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 .
This action is in response to the original application filed on July 9th, 2024.
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 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 recites, “A computer-implemented method for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer-implemented method comprising:” therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“initializing a set of schedules using an initializer that sets an initial set of solutions for the GA to start the optimization;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a set of initial schedules or predictions for a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generating a parent population for the GA, wherein the parent population comprises a collection of potential solutions to the scheduling problem;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a set of predictions or schedules which represent parent nodes within a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“creating a child population via evolution using current probabilistic parameters comprising crossover and mutation operators, wherein new candidate solutions are produced by combining or modifying solutions of the collection of potential solutions from the parent population;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and process parent nodes to generate child nodes in a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate the child population, wherein the new candidate solutions are organized into hierarchical groups, and duplicates from the new candidates are removed;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate child nodes in a GA and identify duplicate nodes and remove them from the program. Further, a human is able to evaluate nodes and sort them using pen and paper. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining a new population from a total population comprising the parent population and the child population, using a custom multi-objective sorting technique, wherein the new population comprises top-performing solutions;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a population of nodes in a GA and sort them based on evaluated values and select the top ranked nodes. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “A computer-implemented method for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer-implemented method comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “A computer-implemented method for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer-implemented method comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a 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”.
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determining if the pre-determined iterations are attained to determine whether the optimization has achieved pre-defined results or further iterations are required, wherein the determination is one of a successful determination or an unsuccessful determination.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a set of nodes and determine if any of generated schedules are successful or not. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “upon the successful determination, rendering final schedules with optimal statistics and trade-offs as output; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“upon the unsuccessful determination, updating the probabilistic parameters of the GA during runtime using the runtime adapter.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “upon the successful determination, rendering final schedules with optimal statistics and trade-offs as output; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“upon the unsuccessful determination, updating the probabilistic parameters of the GA during runtime using the runtime adapter.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the initializer is a Monte Carlo Tree Search (MCTS) initializer.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the initializer is a Monte Carlo Tree Search (MCTS) initializer.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“combine the parent population and the child population;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to take two sets of data or nodes and combine them into one data structure. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“sort the combination based on multiple optimization objectives; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A hum an is able to evaluate and sort nodes of a GA based on a given algorithm. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“select the top-performing solutions for a next generation.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. After sorting, a human is able to reevaluate the generated list and identify top nodes on the sorted list. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“classifying the new population into groups based on whether a crossover or a mutation is performed, facilitating dynamic adaptation of GA parameters to different operators.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and classify data with given classification constraints. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein summary statistics are computed for each group of the groups, and wherein computation of the summary statistics provides insight into performance of different subsets of solutions, and guides adjustments for the probabilistic parameters.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein summary statistics are computed for each group of the groups, and wherein computation of the summary statistics provides insight into performance of different subsets of solutions, and guides adjustments for the probabilistic parameters.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the summary statistics comprises a mean objective value.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the summary statistics comprises a mean objective value.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein adjustments to be made in each probabilistic parameter are calculated based on the summary statistics obtained in a previous step, allowing for informed adjustments to GA's behavior.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein adjustments to be made in each probabilistic parameter are calculated based on the summary statistics obtained in a previous step, allowing for informed adjustments to GA's behavior.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the probabilistic parameters are adjusted based on contribution of hyper-parameters in previous iterations to improve convergence and solution quality, leveraging past performance to inform future parameter adjustments.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the probabilistic parameters are adjusted based on contribution of hyper-parameters in previous iterations to improve convergence and solution quality, leveraging past performance to inform future parameter adjustments.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 11 recites, "A computer-implemented system for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer system comprising: one or more computer processors, one or more computer readable memories, one or more computer readable storage devices, and program instructions stored on the one or more computer readable storage devices for execution by the one or more computer processors via the one or more computer readable memories, the program instructions comprising:" therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“initializing a set of schedules using an initializer that sets an initial set of solutions for the GA to start the optimization;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a set of initial schedules or predictions for a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generating a parent population for the GA, wherein the parent population comprises a collection of potential solutions to the scheduling problem;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a set of predictions or schedules which represent parent nodes within a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“creating a child population via evolution using current probabilistic parameters comprising crossover and mutation operators, wherein new candidate solutions are produced by combining or modifying solutions of the collection of potential solutions from the parent population;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and process parent nodes to generate child nodes in a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate the child population, wherein the new candidate solutions are organized into hierarchical groups, and duplicates from the new candidates are removed;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate child nodes in a GA and identify duplicate nodes and remove them from the program. Further, a human is able to evaluate nodes and sort them using pen and paper. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining a new population from a total population comprising the parent population and the child population, using a custom multi-objective sorting technique, wherein the new population comprises top-performing solutions;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a population of nodes in a GA and sort them based on evaluated values and select the top ranked nodes. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “A computer-implemented system for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer system comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“one or more computer processors, one or more computer readable memories, one or more computer readable storage devices, and program instructions stored on the one or more computer readable storage devices for execution by the one or more computer processors via the one or more computer readable memories, the program instructions comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “A computer-implemented system for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer system comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“one or more computer processors, one or more computer readable memories, one or more computer readable storage devices, and program instructions stored on the one or more computer readable storage devices for execution by the one or more computer processors via the one or more computer readable memories, the program instructions comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a 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”.
