DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the amendments filed 3/25/2026 in which claims 1, 4-6, 9, 11, and 12 have been amended and claim 13 has been added.
Claims 1-13 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP20177436.1, filed on 05/29/2020.
Response to Amendments
Applicant has amended independent claim 1 to be in more conventional U.S. format. In view of the claim amendments, the previous 35 U.S.C. & 112(b) rejection has been withdrawn.
Response to Arguments
Applicant’s arguments filed 3/25/2026 with respect to the rejection of claim 1 under 35 U.S.C. § 101 have been fully considered but are not persuasive. The rejection is maintained as set forth below.
Applicant argues that it is “not practical or feasible for a human to mentally read in training data …, mentally train a Bayesian neural network, mentally generate a multiplicity of synthetic structure data sets …, mentally generate a quality value with an associated uncertainty indication …, or mentally compare the generated uncertainty indications … and select one.”
This argument is not persuasive.
First, under the 2019 PEG and MPEP 2106.04(a)(2)(III), a limitation is properly characterized as a mental process where the claim, under its broadest reasonable interpretation (BRI), covers performance in the human mind, or by a human using a pen and paper, but for the recitation of generic computer components. The relevant inquiry is not whether a human could perform the steps perfectly, quickly, or on a large scale, but whether the recited concept is of a type that can be performed mentally or with pen and paper. The recitation of a generic computer performing the steps does not remove the underlying concept from the mental-process grouping. (See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366 (Fed. Cir. 2011); MPEP 2106.04(a)(2)(III). Second, Applicant’s argument conflates scale and speed with character. The fact that a task involves a “multiplicity” of data variants merely reflects that the mental/mathematical process is performed on more data; it does not change the character of the underlying evaluation, comparison, and selection. The Federal Circuit has held that claims reciting the collection, analysis, and comparison of data remain abstract even when performed on large data sets or repeatedly. (See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016) (“merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes”); SAP Am., Inc. v. InvestPics, LLC, 898 F.3d 1161 (Fed. Cir. 2018)). Third, several recited steps are squarely mental processes even in isolation. Step (e)—“comparing the generated uncertainty indications with a predefined reliability indication and selecting one of the synthetic structure data sets depending thereon”—is an evaluation-and-judgment step (comparing values against a threshold and choosing accordingly) that a person can perform mentally. Step (a), “reading in training data,” is mere data gathering. Fourth, to the extent the “training” and “generating” steps (b), (c), (d) are not fully practicable in the human mind, that is because they recite mathematical concepts (the Bayesian computations of the neural network), which is an independent and sufficient basis for the exception. Applicant appears to argue that a step is abstract only if it can be performed mentally; that is legally incorrect. A step may recite an abstract idea because it is a mathematical concept (a separate grouping under MPEP 2106.04(a)(2)(I)) irrespective of whether it can be performed mentally.
Applicant contends that “while these steps may involve math, they are not directed to mathematical relationships.”
This argument is not persuasive. Applicant appears to invoke the principle that a claim reciting a mathematical concept is not necessarily ineligible merely because it “involves math.” However, MPEP 2106.04(a)(2)(I) identifies three sub-groupings of mathematical concepts: (A) mathematical relationships, (B) mathematical formulas or equations, and (C) mathematical calculations. Here, the Bayesian neural network of step (b) is defined by, and operates through, mathematical relationships and calculations. As the instant application concedes, training such networks is a known statistical/mathematical technique (¶ [0041] of instant USPGPUB, citing Bishop, Pattern Recognition and Machine Learning). The Li reference of record confirms that Bayesian methods and Bayesian neural networks are built upon Bayes’ theorem and its associated probability computations (see Equations (1)–(4)). The determination of a “quality value” together with an “uncertainty indication”—which the specification defines as “a variance, a standard deviation, a probability distribution, a distribution type and/or a progression indication” (¶ [0019] of instant USPGPUB)—is inherently the output of a mathematical calculation. Accordingly, steps (b), (c), and (d) recite mathematical concepts under MPEP 2106.04(a)(2)(I)(B)–(C), regardless of whether they are characterized as “mathematical relationships.”
Applicant further argues that amended limitation (g)—“manufacturing and/or processing the technical product by a production system based on the selected structural data set”—recites “concrete steps,” “cannot be performed mentally,” and is “not [a] mathematical concept,” and therefore the claim is not directed to an abstract idea.
This argument is not persuasive.
First, the presence of a single additional element that is itself non-abstract does not establish that the claim as a whole is not directed to an abstract idea. Under Step 2A, an additional element is not evaluated for whether it is itself abstract; rather, it is evaluated for whether it integrates the recited exception into a practical application (Prong Two) and whether it amounts to significantly more (Step 2B). (MPEP 2106.04(d)). The claim recites some non-abstract additional element (e.g., a generic computer, an output step); the mere presence of such an element does not confer eligibility. (See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014)). Second, limitation (g) is recited at a high level of generality and amounts to no more than instructions to “apply” the result of the abstract idea using a generic tool, and/or generally linking the abstract idea to a particular technological environment (product manufacturing). The claim does not recite any specific manufacturing operation, transformation, machine configuration, or process parameters. The “production system” is described only generically as “a manufacturing installation, a robot or a machine tool” (¶ [0027]), and the “technical product” is described as any of a broad genus—“a turbine blade, a wind turbine, a gas turbine, a robot, a motor vehicle or a component” (¶ [0028]). A nominally recited, generic “manufacture the product” step is mere insignificant post-solution activity and/or a field-of-use limitation that does not impose a meaningful limit on the exception. (MPEP 2106.05(e), (g), (h)). Furthermore, the manufacturing is a contingent limitation. Third, the claim does not effect a particular transformation within the meaning of MPEP 2106.05(c). The recited transformation (manufacturing) is not tied to any specific transformative process; the claim merely states that some product is produced based on the abstractly-derived data set. This is insufficient to confer eligibility. (See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012)).
