DETAILED ACTION
Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-8 are currently pending in this application.
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. No information disclosure statement (IDS) was submitted in this application.
Drawings
4. The drawings submitted on 12/08/2023 are in compliance with 37 CFR § 1.81 and 37 CFR § 1.83 and have been accepted by the examiner.
Claim Rejections - 35 USC § 101 Non-Statutory
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Specifically, representative Claim 1 recites:
A calibration concentration selection method for a gas sensor array, comprising steps of:
step 1: using the gas sensor array to obtain source domain data of a gas mixture formed by n gases, wherein the source domain data comprise a concentration variation sequence of each gas in the gas mixture, and a response variation sequence of each gas in the gas mixture;
step 2: constructing a gas mixture prediction model (AE-BP model) with an auto encoder network (AEN) and a fully connected neural network; using the response variation sequence as an input and an output of the AEN, and training the AEN; extracting effective features of the response variation sequence from a bottleneck layer of a trained AEN; then using the effective features as an input, and using the concentration variation sequence of the source domain data as a training target, so as to train the fully connected neural network; wherein a trained AE-BP model is formed by the trained AEN and a trained fully connected neural network;
step 3: constructing a variational auto-encoder (VAE); identically distributing the response variation sequence of the source domain data through the VAE for generating an identically distributed response variation sequence for each gas in the gas mixture, wherein a total number of response values is N;
step 4: inputting the identically distributed response variation sequence into the trained AE-BP model to output a predicted concentration variation sequence of each gas in the gas mixture, wherein a total number of concentration values is N; and then normalizing the predicted concentration variation sequence according to a desired concentration test range of each gas in a target domain, so as to generate a target concentration variation sequence for each gas;
step 5: sorting a target concentration variation sequence of a kth gas according to a concentration value, and k=1, 2, ..., n; correspondingly sorting target concentration variation sequences of remaining n-1 gases and corresponding n-dimensional identically distributed response variation sequences, thereby obtaining a sorted concentration variation sequence of the kth gas and a corresponding n-dimensional sorted identically distributed response variation sequence; calculating a response gradient sequence of the n-dimensional sorted identically distributed response variation sequence to the sorted concentration variation sequence of the kth gas, so as to obtain n response gradient sequences corresponding to the kth gas; calculating n response gradient sequences for the target concentration variation sequence of each gas, so as to obtain n×n response gradient sequences in total;
step 6: processing each of the response gradient sequences with absolute value calculation and sliding window filtering with a size of T, thereby obtaining a corresponding smoothed gradient sequence;
step 7: calculating a spike of the smoothed gradient sequence, and if the spike is greater than a preset hyperparameter, executing step 8 to find a large gradient concentration interval of the corresponding smoothed gradient sequence; otherwise, executing step 9 for point selection;
step 8: dividing the smoothed gradient sequence, whose spike is greater than the hyperparameter, into P equal parts according to a gradient value, so as to obtain P-1 equal gradient values; traversing through the equal gradient values, and using a maximum entropy thresholding algorithm to calculate an upper region gradient value information entropy and a lower region gradient value information entropy with the gradient value as a dividing line; using an equal gradient value, with which a sum of the upper region gradient value information entropy and the lower region gradient value information entropy is maximized, as a threshold line; then taking a concentration interval in the smooth gradient sequence, which is larger than the threshold line, as a large gradient concentration interval, and obtaining no less than one large gradient concentration interval; then executing step 9 for point selection; and
step 9: assuming that a total number of test points required in the target domain is M, wherein there are M/2 test points in the large gradient concentration interval as well as in all other concentration intervals in the smoothed gradient sequence other than the large gradient concentration interval; weighting the large gradient concentration interval based on a corresponding maximum gradient value, then assigning the test points to the large gradient concentration interval according to weights, and selecting M/2 corresponding concentration test points by random uniform sampling; and selecting M/2 corresponding concentration test points from all other concentration intervals in the smoothed gradient sequence other than the large gradient concentration interval by random uniform sampling.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim.
