DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The present application was filed on 02/21/2022. This action is in response to amendments and remarks filed on 01/07/2026. In the current amendments claim 1 has been amended, no claims were canceled, and no claims were added. Claim 1 is pending and has been examined. Claim 1 is the independent claim.
Priority
As indicated in the previous Office Action, Acknowledgment is made of applicant' s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The examiner notes that a certified copy of the above-noted foreign application (in Chinese) was retrieved on 03/31/2022 as required by 37 CFR 1.55.
Claim Objections
Claim 1 is objected to because of the following informalities:
The claim is generally narrative and is replete with grammatical and idiomatic errors.
Thus, the issues that the examiner has identified herein should be seen as in illustrative list, not an exhaustive one.
For example, in step 3) of claim 1, “of rule neuron” should recite “of a rule neuron”, and “kth output value of rule neuron” should recite “a kth output value of the rule neuron”.
Also, for example, in equation 4) of claim 1, “between kth rule neuron and output neuron” should recite “between a kth rule neuron and the output neuron”.
Claim limitations recited in claim 1 contain inconsistent with punctuation.
“Adjust parameter knowledge using particle filter algorithm; particle filter algorithm consists of three steps: knowledge sampling, knowledge evaluation and knowledge fusion; the knowledge sampling process is” should be “adjust parameter knowledge using particle filter algorithm; particle filter algorithm consists of three steps: knowledge sampling, knowledge evaluation and knowledge fusion; the knowledge sampling process is:”
“knowledge evaluation includes two indexes of knowledge matching degree and knowledge diversity, which are expressed as” should be “knowledge evaluation includes two indexes of knowledge matching degree and knowledge diversity, which are expressed as:”
“Leverage reconstruction knowledge and data to adjust the parameters of prediction model; The objective function of the prediction model is” should be “leverage reconstruction knowledge and data to adjust the parameters of prediction model; The objective function of the prediction model is:”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C 112(b) or 35 U.S.C 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for application subject to pre-AIA 35 U.S.C 112, the application regards, as the invention.
Claim 1 is rejected as failing to define the invention in the manner required by 35 U.S.C. 112(b).
The claim is generally narrative and indefinite, failing to conform with current U.S. practice. It appears to be a literal translation into English from a foreign document and is replete with grammatical and idiomatic errors. As such, the claim is profoundly unclear and replete with grammatical errors and antecedent basis issues. Thus, the issues that the examiner has identified herein should be seen as in illustrative list, not an exhaustive one. The examiner respectfully recommends that Applicant rewrite the entire claim to clarify the subject matter and to conform to the rules of idiomatic English.
The structure which goes to make up the device (i.e., the “intelligent detection system” of claim 1 must be clearly and positively specified. The structure must be organized and correlated in such a manner as to present a complete operative device. The claim must be in one sentence form only. Appropriate correction is required.
Note the format of the claims in the U.S. patents cited below, and in the U.S. patent(s) and published U.S. patent application(s) cited in the accompanying PTO-892 form.
See, for example the format of the claims in U.S. patent nos. 10,506,652 B2, 10,375,620 B2, 9,744,725 B2 and 9,529,866 B2.
Claim 1 is also rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1 recites the limitation "the number of training samples" in lines 32-33 of the claim. There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner has interpreted to be “a number of training samples”.
Claim 1 recites the limitation "the training process" in line 12 on page 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the examiner has interpreted this recitation to be “a training process”.
Claim 1 recites the term “
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” the variables in the equation are not defined in the claim. Therefore, the claim is indefinite and unclear. There is no explanation in the claim itself.
Claim 1 recites term
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the variables in the equation are not defined in the claim. Therefore, the claim is indefinite and unclear. There is no explanation in the claim itself.
Claim 1 recites “randomly initialized in [0;01, 0;1]” is unclear because of the use of improper mathematical notation. The range notation “[0;01, 0;1]” is not a standard way to represent numerical ranges. For examination purposes, the examiner has interpreted this recitation to be “[0,1’]” .
Claim 1 recites “α(t)∈(0;5, 1] and β(t)∈(0, 0;1]” is unclear because of the use of improper mathematical notation. The range notation “α(t)∈(0;5, 1] and β(t)∈(0, 0;1]” is not a standard way to represent numerical ranges. For examination purposes, the examiner has interpreted this recitation to be “α(t)∈(0.5, 1] and β(t)∈(0, 1]”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1: Claim 1 recites an intelligent detection system of effluent total nitrogen (TN) in a wastewater treatment process based on a fuzzy transfer learning algorithm which is directed to a system, one of the four statutory categories of patentable subject matter.