Accordingly, the amendment adding limitation (g) does not remove the claim from the realm of abstract ideas.
Applicant argues that none of the limitations recite an abstract idea “on their own or per se,” and that “the claims as a whole are not directed to mathematical concepts, but rather recite concrete, real-world steps.”
This argument is not persuasive.
First, Applicant misapplies the “recites” inquiry. Under MPEP 2106.04(a)(2), a claim “recites” a judicial exception when it sets forth or describes the exception in one or more limitations. As set forth in the rejection above and reiterated herein, specific, identified claim limitations—steps (a) through (e)—set forth mental processes and mathematical concepts. The Examiner has identified the specific limitations at issue and the specific grouping into which each falls, as required by the 2019 PEG. The exception need not comprise the entirety of the claim to be “recited”; it need only be set forth within a limitation, which it is here. Second, Applicant conflates Step 2A Prong One (whether the claim recites an exception) with Step 2A Prong Two (whether the claim is directed to the exception after considering integration). Applicant’s contention that the “claims as a whole” recite “concrete, real-world steps” is properly addressed at Prong Two, not Prong One. The presence of additional non-abstract elements does not negate the fact that abstract ideas are recited in identified limitations. (MPEP 2106.04(a)).
Accordingly, the result of Step 2A Prong One is YES—the claim recites a judicial exception—and the analysis proceeds to Prong Two.
Applicant argues that the claim integrates any exception into a practical application because it provides an “improvement in the relevant technological field,” pointing to specification ¶¶ [0005]–[0006] and [0011], which describe that conventional expert-driven design was complex and had to be restarted upon a change in design criteria, whereas the invention provides “more robust and/or more reliable design variants,” accounts for material/production variations, and is “easily adaptable to different fields.”
This argument is not persuasive.
First, the alleged improvements Applicant identifies are improvements to the abstract idea itself (the statistical modeling/estimation process), not improvements to a computer or other technology. The “robustness” and “reliability” of the design variants flow directly from the Bayesian analysis “explicitly taking into account” uncertainties (¶ [0011])—that is, from the mathematical concept. An improvement in the accuracy, robustness, or reliability of a statistical/analytical result is an improvement to the abstract idea, not a technological improvement that integrates the exception into a practical application. (See SAP Am. v. InvestPic, 898 F.3d at 1170 (“a claim for a new abstract idea is still an abstract idea”); MPEP 2106.05(a)). Second, to qualify as a technological improvement under MPEP 2106.05(a), the specification must describe the improvement with specificity, and the claim must reflect that improvement. Here:
The claim does not recite any improvement to the functioning of the computer, the neural network architecture, or any manufacturing technology.
The Bayesian neural network is invoked generically and is a known tool (¶ [0041]; see Li reference).
The generically-recited “production system” (¶ [0027]) is not improved by the claim.
The claim thus uses generic computing tools and a generic production system to implement the abstract idea; it does not improve them.
Third, Applicant’s reliance on the “does not seek to tie up the abstract idea” language (citing MPEP 2106.04(a)(1)) is misplaced. While pre-emption is a concern underlying the eligibility inquiry, the absence of complete pre-emption does not by itself establish eligibility. The Federal Circuit has repeatedly held that “the absence of complete preemption does not demonstrate patent eligibility.” (MPEP 2106.04(a)(1)). Where, as here, the claims are shown to be directed to a patent-ineligible concept under the Alice/Mayo framework, “preemption concerns are fully addressed and made moot” and cannot independently confer eligibility. That Applicant’s claim recites various specific data-handling and computational steps (reading in training data, training the network, generating synthetic data sets, etc.) merely reflects that the claim narrows the abstract idea to a particular application; narrowing an abstract idea does not make it non-abstract. (See BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281 (Fed. Cir. 2018)).
Fourth, the additional non-abstract elements—the generic “computer-implemented” environment, the generic Bayesian neural network as a tool, the “outputting” step, and the generically-recited “manufacturing/processing by a production system”—when evaluated individually and in combination, do not integrate the exception into a practical application because they (i) merely apply the exception using generic computer components (MPEP 2106.05(f)), (ii) add insignificant extra-solution activity (outputting) (MPEP 2106.05(g)), and (iii) generally link the exception to a field of use / recite “apply it” instructions (manufacturing) (MPEP 2106.05(e), (h)).
Accordingly, the result of Step 2A Prong Two is NO—the claim does not integrate the recited exception into a practical application—and the claim is directed to the abstract idea.
Applicant argues that the claim contains “significantly more” than the abstract idea, and that because the claims are allegedly “novel and non-obvious over the cited art,” this “weighs against any finding that the elements are merely well known, routine, and/or conventional.”
This argument is not persuasive.
