DETAILED ACTION
Notice of 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 .
Information Disclosure Statement
The information disclosure statement submitted on 1/27/2026 has been considered.
Response to Amendment
Applicant’s Amendment and remarks dated 1/27/2026 have been considered. Claims 1-10 are pending.
Drawing Objections. The drawing objections are withdrawn in view of the replacement drawings submitted by Applicant.
Claim Objections. The objection to claim 3 is withdrawn in view of Applicant’s amendments to such claim.
Response to Arguments
On pages 7-8 of Applicant’s 1/27/2026 Amendment and remarks, Applicant asserts that at least paras. 0055 and 0081 of the instant specification provide written description support for the claim amendments.
The examiner agrees that the portions of the disclosure identified by Applicant provide sufficient written description support for the claim amendments.
On pages 8-9 Applicant’s 1/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, Applicant asserts that the newly-added “c1) measuring, using a real-time air quality sensor located in the venue, physical and chemical properties of indoor air at the venue in real-time to obtain measured data; the real-time air quality sensor connected to the computing device” limitation makes the claims eligible under Step 2A, prong 2 or Step 2B.
The examiner respectfully disagrees. With respect to Step 2A, prong 2, measuring data is merely a data gathering step, and such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
As a whole, the claim does not pertain to any improvement to any computer technology or other technical field. Any improvements are merely to the mental processes of evaluating AI models, and making predictions of concentration of indoor bioaerosols, and not to any actual technical field.
With respect to Step 2B, measuring data is merely a data gathering step, and the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
While Applicant argues that the claims are “not well understood, routine, or conventional in the field”, Applicant has provided no evidence in support of such contention. MPEP 2106.05(d) explains that the well-understood, routine, and conventional activity test is a factor for consideration, and not a standalone test under Step 2B. Because there is no evidence of record establishing that the claim limitations are well-understood, routine, and conventional, or not, the examiner finds that this factor does not favor, nor disfavor, a finding of eligibility.
On pages 9-10 Applicant’s 1/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant asserts that the TANIGUCHI and LIU references do not teach the newly-added limitations related to “using the sensor to measure physical and chemical properties of indoor air at the venue in real-time to obtain the measured data.”
The examiner acknowledges that TANIGUCHI and LIU do not explicitly teach this limitation. The previous rejections under 35 U.S.C. 103 are hereby withdrawn. However, new grounds of rejection, which were necessitated by Applicant’s amendments, are provided herein.
On pages 10-11 Applicant’s 1/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that the TANIGUCHI and LIU references do not adequately disclose the “venue” limitation:
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The examiner respectfully disagrees. In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). TANIGUICHI discloses evaluating the prediction accuracy of a plurality of prediction models and selecting a highest accuracy model. LIU teaches the concept of using neural network models to estimate the concentration of indoor airborne bacteria. Applying the teachings of TANIGUCHI to the indoor venue of LIU, where now a highest accuracy model to estimate the concentration of indoor airborne bacteria is selected as in LIU, would have been straightforward to implement.
The examiner respectfully disagrees with Applicant’s argument that “LIU also does not realize the importance of the venue for the model output.” The models of LIU are specific to certain buildings, which each correspond to the recited “venue.”
On page 11 Applicant’s 1/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that there is no motivation for one of ordinary skill to combine TANIGUCHI with LIU.
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The examiner respectfully disagrees. A motivation to combine TANIGUCHI with LIU was explicitly provided on pages 21-22 of the 9/23/2025 office action, and Applicant has not rebutted that finding. Moreover, Applicant’s argument that there is a “technical incompatibility that renders the intended method and results in TANIGUCHI destroyed” is not persuasive. Modifying the system of TANIGUCHI to consider multiple prediction models and predict the highest accuracy one, for particular indoor bioaerosol readings in a building, would have been straightforward for one of ordinary skill to implement as explained in the detailed rejections.
On pages 11-12 Applicant’s 1/27/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that the dependent claims should be allowed for the same reasons explained with respect to the independent claims.
The examiner respectfully disagrees for the same reasons explained with respect to the independent claims.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-8 are directed to a method (a process), Claim 9 is directed to an apparatus (a machine), and Claim 10 is directed to a non-transitory computer readable medium (an article of manufacture), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “artificial intelligence (AI) models” and “computing devices”).
