DETAILED ACTION
This action is responsive to the claims filed on 04/16/2026. Claims 16, 19-23 and 26-28 are pending for examination.
This action is Final.
Response to Arguments
Applicant’s arguments with respect to the 103 rejection of claims 16, 19-23 and 26-28 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments with respect to the rejection under 35 U.S.C. § 101 have been considered but are not persuasive. Although amended claim 16 now recites, based on the prediction for the pollutant concentration, performing at least one of automatically diverting traffic and automatically making more buses or more trams available, the claim does not recite sufficient technical details for how the predicted pollutant concentration is integrated into or used to control traffic infrastructure or public transportation resources. Rather, the added limitation is recited at a high level of generality as a desired result following the prediction. The specification likewise does not appear to provide adequate technical disclosure explaining how the automatic diversion of traffic or automatic provisioning of buses/trams is actually implemented based on the predicted concentration. Accordingly, the added limitation does not meaningfully integrate the abstract idea into a practical application, and instead amounts to no more than broadly applying the prediction result. The claim therefore remains directed to an abstract idea and does not include significantly more. Accordingly, the rejection of claims 16-23 under 35 U.S.C. § 101 is maintained.
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 16, 19-23 and 26-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Statutory Categories
Claims 16, 19-23 and 26-28 are directed to a method.
Independent Claims – Claims 16
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. Independent claims 16 and 24 recites limitations that are abstract ideas in the form of mental processes:
Claim 16 recites:
generating a synthetic measurement series using an alteration of the measurement series of the pollutant concentrations based on user input, (this limitation merely amounts to performing mathematical calculations, algorithms, or formulas (See specification pages 9, 14, and 20 for the related mathematical disclosure))
determining…the predicted pollutant concentration from the user input that includes the at least one pollutant emission; (this limitation merely amounts to performing mathematical calculations, algorithms, or formulas (See specification pages 9, 14, and 20 for the related mathematical disclosure))
Claim 16 also recites the following additional elements for the purposes of Step 2A Prong Two analysis:
A method comprising: providing a measurement series of pollutant concentrations including a measured value above a defined threshold value; (providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g))
providing a model for a relationship between a physical measured variable and a pollutant emission; (providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g))
training a neural network designed to determine a predicted pollutant concentration from at least one pollutant emission, with the measurement series of the pollutant concentrations and the synthetic measurement series as training data; (the recitation of a neural network used at a high level of generality is being considered as mere instructions to apply an exception under step 2A prong 2, see MPEP 2106.05(f))
by the neural network, (the recitation of a neural network used at a high level of generality is being considered as mere instructions to apply an exception under step 2A prong 2, see MPEP 2106.05(f))
and based on the prediction for the pollutant concentration, performing at least one of: automatically diverting traffic; and automatically making more buses or more trams available. (this limitation is generally linking the use of a judicial exception to a particular technological environment or field of use of traffic diversion and/or bus/tram availability)
The additional limitations fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
This claim recites the following additional elements for the purposes of Step 2B analysis:
A method comprising: providing a measurement series of pollutant concentrations including a measured value above a defined threshold value; (providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g), it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
providing a model for a relationship between the physical measured variable and the pollutant emission; (providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g), it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
training a neural network designed to determine a predicted pollutant concentration from at least one pollutant emission, with the measurement series of the pollutant concentrations and the synthetic measurement series as training data; (the recitation of a neural network used at a high level of generality is being considered as mere instructions to apply an exception under step 2B, see MPEP 2106.05(f))
by the neural network, (the recitation of a neural network used at a high level of generality is being considered as mere instructions to apply an exception under step 2B prong 2, see MPEP 2106.05(f))
and based on the prediction for the pollutant concentration, performing at least one of: automatically diverting traffic; and automatically making more buses or more trams available. (this limitation is generally linking the use of a judicial exception to a particular technological environment or field of use of traffic diversion and/or bus/tram availability, see MPEP 2106.05(f))
The claim also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Dependents of Claim 16
The remaining dependent claims corresponding to independent claim 16 do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The analysis of which is shown below:
The claims below recite additional limitations which fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
The claims also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. The claims are unpatentable.
