Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to submission of application on 1/30/2024.
Claims 1-20 are presented for examination.
Election/Restrictions
Restriction to one of the following inventions is required under 35 U.S.C. 121:
Claims 1-9 are a method of designing a numerical experiment. (G06N 7/00)
Claims 10-15 are a device for obtaining values, constraints, and tuned parameters for executing an algorithm. (G06F 17/11)
Claims 16-20 are a non-transitory machine-readable medium comprising instructions for providing a Graphical User Interface (GUI). (G06F 8/34)
Applicant is required under 35 U.S.C. 121 to elect a single disclosed species, or a single grouping of patentably indistinct species, for prosecution on the merits to which the claims shall be restricted if no generic claim is finally held to be allowable.
The inventions are independent or distinct, each from the other based on the following:
Inventions I-III are directed to related devices, products or processes. The related inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806. 05(j). In the instant case, the inventions, as claimed, have materially different designs, modes of operations, functions or effects. Invention I can have specific functions and structures associated with designing a numerical experiment. Invention II can have specific functions and structures associated with obtaining values, constraints, and tuned parameters for executing an algorithm. Invention III can have specific functions and structures associated with providing a Graphical User Interface at a display that includes icons for selecting values that are associated with a tunable algorithm.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply: The species or groupings of patentably indistinct species require a different field of search (e.g., searching different classes/subclasses or electronic resources, or employing different search strategies or search queries).
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention. Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Applicant is reminded that upon the cancellation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1. 76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
Applicants’ election of claims 1-9, without traverse of claims 10-20, in the telecon with Mr. Andrew Gust, Attorney of Record, on 8/18/2026 is acknowledged. Claims 10-20 are withdrawn.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-9 are directed to a method (i.e., a process); therefore, all pending claims are directed to one of the four categories of invention.
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 recites limitations of:
designing of a numerical experiment, by a processing system including a processor, for tuned parameters and external parameters for a parametrized algorithm – mental process (observation, evaluation, judgement, opinion) as a human mind can design a numerical experiment for tuned parameters and external parameters for a parameterized algorithm.
calculating, by the processing system, Key Performance Indicators (KPIs) for the parametrized algorithm for each combination of the tuned parameters and the external parameters – mathematical concepts (relationships, formulas or equations, calculations).
generating, by the processing system, regression models based on the tuned parameters and the external parameters for each of the KPIs – mental process (observation, evaluation, judgement, opinion) as a human mind can generate a regression model based on tuned parameters and external parameters for each of the KPIs.
optimizing, by the processing system, the regression models with constant external parameters values – mental process (observation, evaluation, judgement, opinion) as a human mind can optimize a regression model with constant external parameter values.
determining optimal values of the tuned parameters – mental process (observation, evaluation, judgement, opinion) as a human mind can determine optimal values for tuned parameters.
executing, by the processing system, the parametrized algorithm with the optimal values of the tuned parameters – mathematical concepts (relationships, formulas or equations, calculations) as executing an algorithm is calculating.
which are abstract ideas, something that can be accomplished by the human mind, or with the aid of pen and paper
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements of:
by a processing system including a processor – computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
by the processing system - computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
The additional elements do not integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception:
The additional elements of:
by a processing system including a processor – computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
by the processing system - computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
The additional limitations do not amount to significantly more than the abstract idea. Therefore, claim 1 is not patent eligible.
The above analysis applies similarly to the dependent claims.
Claim 2 recites the additional limitations of “the optimizing of the regression models is performed by multi-objective optimization utilizing each of the KPIs as an objective function” – mental process (observation, evaluation, judgement, opinion), and “receiving user input of a selection from among multiple Pareto optimal solutions according to the multi-objective optimization” – inputting data is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well-understood, routine, and conventional. See MPEP 2106.05(d)(II)(i).
Claim 3 recites the additional limitations of “the multi-objective optimization of the regression models is based on a Monte Carlo algorithm” – recitation of an algorithm at a high level merely identifies a technology or field of use. See MPEP 2106.05(h).
