DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/28/2026 has been entered.
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 .
This action is in response to the request for continued examination (RCE) filed on 5/28/2026. In the RCE, no claims were amended, cancelled or added. As such, claims 1-18 are pending and have been examined.
Response to Arguments
The RCE filed 5/28/2026 requested consideration of remarks in the inventor’s Declaration Under 37 CFR 1.132 dated and signed 3/27/2026 and concurrently filed with the RCE on 5/28/2026 (see, page 6 of applicant’s remarks referencing “the declaration (Subject Matter Eligibility Declaration)”, hereinafter “Declaration”). Applicant's arguments in the RCE and the inventor’s remarks in the Declaration with respect to the rejections of claims 1-18 under 35 U.S.C. 101 have been carefully and fully considered, but are not persuasive.
Applicant's arguments filed on 5/28/2026 with respect to the 35 U.S.C. 101 rejections of claims 1-18 have been fully considered but they are not persuasive.
For the reasons stated below, the rejections of claims 1-18 under 35 U.S.C. 101 remain and are being maintained.
The Declaration under 37 CFR 1.132 filed 5/28/2026 is insufficient to overcome the rejections of claims 1-18 under 35 U.S.C. 101 as set forth in the last Office action because of the reasons provided below in the “Examiner’s Response” section.
As a preliminary matter, the inventor’s Declaration summarizes the claimed invention, generally states, inter alia, “The claims in this patent application also refer to neural networks, often referred to as artificial neural networks (ANNs).” and “The application is more specifically concerned with so-called explainable or interpretable neural networks.” (see, Declaration page 1).
The Declaration further states “I am informed that, under the law governing patents in the United States, inventions comprising only "mental steps" or "mental processes" may be deemed ineligible for patenting. While I offer no opinion on the ultimate question of whether the pending claims in U.S. Patent Application No. 17/503,6361 are eligible for patenting” before concluding, with apparent reference to disclosed embodiments in the specification and/or the inventor’s personal opinion, but without citing or quoting claim language, that “based on my expertise in designing arid implementing control systems based on neural networks I can unequivocally state that: neural networks are not a "mental step" or "mental process," and would never be understood as such by a person of ordinary skill in the art of neural network technology and/or control systems. Calculating an integral over time of a controller input signal that represents an error in an output of a nonlinear plant is not a calculation that can be practically performed in the human mind and likewise is impractical to carry out using pencil and paper; because of the non-linear transfer function of the nonlinear plant. The pending claims would be understood by persons of ordinary skill in the art to be directed to improvements a technological field, to wit, improvements in the control of nonlinear plants using artificial neural networks.” (see, Declaration page 2, and reproduced in applicant’s remarks, page 7).
Examiner’s Response:
Examiner appreciates the inventor’s summary of the invention and insights into the technical field of the invention. However, as noted above, the Declaration under 37 CFR 1.132 filed 5/28/2026 is insufficient to overcome the rejections of claims 1-18 under 35 U.S.C. 101 as set forth in the last Office action because of the following:
The Declaration’s conclusory statement regarding “neural networks are not a "mental step" or "mental process," and would never be understood as such by a person of ordinary skill in the art of neural network technology and/or control systems” is not persuasive. Even assuming arguendo that this conclusion is correct, which examiner does not concede, the section 101 rejections discussed below detail how independent claim 1, and most of the dependent claims are directed to mathematical concepts and not solely a “mental process” as stated in the Declaration. This is noted in applicant’s own remarks stating “Claims 1-18 are rejected as allegedly directed to a "an abstract idea without substantially more," with the Office taking the position that the claims are directed to mathematical operations.” (applicant’s remarks, page 6).
Also, as noted above, the Declaration’s statement seemingly relies upon and refers to unclaimed embodiments and improvements in stating “Calculating an integral over time of a controller input signal that represents an error in an output of a nonlinear plant is not a calculation that can be practically performed in the human mind and likewise is impractical to carry out using pencil and paper” (Declaration, page 2, and reproduced in applicant’s remarks, page 7) without specifically pointing out claim language for claims that stand rejected under 35 U.S.C. 101.
Further, regarding the purported impracticality of “calculating an integral over time of a controller input signal that represents an error in an output of a nonlinear plant” because this is allegedly “not a calculation that can be practically performed in the human mind and likewise is impractical to carry out using pencil and paper” Id., this argument is unpersuasive because it is not the relative ease or ‘practicality’ of carrying out the abstract idea (i.e., mathematical concepts) that is relevant. Rather, it is the determination of whether the invention as claimed is directed to an abstract idea that is dispositive and controlling. For an evidentiary declaration to be relevant, there must be a nexus between the invention as claimed and the evidence provided in the declaration. See MPEP 716.01(b). Also, to be of probative value, any objective evidence proffered in the Declaration should be supported by actual proof. See MPEP §§ 716.01(c) and 2106.05(a). Here, to the extent the Declaration is relevant, there is insufficient nexus between the claimed invention and the evidence provided within the Declaration.
As explained in the section 101 rejections below, if the claim limitations, under their broadest reasonable interpretations (BRIs), cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit”, “transfer neural network” and “neural network”), the limitations of the claims cover mathematical relationships, mathematical formulas or equations, and mathematical calculations (combined with mental processes in the case of dependent method claim 15 and claims depending therefrom).
Lastly, as detailed in the section 101 rejections below, independent claim 1 primarily recites calculating a controller output signal from a received/observed controller input signal by summing at least a first signal depending on a current value of the controller input signal and a second signal generated and estimating an integral over time of the controller input signal. In the context of the claim limitations, this encompasses a mathematical concept of summing an integral over time corresponds to mathematical concepts.