Claim 19 recites the additional limitation of:
The method as claimed in claim 16, wherein a traffic- related proportion of the pollutant concentration is in a range from 0.3 to 0.5. (a mathematical concept including mathematical formulas, algorithms, or calculations)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 20 recites the additional limitation of:
The method as claimed in claim 16, wherein the pollutant concentration is a nitrogen oxide concentration and the pollutant emission is a nitrogen oxide emission. (Under both step 2A prong II and step 2B this limitation is generally linking the use of a judicial exception to a particular technological environment or field, see MPEP 2106.05(h))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 21 recites the additional limitation of:
The method as claimed in claim 16, wherein the physical measured variable used is a temperature, a wind speed, and/or a traffic level. (Under both step 2A prong II and step 2B this limitation is generally linking the use of a judicial exception to a particular technological environment or field, see MPEP 2106.05(h))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 22 recites the additional limitation of:
The method as claimed in claim 16, further comprising capturing the measurement series of the pollutant concentration and the measurement series of the measured variable using a measurement station within a town. (Step 2A Prong II: providing data is mere data gathering/outputting and is considered insignificant extra-solution activity. See MPEP 2106.05(g). For Step 2B, it should be noted that the courts have recognized receiving or transmitting data over a network as well-understood, routine, and conventional activity. (See MPEP 2106.05(d)(ii) and Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 23 recites the additional limitation of:
The method as claimed in claim 16, wherein the model is a domain-based model. (For the purposes of Step 2A prong 2 and Step 2B: the recitation of a domain based model used at a high level of generality is being considered as mere instructions to apply an exception, see MPEP 2106.05(f))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 26 recites the additional limitation of:
The method as claimed in claim 16, wherein the automatically diverting traffic includes diverting traffic by appropriate traffic lights. (For the purposes of Step 2A prong 2 and Step 2B: this limitation is generally linking the use of a judicial exception to a particular technological environment or field of use of traffic diversion traffic lights, see MPEP 2106.05(f))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 27 recites the additional limitation of:
The method as claimed in claim 16, wherein the generating the synthetic measurement series of the measurement series of the pollutant concentrations includes altering only subranges of the measurement series of the pollutant concentration. (this limitation merely amounts to altering values within predetermined subranges and is considered a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 28 recites the additional limitation of:
The method as claimed in claim 21, wherein the wind speed is a vector field that has a component that is horizontal and vertical relative to the earth's surface. (For the purposes of Step 2A prong 2 and Step 2B: this limitation is generally linking the use of a judicial exception to a particular technological environment or field of use of wind speed on earth, see MPEP 2106.05(f))
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is 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.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
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 16, 21-23 and 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2016/0125307 A1), hereafter referred to as Zhang, in view of Sharp et al., (US 7302313 B2), hereafter referred to as Sharp, and in further view of Xipeng et al. (Xipeng, Z., Shunsheng, Y., Wenchuan, X., & Yu, C. (2018). RBF Neural Network-Based Prediction and Inverse Calculation of Air Pollutant Emission Concentration. American Journal of Biological and Environmental Statistics, 4(2), 66-73.), hereafter referred to as Xipeng, and Goodfellow et al. (Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.), hereafter referred to as Goodfellow and Pandiyaraj et al., (Pandiyaraj, G. S., Pugazhendhi, B. B., & Umamaheswari, R. (2018). Traffic management based air pollution monitoring system. International Conference on Emerging Trends in Science, Technology and Engineering.), hereafter referred to as Pandiyaraj.
Claim 16: Zhang teaches:
providing a model for a relationship between a physical measured variable and a pollutant emission; (Zhang, paragraph 5, “The co-training framework may generate inference models, i.e., classifiers, which are used to interpolate air qualities for additional areas based on a limited set of measured air quality data from a small number of areas. The models may be used to infer the air quality of the additional areas based on real-time air quality data from existing air quality monitor stations and other forms of collected spatial or temporal data.”)
