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 .
Claim Status
Claims 1-20 are pending.
Priority
This application claims benefit of application no. 63/404,386, filed 09/07/2022. Therefore, the instant application has the effective filing date of 07 September 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/06/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings, submitted on 09/06/2023, are accepted by the examiner.
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 1-20 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-20 are directed to a statutory category (apparatus).
Therefore, in accordance with MPEP § 2106.03 all claims have patent eligible subject matter.
[Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claim 1: simulate, as a function of the scenario data, emission of the chemical at the site and transport and dispersion of a plume of the chemical across the site in space and time; (mental process, mathematical concept)
assess an extent to which concentrations of the chemical in the airborne plume encounter areas containing people, as a function of time of day and projected human activity and movement across the site; (mathematical concept, mental process)
determine, based on the simulation and assessment, recommended sampler device locations to obtain a target quality and target quantity of samples of the chemical for use in a sampling and analysis plan to determine an effect of the chemical on the human population at the site (mental process)
Claim 18: present an optimality score indicative of a divergence from recommended sampler device locations due to sampler device placement restriction (mental process)
Claim 20: wherein to present the recommended sampler device locations comprises to produce a sampling and analysis plan document (mental process)
Step 2A – Prong One Analysis:
Analysis techniques such as characterizing data, requiring nothing more than the human mind and pen/paper, read on observations, evaluations, judgments, and opinions, and fall under the mental process grouping of abstract ideas.
Analysis techniques such as simulating emission and concentrations as functions recite mathematical calculations, formulas, and/or relationships that fall under the mathematical concept grouping of abstract ideas.
Therefore, the claims appear to recite judicial exceptions.
[Eligibility Step 2A – Prong One: YES]
Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)). Additional elements are recited, categorized, and analyzed below.
Data Gathering/Outputting Elements:
Claim 1: obtain scenario data indicative of emission of an airborne chemical at a site with a human population, wherein the scenario data includes meteorological data for the site and a location of an emission source for the airborne chemical; present the recommended sampler device locations
Claim 2: wherein to obtain scenario data comprises to obtain sector data indicative of locations and shapes of sectors within the site, including population data indicative of the human population in each sector and activity data indicative of a level of human activity per hour in each sector.
Claim 3: wherein to obtain the sector data comprises to obtain data indicative of at least one sector in which sampler device placement is prohibited
Claim 4: wherein to obtain the scenario data comprises to obtain emission source data indicative of properties of the emission source, including the size of the emission source and temperature over time for the emission source
Claim 5: wherein to obtain the scenario data comprises to obtain data indicative of a location of the site and a perimeter of the site.
Claim 6: wherein to obtain the scenario data comprises to obtain data indicative of perimeters of one or more buildings at the site.
Claim 7: wherein to obtain the scenario data comprises to obtain data indicative of an identity and one or more properties of the chemical.
Claim 8: wherein to obtain the scenario data comprises to obtain data indicative of a profile of the chemical.
Claim 9: wherein to obtain the scenario data comprises to obtain data indicative of a sector of heightened interest to be prioritized for sampler device placement.
Claim 10: wherein to obtain the scenario data comprises to obtain data indicative of air sampling parameters for the chemical.
Claim 11: wherein to obtain the scenario data comprises to obtain data indicative of an analytical method, equipment, sample media, sampling duration, sample rate, sampling volume, and a quantity of sampler devices.
Claim 12: wherein to obtain the scenario data comprises to obtain data indicative of a predefined sampler device location that cannot be modified.
Claim 13: wherein to obtain the scenario data comprises to obtain data indicative of a field blank or duplicate samples.
Claim 14: wherein to obtain the meteorological data comprises to obtain data indicative of historical weather data for the site for multiple years and data indicative of hourly air temperature, wind speed, wind direction, and atmospheric stability measures.
Claim 15: wherein the circuitry is further configured to obtain simulation data indicative of parameters under which to simulate emission of the chemical at the site, wherein the simulation data includes data indicative of start and end dates for a sampling window, data indicative of a number of steps per hour, data indicative of a size of each cell in a grid to be overlaid onto the site, data indicative of a minimum distance between sampler devices, and data indicative of a sampling duration.
Claim 16: wherein the circuitry is further configured to: obtain data indicative of user defined candidate sampler device locations; and display differences between the recommended sampler devices locations and the candidate sampler device locations.
Claim 17: wherein the circuitry is further configured to present a recommended date and time at which to begin sample collection.
Claim 19: wherein the circuitry is further configured to display plume, activity, population, or exposure risk potential over time.
Step 2A – Prong Two Analysis:
The data gathering elements merely gather data necessary to enact the judicial exceptions upon and are thus classified as insignificant extra-solution activity. Such activities do not integrate the judicial exceptions into practical application per MPEP2106.05(g).