If this particular arrangement provides a technological advantage, this advantage is not sufficiently reflected in the claims. At the level of detail provided, the claim limitations are seen to encompass mathematical concepts.
As also detailed in the 101 rejections below, the recited “controller circuit, comprising…and a neural network” are generically recited. Regarding the “neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. Thus, the claims do not recite a practical application or otherwise overcome the rejections under 101.
In particular, claim 1 recites, inter alia, “controller circuit, comprising … a neural network” – which is generically recited as detailed in the section 101 rejections below. Regarding the “neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “input signal”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite an abstract idea (i.e., mathematical concepts). Applicant’s argument that the controller circuit performs more than summing or integrating signals is acknowledged. However, the claimed steps still amount to mathematical operations performed on input data because generating a control signal are all described at a high level of abstraction and do not specify how specify how the neural network is implemented or how the control is technologically improved. The mere presence of a “nonlinear plant” terminology does not confer eligibility. The claim does not recite any particular improvement to the operation of a control technology itself, nor do they specify a concrete technological solution to a technical problem. Instead, the claims broadly apply a mathematical model to a result-oriented function of generating a control signal. Therefore, the rejections under 35 U.S.C. 101 are maintained.
Regarding the 35 U.S.C. 101 rejection of claim 1, applicant asserts, “that while the Final Office Action's "Response to Arguments" distinguishes the present claims from the machine-learning-based claims in the Desjardins2 decision, which the Applicant discussed in its previous response, the guidance provided by Director Squires to examiners is not limited to improvements in machine-learning technology itself, but is more generally related to the question of subject matter eligibility, and particularly to the question of establishing whether claims are directed to an eligible improvement in technology.” before concluding “In view of the Subject Matter Eligibility Declaration filed herewith, the rejections under 35 U.S.C. § 101 should be withdrawn.” (applicant’s remarks, pages 6-7, referencing Desjardins and the aforementioned Declaration).
Examiner’s Response:
The examiner respectfully disagrees. First, the Declaration’s assertions and conclusions are addressed above.
Second, regarding applicant’s apparent, continued reliance on the decision of the Appeals Review Panel in Ex parte Desjardins, No. 2024-000567 (P.T.A.B. Sept. 26, 2025), hereinafter Desjardins, in Desjardins, unlike in the claims at issue here, the appellants specifically argued that the claimed invention “address[es] challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training”. Desjardins, op. at 7. That is, the appellant in Desjardins specifically alleged that the claimed subject matter improves machine learning itself. In contrast, Applicant in the instant case does not point to any specific claim language that characterizing an improvement, and does not point to any claim language that is analogous to the claims at issue in Desjardins. Regarding the Director's Decision cited by applicant, that particular eligibility determination was grounded in a finding that the claims improved machine learning technology, rather than merely applying mathematical calculations. In contrast, the present claims do not recite the functioning of a neural network or to machine learning technology itself. The claims merely recite using a neural network to estimate an integral over time and to generate control signal, without specifying any improvement to how the neural network is used a tool to perform mathematical processing, rather than being the subject of a technological improvement. Therefore, the rejections under 35 U.S.C. 101 are maintained.
Applicant's arguments filed on 5/28/2026 with respect to 35 U.S.C. 102 rejections of claims have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 102 rejection of claim 1, applicant asserts, “claim 1 specifies, among other things, that the neural network is configured to calculate a controller output signal by summing a first signal and a second signal, where the first signal depends "on a current value of the controller input signal" and the second signal that is "generated at least in part by a first neural network estimating an integral over time of the controller input signal." Note that claim 1 specifies that the "controller input signal" represents "an error in an output of the nonlinear plant." The Final Office Action, at page 25, refers to "Page 517 & III. Learning Algorithm of RLLS" in Hwang's disclosure, but does not explain where a sum that involves a "first signal depending on a current value of the controller input signal" and a second signal that is "generated at least in part by a first neural network estimating an integral over time of the controller input signal" can be found in that disclosure. To be sure, numerous sums are disclosed in that section of Hwang, and the section refers several time to "back-propagating" certain errors, but the section does not appear to disclose the summing of any of those error with a signal that is "generated at least in part by a first neural network estimating an integral over time of the controller input signal," i.e., a signal generated by a neural network that estimates an integral over time of a signal that represents error in the output of a nonlinear plant. (applicant’s remarks, page 8, emphasis in original).
Applicant then asserts “The Final Office Action's "Response to Arguments" responds by asserting that Hwang's "evaluation predictor (EP) is long-term policy, which computes a utility index based on successive state transitions," and that this "utility index is updated using both current and future state information, thereby representing an accumulated (i.e. integrated) measure of system performance over time." The "Response to Arguments" continues, "Since the controller input signal represents error, and the utility index is derived from repeated observation of that signal across time steps, the EP network estimates an integrated effect of the controller input signal over time." (applicant’s remarks, pages 8-9).
Finally, Applicant asserts “Assuming for the sake of argument that Hwang's EP network "estimates an integrated effect of the controller input signal over time," thus matching the claim's recitation of a "second signal," Hwang nevertheless fails to anticipate the claims because it does not disclose (or suggest) that the EP network's estimated effect of the controller input signal over time is summed with the first signal, i.e., with a "first signal depending on a current value of the controller input signal." Hwang shows an output p being fed to a box labeled "Critic," but does not show that that this signal p is summed with a signal that depends on a current value of the controller input signal.” before concluding “the rejections under 35 U.S.C. § 102 should be withdrawn.” (applicant’s remarks page 9).