Claim 4 recites the additional limitations of “the multi-objective optimization of the regression models is based on an evolutionary algorithm” – description of an algorithm at a high level merely identifies a technology or field of use. See MPEP 2106.05(h).
Claim 5 recites the additional limitations of “the optimizing of the regression models is performed by constrained single-objective optimization” – further details of the mental process, as such a mental process (observation, evaluation, judgement, opinion), “one of the KPIs is used as a main objective, wherein other KPIs are used as constraints” – further details of the mental process, as such a mental process (observation, evaluation, judgement, opinion), and “a single optimal solution is selected automatically” – further details of the mental process, as such a mental process (observation, evaluation, judgement, opinion).
Claim 6 recites the additional limitations of “the constrained single-objective optimization is performed based on a Monte Carlo algorithm” – mathematical concepts (relationships, formulas or equations, calculations) as performing an optimization with a Monte Carlo algorithm is performing a calculation.
Claim 7 recites the additional limitations of “the designing of a numerical experiment is based on a uniformly distributed sequence” – further details of the mental process (observation, evaluation, judgement, opinion) as a human mind can design a numerical experiment based on a uniformly distributed sequence.
Claim 8 recites the additional limitations of “the uniformly distributed sequence is a sequence of random points” - further details of the mental process (observation, evaluation, judgement, opinion), as such, a mental process.
Claim 9 recites the additional limitations of “the uniformly distributed sequence is a sequence of Sobol points” - further details of the mental process (observation, evaluation, judgement, opinion), as such, a mental process.
The dependent claims do not integrate the abstract idea into a practical application. Nor do they among to significantly more.
Therefore, claims 1-9 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1 is rejected under 35 U.S.C. § 103 as being unpatentable over Gunawan, et al (Fine-Tuning Algorithm Parameters Using the Design of Experiments Approach, herein Gunawan), and Ribeiro, et al (A Memetic Algorithm for Multi-Attribute Vehicle Routing Optimization, herein Ribeiro).
Regarding claim 1,
Gunawan teaches A method (Gunawan, abstract, line 5 “In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning.” In other words, framework to find a good initial range of parameter values is a method.) comprising:
designing of a numerical experiment, [by a processing system including a processor], for tuned parameters and external parameters for a parametrized algorithm (Gunawan, Figure 1, and, abstract, line 7 “We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter search space of the important parameters. A model based on the Response Surface methodology is then proposed to define the promising initial range for the important parameter values. We show how our approach can be embedded with existing automated parameter tuning configurators, namely ParamILS and RCS (Randomized Convex Search), to tune target algorithms and demonstrate that our proposed methodology leads to improvements in terms of the quality of the solutions.” And, page 280, paragraph 1, line 4 “Parameters which are determined to be unimportant (in that the solution quality is insensitive to the values of these parameters) are set to some constant values so that the resulting parameter space that needs to be explored is reduced.” Examiner notes that the specification of the instant application recites “As an example, the method and apparatus can include one or more of: (a) splitting algorithm parameters into two groups: internal (tuned) and external parameters such as location, demand-supply parameters…” (Specification, paragraph [0016], line 3.) Therefore, examiner is interpreting external parameters as being fixed and not tuned. In other words, experiment design is designing of a numerical experiment, algorithms is algorithm, tuning parameters is tuned parameters and parameters set to constant values are external parameters.)