As such, the additional elements, when viewed separately and in the context of a whole claimed invention, do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
Receiving or transmitting data over a network and storing and retrieving information in memory is found well-understood, routine, and conventional per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, respectively. It is also important to note that selecting information, based on types of information and availability of information in a particular environment, such as airborne chemical monitoring for collection, analysis and display, is also classified as insignificant extra-solution activity and considered at step 2a prong two and step 2b (MPEP 2106.05 g).
As such, the additional elements are further found to lack inventive concept.
[Eligibility Step 2B: NO]
Therefore, claims 1-20 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
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 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808; 2022).
Eichenlaub et al. describes emissions detection system and methods.
Claim 1 is directed to circuitry configured to obtain scenario data indicative of emission of an airborne chemical at a site with a human population, wherein the scenario data includes meteorological data for the site and a location of an emission source for the airborne chemical.
Eichenlaub et al. teaches one or more methods of locating and quantifying emissions at a site [0013] based on obtaining certain parameters using one or more air quality monitors provided at the site including various sensors that are able to obtain atmospheric parameters like wind speed, wind direction, temperature, pressure, and humidity at the site [0013]; and a platform that integrates detector data, meteorological conditions, and cloud analytics to detect and quantify methane emissions for remote locations [0247].
Claim 1 is further directed to simulating, as a function of the scenario data, emission of the chemical at the site and transport and dispersion of a plume of the chemical across the site in space and time.
Eichenlaub et al. teaches an aspect of the system may use a reduced order model rather than a full dispersion advection transport model for the simulation of transport of the trace gas of interest; in particular, Gaussian Plume modeling may be used that uses a gaussian approximation of the plume geometry to approximate dispersion, where some approximation of the dispersion width can be obtained using Pasquill curves which may depend on the atmospheric stability class at the time of transport [0228]; and further, for each emission source, a simulated plume model is generated, based on a wind-direction [0015].
Eichenlaub et al. does not teach assessing an extent to which concentrations of the chemical in the airborne plume encounter areas containing people, as a function of time of day and projected human activity and movement across the site (claim 1)
Park et al. describes how individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored.
Park et al. teaches examining how personal or individual exposure levels may be different depending on whether human mobility and hourly variation of air pollution are considered, four types of personal exposure levels are estimated through (1) considering both human mobility and hourly pollution levels, in which all hourly ozone prediction maps and the simulated daily movement data are combined to calculate personal mobility-based exposure estimates (page 5, column 1); the ozone concentration in a specific activity location is extracted from the ozone map at the corresponding hour (page 5, column 1); and the total mobility-based exposure for an individual over the course of a day is calculated by summing up all exposure values (page 5, column 1).
Park et al. further teaches in future studies, air dispersion models that are less affected by the number of monitoring sites, and use emission, meteorological, and topographic data, which address the dispersion of air pollutants in the atmosphere, can be used to improve the accuracy of air pollution estimates (page 9, column 2).
Therefore Park et al. provides sufficient motivation to combine data regarding an assessment of the extent of which the concentration of airborne chemicals encounter areas containing people based on projected activity and movement as a function of time, with information regarding the dispersion, emission, and meteorological conditions of the chemical emission site. As such, it would be obvious for one of ordinary skill in the art to combine the methods of Eichenlaub et al. with the method of Park et al. in order to improve the accuracy of air pollution estimates, with a reasonable expectation of success and each element merely performing the same function as they do separately.
Eichenlaub et al. further teaches a location method, which may include providing a first air quality monitor comprising: a first sensor responsive to the target substance and a first location at which the first air quality monitor is located on the site [0018].
Eichenlaub et al. does not explicitly teach determining, based on the simulation and assessment, recommended sampler device locations to obtain a target quality and target quantity of samples of the chemical for use in a sampling and analysis plan to determine an effect of the chemical on the human population at the site; and presenting the recommended sampler device locations (claim 1).
Mano et al. describes an Information Theory Solution approach to the Air Pollution Sensor Location–Allocation Problem.
Mano et al. teaches the first step toward air pollution mitigation is monitoring and identifying its source (page 1, column 1); the deployment of a sensor array always involves a tradeoff between cost and performance; the performance of the network heavily depends on optimal deployment of the sensors (page 1, column 1); the latter is known as the location–allocation problem (page 1, column 1); here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions; and the sensors are then placed in those locations identified as the most informative (page 1, column 1).
Mano et al. teaches a in this work, the pollution dispersion model consisted of a Gaussian plume model to describe the dispersion of continuous emissions in steady-state conditions and a Gaussian puff model that simulated instantaneous emissions (page 2, column 1); and presenting the optimal deployments of 30 sensors, as dictated by Algorithm 1 for the two DMs, entropy and hotspot in Figure 5 (page 9, column 1), which show the locations identified by the entropy DM marked in blue, and the locations identified by the hotspot DM marked in red (page 9, column 1).
Therefore Eichenlaub et al. in view of Park et al. teach a reason to the combination of emission modelling via Gaussian plume modelling with human exposure modelling. Mano et al. teaches using Gaussian plume modelling as an integral step in identifying and presenting optimal sensor placement locations. As such, it would be obvious to one of ordinary skill in the art to combine the methods of Eichenlaub et al. in view of Park et al. with the method of Mano et al., with each element merely performing the same function as they do separately, in order to result in a predictable combination for airborne chemical pollutant monitoring.