Step 2A Prong 1: The claim recites the limitations
the fuzzy-neural-network prediction model includes: 1) input layer: there are 5 neurons in this input layer, the output of input neuron is: x p(t)=op(t), (1) where xp(t) is pth output value of input neuron at time t, t=1, T, p=1,…, 5 - In the context of the claim limitation, this encompasses the mental process of evaluating output for the prediction.
membership function layer: there are 10 neurons in the membership function layer, an output of membership function neuron is:
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where ϕk(t) is kth output value of membership function neuron at time t, k=1, . . . , 10, cpk(t) is pth center of kth membership function neuron at time t, which is randomly initialized in [−1, 1]; σpk(t) is pth width of the kth membership function neuron at time t, which is randomly initialized in [−1, 1] - In the context of the claim limitation, this encompasses the mathematical concept of calculating an output of a neuron using the above-noted equation.
3) rule layer: there are 10 neurons in the rule layer, and an output of rule neuron is:
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where vk(t) is the kth output value of rule neuron at time t - In the context of the claim limitation, this encompasses the mathematical concept of calculating an output of a neuron using the above-noted equation.
output layer: an output of output neuron is:
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where y(t) is the output of the fuzzy-neural-network prediction model at time t, wk(t) is the connecting weight between kth rule neuron and output neuron - In the context of the claim limitation, this encompasses the mathematical concept of calculating an output of an output neuron using the above-noted equation.
establish a reference model of the effluent TN to acquire knowledge a structure of the reference model is same as the fuzzy-neural-network the prediction model, the number of training samples of the reference model is N - In the context of the claim limitation, this encompasses the mental process of observing models are same.
construct the reference model:
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where yZ(n) is an output of the reference model at time n, n=1, . . . , N, wZ k(n) is a connecting weight between kth rule neuron and output neuron at time n, which is randomly initialized in [0, 1], cZ pk(n) is the pth center of the kth membership function neuron at time n, which is randomly initialized in [−1, 1], σZ pk(n) is the pth width of the kth membership function neuron at time n, which is randomly initialized in [−1, 1] - In the context of the claim limitation, this encompasses the mathematical concept of construct of reference model.
center, width and weight of the reference model are updated as:
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where E(n) is an objection function of the reference model at time n, yZd(n) is a desired output of the reference model at time n, λ is learning rate of the gradient descent algorithm, which is randomly initialized in [0;01, 0;1] - In the context of the claim limitation, this encompasses the mathematical concept of updating weight.
compute E(n+1) using equation (6), if n<N or E(n+1)>0;01, n=n+1, go to step (2) else stop the training process, given
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to the reference model - In the context of the claim limitation, this encompasses the mental process of evaluating E(n+1).
acquire parameter knowledge from the reference model; the parameter knowledge is given as
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where
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is kth parameter knowledge extracted from the reference model at time n,
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is the parameter knowledge extracted from the reference model at time n - In the context of the claim limitation, this encompasses the mathematical concept of updating weight.
1) adjust parameter knowledge using particle filter algorithm; the particle filter algorithm consists of three steps: knowledge sampling, knowledge evaluation and knowledge fusion; the knowledge sampling process is
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where Kl(t) is lth sampling knowledge, l=1, . . . , L, L=30 is the number of sampling, δl(t) is random sampling vector, which is a randomly initialized in [0, 1]; the knowledge evaluation includes two indexes of knowledge matching degree and knowledge diversity, which are expressed as
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where
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is an importance weight of the lth sampling knowledge at time t,
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is a knowledge matching degree between lth sampling knowledge and training data at time t,
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is an output of the fuzzy-neural-network prediction model with Kl(t) as parameter at time t, yd(t) is the desired output of the fuzzy-neural-network prediction model at time t, Dl(t) is the knowledge diversity of lth sampling knowledge at time t; based on the sampling knowledge and the importance weight, the knowledge fusion is expressed as
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where
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is the reconstruction knowledge at time t,
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is kth reconstruction knowledge
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- In the context of the claim limitation, this encompasses the mathematical concept of parameter knowledge particle filter algorithm.