[calculating, by the processing system, Key Performance Indicators (KPIs) for the parametrized algorithm for each combination of the tuned parameters and the external parameters] ;
generating, by the processing system, regression models based on the tuned parameters and the external parameters for each of the KPIs (Gunawan, page 280, paragraph 1, line 1 “Consider an algorithm (called the target algorithm) to solve a particular problem that requires a number of parameters to be set prior to the execution of the algorithm. A factorial experiment design is applied to first screen and rank the parameters. Parameters which are determined to be unimportant (in that the solution quality is insensitive to the values of these parameters) are set to some constant values so that the resulting parameter space that needs to be explored is reduced. A first-order polynomial model based on RSM is then built to define the promising initial range for the important parameter values. We apply our proposed approach to two different automated tuning configurators, ParamILS (Hutter et al., 2009) and RCS (Lau and Xiao, 2009). Each configurator is applied to a target algorithm for solving the Traveling Salesman Problem (TSP) and Quadratic Assignment Problem (QAP), respectively.” In other words, polynomial model is regression model, important parameters is tuned parameters, parameters set to constant values are external parameters, and a first-order polynomial model based on RSM is then built is generating regression models based on the parameters. Examiner notes that processing system and KPIs are previously mapped to Ribeiro.);
optimizing, by the processing system, the regression models with constant external parameters values and determining optimal values of the tuned parameters (Gunawan, page 280, paragraph 4, line 1 “The Automated Tuning problem is defined as follows: Definition: Given a target algorithm TA parameterized by a set of parameters X with their respective intervals, a set of training instances Itr, and a meta-function H(x) that measures the algorithm performance on a fixed parameter setting x over a set of
problem instances, the goal is to determine a configuration x* such that H(x*) is minimized over Itr.” And, page 280, paragraph 5, line 4 “In our paper, the goal is to optimize x over the given set of training instances Itr and subsequently verify the quality of this parameter setting on a set of testing instances.” In other words, set of parameters is parameter, and optimize x over the given set of training instances is optimizing… the regression models… and determining optimal values of the tuned parameters.); and
executing, by the processing system, the parametrized algorithm with the optimal values of the tuned parameters (Gunawan, page 280, paragraph 5, line 4 “In our paper, the goal is to optimize x over the given set of training instances Itr and subsequently verify the quality of this parameter setting on a set of testing instances.” In other words, subsequently verify the quality of this parameter setting on a set of testing instances is executing the parameterized algorithm with the optimal values of the tuned parameters.) .
Thus far, Gunawan does not explicitly teach by a processing system including a processor.
Ribeiro teaches by a processing system including a processor (Ribeiro, page 41, paragraph 3, line 1 “All the tests and experiments mentioned in this chapter were performed on a laptop with an Intel(R) Core(TM) i7-6500U, 2.50GHz processor, with 16.0 GB of RAM and a Windows 10 of 64 bits operating system. The Memetic Algorithm was coded in C++.” In other words, laptop with an Intel(R) Core(TM) is a processing system including a processor.) .
Ribeiro teaches calculating, by the processing system, Key Performance Indicators (KPIs) for the parametrized algorithm for each combination of the tuned parameters and the external parameters (Ribeiro, Table 5.9, and, page 49, paragraph 2, line 3 “Therefore, to assess the quality of the output routes of the algorithm, some KPIs (Key Performance Indicators) were selected. These do not directly reflect the quality of the algorithm, as they depend also on the input data, but can serve as an objective guide to help evaluate the created routes.”
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In other words, Key Performance Indicators (KPI) is Key Performance Indicators, and Table 5.9 shows calculating KPIs for the parameterized algorithm for each combination of the tuned parameters and external parameters.)
Both Gunawan and Ribeiro are directed to tuning algorithm parameters, among other things. Gunawan teaches a method comprising: designing of a numerical experiment,
In view of the teaching of Gunawan, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Ribeiro into Gunawan. This would result in a method comprising: designing of a numerical experiment, by a processing system including a processor, for tuned parameters and external parameters for a parametrized algorithm; calculating, by the processing system, Key Performance Indicators (KPIs) for the parametrized algorithm for each combination of the tuned parameters and the external parameters; generating, by the processing system, regression models based on the tuned parameters and the external parameters for each of the KPIs; optimizing, by the processing system, the regression models with constant external parameters values and determining optimal values of the tuned parameters; and executing, by the processing system, the parametrized algorithm with the optimal values of the tuned parameters.