Claim 2 is directed to wherein to obtain scenario data comprises to obtain sector data indicative of locations and shapes of sectors within the site, including population data indicative of the human population in each sector and activity data indicative of a level of human activity per hour in each sector.
Park et al. teaches in the report, census tracts within the Southern California Association of Governments’ area are aggregated into Regional Statistical Areas (RSAs) (see Table 1 and Fig. 3) (page 3, column 2); the report provides the percentage of work trips and non-work trips within each RSA and from one RSA to another (page 3, column 2); the simulated data include each individual's trip information, such as latitude, longitude, activity start time, activity type, and duration (page 4, column 1).
Claim 3 is directed to wherein to obtain the sector data comprises to obtain data indicative of at least one sector in which sampler device placement is prohibited.
Eichenlaub et al. teaches second, the sensor may only be deployed in an allowed area of the site. In the case of oil and gas pads, the site boundary is generally allowable as it is part of the site and far enough away from the hazard zone around the equipment groups [0270]. Eichenlaub et al. further illustrates a keep-out zone as part of its plan of an example site under monitoring [fig. 22].
Claim 14 is directed to wherein to obtain the meteorological data comprises to obtain data indicative of historical weather data for the site for multiple years and data indicative of hourly air temperature, wind speed, wind direction, and atmospheric stability measures.
Eichenlaub et al. teaches detector placement is initially decided prior to the testing campaign by studying multiple wind rose diagrams from historical weather stations data and identifying the most likely dominant wind directions around the location of the testing; visualization of time series and hourly aggregated statistics of concentration, wind speed, and wind direction from all detectors [0250]; some approximation of the dispersion width can be obtained using Pasquill curves which may depend on the atmospheric stability class at the time of transport and distance between source and sensor [228]; and obtaining atmospheric parameters like wind speed, wind direction, temperature, pressure, and humidity at the site [0013] that are updated hourly [0299].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808; 2022), as applied to claims 1-3 and 14 previously, and in further view of Zhou et al. (Theoretical and Applied Climatology; Vol. 141: 857-868; 2020) and Page et al. (2011/0009986).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al. in view of Park et al. and Mano et al. do not teach obtaining emission source data indicative of properties of the emission source, including the size of the emission source and temperature over time for the emission source (claim 4).
Zhou et al. describes the impact of secondary inorganic aerosol emissions change on surface air temperature in the Northern Hemisphere.
Zhou et al. teaches there is a regional difference in temperature changes caused by this difference in aerosol pollutant emissions (page 9, column 1); temperature changes over the emission source regions are mainly driven by radiative forcing (page 1, column 1); such conclusions are discovered by using the left side of the equation as the local variation of temperature, which represents changes in local temperature over time (page 3, column 1); and the response of surface temperature to the change of SIOAs concentrations distribution is different between the aerosol source area and the distant area (page 5, column 2), in which in the aerosol source area, the trend of temperature change is opposite to that of SIOAs emissions (page 5, column 2).
Zhou et al. does not explicitly teach identifying the size of the aerosol source area.
Page et al. describes a system and method for dynamically controlling odor emission.
Page et al. teaches the processing module may be further adapted to receive or retrieve from a database at least one distance input indicative of a distance between the odor emission site and at least one remote site [0027]; in one aspect, the distance corresponds to a geographical area defined with respect to the emission site [0027].
Page et al. further teaches in some instances, the unpleasant odor emissions produced by those enterprises corresponds to pollutants released in the surrounding environment [0002].
Therefore Zhou et al. teaches temperature change over time in an emission source area is of interest in airborne pollutant monitoring. Page et al. further teaches that the size of said emission area is also of interest. As such it would be obvious to one of ordinary skill in the art to combine the techniques of Zhou et al. and Page et al. with that of Eichenlaub et al. in view of Park et al. and Mano et al., with the expectation of predictable results and each element merely performing the same function as they do separately.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of Page et al. (2011/0009986).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Claim 5 is directed to wherein to obtain the scenario data comprises to obtain data indicative of a location of the site and a perimeter of the site.
Eichenlaub et al. teaches in one configuration, a location method for locating an emission source of a target substance at a site is disclosed [0018].
Eichenlaub et al. does not teach obtaining the perimeter of the site.
Page et al. describes a system and method for dynamically controlling odor emission, as taught above.
Page et al. teaches the processing module may be further adapted to receive or retrieve from a database at least one distance input indicative of a distance between the odor emission site and at least one remote site; in one aspect, the distance corresponds to a perimeter defined around the emission site; and in another aspect, the distance corresponds to a geographical area defined with respect to the emission site [0027].
Page et al. teaches in some instances, the unpleasant odor emissions produced by those enterprises corresponds to pollutants released in the surrounding environment [0002].
