sensor data of the pneumatic system by means of a set of sensors; Extract features from the acquired sensor data; Determine deviations of extracted features from learned features of the normal state model using a distance metric…; Calculate an anomaly score from the determined deviations and Output of the calculated anomaly score.” Para 49, “ the normal state data may comprise pressure signals and/or flow signals” Para 112, “features are extracted by means of an extractor 304 , i.e. quantities are derived that provide information about the functioning of the pneumatic system… include the actuator features “reaction time extension”, “travel time extension”, “reaction time retraction”, and “travel time retraction””. – EN: this denotes the trained ML model is applied to sensor data, which includes “pressure signals and/or flow signals”, to perform “leakage detection” (para 5). The model operates by extracting specific features from this data, such as “reaction time extension” and “travel time extension”, and then “determining deviations” of these extracted features from learned norms. These determined deviations constitute the “leak patterns” because they are distinct data signatures used to identify the presence of a leak.)
Streichert does not explicitly disclose:
“identifying at least one shutdown event in a compressed air system comprising at least one compressed air client consuming compressed air delivered by at least one air compressor, during the at least one shutdown event demand of compressed air by the at least one compressed air client is at least partially reduced;” and “…the at least one shutdown event”
However, Ma teaches:
identifying at least one shutdown event in a compressed air system comprising at least one compressed air client consuming compressed air delivered by at least one air compressor, during the at least one shutdown event demand of compressed air by the at least one compressed air client is at least partially reduced; (Section 5.2.2 and Figure 5 – EN: Ma describes analyzing compressed air consumption in a ceramic moulding workshop where operations follow a “two-shift rotation”. Ma identifies specific time windows like nights where production activity is reduced or stopped, and the air compressor is expected to stop or reduce output.)
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the at least one shutdown event (Section 5.2.2 and Figure 5 – EN: Ma describes analyzing compressed air consumption in a ceramic moulding workshop where operations follow a “two-shift rotation”. Ma identifies specific time windows like nights where production activity is reduced or stopped, and the air compressor is expected to stop or reduce output.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the leak detection using machine learning system of Streichert with the identified shutdown events of Ma. The motivation for doing so would be to improve the accuracy of leak detection by focusing the analysis on periods of reduced production activity where compressed air demand is minimal or stopped, thereby making leaks more distinguishable from normal consumption. Ma explicitly teaches this benefit, stating that “The use of compressed air at night must also be investigated to determine whether a leak or other abnormal air use occurs to reduce the amount of compressed air used and the operating time of air compressors, ultimately reducing energy consumption.” (Ma, section 5.2.2)
Claim 2
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
wherein the at least one trained ML model is trained to detect the at least one compressed air leak (Para 6, “Provide a trained normal state model as a one-class model that has been trained in a training phase”, Para 49, “ In a further, preferred embodiment of the invention, the calculated and output anomaly score is used for anomaly detection, in particular for leakage detection”) based on analysis of the pressure data and the flow rate data (Para 5, “Extract features from the acquired sensor data;” Para 32, “For example, one or more of the following sensors may be used: Flow meter, Pressure sensor…”) accumulated during a predefined time period. (Para 69, The time window length can be specified as a static value in the unit number of cycles or in time units, such as 10 seconds. Para 68, “a result of the pattern recognition algorithm can be used to calculate time windows in which feature extraction is performed.”)
Claim 3
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
wherein the pressure data and the flow rate data are measured by at least one pressure sensor and at least one flow sensor respectively which are deployed in the compressed air system. (Para 107, “ In a step 204 , sensor data of the pneumatic system 100 is continuously acquired by means of a set of sensors. The set of sensors includes at least the sensors m1 and m2 described above. In addition, several of these or other types of sensors (for example, flow sensors, pressure sensors, microphones, structure-borne sound pickups) may collect sensor data.”)
Claim 5
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
wherein the at least one trained ML model is further trained to detect the at least one compressed air leak based on analysis of aggregated flow rate data aggregating flow rate data measured by a plurality of flow sensors deployed to measure the air flow rate at different locations in the compressed air system. (Para 60, “Alternatively or in addition, the anomaly score can also be output locally for certain subgroups and/or functional units of a pneumatic system… This involves processing a large number of sensor signals from different sensors, which is advantageous for the efficiency and scalability of anomaly detection and rectification.” – EN: this denotes the use of multiple sensors (including flow meters [para 33]) across various functional units/sub-groups to calculate anomaly scores [anomaly score is derived from the trained ML model]. Under BRI, “processing a large number of sensor signals” (which include flow sensors) to output a score maps to “aggregating flow rate data” from sensors at “different locations”.)