Examiner Response:
The examiner respectfully disagrees. As explicitly disclosed on page 517 of Hwang, the reinforcement predictor network produces an output signal that is a function of the current state vector, which corresponds to the current controller input signal representing error. This satisfies the claimed first signal that depends on the current value of the controller input signal. Additionally, Hwang’s learning algorithm of RLLS (Page 517 Section II and III) explicitly discloses an evaluation predictor (EP) is long-term policy, which computes a utility index based on successive state transitions. This utility index is updated using both current and future state information, thereby representing an accumulated (i.e. integrated) measure of system performance over time. Since the controller input signal represents error, and the utility index is derived from repeated observation of that signal across time steps, The EP network estimates an integrated effect of the controller input signal over time. As such, the output of the long-term policy selector corresponds to an integrated measure of system performance over time, even if the term “integral” is not explicitly used.
Moreover, regarding applicant’s assertions regarding the alleged deficiencies of Hwang vis-à-vis allegedly “not show[ing] that that this signal p is summed with a signal that depends on a current value of the controller input signal” (applicant’s remarks, page 9), the claimed “summing at least a first signal depending on a current value of the controller input signal and a second signal” recited in claim 1 is disclosed by Hwang, as detailed in the section 102 rejections below, this “summing at least a first signal depending on a current value of the controller input signal and a second signal” and the other limitations of claim 1 are disclosed in the cited portions (e.g., page 517 and the algorithm depicted in Sect. III LEARNING ALGORITHM OF RLLS) of Hwang. Therefore, Hwang anticipates claim 1, and the rejections under 35 U.S.C. 102 are maintained.
Applicant’s response does not include arguments with respect to the 35 U.S.C. 103 rejections
Applicant does not proffer any arguments regarding the 35 U.S.C. 103 rejections of the dependent claims. Indeed, applicant’s remarks and the Declaration are wholly silent regarding the section 103 rejections.
Examiner Response:
Notwithstanding applicant’s omission of remarks or arguments regarding the section 103 rejections, examiner notes that each of the remaining claims depends on claim 1. All of the limitations of the dependent claims rejected under 35 U.S.C. 103 are taught by Hwang in view of various other applied references, as detailed in the section 103 rejections below.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claims 1-18 are 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 is directed to a controller circuit, corresponding to a machine, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
calculate the controller output signal from the controller input signal by summing at least a first signal depending on a current value of the controller input signal and a second signal generated…estimating an integral over time of the controller input signal - In the context of the claim limitation, this encompasses a mathematical concept of summing an integral over time.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “a neural network”, “first neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “input signal”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., a “controller circuit” and “neural network”), the limitations of claim 1 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations. Accordingly, claim 1 recites an abstract idea.
The claim also recites “a controller output signal for input to a nonlinear plant”, “a controller input signal representing an error in an output of the nonlinear plant”, which recite 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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Furthermore, the recitations of “a controller output…”, “a controller input…” are directed to insignificant extra-solution activity that is 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.
Claim 2:
Step 1: Claim 2 is directed to a controller circuit as depending from claim 1, corresponding to a machine, one of the statutory categories. The analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites the limitations:
calculate the controller output by summing the first signal and the second signal with a third signal generated at least in part by a second neural network estimating a differential of the controller input signal - In the context of the claim limitation, this encompasses a mathematical concept of summing.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “neural network” , “by a second neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “neural network” and “by a second neural network”, no details of the neural networks or its training are recited and the neural networks are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network” and “second neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “input signal”). The neural networks are recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “second neural network”), the limitations of claim 2 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 3:
Step 1: Claim 3 is directed to a controller circuit as depending from claim 2, corresponding to a machine, one of the statutory categories. The analysis for patent eligibilities of claim 2, and of base claim 1 are incorporated herein.
Step 2A Prong 1: Please see analysis of claims 1 and 2 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein at least one of the first and second neural networks is a recurrent neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “the first and second neural networks” and “a recurrent neural network”, no details of the neural networks or its training are recited and “the first and second neural networks” and the “recurrent neural network” are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “the first and second neural networks” and the “recurrent neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “input signal”). The neural networks are recited at a high level of generality and therefore are being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “first and second neural networks”), the limitations of claim 3 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 4:
Step 1: Claim 4 is directed to a controller circuit as depending from claim 3, corresponding to a machine, one of the statutory categories. The analysis for patent eligibilities of claim 3, intervening claim 2, and of base claim 1 are incorporated herein.
Step 2A Prong 1: Please see analysis of claims 1-3 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the second neural network is a recurrent neural network and a weighted version of the first signal is linked to an input of the second neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “the second neural network”; “a recurrent neural network”, no details of the neural networks or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “the second neural network”; “a recurrent neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “weighted version of the first signal”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit”, “recurrent neural network” and “second neural network”), the limitations of claim 4 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 5:
Step 1: Claim 5 is directed to a controller circuit as depending from claim 1, corresponding to a machine, one of the statutory categories. The analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: Please see analysis of claim 1 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the input weights to the first neural network are non-trainable weights” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “the first neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “the first neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “input weights”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit”, “first neural network”), the limitations of claim 5 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 6:
Step 1: Claim 6 is directed to a controller circuit as depending from claim 1, corresponding to a machine, one of the statutory categories. The analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites the limitations:
calculate the controller output signal by summing the first and second signals using trainable weights - In the context of the claim limitation, this encompasses a mathematical concept of summing signals using weights.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “the neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “input signal”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “neural network”), the limitations of claim 6 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 7:
Step 1: Claim 7 is directed to a controller circuit as depending from claim 2, corresponding to a machine, one of the statutory categories. The analysis for patent eligibilities of claim 2 and of base claim 1 are incorporated herein.