Sharp, in the same field of air quality tracking, teaches the following limitations which Zhang fails to teach:
A method comprising: providing a measurement series of pollutant concentrations including a measured value above a defined threshold value; (Sharp, col. 6, lines 13-20, “an air quality monitoring system comprises at least one air quality sensor for acquiring sensor data at a selected indoor location, a control unit for generating a grab sample command in response to the acquired sensor data meeting a predetermined criteria, and a grab sample unit for acquiring an air sample at the selected indoor location in response to the grab sample command from the control unit.”,
Sharp, col. 20, lines 33-37, “The criteria for triggering a grab Sample may include concentrations above the average concentration, above a predetermined threshold value,”, Sharp collects air samples (including air concentrations) when the acquired sensor data meet a predetermined threshold.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Sharp into that of Zhang, because Zhang relies on monitor-station measurement series for air-quality modeling/inference, and Sharp teaches a well-known technique for triggering acquisition/collection of air samples (and thus pollutant concentration measurements) when sensed air-quality data meets predetermined criteria (i.e., exceeds a defined threshold). A motivation for combining would have been to improve the relevance and efficiency of Zhang’s measurement-series collection by ensuring the collected/used concentration series includes pollutant events of interest (e.g., exceedances above a defined threshold), thereby focusing training/inference on higher-impact pollution episodes and reducing unnecessary sampling/processing when conditions are normal. Sharp supports this motivation by expressly disclosing “a control unit for generating a grab sample command in response to the acquired sensor data meeting a predetermined criteria” (Sharp, col. 6, lines 13-20).
Xipeng, in the same field of pollution inference, teaches the following limitation which the above fails to teach:
training a neural network, designed to determine a predicted pollutant concentration from at least one pollutant emission, with the measurement series of the pollutant concentrations… as training data; (Xipeng, Abstract, “A data prediction model for RBF neural network was created. Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors…”; Xipeng, page 67, col. 1, paragraph 2, “the available air pollution concentration data were used to predict the pollutant concentration at the unknown positions”; Xipeng, page 67, col. 2, section 2.1, “RBF neural network has an input layer, hidden layer and output layer”; Xipeng, page 67, col. 2, section 2.1, “The Gaussian plume diffusion model-based experimental data were used to create a neural network model. The first 200 groups of data were used for a drill…”; Xipeng, page 69, Formula (3), defining C(x, y, z) as pollutant concentration and Q as pollution source intensity. Xipeng teaches an RBF neural network used to predict pollutant concentration. In particular, Xipeng discloses that measured air pollution data are used to predict pollutant concentration at unknown positions. Xipeng further discloses that the RBF neural network has an input layer, hidden layer, and output layer, and that Gaussian plume diffusion model-based experimental data were used to create the neural network model, with a first set of data used for a “drill,” i.e., training. Xipeng’s Gaussian plume model defines C(x, y, z) as pollutant concentration and Q as pollution source intensity. Thus, Xipeng teaches a neural-network model trained using pollutant-concentration data and designed to determine predicted pollutant concentration, wherein the pollutant concentration is related to at least one pollutant source intensity/emission value Q.)
determining by the neural network, the predicted pollutant concentration from the user input that includes the at least one pollutant emission pollutant emission. (Xipeng, page 67, col. 1 paragraph 2, “To solve the problem, the available air pollution concentration data were used to predict the pollutant concentration at the unknown positions”, Xipeng explains that “available air pollution concentration data were used to predict the pollutant concentration at the unknown positions,” which is an inference/use step: the trained prediction model is applied to input values to output predicted pollutant concentration for new/unknown conditions. In the context of Xipeng’s plume-model-based framework (used to create/train the neural network model), supplying the relevant model inputs, including source/emission-related measurements, corresponds to providing an input pollutant emission (here, via an input value provided to the model), so the reference teaches using the trained neural network to output pollutant concentration in response to an input emission value.)
measurement series of the pollutant concentrations, (Xipeng, Abstract, “Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors”; Xipeng, page 67, col. 1, paragraph 2, “the available air pollution concentration data were used to predict the pollutant concentration at the unknown positions”; Xipeng, page 67, col. 2, section 2.1, “The Gaussian plume diffusion model-based experimental data were used to create a neural network model. The first 200 groups of data were used for a drill…”; Xipeng, page 67, col. 2, section 2.1, “the concentration data were normalized first”; Xipeng, Figure 1, “Comparison of BP\RBF-based Pollutant Concentration Prediction Results And Actual Value.” Xipeng teaches that measured air-pollution concentration data are used to predict pollutant concentration at unknown positions. Xipeng further teaches using Gaussian plume diffusion model-based experimental data to create and train a neural network model, and describes the training data as concentration data. Thus, Xipeng teaches that the measurement series used for neural-network training is a measurement series of pollutant concentrations.