One or ordinary skill in the art would be motivated to do this because producing better methods to solve the vehicle routing problem (VRP) provides more efficiency and cost savings to the real world problem of freight transportation. (Ribeiro, page 1, paragraph 1, line 1 “In the European Union, road remains the predominant method for freight transportation, accounting for 51% of goods shipped (Directorate-General for Mobility and Transport - European Commission, 2020). The growth of transport-related energy consumption and the resultant negative effects on the environment represent a global concern affecting almost any market sector. Due to its central role in most supply chains, the optimization of transportation planning hides a huge potential for economic and environmental cost reduction.”)
Claims 2-6 are rejected under 35 U.S.C. § 103 as being unpatentable over Gunawan, Ribeiro, and Rabbani, et al (A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation, herein Rabbani).
Regarding claim 2,
The combination of Gunawan and Ribeiro teaches the method of claim 1, wherein
the optimizing of the regression models is performed by multi-objective optimization utilizing each of the KPIs as an objective function (Ribeiro, Table 5.9, and, abstract, paragraph 3, line 1 “The first step was to mathematically formulate this novel multi-attribute VRP. Based on it, the proposed solution methodology constitutes a Memetic Algorithm, a Genetic Algorithm complement with Local Search.” And, page 49, paragraph 2, line 3 “Therefore, to assess the quality of the output routes of the algorithm, some KPIs (Key Performance Indicators) were selected. In other words, from prior mapping, optimization is optimization, KPI is KPI, and multi-attribute VRP (Vehicle Routing Problem) is multi-objective.), and further comprising
receiving user input of a selection (Ribeiro, Figure 5.3, and Figure 5.4.
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In other words, Figure 5.3 shows receiving user input, and Figure 5.4 shows user interface for receiving input.).
Thus far, the combination of Gunawan and Ribeiro does not explicitly teach selection from among multiple Pareto optimal solutions according to the multi-objective optimization.
Rabbani teaches selection from among multiple Pareto optimal solutions according to the multi-objective optimization (Rabbani, Fig. 2, and, page 946, column 1, paragraph 1, line 28 “The first study that utilized an exact multi-objective solution approach (i.e., the lexicographic weighted Tchebycheff) to achieve a comprehensive Pareto optimal solutions for this class of problems was reported by Samanlioglu (2013). Later, Yu and Solvang (2016) and Zhao, Huang, Lee, and Peng (2016) applied the augmented epsilon-constraint method for this purpose.” And, page 952, column 2, paragraph 6, line 1 “Determine the most preferred solution among the Pareto optimal set. After the algorithm has converged to the Pareto optimal solutions, we rank the obtained solutions using TOPSIS method ( Sun & Lin, 2009 ) by considering the objective functions as its criteria.”
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In other words, determine the most preferred solution among the Pareto optimal set is selection from among multiple Pareto optimal solutions according to the multi-objective optimization. )
Both Rabbani and the combination of Gunawan and Ribeiro are directed to optimizing algorithms, among other things. The combination of Gunawan and Ribeiro teaches the method of claim 1 but does not explicitly teach selection from among multiple Pareto optimal solutions according to the multi-objective optimization. Rabbani teaches selection from among multiple Pareto optimal solutions according to the multi-objective optimization.
In view of the combination of Gunawan and Ribeiro, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Rabbani into the combination of Gunawan and Ribeiro. This would result in the method of claim 1 and selection from among multiple Pareto optimal solutions according to the multi-objective optimization.
One of ordinary skill in the art would be motivated to do this in order to better function with uncertainty by moving from a deterministic method to a stochastic method by using a genetic algorithm combined with a Monte Carlo simulation. (Rabbani, abstract, line 1 “The present study extends a multi-objective mathematical model in the context of industrial hazardous waste management, which covers the integrated decisions of three levels with locating, vehicle routing, and inventory control. Analyzing these decisions simultaneously not only may lead to the most effective structure in the waste management network, but also may reduce the potential risk of managing the hazardous waste. Furthermore, because of the inherent complexity of the waste management system, uncertainty is inevitable and should be acknowledged to guarantee reliability in the decision-making process. From this perspective, the proposed model is novel in the following three aspects: (1) shifting from a deterministic to stochastic environment; (2) considering a multi-period planning horizon; and (3) incorporating the inventory decisions into the problem.”)
