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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/30/2025 has been entered.
Effective Filing Date
The effective filing date of 04/25/2022 is acknowledged.
Information Disclosure Statement
The information disclosure statement(s) submitted on 04/25/2022 and 04/28/2022 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Status of Claims
The present application is being examined under the claims filed on 12/30/2025.
Claim(s) 1, 2, 4, 9, 10, 11, 13, 18, 19 is/are rejected.
Claim(s) 1, 2, 4, 9, 10, 11, 13, 18, 19 is/are pending.
Prior Art References
Katoch et al. A review on genetic algorithm past present and future 2020 (Hereafter, “Katoch”).
Camilo et al. In Vitro Fertilization Genetic Algorithm 2011 (Hereafter, “Camilo”).
US20030084011A1 - Methods For Solving The Traveling Salesman Problem (Hereafter, “Ravindra”).
US5819244A - Adaptive Computing Systems, Computer Readable Memories And Processes Employing Hyperlinear Chromosomes (Hereafter, “Smith”).
US20050074097A1 - Method Of Determining Parameters Of A Sample By X-ray Scattering Applying An Extended Genetic Algorithm With Truncated Use Of The Mutation Operator (Hereafter, “Vincent”).
US5390283A - Method For Optimizing The Configuration Of A Pick And Place Machine (Hereafter, “Eshelman”).
Response to Arguments/Remarks
Regarding the 35 U.S.C. 112(b) rejections.
Examiner finds the amended claim language to resolve the 35 U.S.C. 112(b) rejections noted in the previous office action and thus withdraws them.
Regarding the 35 U.S.C. 103 rejections.
Applicant remarks:
“Camilo fails to disclose or suggest random selection of a segment from a best solution amongst the one or more best solutions where the segment comprises a subset of elements of the best solution and indices representing addresses corresponding to the subset of elements, as recited in amended claim 1.”
(pg. 10)
Examiner response:
Examiner respectfully disagrees. Camilo discloses the random selection of a segment of the "father" individual which is defined as the fittest and therefore the best solution in a population of solutions. This segment and its complement is then used in crossover as shown in Fig. 4 of Camilo.
Applicant remarks:
“Camilo does not teach or suggest that the indices of the subset of elements of the randomly selected segment are one of continuous and noncontiguous, as claimed in amended claim 1. […] Camilo does not select randomly selecting any "segment" comprising subset of elements and corresponding indices which are either contiguous or non-contiguous for eventually generating an augmented population to expand search space to find an optimal solution in the manner recited in amended claim 1.”
(pg.10-11)
Examiner response:
Examiner respectfully disagrees. Camilo discloses single point crossover which is consistent with a randomly selected continuous segment. Camilo's teaching or lack thereof of a noncontiguous segment is a moot point because Katoch offers further details regarding k-point crossover which is consistent with a randomly selected noncontiguous segment. Refer to the updated claim mapping in this document.
Applicant remarks:
“In other words, Camilo does not teach or suggest random selection of a segment from the designated elements, and selection of indices that represent addresses of elements in the segment, as required by amended claim 1.”
(pg. 11)
Examiner response:
Examiner respectfully disagrees. Camilo discloses single point crossover which is consistent with a randomly selected continuous segment and necessarily requires determining an index at which crossover is to occur.
Applicant remarks:
“That is, Camilo does not contemplate "slicing and dicing" of the designated elements to select sub-elements within segments of designated elements.”
(pg. 11)
Examiner response:
Examiner respectfully disagrees. Camilo discloses single point crossover which is consistent with a randomly selected continuous segment. Camilo's teaching or lack thereof of a noncontiguous segment is a moot point because Katoch offers further details regarding k-point crossover which is consistent with a randomly selected noncontiguous segment. Refer to the updated claim mapping in this document.
Applicant remarks:
“Camilo does not pre-empt random selection of some segments from 'father' chromosome before recombination, and the selection in Camilo is limited to selection of the best 'father' chromosome for recombining with 'mother' chromosome for generating superior quality offspring.”
(pg. 11)
Examiner response:
Examiner respectfully disagrees. Camilo discloses single point crossover which is consistent with a randomly selected continuous segment. Camilo's teaching or lack thereof of a noncontiguous segment is a moot point because Katoch offers further details regarding k-point crossover which is consistent with a randomly selected noncontiguous segment. Refer to the updated claim mapping in this document.
