DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 19 May 2025 has been entered.
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.
Claims 1,3-5, 8-11 and 13-15 are 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.
With respect to Claim 1, line 10 recites “a data processing device”. It is not clear if this is the same “a data processing device” from lines 8-9 or a second device. For purposes of examination, they will be treated as the same device. Remaining claims rejected due to dependency.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-5 and 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Banerjee et al. (U.S. Publication No. 2019/015439, hereinafter Banerjee) in view of Lane et al. (U.S. Publication No. 2014/0061962, hereinafter Lane).
With respect to Claim 1, Banerjee discloses a method [see fig 5 unless otherwise noted] for leakage detection on a pipe [420] carrying a flow of a medium, the method comprising
ascertaing a value for a change in the flow rate [528] of the medium, for a pressure change [524] in the medium and a temperature value change [512-1, 512-2] at each of a plurality of measurement points [the aforementioned sensors take measurements at different points along 420] on a measurement section of the pipe carrying a flow, wherein a length of the measurement section is such that at least some of the plurality of measurement points are outside a same measuring housing [para 27 and 28 indicate that the system is replicated multiple building, each would have a leak detector, each in its own housing] and wherein the ascertained values are recorded and statistically evaluated by a data processing device [536; para 47],
identifying, by the data processing device, a pattern in a group of values that formed from the ascertained values [para 51] and
determining, by the data processing device, a likelihood of a presence of a leak in the measurement section of the pipe carrying a flow based on the identified pattern [Para 51 and 60], by:
applying a classification algorithm and a pattern analysis algorithm to the identified pattern [para 60, an algorithm determines a combined score an compares that to a threshold to classify the pattern as indicating a leak], and
applying a learning algorithm that comprises the classification algorithm and the pattern analysis algorithm to the group of values [para 47], wherein output of the learning algorithm is indicative of the likelihood of the presence of a leak [para 60].
Banerjee does not specifically disclose that the learning algorithm is trained using stored and/or simulated values before being applied to the ascertained values and/or the group of values, wherein the stored and/or simulated values are associated with an actual and/or a simulated presence of a leak on an object carrying a flow of a medium, machine learning is typically trained on datasets that represent the values being analyzed. Various groups of values for pressure, temperature and flow changes get fed into the algorithm and either indicate a leak or don’t indicate a leak. This changes the weights in the algorithms such that when a novel data set is input, the correct indication of leakage is inferred base on the prior trained data. Learning algorithms are commonly trained on stored and simulated values.
Lane discloses using a learning algorithm to determine a leak and uses known and simulated values. See para 100.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to train Banerjee’s learning algorithm with stored and simulated values associated with actual or simulated leaks so that the algorithm can accurately determine leaks.
With respect to Claim 3, Banerjee discloses that the identified pattern is stored as a dataset in a database and/or is assigned to a dataset stored in a database. See para 47 which uses cloud analyzer 108 to identify the pattern via machine learning. Clouds store data in databases and machine learning requires datasets.
With respect to Claim 4, Banerjee discloses that the identified pattern is compared with one or more stored patterns [the training data inherent to machine learning is taken to be the stored pattern].
With respect to Claim 5, Banerjee discloses that a characteristic pattern serves as a criterion for the presence of a leak in the measurement section of the object carrying a flow. Para 47, machine learning to determine leaks.
With respect to Claim 9, Banerjee discloses that the change in the flow rate is measured by means of an acoustic, preferably ultrasound-based, method. See para 50.
With respect to Claim 10, Banerjee discloses that the data from an external source, in particular concerning the weather in the surroundings of the measurement section and/or of a measurement point, are processed by the data processing device, in particular linked with the group of values and/or added to the group of values. See para 71.
With respect to Claim 11, Banerjee discloses that the ascertained values from different measurement points are transmitted to a central data processing device [108], preferably wirelessly [para 28].