Claim 6
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
wherein the at least one trained ML model is further trained to detect the at least one compressed air leak based on analysis of an averaged air flow rate averaging the measured air flow rate to compensate for compressor load and unload periods. (Para 75, “the extracted features can comprise statistical characteristics and in particular mean values… of the sensor data” Para 80, “A differentiator for determining deviations of extracted features from learned features of the normal state model” – EN: this denotes using mean values of sensor data which include flow sensor data as noted in (Para 32) to train a model. Additionally, averaging data over a production cycle results in compensating for fluctuations such as load and unload periods within that cycle.)
Claim 7
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
wherein the at least one trained ML model is further trained to detect the at least one compressed air leak based on reference pressure and/or reference air flow defining typical demand of compressed air in the compressed air system having no compressed air leaks. (Para 5, “Provide a trained normal state model as a one-class model that has been trained in a training phase with normal state data representing a normal state of the pneumatic system.” Para 14, “In the normal state, there are no anomalies, leaks or other faults, and the production process is functioning perfectly.”)
Claim 8
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
wherein the at least one trained ML model is further trained to detect the at least one compressed air leak based on normalized flow rate data in which the air flow rate is normalized according to the pressure. (Para 113, “The extracted features can be normalized to simplify their representation in an n-dimensional space. This is particularly advantageous if the features derived from the sensor data contain different physical quantities and/or magnitudes (for example, pressure and time) that are to be further processed together.” – EN: this denotes extracting features from flow and pressure. And these features are “normalized… to simplify their representation… advantageous if the features… contain different physical quantities… for example, pressure”. By normalizing flow features and pressure features into a common “n-dimensional space” so they can be “processed together”, the flow rate is being normalized according to or in relation to the pressure data and its magnitude. In the BRI of the claim, this maps to normalizing the flow data in a way that accounts for pressure.)
Claim 11
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
“…identified by the at least one trained ML model which is further trained to detect … based on measured pressure data and flow rate data” (Para 21, “In a preferred embodiment of the invention, the normal state model is a statistical model and/or machine learning model.” Para 15, “A pneumatic system can have more than one operating state… In this case, the normal state of the pneumatic system must be learned for each operating state during the training phase.” Para 16, “These features are based on measured state data of the normal state, such as pressure, pressure curve, flow, flow curve or time stamp. The One-Class model learns these features” -- EN: this denotes the use of a trained ML model that identifies specific operating states of the system and is learned based on pressure and flow rate data.)
Streichert does not explicitly disclose:
“wherein the at least one shutdown event is identified” and “at least one shutdown event”
However Ma teaches:
“wherein the at least one shutdown event is identified” and “at least one shutdown event”
(Ma reference, Section 2.2.2, Figure 5 – EN: this denotes identifying segments where the air compressor usage is significantly reduced or in a low usage rate)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to use the machine learning model that is trained based on pressure and flow rate data of Streichert to identify shutdown events of Ma. The motivation for doing so would be to improve the accuracy of leak detection by focusing the analysis on periods of reduced production activity where compressed air demand is minimal or stopped, thereby making leaks more distinguishable from normal consumption. Ma explicitly teaches this benefit, stating that “The use of compressed air at night must also be investigated to determine whether a leak or other abnormal air use occurs to reduce the amount of compressed air used and the operating time of air compressors, ultimately reducing energy consumption.” (Ma, section 5.2.2) Furthermore, it would have been obvious to utilize the trained ML model of Streichert to automatically identify these shutdown events as taught by Ma to enable a “self-organizing” and “unmanned” operation, as Ma discloses that “The equipment can realise the self-learning function, accumulate experience and form a knowledge base. (Ma, Section 5.1)” And utilizes “Data mining algorithms (Ma page 6)” such as “deep convolutional networks (Ma page 6)”.
Claim 12
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
wherein the at least one trained ML model is further trained to… (As established in claim 11, Streichert teaches the use of a machine learning model trained to learn specific operating states)
Streichert does not explicitly disclose:
“…filter out at least one potential shutdown event detected while the air flow rate in the compressed air system exceeds a certain threshold.”
However, Ma teaches:
“…filter out at least one potential shutdown event detected while the air flow rate in the compressed air system exceeds a certain threshold.” (Section 5.2.2, “The air compressor stopped working between 16:00 to 6:00 the next day. [valid shutdown event – air consumption stopped as part of normal operation cycle, in this case it was due to shift rotation] … However, during the three nights… especially on the night of March 25, which showed a large amount of air consumption. [exceeding threshold]… Did the company require overtime production?” – EN: this denotes analyzing a standard “shutdown” window (16:00-6:00). When the system detects that the air flow is too high (“large amount of consumption”), it does not treat that period as a shutdown, instead it” filters” it from the shutdown category by identifying it as “overtime production”.
