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 .
DETAILED ACTION
1. Claims 1-3 are presented for examination.
Claim Objections
2. Claim 3 is objected to because of the following informalities:
As per Claim 3, it recites the limitation “a search mode of a algorithm” in the step S2.4.2.1 which would be better as “a search mode of an algorithm”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claims 1-3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 1, it recites the limitation “a better solution” in the step S2 is vague and indefinite since “better” does not set a range.
As per Claim 1, it recites the limitation “optimization effect” in the step S2 is vague and indefinite since “optimization effect” does not set a range.
As per Claim 3, it recites the limitation “
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” in S2.4.2.4. The word “respectively” distributes the two individuals over the two named formulas, so that
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is substituted into formula (2) only and
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is substituted into formula (8) only. But the same step then recites “determining whether
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and
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meet the constraints in formula (2) to formula (8)”, which requires each intermediate individual to be tested against all seven constraints, and there is no recitation supplying the constraint values of formulas (3) to (7) for either individual under the distributive reading. Thus, it is unclear how the scope of the substituting step can be determined.
As per Claim 3, it recites the limitation “updating the objective function value of “pBesti to Xi(t)” in S2.4.2.5. An objective function value is a scalar and Xi(t) is an individual of the population, so an objective function value cannot be updated to an individual, and the parallel recitation at the end of S2.4.2.3 instead recites that “a variable value of pBesti is updated to Xi(t)”. It is therefore unclear whether S2.4.2.5 updates pBest itself, the objective function value F(gBest), or both, and it is unclear how the scope of the updating step can be determined.
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.
4. Claims 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) The claim 1-11 is directed methods and fall within the statutory category of processes.
(Step 2A – Prong One) For the sake of identifying the abstract ideas, a copy of the claim is provided below. Abstract ideas are bolded.
Claim 1 recites:
S1: determining an objective function for weight optimization of the disc spring and key parameters to be solved (under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion); and
S2: optimizing a structure of an original Harris Hawks optimization (under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion), wherein first, eliminating steps of centralized calculation of objective function values in the original Harris Hawks optimization except a step of population initialization (under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion), and recording a result when a better solution is obtained, to save calculation time (insignificant extra-solution activity – data outputting); second, introducing a random unit permutation mechanism before an end of each iterative optimization of the original Harris Hawks optimization (under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion) to enhance global search performance; third, deleting a step of searching for an energy factor E with an absolute value greater than or equal to 1 in the original Harris Hawks optimization (under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion) to reduce an influence of the random unit permutation mechanism on an optimization effect; referring to an optimized Harris Hawks optimization as a random unit permutation-based Harris Hawks optimization (under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion), and solving the key parameters by means of the random unit permutation-based Harris Hawks optimization to obtain the key parameters for weight optimization of the disc spring (under its broadest reasonable interpretation, a mathematical concept and a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion).
Therefore, the limitations, under the broadest reasonable interpretation, have been identified to recite judicial exceptions, an abstract idea.
(Step 2A – Prong Two: integration into practical application) This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements of “disc spring” is an insignificant extra-solution activity which is generally linking the use of a judicial exception to a particular technological environment or field of use.
Further Claims recite the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering/outputting (see MPEP 2106.05(g)): “recording a result when a better solution is obtained, to save calculation time”
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
(Step 2B - inventive concept) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “disc spring” is an insignificant extra-solution activity which is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP §2106.05(h)).
Further as discussed above Claim 1 recites the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering/outputting (see MPEP 2106.05(g)) which is the element that the courts have recognized as well-understood, routine, conventional activity (see MPEP 2106.05(d) II. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93): “recording a result when a better solution is obtained, to save calculation time (insignificant extra-solution activity – data outputting)”.
Further dependent claims 2-3 recite:
(Claim 2)
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( a mathematical concept and a mental process)
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( a mathematical concept and a mental process)
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( a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
(Claim 3)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(a mathematical concept and a mental process)
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(insignificant extra-solution activity – outputting and/ or generally linking the use of a judicial exception to a particular technological environment or field of use)
Considering the claim both individually and in combination, there is no element or combination of elements recited contains any “inventive concept” or adds “significantly more” to transform the abstract concept into a patent-eligible application.
