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 .
Response to Amendment
The amendment filed March 3, 2026 has been entered. Claims 1-19 remain pending in the instant application. Applicant’s amendments to the Claims have overcome each and every 112(b) rejection previously set forth in the Non-Final Office Action mailed September 4, 2025. Applicant’s acceptance of suggested title, Method and Tool for Pareto-Optimization of a Complex System, is noted. Regarding the objection to the Abstract previously set forth, the Abstract of the Specification “must commence on a separate sheet, preferably following the claims, under the heading ‘Abstract’ or ‘Abstract of the disclosure,’” see MPEP § 608.01(b) referencing 37 C.F.R. 1.72. The objection to the Abstract is maintained, and a corrected Abstract sheet is requested.
Response to Arguments
Applicant’s arguments, filed March 3, 2026, regarding rejections under 35 U.S.C 101 have been fully considered, but they are not persuasive.
First, Applicant argues that the claims do not recite mental processes and/or mathematical concepts under Step 2A prong I of the abstract idea analysis. Specifically, Applicant argues that building a surrogate model does not constitute a mathematical concept because the claim itself does not recite mathematical relationships, formulae, or calculations. Applicant also argues that the human mind is not equipped to perform the method of Claim 1, specifically “repeating steps of applying multi-objective optimization.”
Regarding Applicant’s argument that the claims do not recite a mathematical concept, the Examiner disagrees. The term “surrogate model” has is customarily understood in the art as being a simplified mathematical model to map inputs to outputs. Given its broadest reasonable interpretation in light of the specification, “building a surrogate model” comprises formulating a mathematical equation, which is also performable in the human mind. Furthermore, the claims and specification do not define a “surrogate model” or “building a surrogate model” in any way that would preclude these interpretations of the limitations as mathematical concepts and/or mental processes.
Regarding Applicant’s argument that the human mind is not equipped to perform the method of Claim 1, the Examiner disagrees. As claimed, “repeating the steps of applying multi-objective optimization […] until an abort criterion is met” does not preclude simple optimization processes that may be performed by a human using pen and paper. For instance, the multi-objective optimization steps may only need to be repeated once more before an abort criterion is met, which may be practicably performed by a human with pen and paper.
Applicant argues that the claim recites additional elements that integrate the judicial exceptions into a practical application. Specifically, Applicant argues that a combination of claim elements are directed to an improvement in the function of a computer, or an improvement to other technology or technical field. Applicant points to paragraphs [0048] and [0052] of the instant specification as describing a technological improvement. Applicant argues that the claimed solution allows a user to investigate tentative results generated by the surrogate model instead of having to wait until the layout space has been thoroughly simulated. Applicant further points to Ex Parte Desjardins as support for using a technological improvement to provide a practical application.
Regarding Applicant’s argument that the claims integrate the judicial exception(s) into a practical application by providing an improvement in technology, the Examiner notes that “the judicial exception alone cannot provide the improvement,” see MPEP § 2106.05(a) referenced by MPEP § 2106.04(d)(1). While the improvement can be provided by one or more additional element(s) in combination with the judicial exception(s), the additional elements of Claim 1 merely recite generic computer components as instructions to apply the abstract idea(s) on a computer, insignificant extra-solution activity, and/or a general field of use and technological environment, see MPEP § 2106.05(f)-(h). In the instant claims, using a system modeling module, without any further claimed detail regarding implementation of the module, recites mere instructions to apply the exception on a computer. Providing a user interface may also be considered to be insignificant extra-solution activity of data gathering, wherein the user interface merely enables the user to perform further mental evaluations and mathematical calculations, i.e., “avoiding submission of specific layout configurations that are not likely to be optimal” (e.g., remarks, page 17) and “verifying objective responses predicted by the surrogate model” (e.g., Claim 1). Thus, the additional elements in the claims are not considered to provide a practical application through a technological improvement, as the additional elements merely facilitate an improvement to the recited abstract ideas of multi-objective optimization and surrogate modeling.
Regarding Ex Parte Desjardins, Applicant has not pointed out specifically how the technological improvement in Ex Parte Desjardins is analogous to the alleged technological improvement in the instant claims, beyond merely stating that both the instant claims and Ex Parte Desjardins provide technological improvements to their respective fields.
Applicant finally argues that the claims provide significantly more under step 2B of the abstract idea analysis. Specifically, Applicant argues that the features identified as insignificant extra-solution activity are not well-understood, routine conventional activity. Applicant further argues that the alleged technological improvements recited in the claims are not well-understood, routine conventional activity. Applicant alleges that appropriate Berkheimer support was not provided in determining that additional elements were well-understood, routine conventional activity.
Regarding Applicant’s arguments that proper Berkheimer support was not provided for insignificant extra-solution activity identified as well-understood, routine conventional activity, the Examiner notes that the previous office action provides a citation to one or more of the court decisions discussed in MPEP § 2106.05(d)(II) as noting the well-understood, routine conventional nature of the additional elements(s). The Examiner points to page 7, of the previous office action, where “processing input data” and “receiving objective responses” were considered to be receiving or transmitting data over a network and storing and retrieving information in memory, which are elements that the courts have recognized as well-understood, routine conventional activity.
Regarding Applicant’s argument that the technological improvements in the specification were not considered, and appropriate Berkheimer support was not provided for technological improvements recited in the claims, the Examiner notes that whether or not a recited abstract idea is well-understood, routine conventional activity is not a factor in step 2B of the abstract idea analysis. Only additional elements that are not identified as abstract ideas are evaluated for well-understood, routine conventional activity. As explained above, any recited technological improvement in the claims are merely improvements to an abstract idea provided by further judicial exceptions. Thus, these technological improvements are not evaluated under step 2B, and Berkheimer support is not required.
An updated rejection under 35 U.S.C 101, necessitated by Applicant’s amendment, is provided below.
Applicant’s arguments regarding rejections under 35 U.S.C 102(a)(1) and 35 U.S.C 103 have been fully considered, but they are not persuasive.
Applicant first argues that Palm fails to teach “wherein determining the one or more consecutive further sets of layout parameter values is based on employing the surrogate model. Specifically, Applicant argues that step 5) on page 3, reciting that “[t]he surrogate model enables system architects to now quantify indicator trade-offs,” is not the same as determining further set of layout parameter values.
Regarding this argument, the Examiner notes that step 5) further recites that “[i]f higher accuracy is required, the model may be refined a) by newly defining the chosen Hyper Space segment (i.e. loop back to Define Hyper Space [or] by increasing the surrogate model itself (i.e., loop back to Design of virtual Experiments (DovE)).” Iteratively looping back to defining the hyper space is equivalent to determining further sets of layout parameter values.
Applicant further argues that Palm does not disclose the amended limitations of employing the surrogate model to “calculate layout parameters for Pareto-optimal points not included in any of the objective responses” and “verifying objective responses […] by submitting the specific layout configuration to the one or more system modelling modules.”
