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 1-10 are currently pending and under exam herein.
Claims 1, 3, and 8-10 are rejected.
Claims 2 and 4-7 are objected to.
Priority
Applicant’s claim for domestic benefit to the earlier filed international application PCT/CN2021/122732, filed October 9, 2021, which claims priority to the foreign application CN202011616109.X, filed December 30, 2020, is acknowledged. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. At this point in the examination, the effective filing date of claims 1-10 is December 30, 2020.
Information Disclosure Statement
The information disclosure statement (IDS), submitted August 12, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 8-10 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 8-10, they recite a computer apparatus, a computer-readable storage medium, and a computer program product that implement the steps of the method in claim 1; respectively. The published specification discloses that in step 1) of claim 1 the air pollutant and bacteria concentrations are obtained by installing TS WES-C air pollutant detectors and Anderson impaction air microbial samplers at air vents and seats in a train compartment which are transmitted to a data storage platform (paragraphs 0050-0051). Which demonstrates that other machines performed the data gathering steps and not the claimed computer program executed by a computer apparatus or stored on a computer-readable storage medium program being executed on a processor.
MPEP 2161.01(I) states, “… original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. Since claims 8-10 are relying on a computer to perform a step that cannot be performed by an algorithm alone, an issue of written description arises because the claimed invention has not been described with sufficient particularity such that once skilled in the art would recognize that the inventor had possession of the claimed invention at the time of filing (MPEP 2163(I)(A)).
Therefore, the claims are rejected because the specification fails to provide adequate written description of the structure necessary for the computer program executed by a computer apparatus or stored on a computer-readable medium that can implement the steps of the method in claim 1.
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 9-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 9, the claim does not fall within at least one of the four categories of patent eligible subject matter because it is directed to a signal per se (i.e., mere information in the form of data). MPEP 2106.03(I) states, “Even when a product has a physical or tangible form, it may not fall within a statutory category. For instance, a transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter. Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03. As such, a transitory, propagating signal does not fall within any statutory category. Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294, 112 USPQ2d 1120, 1133 (Fed. Cir. 2017); Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03.”
The specification does not provide a definition for the limitation “a computer-readable storage medium” that excludes transitory signals and MPEP 2106.03(II) states, “… the BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves).” It is suggested to amend the claim or specification to exclude transitory signals to overcome this rejection.
Regarding claim 10, the claim does not fall within at least one of the four categories of patent eligible subject matter because it is directed to software per se (i.e., a product that does not have a physical or tangible form) since the computer program product is claimed without any structural recitations (MPEP 2106.03(I)). It is recommended to amend the claim to recite structural components to overcome this rejection.
Claims 1, 3, and 8-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
The first part of the eligibility analysis evaluates whether a claim falls within any statutory category (MPEP 2106.03). Claims 1, 3, and 8-10 recite a series of data analysis steps in order to calculate the necessary ventilation rates in a train compartment based on the level of bacterial colonies and claim 8 also recites a computer system that performs the steps. Claims 1 and 3 are directed to a method and claim 8 is directed to a machine and fall within one of the statutory categories of invention (Step 1: YES). While claims 9-10 do not recite statutory categories of invention, the subject matter eligibility analysis will continue in the interest of compact prosecution.
Step 2A, prong 1:
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, prong 1). In the instant application, the claims recite the following limitations that equate to those concepts:
Claims 1 and 8-10 recite: 2) establishing, according to the PM2.5 concentration, PM10 concentration, CO concentration, NO2 concentration, SO2 concentration, O3 concentration, and the total number of bacterial colonies at each detection point in a compartment, a mapping relationship between the total number of bacterial colonies D and the concentration of air pollutants d in each micro environmental unit, wherein the micro environmental unit is the detection point; 3) selecting a measured air pollutant concentration data set with a time length of N minutes, calculating the total number of bacterial colonies according to the mapping relationship, denoting a time series of the total number of bacterial colonies at the ith seat as
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denoting a time series of the total number of bacterial colonies at the jth air supply port or air exhaust port as
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, performing hypothesis test by using Granger causality test to determine whether there is causality between
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and
Y
N
j
, and then obtaining a test result set of each seat detection point, m air supply ports and n air exhaust ports; 4) obtaining a nonlinear description model base of all seat detection points according to the mapping relationship and the test result set; and 5) inputting ventilation rates of all air supply ports and all air exhaust ports of the train to a grey wolf optimizer, calculating fitting results of the total number of bacterial colonies at the air supply ports/air exhaust ports under different ventilation rates, inputting the fitting results to the nonlinear description model base to obtain a fitting result of the total number of bacterial colonies at each seat, and determining the ventilation rates of all the air supply ports and all the air exhaust ports by using the fitting result of the total number of bacterial colonies at each seat.
