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 . The rejections and rejections from the Office Action of 11/3/2025 are hereby withdrawn. New grounds for rejection are presented below.
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.
Claim(s) 1-6, 9-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Muralidhar et al. (US 20190285504 A1)[hereinafter “Muralidhar”]; Hirst et al., Locating and quantifying gas emission sources using remotely obtained concentration data, Atmospheric Environment 74, 2013 [hereinafter “Hirst”]; Bennetts et al., Creating true gas concentration maps in presence of multiple heterogeneous gas sources, IEEE Sensors, 2012 [hereinafter “Bennetts”]; and Dong et al., The Gas Leak Detection Based on a Wireless Monitoring System, IEEE, 2019 [hereinafter “Dong”].
Regarding Claims 1 and 11, Muralidhar discloses a gas leak detection system/method [Paragraph [0045] – “Server 604 receives the sensor data from the motes 402/404 via the base station 602. The server 604 then identifies, based on the data, the gas leak source location.”], comprising:
non-transitory computer readable storage media [Paragraph [0082]] that stores:
measured gas concentrations output by a plurality of the sensor units deployed at a site; and wind speeds and wind directions at the site [See Fig. 8 and Paragraph [0047] – “FIG. 8 is a diagram illustrating time synchronized gas sensor and wind sensor data over a sample 4 hour interval. As described above, the data was collected from seven (CH.sub.4) gas sensors (Sensor 1-Sensor 7) arranged in a perimeter around a CH.sub.4 gas source, and two wind sensors—one inside and one outside the perimeter of gas sensors. As shown in FIG. 8, the sensor peaks (i.e., indicating that the sensor detects the gas) occur at random meaning that, as described above, wind angle is stochastic in short timescale and lengthscale.”Paragraph [0038] – “According to an exemplary embodiment, each of the motes 404 includes at least one wind direction and wind speed sensor.”];
a processing unit [Paragraph [0082]] that:
identifies distances and bearings between each of the sensor units [See Fig. 9 and Paragraph [0051] – “As provided above, the motes with gas sensors form a perimeter around a gas leak source (labeled “source” in FIG. 9). Thus, the points 902 used in FIG. 9 are not necessarily meant to indicate that there is a gas sensor present at each point 902, but merely to orient the location of the sensors (see, e.g., “Sensor 1” and “Sensor 2”) on the (x,y) grid. However, it is within the scope of the present techniques to include a sensor at each of the points 902, if so desired.”] and each of a plurality of locations at the site [See Fig. 10 and Paragraph [0056] – “Here, however, there are three lines 1002, 1004 and 1006 corresponding to the three sensors, i.e., Sensor 1, Sensor 2 and Sensor 3, respectively, registering peaks. The intersection point between any two of these lines is a possible location for the leak. For instance, the intersection of lines 1002 and 1004 indicates a location represented by a square, the intersection of lines 1002 and 1006 indicates a location represented by a diamond, and the intersection of lines 1004 and 1006 indicates a location represented by a triangle. The actual location of the leak (“source”) lies in between these points.”See Figs. 15 and 16 and Paragraphs [0065]-[0066], describing how intersection locations are fit to equipment locations.].
Muralidhar fails to disclose that the processing unit:
for each of the plurality of sensor units, calculates a background gas concentration based on the wind direction at the site and adjusts the measured gas concentration to calculate a background-adjusted gas concentration;
estimates surface layer air mixing conditions at the site; and
estimates gas emission rates at each of the plurality of locations at the site by providing the background-adjusted gas concentrations at each of the sensor units to a gas transport model that estimates gas emission rates at each of the plurality of locations at the site based on the wind speeds and wind directions at the site, the surface layer air mixing conditions at the site, and the background-adjusted gas concentrations at each of the sensor units.