Applicant remarks:
“Further, Camilo's recombination procedure (see, for instance, page 61 section [2.4]) is strictly a two-parent crossover operation and does not relate to generation of augmented population applied across every candidate solution of the received population, as claimed in the amended claim 1.”
(pg. 11)
Examiner response:
Examiner respectfully disagrees and refers to page 61 of Camilo (Camilo 61, "Analyzing the population N’, the best individual is reserved as father and the others as mothers").
Applicant remarks:
“In addition, Camilo does not teach generating a "complement of the randomly selected segment", nor does it disclose using this complement of the randomly selected segment to generate a distinct second population, as required by the amended claim 1. In fact, Camilo's notion of "complement" (at page 60 section [2.1] and page 61 section [2.4]) is fundamentally different from the "complement" recited in the claimed invention. The "complement" in Camilo is merely a default remainder of a parent chromosome produced by donation of elements during recombination. Camilo, (at page 63 section [3], Fig.4), merely performs a traditional recombination in which a mother donates one element, and the father provides the rest of the chromosome which is not a 'complement' as required in the amended claim 1. Applicant submits that the claimed invention recites generating a second population in respect of "a complement" of the "randomly selected segment", the complement being elements of the best solution other than the subset of elements of the randomly selected segment.”
(pg. 11-12)
Examiner response:
Applicant's arguments have been considered but they are not persuasive. It remains unclear to the examiner what distinguishes the notion of a complement as taught in Camilo from what is being claimed as the complement.
Applicant remarks:
“Further, Camilo relies on a "single best solution" ("Father"), and there is no teaching or suggestion that the described procedure should be repeated for each of multiple best solutions, nor is there any indication that distinct augmented populations should be produced for each of the one or more best solutions.”
(pg. 12)
Examiner response:
Applicant's arguments have been considered but they are not persuasive. Examiner notes that the claim language does not require contemplation of multiple best solutions as it is directed in the alternative toward "one or more best solutions".
Applicant remarks:
“Camilo, at pages 61 and 62 section [2.4], replaces weak individuals in a population with offspring, and does not teach merging multiple distinct populations into a single augmented population while retaining all individuals. Camilo fails to disclose or suggest concurrently maintaining original population alongside both first and second populations and then merging all three.”
(pg. 12)
Examiner response:
Applicant's arguments have been considered and they are persuasive. Additional reference "Eshelman" more closely teaches the formation of a single augmented population following the merging process. Refer to the updated claim mapping of this document.
Applicant remarks:
“Katoch does not disclose or suggest random selection of a segment from a best solution amongst the one or more best solutions where the segment comprises a subset of elements of the best solution and indices representing addresses corresponding to the subset of elements, as recited in amended claim 1.”
(pg. 13)
Examiner response:
Applicant's arguments have been considered and Examiner agrees. Camilo teaches the "random selection of a segment from a best solution amongst the one or more best solutions where the segment comprises a subset of elements of the best solution and indices representing addresses corresponding to the subset of elements". Refer to the update claim mapping of this document.
Applicant remarks:
“Further, Katoch also fails to teach that the indices of the subset of elements of the randomly selected segment are one of continuous and non-contiguous, as claimed in amended claim 1.”
(pg. 13)
Examiner response:
Examiner respectfully disagrees. Katoch offers further details regarding k-point crossover which is consistent with a randomly selected noncontiguous segment. Refer to the updated claim mapping in this document.
Applicant remarks:
“Moreover, Katoch does not disclose or suggest generation of first population using a randomly selected segment and a second population of candidate solutions using complement of the randomly selected segment, as required by amended claim 1.”
(pg. 13)
Examiner response:
Examiner agrees, but the point is moot. This is disclosed in Camilo. Refer to the updated claim mapping in this document.
Applicant remarks:
“Further, Katoch offers no suggestion or teaching on repeating the claimed process across multiple best solutions or subsequently merging the resulting populations with the original population, as recited in amended claim 1.”
(pg. 13)
Examiner response:
Applicant's arguments have been considered but they are not persuasive. Examiner notes that the claim language does not require contemplation of multiple best solutions as it is directed in the alternative toward "one or more best solutions".
Applicant remarks:
“Katoch neither proposes nor implies generating augmented population with respect to multiple best solutions, as required by amended claim 1.”
(pg. 13)
Examiner response:
Applicant's arguments have been considered and they are persuasive. Additional reference "Eshelman" more closely teaches the formation of a single augmented population following the merging process. Refer to the updated claim mapping of this document.