Xipeng further teaches that the pollutant-concentration measurement data are processed based on model input; (Xipeng, page 67, col. 2, section 2.1, “RBF neural network has an input layer, hidden layer and output layer”; Xipeng, page 67, col. 2, section 2.1, “δx(t): Euclidean distance between input parameter vector and central vector”; Xipeng, page 69, Formula (3), defining C(x, y, z) as “Pollutant concentration at (x, y, z)” and Q as “Pollution source intensity.” Xipeng teaches that the RBF neural network receives input through an input layer and operates on an input parameter vector. Xipeng further teaches that pollutant concentration C(x, y, z) is modeled using a pollution source intensity Q. Under a broad but reasonable interpretation, a user input corresponds to input information provided to the model, and Xipeng teaches input parameters used by the model to determine pollutant concentration. Thus, Xipeng supports the “based on user input” aspect of the limitation when the pollutant-concentration measurement series is generated or processed based on input parameters supplied to the model.)
training a neural network, designed to determine a predicted pollutant concentration from at least one pollutant emission; (Xipeng, page 67, col. 1, paragraph 2, “the available air pollution concentration data were used to predict the pollutant concentration at the unknown positions”; Xipeng, page 67, col. 2, section 2.1, “RBF neural network has an input layer, hidden layer and output layer”; Xipeng, page 67, col. 2, section 2.1, “The Gaussian plume diffusion model-based experimental data were used to create a neural network model. The first 200 groups of data were used for a drill…”; Xipeng, page 69, Formula (3), defining C(x, y, z) as “Pollutant concentration at (x, y, z)” and Q as “Pollution source intensity.”, Xipeng teaches training an RBF neural network to predict pollutant concentration. Xipeng further teaches that the Gaussian plume diffusion model relates pollutant concentration C(x, y, z) to pollution source intensity Q. Under a broad but reasonable interpretation, pollution source intensity Q corresponds to at least one pollutant emission because it is the source/emission term used to calculate pollutant concentration. Thus, Xipeng teaches training a neural network designed to determine a predicted pollutant concentration from at least one pollutant emission.)
Zheng discloses a neural-network framework that takes pollutant emissions, meteorological parameters, and other measured variables as inputs to predict pollutant concentrations via co-trained spatial and temporal classifiers. In particular, Zheng et al. describe extracting spatial and temporal features from multiple sensor streams and feeding them into a neural network to output inferred air-quality indices and concentration values. Xi-Peng then shows how, “according to the measured values and the predicted data,” a Gaussian-plume diffusion model can be coupled with a quadratic optimization and inversion method to directly compute pollutant emission rates from measured concentration data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Zhang and Sharp by incorporating the teachings of Xipeng to perturb input of the models training data. A motivation of which would be to use RBF (Radial Basis Function) neural networks to infer pollutant indexes, achieving higher accuracy when compared to other neural networks like BP (Back-propagation), (Goodfellow, page 5, paragraph 5, “The adversarial training procedure can be seen as minimizing the worst case error when the data is perturbed by an adversary. That can be interpreted as learning to play an adversarial game, or as minimizing an upper bound on the expected cost over noisy samples with noise from U(−ε, ε) added to the inputs. Adversarial training can also be seen as a form of active learning, where the model is able to request labels on new points. In this case the human labeler is replaced with a heuristic labeler that copies labels from nearby points.”).
Goodfellow, in the same field of data augmentation, teaches the following limitations which the above fails to teach:
training a neural network, … with the measurement series… and the synthetic measurement series as training data; (Goodfellow, Abstract, “adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset”; Goodfellow, page 3, paragraph 2, “Let θ be the parameters of a model, x the input to the model, y the targets associated with x … and J(θ, x, y) be the cost used to train the neural network”; Goodfellow, page 3, paragraph 2, “η = εsign(∇xJ(θ, x, y)). We refer to this as the ‘fast gradient sign method’ of generating adversarial examples”; Goodfellow, page 5, paragraph 2, “This approach means that we continually update our supply of adversarial examples”; Goodfellow, page 5, paragraph 5, “The adversarial training procedure can be seen as minimizing the worst case error when the data is perturbed by an adversary.”, Goodfellow teaches using adversarial examples during neural-network training. The adversarial examples are synthetic examples because they are generated by perturbing original examples from the dataset. Goodfellow further teaches that these perturbed examples are used in adversarial training. Thus, Goodfellow teaches using a synthetic measurement series as training data.)