Step 2A Prong 1: Please see analysis of claims 1 and 2 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the neural network further comprises at least one transfer neural network having at least one output from the first and second neural networks as an input, the calculated controller output signal being based on the output of the at least one transfer neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “the neural network”; “transfer neural network", no details of the neural network and the transfer neural network and their training are recited and the neural networks are recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network” and “transfer neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “output from the first and second neural networks as an input”). The neural networks are recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit”, “transfer neural network” and “neural network”), the limitations of claim 7 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 8:
Step 1: Claim 8 is directed to a controller circuit as depending from claim 7, corresponding to a machine, one of the statutory categories. The analysis for patent eligibilities of claim 7, intervening claim 2, and of base claim 1 are incorporated herein.
Step 2A Prong 1: The claim recites the limitations:
at least one rectified linear unit transfer function layer transforming an output x from one of the first, second, and third neurons to an output y of the at least one rectified linear unit transfer function layer according to
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- In the context of the claim limitation, this encompasses a mathematical concept of a transfer function layer.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “at least one transfer neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “transfer neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “transfer neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “output from the first and second neural networks as an input”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “transfer neural network”), the limitations of claim 8 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 9:
Step 1: Claim 9 is directed to a controller circuit as depending from claim 8, corresponding to a machine, one of the statutory categories. The analysis for patent eligibilities of claim 8, intervening claims 2 and 7, and of base claim 1 are incorporated herein.
Step 2A Prong 1: The claim recites the limitations:
wherein at least one of the vectors w and b comprises trainable parameters - In the context of the claim limitation, this encompasses mathematical concept of vector and trainable parameters.
Step 2A Prong 2: Please see analysis of claim 8 above.
Step 2B Analysis: Please see analysis of claim 8 above.
Claim 10:
Step 1: Claim 10 is directed to a controller circuit as depending from claim 8, corresponding to a machine, one of the statutory categories. The analysis for patent eligibilities of claim 7, intervening claim 2, and of base claim 1 are incorporated herein.
Step 2A Prong 1: Please see analysis of claim 8 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the at least one transfer neural network comprises three rectified linear unit transfer function layers corresponding to the first, second, and third signals, respectively” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “transfer neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “transfer neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “output from the first and second neural networks as an input”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “transfer neural network”), the limitations of claim 10 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 11:
Step 1: Claim 11 is directed to a controller circuit as depending from claim 7, corresponding to a machine, one of the statutory categories. The analysis for patent eligibilities of claim 7, intervening claim 2, and of base claim 1 are incorporated herein.
Step 2A Prong 1: Please see analysis of claims 1, 2 and 7 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the at least one transfer neural network comprises any one or more of any of: a leaky rectified linear unit transfer function layer; a parametric rectified linear unit transfer function layer; and a Gaussian error linear unit” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “transfer neural network”, Reword to say, aside from reciting “one transfer neural network comprises three rectified linear unit transfer function layers corresponding to the first, second, and third signals”, no further details of the neural network are recited or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “transfer neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “output from the first and second neural networks as an input”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “transfer neural network”), the limitations of claim 11 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 12:
Step 1: Claim 12 is directed to a controller circuit as depending from claim 1, corresponding to a machine, one of the statutory categories. The analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites the limitations:
a layer clamping a sum formed from at least the first and second signals to a predetermined range - In the context of the claim limitation, this encompasses a mathematical concept of clamping a sum of a predetermined range.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “the neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “neural network”, the neural network further comprises a layer clamping a sum formed from at least the first and second signals to a predetermined range”, no further details of the neural network are recited or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “input signal”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “neural network”), the limitations of claim 12 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 13:
Step 1: Claim 13 is directed to a controller circuit as depending from claim 1, corresponding to a machine as depending from claim 1, one of the statutory categories. The analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: Please see analysis of independent claim 1 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “the neural network comprises a feedback signal, based on a sum formed from at least the first and second signals, fed into the first neural network and configured to prevent integral windup in the first neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “neural network”, the neural network comprises a feedback signal, based on a sum formed from at least the first and second signals, fed into the first neural network and configured to prevent integral windup in the first neural network”, no further details of the neural network are recited or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “data points”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer.
If the claim limitations, under their broadest reasonable interpretations, cover mathematical relationships, mathematical formulas or equations, or mathematical calculations, then they fall within the “Mathematical Concepts” grouping of abstract ideas. See MPEP 2106.04(a)(2) § I. But for the recitation of generic computer components (i.e., “controller circuit” and “neural network”), the limitations of claim 13 cover mathematical relationships, mathematical formulas or equations, and mathematical calculations.
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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 14:
Step 1: Claim 14 is directed to a controller circuit as depending from claim 1, corresponding to a machine, one of the statutory categories. The analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: Please see analysis of independent claim 1 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “nonlinear plant is a power converter circuit” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). 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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 15:
Step 1: Claim 15 is directed to a method of training controller circuit, corresponding to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
tuning trainable weights…using a reward function - In the context of the claim limitation, this encompasses a mental process of evaluating the weights according to the reward function.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “of the neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “data points”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mental processes) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite an abstract idea (i.e., mathematical concepts of base claim 1 combined with mental processes of claim 15). 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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 16:
Step 1: Claim 16 is directed to a method of training controller circuit as depending from claim 16, which corresponds to a process, one of the statutory categories. The analysis for patent eligibility of claim 15 is incorporated herein.