First, Applicant’s argument improperly conflates the § 101 inquiry with the §§ 102/103 inquiries. As Applicant itself acknowledges, “the search for an inventive concept under § 101 is not equivalent to analysis under § 102 or § 103.” The Federal Circuit has squarely held that “the question of whether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question of fact” that is distinct from patentability under §§ 102 and 103. (Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018)). Critically, a claim may recite a novel and non-obvious abstract idea and still be patent-ineligible. “Groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the § 101 inquiry.” (Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); see also SAP Am. v. InvestPic, 898 F.3d at 1163 (“a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.”)). Accordingly, even if the claim were found novel and non-obvious over the art of record, that finding would not establish that the additional elements amount to significantly more. Second, the proper Step 2B inquiry examines whether the additional elements—i.e., the claim elements beyond the abstract idea—individually and as an ordered combination, provide an inventive concept. The additional elements here are: (i) the generic “computer-implemented” environment; (ii) the generic Bayesian neural network used as a tool to perform the mathematical operations; (iii) the “outputting” step; and (iv) the generically-recited “manufacturing/processing by a production system.” Applicant’s argument that “the claims as a whole contain significantly more” impermissibly relies on the abstract idea itself (the specific data-processing and Bayesian-estimation steps) to supply the inventive concept. It is established that the abstract idea cannot furnish the inventive concept; “it has been clear since Alice that a claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention ‘significantly more’ than that ineligible concept.” (BSG Tech v. BuySeasons, 899 F.3d at 1290). Third, the record affirmatively establishes that the additional elements are well-understood, routine, and conventional, satisfying the evidentiary standard of Berkheimer and MPEP 2106.05(d):
The Bayesian neural network is expressly acknowledged in the specification as trainable by known methods described in a published textbook—“Efficient training methods for such Bayesian neural networks can be gathered for example from the textbook ‘Pattern Recognition and Machine Learning’ by Christopher M. Bishop, Springer 2011” (¶ [0041]). This is a statement in the specification demonstrating conventionality (MPEP 2106.05(d)(II), citing the Berkheimer Memorandum, option 1).
The Li reference of record further evidences that Bayesian methods and Bayesian neural networks were widely known statistical techniques predating the application.
The “production system” is described in the specification as pre-existing conventional equipment—“a manufacturing installation, a robot or a machine tool for product production or product processing” (¶ [0027])—and databases of training design variants are described as already “available for a multiplicity of products” (¶ [0031]).
The “outputting” of a data result is a well-understood, routine, and conventional computer function. (MPEP 2106.05(d)(II)).
Fourth, considered as an ordered combination, the additional elements add nothing more than they do individually. Receiving data, performing statistical estimation via a known neural network, comparing/selecting a result, outputting it, and generically manufacturing a product “based on” that result is a conventional arrangement that does not transform the abstract idea into a patent-eligible application. (Alice, 573 U.S. at 225). Fifth, Applicant’s reliance on Alice is misplaced. Applicant states that “[i]n contrast to Alice, the present claims transform Applicant’s embodiments … into a patent-eligible invention.” However, Applicant identifies no specific claim element analogous to the type of unconventional, technology-improving element that has been found to confer eligibility (e.g., BASCOM, DDR Holdings). Rather, like the claims held ineligible in Alice, the present claim recites a fundamental practice (here, statistical estimation and selection) implemented using generic computing components applied to a generic technological field.
Accordingly, the result of Step 2B is NO—the claim does not recite significantly more than the abstract idea.
For at least the reasons set forth above, Applicant’s arguments are not persuasive. The claim (1) recites a judicial exception (mathematical concepts and mental processes) under Step 2A Prong One; (2) does not integrate that exception into a practical application under Step 2A Prong Two; and (3) does not recite significantly more than the exception under Step 2B. The rejection of claim 1 under 35 U.S.C. § 101 is respectfully maintained.
Applicant’s arguments regarding the prior art rejection of claim 1 have been fully considered but are not persuasive.
The Examiner notes that Applicant’s arguments rely substantially on importing limitations from the specification into the claims that are not actually recited. Under the broadest reasonable interpretation (BRI) consistent with the specification, claim terms are given their plain meaning; features described only in the specification are not read into the claims. (See MPEP 2111). Several of Applicant’s arguments (e.g., defining “quality value” by reference to spec ¶ [0032], or “synthetic” as “new structures not present in the training data”) impermissibly narrow the claims beyond their recited scope.
Applicant argues that Dweik “does not teach or suggest any quality value,” and that a quality value must, per spec ¶ [0032], “indicate whether and to what extent a requirement … concerning a design criterion is satisfied.”
This argument is not persuasive.
First, Applicant improperly imports limitations from the specification. Claim 1 recites a “training quality value quantifying a predefined design criterion.” The claim does not require that the quality value “indicate whether and to what extent a requirement … is satisfied”; that is language from ¶ [0032] of the specification, not the claim. Under BRI, a “quality value quantifying a predefined design criterion” is any value that numerically characterizes a design/performance criterion of the product. (MPEP 2111.01). Second, Dweik expressly teaches such values. Dweik discloses predicting “one or more characteristics of the mechanical device” (¶ [0004], [0033]), and these characteristics are explicitly performance/design criteria of the product—e.g., “fluid flow,” “thermal characteristics” such as a “temperature,” “heat capacity,” “thermal expansion,” “thermal conductivity,” “thermal stress” (¶ [0040]), and “combustion characteristics” such as a “heating value,” “elemental composition,” “moisture content,” “density,” etc. (¶ [0041]). Each such predicted characteristic is a value that quantifies a design/performance criterion of the mechanical device, and thus reads on the claimed “quality value” under BRI. The rejection’s citation to Dweik ¶¶ [0033]–[0035] and [0040]–[0041] establishes that Dweik generates and uses these quantified characteristics. Third, to the extent Applicant contends the mapping was not sufficiently explained, the mapping is clarified above and reiterated: Dweik’s quantified predicted “characteristics” (¶¶ [0040]–[0041]) correspond to the claimed “quality value quantifying a predefined design criterion.”
