DETAILED ACTION
Claims 1 – 16 have been presented for examination. Claim 1 is currently amended. Claim 17 is cancelled.
The instant office action is in response to the amendments dated 04/06/2026.
Discussion of Claim Rejections under 35 U.S.C. 101
Applicant’s amendments and arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “To directly address this concern, Applicant has amended independent claim 1 to recite a specific manner in which the parametric Computer-Aided Design representation is configured. …
Accordingly, amended claim I no longer merely recites a generic instruction to apply the results of an abstract calculation. Rather, the claim specifies that the feature values of the design parameters of the representative data sample are assigned as particular elements of a parametric geometric representation, namely NURBS control points or FFD displacement variables. This claimed assignment uses the selected representative data sample to configure a parametric CAD representation in a specific technical manner, thereby constructing a geometric design for manufacturing the physical object …
Instead, amended claim 1 recites a particular technical use of the selected representative data sample in a CAD process by assigning its design parameter feature values as NURBS control points or FFD displacement variables. In this way, the claimed invention uses the defined metric and the selected representative data sample to automatically control the construction of a manufacturing-ready geometric definition, thereby transforming design data into a functional engineering design asset.
Overall, amended claim 1 requires the configuration of a specific technical CAD environment, namely a parametric CAD representation, to produce a tangible technical result, namely a geometric design for manufacturing a physical object.”
Examiner notes that the claimed “assigning feature values of design parameters of the at least one representative data sample as control points of a Non-Uniform Rational B-Spline representation or as displacement variables for a set of deformation control points specifying a Free-Form Deformation” covers well-understood, routine, conventional activity (see Papakipos et al. (US 2005/0073520) Paragraph 54 “NURBS are well known and are widely used as mathematical parametric models of 3D objects”, and Paragraph 87 “As is well known in the art, a NURBS model includes "knot vectors" which, in addition to the NURBS control points, define the curve”). Therefore, the “performing” does not amount to a practical application, and does not amount to significantly more than the abstract idea.
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 – 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claim 1 recites at Step 1 a statutory category (i.e. a process) computer-implemented method for performing a design process by analysing design data of a physical object, the computer-implemented method comprising: determining plural concept candidates from the obtained dataset (D) based on at least a similarity of feature values of the design features (fi, ... , fNF), each concept candidate including a group of data samples, for generating plural concept candidate configurations; calculating a metric ( Q) for the concept candidate configurations, the calculated metric (Q) defining a quality of the generated concept candidate configurations, the metric (Q) evaluating the design features (fi, ... ,fNF) of different description spaces of the plurality of description spaces, wherein the metric (0) is configured to define the quality based on a distance to a Pareto front for each of the plurality of description spaces; evaluating the plural concept candidate configurations based on the calculated metric (Q) to generate concepts; determining at least one representative data sample for each of the concepts based on at least one selection criterion, thereby compressing the dataset to a reduced number of representative data samples. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “evaluating” amounts to modeling actions recited at a high-level of generality which requires no more than judgements or evaluations. The recited limitations in part, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). The “calculating” recite performing metrics calculations. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: that the method is computer-implemented; obtaining a dataset (D) including a plurality of data samples (xi, ... , xNv) of design data, each data sample xi representing a design variation of the physical object and comprising a plurality of design features (fi, ... , fNp), each design feature fi included in at least one of a plurality of description spaces; that reduces the required storage size and processing requirements for subsequent data analysis; outputting the determined at least one representative data sample for each of the concepts; performing the design process for the physical object by configuring a parametric Computer-Aided Design representation based on the output at least one representative data sample for each of the concepts to construct a geometric design for manufacturing the physical object, wherein configuring the parametric Computer-Aided Design representation comprises assigning feature values of design parameters of the at least one representative data sample as control points of a Non-Uniform Rational B-Spline representation or as displacement variables for a set of deformation control points specifying a Free-Form Deformation. The “computer-implemented” and “storage size and processing requirements” are recited at a high-level of generality such that it amounts to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “obtaining” amounts to insignificant data gathering since it is recited at a high-level of generality (see MPEP 2106.05(g)). The “outputting” amounts to insignificant data gathering since it is recited at a high-level of generality. The “performing” covers well-understood, routine, conventional activity for configuring the parametric Computer-Aided Design (see Papakipos Paragraph 54 “NURBS are well known and are widely used as mathematical parametric models of 3D objects”, and Paragraph 87 “As is well known in the art, a NURBS model includes "knot vectors" which, in addition to the NURBS control points, define the curve”). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “computer-implemented” and “storage size and processing requirements” amount to no more than mere instructions to apply the judicial exception using generic computer components. The additional elements do not amount to a particular machine (see MPEP 2106.05(b)(I)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The “obtaining” and “outputting” covers well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). The ”performing” covers well-understood, routine, conventional activity (see Papakipos Paragraph 54 “NURBS are well known and are widely used as mathematical parametric models of 3D objects”, and Paragraph 87 “As is well known in the art, a NURBS model includes "knot vectors" which, in addition to the NURBS control points, define the curve”). Considering the additional elements in combination does not add anything more than when considering them individually since the ”determining” and “calculating” and “evaluating” and “performing” requires no more than generic computer functions. For at least these reasons, the claim is not patent eligible.
