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 .
Priority
Acknowledgement is made of the applicant’s claim for Foreign priority to Japanese Patent Application No. JP 2022098002, filed on June 17, 2022.
Claim Objections
Claim 8 objected to under 37 CFR 1.75 as being a substantial duplicate of claim 1. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-8 are within the four statutory categories (a process, machine, manufacture or composition of matter).
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Claims 1-6 and 8 are directed to a non-transitory computer-readable storage medium which are machines. Claims 7 is directed to a method consisting of a series of steps, meaning that it is directed to the statutory category of process.
Regarding claim 1, the following claim elements are abstract ideas:
setting a constraint on the combination of the plurality of explanatory variables and controlling an intermediate variable that varies the explanatory variables of each of the plurality of individuals independently of the constraint (This is an abstract idea of a “mental process.” It describes applying mathematical and logical reasoning to control variables and adjust them subject to constraints – a form of optimization that can be performed conceptually in the human mind or with pen and paper. For example, a person could mentally choose limits on several variables, then adjust one variable independently of those limits to observe how outcomes change, before enforcing the constraint again. Because this step involves judgement, comparison, and arithmetic manipulation that can be practically performed in the human mind, it falls within the mental processing grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).);
evaluating an objective function obtained using the explanatory variables varied by the intermediate variable (This is an abstract idea of a “mental process.” It involves calculating and comparing results of a function based on variable inputs, which is a form of mathematical reasoning that can be performed mentally or with pen and paper. For example, a person could assign trial values to explanatory variables, substitute them into an equation representing an objective, and mentally determine which combination yields the smallest or largest value. Such evaluation of a function through observation, computation, and judgement constitutes an act that can practically be performed in the human mind.).
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer-readable recording medium (This is a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05.)
an arithmetic program of searching for a combination of a plurality of explanatory variables such that an objective variable satisfies a predetermined condition by evolving a plurality of individuals each identified by the plurality of explanatory variables (This recitation represents mere instructions to apply an abstract idea using a generic computer. The recited “arithmetic program” performs steps of searching, selecting, and evolving variables – activities that amount to logical reasoning and decision-making that could practically be performed in the human mind or with pen and paper. The computer is used only to automate these mental operations and does not provide any improvement to computer technology or another technical field. Accordingly, this element is considered extra-solution activity. See MPEP 2106.05(f) and MPEP 2106.05(g).),
the arithmetic program causing a computer to execute processing comprising (This is merely an instruction to apply the abstract idea on a generic computer. See MPEP 2106.05.):
a computer (This is a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract ideas:
selecting the combination of the plurality of explanatory variables according to values of the explanatory variables varied by the intermediate variable (This is an abstract idea of a “mental process.” It involves reviewing and comparing the values of explanatory variables and selecting a combination based on those values – an act of judgment and logical reasoning that could be practically performed in the human mind or with pen and paper. A person could mentally evaluate variable values, determine which satisfy a desired condition, and select the corresponding combination. As such, it falls within the mental process grouping of abstract ideas.).
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, claim 3 recites the following abstract ideas:
setting an upper limit number on a number of the plurality of explanatory variables as the constraint and selecting, as the combination, explanatory variables with a largest value for the upper limit number from among the explanatory variables varied by the intermediate variable (This is an abstract idea of a “mental process.” It involves setting a numerical limit and selecting variables with the largest values within the limit – an evaluative task that can be performed through observation, comparison, and judgement. For example, a person could mentally review variable values, decided the maximum number to include, and choose those with the highest values accordingly. Such analysis and selection can be practically performed in the human mind or with pen and paper.).
Regarding claim 4, the rejection of claim 1 is incorporated herein. Further, claim 4 recites the following abstract ideas:
setting a total value of the plurality of explanatory variables to a predetermined value as the constraint and converting the values of the explanatory variables varied by the intermediate variable according to a ratio such that the total value becomes the predetermined value (This is an abstract idea of a “mental process.” It involves setting a total target value and proportionally adjusting individual variable values so that their sum equals that target – an arithmetic and logical adjustment that can be carried out mentally or with pen and paper. A person could review variable values, calculate proportional ratios, and manually scale each value until the total matches the predetermined constraint. Since this involves mathematical reasoning and human judgement, it falls within the mental process grouping of abstract ideas.).