Applicant argues Dweik fails to teach a quality value together with an associated uncertainty indication, contending that Dweik’s ¶ [0037] “classification confidence” (how likely an input belongs to a class) “is not the same as an associated uncertainty indication connected with an associated quality value.”
This argument is not persuasive, and further, this limitation is addressed by the combination as a whole.
First, the rejection relies on the combination of Dweik and Li for the Bayesian-network aspects of the claim. Applicant’s argument attacks Dweik individually for teaching the “uncertainty indication,” but “one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references.” (In re Keller, 642 F.2d 413 (Fed. Cir. 1981); In re Merck & Co., 800 F.2d 1091 (Fed. Cir. 1986); MPEP 2145(IV)).
Second, Dweik itself provides a reasonable teaching of an uncertainty indication associated with a value. Dweik expressly employs “Bayesian models (e.g., Bayesian networks)” and “probabilistic classification models” (¶ [0037]), and describes a machine learning process that outputs a “confidence” that an input belongs to a class—“f(x)=confidence(class)” (¶ [0037]). A confidence measure associated with a predicted characteristic is a measure of the certainty/uncertainty of that predicted value. Under BRI, an “uncertainty indication” is any indication of the uncertainty associated with the quality value; Dweik’s confidence/probabilistic output associated with its predicted characteristics reads on this. Third, Li expressly and repeatedly teaches that Bayesian methods and Bayesian neural networks produce a predicted value together with an associated uncertainty/predictive distribution. Li teaches that “the result of Bayesian training is a posterior distribution over network weights,” yielding “a distribution over the outputs of the network, which is known as the predictive distribution,” and that “the full predictive distribution also tells how uncertain this prediction is” (Li, § 2.3). Li further teaches that the Bayesian approach provides “an indication of the degree of uncertainty in the predictions” (Li, § 3.2, discussing the Bayesian neural network of Vahidinasab et al.). Thus, the combination teaches producing a quality value (Dweik’s quantified characteristic) together with an associated uncertainty indication (Li’s predictive distribution/uncertainty measure) when implemented via a Bayesian neural network.
The rationale to combine is set forth in the rejection: a person of ordinary skill would have been motivated to implement Dweik’s machine-learning prediction of design characteristics using the Bayesian neural network of Li in order to obtain the recognized benefits of Bayesian methods—namely, quantifying and providing “the degree of uncertainty in the predictions” (Li, § 3.2), which improves the “accuracy and reliability” of the modeling (Li, Abstract). This is the use of a known technique (Bayesian NN with uncertainty output) to improve a similar device/method (Dweik’s ML-based prediction) in the same way, and combining prior art elements according to known methods to yield predictable results. (MPEP 2143(A), (C)).
Applicant argues Dweik “does not generate synthetic structure data sets,” because the cited paragraphs are “directed to choosing and assembling stored data elements” and do not “creat[e] synthetic (for example, new structures not present in the training data) structure data sets.”
This argument is not persuasive.
First, Applicant again imports an unrecited limitation. Claim 1 recites “generating a multiplicity of synthetic structure data sets.” The claim does not require that the synthetic structure data sets be “new structures not present in the training data”; Applicant’s parenthetical “(for example …)” concedes this is merely an example from the specification, not a claim requirement. Under BRI, “synthetic structure data sets” are structure data sets that are generated/produced by the method, which is fully consistent with Dweik’s generation of structure data sets. Second, Dweik expressly generates structure data sets. Dweik’s modeling component “generate[s] a three-dimensional model of a mechanical device” and generates structure data by, e.g., “comput[ing] the one or more mechanical elements” such that they are “computationally derived elements” (¶ [0032]). Dweik further teaches generating a 3D model / computational domain “from an image” and “from a traced image” (¶ [0028]), and generating and combining structure data sets, including integrating a “first 3D model” and “second 3D model” to “generate a 3D model for a device” (See at least ¶ [0037] and [0042]) that is a “combination of two or more 3D models”—i.e., producing a structure data set representing a design variant not identical to any single stored element. These generated/computed structure data sets read on the claimed “synthetic structure data sets” under BRI. Even under Applicant’s narrower reading, the rejection relies on the combination; Applicant’s argument that Dweik alone does not generate “new” structures does not address the combined teachings. Moreover, Dweik’s generation of new combined 3D models (¶ [0042]) does produce structures not identical to individual stored training elements.
Applicant argues limitation (d) is not taught because Dweik is allegedly “silent regarding any quality value, any associated uncertainty indication, and any generation of synthetic structure data sets.”
This argument is not persuasive because it is entirely derivative of Applicant’s arguments regarding limitations (a), (b), and (c), which have been rebutted above. As established: (i) Dweik generates quantified predicted characteristics (quality values) for the modeled structures (¶¶ [0040]–[0041]); (ii) Dweik in combination with Li teaches associating an uncertainty indication with each such value via the Bayesian neural network (Dweik ¶ [0037]; Li §§ 2.3, 3.2); and (iii) Dweik generates synthetic (computationally-derived) structure data sets (¶¶ [0028], [0032], [0037] [0042]). Dweik further teaches performing the prediction “for each” modeled data set iteratively (see Dweik Fig. 14, steps 1404–1408; ¶¶ [0067]–[0069], determining whether the machine learning process “has generated new output” and updating accordingly). Accordingly, Dweik teaches limitation (d).
Applicant argues that Li is “directed to applying Bayesian methods to wind potential, short term forecasting, extreme wind modeling, and wind turbine lifetime,” and “does not teach or suggest generating synthetic structure data sets for a technical product nor any related quality value.”