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to train the leak detection machine learning model of Streichert to filter out shutdown events based on a flow rate threshold. The motivation for doing so would to ensure the accuracy of leak detection analysis by preventing the system from combining real production usage (such as overtime) with leakage during expected shutdown windows. Ma explicitly teaches analyzing flow rates during these windows to identify anomalies, noting that “a large amount of air consumption” (Ma, section 5.2.2) during a standard shutdown time prompted the determination of “overtime production” rather than a leak. Ma Section 5.2.2, “ However, during the three nights from 16:00 on March 25 to 6:00 on March 28, compressed air was used erratically on the 2nd floor, especially on the night of March 25, which showed a large amount of air consumption. Did the company require overtime production?”
Claim 13
Streichert in view of Ma teaches all the limitations of claim 1, Ma further teaches:
wherein the at least one shutdown event is identified according to an indication received from at least one control unit of the compressed air system. (Section 5.2.1 and 5.2.2, “The use of compressed air is analysed based on the EMS [Control Unit] to identify any unreasonable production status [indication]… The air compressor stopped working between 16:00 to 6:00 the next day [Shutdown Event/reduced activity]… Operators work in a two-shift rotation [normal operation cycle]…” – EN: this denotes using EMS to identify recurring non-working hours which are part of the factory’s standard daily shift cycle, which fits the definition of a shutdown event as a period of reduced activity like a break or recess rather than a maintenance shutdown.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the leak detection using machine learning system of Streichert with the identification of shutdown events via a control unit of Ma. The motivation for doing so would be to accurately determine the operational status of the system to pinpoint non-working periods where consumption can be minimized, thereby improving the overall energy efficiency and costs. Ma Section 5.2.1, “The use of compressed air is analysed based on the EMS to identify any unreasonable production status. Finally, reducing the amount of compressed air can save energy and reduce consumption and production costs.”
Claim 16
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
“further comprising transmitting at least one alert reporting the at least one detected compressed air leak.” (Para 59, “when a configurable threshold value of the anomaly score is exceeded, the operator of the pneumatic system is alerted. This can happen, for example, by means of a warning message”)
Claim 17
Streichert teaches:
A system for detecting leaks in a compressed air system using machine learning (ML),
Para 97, “The present invention relates to a method and a device for monitoring the condition of pneumatic systems, in particular for detecting anomalies such as leaks.” Para 98, “FIG. 1 shows an overview illustration of a pneumatic system 100 with a condition monitoring unit 114 .” Para 100, “The condition monitoring unit 114 includes, for example, models and their parameters, training and inference algorithms, training data, state data, meta-parameters, and configuration parameters (not shown). “Comprising: at least one processor configured to execute a code, the code comprising: code instructions…” and “code instructions…” and “code instructions …” (Para 79, “the corresponding functional features of the method are formed by corresponding representational modules, in particular by hardware modules or microprocessor modules, of the system or product, and vice versa.” Para 87, “a computer program comprising instructions which, when the computer program is executed by a computer, cause the computer program to execute the method described above.”)
The remaining limitations are substantially the same as limitations of claim 1, therefore it is rejected under the same rationale.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over patent application US 2023/0088241 A1 Streichert et al., hereinafter “Streichert” in view of non-patent literature Ma et al. (“Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries”, hereinafter “Ma”) further in view of patent application US 2008/0314122 A1 Hunaidi et al.
Claim 4
Streichert in view of Ma teaches all the limitations of claim 3, Streichert further teaches:
wherein the pressure data used for detecting the at least one compressed air leak is measured by at least one… pressure sensor… deployed in the compressed air system. (Para 107, “ In a step 204 , sensor data of the pneumatic system 100 is continuously acquired by means of a set of sensors. The set of sensors includes at least the sensors m1 and m2 described above. In addition, several of these or other types of sensors (for example, flow sensors, pressure sensors, microphones, structure-borne sound pickups) may collect sensor data.”)
Streichert in view of Ma does not explicitly disclose:
“…at least one selected closest pressure sensor which is located at a shortest distance from the at least one flow sensor among a plurality of pressure sensors…”
However, Hunaidi teaches:
“…at least one selected closest pressure sensor which is located at a shortest distance from the at least one flow sensor among a plurality of pressure sensors…” (Figure 2, EN: this denotes a flow meter 32 and multiple pressure gauges 34, 36, and 38. Among the plurality of pressure gauges, pressure gauge 36 corresponds to the “selected closest pressure sensor”. See Para 44, “Pressure sensor 36 senses pressure at the flow meter 32.” Therefore, the reference explicitly teaches the pressure being measured is from the closest flow meter.)