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, claim 5 recites the following abstract ideas:
setting an upper limit on a maximum number of generations of the evolution according to a hypervolume in a space of the objective function (This is an abstract idea of a “mental process.” It involves conceptually analyzing a mathematical space representing objective function values, determining a hypervolume measure, and deciding on a numerical upper limit for iterations based on that analysis. Such reasoning – evaluating performance metrics and setting limits accordingly – can be performed in the human mind or with pen and paper through observation, estimation, and judgment.),
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
the hypervolume includes volume or area obtained from a reference point and a set of solutions for each generation in the space of the objective function (This limitation amounts to insignificant extra-solution activity. It merely defines how the hypervolume is measured – by calculating a geometric volume or area from a reference point and a set of solutions – which represents a mathematical characterization of results rather than a practical application. Such definition of metrics or visualization of data in objective space is an ancillary computation that does meaningfully limit the abstract idea.).
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, claim 6 recites the following abstract ideas:
setting a minimum number of generations of the evolution and making the maximum number of generations larger than the minimum number of generations (This is an abstract idea of a “mental process.” It involves determining lower and upper numeric limits for iterative steps based on reason based on reasoning and judgment. A person could mentally decide, for example, to run at least a certain number of iterations and stop after a larger threshold – an evaluative task involving logical decision-making and numerical comparison that can be performed in the human mind or with pen and paper. See MPEP 2106.04(a)(2)(III).).
Regarding claim 7, the following claim elements are abstract ideas:
searching for a combination of a plurality of explanatory variables such that an objective variable satisfies a predetermined condition by evolving a plurality of individuals each identified by the plurality of explanatory variables (This is an abstract idea of a “mental process.” It describes evaluating and selecting combinations of variables to meet a desired outcome – essentially an optimization or decision-making process that can be performed conceptually. A person could mentally or manually test various variable combinations (e.g., through calculation or reasoning) to see which combination satisfies a goal condition. Such mental evaluation and iterative adjustment toward a target outcome constitute observation, comparison, and logical judgment that can be practically performed in the human mind or with pen and paper.),
setting a constraint on the combination of the plurality of explanatory variables and controlling an intermediate variable that varies the explanatory variables of each of the plurality of individuals independently of the constraint (This is an abstract idea of a “mental process.” It describes applying mathematical and logical reasoning to control variables and adjust them subject to constraints – a form of optimization that can be performed conceptually in the human mind or with pen and paper. For example, a person could mentally choose limits on several variables, then adjust one variable independently of those limits to observe how outcomes change, before enforcing the constraint again. Because this step involves judgement, comparison, and arithmetic manipulation that can be practically performed in the human mind, it falls within the mental processing grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).);
evaluating an objective function obtained using the explanatory variables varied by the intermediate variable (This is an abstract idea of a “mental process.” It involves calculating and comparing results of a function based on variable inputs, which is a form of mathematical reasoning that can be performed mentally or with pen and paper. For example, a person could assign trial values to explanatory variables, substitute them into an equation representing an objective, and mentally determine which combination yields the smallest or largest value. Such evaluation of a function through observation, computation, and judgement constitutes an act that can practically be performed in the human mind.).
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
An arithmetic method implemented by a computer (This is a generic instruction to apply the abstract idea on a computer. The recitation merely automates the conceptual process using conventional computer components without improving their function. It represents insignificant extra-solution activity that does meaningfully limit the abstract idea. See MPEP 2106.05(f) and MPEP 2106.05(g).)