A method for predicting concentration of indoor bioaerosols, comprising steps of: (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally predict a concentration of indoor bioaerosols, e.g., a prediction of 0% for clean room with state-of-the-art filtering)
b) evaluating a prediction accuracy of each of the plurality of Al models for a venue; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally evaluate the prediction accuracy of each of the AI models for a venue, such as by mentally reviewing the outputs of an analysis of the prediction accuracy for each model)
c) choosing a best model from the plurality of Al models for the venue; and (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally choose the model with the highest accuracy as the recited “best model”)
e) generating a prediction of concentration of indoor bioaerosols ... for the venue using the measured data (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally predict a concentration of indoor bioaerosols using measured data for a venue, e.g., a prediction of 0% for clean room with state-of-the-art filtering)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “artificial intelligence (AI) models” and “computing devices”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “a) providing a plurality of artificial intelligence (AI) models on a computing device” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of AI models on generic computing devices. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (AI models, without specific details about such AI model’s architecture, training, configuration, hyperparameters, etc.). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “c1) measuring, using a real-time air quality sensor located in the venue, physical and chemical properties of indoor air at the venue in real-time to obtain measured data; the real-time air quality sensor connected to the computing device” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “d) inputting the measured data into the best model on the computing device” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of AI models. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (AI models, without specific details about such AI model’s architecture, training, configuration, hyperparameters, etc.). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “... by the best model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of AI models. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (AI models, without specific details about such AI model’s architecture, training, configuration, hyperparameters, etc.). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “artificial intelligence (AI) models” and “computing devices”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “a) providing a plurality of artificial intelligence (AI) models on a computing device” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “c1) measuring, using a real-time air quality sensor located in the venue, physical and chemical properties of indoor air at the venue in real-time to obtain measured data; the real-time air quality sensor connected to the computing device” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “d) inputting the measured data into the best model on the computing device” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “... by the best model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 2
Step 2A, Prong 2
Regarding the “wherein the plurality of Al models includes one or more of a linear regression model, a lasso regression model, a random forest (RF) model, an extreme gradient boosting model, a multilayer perceptron model, an LSTM model, and a recurrent neural network model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of an AI model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (an AI model of a particular type, which still does not claim sufficient details regarding the architecture, configuration, training, hyperparameters, etc.). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (particular categories of machine learning models). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the plurality of Al models includes one or more of a linear regression model, a lasso regression model, a random forest (RF) model, an extreme gradient boosting model, a multilayer perceptron model, an LSTM model, and a recurrent neural network model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)). Moreover, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 3
Step 2A, Prong 1
h) finding, for each of the plurality of Al model, a difference data between predicted test data and measured test data; (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally subtract the measured test data from the predicted test data to determine difference data; the examiner further notes that this subtraction operation is a mathematical calculation, which is another type of abstract idea)
i) determining one of the plurality of Al models that has a best difference data as the best model. (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally determine that the AI model that has the highest accuracy, or the lowest difference data, as the best model)
Step 2A, Prong 2
Regarding the “f) inputting test data for the venue into each of the plurality of Al models” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of AI models. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (AI models, without specific details about such AI model’s architecture, training, configuration, hyperparameters, etc.). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “g) applying more than one pair of input and output time windows” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result (applying any pair of time windows to a generic AI model, without any particular guidance as to how to select the time windows and how to configure the AI model to utilize such time windows). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “f) inputting test data for the venue into each of the plurality of Al models” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “g) applying more than one pair of input and output time windows” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 4
Step 2A, Prong 1
wherein the difference data comprises one or more of a mean squared error (MSE), a root-mean-square error (RMSE) and a value on a revised version of the Willmott's index (WI). (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally subtract the measured test data from the predicted test data to determine difference data and then perform calculations of MSE or RMSE; the examiner further notes that this operation is a mathematical calculation, which is another type of abstract idea)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 2
Regarding the “wherein the more than one pair of input and output time windows comprises a real-time window pair” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result (applying any pair of time windows to a generic AI model, without any particular guidance as to how to select the time windows and how to configure the AI model to utilize such time windows). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein the more than one pair of input and output time windows comprises a real-time window pair” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation attempts to cover a solution to an identified problem with no restriction on how the result is accomplished, or provides no description of the mechanism for accomplishing the result. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 6
Step 2A, Prong 1
the method further comprising a step of determining which one of the plurality of input features is more important than another one by conducting a permutation importance analysis (under the broadest reasonable interpretation, a human can mentally perform this limitation, for example, a human can mentally perform a permutation importance analysis to determine the input feature that is most important to the accuracy of the AI models, e.g., trying different permutations of input features and mentally determining which permutation has the highest accuracy)
Step 2A, Prong 2
Regarding the “wherein the measured data comprises a plurality of input features” limitation, this limitation merely describes the types of input data being processed, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the measured data comprises a plurality of input features” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 7
Step 2A, Prong 2
Regarding the “wherein the plurality of input features comprises one or more of temperature, relative humidity (RH), concentrations of CO2, total volatile organic compounds (TVOCs), PM2.5 and PM10” limitation, this limitation merely describes the types of input data being processed, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the plurality of input features comprises one or more of temperature, relative humidity (RH), concentrations of CO2, total volatile organic compounds (TVOCs), PM2.5 and PM10” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 8
Step 2A, Prong 2
Regarding the “wherein the plurality of input features comprises concentrations of more than one biological matters” limitation, this limitation merely describes the types of input data being processed, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B
Regarding the “wherein the plurality of input features comprises concentrations of more than one biological matters” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 9
Step 2A, Prong 1
Claim 9 recites an apparatus that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (“one or more processors”, “memory storing computer-executable instructions”, “artificial intelligence (AI) models”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 9 recites an apparatus that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (“one or more processors”, “memory storing computer-executable instructions”, “artificial intelligence (AI) models”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 9 recites an apparatus that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 9. While claim 9 recites additional generic computing components (“one or more processors”, “memory storing computer-executable instructions”, “artificial intelligence (AI) models”), such additional generic computing components do not change the analysis under Step 2B.
Regarding Claim 10
Step 2A, Prong 1
Claim 10 recites a non-transitory computer readable medium that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 10. While claim 10 recites additional generic computing components (processor”, “non-transitory computer readable medium”, “computing device” and “artificial intelligence (AI) models”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 10 recites a non-transitory computer readable medium that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 10. While claim 10 recites additional generic computing components (processor”, “non-transitory computer readable medium”, “computing device” and “artificial intelligence (AI) models”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 10 recites a non-transitory computer readable medium that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 10. While claim 10 recites additional generic computing components (processor”, “non-transitory computer readable medium”, “computing device” and “artificial intelligence (AI) models”), such additional generic computing components do not change the analysis under Step 2B.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-2 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210012244 A1, hereinafter referenced as TANIGUCHI, in view of Liu, Zhijian, et al. "Quick estimation model for the concentration of indoor airborne culturable bacteria: an application of machine learning." International journal of environmental research and public health 14.8 (2017): 857, hereinafter referenced as LIU, and further in view of US 20210364184 A1, hereinafter referenced as SAVAKKANAVAR.