However, Hirst discloses a method for locating gas emission sources [See Title] which:
calculates a background gas concentration based on the wind direction at a site [Page 154 – “In designing a Gauss-Markov random field to model the background field, we seek to capture two effects. First, the background should be smooth. Second, since the background concentration travels with wind, it should be smoother along the direction of the wind. To model these two effects, we introduce two different types of edges in the graphical model. The first are edges connecting adjacent measurements vertices. These ensure overall smoothness of the background with respect to time and space. The second type of edge concerns the wind. At each measurement point, we consider the line along the wind direction from this point. We find the next measurement point that crosses this line, and connect an edge in the graph between these two points. Thus the second point is as close as possible to directly in line with the wind from the initial point. “Wind-linked” points along the trajectory for the landfill application is given in Fig. 15.”] and adjusts the measured gas concentration to calculate a background-adjusted gas concentration [See Figure 8(a) – “Fig. 8. Plume concentrations for the synthetic problem: (a) Detail of central region of Fig. 6 showing simulated concentrations along flight path as black dots, size proportional to concentration. Blue dots are also used to indicate source locations. Background concentration is 1.8 ppm.”];
estimates air mixing conditions at the site; and uses a gas transport model to estimate gas emissions rates at each of a plurality of locations at the site based on the wind speeds and wind directions at the site, the air mixing conditions at the site, and the background-adjusted gas concentrations at each of the sensor units [Section 3.1 (gas transport model used to estimate emissions rates based on the wind field with consideration of background concentration):
PNG
media_image1.png
502
716
media_image1.png
Greyscale
Section 3.1.1 (The wind field is described by wind vector U, which represents both speed and direction. The gas transport model is a Gaussian plume model.):
PNG
media_image2.png
206
718
media_image2.png
Greyscale
Section 3.2 (The estimation and use of air mixing conditions at the site in calculating estimated gas emission rates) – “Initial parameter estimation is performed assuming a spatial grid of potential source locations. Subsequent grid-free Bayesian mixture modelling (see Section 3.3) uses estimates so found as a starting solution. Given measured concentrations y on trajectory x and associated wind field data
PNG
media_image3.png
30
200
media_image3.png
Greyscale
, initial point estimates for source emission rates s and background parameters b are obtained[.]”Section 3.3 (The estimation and use of air mixing conditions at the site in calculating estimated gas emission rates) – “Full parameter estimation is performed using a mixture modelling approach, using estimates from initial optimisation (see Section 3.2) as starting solution. We assume that each of m sources can be represented as a two-dimensional Gaussian kernel located at zj with half width wj (corresponding to the standard deviation of the Gaussian) and source emission rate sj. Using reversible jump Markov chain Monte Carlo (RJMCMC) simulation (Green, 1995), we treat m as a random variable, and estimate the joint distribution of m and all other model parameters. We can also make inferences about apparent bias and/or uncertainty in wind field parameters and measurement error. Bias-correction of wind direction proves to be important in some applications.”].
It would have been obvious to use the method for locating gas emission sources of Hirst in the context of a ground-based system like that of Muralidhar because doing so is an effective manner of determining emission rates. It would have been obvious to calculate surface layer air mixing conditions as the air mixing conditions because the sensors of Muralidhar are ground-based [See Figs. 4 and 7].
Muralidhar, as modified, would disclose that the processing unit, for each of the plurality of locations at the site: calculates two or more estimated gas emission rates at the location, each based on the background-adjusted gas concentration at a different one of the plurality of sensor units [Application of the background-adjusted (i.e., the “smooth” background) gas emission rates of Hirst to the gas measurements of Muralidhar over time being an analysis of two or more rates through the measurement period (see Fig. 8 of Muralidhar).].
Muralidhar also fails to disclose the processing unit, for each of the plurality of locations at the site, calculates a variance between the two or more estimated gas emission rates at the location; and
calculates a probability that a gas leak is occurring at the location at the site, wherein the probability that a gas leak is occurring at the location is inversely proportional to the variance between the two or more estimated gas emission rates at the location.