Regarding claim 8, the following claim elements are abstract ideas:
setting a constraint on the combination of the plurality of explanatory variables and controlling an intermediate variable that varies the explanatory variables of each of the plurality of individuals independently of the constraint (This is an abstract idea of a “mental process.” It describes applying mathematical and logical reasoning to control variables and adjust them subject to constraints – a form of optimization that can be performed conceptually in the human mind or with pen and paper. For example, a person could mentally choose limits on several variables, then adjust one variable independently of those limits to observe how outcomes change, before enforcing the constraint again. Because this step involves judgement, comparison, and arithmetic manipulation that can be practically performed in the human mind, it falls within the mental processing grouping of abstract ideas. See MPEP 2106.04(a)(2)(III).);
evaluating an objective function obtained using the explanatory variables varied by the intermediate variable (This is an abstract idea of a “mental process.” It involves calculating and comparing results of a function based on variable inputs, which is a form of mathematical reasoning that can be performed mentally or with pen and paper. For example, a person could assign trial values to explanatory variables, substitute them into an equation representing an objective, and mentally determine which combination yields the smallest or largest value. Such evaluation of a function through observation, computation, and judgement constitutes an act that can practically be performed in the human mind.).
The following claim elements are additional elements which, taken alone or in combination with the other elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer-readable recording medium (This is a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05.)
an arithmetic program of searching for a combination of a plurality of explanatory variables such that an objective variable satisfies a predetermined condition by evolving a plurality of individuals each identified by the plurality of explanatory variables (This recitation represents mere instructions to apply an abstract idea using a generic computer. The recited “arithmetic program” performs steps of searching, selecting, and evolving variables – activities that amount to logical reasoning and decision-making that could practically be performed in the human mind or with pen and paper. The computer is used only to automate these mental operations and does not provide any improvement to computer technology or another technical field. Accordingly, this element is considered extra-solution activity. See MPEP 2106.05(f) and MPEP 2106.05(g).),
the arithmetic program causing a computer to execute a process comprising (This is merely an instruction to apply the abstract idea on a generic computer. See MPEP 2106.05.):
a computer (This is a high-level recitation of generic computer components for performing the abstract idea. See MPEP 2106.05.)
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.
Claims 1-8 are rejected under the 35 U.S.C. 103 as being unpatentable over Cantin (Pub. No.: US 20120005138 A1 (Filed: 2012)). in view of Chen et al., (NPL: “Utilizing Dependence among Variables in Evolutionary Algorithms for Mixed-Integer Programming: A Case Study on Multi-Objective Constrained Portfolio Optimization” (Published: February 2021)).
Regarding claim 1, Cantin teaches the following limitations:
A non-transitory computer-readable recording medium storing (Cantin, paragraph [0061] “ Any combination of one or more computer readable medium(s) may be utilized… A computer readable storage medium may be…would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.”) an arithmetic program of searching for a combination of a plurality of explanatory variables such that an objective variable satisfies a predetermined condition by evolving a plurality of individuals each identified by the plurality of explanatory variables, the arithmetic program causing a computer to execute processing comprising (Cantin, paragraph [0003] “Some embodiments include a method directed to determining a plurality of constraint compliant values for each of a plurality of constrained variables (explanatory variables) of an optimization problem, wherein a constraint condition mutually constrains possible values… the method is further directed to generating a population of constraint compliant candidate solutions (individuals)…while running the computer-based simulation with the population of constraint compliant candidate solutions, determining that a mutated candidate solution…fails to comply with the constraint condition…modifying the mutated candidate solution to use at least one value randomly selected from the plurality of constraint compliant values for a corresponding one of the plurality of constrained variables resulting in a constraint compliant mutated candidate solution that complies with the constraint condition.” Paragraph [0016] “ The description that follows includes exemplary systems, methods, techniques, instruction sequences, and computer program products that embody techniques of the present inventive subject matter…embodiments can utilize specific types of evolutionary algorithms (e.g., genetic algorithms, genetic programming, evolutionary programming, evolution strategy, etc.) suited to fit a particular type of optimization problem being solved.” – Evolves a population of candidate solutions (individuals) defined by constrained variables (explanatory variables) to find combinations that satisfy a constraint condition (objective variable meeting a predetermined condition).):