Regarding Claim 1
TANIGUCHI discloses:
a) providing a plurality of artificial intelligence (AI) models on a computing device; (TANIGUCHI, para. 0021: A model generation program according to the present invention causes a computer to execute the processes of: ... determining a prediction model used for the progress prediction from among the plurality of the learned prediction models based on an evaluation result regarding the prediction accuracy and an evaluation result regarding the graph shape or the number of defective samples.”;
TANIGUCHI, para. 0080: “The model selection unit 13 selects a prediction model that has a high prediction accuracy and has a small number of samples (hereinafter referred to as the number of defective samples) that cannot be interpreted from among a plurality of prediction models (learned models having mutually different values of the regularization parameters that affect the term of the strong regularization variable) learned by the model learning unit 12.”;
TANIGUCHI, para. 0246: “FIG. 19 is a schematic block diagram showing a configuration example of a computer according to each exemplary embodiment of the present invention. A computer 1000 includes a CPU 1001, a main storage device 1002, an auxiliary storage device 1003, an interface 1004, a display device 1005, and an input device 1006.”)
b) evaluating a prediction accuracy of each of the plurality of Al models ...; (TANIGUCHI, para. 0257: “The accuracy evaluation means 63 (for example, a part of the model selection unit 13, a part of the model selection unit 223, the model evaluation unit 131), evaluates the prediction accuracy of each of the plurality of learned prediction models using the predetermined verification data.”)
c) choosing a best model from the plurality of Al models ...; (TANIGUCHI, para. 0089: “Next, the model selection unit 13 selects a prediction model having a high prediction accuracy and a small number of defective samples from among the plurality of learned prediction models, and stores it in the model storage unit 14 (step S104).”;
TANIGUCHI, para. 0259: “The model determination means 65 (for example, a part of the model selection unit 13, a part of the model selection unit 223, the model determination unit 132) determines, based on the evaluation result regarding the prediction accuracy and the evaluation result regarding the graph shape or the number of defective samples, a single prediction model to be used for the progress prediction from among the plurality of learned prediction models.”)
d) inputting the measured data into the best model on the computing device; and (TANIGUCHI, para. 0080: “When the prediction target data is input, the model application unit 15 uses the prediction model stored in the model storage unit 14 to perform progress prediction.”;
TANIGUCHI, para. 0082: “When the prediction target data is input, the model application unit 15 uses the prediction model stored in the model storage unit 14 to perform progress prediction.”;
TANIGUCHI, para. 0092: “When the prediction target data is input, the model application unit 15 reads the prediction model stored in the model storage unit 14 (step S202), applies the prediction target data to the read prediction model, and obtains a predicted value at each prediction time point included in the evaluation target period (step S203).”;
TANIGUCHI, para. 0263: “The prediction means 67 (for example, the model application unit 15, the model application unit 25), when the prediction target data is given, uses the prediction model stored in the model storage means 66 to perform progress prediction.”);
TANIGUCHI, para. 0246: “FIG. 19 is a schematic block diagram showing a configuration example of a computer according to each exemplary embodiment of the present invention. A computer 1000 includes a CPU 1001, a main storage device 1002, an auxiliary storage device 1003, an interface 1004, a display device 1005, and an input device 1006.”)
However, TANIGUCHI fails to explicitly teach:
A method for predicting concentration of indoor bioaerosols, comprising steps of:
... for a venue
... for the venue
c1) measuring, using a real-time air quality sensor located in the venue, physical and chemical properties of indoor air at the venue in real-time to obtain measured data; the real-time air quality sensor connected to the computing device
e) generating a prediction of concentration of indoor bioaerosols by the best model for the venue using the measured data.