Therefore Page et al. teaches obtaining the perimeter of an emission site as a part of a problem that can be applied to airborne chemical pollutants. As such it would be obvious to one of ordinary skill in the art to apply this technique to the method of Eichenlaub et al. in view of Park et al. and Mano et al. with a reasonable expectation of success and each element merely performing the same function as they do separately.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22" 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of Marks et al. (2019/0047269).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Claim 6 is directed to wherein to obtain the scenario data includes obtaining data indicative of perimeters of one or more buildings at the site.
Marks et al. describes a method of making a barrier film substrate.
Marks et al. teaches volatile organic compounds (VOCs) are organic chemicals that have a high vapor pressure at ordinary room temperature [0002]; a higher concentration of VOCs in a building structure may be caused in part by inadequate ventilation as well as multiple sources of VOC emission within the building structure [0002]; accordingly, it is desirable to prevent passage of VOCs, gases, and water vapor into a structure [0003]; this disclosure is directed to a vapor intrusion barrier to attenuate and/or slow down the transmission of hazardous substances, pollutants, and/or contaminants, including but not limited to: VOCs, methane, radon, and water vapor [0008]; more specifically, a non-limiting example of the disclosure herein is a barrier film substrate for use as a barrier installed within or around a building envelope, or at development or cleanup sites, to minimize transmission of chemical vapors including volatile organic compounds (“VOC”), methane, radon, and water; and for the purposes of this application, the “building envelope” may include all areas adjacent to and within a perimeter of a building structure [0008].
Therefore Marks et al. provides sufficient motivation to obtain and use the perimeter of a building within a VOC emission source in order to prevent passage of VOCs and gases into a structure and minimize transmission of chemical vapors including volatile organic compounds (“VOC”) or pollutants. As such, it would be obvious for one of ordinary skill in the art to obtain the perimeter of a building as part of airborne chemical pollutant monitoring system, with a reasonable expectation of success, yielding predictable results, with each element merely performing the same function as they do separately.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of Fumarola et al. (Science for Environment Policy; Issue 4; 2008).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al. in view of Park et al. and Mano et al. do not teach obtaining data indicative of an identity and one or more properties of the chemical (claim 7).
Fumarola et al. describes issues in air quality and emissions trading.
Fumarola et al. teaches recent research suggests that it is important to identify the chemical, physical and biological properties of particulate matter (PM), as the adverse health effects of pollution are not only defined by the size and concentration of particles (page 3, column 1).
Claim 8 is directed to wherein to obtain the scenario data includes obtaining data indicative of a profile of the chemical.
Fumarola et al. teaches modelling techniques were used to match the chemical profile of pollutants to their source (page 5, column 1); and identifying the chemical composition and concentrations of two size fractions of particles, PM2.5 and PM10 (page 3, column 1).
Fumarola et al. further teaches Chemical Transport Models are an essential computational method used to predict air quality, which are closely aligned to weather prediction models (page 2, column 1); they also incorporate the complex reactions of pollution emissions, their dispersal in the atmosphere, the chemical transformations they undergo and their removal processes (page 2, column 1).
Therefore Fumarola et al. teaches it is important to identify and use the identity and profile of the emitted chemical in combination with chemical transport models, which model emission and dispersion as integral parts of airborne chemical pollutant monitoring. As such, it would be obvious for one of ordinary skill in the art to combine the methods of Fumarola et al. with the method of Eichenlaub et al. in view of Park et al. and Mano et al., which use chemical transport modelling, with each element merely performing the same function they do separately in order to yield predictable results with a reasonable expectation of success.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of Nagendra et al. (Sustainable Cities and Society; Vol. 44; 520-535; 2019).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al. in view of Park et al. and Mano et al. do not teach wherein to obtain the scenario data includes obtaining data indicative of a sector of heightened interest to be prioritized for sampler device placement (claim 9).
Nagendra et al. describes visualizing spatio-temporal variation of pollutants at urban hotspots
Nagendra et al. teaches the current fixed station monitoring is not providing spatial variations of the pollutant concentrations across the city and tend to underestimate the personal exposure, as it does not take into account all locations and activities which contribute to an individual’s exposure (page 2, column 1); the air quality information across these stations are not representing the air quality at hot spots i.e, traffic congestion and street canyons etc; so, the current stationary monitoring network is not useful for personal exposure assessment and health risk analysis (page 2, column 1).
Nagendra et al. teaches therefore, there is an immediate requirement to complement existing air quality monitoring network with flexible and affordable alternative technologies, to improve spatial and temporal resolution of air quality data for both scientific and public awareness purposes (page 2, column2); and in this background, using large numbers of low-cost sensors simultaneously with equivalent instrumentation can improve confidence in the measurements made (page 2, column 2).