This argument is not persuasive.
First, Li is relied upon for its teaching of Bayesian neural network methodology and the associated uncertainty/predictive-distribution output—not for teaching the design-of-a-technical-product context, which is supplied by Dweik. Applicant’s argument that Li does not teach “generating synthetic structure data sets for a technical product” attacks Li individually for elements taught by Dweik, contrary to In re Keller. Second, that Li’s exemplary applications concern wind energy does not limit the applicability of its Bayesian methodology teachings. A reference is available for all that it teaches to one of ordinary skill, and its teachings are not limited to the specific embodiments or preferred field disclosed. (In re Heck, 699 F.2d 1331 (Fed. Cir. 1983); MPEP 2123). Li broadly teaches the general advantages of Bayesian methods and Bayesian neural networks for “statistical modeling and data analysis for the quantity of interest with uncertainty and variability” (Li, Abstract), and expressly notes that “Bayesian neural networks can be used to improve the performance of those efforts” across “regression, density estimation, prediction and classification” (Li, § 4). Notably, Li is directed to wind turbines—which is precisely one of Dweik’s and the instant application’s recited technical products (a “wind turbine” / “turbine blade”; Dweik ¶ [0028]; instant spec ¶ [0028]). The references are therefore in analogous and closely related art.
Third, the motivation to combine is properly grounded in Li’s own stated benefits: applying Bayesian methods provides “more accurate, reliable, and adaptive modeling” and “an indication of the degree of uncertainty in the predictions” (Li, Abstract; § 3.2). A person of ordinary skill designing technical products with Dweik’s machine-learning system would have been motivated to adopt Li’s Bayesian neural network approach precisely to obtain these recognized advantages of explicitly quantifying prediction uncertainty. This is a combination of known prior art elements according to known methods to yield predictable results, and the use of a known technique to improve a similar method in the same way. (MPEP 2143(A), (C)).
Applicant argues that Li “discusses probability distribution for wind speed, but does not compare generated uncertainty indications with a predefined reliability indication in order to select one of the synthetic structure data sets.”
This argument is not persuasive.
Li teaches the core concept of comparing uncertainty/probabilistic measures against a threshold to make a selection. Li teaches “Bayesian model selection indicates that one could choose the ‘best’ model with some model selection criteria such as Bayesian factor or the highest posterior model probability given the data” (Li, § 2.4), i.e., selecting based on a probabilistic/uncertainty criterion. Li further teaches use of a threshold in the context of uncertainty—e.g., Chiodo and Lauria’s Bayesian estimation of a “safety horizon … in which the wind gust amplitude is smaller than a pre-selected threshold value with a given high probability” (Li, § 3.3), and enabling “predicting, for a specified level of probability, the interval within which the generated power should be observed” (Li, § 3.2). These teach comparing an uncertainty/probability measure against a predefined criterion (a “predefined reliability indication”) and making a determination based thereon. Li supplies the teaching of comparing an uncertainty/probability measure against a predefined threshold. Applicant appears to import the unrecited requirement that the selection be of a “synthetic structure data set” limited to “new structures.” As established above, “synthetic structure data sets” under BRI encompass Dweik’s generated/computed structure data sets.
Applicant further argues that “[n]either reference teaches or suggests using the selected synthetic structure data set to actually manufacture or process a physical technical product by a production system.”
This argument is not persuasive.
Dweik is expressly directed to a design tool whose purpose is producing physical products. Dweik describes the invention as applicable to “optimized machine design factoring in cost of materials in real-time” and to physical systems including “an automobile, an aircraft, a water craft, … pumps, engines” (Dweik ¶ [0028]–[0029]). Dweik’s generated 3D models and physics modeling data are used to inform the design and production of these physical devices. One of ordinary skill would understand that the output design data is used to manufacture the corresponding physical product. Furthermore, it is well understood that the entire purpose of a computer-aided design system is to generate a design that is then physically produced. Providing a design output to a production system (manufacturing installation, robot, or machine tool) to manufacture the corresponding product is a conventional and expected final step, and would have been obvious to one of ordinary skill in order to realize the practical benefit of the design method—i.e., actually obtaining the designed product. (MPEP 2143(A), combining prior art elements to yield predictable results; MPEP 2144.03 regarding matters within the ordinary skill/knowledge in the art).
For at least the reasons set forth above, Applicant’s arguments are not persuasive. Applicant’s arguments rely substantially on (i) importing unrecited limitations from the specification into the claims (e.g., the specific definition of “quality value” and the requirement that “synthetic” data be “new structures not present in the training data”), and (ii) attacking the references individually rather than addressing the combined teachings. When the claims are given their broadest reasonable interpretation and the references are considered in combination, the cited combination of Dweik and Li teaches or suggests each limitation of claim 1. The rejection is respectfully maintained.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “design system” in claim 10, which is interpreted to have structure as in instant para [0057] “The design system KS has one or more processors PROC for carrying out the required method steps, and also one or more memories MEM for storing data to be processed”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1: Claim 1 is directed to a computer-implemented design method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
Step 2A Prong 1: Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented design method for generating structure data sets specifying a technical product, comprising:
a) reading in training data, wherein the training data includes, for a multiplicity of design variants of the technical product, in each case a training structure data set specifying the respective design variant and also a training quality value quantifying a predefined design criterion;
b) training a Bayesian neural network on the basis of the training data, to determine an associated quality value together with an associated uncertainty indication on the basis of a structure data set;
c) generating a multiplicity of synthetic structure data sets and feeding the generated multiplicity of synthetic structure data sets into the trained Bayesian neural network;
d) generating a quality value with an associated uncertainty indication for each of the synthetic structure data sets by the trained Bayesian neural network;
e) comparing the generated uncertainty indications with a predefined reliability indication and selecting one of the synthetic structure data sets depending thereon;
f) outputting the selected structure data set for the purpose of producing the technical product; and
g) manufacturing and/or processing the technical product by a production system based on the selected structural data set.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitations in the human mind. Specifically, the following limitations, under their broadest reasonable interpretation, recite a mental process and/or a mathematical concept:
Limitation (a), “reading in training data,” recites the collection/receipt of data, which is a form of data gathering that can be performed in the human mind or with pen and paper (observation/evaluation).