However, Cantin does not teach, but Cantin in view of Chen teaches the limitation:
setting a constraint on the combination of the plurality of explanatory variables (Cantin, paragraph [0003] “Some embodiments include…wherein a constraint condition mutually constrains possible values that can be used for the plurality of constrained variables, and wherein the plurality of constraint compliant values comply with the constraint condition.” Paragraph [0028] “The first constraint expression 210 constrains the variables C and D according to a first mathematical and/or logical relationship (e.g., the inequality statement C+D>6, meaning that the sum of variables C and D must be greater than six units).” -setting a constraint (C+D>6) that explicitly operates on the sum (combination) of two variables).) and controlling an intermediate variable (Chen, Abstract “In this work, we turn to Multi-Objective Evolutionary Algorithms (MOEAs) for finding elegant solutions for such problems. In this framework, we investigate a multi-objective constrained portfolio optimization problem…We thus, propose a Compressed Coding Scheme (CCS), compressing the two dependent variables into one variable to utilize the dependence and thereby meeting this challenge.” Section 3.2 “The basic idea is to use a real-valued vector with length N to represent a solution, which is defined in Eq. (12):
c
=
c
1
,
c
2
,
…
c
N
” – this defines the single “intermediate variable” (c) as a real-valued vector, which the Evolutionary Algorithm (EA) directly controls.) that varies the explanatory variables (Chen, section 3.2 “Fig. 4 shows that CCS represents the selection and weight based on one string of real numbers in
[
0,1
]
N
for a multi-mapping, where one vector c is able to represent both selection and weight tasks simultaneously.” Section 4.1 “By using CCS, all solutions are represented as real-valued vectors. Therefore, the search operators for continuous variables can be directly employed here.” – This shows the action of variation is executed using simple continuous operators. The vector c serves as the single source of variation for the final explanatory variables (selection s and weight w).) of each of the plurality of individuals independently of the constraint (Chen, page 4, Introduction “In this way, not only the
reusing of the existing search operators is simplified, but also the dependence can be utilized for a more effective search.” – The variation occurs in the real-valued domain [0,1] using simplified operators, bypassing the need for immediate checks against rigid constraints (which are only enforced later during the decoding and repair steps).)
evaluating an objective function obtained using the explanatory variables varied by the intermediate variable (Chen, section 2 “This paper considers the following bi-objective model [34], which includes maximizing the return and minimizing the risk simultaneously. Meanwhile, it meets the four practical constraints [20] mentioned above, namely, cardinality, quantity, pre-assignment, and round lot constraints.” Section 3.2 “Firstly, a solution is converted into the selection of assets
s
1
,
…
,
s
N
…Then the weight of the assets is given in Eq. (13). An example of decoding is presented in Fig. 5.” Section 4.3 “For the fitness evaluation, each solution c in P is decoded by the decoding scheme to a portfolio w (lines 2-4)… Finally, the best individuals are selected by an MOEA selection strategy in each iteration as the next population P.” – This teaches evaluating an objective function (risk and return) based on explanatory variables (asset selection and weights) that are varied indirectly through the intermediate real-value vector (the CCS solution c). The algorithm decodes the intermediate variable into the actual explanatory variables used to compute objective functions, satisfying the requirement that the objective is evaluated using variables derived via the intermediate representation.).
Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having optimization methods of Cantin and Chen before them, to incorporate the technique of independent variable variation of Chen into a framework that requires guaranteed constraint compliance as taught by Cantin. One would have been motivated to make such a combination in order to improve the global search capability and efficiency of constrained optimization by suppressing the limitation on individual movement caused by rigid constraints. This would allow the system to efficiently explore a wider search space and still guarantee that the final set of solutions adhere to all complex constraints.
Regarding claim 2, Cantin in view of Chen, as outlined above, all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1, mutatis mutandis, Cantin in view of Chen further teachers:
selecting the combination of the plurality of explanatory variables (Chen, section 3.2, “The basic idea is to use a real-valued vector with length N to represent a solution…The elements of a vector are utilized to represent not only the selection but also the weight. More specifically, the L pre-assigned assets are selected.” – the purpose of the intermediate variable (c) is tied to selection the final explanatory variables (selection s and weight w).) according to values of the explanatory variables varied by the intermediate variable (Chen, section 3.2 “Meanwhile, among the assets which are not pre-assigned, the
K
-
L
assets with highest values are selected. Then the
K
elements of the vector are applied again to represent the weight.” – This shows the selection rule. The final combination of assets is chosen directly according the magnitude (the values) of the components in the intermediate vector (c). The assets corresponding to the highest values of the intermediate vector are chosen to be the final combination.).