Therefore Nagendra et al. provides sufficient motivation for one of ordinary skill in the art to account for sectors of heightened interest to be prioritized for sampler device placement, in the form of hotspots, in order to improve the personal exposure assessment, health risk analysis, and confidence in measurements within an air monitoring and device placement system, such as that of Eichenlaub et al. in view of Park et al. and Mano et al. with a reasonable expectation of success.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14, and in further view of McCammon et al. (NIOSH Manual of Analytical Methods; 5th Edition; 2016).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al. in view of Park et al. and Mano et al. do not teach wherein to obtain the scenario data includes obtaining data indicative of air sampling parameters for the chemical (claim 10).
McCammon et al. describes general considerations for sampling airborne contaminants.
McCammon et al. teaches in choosing methods for sampling of chemical and biological agents related to occupational exposures, thorough advance planning is required (page 2, column 1); once the sampling media and measurement method are chosen, the specific sampling parameters need to be determined [Eller 1986; ASTM International 2013] (page 7, column 1); and sampling parameters that should be considered might include: a) type of sample; b) contaminant(s) to be sampled; c) physical nature of airborne sample (vapor and/or aerosol); d) duration of sample collection; e) potential interferences; and f) estimated contaminant concentrations (page 2, column 1).
Therefore McCammon et al. provides sufficient motivation for one of ordinary skill in the art to determine sampling parameters for airborne chemical agents. As such it would be obvious to one of ordinary skill in the art to combine this technique with the method of Eichenlaub et al. in view of Park et al. and Mano et al. in order to thoroughly plan the sampling process of the air monitoring system with a reasonable expectation of success.
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of McCammon et al. (NIOSH Manual of Analytical Methods; 5th Edition; 2016), EPA (US EPA; Publication 9360.8-17FS; 1993), and Shoeib et al. (Environmental Science & Technology; Vol. 36: 19; 2002).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Claim 11 is directed wherein to obtain scenario data includes obtaining data indicative of an analytical method, equipment, sample media, sampling duration, sample rate, sampling volume, and a quantity of sampler devices.
Eichenlaub et al. further teaches the number of sensors and their position can then be chosen for the site by selecting the best optimization result with a sufficient margin of safety to guarantee operation over time [0207]; or sensor deployment techniques discussed in connection with FIG. 4C can be used to select the number of sensors, or sensor position [0213].
Eichenlaub et al. does not teach obtaining data indicative of an analytical method, equipment, sample media, sampling duration, sample rate, or sampling volume.
McCammon et al. describes general considerations for sampling airborne contaminants, as taught above.
McCammon et al. teaches in choosing methods for sampling of chemical and biological agents related to occupational exposures, thorough advance planning is required (page 2, column 1); once the sampling media and measurement method are chosen, the specific sampling parameters need to be determined (page 7, column 1); and generally, the parameters which must be considered are flow rate, total sample volume, sampling time, and limit of quantitation (LOQ) (page 7, column 1).
Claim 13 is directed to wherein to obtain the scenario data includes obtaining data indicative of a field blank or duplicate samples.
McCammon et al. teaches certain numbers of blanks are required by the analytical laboratory for each set of samples to be analyzed (page 11, column 1); and the general recommendation for the number of field blanks is three field blanks for each batch of samples (page 12, column 1). McCammon et al. teaches take a minimum of three replicate samples for every set of field samples to assure quality of the sampling procedures (page 19, column 1).
Eichenlaub et al. in view of McCammon do not explicitly teach obtaining data indicative of equipment, sampling duration, or sample rate (claim 11).
EPA describes personal air sampling and air monitoring requirements.
EPA teaches examples of elements to include in an area air sampling strategy include: the locations where air sampling will be performed; the hazardous substances that will be sampled during the task; the duration of the sample; the equipment that will be used to sample for the different hazardous substances; and collection of meteorological data (page 4, column 2).
Eichenlaub et al. in view of McCammon et al. and EPA do not explicitly teach obtaining data indicative of sample rate.
Shoeib et al. describes the characterization and comparison of three passive air samplers for persistent organic pollutants.
Shoeib et al. teaches passive samplers are chemical accumulators that can be used to assess ambient concentrations or fugacities in either homogeneous or heterogeneous media into which they are deployed (page 1, column 1); they are increasingly employed in investigations of persistent organic pollutants (POPs) (page 1, column 1); organic media such as the ones mentioned above have a high capacity for organic chemicals (page 1, column 1); this property allows them to accumulate airborne contaminants; the extent to which the chemicals are enriched relative to air is dependent on the passive sampler medium (PSM)−air partition coefficient, KPSMA (page 1, column 1); and in order to use passive samplers quantitatively to assess ambient air concentrations, it is necessary to know the sampling rate and KPSM-A (page 1, column 2).
Therefore McCammon et al. provides sufficient motivation for one of ordinary skill in the art to determine sampling parameters for airborne chemical agents. As such it would be obvious to one of ordinary skill in the art to combine this technique with the method of Eichenlaub et al. in view of Park et al. and Mano et al. in order to thoroughly plan the sampling process with a reasonable expectation of success.