However, in a related field of endeavor (neural networks to estimate the indoor concentration of airborne culturable bacteria, see p. 2, section 1), LIU teaches:
A method for predicting concentration of indoor bioaerosols, comprising steps of: (LIU, p. 2, section 1: “In this communication, we aim to propose a quick estimation method of the concentration of indoor airborne culturable bacteria using ANN models. Our results show that with the simple inputs of indoor PM2.5 and PM10, temperature, relative humidity, and CO2 concentration, the model trained from our experimental database with 249 data groups can effectively predict the concentration of indoor airborne culturable bacteria with relatively low root mean square errors (RMS errors).”; Examiner’s Note: The TANIGUCHI-LIU combination now utilizes the techniques of TANIGUCHI with respect to selecting a particular, high accuracy machine learning model, in order to estimate concentrations of indoor airborne culturable bacteria (corresponding to recited “indoor bioaerosols”) of LIU)
b) evaluating a prediction accuracy of each of the plurality of Al models for a venue; (LIU, p. 2, section 2: “To develop a model for estimating the concentration of indoor airborne culturable bacteria, we chose a series of independent variables that can be easily measured inside a building, .... All the data were measured in various buildings in Baoding”; Examiner’s Note: LIU teaches estimating the concentration of indoor airborne culturable bacteria in a building (corresponding to recited “venue”); TANIGUCHI-LIU combination now utilizes the techniques of TANIGUCHI with respect to evaluating accuracy of a plurality of models to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU)
c) choosing a best model from the plurality of Al models for the venue; (LIU, p. 2, section 2: “To develop a model for estimating the concentration of indoor airborne culturable bacteria, we chose a series of independent variables that can be easily measured inside a building, .... All the data were measured in various buildings in Baoding”; Examiner’s Note: LIU teaches estimating the concentration of indoor airborne culturable bacteria in a building (corresponding to recited “venue”); TANIGUCHI-LIU combination now utilizes the techniques of TANIGUCHI with respect to choosing a high accuracy model (corresponding to recited “best model”) from a plurality of models to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU)
e) generating a prediction of concentration of indoor bioaerosols by the best model for the venue ... (LIU, p. 2, section 2: “To develop a model for estimating the concentration of indoor airborne culturable bacteria, we chose a series of independent variables that can be easily measured inside a building, .... All the data were measured in various buildings in Baoding”; Examiner’s Note: LIU teaches estimating the concentration of indoor airborne culturable bacteria in a building (corresponding to recited “venue”); the TANIGUCHI-LIU combination now utilizes the techniques of TANIGUCHI with respect to using a selected model (based on high accuracy) to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI the teachings of LIU as explained above. As disclosed by LIU, one of ordinary skill would have been motivated to do so because “many indoor microorganisms are potential threats to human health” and determining the concentration of such indoor microorganisms will help to “reduce the potential threats of indoor inhalable microorganisms.” (LIU, p. 1, section 1). One of ordinary skill in the art would further understand the benefit of using the teachings of LIU, which teach an estimation technique that “can dramatically reduce the measurement time from days to seconds, saving much time, economic cost, and manpower.” (LIU, p. 7, section 4).
However, TANIGUICHI and LIU fail to explicitly teach:
c1) measuring, using a real-time air quality sensor located in the venue, physical and chemical properties of indoor air at the venue in real-time to obtain measured data; the real-time air quality sensor connected to the computing device
... using the measured data.
However, in a related field of endeavor (“accurately measuring and controlling air quality of indoor environment using IoT technology and Artificial Intelligence”, para. 0001), SAVAKKANAVAR teaches and makes obvious:
c1) measuring, using a real-time air quality sensor located in the venue, physical and chemical properties of indoor air at the venue in real-time to obtain measured data; the real-time air quality sensor connected to the computing device (SAVAKKANAVAR, para. 0025: “The system 110 might include a server or a computer or a laptop, a smart phone or any electronic device comprising an application to execute functions for measuring air quality of indoor environment and controlling the air quality.”
SAVAKKANAVAR, para. 0031: “Referring to FIG. 1, the system 110 might be communicatively coupled to the indoor sensors 150, 152, 154, and 156. In one example, the indoor sensor 150 may indicate a sensor configured for measuring the amount of at least one or more volatile organic compounds (□VOCs□), carbon dioxide, carbon monoxide, methane gas, or a combination thereof in the air, and wherein the solid particles and/or liquid droplets are mold spores, bacteria, dust mites, dust, PM2.5, insect faeces, pollen, smoke, dander, saliva, mucus, other airborne allergens, or a combination thereof.”;
SAVAKKANAVAR, para. 0032: “In one example, the indoor sensor 152 may indicate a sensor configured for sensing parameter that can affect IAQ parameters. For example, the indoor sensor 154 may include occupancy sensors, activity sensors, sunlight sensors, ground-moisture sensors, and so on.”
SAVAKKANAVAR, para. 0061: “Specifically, the system 110 might learn the pattern of the IAQ parameters measured and set desired IAQ parameters automatically monitor air quality, air pollutant signatures, and thermal comfort levels in real-time. In addition, based on a mathematical classifier (i.e., algorithm) trained on a supervised machine-learning method (such as SVM), the system 110 might automatically turn on/off, power up/down and/or open/close the healthy gas storage container 175, the air conditioned unit 180 and operate the plurality of indoor filters 160, 162, according to different air-related data measured in real-time using the plurality of indoor sensors 150, 152, 154, 156, the indoor air sample analyser 170, the healthy gas storage container 175, the air conditioned unit 180, the external sensors 200, the outdoor air sample analyser 205, the environment server 210, the outdoor temperature sensor 215, and the outdoor healthy air sensor 220 to control the air quality of the indoor environment 107.”;
Examiner’s Note: SAVAKKANAVAR discloses monitoring, using indoor real-time sensors, properties such as the amount of “carbon dioxide, carbon monoxide, methane gas” in the air (corresponding to recited “chemical properties”) and sensors including “occupancy sensors, activity sensors, sunlight sensors, ground-moisture sensors” (corresponding to recited sensors for “physical properties”), where such sensors are connected to a computing device; the TANIGUCHI-LIU-SAVAKKANAVAR combination now utilizes the techniques of TANIGUCHI with respect to using a selected model (based on high accuracy) to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU, where real-time sensor data is collected for the building or room as in SAVAKKANAVAR)
e) generating a prediction of concentration of indoor bioaerosols by the best model for the venue using the measured data. (SAVAKKANAVAR, para. 0031: “Referring to FIG. 1, the system 110 might be communicatively coupled to the indoor sensors 150, 152, 154, and 156. In one example, the indoor sensor 150 may indicate a sensor configured for measuring the amount of at least one or more volatile organic compounds (□VOCs□), carbon dioxide, carbon monoxide, methane gas, or a combination thereof in the air, and wherein the solid particles and/or liquid droplets are mold spores, bacteria, dust mites, dust, PM2.5, insect faeces, pollen, smoke, dander, saliva, mucus, other airborne allergens, or a combination thereof.”;
SAVAKKANAVAR, para. 0032: “In one example, the indoor sensor 152 may indicate a sensor configured for sensing parameter that can affect IAQ parameters. For example, the indoor sensor 154 may include occupancy sensors, activity sensors, sunlight sensors, ground-moisture sensors, and so on.”