Regarding this argument, the Examiner disagrees. On page 4 of Palm, figure 6 discloses Pareto-optimal fronts, wherein fronts interpolate Pareto-optimal layouts not specifically simulated. On page 3, step 5) of palm discloses that “it is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept.” Deciding if a reached surrogate model accuracy is sufficient is interpreted as verifying objective responses of the surrogate model for specific layouts.
An updated rejection under 35 U.S.C 102(a)(1), necessitated by Applicant’s amendment, is provided below.
Specification
The abstract of the disclosure is objected to because the abstract includes legal phraseology (i.e., “wherein”), and the length of the abstract exceeds 150 words. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claim 1, the limitation wherein the surrogate model is employed to calculate layout parameters for Pareto-optimal points not included in any of the objective responses constitutes new matter. As understood by the Examiner, the objective responses are results of virtual experiments performed on a system having specified layout parameters. These objective responses are evaluated to reach a pareto-optimal layout. This limitation appears to express that pareto-optimal points and objective responses are of the same typology. This is inconsistent with the claimed invention, in which the pareto-optimal points (defining pareto-optimal layouts) are inputs, and objective responses are outputs of experiments performed using defined layout parameters. Furthermore, the specification does not appear to recite the language used in this claim limitation. Applicant is advised to either change the claim language to reflect language used in the specification, or point to specific locations in the specification for support of this claim limitation.
Claim 1 further recites the limitation wherein the method further comprises verifying objected responses predicted by the surrogate model. This limitation constitutes new matter. The specification is devoid of any mention of verifying or verification. Applicant is advised to either change the claim language to reflect language used in the specification, or point to specific locations in the specification for support of this claim limitation.
Regarding Claims 2-13, the claims require the limitations of Claim 1, on which these claims depend, and the claims are rejected under 35 U.S.C 112(a) for the same reasons.
Regarding Claim 14, the claim recites the same language identified as new matter in Claim 1, and the claim is rejected under 35 U.S.C 112(a) for the same reasons.
Regarding Claims 15-18, the claims require the limitations of Claim 14, on which these claims depend, and the claims are rejected under 35 U.S.C 112(a) for the same reasons.
Regarding Claim 19, the claim recites the same language identified as new matter in Claim 1, and the claim is rejected under 35 U.S.C 112(a) for the same reasons.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts without significantly more.
The following is an analysis of independent claim 1 based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
Yes: Claims 1-13 are directed to a method.
Step 2A Prong I, judicial Exception:
The Examiner submits that the foregoing claim limitations constitute mental processes and/or mathematical concepts, given their broadest reasonable interpretation. Abstract ideas are bolded.
Claim 1 recites the limitations:
1. A computer-implemented method for designing a Pareto-optimal layout of a system to be modeled, the method comprising:
processing input data relating to defining a layout space of layout parameters of a system to be modeled, a target space of target parameters of the system, and constraints in the layout parameters and the target parameters;
determining one or more sets of layout parameter values, each set specifying a layout configuration of the system to be modeled;
receiving, from one or more system modelling modules, objective responses for the system having layout configurations specified by the one or more sets of layout parameter values, wherein each objective response corresponds to a target parameter value achieved by the system when having a layout configuration specified by one of the sets of layout parameter values;
applying multi-objective optimization with respect to the target parameters to determine one or more further sets of layout parameter values;
receiving objective responses for the system having layout configurations specified by the one or more further sets of layout parameter values;
building a surrogate model that approximates a relation between the target parameters and the layout parameters;
repeating the steps of applying multi-objective optimization to determine one or more consecutive further sets of layout parameter values and receiving respective objective responses until an abort criterion is met, wherein determining the one or more consecutive further sets of layout parameter values is based on employing the surrogate model; and
providing a user interface for Pareto-optimal design of the system to be modeled, the user interface configured for selecting a specific layout configuration on basis of visualizing objective trade-offs inferred from the objective responses for the respective one or more sets of layout parameter values, wherein the trade-offs are inferred from the objective responses based on determining Pareto-optimal points.
wherein the surrogate model is employed to calculate layout parameters for Pareto- optimal points not included in any of the objective responses, and wherein visualizing the objective trade-offs comprises displaying a Pareto front,
wherein the method further comprises verifying objective responses predicted by the surrogate model for the specific layout configuration by submitting the specific layout configuration to the one or more system modelling modules.
The limitations determining one or more sets of layout parameter values, selecting a specific layout configuration, repeating the steps of applying multi-objective optimization […] until an abort criterion is met, employing a surrogate model, and verifying objective responses are abstract ideas because they are directed to mental processes, observations, evaluations, judgements, and opinions. A user can perform the mental judgement of determining values, selecting a layout configuration, and determining if a criterion is met. A user may use pen and paper to perform the determination and selection.
The limitations applying multi-objective optimization, building a surrogate model, and employing a surrogate model and are abstract ideas because they are directed to mathematical concepts, relationships, formulas, and calculations. As disclosed in paragraph [0044] of the instant specification, the surrogate model s is a mathematical function; in paragraph [0048] of the instant specification, the multi-objective optimization is disclosed to use the surrogate model. Furthermore, multi-objective optimization is itself a mathematical concept, given its broadest reasonable interpretation.
Step 2A Prong II, Integration into a Practical Application:
Claim 1 recites the following additional claim limitations outside the abstract idea which only present general fields of use, mere instructions to apply an exception, and/or insignificant extra-solution activity:
A computer-implemented method for designing a Pareto-optimal layout of a system to be modeled (general field of use, see MPEP § 2106.05(h)).
processing input data relating to defining a layout space of layout parameters of a system to be modeled, a target space of target parameters of the system, and constraints in the layout parameters and the target parameters (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
receiving, from one or more system modelling modules, objective responses for the system having layout configurations specified by the one or more sets of layout parameter values (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
wherein each objective response corresponds to a target parameter value achieved by the system when having a layout configuration specified by one of the sets of layout parameter values (general field of use, see MPEP § 2106.05(h)).
receiving objective responses for the system having layout configurations specified by the one or more further sets of layout parameter values (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
providing a user interface for Pareto-optimal design of the system to be modeled (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
wherein visualizing the objective trade-offs comprises displaying a Pareto front (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
submitting the specific layout configuration to the one or more system modelling modules (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
ADDITIONAL ELEMENTS:
Claim 1 recites the following additional elements:
“Computer-implemented, “one or more system modelling modules,” and “user interface” are high level recitations of generic computer components, computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f). Therefore, the claim does not integrate the recited abstract ideas into a practical application.
Step 2B, Significantly More:
When considered individually or in combination, the additional limitations and elements of claim 1 do not amount to significantly more than the judicial exceptions for the same reasons above as to why the additional limitations do not integrate the abstract idea into a practical application.