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Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the leak detection using machine learning system and the identified shutdown events of Streichert in view of Ma with the selection of pressure data acquired from the pressure sensor that is closest to the flow sensor of Hunaidi. The motivation for doing so would be to ensure the pressure data correlates accurately with the flow data, as Hunaidi teaches that pressure is not constant along the length of the pipe but rather varies based on location. “Discrepancy in predicted leak location is believed to be due to variation of acoustic velocity along the pipe… negative pressure in the pipe… is believed to be highest near the free end… and becomes less severe in the direction of the pumping station” (Hunaidi, Para 64)
Claim 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over patent application US 2023/0088241 A1 Streichert et al., hereinafter “Streichert” in view of non-patent literature Ma et al. (“Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries”, hereinafter “Ma”) further in view of non-patent literature Ebin John Daniel (“ANALYZING COMPRESSED AIR DEMAND TRENDS TO DEVELOP A METHOD TO CALCULATE LEAKS IN A COMPRESSED AIR LINE USING TIME SERIES PRESSURE MEASUREMENTS”, hereinafter “Daniel”)
Claim 9
Streichert in view of Ma teaches all the limitations of claim 1, Daniel further teaches:
further comprising estimating an amount of leaked compressed air by: (Page 26, “By utilizing the pressure difference measured across the piping… a continuous leak measurement algorithm can be developed to account for the volumetric loss of air, Qleaks, through said leaks.”) receiving a plurality of air flow rates measured in the compressed air system for a plurality of corresponding pressure levels,”) (Page 30, “The manufacturer of the ultrasound gun will provide a data table showing the instantaneous volumetric flow rate of the compressed air in relation to the decibel readings. The ultrasound gun used for the purposes of this paper is provided in Table 2.2 [40]. The inner cells of Table 2.2 are the instantaneous volumetric flow rates in m3/min determined by the corresponding decibel reading from the first column, to the corresponding pressure from the first row.”) computing a flow to pressure relation based on the plurality of measured air flow rates and the plurality of corresponding pressure levels, the flow to pressure relation is indicative of the at least one compressed air leak which induces a pressure dependent air flow rate, and (Page 50, “As discussed in section 2.4, the volumetric flow rate of the air through the piping is dependent on the source pressure and end pressure, the relationship was found to be as shown in equation 2.18.”)
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inferring the amount of leaked compressed air based on the flow to pressure relation which is indicative of a pressure dependent air flow rate induced by the at least one air leak. (Page 53, “Figure 3.1, shows the instantaneous leak rates, qleaks, calculated at every second using equation 2.18 and compared to the total air rate consumed”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the leak detection using machine learning system and the identified shutdown events of Streichert in view of Ma with estimation of leaked air by computing a flow to pressure relation of Daniel. The motivation for doing so would be obtain a more accurate estimate of the air loss by accounting for the pressure dependent nature of the leaks, as Daniel teaches that a benefit of utilizing such a relationship is that “the variations in the volumetric flow rate of air lost through the orifices will be accounted for which has usually been overlooked in traditional methods and provide a higher degree of accounting detail” (Daniel, page 23)
Claim 10
Streichert in view of Ma further in view of Daniel teaches all the limitations of claim 9, Streichert further teaches:
further comprising actively controlling the at least one air compressor to deliver compressed air to induce the plurality of pressure levels in the compressed air system. (Para 19, “In this case, the throttle valve and/or another suitable controller can be used to regulate the supply pressure of the supply lines, resulting in different travel times and/or reaction times with otherwise constant components.” – EN: this denotes using a “controller” to “regulate the supply pressure” to create different operating states (pressure levels). Under BRI, regulating supply pressure via a controller to induce specific levels maps to “actively controlling” the air supply/compressor system to induce those levels.)