Regarding claim 3,
The combination of Gunawan, Ribeiro, and Rabbani teaches the method of claim 2, wherein
the multi-objective optimization of the regression models is based on a Monte Carlo algorithm (Rabbani, Fig. 2, and, abstract, line 11 “In terms of methodological contribution, a new simheuristic approach that is an integration of Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Monte Carlo simulation is developed to overcome the stochastic combinatorial optimization problem of this study. Our findings verify the efficiency of the proposed approach as it is able to find a high-quality solution within a relatively reasonable computational time.” In other words, combinatorial optimization is multi-objective optimization and Monte Carlo simulation is Monte Carlo algorithm. ).
Regarding claim 4,
The combination of Gunawan, Ribeiro, and Rabbani teaches the method of claim 2, wherein
the multi-objective optimization of the regression models is based on an evolutionary algorithm (Rabbani, Fig. 2. and, abstract, line 11. See above mapping. In other words, Genetic Algorithm is based on an evolutionary algorithm.)
Regarding claim 5,
The combination of Gunawan, Ribeiro, and Rabbani teaches the method of claim 1, wherein
the optimizing of the regression models is performed by constrained single-objective optimization (Rabbani, Fig. 2, and, page 953, column 2, paragraph 1, line 1 “We begin the solution procedure by solving three deterministic (considering mean value of uncertain parameters) single objective problems, in each of which the objective function is considered as a single objective for the problem and their minimum values are calculated in the absence of other objectives.” In other words, solving three deterministic single objective problems is the optimizing of the regression models is performed by constrained single-objective optimization.) , wherein
one of the KPIs is used as a main objective, wherein other KPIs are used as constraints (Ribeiro, Table 5.9, In other words, number or customers visited per trip is a main objective and weight occupation is a constraint.) , and wherein
a single optimal solution is selected automatically (Rabbani, Fig. 2, In other words, from Fig. 2, reaching the stopping criteria determining the most preferred solution is the optimal solution is selected automatically.) .
Regarding claim 6,
The combination of Gunawan, Ribeiro, and Rabbani teaches the method of claim 5, wherein
the constrained single-objective optimization is performed based on a Monte Carlo algorithm (Rabbani, Fig. 2, In other words, from Fig. 2, Apply Monte Carlo sampling and generated a certain number of realization is the constrained single-objective optimization is performed based on a Monte Carlo algorithm.) .
Claims 7-9 are rejected under 35 U.S.C. § 103 as being unpatentable over Gunawan, Ribeiro, and Lai, et al (Evaluating emissions in a modern compression ignition engine using multi-dimensional PDF-based stochastic simulations and statistical surrogate generation, herein Lai).
Regarding claim 7,
The combination of Gunawan and Ribeiro teaches the method of claim 1, wherein the designing of a numerical experiment is
Thus far, the combination of Gunawan and Ribeiro does not explicitly teach based on a uniformly distributed sequence.