and generating a synthetic measurement series using an alteration of the measurement series,… based on user input (Goodfellow, page 5, paragraph 1, “We found that training with an adversarial objective function based on the fast gradient sign method was an effective regularizer:
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In all of our experiments, we used α = 0.5. Other values may work better; our initial guess of this hyperparameter worked well enough that we did not feel the need to explore more. This approach means that we continually update our supply of adversarial examples, to make them resist the current version of the model.”, Goodfellow, Abstract, “adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset”; Goodfellow, page 3, paragraph 2, “Let θ be the parameters of a model, x the input to the model, y the targets associated with x … and J(θ, x, y) be the cost used to train the neural network”; Goodfellow, page 3, paragraph 2, “η = εsign(∇xJ(θ, x, y)). We refer to this as the ‘fast gradient sign method’ of generating adversarial examples”; Goodfellow, page 5, paragraph 2, “This approach means that we continually update our supply of adversarial examples”; Goodfellow, page 6, paragraph 5, “it is usually better to just perturb the original input.” Goodfellow teaches generating synthetic/adversarial examples by perturbing an original input example from a dataset. Goodfellow identifies x as the input to the model and teaches generating an altered example by adding perturbation η to that input. Under a broad but reasonable interpretation, “user input” includes input data supplied to the model. Therefore, Goodfellow teaches generating a synthetic measurement series using an alteration based on the supplied input data, where the synthetic series is generated by perturbing the original input measurement series.)
Zheng teaches using a neural network to predict pollutant concentrations from emission data, and Xipeng demonstrates inverting measured concentration values into emission rates via a Gaussian‐plume/RBF neural-network framework . Goodfellow uses the “fast gradient sign method” for numerically perturbing inputs, and the mixed-loss objective to synthesize new training examples from original and perturbed inputs. A person of ordinary skill in the art seeking to improve pollutant-emission prediction would have been motivated to apply these adversarial‐perturbation and synthetic-data techniques to the measured concentration inputs taught by Zheng, Sharp, and Xipeng—thereby generating perturbed concentration series (C₁), computing their corresponding emission outputs (E₁), and producing a synthetic measurement series, with each perturbation inherently tied to the model’s relative output change ΔE/E₀. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Zhang by incorporating the teachings of Goodfellow to perturb input of the models training data. A motivation of which would be to use adversarial examples to further minimize the worst case error and provide a form of active learning, (Goodfellow, page 5, paragraph 5, “The adversarial training procedure can be seen as minimizing the worst case error when the data is perturbed by an adversary. That can be interpreted as learning to play an adversarial game, or as minimizing an upper bound on the expected cost over noisy samples with noise from U(−ε, ε) added to the inputs. Adversarial training can also be seen as a form of active learning, where the model is able to request labels on new points. In this case the human labeler is replaced with a heuristic labeler that copies labels from nearby points.”).
Pandiyaraj, in the same field of traffic safety analysis, teaches the following limitations which the above fails to teach:
And based on the prediction for the pollutant concentration, performing at least one of: automatically diverting traffic; and automatically making more buses or more trams available. (Pandiyaraj, abstract, “The main objective of this paper is to introduce an equipment which manages the road traffic based on monitoring the air pollution level by pollutant gas sensor…. The experimental results show that the proposed system can provide least polluted path and reduces air pollution by diverting the road traffic dynamically.”, Page 183, col. 2, paragraph 1, “This process will compare the sensed data with the threshold value in all the junctions. Then the Least polluted path is identified by an algorithm named Analytical Hierarchy Process. This Algorithm will compare all the junctions which have crossed the threshold limit and selects the least polluted path. The traffic lights are controlled based on the least polluted path chosen by the above Algorithm.”, Pandiyaraj teaches a system that manages road traffic based on air-pollution information obtained from pollutant gas sensors. The claim requires that, based on the predicted pollutant concentration, at least one listed action is performed. Pandiyaraj teaches the listed action of “automatically diverting traffic” because the system compares pollution levels at road junctions, identifies the least polluted path, and diverts vehicles to that path. The phrase “diverting the road traffic dynamically” supports that the traffic diversion is not merely a passive recommendation, but an active traffic-management response. Thus, Pandiyaraj teaches the automatic traffic-diversion alternative of the claimed “at least one of” limitation.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the pollutant-concentration prediction system of Zhang, Sharp, Xipeng, and Goodfellow by incorporating the traffic-management teachings of Pandiyaraj. Pandiyaraj teaches a known downstream use of air-pollution information: dynamically managing traffic based on monitored pollutant levels, including selecting a least-polluted path and controlling traffic lights based on that path. A POSITA would have been motivated to combine these teachings so that the predicted pollutant concentration is not merely calculated, but is used to automatically reduce further pollutant accumulation and reduce user exposure by diverting vehicles away from higher-pollution areas. Such a modification would have amounted to applying a known traffic-control response to the known problem of traffic-related air pollution, with predictable results.