Claim Rejections - 35 U.S.C. § 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.
Claim(s) 1, 2, 9, 10, 11, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Katoch in view of a second embodiment of Katoch (Katoch2) in further view of Camilo in further view of Eshelman.
Claim(s) 4, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Katoch in view of a second embodiment of Katoch (Katoch2) in further view of Camilo in further view of Eshelman in further view of Ravindra.
In reference to claim 1.
“1. (Currently Amended) A method for augmenting a population of candidate solutions including one or more best solutions, wherein the method is implemented by a processor executing program instructions stored in a memory, the method comprising:”
Katoch teaches:
“a. receiving, by the processor, a population of the candidate solutions including one or more best solutions,”
(Katoch 8092, “Chromosomes are considered as points in the solution space.”)
The received “population of candidate solutions” is taught by “chromosomes” in Katoch and necessarily contains at least one “best” solution.
“wherein the one or more best solutions have highest fitness values in the population of the candidate solutions;”
(Katoch 8094, “Genetic algorithm (GA) is an optimization algorithm that is inspired from the natural selection. It is a population based search algorithm, which utilizes the concept of survival of fittest [135]. The new populations are produced by iterative use of genetic operators on individuals present in the population. The chromosome representation, selection, crossover, mutation, and fitness function computation are the key elements of (Y) of n chromosomes are initialized randomly. The fitness of each chromosome in Y is computed”)
A second embodiment of Katoch teaches:
“the segment comprising a subset of elements of the best solution and indices representing addresses corresponding to the subset of elements, wherein the indices of the subset of elements of the randomly selected segment are one of continuous and non-contiguous;”
(Katoch2 8098, “In a single point crossover, a random crossover point is selected.”)
The use of “continuous” elements in segment is taught by “single point crossover”.
(Katoch2 8098, “In a two point and k-point crossover, two or more random crossover points are selected and the genetic information of parents will be swapped as per the segments that have been created”)
The use of “non-contiguous” elements in segment is taught by “k-point crossover”.
Motivation to combine Katoch, a second embodiment of Katoch.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Katoch, a second embodiment of Katoch.
Katoch discloses generic genetic algorithm methodologies.
A second embodiment of Katoch discloses specific crossover techniques for use in genetic algorithms.
One would be motivated to combine these references because modulating the crossover technique of a genetic algorithm would be obvious to try to a person having ordinary skill in the art in order to achieve better results when traversing a search space with genetic algorithms.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
Camilo teaches:
“b. randomly selecting, by the processor, a segment from a best solution amongst the one or more best solutions,”
(Camilo 60, “4. The fittest (father) and the N’ individuals are recombined.”)
“c. generating, by the processor, a first population of candidate solutions with respect to the randomly selected segment of the best solutions by replacing elements of each of the candidate solutions of the received population with the subset of elements of the randomly selected segment, wherein position of the elements of each of the candidate solutions that is replaced is same as the indices corresponding to the subset of elements of the randomly selected segment;
d. generating, by the processor, a second population of candidate solutions with respect to a complement of the randomly selected segment of the best solutions by replacing elements of each of the candidate solutions of the received population with the subset of elements of the complement of the randomly selected segment, wherein position of the elements of each of the candidate solutions that is replaced is same as the indices corresponding to the subset of elements of the complement of the randomly selected segment, and wherein the complement of the randomly selected segment comprises elements of the best solution excluding the subset of elements of the randomly selected segment; and”
(Camilo Fig. 4, “First group of Sons” teaches the “first population” and “Second group of Sons” teaches the “second population”)
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“e. repeating, by the processor, steps b, c and d for remaining of the one or more best solutions of the population until the first population and the second population of candidate solutions have been generated with respect to each of the remaining of the one or more best solutions; and”
(Camilo 60, “4. The fittest (father) and the N’ individuals are recombined.”)
Camilo is taught in the context of a single “best solution” and thus teaches “repeating […] steps a, b, c, and d for the remaining […] best solutions”
Motivation to combine Katoch, a second embodiment of Katoch, Camilo.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Katoch, a second embodiment of Katoch.
Katoch, a second embodiment of Katoch discloses elements of genetic algorithm procedure.
Camilo discloses a module for genetic algorithms for preserving the strongest performer(s) each generation as elitism.