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.
5. Claims 1 and 2 are rejected under 35 U.S.C. 103 as being unpatentable over Heidari (“Harris hawks optimization: Algorithm and applications”) in view of Rao (“Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems”), further in view of Jia (“Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation”), further in view of Shami (“Particle Swarm Optimization: A Comprehensive Survey”).
As per Claim 1, Heidari teaches a method for optimizing a weight … (§4.6.2 Tension/compression spring design, pg. 863 “In this case, our intention is to minimize the weight of a spring.”), comprising:
S1: determining an objective function for weight optimization of the … spring and key parameters to be solved (§4.6.2, pg. 863 “Design variables for this case are wire diameter (d), mean coil diameter (D), and the number of active coils (N).”; §4.6, pg. 860: the weight objective and the design variables to be solved are determined for a constrained spring design task, with the constraints handled by integrating the optimizer with a constraint handling technique);
S2: optimizing a structure of an original Harris Hawks optimization … (Algorithm 1, pg. 854 “
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”; pg. 852
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; §3.3, pgs. 852-853: the original Harris Hawks optimization, with its population initialization step, its per-iteration energy factor E that switches the algorithm between the exploration phase at |E| greater than or equal to 1 and the exploitation phase at |E| less than 1, its Levy-flight-based position updates, and its greedy keep-the-better selectors, is the algorithm whose structure the claim modifies); and
solving the key parameters by means of the … Harris Hawks optimization to obtain the key parameters for weight optimization of the … spring (§4.6.2, pg. 864 “
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“: the Harris Hawks optimization is executed on the constrained spring formulation and returns the best design that is the solved key parameters).
However, Heidari fails to teach explicitly optimizing a weight of a disc spring; wherein first eliminating steps of centralized calculation of objective function values in the original Harris Hawks optimization except a step of population initialization, and recording a result when a better solution is obtained, to save calculation time; second, introducing a random unit permutation mechanism before an end of each iterative optimization of the original Harris Hawks optimization to enhance global search performance; and third, deleting a step of searching for an energy factor E with an absolute value greater than or equal to 1 in the original Harris Hawks optimization to reduce an influence of the random uniter permutation mechanism on an optimization effect; referring to an optimized Harris Hawks optimization as a random unit permutation-based Harris Hawks optimization.
Rao teaches optimizing a weight of a disc spring (§5.3.6, pg. 309 “
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; §5.3.7, pg. 311 “
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.”: the constrained minimum-weight design of a Belleville spring (i.e., the “disc spring” as claimed) is formulated over the same four key parameters the claim recites and is solved by population-based optimization algorithms; Examiner's Note - the claimed “disc spring” corresponds to Rao's Belleville spring, the objective function and constraints recited in claim 2 for the disc spring being the printed Belleville-spring objective and constraints of Rao's Appendix C.6 (Rao: App. C.6, pg. 315)). In particular, Rao teaches the constrained minimum-weight Belleville spring design problem, formulated over the external diameter, the internal diameter, the thickness and the height of the spring, together with its solution by population-based optimization algorithms.
Heidari and Rao are analogous art because they are both from the same field of endeavor, metaheuristic optimization of constrained mechanical engineering design problems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Rao into Heidari's invention for the purpose of solving real-world engineering optimization problems by a Harris hawks optimization algorithm to provide a benchmark mechanical design optimization problem which has real world applications, on which the effectiveness of a population-based optimization method is tested based on the best solution, convergence rate and computational effort (Rao: Abstract, pg. 303).
However, Heidari as modified by Rao fails to teach explicitly wherein first eliminating steps of centralized calculation of objective function values in the original Harris Hawks optimization except a step of population initialization, and recording a result when a better solution is obtained, to save calculation time; second, introducing a random unit permutation mechanism before an end of each iterative optimization of the original Harris Hawks optimization to enhance global search performance; and third, deleting a step of searching for an energy factor E with an absolute value greater than or equal to 1 in the original Harris Hawks optimization to reduce an influence of the random uniter permutation mechanism on an optimization effect; referring to an optimized Harris Hawks optimization as a random unit permutation-based Harris Hawks optimization.