Claim 3 recites: wherein in step 3), the test result set is
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The limitations in claims 1 and 8-10 of mapping a relationship between air pollutants and bacterial colonies, calculating the total number of bacterial colonies from selected air pollution data and performing Granger causality, obtaining a nonlinear model of all seat detection points, inputting ventilation rates into a grey wolf optimizer, calculating fitting results of bacterial colonies at the air supply/exhaust ports, inputting the fitting results to the nonlinear description model base, and determining ventilation rates of air supply/exhaust ports are verbal recitations of mathematical calculations. Likewise, the limitations in claim 3 further limit the mathematical calculations in claim 1 to perform Granger causality by describing the arrangement of test results.
Therefore, these limitations fall under the “Mathematical concepts” grouping of abstract ideas (Step 2A, prong 1: YES).
Step 2A, prong 2:
Claims found to recite a judicial exception under Step 2A, prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application (Step 2A, prong 2). The claims recite the following additional elements:
Claims 1 and 8-10 recite: 1) detecting PM2.5 concentration, PM10 concentration, CO concentration, NO2 concentration, SO2 concentration, O3 concentration, and the total number of bacterial colonies at an air supply port, an air exhaust port and a seat of a train…
Claim 8 recites: A computer apparatus, comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program…
Claim 9 recites: A computer-readable storage medium, storing a computer program/instruction, wherein when the computer program/instruction is executed by a processor…
Claim 10 recites: A computer program product, comprising a computer program/instruction, wherein when the computer program/instruction is executed by a processor…
The additional elements of detecting air pollutant concentrations and bacterial colonies recited in claims 1 and 8-10 amounts to necessary data gathering required for use by the judicial exception which is insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements in claims 8-10 of a computer apparatus, a memory, a processor, a computer-readable storage medium storing computer program/instructions, and a computer program product with instructions only recites the idea that the steps of claim 1 are performed by a computer which amounts to instructions to apply the judicial exception in a generic computer environment (MPEP 2106.05(f)).
Therefore, the judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies/uses the recited judicial exception in some other meaningful way and the claims are directed to the judicial exception (Step 2A, prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). Claims 8-10 recite additional elements that equate to mere instructions to apply the recited judicial exception in a generic computing environment. Claims that amount to nothing more than instructions to apply the judicial exception using a generic computer do not render an abstract idea eligible. Alice Corp., 576 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The additional element recited in claim 1 of detecting air pollutants and bacterial colonies are WURC because paragraph 0050 of the published specification states the detection of air pollutants and bacterial colonies was performed using commercially available devices that perform such measurements.
As such, the combination of additional elements recited in the claims is well-understood, routine, and conventional. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transform the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO) and claims 1, 3, and 8-10 are not patent eligible.
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.
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.
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.
Claims 1, 3, and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Tseng et al. (Building and Environment, vol. 46, no. 12, pp. 2578-89) and Fan et al. (Science of The Total Environment, vol. 672, pp. 834-45) in view of Liu et al. (CN110395286A), Bo et al. (CN109993281A), and Faris et al. (Neural Computing and Applications, vol. 30, no. 2, pp. 413-35). The italicized text corresponds to the instant claim limitations.
In the following rejection, reference will be made to the following documents for English translations of the prior art:
US11999388B2 for Liu et al. (CN110395286A).
The provided machine-generated translations from Espacenet for Bo et al. (CN109993281A).