It would be further obvious to combine the methods of Eichenlaub et al. in view of Park et al. and Mano et al. in view of McCammon et al. with EPA, as it further teaches methods that can be combined with the air monitoring plan with each element merely performing the same function as they do separately, in order to yield predictable results. It would be further obvious to include sample rate, according to the sufficient motivation present by Shoeib et al., as it teaches the rate is another parameter necessary to obtain in order to conduct monitoring of airborne chemical contaminants with a reasonable expectation of success.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of EPA (US EPA; Publication 9360.8-17FS; 1993).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al. in view of Park et al. and Mano et al. do not teach wherein to obtain the scenario data includes obtaining data indicative of a predefined sampler device location that cannot be modified (claim 12).
EPA describes personal air sampling and air monitoring requirements, as taught previously.
EPA teaches air monitoring refers to the use of direct reading instruments producing instantaneous data (page 2, column 1) that can be performed to identify the existence of a hazardous atmosphere (page 2, column 2), which may be in the form of vapor, gases, aerosols, and dusts (page 1, column 2); and OSHA requirements for monitoring at uncontrolled hazardous waste sites are codified at 29 CFR 1910.10(h) (page 1, column 1).
EPA teaches information about the potential chemical hazards is gathered during the initial entry and used to make decisions about air sampling needs (page 3, column 2); and for any contaminants discovered during the initial entry that are regulated by 29 CFR 1910.1000 or that could be considered hazardous, the air sampling needs must be assessed according to the requirements (page 3, column 2).
Therefore, EPA teaches the air sampling needs of samples that may be airborne chemicals, must be assessed according to requirements, which include a mandatory sampling site. As such, it would be obvious to one of ordinary skill in the art to apply this technique of acquiring and adhering to sampling site requirements to the base product of an air monitoring and device placement system in order to yield predictable results that fully account for potential hazardous contaminants.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of Taino et al. (Environment International; Vol. 147: 105954; 2021).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Claim 15 is directed to wherein the circuitry is further configured to obtain simulation data indicative of parameters under which to simulate emission of the chemical at the site, wherein the simulation data includes data indicative of start and end dates for a sampling window, data indicative of a number of steps per hour, data indicative of a size of each cell in a grid to be overlaid onto the site, data indicative of a minimum distance between sampler devices, and data indicative of a sampling duration.
Mano et al. further teaches a simulation was carried out to evaluate sensor deployment strategies (page 3, column 1), in which the region of interest, Ω, was modeled as a flat 500×500 m area divided into a grid of 5 × 5 m cells, {𝜔}∈Ω (page 3, column 1); the meteorology input for the GRAL simulation was obtained from real-life data collected by the Israel Meteorology Service at the Hadera Port station on 1 May 2020; for 𝑇, the total time in hours of the analysis, the temporal resolution of the meteorological data in minutes is ΔΦ; thus, for a 𝑇=24 h period (page 4, column 1); the union of all the cells’ neighboring sensors is then denoted by 𝐵𝑑 (page 5, column 1); 𝐵𝑑 allows for avoiding placing sensors too close to each other, which would have represented the same information; and using the notation above, at each iteration, the candidate locations for placing a sensor are Ω𝑁=Ω\(Ω𝑑∪ 𝐵𝑑); and for N, the maximum candidate locations for placing a sensor in each iteration, the locations are selected by taking the minimum between |Ω𝑁| and 𝑁 locations in Ω𝑁 with the highest 𝐷𝑀 score (page 5, column 1).
Therefore Mano et al. teaches obtaining emission simulation parameters including the size of cells in a grid, start and end dates, sampling duration, and minimum distance between sensors.
Mano et al. does not teach including data indicative of a number of steps per hour.
Taino et al. reviews evidence between air pollution, physical activity and health.
Taino et al. teaches few modelling studies have explored exposure contrasts for pedestrians (page 6, column 1); Mölter and Lindley (2015) found slightly lower exposures in the simulated lowest NO2 and PM10 exposure route versus the shortest routes to schools; Elford and Adams (2019) used a similar simulation model in Toronto, Canada, to estimate exposures to UFPs during school commutes (page 6, column 1); others highlighted the relationship between PM10 (μg/m3) and daily step count (page 3, table 1); the relationship between O3 (μg/m3) and daily step count (page 3, table 1); associated an increase in ambient PM2.5 concentration with a reduction in weekly total hours of walking (page 3, table 1), and an increase in the ambient PM2.5 concentration with a reduction in weekly total minutes of walking (page 3, table 1).
Therefore Taino et al. teaches though there are few models that simulate airborne chemical pollutants and pedestrian activity, the studies find associations between such pollutants and daily step count, hours spent walking, and minutes spent walking. Therefore Taino et al. provides sufficient evidence that one of ordinary skill in the art can simulate chemical air pollutants using walking activity data measured in step count. As such, it would be obvious to one of ordinary skill in the art to conduct such simulations with an hourly step count due to an obvious change in scale/proportion yielding predictable results.
It would be further obvious to one of ordinary skill in the art to combine the method of Taino et al. with that of Eichenlaub et al. in view of Park et al. and Mano et al., with each element merely performing the same function as they do separately in order to yield predictable results that account for an associated parameter.