The additional elements “computer-implemented,” “one or more system modelling modules,” and “user interface” reciting generic computer components as mere instructions to apply on a computer per MPEP § 2106.05(f) are carried over and do not provide significantly more than the abstract idea. The examiner also notes that the specification does not define the structures of the additional elements in any way that could be used to integrate the abstract idea into a practical application.
The additional limitations identified as mere instructions to apply an exception, insignificant extra-solution activity, or general field of use above are carried over and also do not provide significantly more than the abstract idea. See MPEP § 2106.04(d) referencing MPEP § 2106.05(f), MPEP § 2106.05(g), and MPEP § 2106.05(h).
The insignificant extra solution activities of processing input data, receiving objective responses, providing a user interface, displaying a Pareto front, and submitting the specific layout configuration are considered to be further well understood, routine and conventional, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory […] i. Recording a customer’s order […] iv. Presenting offers and gathering statistics.”
Considering the claim limitations in combination and the claims as a whole does not change this conclusion, and Claim 1 is ineligible under 35 U.S.C 101.
Regarding Claim 2, the claim recites The computer-implemented method of claim 1, wherein determining the sets of one or more layout parameter values is based on applying a space-filling algorithm; this limitation is considered to constitute the additional mathematical concept of a space-filling algorithm under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 2 is ineligible under 35 U.S.C 101.
Regarding Claim 3, the claim recites The computer-implemented method of claim 1, wherein applying multi-objective optimization to determine the one or more further sets and the consecutive further sets, respectively, of layout parameter values is based on employing all previously received objective responses; this limitation is considered to constitute additional mental processes/ mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user may use pen and paper to perform optimization and determination based on previously received responses.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mathematical concepts/ mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 3 is ineligible under 35 U.S.C 101.
Regarding Claim 4, the claim recites The computer-implemented method of claim 1, wherein the layout space corresponds to a Cartesian product of a design space of design parameters and a use space of use case parameters; this limitation is considered to be directed to the additional mathematical concept of a Cartesian product under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III).
and wherein the design parameters and the use case parameters correspond to the layout parameters; this limitation is considered to constitute additional mental processes/ mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user may use pen and paper to determine design and use case parameters corresponding to layout parameters.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes/ mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 4 is ineligible under 35 U.S.C 101.
Regarding Claim 5, the claim recites The computer-implemented method of claim 1, further comprising sending the sets of layout parameter values, the further sets of layout parameter values, and each of the consecutive further sets of layout parameter values to one or more remote system modelling modules for providing the respective objective responses; this limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory.”
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 5 is ineligible under 35 U.S.C 101.
Regarding Claim 6, the claim recites The computer-implemented method of claim 1, further comprising determining that the abort criterion is met by determining that a predetermined level of convergence is reached; this limitation is considered to constitute additional mental processes/ mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user may use pen and paper to determine if the optimization has converged.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes/ mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 6 is ineligible under 35 U.S.C 101.
Regarding Claim 7, the claim recites The computer-implemented method of claim 1, further comprising performing a statistical test that a relation between the layout parameters and the target parameters inferred from the objective responses achieves a predetermined significance level; this limitation is considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). This limitation is directed to calculating a p-value.
and wherein the user interface instructs a user; this limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory […] iv. Presenting offers and gathering statistics.”
to refine the input data if the relation does not achieve the predetermined significance level; this limitation is considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user can perform the mental judgement of determining if a relation achieves a significance level. A user may use pen and paper to perform the determination.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 7 is ineligible under 35 U.S.C 101.
Regarding Claim 8, the claim recites The computer-implemented method of claim 1, wherein the system to be modeled is a neural network for pattern recognition; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
wherein the layout parameters comprise a network depth, a number of input features, and a kernel size, and wherein the target parameters comprise a precision, a recall, and a computing time; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 8 is ineligible under 35 U.S.C 101.
Regarding Claim 9, the claim recites The computer-implemented method of claim 1, wherein the system to be modeled is an electric vehicle; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
wherein the layout parameters comprise a peak motor power, a battery capacity, a number of gears, a gear ratio, and an upshift threshold; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
and wherein the target parameters comprise a total range of the vehicle and an acceleration time of the vehicle; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 10 is ineligible under 35 U.S.C 101.
Regarding Claim 10, the claim recites The computer-implemented method of claim 2, wherein the surrogate model is employed to calculate a continuous Pareto front over the Pareto-optimal points; this limitation is considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). This limitation recites the mathematical concept of using a numerical surrogate model to calculate a pareto front.
and wherein visualizing objective trade-offs comprises displaying the continuous Pareto front; his limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory […] iv. Presenting offers and gathering statistics.”
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 10 is ineligible under 35 U.S.C 101.
Regarding Claim 11, the claim recites The computer-implemented method of claim 1, further comprising performing a statistical test that the surrogate model achieves a predetermined significance level; this limitation is considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). This limitation is directed to calculating a p-value.
and wherein the user interface instructs a user; this limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory […] iv. Presenting offers and gathering statistics.”
to choose a different model family for building the surrogate model if it is determined that the predetermined significance level is not achieved; this limitation is considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user can perform the mental judgement of determining if a relation achieves a significance level. A user may use pen and paper to perform the determination.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 11 is ineligible under 35 U.S.C 101.
Regarding Claim 12, the claim recites The computer-implemented method of claim 1, wherein the user interface is configured for preparing output data reflecting layout parameter sensitivities; this limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory […] iv. Presenting offers and gathering statistics.”
based on analyzing a relation between the objective responses and the layout parameters; this limitation is considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user can perform the mental evaluation of analyzing a relation between responses and parameters.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 12 is ineligible under 35 U.S.C 101.
Regarding Claim 13, the claim recites The computer-implemented method of claim 12, wherein the sensitivities determined from the surrogate model capture correlation of a plurality of the layout parameters with one of the target parameters; this limitation is considered to constitute additional mental processes/ mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). A user may perform the mental judgement of correlating layout and target parameters. A user may use pen and paper to perform the correlation.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mental processes under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 13 is ineligible under 35 U.S.C 101.
The following is an analysis of independent claim 14 based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category:
Yes: Claims 14-18 are directed to a machine.
Step 2A Prong I, judicial Exception:
The Examiner submits that the foregoing claim limitations constitute mental processes and/or mathematical concepts, given their broadest reasonable interpretation. Abstract ideas are bolded.