Dependent claim 2 – 4, 6 – 8, 12 – 13 and 15 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s): Claim 2 the metric (Q) is configured to evaluate the design features (fi, ... , fNF) of at least three of the different description spaces; Claim 3 wherein the similarity of feature values of the design features (/1 , ... ,fNF) includes at least a similarity in a first description space, in a second description space and in a third description space; Claim 4 wherein the metric ( Q) is configured to define the quality further based on at least one of a performance value and an inclusion of predefined data samples in the concept candidate configurations for each of the plurality of description spaces; Claim 6 wherein the at least one selection criterion comprises at least one of a predefined preference criterion, in particular a high performance, or low maintenance cost, or low weight, or any other criterion relevant to performance, a determination criterion calculated based on a composition of the concept, in particular based on a distance to a mean computed based on feature values of the design features (/1 , ... ,fNF) of the data samples (xi,xj ... ) of the concept, and a suitability as a starting point for performing the optimization process for the physical object, in particular preferring low variations of feature values in all description spaces for a small variation of the feature values of the representative data sample xi; Claim 7 wherein the metric ( Q) outputs increased numerical values for an increased quality of the concept candidate configuration; Claim 8 wherein the quality of a particular concept candidate configuration depends on a number of data samples of the dataset (D) being included in all of the plural concept candidates of the particular concept candidate configuration, in particular the quality of the particular concept candidate configuration decreases for an increasing number of data samples of the dataset (D) not included in any of the concept candidates of the concept candidate configuration; and the quality of the particular concept candidate configuration is high in case every data sample of the dataset (D) is associated vvith one concept candidate of the concept candidate configuration; and the quality of the particular concept candidate configuration is high in case the number of data samples of each concept candidate is neither below a first threshold nor above a second threshold; the quality of the particular concept candidate configuration is high in case the data samples of all concept candidates of the particular concept candidate configuration include all the data samples of a predetermined portion of the data samples in the dataset (D) or a portion of the predetermined portion that is neither below a first threshold nor above a second threshold; the quality of the particular concept candidate configuration is high in case each concept candidate approximates predetermined target characteristics in each description space, wherein, in particular, the target characteristics base at least on value ranges for particular feature values in particular description spaces, on a distance of the particular feature values of the particular description spaces to predetermined feature values; Claim 12 wherein calculating the metric ( Q) for the concept candidate configurations further comprises regarding additionally preferred features values of the design features (fv ... , fNF) represented in concept candidates by Qa of one concept candidate if the preferred feature values are not included in a concept candidate according to [Equation] wherein [Equations] with Pi vvith i = 1, ... , Nvs denoting the set of preferred feature values in a description space i and a function Fp(aa) measures a fulfilment of a requirement on the preferred feature values in the description spaces, and the requirement is formulated by defining a set of data samples of interest which should be included into each concept candidate, and calculating the metric ( Q) by aggregating the individual concept quality measures QaP by computing the sum Q = r:c Qa p' or the product Q = rr:c Qa p ' or by using another monotonic aggregation function of the concept quality measure QaP [for quantifying the quality of a concept candidate configuration]; Claim 13 wherein calculating the metric ( Q) for the concept candidate configurations comprises utilizing mutual information for quantifying how much information is gained about an association of the data samples -with one specific concept candidate in one description space by acquiring knowledge about the association of the data samples with the one specific concept in another description space, and utilizing additionally information gained by knowing an association of data 15 samples vvi.th a union of two concepts candidates provides on the association of data samples·with the intersection of the two concept candidates in one description space, and summing over the gained combinatorial information according to [Equation] wherein I(X, Y) is the mutual information of the sets of variables X and Y, for calculating 20 the metric ( Q) based on information theory
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; Claim 15 wherein performing the design process comprises optimizing a design of the physical object based on a fitness function, wherein the fitness function is based on at least one of the calculated metric ( Q) and the selection criterion.