Claim 21: Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj teaches the limitations of claim 16, Zhang further teaches:
The computer aided method as claimed in claim 16, wherein the physical measured variable used is a temperature, a wind speed, and/or a traffic level. (Zhang, paragraph 37, “The temporal feature extraction module 212 may extract temporal features from various environmental or human movement data of the unlabeled source data 112. The concentration of air pollutants may be influenced by meteorology. Accordingly, the temporal feature extraction module 212 may identify, for example, five categories of features: temperature, humidity, barometric pressure, wind speed, and weather (Such as cloudy, foggy, rainy, Sunny, and Snowy). For example, high wind speed may disperse the concentration of PM, and high humidity may result in a high concentration of Mo. High pressure may result in a relative good AQI. A relatively good AQI may also be present when pressure is high and temperature is low.”)
Claim 22: Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj teaches the limitations of claim 16, Zhang further teaches:
The computer aided method as claimed in claim 16, further comprising capturing the measurement series of the pollutant concentration and the measurement series of the measured variable using a measurement station within a town. (Zhang, paragraph 6, “In at least one embodiment, labeled air quality index data for a pollutant in a region may be obtained from one or more air quality monitor stations.”)
Claim 23: Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj teaches the limitations of claim 16, Zhang further teaches:
The computer aided method as claimed in claim 16, wherein the model is a domain-based model. (Zhang, paragraph 18, “The techniques may use a semi-supervised learning approach based on a co-training framework that trains two separate classifiers, such as a spatial classifier and a temporal classifier.”, By splitting learning into distinct spatial and temporal “domains” and then combining them, Zheng teaches a domain-based modeling approach.)
Claim 26: Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj teaches the limitations of claim 16, Pandiyaraj further teaches:
The method as claimed in claim 16, wherein the automatically diverting traffic includes diverting traffic by appropriate traffic lights. (Pandiyaraj, page 183, section IV, paragraph 1, “This MQ135 Air Quality Sensor gives the concentration of different gases. Micro controller helps in controlling Traffic Signal Lights by the use of Arduino Microcontroller and also used for transferring sensor data towards the cloud to the server, where the Algorithm is processed.”, Page 183, col. 2, last paragraph, “The least weighted path will be assigned higher priority and named as least polluted junction or path to which the vehicles should be diverted to and consequently the highly polluted junction is controlled in an efficient manner.”, Pandiyaraj teaches traffic-signal-light control by a microcontroller. It further teaches that sensed pollutant data are compared with threshold values, a least-polluted path is selected by an algorithm, and the traffic lights are controlled based on that selected path. The “appropriate traffic lights” are taught by the traffic lights at the relevant junctions that are controlled to prioritize the least-polluted path. Because Pandiyaraj expressly states that vehicles are diverted to the least-polluted path and that traffic lights are controlled based on that path, Pandiyaraj teaches diverting traffic by appropriate traffic lights.)
Claim 27: Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj teaches the limitations of claim 16, Pandiyaraj further teaches:
The method as claimed in claim 16, wherein the generating the synthetic measurement series of the measurement series of the pollutant concentrations includes altering only subranges of the measurement series.... (Goodfellow abstract, “adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed in put results in the model outputting an incorrect answer with high confidence”, Page 2, section 3, paragraph 2, “Because the precision of the features is limited, it is not rational for the classifier to respond differently to an input x than to an adversarial input ˜x = x +η if every element of the perturbation η is smaller than the precision of the features… we can make many infinitesimal changes to the input that add up to one large change to the output.”, page 3, figure 1, “By adding an imperceptibly small vector whose elements are equal to the sign of the elements of the gradient of the cost function with respect to the input, we can change GoogLeNet’s classification of the image.” Goodfellow teaches generating adversarial examples, i.e., synthetic training examples, by altering original dataset examples through input perturbations. The claim’s “measurement series of the pollutant concentrations” corresponds to an input data series provided to the model, and Goodfellow’s original example x corresponds to the original input example or input series. Goodfellow’s adversarial example x̃ = x + η corresponds to a synthetic version of the original input data generated by adding perturbation η. Goodfellow also teaches that the perturbation is applied at the level of input features or input-vector elements, because it discusses “every element of the perturbation η,” “many infinitesimal changes to the input,” and adding a vector whose elements are based on the gradient with respect to the input. Under a broad but reasonable interpretation, a “subrange” of a measurement series is a selected portion or subset of input values within that series. Because Goodfellow teaches altering input-vector elements/features to generate synthetic examples, Goodfellow supports altering selected portions of the input series. Therefore, when Goodfellow’s input-perturbation technique is applied to a pollutant-concentration measurement series, altering selected input elements or portions of the series teaches or at least renders obvious “altering only subranges of the measurement series of the pollutant concentration.”)