Claim 14 recites the limitations:
14. A computing system for designing a Pareto-optimal layout of a system to be modeled, the computing system comprising:
a processor; and
a memory storing program instructions executable by said processor, wherein said computing system is configured to:
model, by one or more system modelling modules, a system with specified layout configurations, wherein each layout configuration is specified by a set of layout parameter values;
deliver, from the one or more system modelling modules, objective responses for a layout configuration, wherein each objective response corresponds to a target parameter achieved by the system having the layout configuration specified by the set of layout parameter values;
employ the objective responses to determine further sets of layout parameter values to be modelled;
build a surrogate model that approximates a relation between the target parameters and the layout parameters, wherein determining the one or more consecutive further sets of layout parameter values is based on employing the surrogate model;
provide a graphical user interface for Pareto-optimal design of the system, wherein the graphical user interface is configured to visualize objective trade-offs inferred from the objective responses for the layout parameters, wherein the trade-offs inferred are inferred from the objective responses based on determining Pareto- optimal points; and
repeatedly present to a user, in each repetition, further sets of layout parameter values and corresponding objective results, until the user makes a selection of a specific layout configuration having a set of corresponding layout parameter values.
wherein the surrogate model is employed to calculate layout parameters for Pareto- optimal points not included in any of the objective responses, and wherein visualizing the objective trade-offs comprises displaying a Pareto front,
wherein the method further comprises verifying objective responses predicted by the surrogate model for the specific layout configuration by submitting the specific layout configuration to the one or more system modelling modules.
The limitations visualize objective trade-offs, selection of a specific layout configuration, and verifying objective responses are abstract ideas because they are directed to mental processes, observations, evaluations, judgements, and opinions. A user can perform the mental observation of visualizing tradeoffs. A user can perform the mental judgement of selecting a layout configuration. A user may use pen and paper to perform the visualization and selection.
The limitations model a system, determine further sets of layout parameter values, build a surrogate model, and calculate layout parameters and are abstract ideas because they are directed to mathematical concepts, relationships, formulas, and calculations. As disclosed in paragraph [0044] of the instant specification, the surrogate model s is a mathematical function. As determining layout parameter values may use the mathematical function of the surrogate model, determining layout parameter values is also directed to a mathematical concept.
Step 2A Prong II, Integration into a Practical Application:
Claim 14 recites the following additional claim limitations outside the abstract idea which only present general fields of use, mere instructions to apply an exception, and/or insignificant extra-solution activity:
A computing system for designing a Pareto-optimal layout of a system to be modeled (general field of use, see MPEP § 2106.05(h)).
deliver, from the one or more system modelling modules, objective responses for a layout configuration (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
wherein each objective response corresponds to a target parameter achieved by the system having the layout configuration specified by the set of layout parameter values (general field of use, see MPEP § 2106.05(h)).
provide a graphical user interface for Pareto-optimal design of the system (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
repeatedly present to a user, in each repetition, further sets of layout parameter values and corresponding objective results (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
wherein visualizing the objective trade-offs comprises displaying a Pareto front (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
submitting the specific layout configuration to the one or more system modelling modules (insignificant extra-solution activity of data gathering, see MPEP § 2106.05(g)).
ADDITIONAL ELEMENTS:
Claim 14 recites the following additional elements:
“Computing system,” “processor,” “memory,” “program instructions,” “one or more system modelling modules,” and “user interface” are high level recitations of generic computer components, computer elements used as a tool, and represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f). Therefore, the claim does not integrate the recited abstract ideas into a practical application.
Step 2B, Significantly More:
When considered individually or in combination, the additional limitations and elements of claim 14 do not amount to significantly more than the judicial exceptions for the same reasons above as to why the additional limitations do not integrate the abstract idea into a practical application.
The additional elements “computing system,” “processor,” “memory,” “program instructions,” “one or more system modelling modules,” and “user interface” reciting generic computer components as mere instructions to apply on a computer per MPEP § 2106.05(f) are carried over and do not provide significantly more than the abstract idea. The examiner also notes that the specification does not define the structures of the additional elements in any way that could be used to integrate the abstract idea into a practical application.
The additional limitations identified as mere instructions to apply an exception, insignificant extra-solution activity, or general field of use above are carried over and also do not provide significantly more than the abstract idea. See MPEP § 2106.04(d) referencing MPEP § 2106.05(f), MPEP § 2106.05(g), and MPEP § 2106.05(h).
The insignificant extra solution activities of deliver objective responses, provide a graphical user interface, repeatedly present to a user, in each repetition, further sets of layout parameter values, displaying a Pareto front, and submitting the specific layout configuration are considered to be further well understood, routine and conventional, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iii. Performing repetitive calculations […] iv. Storing and retrieving information in memory […] i. Recording a customer’s order […] iv. Presenting offers and gathering statistics.”
Considering the claim limitations in combination and the claims as a whole does not change this conclusion, and Claim 14 is ineligible under 35 U.S.C 101.
Regarding Claim 15, the claim recites The system of claim 14, further configured to deliver the objective result for a layout configuration by simulating the system having a layout configuration specified by the set of one or more layout parameter values; this limitation is considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The simulating may be performed using the surrogate model, which is a mathematical function.
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations are considered to constitute additional mathematical concepts under step 2A prong I of the abstract idea analysis, see MPEP § 2106.04(a)(2)(III). The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 15 is ineligible under 35 U.S.C 101.
Regarding Claim 16, the claim recites The system of claim 14, further configured to retrieve the sets of layout parameters from the multi-objective optimization module by employing an API exposed by the multi-objective optimization module; this limitation recites the further additional element “API,” which is a high level recitation of generic computer components, computer elements used as a tool, and represents mere instructions to apply the abstract idea on a computer under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(f). This limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory.”
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 16 is ineligible under 35 U.S.C 101.
Regarding Claim 17, the claim recites The system of claim 14, further configured to execute the modeling in parallel; this limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II). Berkheimer support is provided by the following citation from Popovic (Popovic. "Parallel processing in real-time control: a state-of-the-art report." In 1994 Proceedings of IEEE International Conference on Control and Applications, pp. 799-804. IEEE, 1994.), discussing the state of the art in 1994 regarding parallel processing, “In the past, single-processor computer architectures have been engineering norms. This has in the time been replaced by a wide variety of new computer architectures for parallel processing based on parallel hardware configurations and high-speed interconnection links [3]. Recently. even massive parallel processing facilities (neural networks, systolic and hypercube computers etc.) have been released and have found their firm application in solution of complex engineering problems (image analysis, speech recognition, quality control etc.)” (e.g., page 2, column 1, paragraph 2).
Regarding Claim 18, the claim recites The system of claim 14, further configured to process user input related to definitions of a layout space of the system, a target space of the system, and a constraints of the system; this limitation is considered to be insignificant extra-solution activity under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(g). The insignificant extra-solution activity is further well-understood, routine conventional activity under step 2B of the abstract idea analysis, see MPEP § 2106.05(d)(II); “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity […] i. Receiving or transmitting data over a network […] iv. Storing and retrieving information in memory.”
wherein the layout space comprises the layout parameters, the target space comprises the target parameters, and the constraints cover required limitations in the layout parameters and the target parameters; this limitation is considered to merely link the judicial exception to a particular field of use and/or technological environment under step 2A prong II of the abstract idea analysis, see MPEP § 2106.05(h).