Limitation (b), “training a Bayesian neural network on the basis of the training data, to determine an associated quality value together with an associated uncertainty indication,” recites a mathematical concept. A Bayesian neural network is defined by mathematical relationships—Bayes’ theorem and the associated probability computations (see, e.g., the Gong reference, Equations (1)–(4), describing the mathematical basis of Bayesian methods and Bayesian neural networks). Determining a “quality value” and an “uncertainty indication” (e.g., a variance, standard deviation, or probability distribution—see instant application ¶ [0019], [0043]) amounts to performing mathematical calculations/statistical estimation.
Limitation (c), “generating a multiplicity of synthetic structure data sets,” and limitation (d), “generating a quality value with an associated uncertainty indication for each of the synthetic structure data sets,” likewise recite mathematical computation/statistical estimation.
Limitation (e), “comparing the generated uncertainty indications with a predefined reliability indication and selecting one of the synthetic structure data sets depending thereon,” recites a mental process—an evaluation, comparison, and judgment/selection that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)). A person can evaluate uncertainty values against a threshold and select accordingly.
Accordingly, limitations (a)–(e) recite abstract ideas falling within the “mathematical concepts” and “mental processes” groupings of the 2019 PEG (MPEP 2106.04(a)).
Step 2A Prong 2: The claim is evaluated to determine whether the recited judicial exception is integrated into a practical application. It is not.
The claim recites the following additional elements:
A “Bayesian neural network” and the general concept of a “computer-implemented” method;
Limitation (f), “outputting the selected structure data set for the purpose of producing the technical product”; and
Limitation (g), “manufacturing and/or processing the technical product by a production system based on the selected structural data set.”
Analysis of the computer/neural network: The recitation of a “Bayesian neural network” and a generic “computer-implemented” method amounts to merely applying the abstract idea using a computer/generic computing tool as a tool to perform the mathematical operations. The specification confirms that the method may be carried out by generic computing hardware—“one or more computers, processors, application-specific integrated circuits (ASICs), digital signal processors (DSPs) and/or so-called ‘field programmable gate arrays’ (FPGAs)” (¶ [0010]). Merely using a generic computer or generic neural network as a tool to perform an abstract idea does not integrate the exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Analysis of limitation (f) — outputting: “Outputting the selected structure data set” is insignificant extra-solution activity—specifically, mere data output/transmission of the result of the abstract idea. This does not impose a meaningful limit on the exception. (MPEP 2106.05(g)).
Analysis of limitation (g) — manufacturing/processing: The recited step of “manufacturing and/or processing the technical product by a production system based on the selected structural data set” is recited at a high level of generality. The claim does not recite any particular manufacturing technique, transformation, or specific machine operation. The “production system” is described only generically in the specification as “a manufacturing installation, a robot or a machine tool” (¶ [0027]), and the “technical product” is any of “a turbine blade, a wind turbine, a gas turbine, a robot, a motor vehicle or a component” (¶ [0028]). Because the manufacturing step is nominally recited and generically claimed, it amounts to no more than instructions to “apply” the results of the abstract idea in a technological environment and/or generally linking the abstract idea to a field of use (product manufacturing). This does not reflect an improvement to the functioning of a computer or to any other technology. (MPEP 2106.05(e), (h)).
Considered individually and as an ordered combination, the additional elements do not (i) improve the functioning of a computer or other technology; (ii) apply the exception with or by use of a particular machine; (iii) effect a particular transformation of an article to a different state or thing (the claim does not tie the abstract selection to any specific transformative manufacturing operation); or (iv) apply the exception in a meaningful way beyond generally linking it to a technological environment. Accordingly, the judicial exception is not integrated into a practical application, and the claim is directed to the abstract idea.
Step 2B: The claim does not include additional elements that, individually or in combination, amount to significantly more than the judicial exception.
The “Bayesian neural network” and “computer-implemented” recitations amount to generic computer components performing generic computing functions (computation/estimation), which cannot supply an inventive concept. (MPEP 2106.05(f); Alice).
Limitation (f) (“outputting”) is well-understood, routine, and conventional data output/extra-solution activity that does not add significantly more. (MPEP 2106.05(d)(II), 2106.05(g)).
Limitation (g) (“manufacturing and/or processing … by a production system”) is recited generically. The specification treats such production systems (manufacturing installation, robot, machine tool) as pre-existing and conventional (¶ [0027]), and describes them as merely producing the product based on the output data. Generic manufacturing based on design output does not add an inventive concept; it is a well-understood and conventional activity of producing a product from a design specification. (MPEP 2106.05(d)).
Notably, the prior art of record further evidences that the components are known and conventional. The instant application itself directs the reader to a textbook for training such networks—“Efficient training methods for such Bayesian neural networks can be gathered for example from the textbook ‘Pattern Recognition and Machine Learning’ by Christopher M. Bishop, Springer 2011” (¶ [0041]). The Li reference (Li & Shi, Renewable Energy 43 (2012) 1–8) further confirms that Bayesian neural networks and the underlying Bayesian mathematics (Equations (1)–(4)) were well-known statistical techniques.