Step 2A Prong 1: The claim recites the limitations:
wherein the reward function is of the from:
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Step 2A Prong 2: Please see analysis of claim 15 above.
Step 2B Analysis: Please see analysis of claim 15 above.
Claim 17:
Step 1: Claim 17 is directed to a method of training controller circuit as depending from claim 15, corresponding to a process, one of the statutory categories. The analysis for patent eligibility of claim 15 is incorporated herein.
Step 2A Prong 1: Please see analysis of claim 15.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the neural network comprises a transfer neural network and wherein the method comprises: training the neural network using one of a leaky rectified linear unit transfer function layer, a parametric rectified linear unit transfer function layer, and a Gaussian error linear unit for the transfer neural network; and using a rectified linear unit transfer function for the transfer neural network for subsequent operation of the neural network, using weights obtained from the training” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithms. See MPEP 2106.05(f). Regarding the “neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “data points”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts combined with mental processes) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite an abstract idea (i.e., mathematical concepts combined with mental processes). 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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 18:
Step 1: Claim 18 is directed to a method of training controller circuit as depending from claim 17, corresponding to a process, one of the statutory categories. The analysis for patent eligibilities of claim 17, and of base claim 15 are incorporated herein.
Step 2A Prong 1: Please see analysis of claims 15 and 17 above.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “performing additional training of the neural network using the rectified linear unit transfer function for the transfer neural network” – these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. See MPEP 2106.05(f). Regarding the “neural network”, no details of the neural network or its training are recited and the neural network is recited at a high level of generality and can be constructed by hand with pen and paper. The claimed “neural network”, under the broadest reasonable interpretation (BRI), in light of the specification, could be constructed by hand with pen and paper based on a reasonable amount of observed data (i.e., the “data points”). The neural network is recited at a high level of generality and therefore is being interpreted as performing an abstract idea (i.e., mathematical concepts) on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite an abstract idea (i.e., mathematical concepts combined with mental processes). 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 is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 6 and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by non-patent literature Hwang (“Reinforcement learning to adaptive control of nonlinear systems”, 2003, hereinafter “Hwang”).
Claim 1.
Hwang discloses the invention as claimed including a controller circuit, comprising a controller output signal for input to a nonlinear plant (Pages 514-515 & SECTION I. Introduction “In the linearizing scheme, the input v is a training signal, the control signal u controls an identified nonlinear plant to approximate linearization, the yd is the output of linear reference model, and y is the output of the plant…The structured ANNs can drive a nonlinear plant to behave like the reference model” teaches controller signal is the input of the nonlinear plant):
and a neural network configured to calculate the controller output signal from the controller input signal by summing at least a first signal depending on a current value of the controller input signal and a second signal generated at least in part by a first neural network estimating an integral over time of the controller input signal (Page 517 & III. LEARNING ALGORITHM OF RLLS “
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” teaches neural network weights are updated and Page 517 & III. LEARNING ALGORITHM OF RLLS “1) Observe the current state vector
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. 2) Use the EP to have utility index of the state s: p← utility(s). 3)Use the LC to select an action merit u^. 4)Use the RP to perform the reinforcement merit r~. 5) Execute the action u∼ψ(u^,σ), where σ=1/(1+e−r~). 6) Observe the successive new state s(t+1) and reinforcement r=yd−y. 7) Use the EP to perform utility index of next state vector s(t+1): pt+1 ←utility(s(t+1)). 8) p′←r+γpt+1. 9)Adjust the weights of the RP by the back-propagating reinforcement error er=r−r~” teaches linearizing control and reinforcement predictor select action which corresponding to current value of input. evaluation predictor is a long-term policy which corresponding to integrating over time which provide second signal).
Claim 6.
As discussed above, Hwang discloses the controller circuit of claim 1.
Hwang further discloses wherein the neural network is configured to calculate the controller output signal by summing the first and second signals using trainable weights (II. STRUCTURE OF RLLS & Page 515-516 “one needs to estimate the gradient ∂r/∂y; then, the output weights can be trained by the usual delta rule, and the usual gradient-based supervised learning algorithm can train the remaining weights… A simple way is to let u^ equal a weighted sum of the input of the LC network at time t” teaches trainable weighted sum of the input of corresponding output signal by summing the trainable weights).
Claim 15.
As discussed above, Hwang discloses the controller circuit of claim 1.
Hwang further teaches the method comprising: tuning trainable weights of the neural network using a reward function (SECTION II. Structure of RLLS “the EP network adjusts its weights using (5). The weights of the LC network are also adjusted according to the same TD error…The external reinforcement signal r(t) provides the same rough information, and the heuristic reinforcement signal r^ supplies the processed signal to the LC to choose a higher action merit” teaches updating weight corresponding to tunning weight of the network using the reward function).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang (“Reinforcement learning to adaptive control of nonlinear systems”) in view of Simard (US10671908B2).
Claim 2.
As discussed above, Hwang discloses the controller circuit of claim 1.
Hwang does not explicitly teach wherein the neural network is configured to calculate the controller output by summing the first signal and the second signal with a third signal generated at least in part by a second neural network estimating a differential of the controller input signal.