Lai teaches based on a uniformly distributed sequence (Lai, page 1, column 2, paragraph 1, line 3 “The measurement-driven calibration methodology involves the use of Design of Experiments (DoE), where the data point is processed to establish statistical response surface models for determining the variations and the measured response (i.e. engine performance, combustion characteristics, and emissions).” And, page 2, column 2, paragraph 3, line 1 “Based on the PDF transport equation approach, the SRM calculates the progression of scalar variables, such as the mass fraction of chemical species 𝑌𝑗 (𝑗=1, … ,𝑆 where 𝑗 denotes the species index and 𝑆 is the total number of chemical species), and temperature 𝑇 as a function of time 𝑡. The random scalar variables can be combined into a vector 𝝍=(𝜓1, … ,𝜓𝑆,𝜓𝑆+1)=(𝑌1, … ,𝑌𝑆,𝑇), and the joint composition PDF is denoted by 𝑓(𝝍;𝑡).” And, page 4, column 1, paragraph 2, line 1 “Sobol sequences [46] are designed to produce points distributed in a K-dimensional space such that they have low discrepancy*. The resulting points are more uniformly distributed than pseudo-random points (see Figure 2), but avoid the symmetries and inefficiencies associated with points on a grid. An additional advantage of Sobol sequences is that they are easily extensible; existing points do not need to be adjusted in order to maintain the low discrepancy when new points are added.” In other words, design of experiments is designing of a numerical experiment, and the resulting points are uniformly distributed is uniformly distributed sequence.)
Both Lai and the combination of Gunawan and Ribeiro are directed to design of experiments, among other things. The combination of Gunawan and Ribeiro teaches the method of claim 1 but does not explicitly teach the designing of a numerical experiment that is based on a uniformly distributed sequence. Lai teaches the designing of a numerical experiment that is based on a uniformly distributed sequence.
In view of the teaching of the combination of Gunawan and Ribeiro, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Lai into the combination of Gunawan and Ribeiro. This would result in the method of claim 1 where the designing of a numerical experiment is based on a uniformly distributed sequence.
One or ordinary skill in the art would be motivated to do this because improving model based analysis helps to reduce development costs. (Lai, page 1, column 1, paragraph 1, line 1 “ Model-based engineering analyses support decision and policy making processes, reduce vehicular/machine and powertrain development costs, and speed up the progression of technology readiness levels (TRL). The adoption of innovative virtual or digital engineering workflows augmented with experimental data-based analyses is increasingly important given the degree of variability and complexities of the modern powertrains and the emissions (both gas phase and particulate phase) compliance requirements.”)
Regarding claim 8,
The combination of Gunawan, Ribeiro and Lai teaches the method of claim 7, wherein
the uniformly distributed sequence is a sequence of random points (Lai, page 4, column 1, paragraph 2, line 1 “Sobol sequences [46] are designed to produce points distributed in a K-dimensional space such that they have low discrepancy*. The resulting points are more uniformly distributed than pseudo-random points (see Figure 2), but avoid the symmetries and inefficiencies associated with points on a grid.” In other words, sequence is sequence, uniformly distributed is uniformly distributed, and pseudo random points is sequence of random points.).
Regarding claim 9,
The combination of Gunawan, Ribeiro and Lai teaches the method of claim 7, wherein
the uniformly distributed sequence is a sequence of Sobol points (Lai, page 4, column 1, paragraph 2. See mapping of claim 8. In other words, Sobol sequences is sequence of Sobol points.).
The prior art made of record and not used is considered pertinent to applicant’s disclosure:
Chavez, et al “A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with backhauls” discloses a multi-objective ant colony algorithm for the Multi-Depot Vehicle Routing Problem with Backhauls (MDVRPB) where three objectives of traveled distance, traveling times and total consumption of energy are minimized.
Schede, et al “A Survey of Methods for Automated Algorithm Configuration” discloses existing algorithm configuration (AC) literature within
the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry.
Kuttimalai, et al, US 12,536,479 B2 “Methods and Systems for Solving an Optimization Problem Using a Flexible Modular Approach” discloses a method for solving an optimization problem inputted by a user that include: (i) using an interface of a digital computer to receive an indication of (ii) said optimization problem, (iii) one or more optimization problem specifications of a plurality of optimization problem specifications, (iv) at least one generation method and at least one evaluation method, and (v) one or more algorithms of a plurality of algorithms.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BART RYLANDER whose telephone number is (571)272-8359. The examiner can normally be reached Monday - Thursday 8:00 to 5:30.
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/Bart I Rylander/Examiner, Art Unit 2124
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