These limitations have been considered in combination with the limitations required by the claim(s) from which this claim depends. The additional limitations and/or additional elements do not integrate the claim limitations into a practical application (step 2A prong II), or recite significantly more than the abstract idea (step 2B). Therefore, claim 18 is ineligible under 35 U.S.C 101.
Regarding Claim 19, the claim recites substantially similar limitations to claim 1, and the claim is rejected under 35 U.S.C 101 for the same reasons. The additional elements “computer program product,” “non-transitory computer-readable medium,” and “program instructions” represent mere instructions to apply the abstract idea on a computer as in MPEP § 2106.05(f).
Furthermore, the claim is ineligible under Step 1 of the abstract idea analysis, as a “computer program product” does not fall within at least one of the four categories of patent eligible subject matter, see MPEP § 2106.03. The claim is directed to a product lacking a physical or tangible structure in the form of an organizational structure, such as a computer program per se (often referred to as “software per se”). A “computer program product,” comprising, but not limited to, “a non-transitory computer-readable medium,” could be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. Therefore, the claim is ineligible under 35 U.S.C 101. Applicant may amend the claim to “A non-transitory computer program product” to ensure that the claim is eligible under step 1.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7 and 9-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Palm et al. (Palm, Herbert, and Jorg Holzmann. "Hyper space exploration a multicriterial quantitative trade-off analysis for system design in complex environment." In 2018 IEEE International Systems Engineering Symposium (ISSE), pp. 1-6. IEEE, 2018.), hereinafter Palm.
Regarding Claim 1, Palm teaches A computer-implemented method for designing a Pareto-optimal layout of a system to be modeled (“The HSE approach combines methods of virtual prototyping with those of design of virtual experiments based studies for statistical learning […] Statistical learning enables system architects to […] identify Pareto-optimal system solutions.”) (e.g., page 1, abstract).
the method comprising: processing input data relating to defining a layout space of layout parameters of a system to be modeled (“Design solutions are characterized by a set of relevant topological and parametric design variables {d1, d2, ..., di} spanning the i-dimensional Design Space D. Any vector d = (d1, d2, ..., di) [which is an element in] D is representing an individual design layout.”) (e.g., page 2, column 2, last paragraph).
a target space of target parameters of the system (“Any relevant target indicator for evaluation of design alternatives is characterized by a set of target indicator variables {t1, t2, ..., tk} spanning the k-dimensional Target Indicator Space T. Any vector t = (t1, t2, ..., tk) [which is an element in] T is representing an individual multicriterial target achievement measure.”) (e.g., page 3, column 1, paragraph 1).
and constraints in the layout parameters and the target parameters (“Any use case of relevance is characterized by a set of use case variables {u1, u2, …, uj} spanning the j-dimensional Use Case Space U. Any vector u = (u1, u2, ..., uj) [which is an element in] U is representing an individual use case.” The design space D, target indicator space T, and use case space U are interpreted as comprising layout parameter constraints.) (e.g., page 2, column 2, last paragraph to page 3, column 1, paragraph 1).
determining one or more sets of layout parameter values, each set specifying a layout configuration of the system to be modeled (“Design solutions are characterized by a set of relevant topological and parametric design variables {d1, d2, ..., di} spanning the i-dimensional Design Space D. Any vector d = (d1, d2, ..., di) [which is an element in] D is representing an individual design layout.” Design variables d1 through di are interpreted as one or more layout parameter values.) (e.g., page 2, column 2, last paragraph).
receiving objective responses for the system having layout configurations specified by the one or more sets of layout parameter values (“Virtual experiments, i.e. simulations, are carried out according to the experimental test plan,” wherein the results of the simulations are interpreted as objective responses.) (e.g., page 3, column 1, paragraph 3).
wherein each objective response corresponds to a target parameter value achieved by the system when having a layout configuration specified by one of the sets of layout parameter values (“Results of simulations are filed in a storage allowing to assign (d, u) based virtual experiments to their according target measures t,” wherein t is a target indicator variable.) (e.g., page 3, column 1, paragraph 3).
applying multi-objective optimization with respect to the target parameters to determine one or more further sets of layout parameter values (“The surrogate model enables system architects now to quantify target indicator trade-offs. It enables assessment of design family capabilities in terms of use case u specific Pareto-optimal solution alternatives [given by equation (3)].” Assessment of design family capabilities using Pareto-optimal solution alternatives is interpreted as applying multi-objective optimization.) (e.g., page 3, column 1, paragraph 5).
receiving, from one or more system modelling modules, objective responses for the system having layout configurations specified by the one or more further sets of layout parameter values (“If higher accuracy is required, the model may be refined a) by newly defining the chosen Hyper Space segment (i.e., loop back to Define Hyper Space) or b) by selecting more appropriate space filling or by increasing the surrogate model itself (i.e., loop back to Design of virtual Experiments (DovE)).” Once further layout parameter values are specified by defining the hyper space, further virtual experiments may be run to obtain further objective responses.) (e.g., page 3, column 1, last paragraph to column 2, first paragraph).
building a surrogate model that approximates a relation between the target parameters and the layout parameters (“System behavior is manifested in the t(d, u) relation. However, this relationship in most cases in analytically inaccessible. Therefore, a surrogate model in terms of a k-dimensional vector s (of a suited functional family f α(d, u) with parameter vector α) is extracted approximated the t(d, u) relation [given by equation (1)].”) (e.g., page 3, column 1, paragraph 4).
repeating the steps of applying multi-objective optimization to determine one or more consecutive further sets of layout parameter values and receiving respective objective responses until an abort criterion is met (“It is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept [...] This will allow an iterative optimization of both the model and the system itself.”) (e.g., page 3, column 1, last paragraph; column 2, paragraph 1).
wherein determining the one or more consecutive further sets of layout parameter values is based on employing the surrogate model (“The surrogate model enables system architects now to quantify target indicator trade-offs. It enables assessment of design family capabilities in terms of use case u specific Pareto-optimal solution alternatives [given by equation (3)].” Assessment of design family capabilities using the surrogate model is interpreted as determining further sets of layout parameter values, wherein the design family comprises further sets of layout parameter values.) (e.g., page 3, column 1, paragraph 5).
and providing a user interface for Pareto-optimal design of the system to be modeled (“The HSE environment allows running HSE specific tasks of [...] d) visualizing results for adequate analysis and decision making.”) (e.g., page 3, column 2, paragraph 6).
the user interface configured for selecting a specific layout configuration on basis of visualizing objective trade-offs inferred from the objective responses for the respective one or more sets of layout parameter values (Figure 5 discloses different layout configurations, and figure 6 discloses trade-offs for layouts A1 and A2 which are inferred from results of the virtual experiments. The information from figures 5 and 6 may be visualized using the visualization module from figure 4.) (e.g., page 3, column 2, figure 4; page 4, column 1, figure 5; column 2, figure 6).