However, in the same field, analogous art Simard teaches wherein the neural network is configured to calculate the controller output by summing the first signal and the second signal with a third signal generated at least in part by a second neural network estimating a differential of the controller input signal (SUMMARY & Page 23, Column 1 “ For each state being stored, the state component sub-program modifies and stores a current state by adding the previous stored state to a corresponding element of a state contribution vector output by a trainable transition and differential non-linearity component sub-program using the associated state loop and adder each time an input vector is input into the differential RNN” teaches based differential RNN that estimates differential components of the controller input corresponding to summing the signal of multi neural network).
Hwang and Simard are analogous art because they are both directed to using a neural network to contribute to a control signal and to sum outputs of trainable weights.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitations above as taught by Simard into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, the DRNN shows capability to approximate “to improve the convergence of the gradient descent method by increasing the curvature of the Hessian of the objective function in regions where it would otherwise be flat”, as suggested by Simard (Simard, Page 24, Column 4 & SUMMARY).
Claim 3.
As discussed above, Hwang in view of Simard teaches the controller circuit of claim 2.
Hwang does not explicitly disclose wherein at least one of the first and second neural networks is a recurrent neural network.
However in the same field, analogous art Simard teaches wherein at least one of the first and second neural networks is a recurrent neural network (SUMMARY & Page 23, 1st column “Differential recurrent neural network (RNN) implementations described herein generally concern a type of neural network that handles dependencies that go arbitrarily far in time by allowing the network system to store states using recurrent loops” teaches differential neural network).
Hwang and Simard are analogous art because they are both directed to using a neural network to contribute to a control signal and to sum outputs of trainable weights.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Simard into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, the DRNN shows capability to approximate “to improve the convergence of the gradient descent method by increasing the curvature of the Hessian of the objective function in regions where it would otherwise be flat”, as suggested by Simard (Simard, Page 24, Column 4 & SUMMARY).
Claim 4.
As discussed above, Hwang in view of Simard teaches the controller circuit of claim 3.
Hwang does not explicitly disclose wherein the second neural network is a recurrent neural network and a weighted version of the first signal is linked to an input of the second neural network.
However in the same field, analogous art Simard further teaches wherein the second neural network is a recurrent neural network and a weighted version of the first signal is linked to an input of the second neural network (SUMMARY & Page 24, 4th column “The positive and negative contribution gradient vectors are fed from the output of the trainable transition component of the differential RNN. The trainable transition component includes a neural network which is trained by using the gradient vector signals from its output to modify the weight matrix of the neural network via a backpropagation procedure” teaches differential RNN trained by using the gradient from the modifying weight of the matrix via back propagation which corresponding to linked to input of second recurrent neural network).
Hwang and Simard are analogous art because using because they are both directed to using a NN to contribute to a control signal and to sum outputs of trainable weights.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Simard into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, the DRNN shows capability to approximate “to improve the convergence of the gradient descent method by increasing the curvature of the Hessian of the objective function in regions where it would otherwise be flat”, as suggested by Simard (Simard, Page 24, Column 4 & SUMMARY).
Claim 5.
As discussed above, Hwang discloses the controller circuit of claim 1.
Hwang does not explicitly disclose wherein the input weights to the first neural network are non-trainable weights.
However in the same field, analogous art Simard teaches wherein the input weights to the first neural network are non-trainable weights (Page 25 & Column 6 “If the function F that computes the next state xt+1 as a function of the previous state xt and an input it is defined by:x t+1 =F(W, x t ,i t) (1) Stability around a fixed point a=F(W, a, i)
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” teaches fixed point F corresponding to non-trainable weights to the neural network).
Hwang and Simard are analogous art because they are both directed to using a neural network to contribute to a control signal and to sum outputs of trainable weights.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Simard into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, the DRNN shows capability to approximate “to improve the convergence of the gradient descent method by increasing the curvature of the Hessian of the objective function in regions where it would otherwise be flat”, as suggested by (Simard, Page 24, Column 4 & SUMMARY).
Claim 7.
As discussed above, Hwang in view of Simard teaches the controller circuit of claim 2.
Hwang does not explicitly disclose wherein the neural network further comprises at least one transfer neural network having at least one output from the first and second neural networks as an input, the calculated controller output signal being based on the output of the at least one transfer neural network
However in the same field, analogous art Simard teaches wherein the neural network further comprises at least one transfer neural network having at least one output from the first and second neural networks as an input, the calculated controller output signal being based on the output of the at least one transfer neural network (SUMMARY & Page 24, 1st column “the differential RNN includes a state component sub-program for storing states. This state component sub-program includes a state loop with an adder for each state. For each state being stored, the state component sub-program modifies and stores a current state by adding the previous stored state to a corresponding element of a state contribution vector output by a trainable transition and differential non-linearity component sub-program using the associated state loop and adder each time an input vector is input into the differential RNN. During backpropagation, the state component sub-program accumulates gradients of a sequence used to train the differential RNN by adding them to the previous stored gradient and storing the new gradient at each time step starting from the end of the sequence” teaches state loop for each state combining control signal corresponding transfer neural network).
Hwang and Simard are analogous art because they are both directed to using a neural network to contribute to a control signal and to sum outputs of trainable weights.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Simard into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, the DRNN shows capability to approximate “to improve the convergence of the gradient descent method by increasing the curvature of the Hessian of the objective function in regions where it would otherwise be flat”, as suggested by (Simard, Page 24, Column 4 & SUMMARY).