One would be motivated to combine these references because the disclosure of Katoch specifically notes the benefits of elitist selection (Katoch 8097, “Elitism selection was proposed by K. D. Jong (1975) for improving the performance of Roulette wheel selection. It ensures the elitist individual in a generation is always propagated to the next generation. If the individual having the highest fitness value is not present in the next generation after normal selection procedure, then the elitist one is also included in the next generation automatically [88]. The comparison of above-mentioned selection techniques are depicted in Table 3.”)
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
Eshelman teaches:
“f. generating, by the processor, an augmented population by merging the received population of the candidate solutions with the first population and the second population of candidate solutions generated with respect to each of the one or more best solutions, and providing the augmented population for finding an optimal solution in a search space.”
(Eshelman (29), “During survival-selection, on the other hand, instead of replacing the old parent population P(t-1) with the children population C(t) to form P(t), the newly created children must compete with the members of the parent population P(t-1) for survival--i.e., competition is cross-generational. More specifically, the members of P(t-1) and C'(t) are merged […] We shall call this procedure of retaining the best ranked members of the merged parent and child populations population-elitist selection since it guarantees that the best M individuals seen so far shall always survive.”)
Motivation to combine Katoch, a second embodiment of Katoch, Camilo, Eshelman.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Katoch, a second embodiment of Katoch, Camilo, Eshelman.
Katoch, a second embodiment of Katoch, Camilo discloses genetic algorithm methodologies.
Eshelman discloses a specific optimization problem to apply genetic algorithm methodologies to.
One would be motivated to combine these references because the specific implementations of Eshelman could further enhance the combined teaching Katoch, a second embodiment of Katoch, Camilo in order to solve other optimization problems not taught explicitly in Eshelman.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
In reference to claim 2.
Katoch teaches:
“2. (Previously Amended) The method as claimed in claim 1, wherein the population of candidate solutions including the one or more best solutions is any population generated in relation to an optimization problem subsequent to a seed population.”
(Katoch 8094, “Genetic algorithm (GA) is an optimization algorithm that is inspired from the natural selection. It is a population based search algorithm, which utilizes the concept of survival of fittest [135]. The new populations are produced by iterative use of genetic operators on individuals present in the population. The chromosome representation, selection, crossover, mutation, and fitness function computation are the key elements of (Y) of n chromosomes are initialized randomly. The fitness of each chromosome in Y is computed”)
In reference to claim 4.
Ravindra teaches:
“4. (Original) The method as claimed in claim 1, wherein the one of the one or more best solutions is selected randomly or based on user inputs for random selection of segment.”
(Ravindra Table 1 Step 3a)
Examiner notes that selection in genetic algorithms occurs based on a computed fitness function - the value of which depends on a user defined fitness function. Therefore, random selection based on fitness necessarily teaches selection based on user inputs for the random selection of a segment because the user of GA defines the fitness function which in turn randomly decides what chromosome will be selected and therefore what segment will be entered into crossover. Examiner further notes that Ravindra does not necessarily specify that selection is occurring exclusively on the one or more best solutions, but Camilo does teach this limitation (Camilo, pg. 60, “1. Find the fittest individual and label it as Father; […] 4. The fittest (father) and the N’ individuals are recombined.”))
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Motivation to combine Katoch, a second embodiment of Katoch, Camilo, Eshelman, Ravindra.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Katoch, a second embodiment of Katoch, Camilo, Eshelman, Ravindra.
Katoch, a second embodiment of Katoch, Camilo, Eshelman discloses genetic algorithm methodologies.
Ravindra discloses an application of genetic algorithms to the traveling salesman optimization problem.
One would be motivated to combine these references because Ravindra offers a practical application for the theoretical underpinnings of Katoch, a second embodiment of Katoch, Camilo.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In reference to claim 9.
Camilo teaches:
“9. (Original) The method as claimed in claim 1, wherein said method is used with evolutionary computing algorithms subsequent to each step of evaluation of candidate solutions of respective populations until one target condition from a set of target conditions is achieved, said respective populations generated subsequent to a seed population.”
(Camilo Fig. 1)
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In reference to claims 10, 11, 13, 19.
Claim 10 is substantially similar to claim 1, 11 to 2, 13 to 4, and 19 to 1 and are thus rejected using the same art.
In reference to claim 18.
Camilo teaches:
“18. (Original) The system as claimed in claim 10, wherein said system interfaces with an evolutionary computation system executing an Evolutionary Computing (EC) algorithm, said system configured to provide augmentation of any population of candidate solutions including one or more best solutions.”
(Camilo Fig. 1)
Conclusion
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/CODY RYAN GILLESPIE/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147