Jia teaches second, introducing a random unit permutation mechanism before an end of each iterative optimization of the original Harris Hawks optimization to enhance global search performance (§1. Introduction, pg. 3 “a famous mutation operator, known as DE/best/2 is utilized to improve the global search efficiency and population diversity”; §4, pg. 10 “
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”: a mutation mechanism that recombines the best individual with four mutually distinct randomly selected population units is performed within the iteration loop of the Harris Hawks optimization, on the branch of each position update where the original algorithm performed its exploration search, to enhance global search; Examiner's Note - the claimed “random unit permutation mechanism” corresponds to Jia's DE/best/2 mutation mechanism (i.e., the “random unit permutation mechanism” as claimed), which draws a fresh random selection of four distinct population units at each application of the mechanism (Jia: pg. 10); the recited referring to an optimized Harris Hawks optimization as a random unit permutation-based Harris Hawks optimization is a naming of the Harris Hawks optimization as so modified); and third, deleting a step of searching for an energy factor E with an absolute value greater than or equal to 1 in the original Harris Hawks optimization to reduce an influence of the random uniter permutation mechanism on an optimization effect; referring to an optimized Harris Hawks optimization as a random unit permutation-based Harris Hawks optimization (§1, pg. 2; pg. 11, Algorithm 2 “dynamic Harris hawks optimization with a mutation mechanism (DHHO/M)”, “
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”: the original energy-factor-driven exploration search step is removed and the mutation mechanism is performed in its place, Jia stating that the escaping energy cannot be larger than 1 during the latter half of the iterations so that the search agent does not perform a global search; the recited purpose, to reduce an influence of the random unit permutation mechanism on an optimization effect, is an intended use thus no patentable weight given). In particular, Jia teaches a dynamic Harris Hawks optimization in which a DE/best/2 mutation mechanism, built from the best individual and four mutually distinct randomly selected individuals, is performed within each iteration in place of the original energy-factor-driven exploration search, to improve the global search efficiency and population diversity.
Heidari, Rao, and Jia are analogous art because they are all related to population-based metaheuristic optimization algorithms for solving optimization problems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Jia into Heidari as modified by Rao's invention for the purpose of solving real-world engineering optimization problems by a Harris hawks optimization algorithm to provide a benchmark mechanical design optimization problem which has real world applications, on which the effectiveness of a population-based optimization method is tested based on the best solution, convergence rate and computational effort (Rao: Abstract, pg. 303) and to provide improved global search efficiency and population diversity of the Harris Hawks optimization (Jia: §1, pg. 3).
However, Heidari as modified by Rao and Jia fails to teach wherein first eliminating steps of centralized calculation of objective function values in the original Harris Hawks optimization except a step of population initialization, and recording a result when a better solution is obtained, to save calculation time;.
On the other hand, Shami teaches wherein first eliminating steps of centralized calculation of objective function values in the original Harris Hawks optimization except a step of population initialization, and recording a result when a better solution is obtained, to save calculation time (Algorithm 1, pg. 10034 “
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”: each candidate solution is evaluated as it is produced within the iteration loop and the personal best and global best records are updated immediately when a better solution is obtained, with no separate centralized objective-function evaluation step after the population initialization; the recited purpose, to save calculation time, is an intended use thus no patentable weight given). In particular, Shami teaches the particle swarm optimization bookkeeping scheme in which personal best and global best solutions are initialized at population initialization and are thereafter updated by recording a better solution at the moment it is found within the iteration loop.
Heidari, Rao, Jia, and Shami are analogous art because they are all related to population-based metaheuristic optimization algorithms for solving optimization problems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Shami into Heidari as modified by Rao and Jia's invention for the purpose of solving real-world engineering optimization problems by a Harris hawks optimization algorithm to provide a benchmark mechanical design optimization problem which has real world applications, on which the effectiveness of a population-based optimization method is tested based on the best solution, convergence rate and computational effort (Rao: Abstract, pg. 303) and to provide improved global search efficiency and population diversity of the Harris Hawks optimization (Jia: §1, pg. 3) and to provide a best-record bookkeeping scheme that is simple to implement and code and is flexible to hybridize with other optimization algorithms (Shami: §I, pg. 10032).