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang (“Reinforcement learning to adaptive control of nonlinear systems”) in view of Simard (US10671908B2) and further in view of Kang (US20210264247A1).
Claim 8.
As discussed above, Hwang in view of Simard teaches the controller circuit of claim 7.
Hwang in view of Simard does not explicitly teach wherein the at least one transfer neural network comprises at least one rectified linear unit transfer function layer transforming an output x from one of the first, second, and third neurons to an output y of the at least one rectified linear unit transfer function layer according to:
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.
However, in the same field analogous art Kang teaches wherein the at least one transfer neural network comprises at least one rectified linear unit transfer function layer transforming an output x from one of the first, second, and third neurons to an output y of the at least one rectified linear unit transfer function layer according to:
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(Para [0024] “In the context of artificial neural networks, an ReLU provides an activation function that is generally referred to as “rectifier”, which is defined as the positive part of its argument: f(x)=x+=max(0,x), where x is the input to a neuron (102-110). FIG. 2 depicts a ReLU 210 according to one or more embodiments of the present invention. Here, consider that the ReLU 210 receives a vector {right arrow over (x)} as an input and {right arrow over (w)} is a vector of weight assigned to a neuron associated with the ReLU 210. The ReLU 210 computes an output y as a scalar dot product of the input {right arrow over (x)} and the weights {right arrow over (w)}. However, the ReLU 210 only outputs a positive y; if the product of {right arrow over (x)} and {right arrow over (w)} results in a negative value, the output y is 0 (zero)” and Figure 2 teaches rectified linear unit with weight, bias and return the positive value).
Hwang, Simard, and Kang are analogous art because they are all related to training, implementing and using neural networks with trainable weights and bias.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitations above as taught by Kang into the disclosed invention of Hwang in view of Simard.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, deep neural network provide improvement of many tasks such as “large-category image classification and recognition; speech recognition, and nature language processing. Neural networks have demonstrated an ability to learn such skills as face recognition, reading, and the detection of simple grammatical structure”, as suggested by Kang (Kang, Para [0002]).
Claim 9.
As discussed above, Hwang in view of Simard further in view of Kang teaches the controller circuit of claim 8.
Hwang in view of Simard does not explicitly teach wherein at least one of the vectors w and b comprises trainable parameters.
However, in the same field, analogous art Kang teaches wherein at least one of the vectors w and b comprises trainable parameters (Para [0027] “sb provides output of adder tree 320 at given cycle b. The final dot product can be represented as {right arrow over (x)}·{right arrow over (w)}=s0+2s1+ . . . +2B−1sB−1. Alternatively, or in addition, a total accumulated value at the adder tree 320 at any given cycle b can be represented as Sb=2B−b−1sB−1+2B−b−2sB−2+ . . . sb” and Figure 3 teaches weight and b are trainable parameters).
Hwang, Simard, and Kang are analogous art because they are each directed to using trainable weights and bias.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Kang into the disclosed invention of Hwang in view of Simard.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, Deep neural network provides improvement of many tasks such as “large-category image classification and recognition; speech recognition, and nature language processing. Neural networks have demonstrated an ability to learn such skills as face recognition, reading, and the detection of simple grammatical structure”, as suggested by Kang (Kang, Para [0002]).
Claim 12 is re rejected under 35 U.S.C. 103 as being unpatentable over Hwang (“Reinforcement learning to adaptive control of nonlinear systems”) in view of Kang (US20210264247A1).
Claim 12.
As discussed above, Hwang discloses the controller circuit of claim1.
Hwang does not explicitly disclose wherein the neural network further comprises a layer clamping a sum formed from at least the first and second signals to a predetermined range.
However, in the same field, analogous art Kang teaches wherein the neural network further comprises a layer clamping a sum formed from at least the first and second signals to a predetermined range (Para [0019] “A neuron generally is a part of a neural network computer system that determines an output based on one or more inputs (that can be weighted), and the neuron can determine this output based on determining the output of an activation function with the possibly-weighted inputs… sigmoid, which produces an output that ranges between 0 and 1” teaches control output rages between 0 to 1 which clearly indicate signal are bounded).
Hwang and Kang are analogous art because they are both directed to using trainable weights and bias.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitations above as taught by Kang into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, Deep neural network provides improvement of many tasks such as “large-category image classification and recognition; speech recognition, and nature language processing. Neural networks have demonstrated an ability to learn such skills as face recognition, reading, and the detection of simple grammatical structure”, as suggested by Kang (Kang, Para [0002]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hwang (“Reinforcement learning to adaptive control of nonlinear systems”) in view of Simard (US10671908B2) in view of Kang (US20210264247A1) and further in view of Hendrycks (“Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units”).
Claim 10.
As discussed above, Hwang in view of Simard and further in view of Kang teaches the controller circuit of claim 8.
Hwang in view of Simard and further in view of Kang does not explicitly teach wherein the at least one transfer neural network comprises three rectified linear unit transfer function layers corresponding to the first, second, and third signals, respectively.
However, in the same field, analogous art Hendrycks teaches wherein the at least one transfer neural network comprises three rectified linear unit transfer function layers corresponding to the first, second, and third signals, respectively (Abstract & Page 1 “We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks” and Figure 1: The Gaussian Error Linear Unit (µ = 0, σ = 1), the Rectified Linear Unit, and the Exponential Linear Unit (α = 1) teaches neural network comprising three gaussian error linear unit, rectified linear unit and exponential linear unit).