Regarding claim 1, Tseng et al. teach measuring bacteria, PM2.5, and PM10 concentrations at several locations (p. 2579, 2.1 Sampling plan and measurement of indoor air quality, paragraphs 1-4) which discloses 1) detecting PM2.5 concentration, PM10 concentration, … and the total number of bacterial colonies… Tseng et al. teach developing a warning system of indoor bacteria based on monitoring data such as PM2.5 and PM10 concentrations (p. 2579, 1. Introduction, paragraph 4, p. 2580, 2.2 Data analysis, paragraph 1) which discloses 2) establishing, according to the PM2.5 concentration, PM10 concentration, … and the total number of bacterial colonies … a mapping relationship between the total number of bacterial colonies D and the concentration of air pollutants d in each micro environmental unit, wherein the micro environmental unit is the detection point. Tseng et al. teach using the air pollutants data to create nonlinear prediction models for several locations (p. 2581, 3.2.1 Using monitoring data to create prediction model for bacteria concentration, paragraph 1) which discloses 4) obtaining a nonlinear description model base … according to the mapping relationship…
Tseng et al. teach that fresh air circulation and auxiliary ventilation affects indoor bioaerosols and can infiltrate indoors through the ventilation which prompted consideration of air exchange rate as model inputs (p. 2583-85, 3.3 Using both monitoring data and management data, and monitoring data only to create a prediction model for bioaerosol concentrations in office buildings, paragraphs 1-2). Tseng et al. teach indoor bacteria concentration increases when air exchange between indoors and outside is inadequate and that the air exchange rate was an important factor for predicting bacteria concentration (p. 2585, 3.3.1.1 The multiple linear regression model for bacteria concentrations in office buildings, paragraph 1-2). Tseng et al. teach that characteristics of effect factors between bioaerosols and environmental factors can be explained using their models (p. 2587, 3.4 Feasibility of the prediction model for indoor bioaerosol concentrations in office buildings, paragraph 1) and the models will help managers prevent indoor air quality (IAQ) problems and improve conditions (p. 2588, 4. Conclusions, paragraph 1). Since air exchange rate is important for lowering bacteria concentrations indoors, these teachings disclose 5) …, calculating fitting results of the total number of bacterial colonies … under different ventilation rates, inputting the fitting results to the nonlinear description model base to obtain a fitting result of the total number of bacterial colonies … and determining the ventilation rates … by using the fitting results of the total number of bacterial colonies…
Regarding claims 8-10, Tseng et al. teach performing their analyses using Microsoft Excel® and IBM’s SPSS statistics software (p. 2580, 2.2 Data analysis, paragraph 1) which discloses the claim 8 limitation of a computer apparatus, comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program…, the claim 9 limitation of a computer-readable storage medium, storing a computer program/instruction…, and the claim 10 limitation of a computer program product, comprising a computer program/instruction… because Microsoft Excel® and SPSS are a computer programs and using them inherently requires a fully functioning computer that has a processor, memory, and computer-readable storage.
Tseng et al. appears to be silent on the claims 1 and 8-10 limitations of detecting CO concentration, NO2 concentration, SO2 concentration, O3 concentration and detecting the air pollutants and bacteria at an air supply port, an air exhaust port and a seat of a train, establishing, according to the CO concentration, NO2 concentration, SO2 concentration, O3 concentration a mapping relationship, 3) selecting a measured air pollutant concentration data set with a time length of N minutes, calculating the total number of bacterial colonies according to the mapping relationship, denoting a time series of the total number of bacterial colonies at the ith seat as
X
N
i
denoting a time series of the total number of bacterial colonies at the jth air supply port or air exhaust port as
Y
N
j
, performing hypothesis test by using Granger causality test to determine whether there is causality between
X
N
i
and
Y
N
j
, and then obtaining a test result set of each seat detection point, m air supply ports and n air exhaust ports, obtaining a nonlinear description model base of all seat detection points according to the mapping relationship and test result set, 5) inputting ventilation rates of all air supply ports and all air exhaust ports of the train to a grey wolf optimizer, calculating fitting results at the air supply ports/air exhaust ports under different ventilation rates, inputting the fitting results into the nonlinear description model base to obtain the total number of bacterial colonies at each seat, and determining the ventilation rates of all the air supply ports and all the air exhaust ports by using the fitting results at each seat. Tseng et al. also appears to be silent on the limitations of claim 3. However, these limitations were known in the art prior to the effective filing date of the invention as taught by Fan et al., Liu et al., Bo et al., and Faris et al.
Regarding claims 1 and 8-10, Fan et al. teach collecting measurement of bacteria and air pollutants including CO concentration, NO2 concentration, SO2 concentration, O3 concentration (p. 835, 2.1 Sample collection) and creating an aggregated boosted tree to determine the relative influence of environmental factors on bacteria (p. 836, 2.4 Statistical analyses, paragraphs 1-2) which discloses detecting CO concentration, NO2 concentration, SO2 concentration, O3 concentration and establishing, according to the CO concentration, NO2 concentration, SO2 concentration, O3 concentration a mapping relationship.