Examiner’s Note: the TANIGUCHI-LIU-SAVAKKANAVAR combination now utilizes the techniques of TANIGUCHI with respect to using a selected model (based on high accuracy) to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU, where real-time sensor data is measured for the building or room as in SAVAKKANAVAR)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI the teachings of LIU and SAVAKKANAVAR as explained above. As disclosed by SAVAKKANAVAR, one of ordinary skill would have been motivated to do so “to accurately measure air quality of indoor environment and control the air quality so as to improve indoor air quality and livability of subjects (human beings or animals).” (para. 0003).
Regarding Claim 2
TANIGUCHI, LIU, and SAVAKKANAVAR teach the method of claim 1 as explained above. TANIGUCHI further teaches:
wherein the plurality of Al models includes one or more of a linear regression model, a lasso regression model, a random forest (RF) model, an extreme gradient boosting model, a multilayer perceptron model, an LSTM model, and a recurrent neural network model. (TANIGUCHI, para. 0051: “ the prediction model is not particularly limited to a linear model, and for example, a piecewise linear model used for heterogeneous mixture learning, a neural network model, or the like may be used.”
TANIGUCHI, para. 0095: “Here, the regularization parameter corresponds to parameters λ and λ.sub.j (where j=1 to M) used in the penalty term of the error function. Note that “∥.sup.q” in the formula represents a norm, and for example, q=1 (L1 norm) is used in the Lasso method and q=2 (L2 norm) is used in the Ridge regression method. The above description is an example of the regularization parameter used for the linear model, but the regularization parameter is not limited to this.”)
Regarding Claim 9
TANIGUCHI teaches:
a) one or more processors; and (TANIGUCHI, para. 0252: “Also, some or all of the components in each of the above-described exemplary embodiments are implemented by a general-purpose or dedicated circuit (circuitry), a processor or the like, or a combination thereof.”)
b) a memory storing computer-executable instructions that, when executed, cause the one or more processors to (TANIGUCHI, para. 0247: “In that case, the operation of each device may be stored in the auxiliary storage device 1003 in the form of a program. The CPU 1001 reads the program from the auxiliary storage device 1003, expands it in the main storage device 1002, and executes the predetermined processing in each exemplary embodiment according to the program.”;
TANIGUCHI, para. 0248: “The auxiliary storage device 1003 is an example of a non-transitory tangible medium. Other examples of non-transitory tangible medium include a magnetic disk, a magneto-optical disk, CD-ROM, DVD-ROM, a semiconductor memory, or the like that is connected via the interface 1004.”)
The remaining limitations in claim 9 correspond to the method of claim 1, and therefore this claim 9 is rejected for substantially the same reasons explained above with respect to claim 1 under 35 U.S.C. 103 in view of the TANIGUCHI and LIU and SAVAKKANAVAR references.
Regarding Claim 10
TANIGUCHI teaches:
A non-transitory computer readable medium, comprising executable instructions that, when executed by at least one processor of a computing device, direct the at least one processor to perform a method, the method comprising: (TANIGUCHI, para. 0246: “FIG. 19 is a schematic block diagram showing a configuration example of a computer according to each exemplary embodiment of the present invention. A computer 1000 includes a CPU 1001, a main storage device 1002, an auxiliary storage device 1003, an interface 1004, a display device 1005, and an input device 1006.”)
TANIGUCHI, para. 0247: “In that case, the operation of each device may be stored in the auxiliary storage device 1003 in the form of a program. The CPU 1001 reads the program from the auxiliary storage device 1003, expands it in the main storage device 1002, and executes the predetermined processing in each exemplary embodiment according to the program.”;
TANIGUCHI, para. 0248: “The auxiliary storage device 1003 is an example of a non-transitory tangible medium. Other examples of non-transitory tangible medium include a magnetic disk, a magneto-optical disk, CD-ROM, DVD-ROM, a semiconductor memory, or the like that is connected via the interface 1004.”)
The remaining limitations in claim 10 correspond to the method of claim 1, and therefore this claim 10 is rejected for substantially the same reasons explained above with respect to claim 1 under 35 U.S.C. 103 in view of the TANIGUCHI and LIU and SAVAKKANAVAR references.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over TANIGUCHI in view of LIU and SAVAKKANAVAR and further in view of US 20240362472 A1, hereinafter referenced as FU.
Regarding Claim 3
TANIGUCHI, LIU, and SAVAKKANAVAR teach the method of claim 1 as explained above. TANIGUCHI further teaches:
wherein Step b) further comprises steps of: f) inputting test data ... into each of the plurality of Al models; (TANIGUCHI, para. 0080: “When the prediction target data is input, the model application unit 15 uses the prediction model stored in the model storage unit 14 to perform progress prediction.”;
TANIGUCHI, para. 0082: “When the prediction target data is input, the model application unit 15 uses the prediction model stored in the model storage unit 14 to perform progress prediction.”;
TANIGUCHI, para. 0092: “When the prediction target data is input, the model application unit 15 reads the prediction model stored in the model storage unit 14 (step S202), applies the prediction target data to the read prediction model, and obtains a predicted value at each prediction time point included in the evaluation target period (step S203).”;
TANIGUCHI, para. 0263: “The prediction means 67 (for example, the model application unit 15, the model application unit 25), when the prediction target data is given, uses the prediction model stored in the model storage means 66 to perform progress prediction.”;
Examiner’s Note: Pursuant to MPEP 2144.04 VI.B, the examiner notes that the mere duplication of parts or steps (e.g., inputting test data into more than 1 AI model) “has no patentable significance unless a new and unexpected result is produced”)
However, TANIGUCHI fails to explicitly teach:
f) inputting test data for the venue into each of the plurality of Al models g) applying more than one pair of input and output time windows;
g) applying more than one pair of input and output time windows;
h) finding, for each of the plurality of Al model, a difference data between predicted test data and measured test data; and
i) determining one of the plurality of Al models that has a best difference data as the best model.