Claims 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14 previously, and in further view of Kumar et al. (J. Air & Waste Manage. Assoc; Vol. 59:1308–1316; 2009).
Eichenlaub et al., in view of Park et al. and Mano et al., teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al., in view of Park et al. and Mano et al., do not teach wherein the circuitry is further configured to: obtain data indicative of user defined candidate sampler device locations; and display differences between the recommended sampler devices locations and the candidate sampler device locations (claim 16).
Kumar et al. describes an optimal spatial configuration of sample sites for air pollution monitoring.
Kumar et al. teaches a computer program was written in C to identify optimal sample sites (page 4, column 2); input to the program included a set of all potential candidate locations (page 4, column 2); and figure 5 shows ten potential candidate households around an optimal site onto a high-resolution Google map (page 6, fig. 5).
Claim 18 is directed to wherein the circuitry is further configured to present an optimality score indicative of a divergence from recommended sampler device locations due to sampler device placement restrictions.
Kumar et al. teaches finally, identifying optimal sites using the proposed design is relatively straightforward (page 4, column 2); however, recruiting households at or around the optimal sites could be challenging for various reasons, including a household not existing at an optimal site or a household declining to participate in the study (page 4, column 2); thus, the actual household locations because of logistic reasons could be offset by some distance (age 4, column 2). Kumar et al. teaches for finalizing the actual sites, one must try to minimize the distance between optimal and actual sites so that the differences between the air pollution estimates at optimal sites, identified using the proposed sampling design, and the actual locations are minimal (page 4, column 2).
Kumar et al. further teaches the methodologies outlined in this article provide a framework to identify an optimal set of locations that can capture spatial variability in ambient air pollution exposure (page 7, column 2); and unlike the classical random site selection, the sampling methodology presented here (1) computes an optimal number of sites given the specified threshold of the variability to be captured, (2) avoids redundancy by con trolling for spatial autocorrelation, and (3) enables estimation of robust CIs of pollutants (page 7, column 2).
Therefore Eichenlaub et al. in view of Park et al. in view of Mano et al. teach identifying optimal air sampling sites. Kumar et al. provides sufficient motivation to apply its method that includes determining a minimized distance, representing a divergence from the optimal site due to logistical restrictions, over other classic sampling methods. As such it would be obvious to one of ordinary skill in the art to apply the techniques presented by Kumar et al. to method of Eichenlaub et al. in view of Park et al. and Mano et al. with an expectation of predictable results and an improved airborne chemical monitoring system.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14, and in further view of Montrucchio et al. (IEEE Trans Vehicular Tech; Vol. 69: 12; 2020), Ren et al. (Energy & Buildings; Vo. 252: 111620; 2022), and Snyder et al. (Department of Energy; 2011).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al., in view of Park et al. and Mano et al., do not teach wherein the circuitry is further configured to present a recommended date and time at which to begin sample collection (claim 17).
Montrucchio et al. describes a densely-deployed, high sampling rate, open-source air pollution monitoring WSN.
Montrucchio et al. teaches repeating the test for two days, on December the 3rd, and on December the 4th, 2018, from 4:00 p.m. to 4:30 p.m. local time (page 9, column 2); selecting these dates due to the fact that they were at the beginning of an emergency traffic control, which has been in progress until December 8th, due to unhealthy pollution levels in the previous days (page 9, column 2); and the time is representative of the rush hour, with significant traffic increments with respect to other hours (page 9, column 2).
Montrucchio et al. further teaches although mobile sensing can be successfully applied to measure concentration of gases, such as ozone, and ultra-fine particles, it is more frequently adopted in monitoring air pollutants like PM2.5 (page 3, column 2).
Therefore Montrucchio et al. teaches determining recommended sampling dates and times within an air monitoring framework that is applicable to airborne chemical pollutants.
Montrucchio et al. does not explicity teach presenting the recommended sampling schedule.
Ren et al. describes a field study of CO2 and particulate matter characteristics during the transition season in the subway system in Tianjin, China.
Ren et al. teaches for better comparison, the sampling was conducted in three timeframes, each lasting one week (page 2, column 2); the sampling was conducted during three periods in one day: the morning-peak period (beginning at 7:30 am, labeled “period I”), the off-peak period (beginning at 9:30 am, period II), and the evening-peak period (beginning at 18:00 pm, period (page 3, column 1); and the detailed sampling schedule is displayed in table 1 (page 3, column 2).
Montrucchio et al. and Ren et al. do not explicitly teach motivation to display the sampling schedule.
Snyder et al. describes the Pacific Northwest Site Office Environmental Monitoring Plan for the DOE-SC PNNL Site.
Snyder et al. teaches this Environmental Monitoring Plan (EMP) describes air surveillance activities at the DOE-SC PNNL Site (page 5, column 1); good data management, data validation, and statistical treatment practices are essential for the production of quality results (page 27, column 1); and the objectives for data management of environmental surveillance results include: managing data in a manner that assures timely collection, validation, and reporting in accordance with an annual sampling schedule and traceability from scheduling to archiving in the database (page 27, column 1).