wherein the trade-offs are inferred from the objective responses based on determining Pareto-optimal points (Figure 6 discloses trade-offs for layouts A1 and A2 which are inferred from results of the virtual experiments.) (e.g., page 4, column 2, figure 6).
wherein the surrogate model is employed to calculate layout parameters for Pareto optimal points not included in any of the objective responses, and wherein visualizing the objective trade-offs comprises displaying a Pareto front (Figure 6 discloses Pareto-optimal fronts, wherein fronts interpolate Pareto-optimal layouts not specifically simulated.) (e.g., page 4, column 2, figure 6).
wherein the method further comprises verifying objective responses predicted by the surrogate model for the specific layout configuration by submitting the specific layout configuration to the one or more system modelling modules (“It is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept.” Deciding if a reached surrogate model accuracy is sufficient is interpreted as verifying objective responses of the surrogate model for specific layouts.) (e.g., page 3, column 1, last paragraph).
Regarding Claim 2, Palm teaches The computer-implemented method of claim 1, wherein determining the sets of one or more layout parameter values is based on applying a space-filling algorithm (“Dating back to the ideas of Ronald Fisher, space filling algorithms are applied accordingly for choosing effective and efficient candidates for virtual (i.e. simulation based) experiments.”) (e.g., page 3, column 1, paragraph 2).
Regarding Claim 3, Palm teaches The computer-implemented method of claim 1, wherein applying multi-objective optimization to determine the one or more further sets and the consecutive further sets, respectively, of layout parameter values is based on employing all previously received objective responses (“If higher accuracy is required, the model may be refined a) by [...] increasing the surrogate model itself (i.e., loop back to Design of virtual Experiments (DovE)),” wherein increasing the surrogate model is interpreted as utilizing previous surrogate models, wherein the surrogate models are determined based on objective responses.) (e.g., page 3, column 1, last paragraph; column 2, paragraph 1).
Regarding Claim 4, Palm teaches The computer-implemented method of claim 1, wherein the layout space corresponds to a Cartesian product of a design space of design parameters and a use space of use case parameters (“The Hyper Space H is defined as the Cartesian product of Design Space, Use Case Space and Target Indicator Space DxUxT,” wherein the hyper space H is interpreted as the layout space.) (e.g., page 3, column 1, paragraph 1).
and wherein the design parameters and the use case parameters correspond to the layout parameters (“Any vector d = (d1, d2, ..., di) [which is an element in] D is representing an individual design layout [...] Any vector u = (u1, u2, ..., uj) [which is an element in] U is representing an individual use case.”) (e.g., page 2, column 2, last paragraph; page 3, column 1, paragraph 1).
Regarding Claim 5, Palm teaches The computer-implemented method of claim 1, further comprising sending the sets of layout parameter values, the further sets of layout parameter values, and each of the consecutive further sets of layout parameter values to the one or more system modelling modules for providing the respective objective responses (Figure 4 discloses a modeling and simulation environment remote from the HSE environment to which the layout parameters values may be sent.) (e.g., page 3, column 2, figure 4).
Regarding Claim 6, Palm teaches The computer-implemented method of claim 1, further comprising determining that the abort criterion is met by determining that a predetermined level of convergence is reached (“It is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept.” Deciding if model a model accuracy is reached is interpreted as comprising determining that the optimization has converged.) (e.g., page 3, column 1, last paragraph).
Regarding Claim 7, Palm teaches The computer-implemented method of claim 1, further comprising performing a statistical test that a relation between the layout parameters and the target parameters inferred from the objective responses achieves a predetermined significance level (“The surrogate model itself may be characterized by attributes such as validation area, p value [15] or other variables that may be interpreted as target indicators,” wherein the p-value is a predetermined significance level and the surrogate model is an inferred relation between the layout parameters and the target parameters.) (e.g., page 3, column 2, paragraph 1).
and wherein the user interface instructs a user to refine the input data if the relation does not achieve the predetermined significance level (“It is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept [...] The surrogate model itself may be characterized by attributes such as validation area, p value, or other variables that may be interpreted as target indicators.” The p-value may be presented to the system architect by the visualization module in figure 4.) (e.g., page 3, column 1, last paragraph to column 2, first paragraph; column 2, figure 4).
Regarding Claim 9, Palm teaches The computer-implemented method of claim 1, wherein the system to be modeled is an electric vehicle (“Two examples of automotive applications with selected results already published in [22] [23] may demonstrate universality and mightiness of the HSE approach in the following two subsections. Both refer to the development of fully electric vehicles (FEVs). Example 1 follows the question if FEVs may or may not benefit from shiftable gear boxes.”) (e.g., page 4, column 1, paragraph 1).
wherein the layout parameters comprise a peak motor power, a battery capacity, a number of gears, a gear ratio, and an upshift threshold (Reference [23] from Palm, Holzmann et al. (Jorg Holzmann, Herbert Palm and Dieter Gerling: “Virtual Prototyping basierte Trade-off Analysen”, Tag des Systems Engineering, S.99-108, ISBN: 978-3-446-45126-1, Hrsg.: Schulze, S.O., Muggeo C., Hanser Verlag, 2016.), hereinafter Holzmann, discloses the hyper space exploration approach applied to electric vehicles, “In addition to the topological variants "1-speed" and "2-speed" transmissions, the design space also includes variations of various other vehicle parameters such as total mass (mvehicle), maximum engine power (Pmax), and gear ratio.” One of ordinary skill in the art would recognize that total vehicle mass is affected by battery capacity, and optimizing vehicle mass comprises optimizing a battery capacity. Optimizing a gear ratio comprises optimizing an upshift threshold.) (e.g., Holzmann; page 4, last paragraph).
and wherein the target parameters comprise a total range of the vehicle and an acceleration time of the vehicle (Holzmann, referenced by Palm, further discloses, “Figure 3 shows, as a result of the potential analysis in Pareto representation, a typical conflict between a performance indicator (the acceleration time from 0 to 50 km/h, tacc50) and a consumption indicator (energy consumption per 100 km, Econsumption).” Energy consumption directly affects the total range of a vehicle, and a performance indicator of energy consumption is analogous to a target of total range.) (e.g., Holzmann; page 4, last paragraph).
Regarding Claim 10, Palm teaches The computer-implemented method of claim 2, wherein the surrogate model is employed to calculate a continuous Pareto front over the Pareto-optimal points (“A dotted and a dashed line in Fig. 6 represent the fronts of Pareto-optimal solutions for both alternatives A1 and A2, respectively. A 4th grade polynomial has been used as surrogate model base.”) (e.g., page 4, column 1, last paragraph).
and wherein visualizing objective trade-offs comprises displaying the continuous Pareto front (Figure 6 discloses a displayed continuous pareto front.) (e.g., page 4, column 2, figure 6).