However, Bennetts discloses the determination of gas leaks at a plurality of locations relative to measured concentrations by determination of gas presence probabilities based on measurement variances [See Fig. 3 – “Fig. 3. Gas distribution maps for the experiment where the gas sources were separated by 0:5 m. The classification map is given in the form of a maximum a posteriori plot (a). The mean map is shown in (b) and the variance map is shown in (c). In all Figures, the black dashed lines denote the robot’s exploration path. The likelihoods, concentrations and variances of ethanol, 2-propanol and fresh air are shaded in green, red and blue respectively. The actual locations of the ethanol and 2-propanol sources are denoted by green and red circles respectively.”Page 2, first column – “The variance distribution map v(k) is computed from variance contributions integrated in a temporary map V(k)”
Page 2, second column – “In addition to the mean distribution and variance maps, MC Kernel DM+V returns a classification map for each of the detected compounds. These classification maps can be interpreted as the probability of detecting a compound l at a given query location in the explored area.”]. It would have been obvious to take such an approach because doing so would have been an effective manner of identifying the source of gas leaks.
Bennetts fails to disclose that the probability that a gas leak is occurring at each location is inversely proportional to the variance between the two or more estimated gas emission rates at the location.
However, Dong teaches that a virtual gas sensor location corresponding to low measurement variance can be considered to be closest to a leak source [Page 6246 – “The several sensors closest to the leak source should make up a set to participate in the detection of gas leak such as sensor 1–3. However, the location of the source leak is uncertain. The general rule is that the closer the distance to the leak source, the greater the gas concentration. So, the sensor with the largest mean concentration over a period of time was assumed to be the virtual leak source (VLS). In this work, the concentration data collected by the ith sensor at time t is expressed as xi(t) where i ∈ {1, 2, . . . , n}, n is the total number of sensors that detected the third level data. The distance diVLS (t) between the VLS sensor and sensor i can be written as <Eq (4)>. The smaller the diVLS (t), the greater the distance. In addition, the distance diVLS (t) is also fluctuant since the concentration is fluctuant. At a moment, a sensor is close to the VLS sensor, and it may be far away from the VLS sensor at the next moment. So when the mean of diVLS (t) is large and its variance is small, the sensor i is considered close to the leak source in the period of time T.” Note the sensor concentration data is used in the determination of diVLS (t) and its respective variance per Equation (4).]. It would have been obvious to have the probability of a leak inversely tied to variance because Dong teaches that can correlate to the presence of a leak.
The combination would further disclose a graphical user interface [Figs. 15/16 of Muralidhar] that outputs a map or image of the site that includes each of the plurality of locations: and information indicative of the estimated gas emission rates at each respective location over each portion of the map or image depicting the respective location [Paragraph [0066] of Muralidhar – “See, for example, heat map 1602 shown in FIG. 16. A heat map is visual depiction of data where a diagram or ‘map’ represents the data values (in this case density of intersection points) as different colors and/or intensities.”].
Regarding Claims 2 and 12, Hirst discloses that the gas transport model comprises a Gaussian plume model [Section 3.1.1, “Gaussian plume model”].
Regarding Claims 3 and 13, Muralidhar discloses that the processing unit identifies the plurality of locations by:
receiving the map or image of the site [Paragraph [0066] – “The same exemplary well pad site 1502 is used in the example in FIG. 16, where the equipment location boundaries have been highlighted.”]; and
identifying a plurality of two- or three-dimensional grid cells at the site [See Fig. 16].
Regarding Claims 4 and 14, Muralidhar discloses that the processing unit further identifies the plurality of locations by identifying the grid cells that include a probable emissions source [See Fig. 16, “Leak location” as seen in the gridded heat map having grid cells and equipment boundaries.Paragraph [0066] – “Heat map 1602 shows the intersection points 1504 (already spatially filtered as described in conjunction with the description of FIG. 15 above) in space with the highest concentration of intersection points 1504 at the ‘leak location.’”].
Regarding Claims 5 and 15, Muralidhar discloses that the processing unit receives an image of the site [Fig. 15. Paragraph [0066] – “The same exemplary well pad site 1502 is used in the example in FIG. 16, where the equipment location boundaries have been highlighted.”] and the grid cells that include a probable emissions source by analyzing the image of the site using an object recognition algorithm [Paragraph [0065] – “Boundaries are placed around the locations (labeled “Equipment locations”) in the pad well site 1502 containing equipment. Intersection points 1504 are determined using the above-described techniques. However, only those intersection points 1504 within the equipment location boundaries are considered (i.e., all other outlying intersection points 1504 are excluded) thereby increasing the accuracy of the leak source location prediction. By way of example only, the remaining intersection points 1504—those within the equipment location boundaries—can then be subject to cluster analysis as described above.”].