Xipeng further teaches :
measurement series of the pollutant concentrations, (Xipeng, Abstract, “Other air pollution data at the unknown positions were predicted by the data measured by the arranged sensors”; Xipeng, page 67, col. 1, paragraph 2, “the available air pollution concentration data were used to predict the pollutant concentration at the unknown positions”; Xipeng, page 67, col. 2, section 2.1, “The Gaussian plume diffusion model-based experimental data were used to create a neural network model. The first 200 groups of data were used for a drill…”; Xipeng, page 67, col. 2, section 2.1, “the concentration data were normalized first”; Xipeng, Figure 1, “Comparison of BP\RBF-based Pollutant Concentration Prediction Results And Actual Value.” Xipeng teaches that measured air-pollution concentration data are used to predict pollutant concentration at unknown positions. Xipeng further teaches using Gaussian plume diffusion model-based experimental data to create and train a neural network model, and describes the training data as concentration data. Thus, Xipeng teaches that the measurement series used for neural-network training is a measurement series of pollutant concentrations.)
Claims 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj in further view of Sundvor et al. (Sundvor, I., Castell Balaguer, N., Viana, M., Querol, X., Reche, C., Amato, F., Mellios, G., & Guerreiro, C. (2012). Road traffic’s contribution to air quality in European cities (ETC/ACM Technical Paper 2012/14). European Topic Centre on Air Pollution and Climate Change Mitigation.), hereafter referred to as Sundvor.
Claim 19: Zhang, Sharp, Xipeng, Goodfellow, and Antoniou teaches the limitations of claim 17, Antoniou further teaches:
The computer aided method as claimed in claim 16, wherein a traffic- related proportion of the pollutant concentration is in a range from 0.3 to 0.5. (Sundvor, page 7, paragraph 3, “the range of the traffic contribution to urban PM concentrations is from 9-53% for PM10 and 9-66% for PM2.5… the TENs indicate an average estimate of 34 % of urban and local traffic contribution to the measured PM10 concentrations at traffic sites”, The claim recites a “traffic-related proportion of the pollutant concentration” in the range from 0.3 to 0.5. Sundvor teaches traffic contribution percentages to measured pollutant concentrations. The quoted 34% average “urban and local traffic contribution” to measured PM10 concentration corresponds to a numerical proportion of 0.34, which falls within the claimed range of 0.3 to 0.5.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the pollutant-concentration prediction system of Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj by incorporating the traffic-contribution teachings of Sundvor. Zhang already uses traffic-related data in air-quality inference, and Pandiyaraj uses pollution information to manage traffic. Sundvor provides known traffic-related contribution proportions for pollutant concentrations in urban environments, including road-traffic contributions to measured PM concentrations. A POSITA seeking to determine or weight the traffic-related portion of a pollutant concentration would have been motivated to use known traffic-contribution proportions, such as those disclosed by Sundvor, because doing so would allow the model or control method to account for the fraction of pollution attributable to road traffic. This would have been a predictable use of known source-apportionment data in a traffic-influenced air-pollution prediction and traffic-management system.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj, in further view of Forehead et al. (Forehead, H., & Huynh, N. (2018). Review of modelling air pollution from traffic at street-level-The state of the science. Environmental Pollution, 241, 775-786.), hereafter referred to as Forehead.