With respect to Claim 12, Banerjee discloses a non-transitory computer program product comprising instructions for determining a likelihood of a presence of a leak on a pipe [420] carrying a flow of a medium, wherein instructions are for causing a data processing device [536] to:
ascertain a value for change in a flow rate [528] of a medium, for a pressure change [524] in the medium and for a temperature value [512-1, 512-2] change at each of a plurality of measurement points on a measuring section of the pipe, wherein at a length of the measurement section is such that at least some of the plurality of measuring points are outside of a same measuring housing [para 27 and 28 indicate that the system is replicated multiple building, each would have a leak detector, each in its own housing],
identify a pattern in a group of values formed from the ascertained values; determine a likelihood of a presence of a leak in the measurement section of the pipe based on the identified pattern [para 47 for machine learning to determine leak from pressure, flow and temperature data]
applying a classification algorithm and a pattern analysis algorithm to the identified pattern [para 60, an algorithm determines a combined score an compares that to a threshold to classify the pattern as indicating a leak], and
applying a learning algorithm that comprises the classification algorithm and the pattern analysis algorithm to the group of values [para 47], wherein output of the learning algorithm is indicative of the likelihood of the presence of a leak [para 60].
Banerjee does not specifically disclose that the learning algorithm is trained using stored and/or simulated values before being applied to the ascertained values and/or the group of values, wherein the stored and/or simulated values are associated with an actual and/or a simulated presence of a leak on an object carrying a flow of a medium, machine learning is typically trained on datasets that represent the values being analyzed. Various groups of values for pressure, temperature and flow changes get fed into the algorithm and either indicate a leak or don’t indicate a leak. This changes the weights in the algorithms such that when a novel data set is input, the correct indication of leakage is inferred base on the prior trained data. Learning algorithms are commonly trained on stored and simulated values.
Lane discloses using a learning algorithm to determine a leak and uses known and simulated values. See para 100.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to train Banerjee’s learning algorithm with stored and simulated values associated with actual or simulated leaks so that the algorithm can accurately determine leaks.
With respect to Claim 13, Banerjee that the group of values includes values for a change in the flow rate [528] of the medium, for a pressure change [524] in the medium and a temperature value change [512-1, 512-2] at each of a plurality of measurement points [the aforementioned sensors take measurements at different points along 420].
With respect to Claim 14, Banerjee discloses that the data from the external source includes data relating to geological composition of the ground surrounding the measurement section. Para 25 indicates accounting for the expected ground temperature, which varies with location and season. The geological composition necessarily affects the ground temperature, so this external temperature can be considered data related to geological composition.
With respect to Claim 15, Banerjee discloses that wherein the length of the measurement section is such that at least some of the plurality of measurement points are in different weather zones. Para 24 and 71 indicate accounting for weather changes. Para 28 indicates monitoring for leaks on pipes at different buildings. Different locations are known to have different weather patterns.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Banerjee and Lane in further view of Klicpera (U.S. Publication No 2016/0163177, hereinafter Klicpera).
With respect to Claim 8, Banerjee discloses that the value for the change in the flow rate of the medium is ascertained noninvasively. See ultrasonic flow sensor para 50.
Banerjee does not disclose that the value for the pressure change in the medium and the temperature value change are ascertained noninvasively.
Klicpera discloses a similar leak detector wherein the value for the change in flow rate of the medium, the value for the pressure change in the medium and the temperature value change are ascertained noninvasively. See para 46 for flow and temp and para 226 for pressure.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Banerjee to use noninvasive pressure and temperature sensors for the benefit of not having to drill a hole in the pipe, which could weaken the structural integrity.
Response to Arguments
Applicant's arguments filed 19 May 2025 have been fully considered but they are not persuasive.
The arguments pertaining solely to newly added limitations are addressed in the rejection above.
On page 7, the applicant argues that using a learning algorithm reduces error rate. Banerjee specifically uses learning algorithms, see pare 47.
On page 7, the applicant argues that Banerjee’s fig 4 shows 120 only measuring values at a single point, whereas the claims over pipelines that cover long distances. Figure 5 shows a more detailed view of 120. Sensors 528, 512-2, 524 and 512-1 are clearly at different positions along pipe 420.
On page 8, the applicant argues that Banerjee does now show or suggest identifying a pattern in a group of values…which are measured at measurement points not within the same measurement housing…by applying a classification algorithm and a pattern analysis algorithm”. None of Banerjee’s sensors have a housing, but instead are all individually placed inside the pipe 420. Paragraph 28 specifically mentions using patterns to find leaks. In paragraph 60, the patterns of temperature and pressure are used to classify the system as having a leak or not having a leak.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX T DEVITO whose telephone number is (571)270-7551. The examiner can normally be reached 12pm- 8 pm EST M-S.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Breene can be reached on 571-272-4107. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEX T DEVITO/Examiner, Art Unit 2855 /JOHN E BREENE/Supervisory Patent Examiner, Art Unit 2855