Regarding Claims 6 and 16, the combination would disclose that the gas transport model calculates the estimated gas emission rate further based on the height of each air sample collected by the sensor units and the height of each of the probable emission sources [See Fig. 5 of Hirst – “Fig. 5. Illustration of plume model parameters. Red line: source height H. Magenta line: downwind distance dR of measurement location relative to the source. Cyan line: horizontal offset dH. Green line: vertical offset dV.”].
Regarding Claims 9 and 19, the combination fails to disclose that the processing unit calculates the variance between the two or more estimated gas emission rates at each of the plurality of locations by:
estimating the gas emission rate using one or more measured gas concentrations output by a first sensor unit;
estimating the gas emission rate using one or more measured gas concentrations output by a second sensor unit; and
determining the variance between the gas emission rates estimated using the first sensor unit and the second sensor unit.
However, Bennetts discloses the determination of gas leaks at a plurality of locations relative to measured concentrations by determination of gas presence probabilities based on measurement variances [See Fig. 3 – “Fig. 3. Gas distribution maps for the experiment where the gas sources were separated by 0:5 m. The classification map is given in the form of a maximum a posteriori plot (a). The mean map is shown in (b) and the variance map is shown in (c). In all Figures, the black dashed lines denote the robot’s exploration path. The likelihoods, concentrations and variances of ethanol, 2-propanol and fresh air are shaded in green, red and blue respectively. The actual locations of the ethanol and 2-propanol sources are denoted by green and red circles respectively.”Page 2, first column – “The variance distribution map v(k) is computed from variance contributions integrated in a temporary map V(k)”
Page 2, second column – “In addition to the mean distribution and variance maps, MC Kernel DM+V returns a classification map for each of the detected compounds. These classification maps can be interpreted as the probability of detecting a compound l at a given query location in the explored area.”]. It would have been obvious to take such an approach because doing so would have been an effective manner of identifying the source of gas leaks.
Regarding Claims 10 and 20, the combination would disclose displaying information indicative of the calculated probability that the gas leak is occurring at respective locations over each portion of the map or image depicting the respective location [Applying the teachings of Bennetts to Fig. 16 of Muralidhar.See Fig. 3 of Bennetts – “Fig. 3. Gas distribution maps for the experiment where the gas sources were separated by 0:5 m. The classification map is given in the form of a maximum a posteriori plot (a). The mean map is shown in (b) and the variance map is shown in (c). In all Figures, the black dashed lines denote the robot’s exploration path. The likelihoods, concentrations and variances of ethanol, 2-propanol and fresh air are shaded in green, red and blue respectively. The actual locations of the ethanol and 2-propanol sources are denoted by green and red circles respectively.”].
Response to Arguments
Applicant argues:
PNG
media_image4.png
230
857
media_image4.png
Greyscale
PNG
media_image5.png
66
862
media_image5.png
Greyscale
Examiner’s Response:
The Examiner agrees and the corresponding rejections are hereby withdrawn.
Applicant argues:
PNG
media_image6.png
109
849
media_image6.png
Greyscale
Examiner’s Response:
The Examiner respectfully disagrees. Muralidhar discloses a set of geographically dispersed sensors [See Fig. 8 and Paragraph [0047] – “FIG. 8 is a diagram illustrating time synchronized gas sensor and wind sensor data over a sample 4 hour interval. As described above, the data was collected from seven (CH.sub.4) gas sensors (Sensor 1-Sensor 7) arranged in a perimeter around a CH.sub.4 gas source, and two wind sensors—one inside and one outside the perimeter of gas sensors. As shown in FIG. 8, the sensor peaks (i.e., indicating that the sensor detects the gas) occur at random meaning that, as described above, wind angle is stochastic in short timescale and lengthscale.”Paragraph [0038] – “According to an exemplary embodiment, each of the motes 404 includes at least one wind direction and wind speed sensor.”].