At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)) or mathematical concepts (see MPEP 2106.04(a)(2)(III)) since they further modify a parent claim abstract idea limitation. The “metric (Q) is configured to” and “metric (Q) outputs” and “quality of a particular concept candidate configuration depends on” and “reducing the concept quality measure” and “aggregating” and “utilizing mutual information” and “utilizing additionally information gained” and “summing” further limits the parent claim “calculating”. The “similarity of feature values of the design features (/1 , ... ,fNF) includes” further limits the parent claim “determining plural concepts”. The “one selection criterion comprises” further limits the parent claim “determining at least one representative data sample”. The “optimizing” further limits the parent claim “performing”. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention does not further recite any limitations. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception since there are no further recited limitations. For at least these reasons, the claim(s) are not patent eligible.
Dependent claim 5 and 9 – 11 recite(s)at Step 1 the same statutory category as the parent claim(s), and further recite(s): Claim 5 wherein the method includes determining a predetermined number of the concept candidates for the plural concept candidate configurations; or defining different numbers of the concept candidates for the concept candidate configurations from the dataset (D) simultaneously, and evaluating the plural concept candidate configurations based on the metric ( Q) and the different number of concept candidates in order to determine an optimized number of concept candidates for the plural concept candidate configurations; or optimizing, based on the metric ( Q) included in a fitness function, the similarity of the feature values of the design features (fv ... , f NF) of the concept candidates in the step of determining the concept candidate configurations; Claim 9 wherein evaluating the metric ( Q) for the concept candidate configurations comprises maximizing the metric ( Q) using a numerical optimization algorithm, in particular a gradient based algorithm or an evolutionary or swarm-based optimization algorithm, by changing the number of concept candidates of the concept candidate configuration and an association of each data sample of the design data in each description space with none, one or more concept candidates; Claim 10 wherein evaluating the metric ( Q) for the concept candidate configurations comprises using binary variables describing an association of each data sample in each description space to each concept candidate directly as optimization parameters for maximizing the metric (Q); or defining geometrical regions in each description space, which define an affiliation of the data samples to the concept candidates, and using geometric variables characterizing the geometric regions; Claim 11 wherein calculating the metric ( Q) for the concept candidate configurations comprises counting a number ICadof the data samples for each concept candidate in each description space, wherein Cai is the set of data samples associated with concept candidate a in a description space l, counting numbers of data samples associated with multiple concept candidates in one description space, counting numbers of data samples not associated with any concept candidate, determining a size of the concept candidates in each description space, and calculating a concept quality measure Qa of one concept candidate according to [Equation] wherein Nvs denotes a number of the description spaces and Ne the number of concept candidates, and a factor [Equation] with O::;; s::;; 1 which favors the size of each concept to be between sNv and (1 - s)Nv, s where Nv is the total number of data samples in the dataset, and calculating the metric ( Q) by aggregating the individual concept quality measures Qa by computing a sum, Q = I:c Qa, or a product Q = n:c Qa or by using another monotonic aggregation function of the individual concept quality measures Qa for quantifying the quality of a concept candidate configuration. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). The “determining” and “defining” and “evaluating” and “optimizing” recite modeling actions recited at a high-level of generality which requires no more than judgements or evaluations. The recited limitations in part, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). The “maximizing the metric ( Q) using a numerical optimization algorithm” and “using binary variables” and “defining geometrical regions” recite specific mathematical relationships. The “calculating” recites performing mathematical calculations. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention does not recite any further limitation. The claim is directed to an abstract idea.
At Step 2B the claims do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception since there are no further limitations recited. For at least these reasons, the claims are not patent eligible.