Regarding claim 3, Cantin in view of Chen, as outlined above, all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1, mutatis mutandis, Cantin in view of Chen further teachers:
setting an upper limit number on a number of the plurality of explanatory variables as the constraint and selecting, as the combination, explanatory variables with a largest value for the upper limit number from among the explanatory variables varied by the intermediate variable (Chen, section 3.2 “The elements of a vector are utilized to represent not only the selection but also the weight. More specifically, the L pre-assigned assets are selected. Meanwhile, among the assets which are not pre-assigned, the K L assets with highest values are selected. Then the K elements of the vector are applied again to represent the weight.” Fig. 5 “The values of
c
2
and
c
4
are higher than other genes, so the selection of assets is represented as
s
2
=
1
and
s
4
=
1
” -This teaches setting the upper limit number (K) on the number of explanatory variables (assets) to be included in the combination and selecting those with the largest values from the intermediate variable (the CCS vector c). The CCS decoding process directly performs this selection by ranking the variable values and taking the K highest, thereby satisfying the requirement of choosing explanatory variables with the largest values under a set constraint limit.).
Regarding claim 4, Cantin in view of Chen, as outlined above, all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1, mutatis mutandis, Cantin in view of Chen further teachers:
setting a total value of the plurality of explanatory variables to a predetermined value as the constraint and converting the values of the explanatory variables varied by the intermediate variable according to a ratio such that the total value becomes the predetermined value (Chen, section 3.2 “Then the weight of the assets is given in Eq. (13). An example of decoding is presented in Fig. 5.
w
i
=
s
i
c
i
∑
j
=
1
N
s
j
c
j
,
i
=
1,2
,
…
,
N
.” Fig. 5 “Finally, the portfolio w is normalized as
{
0.00
,
0.50
,
0.00
,
0.50
,
0.00
}
.” – This teaches applying a normalization constraint that ensures the total sum of explanatory variables (weights) equals a predetermined total value (100% of the portfolio). The system converts the intermediate variables (the CCS real-valued vector c) into normalized weights by dividing each value by the total sum (
∑
j
s
j
c
j
). This operation adjusts all explanatory variables proportionally so their total equals the predetermined constraint, satisfying the claimed “converting…according to a ratio such that the total value becomes the predetermined value.).
Regarding claim 5, Cantin in view of Chen, as outlined above, all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1, mutatis mutandis, Cantin in view of Chen further teachers:
setting an upper limit on a maximum number of generations of the evolution according to a hypervolume in a space of the objective function, wherein the hypervolume includes volume or area obtained from a reference point and a set of solutions for each generation in the space of the objective function (Chen, page 16, section 5.2, “Inverted Hypervolume(IH).
I
H
is the inverted version of Hypervolume (
H
V
) [45].
H
V
, also known as the size of dominated space, is a quality indicator that rewards the convergence toward the
P
F
as well as the distribution of the representative points along the front. It normalizes the objective space and measures the volume of space, which is bounded by the obtained efficient solutions and a preference point
r
. For each obtained solution
i
∈
Q
, a hypercube
h
c
i
from solution
i
, and the reference point
r
is measured. Generally, higher values of
H
V
are preferable...comparison as
I
G
D
,
I
H
is defined by
I
H
=
v
o
l
u
m
e
(
∪
i
=
1
Q
h
c
r
-
h
c
i
)
, where
h
c
r
is the hypercube among the reference point and representatives. Hence, lower values are better concerning this definition. In addition, the reference point is set to [1:2; 1:2] while all the solutions are normalized for minimizing
f
1
and
-
f
2
.” And “Table 2: The parameter setting for all algorithms…Number of generations 1000” – This teaches setting the upper limit on the number of generations (1000) for the evolutionary process and evaluating process using the hypervolume metric that measures the space bounded by a reference point and the set of solutions in the objective-function space. The reference point defines the bounds, and the obtained solutions in the objective-function space. The reference point defines the bounds, and the obtained solutions from the measured volume, thereby linking generation control with hypervolume-based evaluation of the evolution performance.).