However, in a related field of endeavor (neural networks to estimate the indoor concentration of airborne culturable bacteria, see p. 2, section 1), LIU teaches:
f) inputting test data for the venue into each of the plurality of Al models g) applying more than one pair of input and output time windows; (LIU, p. 2, section 2: “To develop a model for estimating the concentration of indoor airborne culturable bacteria, we chose a series of independent variables that can be easily measured inside a building, .... All the data were measured in various buildings in Baoding”; Examiner’s Note: LIU teaches estimating the concentration of indoor airborne culturable bacteria in a building (corresponding to recited “venue”); the TANIGUCHI-LIU-SAV combination now utilizes the techniques of TANIGUCHI with respect implementing an accurate model to estimate the concentration of indoor airborne culturable bacteria in a particular building as in LIU)
h) finding, for each of the plurality of Al model, a difference data between predicted test data and measured test data; and (LIU, p. 3, section 2: “The training and testing of each percentage were repeated 200 times. RMS errors were calculated from the testing results for comparison.”; (EN): LIU teaches that during testing, root mean square errors were calculated (corresponding to recited “difference data between predicted test data and measured test data”); the TANIGUCHI-LIU-SAVAKKANAVAR combination now utilizes the techniques of TANIGUCHI with respect implementing an accurate model to estimate the concentration of indoor airborne culturable bacteria in a particular building as in LIU)
i) determining one of the plurality of Al models that has a best difference data as the best model. (LIU, p. 3, section 2: “The training and testing of each percentage were repeated 200 times. RMS errors were calculated from the testing results for comparison.”; (EN): LIU teaches that during testing, root mean square errors were calculated (corresponding to recited “difference data”); the TANIGUCHI-LIU-SAVAKKANAVAR combination now utilizes the techniques of TANIGUCHI with respect to choosing a high accuracy model (corresponding to the model having the “best difference data”) from a plurality of models to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI with the teachings of LIU and SAVAKKANAVAR as explained above. As disclosed by LIU, one of ordinary skill would have been motivated to do so because “many indoor microorganisms are potential threats to human health” and determining the concentration of such indoor microorganisms will help to “reduce the potential threats of indoor inhalable microorganisms.” (LIU, p. 1, section 1). One of ordinary skill in the art would further understand the benefit of using the teachings of LIU, which teach an estimation technique that “can dramatically reduce the measurement time from days to seconds, saving much time, economic cost, and manpower.” (LIU, p. 7, section 4).
However, TANIGUCHI and LIU and SAVAKKANAVAR fail to explicitly teach:
g) applying more than one pair of input and output time windows;
However, in a related field of endeavor (handling data drift in machine learning models, see para. 0007), FU teaches:
g) applying more than one pair of input and output time windows; (FU, para. 0163: “The input window is set to 12 samples, and the output window (prediction duration) is 6 samples. This means that 12 previous samples were evaluated to forecast 6 samples ahead which is equivalent to predicting one minute ahead in the case that the data collection rate is 10 s.”;
Examiner’s Note: FU teaches a specific example of an input window of 12 samples and an output window of 6 samples; the TANIGUCHI-LIU-SAVAKKANAVAR-FU combination now utilizes the techniques of TANIGUCHI with respect to inputting data into a high accuracy model from a plurality of models to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU, and now modifies the models of LIU to use input time windows of FU to predict output concentrations over an output time window as in FU; pursuant to MPEP 2144.04 VI.B, the examiner notes that the mere duplication of parts or steps (e.g., “more than one” input-output time window pair) “has no patentable significance unless a new and unexpected result is produced”)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI with the teachings of LIU, SAVAKKANAVAR, and FU, as explained above. As disclosed by FU, one of ordinary skill would have been motivated to do so to account for distribution changes in data. (see para. 0061). One of ordinary skill would further understand the benefit of using input and output time windows to account for changes in concentration due to environmental changes, such as accounting for the opening of a door or window that disturbs the air in the indoor environment.
Regarding Claim 4
TANIGUCHI, LIU, SAVAKKANAVAR, and FU teach the method of claim 3 as explained above. TANIGUCHI further teaches:
wherein the difference data comprises one or more of a mean squared error (MSE), a root-mean-square error (RMSE) and a value on a revised version of the Willmott's index (WI). (TANIGUCHI, para. 0114: “select a model having the smallest number of defective samples from among the models with the prediction accuracy (e.g., Root Mean Squared Error (RMSE) or correlation coefficient) equal to or more than a predetermined threshold.”; (EN): the examiner further notes that LIU also uses RMS error for comparing results as disclosed at p. 3, section 2).
Regarding Claim 5
TANIGUCHI, LIU, SAVAKKANAVAR, and FU teach the method of claim 3 as explained above. However, TANIGUCHI, LIU, and SAVAKKANAVAR do not explicitly teach:
wherein the more than one pair of input and output time windows comprises a real-time window pair.