Under Step 2A, prong 1, claim 1 is found to include at least one judicial exception, that being a mathematical concept and/or mental process. This can be seen in the claim limitation of “step 2: constructing a gas mixture prediction model (AE-BP model) with an auto encoder network (AEN) and a fully connected neural network; using the response variation sequence as an input and an output of the AEN, and training the AEN; extracting effective features of the response variation sequence from a bottleneck layer of a trained AEN; then using the effective features as an input, and using the concentration variation sequence of the source domain data as a training target, so as to train the fully connected neural network; wherein a trained AE-BP model is formed by the trained AEN and a trained fully connected neural network; step 3: constructing a variational auto-encoder (VAE); identically distributing the response variation sequence of the source domain data through the VAE for generating an identically distributed response variation sequence for each gas in the gas mixture, wherein a total number of response values is N; step 4: inputting the identically distributed response variation sequence into the trained AE-BP model to output a predicted concentration variation sequence of each gas in the gas mixture, wherein a total number of concentration values is N; and then normalizing the predicted concentration variation sequence according to a desired concentration test range of each gas in a target domain, so as to generate a target concentration variation sequence for each gas; step 5: sorting a target concentration variation sequence of a kth gas according to a concentration value, and k=1, 2, ..., n; correspondingly sorting target concentration variation sequences of remaining n-1 gases and corresponding n-dimensional identically distributed response variation sequences, thereby obtaining a sorted concentration variation sequence of the kth gas and a corresponding n-dimensional sorted identically distributed response variation sequence; calculating a response gradient sequence of the n-dimensional sorted identically distributed response variation sequence to the sorted concentration variation sequence of the kth gas, so as to obtain n response gradient sequences corresponding to the kth gas; calculating n response gradient sequences for the target concentration variation sequence of each gas, so as to obtain n×n response gradient sequences in total; step 6: processing each of the response gradient sequences with absolute value calculation and sliding window filtering with a size of T, thereby obtaining a corresponding smoothed gradient sequence; step 7: calculating a spike of the smoothed gradient sequence, and if the spike is greater than a preset hyperparameter, executing step 8 to find a large gradient concentration interval of the corresponding smoothed gradient sequence; otherwise, executing step 9 for point selection; step 8: dividing the smoothed gradient sequence, whose spike is greater than the hyperparameter, into P equal parts according to a gradient value, so as to obtain P-1 equal gradient values; traversing through the equal gradient values, and using a maximum entropy thresholding algorithm to calculate an upper region gradient value information entropy and a lower region gradient value information entropy with the gradient value as a dividing line; using an equal gradient value, with which a sum of the upper region gradient value information entropy and the lower region gradient value information entropy is maximized, as a threshold line; then taking a concentration interval in the smooth gradient sequence, which is larger than the threshold line, as a large gradient concentration interval, and obtaining no less than one large gradient concentration interval; then executing step 9 for point selection; and step 9: assuming that a total number of test points required in the target domain is M, wherein there are M/2 test points in the large gradient concentration interval as well as in all other concentration intervals in the smoothed gradient sequence other than the large gradient concentration interval; weighting the large gradient concentration interval based on a corresponding maximum gradient value, then assigning the test points to the large gradient concentration interval according to weights, and selecting M/2 corresponding concentration test points by random uniform sampling; and selecting M/2 corresponding concentration test points from all other concentration intervals in the smoothed gradient sequence other than the large gradient concentration interval by random uniform sampling.”, which is the judicial exception of a mental process and/or a mathematical concept because it is merely a data evaluation including calculations, and/or judgements capable of being performed mentally.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “step 1: using the gas sensor array to obtain source domain data of a gas mixture formed by n gases, wherein the source domain data comprise a concentration variation sequence of each gas in the gas mixture, and a response variation sequence of each gas in the gas mixture, which are merely data gathering steps recited at a high level of generality and therefore merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. The claim also recites “neural network” however the “neural network” is recited at a high level of generality, and merely amounts to the use of computer technology as a tool to apply the abstract idea (see MPEP 2106.05(f)) and/or the use of “neural network” to perform the predictions, that are otherwise abstract, is merely an attempt at limiting the abstract to a particular field of use (See MPEP 2106.05(h)).
The generic data gathering, processing, and output steps, and other elements, are recited so generically (no details whatsoever are provided) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the output from the model. It is unclear if anything is actually calculated.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely insignificant extra-solution activity (claims 1, 8, and 15). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1 amounts to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-8, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements therefore these claims are found ineligible for the reasons described for independent claim 1.
See Supreme court decision in Alice Corporation Pty. Ltd. V. CLS Bank International, et al.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Walsh et al. US 2006/0155486 teaches a computer implemented system and method for analyzing mixtures of gases.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADITYA S BHAT whose telephone number is (571)272-2270. The examiner can normally be reached on Monday-Friday 8 am-6pm.
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10. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADITYA S BHAT/Primary Examiner, Art Unit 2857 September 2, 2026
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