Further regarding claims 1 and 8-10, Liu et al. teach an interior air quality monitoring and ventilation control system method and system for a train that selects suitable ventilation control strategies according to different degrees of air quality and acquires multiple groups of interior and exterior air quality data (p. 5, col. 1, lines 40-59). The data include PM2.5, NO2, and SO2 concentration measurements and are obtained at multiple points (p. 5, col. 2, line 66 – p. 6, col. 3, line 6). The interior air quality detection devices can be arranged at the head, middle, and tail of each compartment and the exterior air quality detection devices are arranged at an outside air inlet of each ventilation duct of each compartment (p. 7, col. 6, lines 56-64). Since the interior detection devices would measure data from the seats near the detection devices, these teachings disclose detecting the air pollutants and bacteria at an air supply port, an air exhaust port and a seat of a train, obtaining a nonlinear description model base of all seat detection points according to the mapping relationship, calculating fitting results at the air supply ports/air exhaust ports under different ventilation rates.
Regarding claims 1, 3, and 8-10, Bo et al. teach in the field of air quality, Granger causality mining can reveal hidden relationships between atmospheric visibility and meteorological factors providing support for air pollution control (paragraph 0004). Bo et al. teach if a prediction error of a time series using only its’ past values (X) is greater than the joint prediction error of values (X) and values (Y) from a different time series, then time series X and Y are said to have Granger causality (paragraph 0005). These teachings disclose 3) selecting a measured air pollutant concentration data set with a time length of N minutes, calculating the total number of bacterial colonies according to the mapping relationship, denoting a time series of the total number of bacterial colonies at the ith seat as
X
N
i
denoting a time series of the total number of bacterial colonies at the jth air supply port or air exhaust port as
Y
N
j
, performing hypothesis test by using Granger causality test to determine whether there is causality between
X
N
i
and
Y
N
j
, and then obtaining a test result set of each seat detection point, m air supply ports and n air exhaust ports, obtaining a nonlinear description model base according to the mapping relationship and test result set, inputting the fitting results into the nonlinear description model base to obtain the total number of bacterial colonies at each seat, and determining the ventilation rates of all the air supply ports and all the air exhaust ports by using the fitting results at each seat.
The teaching of Bo et al. also disclose the claim 3 limitations of wherein in step 3), the test result set is
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Yet further regarding claims 1 and 8-10, Faris et al. teach a grey wolf optimizer (GWO) is a metaheuristic swarm intelligence algorithm that has been widely tailored for many optimization problems given its’ advantages over other swam intelligence methods (Abstract). Faris et al. teach that GWO applies novel parameters to avoid local optima stagnation and has been used in applications including environmental planning problems (p. 414, 1. Introduction, paragraphs 5-8; Figure 2). Faris et al. teach a GWO was used to optimize the parameters of a support vector regressor used to forecast PM2.5 concentrations resulted in lower error rates compared to other optimizers (p. 427-428, 4.4 Environmental modeling applications, paragraphs 1-4) which discloses 5) inputting ventilation rates of all air supply ports and all air exhaust ports of the train to a grey wolf optimizer.
An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Tseng et al. teach the bacteria concentration prediction models will help managers prevent indoor air quality (IAQ) problems and improve conditions (p. 2588, 4. Conclusions, paragraph 1) while Fan et al. teach airborne pollutants had more impact on opportunistic pathogenic bacteria and particulate matter (PM) was significantly related to pathogenic bacteria (p. 843, 4. Conclusion, paragraph 1). One of ordinary skill in the art would be motivated to combine the additional air pollutant measurements taught in Fan et al. with the bacterial concentration prediction model in Tseng et al. in order to better model the composition of airborne bacteria and manage IAQ. There would be a reasonable expectation of success because both teachings pertain to measuring air pollutants and mapping the measurements to bacteria concentration in the air.
Furthermore, Liu et al. teach an invention for interior air quality monitoring and ventilation control in a train (p.5, col. 1, lines 15-19) and improvement of ventilation strategies to achieve health guarantees for interior air quality and comfort for passenger (p. 5, col. 1, lines 38-47). One of ordinary skill in the art would be motivated to combine the bacterial concentration prediction model taught by Tseng et al. and Fan et al. with the ventilation control system taught by Liu et al. to achieve high air quality and comfort in a passenger train. There would be a reasonable expectation of success because Liu et al. measures air pollutant concentrations to adjust ventilation and Tseng et al. measures air pollutants as well with the only difference being the calculation of bacterial concentrations to adjust ventilation.