However, in a related field of endeavor (handling data drift in machine learning models, see para. 0007), FU teaches:
wherein the more than one pair of input and output time windows comprises a real-time window pair. (FU, para. 0047: “Online data may contain one or multiple data samples at a time and is usually used by a trained machine learning model for inferring a real-time system status.”
FU, para. 0163: “The input window is set to 12 samples, and the output window (prediction duration) is 6 samples. This means that 12 previous samples were evaluated to forecast 6 samples ahead which is equivalent to predicting one minute ahead in the case that the data collection rate is 10 s.”;
Examiner’s Note: FU teaches a specific example of an input window of 12 samples and an output window of 6 samples; the TANIGUCHI-LIU-SAVAKKANAVAR-FU combination now utilizes the techniques of TANIGUCHI with respect to inputting data into a high accuracy model from a plurality of models to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU, where LIU also teaches “real-time measurement of the concentration of indoor airborne culturable bacteria, see p. 7, section 3) and now modifies the models of LIU to use input time windows of FU to predict output concentrations over an output time window as in FU; pursuant to MPEP 2144.04 VI.B, the examiner notes that the mere duplication of parts or steps (e.g., “more than one” input-output time window pair) “has no patentable significance unless a new and unexpected result is produced”)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI with the teachings of LIU, SAVAKKANAVAR, and FU, as explained above. As disclosed by FU, one of ordinary skill would have been motivated to do so to account for distribution changes in data. (see para. 0061). One of ordinary skill would further understand the benefit of using input and output time windows to account for changes in concentration due to environmental changes, such as accounting for the opening of a door or window that disturbs the air in the indoor environment.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over TANIGUCHI in view of LIU and further in view of US 20210406707 A1, hereinafter referenced as RESNICK.
Regarding Claim 6
TANIGUCHI, LIU, and SAVAKKANAVAR teach the method of claim 1 as explained above. However, TANIGUCHI fails to explicitly teach:
wherein the measured data comprises a plurality of input features;
the method further comprising a step of determining which one of the plurality of input features is more important than another one by conducting a permutation importance analysis.
However, in a related field of endeavor (neural networks to estimate the indoor concentration of airborne culturable bacteria, see p. 2, section 1), LIU teaches:
wherein the measured data comprises a plurality of input features; (LIU, p. 2, section 2: “To develop a model for estimating the concentration of indoor airborne culturable bacteria, we chose a series of independent variables that can be easily measured inside a building, including: (i) indoor PM2.5, (ii) indoor PM10, (iii) temperature, (iv) relative humidity, and (v) CO2 concentration. All the data were measured in various buildings in Baoding”; (EN): the TANIGUCHI-LIU combination now utilizes the techniques of TANIGUCHI with respect to using a selected model (based on high accuracy) to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI with the teachings of LIU as explained above. As disclosed by LIU, one of ordinary skill would have been motivated to do so because “many indoor microorganisms are potential threats to human health” and determining the concentration of such indoor microorganisms will help to “reduce the potential threats of indoor inhalable microorganisms.” (LIU, p. 1, section 1). One of ordinary skill in the art would further understand the benefit of using the teachings of LIU, which teach an estimation technique that “can dramatically reduce the measurement time from days to seconds, saving much time, economic cost, and manpower.” (LIU, p. 7, section 4).
However, TANIGUCHI and LIU and SAVAKKANAVAR do not explicitly teach:
the method further comprising a step of determining which one of the plurality of input features is more important than another one by conducting a permutation importance analysis.
However, in a related field of endeavor, “imputing data in computer-based reasoning systems”, see para. 0002), RESNICK teaches:
the method further comprising a step of determining which one of the plurality of input features is more important than another one by conducting a permutation importance analysis. (RESNICK, para. 0023: “For example, feature importance may be determined 120 using confidence intervals, frequency of appearance in sets of decision trees, purity of subsections of the model, permutation feature importance, entropy measures (such as cross entropy and KL divergence), variance, accuracy when dropping out data, Bayesian posterior probabilities, or Pearson correlation.”;
Examiner’s Note: the TANIGUCHI-LIU-SAVAKKANAVAR-RESNICK combination now utilizes the techniques of TANIGUCHI with respect to using a selected model (based on high accuracy) to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU, where the features of LIU (e.g., PM2.5, PM10, CO2) are analyzed using the permutation feature importance techniques of RESNICK).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI with the teachings of LIU, SAVAKKANAVAR, RESNICK as explained above. As disclosed by RESNICK, one of ordinary skill would have been motivated to do so in order to determine the most important data for a model in the event that the model needs to impute values. (para. 0016). One of ordinary skill would further understand the benefit of using the permutation feature importance techniques of RESNICK to select the best input variables for the models of LIU, e.g., to reduce the amount of data that needs to be collected and input into the models of LIU)
Regarding Claim 7
TANIGUCHI, LIU, SAVAKKANAVAR, and RESNICK teach the method of claim 6 as explained above. However, TANIGUCHI fails to explicitly teach:
wherein the plurality of input features comprises one or more of temperature, relative humidity (RH), concentrations of CO2, total volatile organic compounds (TVOCs), PM2.5 and PM10.