As per Claim 2, Heidari teaches, the key parameters to be solved are represented by a vector X, where a lower bound of X is represented by a vector LB, an upper bound of X is represented by a vector UB, and X, LB and UB are expressed by formula (12)
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(Heidari: Eq. (1), pg. 852
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)”
Heidari fails to teach explicitly wherein determining the objective function for weight optimization of the disc spring and the key parameters to be solved in S1 comprises steps:
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Rao teaches wherein determining the objective function for weight optimization of the disc spring and the key parameters to be solved in S1 comprises steps:
S1.1: determining the objective function for weight optimization of the disc spring, wherein the objective function is shown by formula (1)
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(Rao: App. C.6. Belleville spring, pg. 315 “
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”: the printed minimum-weight objective is the claimed formula (1), the claimed density ρ = 0.283 lb/in3 being folded into the printed constant 0.07075, which equals ρ/4);
S1.2: determining constraints for weight optimization of the disc spring, wherein the constraints are shown by formula (2) to formula (8)
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(Rao: pg. 309
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; pg. 315 “
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”: Appendix C.6 prints the stress constraint g1 and the load constraint g2 with the same bracket structures and symbols as the claimed formulas (2) and (3), and prints g3 to g7 as the claimed limited-deflection, height-thickness, outer-diameter, diameter-relation and geometric-size constraints of formulas (4) to (8), the printed g7 fraction reading h/(De − Di));α, β and γ are temporary variables and are calculated by formula (9) to formula (11):
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(Rao: pg. 315
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”; pg. 309: Appendix C.6 prints α, β and γ as 6/(π ln K) multiplied by ((K − 1)/K) squared, by ((K − 1)/ln K − 1), and by ((K − 1)/2), with K = De/Di, the claimed formulas (9) to (11));
S1.3: determining parameter values in the constraints shown by formula (2) to formula (8)
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(Rao: App. C.6, pg. 315 “
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”: the claimed ρ = 0.283 lb/in3 following from the printed objective constant 0.07075 = ρ/4);
the relation between a and f(a)
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(Rao: Table 7 Variation of f (a) with a., pg. 308: the claimed piecewise a-to-f(a) relation is the midpoint-binned rendering of the value-for-value identical Table 7 grid, including the repeated 0.51 entries and the 0.50 endpoint);
the outer diameter Dot of the disc spring, the inner diameter Dinn of the disc spring, the thickness ts of the disc spring, and the height h of the disc spring, are the key parameters to be solved
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(Rao: §5.3.6 Belleville spring, pg. 309: the same four design variables, external diameter, internal diameter, thickness and height, are the parameters solved for; candidate solutions in the Harris Hawks optimization are decision vectors bounded below and above by LB and UB vectors, and the vector content is Rao's four Belleville design variables; Examiner's Note - as to the recited numerical bound values of formulas (13) and (14) and of S1.3, Rao prints Dmax = 12.01 in., H = 2 in. and δmax = 0.2 in. as express bounds of the same design task (Rao: App. C.6, pg. 315), so the general conditions of the claimed bounded design variables are disclosed, and prescribing the particular recited lower and upper bound values is a routine selection of workable ranges (MPEP 2144.05(II)(A))); and
S1.4: transforming the objective function shown by formula (1) with the vector X to obtain a final objective function which is expressed by formula (15)
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(Rao: App.
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211
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pg. 315: the printed objective is the claimed final objective function of formula (15), with X1, X2 and X3 corresponding to De, Di and t).
Allowable Subject Matter
6. Claim 3 would be allowable if rewritten to overcome the rejection under 35 U.S.C. 112(b) set forth in this Office action and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record, alone or in combination, does not teach or fairly suggest the intermediate individuals
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31
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and
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of formulas (31) and (32).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Eskandar (“Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems”) discloses constrained metaheuristic optimization of mechanical engineering design problems including a minimum-mass multiple disc clutch brake formulation.
Sadollah (“Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems”) discloses a population-based algorithm for solving constrained engineering design optimization benchmark problems.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/ Primary Examiner, Art Unit 2188
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