Regarding Claim 11, Palm teaches The computer-implemented method of claim 1, further comprising performing a statistical test that the surrogate model achieves a predetermined significance level (“The surrogate model itself may be characterized by attributes such as validation area, p value [15] or other variables that may be interpreted as target indicators,” wherein the p-value is a predetermined significance level.) (e.g., page 3, column 1, paragraph 1).
and wherein the user interface instructs a user to choose a different model family for building the surrogate model if it is determined that the predetermined significance level is not achieved (“It is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept [...] The surrogate model itself may be characterized by attributes such as validation area, p value, or other variables that may be interpreted as target indicators.” The p-value may be presented to the system architect by the visualization module in figure 4.) (e.g., page 3, column 1, last paragraph to column 2, first paragraph; column 2, figure 4).
Regarding Claim 12, Palm teaches The computer-implemented method of claim 1, wherein the user interface is configured for preparing output data reflecting layout parameter sensitivities based on analyzing a relation between the objective responses and the layout parameters (Figure 8 discloses a stability gain analysis for yaw control on a 4-wheel vs. 2-wheel drive train. The stability gain is interpreted as an objective response, and the yaw control is interpreted as a layout parameter.”) (e.g., page 5, column 1, figure 8).
Regarding Claim 13, Palm teaches The computer-implemented method of claim 12, wherein the sensitivities determined from the surrogate model capture correlation of a plurality of the layout parameters with one of the target parameters (“Designing an active yaw controller may become a complex task when extending the Design Space (spanned by all controller variables) by the Use Case Space (spanned in our example by the two use case variables ax and r). [...] Fig. 8 shows the quantified dependency of the vehicle stability (expressed by target indicator gainstab) as a function of road curvature (expressed by use case variable r) for all considered controller layouts and use cases of longitudinal acceleration ax.” The vehicle stability is interpreted as a target parameter, and the use case variables r and ax are interpreted as layout parameters.) (e.g., page 5, column 1, paragraph 2).
Regarding Claim 14, Palm teaches A computing system for designing a Pareto-optimal layout of a system to be modeled, the computing system comprising: a processor; and a memory storing program instructions executable by said processor (“The HSE Environment allows running HSE specific tasks of a) Hyper Space definition, DovE space filling and script control b) building a surrogate model c) analyzing and optimizing surrogate models and system designs and d) visualizing results for adequate analysis and decision making.”) (e.g., page 3, column 2, paragraph 6).
wherein said computing system is configured to: model, by one or more system modelling modules, a system with specified layout configurations (“Virtual experiments, i.e. simulations, are carried out according to the experimental test plan,” wherein the experimental test plan comprises a specified layout configuration.) (e.g., page 3, column 1, paragraph 3).
wherein each layout configuration is specified by a set of layout parameter values (“Design solutions are characterized by a set of relevant topological and parametric design variables {d1, d2, ..., di} spanning the i-dimensional Design Space D. Any vector d = (d1, d2, ..., di) [which is an element in] D is representing an individual design layout.” Design variables d1 through di are interpreted as one or more layout parameter values.) (e.g., page 2, column 2, last paragraph).
deliver, from the one or more system modelling modules, objective responses for a layout configuration, wherein each objective response corresponds to a target parameter achieved by the system having the layout configuration specified by the set of layout parameter values (“Results of simulations are filed in a storage allowing to assign (d, u) based virtual experiments to their according target measures t,” wherein t is a target indicator variable.) (e.g., page 3, column 1, paragraph 3).
employ the objective responses to determine further sets of layout parameter values to be modelled (“The surrogate model enables system architects now to quantify target indicator trade-offs. It enables assessment of design family capabilities in terms of use case u specific Pareto-optimal solution alternatives [given by equation (3)].” Assessment of design family capabilities using the surrogate model is interpreted as determining further sets of layout parameter values, wherein the design family comprises further sets of layout parameter values.) (e.g., page 3, column 1, paragraph 5).
build a surrogate model that approximates a relation between the target parameters and the layout parameters (“System behavior is manifested in the t(d, u) relation. However, this relationship in most cases in analytically inaccessible. Therefore, a surrogate model in terms of a k-dimensional vector s (of a suited functional family f α(d, u) with parameter vector α) is extracted approximated the t(d, u) relation [given by equation (1)].”) (e.g., page 3, column 1, paragraph 4).
wherein determining the one or more consecutive further sets of layout parameter values is based on employing the surrogate model (“The surrogate model enables system architects now to quantify target indicator trade-offs. It enables assessment of design family capabilities in terms of use case u specific Pareto-optimal solution alternatives [given by equation (3)].” Assessment of design family capabilities using the surrogate model is interpreted as determining further sets of layout parameter values, wherein the design family comprises further sets of layout parameter values.) (e.g., page 3, column 1, paragraph 5).
provide a graphical user interface for Pareto-optimal design of the system (“The HSE environment allows running HSE specific tasks of [...] d) visualizing results for adequate analysis and decision making.”) (e.g., page 3, column 2, paragraph 6).
wherein the graphical user interface is configured to visualize objective trade-offs inferred from the objective responses for the layout parameters (Figure 5 discloses different layout configurations, and figure 6 discloses trade-offs for layouts A1 and A2 which are inferred from results of the virtual experiments. The information from figures 5 and 6 may be visualized using the visualization module from figure 4.) (e.g., page 3, column 2, figure 4; page 4, column 1, figure 5; column 2, figure 6).
wherein the trade-offs inferred are inferred from the objective responses based on determining Pareto- optimal points (Figure 6 discloses trade-offs for layouts A1 and A2 which are inferred from results of the virtual experiments.) (e.g., page 4, column 2, figure 6).
and repeatedly present to a user, in each repetition, further sets of layout parameter values and corresponding objective results, until the user makes a selection of a specific layout configuration having a set of corresponding layout parameter values (“It is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept [...] This will allow an iterative optimization of both the model and the system itself.” The optimization may be facilitated by the visualization module in figure 4.) (e.g., page 3, column 1, last paragraph; column 2, paragraph 1 and figure 4s).
wherein the surrogate model is employed to calculate layout parameters for Pareto optimal points not included in any of the objective responses, and wherein visualizing the objective trade-offs comprises displaying a Pareto front (Figure 6 discloses Pareto-optimal fronts, wherein fronts interpolate Pareto-optimal layouts not specifically simulated.) (e.g., page 4, column 2, figure 6).
wherein the method further comprises verifying objective responses predicted by the surrogate model for the specific layout configuration by submitting the specific layout configuration to the one or more system modelling modules (“It is also up to the system architect to decide if a reached model accuracy is sufficient to meet requirements for proof-of-concept.” Deciding if a reached surrogate model accuracy is sufficient is interpreted as verifying objective responses of the surrogate model for specific layouts.) (e.g., page 3, column 1, last paragraph).