leverage reconstruction knowledge and data to adjust parameters of the fuzzy-neural-network prediction model; an objective function of the fuzzy-neural-network prediction model is
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where
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is the objection function of the fuzzy-neural-network prediction model at time t,
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is an output error of the fuzzy-neural-network prediction model at time t;
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and
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are balancing parameter, an updating process of
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are
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- In the context of the claim limitation, this encompasses the mathematical concept of calculation of objective function.
compute EKD(t+1) using equation (17), if t<Q or EKD(t+1)>0.01, t=t+1, go to step (2); else stop the training process, given cpk(t), σpk(t), wk(t) to the fuzzy-neural-network prediction model - In the context of the claim limitation, this encompasses the mathematical concept of calculation of EKD(t+1).
4) effluent total nitrogen TN concentration prediction; the number of testing samples is M; the testing samples are used as the input of the fuzzy-neural-network prediction model, the output of the fuzzy-neural-network prediction model is soft-computing values of the effluent TN concentration - In the context of the claim limitation, this encompasses the mental process of evaluating output of prediction model
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “a detection instrument, a data processing module, a TN prediction module, a data storage module”; “the detection instrument contains ammonia nitrogen (NH4-N) detector, nitrate nitrogen (NO3-N) detector, suspended solids (SS) concentration detector, a biochemical oxygen demand (BOD) detector and a total phosphorus (TP) detector; the detection instrument is connected with programmable logic controller”, “the programmable logic controller is connected with the data processing module by fieldbus; variables of the wastewater treatment process are analyzed by principal component analysis, and input variables of The TN prediction module are selected as: NH4-N, NO3-N, SS, BOD and TP of the wastewater treatment process measured by the ammonia nitrogen (NH4-N) detector, the nitrate nitrogen (NO3-N) detector, the suspended solids (SS) concentration detector, the biochemical oxygen demand (BOD) detector and the total phosphorus (TP) detector, respectively, an output value of the TN prediction module is a TN value of the waste water treatment process”, “the data processing module is connected with data storage module by the fieldbus”, “the data storage module is connected with the TN prediction module using communication interface”, “the TN prediction module comprise the following steps: (1) establish prediction model of effluent TN based on a fuzzy-neural-network the fuzzy-neural-network contains four layers: an input layer, a membership function layer, a rule layer and an output layer, the fuzzy-neural-network is 5-10-10-1, including 5 neurons in the input layer, 10 neurons in the membership function layer, 10 neurons in the rule layer and 1 neurons in the output layer”, “leverage gradient descent algorithm to train the reference model”, “(3) leverage parameter knowledge and data to train prediction model” – These additional elements can be considered as “generally linking the use of judicial exception to a particular technological environment or field of use”. See MPEP 2106.05(h). The claim also recites that “connecting weights between the input layer and the membership function layer are assigned as 1, connecting weights between the membership layer and the rule function layer are assigned 1, connecting weights between the rule layer and the output layer are randomly initialized in [−1, 1]”, “the number of training samples of a fuzzy-neural-network prediction model is T, an input of the fuzzy-neural-network prediction model is o(t)=[o1(t), o2(t), o3(t), o4(t), o5 (t)] at time t, ol(t) represents NH4-N concentration at time t; o2(t) represents NO3-N concentration at time t, o3(t) represents SS concentration at time t, o4(t) represents BOD value at time t, and o5(t) represents TP concentration at time t, an output of the fuzzy-neural-network prediction model is y(t) and an actual output is yd(t)” which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element directed to mere instruction to apply the judicial exception. Mere instructions to apply a judicial exception do not amount to significantly more than the judicial exception. See MPEP 2106.05(f). Furthermore, the recitation of “connecting weights”, “the number of the training sample…”; are directed to insignificant extra-solution activities that are well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Response to Arguments
Applicant's arguments filed on 01/07/2026 with respect to claim objections of claim 1 have been fully considered but it are not persuasive in part.
With respect to the claim objections to claim 1, applicant generally asserts, “In response, Applicant has amended claim 1 to correct informalities.
It is believed that the amendment has overcome the objections to claim 1. Withdrawal of the objection is requested” (Remarks Pg.7).