Claim 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over patent application US 2023/0088241 A1 Streichert et al., hereinafter “Streichert” in view of non-patent literature Ma et al. (“Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries”, hereinafter “Ma”) further in view of non-patent literature Ravichandran et al. (“Ensemble-based machine learning approach for improved leak detection in water mains”, hereinafter “Ravichandran”)
Claim 14
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
“wherein the at least one trained ML model is trained to identify at least one of the plurality of compressed air leak patterns using a plurality of training samples comprising pressure data and flow rate data” (Para 6-10, “Provide a trained normal state model as a one-class model that has been trained in a training phase with normal state data representing a normal state of the pneumatic system; Continuous acquisition of sensor data of the pneumatic system by means of a set of sensors; Extract features from the acquired sensor data; Determine deviations of extracted features from learned features of the normal state model using a distance metric…; Calculate an anomaly score from the determined deviations and Output of the calculated anomaly score.” Para 49, “ the normal state data may comprise pressure signals and/or flow signals” Para 112, “features are extracted by means of an extractor 304 , i.e. quantities are derived that provide information about the functioning of the pneumatic system… include the actuator features “reaction time extension”, “travel time extension”, “reaction time retraction”, and “travel time retraction””. – EN: this denotes the trained ML model is applied to sensor data, which includes “pressure signals and/or flow signals”, to perform “leakage detection” (para 5). The model operates by extracting specific features from this data, such as “reaction time extension” and “travel time extension”, and then “determining deviations” of these extracted features from learned norms. These determined deviations constitute the “leak patterns” because they are distinct data signatures used to identify the presence of a leak.) “measured in at least one (Para 17, “For example, a pneumatic system can be a single pneumatic actuator or a plurality of actuators. The plurality of actuators can be operated independently of each other. A plurality of actuators may be arranged on a valve island that can control multiple valves at once.” Para 98, “FIG. 1 shows an overview illustration of a pneumatic system 100 with a condition monitoring unit 114” – Examiner’s note: this denotes a pneumatic system that is equivalent to the broadest reasonable interpretation of “Compressed air system” because the pneumatic system comprises actuators that function as the required “compressed air clients” consuming air delivered via values and supply lines. not having compressed air leaks (Para 14, “The normal condition model describes the normal condition of the pneumatic system. In the normal state, there are no anomalies, leaks or other faults, and the production process is functioning perfectly.”)
Streichert in view of Ma does not explicitly disclose:
“ML model is trained (…) in at least one another (…) system having at least one (…) leak”-- (Examiner’s note: Streichert does not teach the ML model being trained on data from other systems having leaks.)
However, Ravichandran teaches:
“ML model is trained (…) in at least one another (…) system (Abstract, “The training and validation data sets have been collected over several months from multiple cities across North America.”) having at least one (…) leak” (Page 316 – Case Study, “The operational data set includes 13,861 negative samples and 54 leak cases observed over 3 months.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the leak detection using machine learning system and the identified shutdown events of Streichert in view of Ma to train the ML model by using leak data from other systems as disclosed by Ravichandran. The motivation for doing so would be in order “For adequate performance, the training data set must… ensure sufficient diversity in the data set.” (Page 310, Ravichandran).
Claim 15
Streichert in view of Ma teaches all the limitations of claim 1, Streichert further teaches:
“wherein the at least one trained ML model is trained to identify at least one of the plurality of compressed air leak patterns using a plurality of training samples comprising pressure data and flow rate data measured in the at least one compressed air system…” (EN: This limitation is similar to claim 14 with the difference being “in the… system”. Streichert teaches this in Para 19, “Experiments have shown that the characteristics of the normal state can depend strongly on the settings of a throttle between a valve and an actuator. A normal state model can therefore be specific to an actuator.” – By stating that the model must be “specific to” the throttle of an actuator, this denotes that the training data must originate from that specific system rather than a different one.) “…while not having compressed air leaks.” (Para 14, “The normal condition model describes the normal condition of the pneumatic system. In the normal state, there are no anomalies, leaks or other faults, and the production process is functioning perfectly.”)
Streichert in view of Ma does not explicitly disclose:
“ML model is trained … in … system while having at least one … leak”
However, Ravichandran teaches:
“ML model is trained … in … system while having at least one … leak” (Page 316 – Case Study, “The operational data set includes 13,861 negative samples and 54 leak cases observed over 3 months.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the leak detection using machine learning system and the identified shutdown events of Streichert in view of Ma to train the model with leak data as disclosed by Ravichandran. The motivation for doing so would be in order “For adequate performance, the training data set must… ensure sufficient diversity in the data set.” (Page 310, Ravichandran).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered
pertinent to applicants’ disclosure.
For example, patent application US 11041779 B1 Bagwell et al. discloses a machine learning algorithm used to analyze gas flow data which is used to determine when and where a gas leak maybe be present.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAYMUR RAHMAN ALI whose telephone number is (571)272-0007. The examiner can normally be reached Mon-Fri. 9:30 am - 6:30 pm.
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/NAYMUR RAHMAN ALI/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123