Additionally, one of ordinary skill in the art would be motivated to employ the Granger causality data mining method taught by Bo et al. to reveal hidden causal relationships between seats on a passenger train and the ventilation ducts of the train in order to improve IAQ of the train because Tseng et al. teach suspended microorganisms in the air can enter indoor environments through air conditioning system (p.2580, 2.1 Sampling plan and measurement of indoor air quality, paragraph 5) . In light of those teachings, one or ordinary skill would seek to discover which vents were causally responsible for contaminating passenger seats in order to mitigate and prevent further contamination. There would be a reasonable expectation of success because Bo et al. teach their method can support air pollution control (paragraph 0004) and Granger causality uses time series datasets (paragraph 0005) which is the type of data collected by Tseng et al. (p. 2579, 2.1 Sampling plan and measurement of indoor air quality, paragraph 1).
Lastly, one of ordinary skill in the art would be motivated to improve indoor air quality management by incorporating the metaheuristic grey wolf optimizer (GWO) on the prediction model taught by Tseng et al. to optimize the models parameters by inputting ventilation rates into a GWO to improve prediction of bacterial concentrations because Faris et al. teach a GWO reduced the error rates on a support vector regression (SVR) model used in the prediction of PM2.5- concentrations based on a time-series dataset of the air pollutant (p. 427-428, 4.4 Environmental modeling, paragraph 3). There would be a reasonable expectation of success because Faris et al. teach that GWO has successfully been utilized in various mathematical modeling problems (p. 421-428, 4 Applications of GWO). The invention of claims 1, 3, and 8-10 are therefore prima facie obvious.
Conclusion
Claims 2 and 4-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 2 and 5-7, the claims are directed to eligible subject matter under 35 U.S.C. 101 because the combination of additional elements in claim 2 of detecting particulate matter, gaseous pollutants, and bacteria with training a deep belief network is not well-understood, routine, and conventional activity in the prior art and the combination provides an inventive concept. Claims 5-7 are eligible because they depend from claim 2.
Regarding claim 4, the claim is also eligible under 35 U.S.C. 101 because the combination of the additional elements of detecting particulate matter, gaseous pollutants, and bacteria with using a deep echo state network is not well-understood, routine, and conventional activity.
Regarding claim 2, the prior art does not teach mapping air pollutant concentrations to bacterial concentration by training a deep belief network (DBN). The closest prior art is Xing et al. (Applied Sciences, vol. 9, no. 18, p. 3765) which teaches training a DBN to predict PM2.5 concentrations based on influencing factors (p. 1-2, 1. Introduction), but appears to lack any teaching, suggestion, or motivation that would lead a person having ordinary skill in the art to use a DBN to establish a mapping relationship between air pollutants and bacterial colonies in the air.
Regarding claim 4, the prior art does not teach training a deep echo state network to establish a nonlinear description model base of bacterial concentrations at seats using vents that have Granger causality with the seat. The closest prior art is Zhang et al. (Environmental Technology, vol. 41, no. 15, pp. 1937-49) which teaches an echo state network (ESN) to predict PM2.5 and PM10 levels based on other air pollutant concentrations (p. 1938, 1. Introduction, paragraph 5; p. 1943, 3.1.2 Analysis of the data, paragraph 1). However, a deep echo state network (DeeESN) is different, and the prior art does not provide a teaching, suggestion, or motivation to use a DeeESN to create a nonlinear description model base of bacterial concentration at seats based on bacterial concentration at vents that have Granger causality with the seats.
Regarding claim 5, it is free of the prior art because it depends from claim 2 and the prior art does not teach measuring bacterial colonies at different ventilation rates at vents and calculating fitting results of the total number of bacterial colonies by performing least square fitting to obtain a polynomial expression g(vk) of the total number of bacterial colonies Ŝk with respect to the ventilation rate vk.
Regarding claim 6, it is free of the prior art because it depends from claims 2 and 5 and the prior art does not teach setting an optimization objective of simultaneously minimizing the total number of bacterial colonies at each seat.
Regarding claim 7, it is free of the prior art because it depends from claims 2, 5, and 6 and the prior art does not teach selecting a non-dominated solution NS*= arg min E, which minimizes an evaluation index E =
∑
k
=
1
m
+
n
Ŝ
k
+
V
a
r
(
Ŝ
)
to determine ventilation rates NS* of all the vents.
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/T.Y.O./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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