Examiner Response:
Arguments are persuasive in part, but objections to claim 1 remain, as discussed above. Although the claim has been amended in response to the previously raised objections, the amendments do not overcome all of the outstanding issues identified in the prior Office Action. The objections set forth above remain applicable to the claim as currently presented. Therefore, the claim 1 remains objected to for the reasons detailed above.
Applicant's arguments filed on 01/07/2026 with respect to the 35 U.S.C. 112(b) rejections of claim 1 have been fully considered but are not persuasive in part.
With respect to the 35 U.S.C. 112(b) rejection of claim 1, applicant generally asserts, “Applicant has amended claim 1 to address the 112(b) issues. It is believed that the amendment has overcome the 112(b) rejections. Withdrawal of the rejection is requested” (Remarks Pg.7).
Examiner Response:
Arguments are persuasive in part. However, claim 1 remains rejected under 112(b), as discussed above. Although the claim has been amended in view of the rejections previously made under 35 U.S.C. 112(b), the amendments do not overcome all of the outstanding issues. The rejections set forth above under 35 U.S.C. 112(b), including both those previously made and the new grounds of rejection, remain applicable to the claim as presently amended. Therefore, the rejection under 35 U.S.C. 112(b) is maintained.
Applicant's arguments filed on 01/07/2026 with respect to 35 U.S.C. 101 rejections of claim 1 have been fully considered but are not persuasive.
With respect to the 35 U.S.C. 101 rejection of claim 1, applicant asserts, “The intelligent detection system of effluent total nitrogen based on fuzzy transfer learning algorithm recited in claim 1 contains several functional modules. The input variables
of the TN prediction module, NH4-N, NO3-N, SS, BOD, and TP of a wastewater treatment process are measured by the ammonia nitrogen (NH4-N) detector, the nitrate nitrogen (NO3-N) detector, the suspended solids (SS) concentration detector, the biochemical oxygen demand
(BOD) detector and the total phosphorus (TP) detector, respectively. The TN prediction module carries out the following four steps:
(1) establish prediction model of effluent TN based on a fuzzy-neural-network
(2) establish a reference model of the effluent TN to acquire knowledge
(3) leverage parameter knowledge and data to train prediction model;
(4) effluent total nitrogen TN concentration prediction.
The intelligent detection system of the present inventio can make use of historical
prediction knowledge of TN to make up for the deficiency of the current prediction data.
Moreover, the intelligent prediction method also reduces the measurement cost, increases the
prediction accuracy and improves the benefit of the wastewater treatment plant” (Remarks Pg. 8).
Examiner Response:
The examiner respectfully disagrees. As indicated above, the four recited steps “(1) establish prediction model of effluent TN based on a fuzzy-neural-network
(2) establish a reference model of the effluent TN to acquire knowledge
(3) leverage parameter knowledge and data to train prediction model;
(4) effluent total nitrogen TN concentration prediction” are directed to an abstract idea.
More specifically, these steps recite mathematical concepts and mental processes. The establishment of prediction and reference models, the acquisition of knowledge, and the training of the prediction model involve mathematical relationships, calculations, and mathematical algorithms processing. The claim further recites an equation for determining or predicting the TN concentration. The equation can be evaluated by substituting formula and performing the corresponding computations. These calculations can be performed conceptually or with the aid of pen and paper.
Furthermore, the “intelligent detection system of effluent total nitrogen based on fuzzy transfer learning algorithm recited in claim 1 contains several functional modules. The input variables of the TN prediction module, NH4-N, NO3-N, SS, BOD, and TP of a wastewater treatment process are measured by the ammonia nitrogen (NH4-N) detector, the nitrate nitrogen (NO3-N) detector, the suspended solids (SS) concentration detector, the biochemical oxygen demand (BOD) detector and the total phosphorus (TP) detector” – these additional elements merely collect data for use in the mathematical model and equation. The claim does not recite any specific improvement to the structure or operation of the detectors, nor any improvement to the wastewater treatment process itself. Instead, the detectors operate in their ordinary and conventional manner to provide input data for mathematical processing. When considered individually and as an ordered combination, the additional elements do not amount to significantly more than the abstract idea itself. Therefore, the rejection of claim 1 under 35 U.S.C. 101 is maintained.
Conclusion
8. THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lokesha Patel whose telephone number is (571)272-6267. The examiner can normally be reached 8 AM - 4 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LOKESHA PATEL/ Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125