Dependent claim 14 and 16 recite(s)at Step 1 the same statutory category as the parent claim(s) , and further recite(s): Claim 14 associating the at least one new data sample xi to a specific concept based on the available feature values for the plurality of design features ([i, ... , fNF), predicting feature values for at least one design feature of the plurality of design features (/1 , ... , f NF) of the new data sample xi for which the feature values for at least one design feature of the plurality of design features (fv ... , fNF) are unavailable based on the associated specific concept. At Step 2A, Prong I the recited limitations in part, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). The “associating” and “predicting” recite modeling actions recited at a high-level of generality which requires no more than judgements or evaluations. The recited limitations in part, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover mathematical concepts (see MPEP 2106.04(a)(2)(I)). Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: Claim 14 wherein performing the design process comprises obtaining at least one new data sample xj wherein for the at least one new data sample xjfor at least one of the description spaces the feature values for at least one design feature of the plurality of design features ([i, ... , fN F) are unavailable; Claim 16 wherein the dataset includes data samples (xv ... , xNv) of engineering design data, each data sample xi representing a design of the physical object, each of the plural description spaces is characterized by a single design feature fi 15 or a group of design features (h[j, ... ), wherein the group of design features (fi,[j, ... ) includes one of a set of design data parameters of the physical object, a set of geometrical features of the physical object, a set of performance values of the physical object for defined conditions, and a latent representation of a machine learning approach, in particular of an auto-encoder or of a principal/independent component analysis PCA/ICA. The “obtaining” amounts to insignificant data gathering since it is recited at a high-level of generality (see MPEP 2106.05(g)). The and “dataset includes” and “plural description spaces is characterized by” further limits the parent claim “obtaining” to specify more details about the dataset. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The “obtaining” and “dataset includes” and “plural description spaces is characterized by” covers well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). For at least these reasons, the claim is not patent eligible.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter, subject to overcoming the 101 rejection.
None of the prior art of record taken individually or in combination discloses the claim 1 (and claims 2 – 16 by incorporation) computer-implemented method comprising: “wherein the metric (Q) is configured to define the quality based on a distance to a Pareto front for each of the plurality of description spaces”, in combination with the remaining elements and features of the claim. It is for these reasons that the applicant’s invention defines over the prior art of record.
Del Rosario et al. “Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization” teaches using a naïve distance to Pareto front to evaluate design candidates and how this is problematic. However, does not appear to explicitly disclose t calculated metric (Q) defining a quality of the generated concept candidate configurations, the metric (Q) evaluating the design features (fv ... , fNp) of different description spaces of the plurality of description spaces based on a distance to a Pareto frontier for each of a plurality of description spaces.
Behandish et al. (US 2020/0209832) teaches a mapping between a design space and a behavioral space, and enumerating designs along a pareto front. However, does not appear to explicitly disclose t calculated metric (Q) defining a quality of the generated concept candidate configurations, the metric (Q) evaluating the design features (fv ... , fNp) of different description spaces of the plurality of description spaces based on a distance to a Pareto frontier for each of a plurality of description spaces.
Ferretti et al. “Leveraging Prior Knowledge for Effective Design-Space Exploration in High-Level Synthesis” teaches searching for the set of pareto-optimal points in a design space. However, does not appear to explicitly disclose t calculated metric (Q) defining a quality of the generated concept candidate configurations, the metric (Q) evaluating the design features (fv ... , fNp) of different description spaces of the plurality of description spaces based on a distance to a Pareto frontier for each of a plurality of description spaces.
Lanfermann et al. “An Effective Measure to Identify Meaningful Concepts in Engineering Design Optimization” teaches calculating a metric (Q) for the concept candidate configurations, the calculated metric (Q) defining a quality of the generated concept candidate configurations, the metric (Q) evaluating the design features (fv ... , fNp) of different description spaces of the plurality of description spaces based on concept overlap. However, does not appear to explicitly disclose t calculated metric (Q) defining a quality of the generated concept candidate configurations, the metric (Q) evaluating the design features (fv ... , fNp) of different description spaces of the plurality of description spaces based on a distance to a Pareto frontier for each of a plurality of description spaces.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALFRED H. WECHSELBERGER whose telephone number is (571)272-8988. The examiner can normally be reached M - F, 10am to 6pm.
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/ALFRED H. WECHSELBERGER/ExaminerArt Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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