Regarding Claim 15, Palm teaches The system of claim 14, further configured to deliver the objective result for a layout configuration by simulating the system having a layout configuration specified by the set of one or more layout parameter values (“Results of simulations are filed in a storage allowing to assign (d, u) based virtual experiments to their according target measures t,” wherein t is a target indicator variable.) (e.g., page 3, column 1, paragraph 3).
Regarding Claim 16, Palm teaches The system of claim 14, further configured to retrieve the sets of layout parameters from a multi-objective optimization module by employing an API exposed by the multi-objective optimization module (Figure 4 discloses a DovE and Script Control module coupled to an Analysis & Optimization module, wherein the script control is analogous to an API.) (e.g., page 3, column 2, figure 4).
Regarding Claim 17, Palm teaches The system of claim 14, further configured to execute the modeling in parallel (Figure 7 discloses two simulations of a vehicle’s lateral stability, which one of ordinary skill in the art could execute in parallel using any multi-core or multi-threaded processor.) (e.g., page 4, column 2, figure 7).
Regarding Claim 18, Palm teaches The system of claim 14, further configured to process user input related to definitions of a layout space of the system (“Design solutions are characterized by a set of relevant topological and parametric design variables {d1, d2, ..., di} spanning the i-dimensional Design Space D. Any vector d = (d1, d2, ..., di) [which is an element in] D is representing an individual design layout.”) (e.g., page 2, column 2, last paragraph).\
a target space of the system (“Any relevant target indicator for evaluation of design alternatives is characterized by a set of target indicator variables {t1, t2, ..., tk} spanning the k-dimensional Target Indicator Space T. Any vector t = (t1, t2, ..., tk) [which is an element in] T is representing an individual multicriterial target achievement measure.”) (e.g., page 3, column 1, paragraph 1).
and constraints of the system (“Any use case of relevance is characterized by a set of use case variables {u1, u2, …, uj} spanning the j-dimensional Use Case Space U. Any vector u = (u1, u2, ..., uj) [which is an element in] U is representing an individual use case.” The design space D, target indicator space T, and use case space U are interpreted as comprising layout parameter constraints.) (e.g., page 2, column 2, last paragraph to page 3, column 1, paragraph 1).
wherein the layout space comprises the layout parameters (“Design solutions are characterized by a set of relevant topological and parametric design variables {d1, d2, ..., di} spanning the i-dimensional Design Space D. Any vector d = (d1, d2, ..., di) [which is an element in] D is representing an individual design layout.”) (e.g., page 2, column 2, last paragraph).
the target space comprises the target parameters (“Any relevant target indicator for evaluation of design alternatives is characterized by a set of target indicator variables {t1, t2, ..., tk} spanning the k-dimensional Target Indicator Space T. Any vector t = (t1, t2, ..., tk) [which is an element in] T is representing an individual multicriterial target achievement measure.”) (e.g., page 3, column 1, paragraph 1).
and the constraints cover required limitations in the layout parameters and the target parameters (“Any vector u = (u1, u2, ..., uj) [which is an element in] U is representing an individual use case,” wherein the use case is interpreted as limiting the layout and target parameters.) (e.g., page 3, column 1, paragraph 1).
Regarding Claim 19, Palm teaches A computer program product for designing a Pareto-optimal layout of a system to be modeled, comprising a non-transitory computer-readable medium encoded with program instructions (“The HSE Environment allows running HSE specific tasks of a) Hyper Space definition, DovE space filling and script control b) building a surrogate model c) analyzing and optimizing surrogate models and system designs and d) visualizing results for adequate analysis and decision making.” The HSE environment is interpreted as a computer program product.) (e.g., page 3, column 2, paragraph 6).
The remaining limitations of Claim 19 recite substantially similar limitations to Claim 1, and the claim is rejected under 35 U.S.C 102(a)(1) for the same reasons.
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.
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.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Palm in view of Ahmad et al. (Ahmad, Fadzil, Nor Ashidi Mat Isa, Zakaria Hussain, and Siti Noraini Sulaiman. "A genetic algorithm-based multi-objective optimization of an artificial neural network classifier for breast cancer diagnosis." Neural Computing and Applications 23, no. 5 (2013): 1427-1435.), hereinafter Ahmad.
Regarding Claim 8, Palm teaches The computer-implemented method of claim 1. However, Palm does not appear to teach wherein the system to be modeled is a neural network for pattern recognition, wherein the layout parameters comprise a network depth, a number of input features, and a kernel size, and wherein the target parameters comprise a precision, a recall, and a computing time.
On the other hand, Ahmad, which relates to multi-objective optimization of neural networks, does teach wherein the system to be modeled is a neural network for pattern recognition (“This work proposes a genetic algorithm-based multi-objective optimization of an Artificial Neural Network classifier, namely GA-MOO-NN, for the automatic breast cancer diagnosis.”) (e.g., page 1, abstract).
wherein the layout parameters comprise a network depth, a number of input features, and a kernel size (“P bits random generator allows 2P different combinations of initial weight. Q bits which are dedicated for the hidden nodes permit the GA to explore up to a maximum of 2Q hidden nodes size. R represents the number of attributes/features being fed to the ANN.” Exploring the hidden node design space is interpreted as comprising layout parameters of a network depth and kernel size for each hidden layer.) (e.g., page 3, column 1, paragraph 2).
and wherein the target parameters comprise a precision, a recall, and a computing time (“Our proposed objective to be minimized in this study is the total number of network connections as represented by Eq. 1, where α is the number of selected feature subsets, β is the number of hidden nodes and γ is the number of outputs [...] The second objective is the [squared error percentage] which is calculated using Eq. 2 as proposed in [28], where p is the number of output nodes, q is the number of examples Aj i is the actual output value of i-th output node for j-th pattern and T j i is the target output value of i-th output node for j-th pattern.” The total number of network connections directly corresponds to a computing time, and the squared error percentage is analogous to a precision and recall.) (e.g., page 3, column 1, last paragraph; page 3, column 2, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Palm with Ahmad. The claimed invention is considered to be merely substituting one known element for another to obtain predictable results, see MPEP § 2143(I)(B). Palm teaches a method for multi-objective optimization of a vehicle system. However, Palm does not teach wherein the system is a neural network. On the other hand, Ahmad does teach multi-objective optimization of a neural network. As both Palm and Ahmad relate to multi-objective optimization, one of ordinary skill could have used the method of Palm and merely substituted the neural network of Ahmad for the vehicle of Palm; as the parameters and objectives of both Palm and Ahmad would have been known in the art, the results of the substitution would have been predictable. Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine Palm with Ahmad in order to perform multi-objective optimization on a neural network in the same way as Palm.
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.
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/K.H.T./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189