Claim 20: Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj teaches the limitations of claim 16, Forehead in the same field of pollution analysis, teaches the following which the above fails to teach:
The computer aided method as claimed in claim 16, wherein the pollutant concentration is a nitrogen oxide concentration (Forehead, page 776, col. 1, paragraph 2, “Diesel vehicles produce most of the particles of 2.5 microns and smaller (PM2.5) and oxides of nitrogen (NOx).”)
and the pollutant emission is a nitrogen oxide emission. (Forehead, page 776, col. 2, paragraph 2, “The composition of the mixture of gases and particles changes with time after release from the exhaust pipe. For example, the concentrations of particular species, such as NOx, can determine the production of secondary pollutants such as ozone”)
Zheng teaches a neural‐network based scheme for inferring air quality indexes by co‐training spatial and temporal classifiers on sensor‐derived concentration inputs. However, it does not address how to select or weight specific pollutant species like nitrogen oxide. Forehead highlights the critical role of identifying NOₓ in traffic‐related air pollution. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Zheng, Sharp, Xipeng, Goodfellow, and Pandiyaraj with Forehead (i.e. identifying NO levels in emission/concentrations). A motivation would have been to provide a way to track specific environmental harming pollutants. (Forehead, page 776, col. 2, paragraph 3, “Although numbers of vehicles on roads continue to increase, emissions regulations have mandated increased efficiency of engine technologies to reduce outputs of harmful emissions. Older, carbureted cars released 10 times the HC, 4 times the CO and 3 times the NOx of newer multi-point ignition engines (Qu et al., 2015). However, while newer cars release less pollution, the expected reduction in emissions from modern vehicles will only be realized if their emissions control equipment is properly maintained”, tracking specific emissions like NOx provides further detailed analysis of the pollution concentration of an environment.)
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj, in further view of Suebyat et al. (Suebyat, K., & Pochai, N. (2018). Numerical simulation for a three-dimensional air pollution measurement model in a heavy traffic area under the Bangkok Sky Train platform. Abstract and Applied Analysis, 2018, Article 9025851. DOI: 10.1155/2018/9025851.), hereafter referred to as Suebyat.
Claim 28: Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj teaches the limitations of claim 21, Suebyat in the same field of windspeed analysis, teaches the following which the above fails to teach:
The method as claimed in claim 21, wherein the wind speed is a vector field that has a component that is horizontal and vertical relative to the earth's surface. (Suebyat, page 3, col. 1, paragraph 2, “The vector𝑉 is the wind velocity field(m/sec); 𝐾 is the eddy-diffusivity or dispersion tensor(m2/sec).= (𝜕/𝜕𝑥) → 𝑖 +(𝜕/𝜕𝑦) → 𝑗 +(𝜕/𝜕𝑧)→ 𝑘,⊗ is matrix multiplication, and𝑅(𝑥,𝑦,𝑧,𝑡) describes sources or sinks of air pollutants (sec−1).”, Suebyat expressly identifies V as a “wind velocity field,” which teaches the “vector field” portion of the limitation. Suebyat further decomposes wind velocity into directional components u, v, and w in the x, y, and z directions. The x- and y-directions correspond to horizontal components in the modeled ground/street domain, while the z-direction corresponds to the vertical direction.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the pollutant-concentration prediction system of Zhang, Sharp, Xipeng, Goodfellow, and Pandiyaraj by incorporating the three-dimensional wind-velocity-field teachings of Suebyat. Xipeng uses pollutant-dispersion modeling in which wind speed and pollutant diffusion affect predicted concentration, and Suebyat teaches a three-dimensional air-pollution model in a heavy-traffic urban environment using wind-velocity components in the x, y, and z directions. A POSITA would have been motivated to represent wind speed as a vector field having horizontal and vertical components because three-dimensional wind movement affects pollutant transport and dispersion, especially in urban traffic environments. Incorporating Suebyat’s vector-field representation would have predictably improved the physical accuracy of pollutant concentration modeling by accounting for both horizontal advection and vertical dispersion.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Negrenti, E. (1999). The ‘corrected average speed’approach in ENEA’s TEE model: an innovative solution for the evaluation of the energetic and environmental impacts of urban transport policies. Science of the total environment, 235(1-3), 411-413.
Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017). Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environmental pollution, 231, 997-1004.
Freeman, B. S., Taylor, G., Gharabaghi, B., & Thé, J. (2018). Forecasting air quality time series using deep learning. Journal of the Air & Waste Management Association, 68(8), 866-886.
Bui, T. C., Le, V. D., & Cha, S. K. (2018). A deep learning approach for forecasting air pollution in South Korea using LSTM. arXiv preprint arXiv:1804.07891.
Stockie, J. M. (2011). The mathematics of atmospheric dispersion modeling. Siam Review, 53(2), 349-372.
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017). Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204.
Han, K., Liu, H., Gayah, V. V., Friesz, T. L., & Yao, T. (2016). A robust optimization approach for dynamic traffic signal control with emission considerations. Transportation Research Part C: Emerging Technologies, 70, 3-26.
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/H.B.Y./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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