However, in a related field of endeavor (neural networks to estimate the indoor concentration of airborne culturable bacteria, see p. 2, section 1), LIU teaches:
wherein the plurality of input features comprises one or more of temperature, relative humidity (RH), concentrations of CO2, total volatile organic compounds (TVOCs), PM2.5 and PM10. (LIU, p. 2, section 2: “To develop a model for estimating the concentration of indoor airborne culturable bacteria, we chose a series of independent variables that can be easily measured inside a building, including: (i) indoor PM2.5, (ii) indoor PM10, (iii) temperature, (iv) relative humidity, and (v) CO2 concentration. All the data were measured in various buildings in Baoding”; (EN): the TANIGUCHI-LIU-SAVAKKANAVAR-RESNICK combination now utilizes the techniques of TANIGUCHI with respect to using a selected model (based on high accuracy) to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU using the specific input features of LIU)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI with the teachings of LIU, SAVAKKANAVAR, RESNICK as explained above. As disclosed by LIU, one of ordinary skill would have been motivated to do so because “many indoor microorganisms are potential threats to human health” and determining the concentration of such indoor microorganisms will help to “reduce the potential threats of indoor inhalable microorganisms.” (LIU, p. 1, section 1). One of ordinary skill in the art would further understand the benefit of using the teachings of LIU, which teach an estimation technique that “can dramatically reduce the measurement time from days to seconds, saving much time, economic cost, and manpower.” (LIU, p. 7, section 4).
Regarding Claim 8
TANIGUCHI, LIU, SAVAKKANAVAR, and RESNICK teach the method of claim 6 as explained above. However, TANIGUCHI fails to explicitly teach:
wherein the plurality of input features comprises concentrations of more than one biological matters.
However, in a related field of endeavor (neural networks to estimate the indoor concentration of airborne culturable bacteria, see p. 2, section 1), LIU teaches:
wherein the plurality of input features comprises concentrations of more than one biological matters. (LIU, p. 2, section 2: “To develop a model for estimating the concentration of indoor airborne culturable bacteria, we chose a series of independent variables that can be easily measured inside a building, including: (i) indoor PM2.5, (ii) indoor PM10, (iii) temperature, (iv) relative humidity, and (v) CO2 concentration. All the data were measured in various buildings in Baoding ... Descriptive statistics of the 249 measured data groups are shown in Table 1, which shows that the data ranges of all our measured variables are wide enough for a machine learning model training”; (EN): TABLE 1 of LIU also discloses that “bacterial concentration” is a measured variable, so LIU teaches at least 2 concentrations of biological matters (CO2 and bacterial concentrations); the TANIGUCHI-LIU-SAVAKKANAVAR-RESNICK combination now utilizes the techniques of TANIGUCHI with respect to using a selected model (based on high accuracy) to estimate the concentration of indoor airborne culturable bacteria in a building as in LIU using the specific input features of LIU)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of TANIGUCHI with the teachings of LIU, SAVAKKANAVAR, RESNICK as explained above. As disclosed by LIU, one of ordinary skill would have been motivated to do so because “many indoor microorganisms are potential threats to human health” and determining the concentration of such indoor microorganisms will help to “reduce the potential threats of indoor inhalable microorganisms.” (LIU, p. 1, section 1). One of ordinary skill in the art would further understand the benefit of using the teachings of LIU, which teach an estimation technique that “can dramatically reduce the measurement time from days to seconds, saving much time, economic cost, and manpower.” (LIU, p. 7, section 4).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220136730 A1 (Schoch). “The computer implemented airborne viral infection risk analysis and air quality calculation system 122 includes (shown schematically) a processor including processing circuitry 124, and a non-transitory computer-readable medium 128, the non-transitory computer-readable medium 128 containing one or more sets of computer instructions configured to instruct the processing circuitry to perform a plurality processing steps for receiving real-time air quality sensor measurements, receiving and analyzing the real-time air quality measurements of the plurality of air quality sensors and calculating an airborne viral infection risk score and the air quality indicators of one or more buildings having one or more monitored air spaces, and other process reporting and message or warning steps as discussed herein. Sensor measurement data and metadata about the measurements, analysis data or metadata from the calculating processes, generated reports, etc. may be stored to database 126. A network interface device 130 interfaces the computer implemented airborne viral infection risk analysis and air quality calculation system 122 to the wide area network 118.” (para. 0167).
US 20220399105 A1 (Wagner Block). “The sensor array (60) may include one or more sensors of varying types that may be distributed in and around an operating room that the ORMS (50) is configured for. The sensor array (60) detects one or more conditions, events, and information about the operating room and may include sensors such as temperature sensors, relative humidity sensors, CO.sub.2 sensors, proximity sensors, motion sensors, vibration sensors, image capture devices, sound capture devices, door position sensors, differential pressure sensors, air flow/velocity and air quality and toxicity sensors, near real-time biological aerosol pathogenic organism detectors, patient physiological sensors, and other types of sensors, depending upon a particular implementation. In various embodiments, one or more of these sensors capture video, and in other embodiments one or more of these sensors will not capture video. Data produced by the sensor array (60) may be received and processed in order to determine various characteristics associated with an operating room, personnel, or patients, for example, as will be described in more detail below.” (para. 0025).
US 20200224915 A1 (Nourbakhsh). “In particular, the control system 201 receives the inputs from the various system components 202, 203, 204, 208, 210, etc., transmits outputs to other system components (e.g., device controllers 204 and HVAC controllers 203), and derives and executes, according to various embodiments, optimizing control policies to maximize or enhance chosen utility functions for building performance in order to effect low air pollution exposure for building occupants. The localized air quality sensors 202 may provide real-time air quality values to the control system 201, which may be time-stamped and stored in a database of the control system 201 for historical analysis.” (para. 0025).
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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