Therefore Montrucchio et al. teaches determining recommended sampling dates and times within an air monitoring framework that is applicable to airborne chemical pollutants; Ren et al. teaches displaying a sampling schedule that includes dates and times; and Snyder et al. teaches the inclusion of sampling schedules are essential in producing quality air monitoring results. As such, it would be obvious to one of ordinary skill art to combine the methods of Eichenlaub et al. in view of Park et al. and Mano et al., with the methods of Montrucchio et al. in view of Ren et al., with each element merely performing the same function as they do separately, and a reasonable expectation of success and an improved air monitoring system, based on the teachings of Snyder et al.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14, and in further view of Williamson et al. (Frontiers in Public Health; Vol. 8: 85; 2020).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Eichenlaub et al. in view of Park et al. and Mano et al. do not teach wherein the circuitry is further configured to display plume, activity, population, or exposure risk potential over time (claim 19).
Williamson et al. describes AQVX, an Interactive Visual Display System for Air Pollution and Public Health.
Williamson et al. teaches the AQVx system ingests, processes and displays a range of model, satellite data sources, listed in Table 1 (page 2, column 2); operators, appreciated the availability of visible animated smoke plumes in the interface (page 9, column 1); to enable visualization of broader population exposure, AQVx integrated Geoscience Australia's National Exposure Information System (NEXIS) population density product and provided the number of people per 10 km2 (page 4, column 2); incorporates emission inventories from urban, traffic, industrial, biogenic and sea-salt production (page 2, column 2); and the AQVx system allowed fire managers from the Department of Environment, Land, Water and Planning (DELWP) to query and validate modeled air quality, and identify potential exposure risks to populated areas (page 2, column 2).
Williamson et al. further teaches Air Quality Visualization (AQVx) was developed to allow the overlaying, synchronization and comparison of a range of maps and data layers, in order to both assess the potential public health impact of landscape fire smoke plumes, and the accuracy of dispersion models (page 1, column 1).
Therefore Williamson et al. provides sufficient motivation to display the named variables within an air monitoring system that uses dispersion modelling. As such, it would be obvious for one of ordinary skill in the art to display the variables with a reasonable expectation of success when combined with the method of Eichenlaub et al. in view of Park et al. and Mano et al.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Eichenlaub et al. (2023/0304982) in view of Park et al. (Health & Place; Vol. 43:85-94; 2017) and Mano et al. (Sensors; Vol. 22: 3808: 2022), as applied to claims 1-3 and 14, and in further view of Snyder et al. (Department of Energy; 2011).
Eichenlaub et al. in view of Park et al. and Mano et al. teach a method of identifying recommended air sampling device locations that account for emission site and human exposure modelling.
Claim 20 is directed to wherein to present the recommended sampler device locations includes producing a sampling and analysis plan document.
Eichenlaub et al. teaches referring now to FIG. 21, a plan 2100 of an example site under monitoring is illustrated [0261].
Therefore Eichenlaub et al. teaches producing a sampling plan document.
Eichenlaub et al. does not teach producing an analysis plan document.
Snyder et al. describes the Pacific Northwest Site Office Environmental Monitoring Plan for the DOE-SC PNNL Site, as taught previously.
Snyder et al. teaches this Environmental Monitoring Plan (EMP) describes air surveillance activities at the DOE-SC PNNL Site (page 5, column 1); this EMP is being implemented to assure that potential radiological impacts associated with DOE-SC PNNL Site activities are evaluated appropriately and consistently, and are not having an unacceptable impact on human or ecological health (page 11, column 1).
Snyder et al. teaches a number of additional specific plans are developed to support this initial EMP (page 21, column 1); the plans describe EMP activities in more detail and may be media- or contaminant-specific (e.g., air, soil, tritium) (page 21, column 1); current plans are limited to radiological air sampling of offsite air and include: Sampling and Analysis Plan (SAP), Data Management Plan (DMP), Dose Assessment Guidance (DAG), and Effluent Management QA Plan (page 21, column 1).
Snyder et al. further teaches the current sampling locations (see Figure 2.1) were determined based on several factors, including access, power availability, and atmospheric dispersion modeling results (page 26, column 1).
Therefore Snyder et al. teaches obtaining and modelling parameters analogous to the method of Eichenlaub et al. in view of Park et al. and Mano et al. in order to determine recommended sampling device locations. It further provides sufficient motivation for one of ordinary skill in the art to include an environmental monitoring plan that additionally includes a sampling and analysis plan document. As such, it would be obvious for one of ordinary skill in the art to combine the methods of Eichenlaub et al. in view of Park et al. and Mano et al. with that of Snyder et al. with each element merely performing the same function as they do separately in order to yield predictable results and an improved air monitoring system.
Conclusion
No claims are currently allowed.
Correspondence
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