Hwang, Simard, Kang and Hendrycks are analogous art because they are each directed to nonlinear plant processes that use a neural network/NN.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Hendrycks into the disclosed invention of Hwang in view of Simard further in view of Kang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks”, as suggested by Hendrycks (Hendrycks, Page 1 & Abstract).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Hwang (“Reinforcement learning to adaptive control of nonlinear systems”) in view of Simard (US10671908B2)and further in view of Hendrycks (“Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units”).
Claim 11.
As discussed above, Hwang in view of Simard teaches the controller circuit of claim 7,
Hwang in view of Simard does not explicitly teach wherein the at least one transfer neural network comprises any one or more of any of: a leaky rectified linear unit transfer function layer; a parametric rectified linear unit transfer function layer; and a Gaussian error linear unit.
However, in the same field, analogous art Hendrycks teaches wherein the at least one transfer neural network comprises any one or more of any of: a leaky rectified linear unit transfer function layer; a parametric rectified linear unit transfer function layer; and a Gaussian error linear unit (Figure 1 “Figure 1: The Gaussian Error Linear Unit (µ = 0, σ = 1), the Rectified Linear Unit, and the Exponential Linear Unit (α = 1)” teaches the gaussian error linear unit).
Hwang, Simard and Hendrycks are analogous art because they are each directed to nonlinear plant processes that use a neural network/NN.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitations above as taught by Hendrycks into the disclosed invention of Hwang in view of Simard.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks”, as suggested by Hendrycks (Hendrycks, Page 1 & Abstract).
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang (“Reinforcement learning to adaptive control of nonlinear systems”) in view of Sekiguchi (US20220051097A1).
Claim 13.
As discussed above, Hwang teaches the controller circuit of claim 1.
Hwang does not explicitly teach wherein the neural network comprises a feedback signal, based on a sum formed from at least the first and second signals, fed into the first neural network and configured to prevent integral windup in the first neural network.
However, Sekiguchi teaches wherein the neural network comprises a feedback signal, based on a sum formed from at least the first and second signals, fed into the first recurrent neural network and configured to prevent integral windup in the first recurrent neural network (Para [0008] “A control device according to the present disclosure controls an output voltage of a converter. The control device includes: a neural network configured to generate a control signal for controlling a power supply block based on a detection signal from an output stage of the power supply block that supplies power to a load of the converter; a model generator configured to generate a model of a nonlinear dynamic system by machine-learning from the detection signal” and Para [0103] “The PID model part performs a feedback control by using coefficients of a deviation from a target value and integration and differentiation of the deviation as parameters” teaches neural network receives multiple summed signal and use them to generate control in a power converter).
Hwang and Sekiguchi are analogous art because they are each directed to using NNs based on a controller to control mechanisms.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Sekiguchi into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, controller of the neural network “due to the galvanic insulation, noise generated in the power supply block 30 is blocked by the galvanic insulation, so that noise resistance of the control block 20 is improved” (Sekiguchi, Para [0096]).
Claim 14.
As discussed above, Hwang discloses the controller circuit of claim 1,
Hwang does not explicitly disclose wherein the nonlinear plant is a power converter circuit.
However, in the same field, analogous art Sekiguchi teaches wherein the nonlinear plant is a power converter circuit (Para [0008] “a neural network configured to generate a control signal for controlling a power supply block based on a detection signal from an output stage of the power supply block that supplies power to a load of the converter; a model generator configured to generate a model of a nonlinear dynamic system by machine-learning from the detection signal” teaches power supply block (power converter circuit)).
Hwang and Sekiguchi are analogous art because they are both directed to using neural networks/NNs based on a controller to control mechanisms.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Sekiguchi into the disclosed invention of Hwang.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, controller of the neural network “due to the galvanic insulation, noise generated in the power supply block 30 is blocked by the galvanic insulation, so that noise resistance of the control block 20 is improved” as suggested by Sekiguchi (Sekiguchi, Para [0096]).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant's disclosure.
For example, non-patent literature Gunther, Johannes, et al. ("Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison." Plos One 15.12 (2020): e0243320, hereinafter “Gunther”) discloses “The aim of this work is to extend the classic PID controller framework, composed of the PID controller and the plant, with as few changes to the physical setup of the control system as possible. Maintaining the core structure of the classic PID control framework allows for easy adoption to existing industrial applications. This structure is preserved by restricting the neural network’s inputs to signals which are already available in the closed loop control setting.” (see, pages 3-4).
Also, for example, Linkser (U.S. Publication No. 2007/0022068 A1, hereinafter “Linkser”) discloses “The optimal control problem consists of using the measurements, or estimates thereof, to generate control signals that have the effect of altering the plant state in such a manner as to minimize a specified measure of the error between the actual and a specified desired (or target) plant state at one or more future times. A function, called the "cost-to-go", is typically specified. This function describes the cost of a process that generates and applies a succession of control signals to the plant, resulting in a succession of plant states over time. Given the current plant state or an estimate thereof, or a measurement vector that conveys information about the plant state, it is desired to generate a sequence of control signal outputs” and “one can combine the output from a nonlinear ANN with that of a (non-neural) KF algorithm, to improve predictions when applied to a nonlinear plant process.” (see, paragraphs 5 and 36).
The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
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/RANDALL K. BALDWIN/Primary Examiner, Art Unit 2125
1 The instant application.
2 Examiner notes that applicant is apparently referring to Appeals Review Panel in Ex parte Desjardins, No. 2024-000567 (hereinafter “Desjardins”).
Prosecution Insights