Considered as an ordered combination, the additional elements add nothing that is not already present when the steps are considered separately. The combination of receiving data, performing statistical/mathematical estimation via a neural network, comparing/selecting a result, outputting it, and then generically manufacturing a product does not provide an inventive concept.
For the reasons above, claim 1 is directed to an abstract idea (mathematical concepts and mental processes) without significantly more, and is therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Dependent Claims
Dependent claims 2-13 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims 2-9 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Specifically, claims 2-6 recite particular types of generative processes (such as variational autoencoders or generative adversarial networks), training approaches, or data inputs, which are mathematical techniques and models routinely used in the field of machine learning and data generation. Claims 7-9 recite specifying uncertainty indication by variance, standard deviation, or probability distribution, or handling multiple criteria, which are also mathematical concepts and standard data processing steps. These limitations do not add significantly more than the abstract idea itself, nor do they amount to an inventive concept or practical application beyond the judicial exception.
Therefore, dependent claims 2-13 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claim(s) 1-13 is/are ineligible under 35 U.S.C. §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-13 are rejected under 35 U.S.C. 103 as being obvious over Dweik et al. (USPGPUB US 20180260501 A1, hereinafter “Dweik”) in view of Li, Gong, and Jing Shi. "Applications of Bayesian methods in wind energy conversion systems." Renewable Energy 43 (2012): 1-8. (Hereinafter “Li”)
Regarding claim 1, Dweik teaches a computer-implemented design method for generating structure data sets specifying a technical product (Dweik, Fig. 1, [0028], [0031]), comprising:
a) reading in training data, wherein the training data includes, for a multiplicity of design variants of the technical product, in each case a training structure data set specifying the respective design variant and also a training quality value quantifying a predefined design criterion are read in as training data; (Dweik, at least [0033]-[0035], “Control volumes can also simulate run conditions for the preconfigured elements, the preconfigured components and/or a system associated with the 3D model. The preconfigured elements and/or the preconfigured components can be included in the library of data elements 114…. the modeling component 104 can employ 3D computer-aided design (CAD) data to automatically create computational domains and/or control volumes (e.g., chambers/elements/components) for the 3D model that can be employed (e.g., by the machine learning component 106) to generate predictions for simulated machine conditions for a device associated with the 3D model…The one or more characteristics determined by the machine learning component 106 can include, for example, one or more fluid characteristics associated with the one or more 3D models generated by the modeling component 104, one or more thermal characteristics associated with the one or more 3D models generated by the modeling component 104, one or more combustion characteristics associated with the one or more 3D models generated by the modeling component 104, one or more electrical characteristics associated with the one or more 3D models generated by the modeling component 104 and/or one or more other characteristics associated with the one or more 3D models generated by the modeling component 104.”)
b) training a Bayesian neural network on the basis of the training data, to determine an associated quality value together with an associated uncertainty indication on the basis of a structure data set; ([0037] “The machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can employ, for example, a support vector machine (SVM) classifier to learn and/or generate inferences with respect to the one or more 3D models generated by the modeling component 104. Additionally or alternatively, the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can employ other classification techniques associated with Bayesian networks, decision trees and/or probabilistic classification models. Classifiers employed by the machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can be explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via receiving extrinsic information). For example, with respect to SVM's that are well understood, SVM's are configured via a learning or training phase within a classifier constructor and feature selection module. A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class—that is, f(x)=confidence(class).”; [0038])
c) generating a multiplicity of synthetic structure data sets and feeding the generated multiplicity of synthetic structure data sets into the trained Bayesian neural network; ([0031], [0035], [0037] “the machine learning component 106 can perform a probabilistic based utility analysis that weighs costs and benefits related to the one or more 3D models generated by the modeling component 104. The machine learning component 106 (e.g., one or more machine learning processes performed by the machine learning component 106) can also employ an automatic classification system and/or an automatic classification process to facilitate learning and/or generating inferences with respect to the one or more 3D models generated by the modeling component 104”)
d) generating a quality value with an associated uncertainty indication for each of the synthetic structure data sets by the trained Bayesian neural network; ([0037] “ In an aspect, the machine learning component 106 can also employ measured data and/or streamed data to set boundary conditions for one or more machine learning processes. For example, the machine learning component 106 can also employ measured data and/or streamed data to set boundary conditions for supply chambers and sink chambers and/or to establish driving forces for simulated physics phenomena (e.g., fluid dynamics, thermal dynamics, combustion dynamics, angular momentum, etc.).”)
and g) manufacturing and/or processing the technical product by a production system based on the selected structural data set. (Dweik ¶ [0028]–[0029])
Dweik does appear to expressly teach:
e) comparing the generated uncertainty indications with a predefined reliability indication and selecting one of the synthetic structure data sets depending thereon; f) outputting the selected structure data set for the purpose of producing the technical product;
However Li teaches
e) comparing the generated uncertainty indications with a predefined reliability indication and selecting one of the synthetic structure data sets depending thereon; (Li, 2.4. Bayesian model selection and averaging section and 3.1. Wind resource assessment and estimation, 4th paragraph) and
f) outputting the selected structure data set for the purpose of producing the technical product (Li, 3.4. Reliability evaluation of wind turbine components section)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to modify Dweik’s system which includes a machine learning component with Li’s Bayesian neural network model to provide a natural way to handle missing data, allow combination of data with domain knowledge, facilitate learning about causal relationships between variables, provide a method for avoiding the overfitting of data, predict with good accuracy even with rather small sample sizes, and can be easily combined with decision analytic tools. (See Li, 1. Introduction section, 3rd paragraph)
Regarding claim 2, Dweik in combination with Li teaches the method as claimed in claim 1, Dweik further teaches wherein the synthetic structure data sets are generated by a trainable generative process. (Dweik, [0037])
Regarding claim 3, Dweik in combination with Li teaches the method as claimed in claim 2, Dweik further teaches wherein the generative process is carried out by a variational autoencoder (Dweik, [0038] “a set of stacked auto-encoder computations”) and/or by generative adversarial networks.