Applicant argues:
PNG
media_image7.png
314
860
media_image7.png
Greyscale
Examiner’s Response:
The Examiner agrees that Hirst discloses measurements collected by an aircraft. However, it would have been obvious to use the method for locating gas emission sources of Hirst in the context of a ground-based system like that of Muralidhar because doing so is an effective manner of determining emission rates and it would have been obvious to calculate surface layer air mixing conditions as the air mixing conditions because the sensors of Muralidhar are ground-based [See Figs. 4 and 7]. The Examiner respectfully disagrees with the assertion that the technique of Hirst is “completely different” from that recited, see the grounds for rejection in light of the pertinent claim limitations above.
Applicant argues:
PNG
media_image8.png
346
864
media_image8.png
Greyscale
Examiner’s Response:
Bennetts is not cited to with regards to the “inversely proportional” language; Dong is cited with regards to that limitation. The instant claims do not recite a variance comparison that is “a comparison of emission rates for a single location estimated independently using sensor data from multiple disparate sensors.”
Applicant argues:
PNG
media_image9.png
215
858
media_image9.png
Greyscale
Examiner’s Response:
Applicant’s argument is not convincing because this is a different variance than the one cited to in Dong.
Applicant argues:
PNG
media_image10.png
57
857
media_image10.png
Greyscale
Examiner’s Response:
Dong is not relied on for disclosing a distance estimate.
Applicant argues:
PNG
media_image11.png
545
860
media_image11.png
Greyscale
Examiner’s Response:
The Examiner respectfully disagrees. It would have been obvious to use the method for locating gas emission sources of Hirst in the context of a ground-based system like that of Muralidhar because doing so is an effective manner of determining emission rates. It would have been obvious to calculate surface layer air mixing conditions as the air mixing conditions because the sensors of Muralidhar are ground-based [See Figs. 4 and 7]. The iterative method of optimizing background gas concentration and adjusted gas concentration reads on the order of steps recited in Claim 1. There is no evidence the proposed combination would not be enabled.
Applicant argues:
PNG
media_image12.png
218
855
media_image12.png
Greyscale
Examiner’s Response:
The Examiner respectfully disagrees. Dong is cited merely as disclosing the “inversely proportional” relationship between variance and emissions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Allen et al., Improving pollutant source characterization by better estimating wind direction with a genetic algorithm, Atmospheric Environment 41, 2007
Bennetts et al., Robot Assisted Gas Tomography, IEEE ICRA, 2014
Haupt et al., Validation of a Receptor–Dispersion Model Coupled with a Genetic Algorithm Using Synthetic Data, American Meteorological Society, 2006
Long et al., Assessing sensitivity of source term estimation, Atmospheric Environment 44, 2010
US 20200232959 A1 – APPARATUS AND METHODS FOR REDUCING FUGITIVE GAS EMISSIONS AT OIL FACILITIES
US 20180321208 A1 – DETERMINING THE NET EMISSIONS OF AIR POLLUTANTS
US 20140032129 A1 – Methods For Gas Leak Detection And Localization In Populated Areas Using Multi-point Analysis
US 20040215402 A1 – Measuring And Analyzing Multi-dimensional Sensory Information For Identification Purposes
US 20220366108 A1 – Determining Gas Leak Flow Rate In A Wellbore Environment
US 10948471 B1 – Leak Detection Event Aggregation And Ranking Systems And Methods
US 10386258 B1 – Systems And Methods For Detecting Changes In Emission Rates Of Gas Leaks In Ensembles
US 10031040 B1 – Method And System For Analyzing Gas Leak Based On Machine Learning
US 10962437 B1 – Aggregate Leak Indicator Display Systems And Methods
US 20190340914 A1 (see Paragraph [0052]) – INFRARED IMAGING SYSTEMS AND METHODS FOR GAS LEAK DETECTION
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 9AM-5PM EST.
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, Arleen Vazquez can be reached at (571) 272-2619. 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.
/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
Prosecution Insights