Regarding claim 6, Cantin in view of Chen, as outlined above, all the elements of claim 1, therefore is rejected for the same reasons as those presented for claim 1, mutatis mutandis, Cantin in view of Chen further teachers:
setting a minimum number of generations of the evolution and making the maximum number of generations larger than the minimum number of generations (Chen, page 16, “Table 2: The parameter setting for all algorithms…Number of generations 1000” Section 4.3 “While the stopping criteria are not met [42], the new candidate solution is generated with a search operator (line 9)… Finally, the best individuals are selected by an
M
O
E
A
selection strategy in each iteration as the next population
P
.” – This teaches setting defined evolutionary limits for the algorithm. The reference specifies a fixed maximum number of generations (1000) while also describing an iterative process that continues until stopping criteria are satisfied, implying a minimum number of generations must be executed before convergence. Together, these establish a lower and upper bound of the number of generations used during the evolutionary search.).
Regarding claim 7, Cantin teaches the following limitations:
An arithmetic method implemented by a computer of searching for a combination of a plurality of explanatory variables such that an objective variable satisfies a predetermined condition by evolving a plurality of individuals each identified by the plurality of explanatory variables, the arithmetic method comprising(Cantin, paragraph [0003] “Some embodiments include a method directed to determining a plurality of constraint compliant values for each of a plurality of constrained variables (explanatory variables) of an optimization problem, wherein a constraint condition mutually constrains possible values… the method is further directed to generating a population of constraint compliant candidate solutions (individuals)…while running the computer-based simulation with the population of constraint compliant candidate solutions, determining that a mutated candidate solution…fails to comply with the constraint condition…modifying the mutated candidate solution to use at least one value randomly selected from the plurality of constraint compliant values for a corresponding one of the plurality of constrained variables resulting in a constraint compliant mutated candidate solution that complies with the constraint condition.” Paragraph [0016] “ The description that follows includes exemplary systems, methods, techniques, instruction sequences, and computer program products that embody techniques of the present inventive subject matter…embodiments can utilize specific types of evolutionary algorithms (e.g., genetic algorithms, genetic programming, evolutionary programming, evolution strategy, etc.) suited to fit a particular type of optimization problem being solved.” – Evolves a population of candidate solutions (individuals) defined by constrained variables (explanatory variables) to find combinations that satisfy a constraint condition (objective variable meeting a predetermined condition).):
However, Cantin does not teach, but Cantin in view of Chen teaches the limitation:
setting a constraint on the combination of the plurality of explanatory variables (Cantin, paragraph [0003] “Some embodiments include…wherein a constraint condition mutually constrains possible values that can be used for the plurality of constrained variables, and wherein the plurality of constraint compliant values comply with the constraint condition.” Paragraph [0028] “The first constraint expression 210 constrains the variables C and D according to a first mathematical and/or logical relationship (e.g., the inequality statement C+D>6, meaning that the sum of variables C and D must be greater than six units).” -setting a constraint (C+D>6) that explicitly operates on the sum (combination) of two variables).) and controlling an intermediate variable (Chen, Abstract “In this work, we turn to Multi-Objective Evolutionary Algorithms (MOEAs) for finding elegant solutions for such problems. In this framework, we investigate a multi-objective constrained portfolio optimization problem…We thus, propose a Compressed Coding Scheme (CCS), compressing the two dependent variables into one variable to utilize the dependence and thereby meeting this challenge.” Section 3.2 “The basic idea is to use a real-valued vector with length N to represent a solution, which is defined in Eq. (12):
c
=
c
1
,
c
2
,
…
c
N
” – this defines the single “intermediate variable” (c) as a real-valued vector,