Regarding claim 4, Dweik in combination with Li teaches the method as claimed in claim 2, Li further teaches wherein the generative process is trained on the basis of the training structure data sets, to reproduce training structure data sets on the basis of random data fed in, wherein a multiplicity of random data are generated and fed into the trained generative process, and wherein the synthetic structure data sets are generated by the trained generative process on the basis of the fed-in multiplicity of generated random data. (Li, 2.2. Hierarchical modeling and Bayesian network section)
Regarding claim 5, Dweik in combination with Li teaches the method as claimed in claim 2, Dweik further teaches wherein further structure data sets are fed into a trained generative process, and wherein the synthetic structure data sets are generated by the trained generative process depending on the further structure data sets fed in. (Dweik, [0074]-[0076])
Regarding claim 6, Dweik in combination with Li teaches the method as claimed in claim 2, Li further teaches wherein the generative process is trained, on the basis of the training structure data sets, to reproduce training structure data sets on the basis of random data fed in, wherein a multiplicity of data values are generated and fed into the trained generative process, wherein for a data value respectively fed in, a synthetic structure data set is generated by the trained generative process, and an associated quality value with an associated uncertainty indication is generated by the trained Bayesian neural network on the basis of the synthetic structure data set, wherein in the context of an optimization method an optimized data value is ascertained in such a way that an uncertainty quantified by the respective uncertainty indication is reduced and/or a design criterion quantified by the respective quality value is optimized, and wherein the synthetic structure data set generated for the optimized data value is output as selected structure data set. (Li, 2.3. Bayesian neural network section)
Regarding claim 7, Dweik in combination with Li teaches the method as claimed in claim 1, Dweik further teaches wherein a respective uncertainty indication is specified by a variance, a standard deviation, a probability distribution, a distribution type and/or a progression indication. (Dweik, [0037])
Regarding claim 8, Dweik in combination with Li teaches the method as claimed in claim 1, Dweik further teaches wherein the uncertainty indication generated for the selected structure data set is output in a manner assigned to the selected structure data set. (Dweik, [0069])
Regarding claim 9, Dweik in combination with Li teaches the method as claimed in claim 1, Li further teaches wherein a plurality of design criteria are predefined, wherein the Bayesian neural network is trained to determine criterion-specific uncertainly indications for criterion-specific quality values, wherein a plurality of criterion-specific uncertainty indications are generated for each of the synthetic structure data sets by the trained Bayesian neural network, and wherein one of the synthetic structure data sets is selected depending on the generated criterion-specific uncertainly indications. (Li, 2.3. Bayesian neural network section)
Regarding claim 10, Dweik in combination with Li teaches a design system for generating structure data sets specifying a technical product, configured for carrying out a method as claimed in claim 1. (Dweik, Fig. 4, element 102, 112, 110)
Regarding claim 11, Dweik in combination with Li teaches a computer program product, comprising a non-transitory computer readable hardware storage device having computer readable program code stored therein, the program code executable by a processor of a computer system to implement the method as claimed in claim 1. (Dweik, [0030])
Regarding claim 12, Dweik in combination with Li teaches a non-transitory computer-readable storage medium comprising a computer program product having computer readable program code executable by a processor of a computer system to implement the method as claimed in claim 1. (Dweik, [0030])
Regarding claim 13, Dweik in combination with Li teaches the method as claimed in claim 2, Dweik further teaches wherein the production system is a manufacturing facility, a robot, or a machine tool (Dweik, [0029]), and wherein a respective quality value indicates the extent to which a requirement relating to a design criterion for the technical product is fulfilled. (As set forth in the base rejection of claim 1, Dweik teaches predicting and generating quantified “characteristics” of the modeled mechanical device that quantify design/performance criteria of the product—for example, quantified thermal characteristics such as “a temperature,” “a heat capacity,” “thermal expansion,” “thermal conductivity,” and “thermal stress” (Dweik, ¶ [0040]), and quantified combustion characteristics such as a “temperature measurement,” “a heating value,” “an elemental composition,” “a moisture content,” and “a density” (Dweik, ¶ [0041]). These quantified characteristics correspond to the claimed “quality value” (see claim 1 rejection). Furthermore, Dweik teaches that these predicted quality values are used to evaluate the design against design criteria/requirements. Dweik discloses that the machine learning component performs a “probabilistic based utility analysis that weighs costs and benefits related to the one or more 3D models” (Dweik, ¶ [0037]), and that the design system generates and evaluates models to determine “impact of a fluid with respect to a design of the device or product” (Dweik, ¶ [0003], [0045]). Dweik’s quantified characteristics thus indicate the degree to which the design of the product meets its intended performance requirements (e.g., the extent to which a thermal or fluid-flow requirement is met by a particular design variant).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANISS CHAD/
Supervisory Patent Examiner
Art Unit 3662
Prosecution Insights