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“further comprising determining if the pre-determined iterations are attained to determine whether the optimization has achieved pre-defined results or further iterations are required, wherein the determination is one of a successful determination or an unsuccessful determination.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a set of nodes and determine if any of generated schedules are successful or not. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “upon the successful determination, rendering final schedules with optimal statistics and trade-offs as output; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“upon the unsuccessful determination, updating the probabilistic parameters of the GA during runtime using the runtime adapter.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “upon the successful determination, rendering final schedules with optimal statistics and trade-offs as output; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“upon the unsuccessful determination, updating the probabilistic parameters of the GA during runtime using the runtime adapter.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the initializer is a Monte Carlo Tree Search (MCTS) initializer.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the initializer is a Monte Carlo Tree Search (MCTS) initializer.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein the custom multi-objective sorting technique is used to: combine the parent population and the child population;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to take two sets of data or nodes and combine them into one data structure. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“sort the combination based on multiple optimization objectives; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A hum an is able to evaluate and sort nodes of a GA based on a given algorithm. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“select the top-performing solutions for a next generation.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. After sorting, a human is able to reevaluate the generated list and identify top nodes on the sorted list. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“further comprising classifying the new population into groups based on whether a crossover or a mutation is performed, facilitating dynamic adaptation of GA parameters to different operators.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and classify data with given classification constraints. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein summary statistics are computed for each group of the groups, and wherein computation of the summary statistics provides insight into performance of different subsets of solutions, and guides adjustments for the probabilistic parameters.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein summary statistics are computed for each group of the groups, and wherein computation of the summary statistics provides insight into performance of different subsets of solutions, and guides adjustments for the probabilistic parameters.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 18
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein adjustments to be made in each probabilistic parameter are calculated based on the summary statistics obtained in a previous step, allowing for informed adjustments to GA's behavior.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein adjustments to be made in each probabilistic parameter are calculated based on the summary statistics obtained in a previous step, allowing for informed adjustments to GA's behavior.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 19
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the probabilistic parameters are adjusted based on contribution of hyper-parameters in previous iterations to improve convergence and solution quality, leveraging past performance to inform future parameter adjustments.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the probabilistic parameters are adjusted based on contribution of hyper-parameters in previous iterations to improve convergence and solution quality, leveraging past performance to inform future parameter adjustments.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 20
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 20 recites, "A non-transitory computer-readable storage medium having stored thereon computer executable instruction which when executed by one or more processors, cause the one or more processors to carry out operations for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the operations comprising perform the operations comprising:" therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“initializing a set of schedules using an initializer that sets an initial set of solutions for the GA to start the optimization;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a set of initial schedules or predictions for a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generating a parent population for the GA, wherein the parent population comprises a collection of potential solutions to the scheduling problem;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a set of predictions or schedules which represent parent nodes within a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“creating a child population via evolution using current probabilistic parameters comprising crossover and mutation operators, wherein new candidate solutions are produced by combining or modifying solutions of the collection of potential solutions from the parent population;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and process parent nodes to generate child nodes in a GA. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate the child population, wherein the new candidate solutions are organized into hierarchical groups, and duplicates from the new candidates are removed;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate child nodes in a GA and identify duplicate nodes and remove them from the program. Further, a human is able to evaluate nodes and sort them using pen and paper. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining a new population from a total population comprising the parent population and the child population, using a custom multi-objective sorting technique, wherein the new population comprises top-performing solutions;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a population of nodes in a GA and sort them based on evaluated values and select the top ranked nodes. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “A non-transitory computer-readable storage medium having stored thereon computer executable instruction which when executed by one or more processors, cause the one or more processors to carry out operations for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the operations comprising perform the operations comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “A non-transitory computer-readable storage medium having stored thereon computer executable instruction which when executed by one or more processors, cause the one or more processors to carry out operations for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the operations comprising perform the operations comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a 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”.
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
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.
Claims 1-3, 5-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sana et al, (Sana et al, “Application of genetic algorithm to job scheduling under ergonomic constraints in manufacturing industry”, 2019, hereinafter “Sana”) in view of Cai et al, (Cai et al, “A genetic algorithm for scheduling staff of mixed skills under multi-criteria”, 2000 , hereinafter “Cai”).
Regarding claim 1, Sana discloses, “A computer-implemented method for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer-implemented method comprising:” (Computational study, pp. 2074; “In the algorithm, there are many factors and parameters which are highly configurable. The algorithm is implemented in software using java programming language with the aim of obtaining the maximum flexibility in the introduction of data and visualization of results. The rotations of production scheduling in 32 workstations with 32 employees with 22 jobs (Table 4) are selected in a plastic industry. The workers employed in the job rotation have sufficient training and the workstations are located in the same area and each rotation causes no interruptions in the process.” Sana discloses using java to encode their algorithm and execute on a computing system. They disclose their own proposed Genetic Algorithm, GA, to solve a scheduling problem.)
“initializing a set of schedules using an initializer that sets an initial set of solutions for the GA to start the optimization;” (Generation the initial population of solutions, pp. 2070; “The algorithm initiates a set of solutions with a matrix scheme, where the size of the array is determined by the number of workers involved in the rotation (J) and the span of rotation is considered by intervals (T).” Sana discloses a random generation of the initial population, i.e. schedules. This is the start of an iterative process to optimize workers schedules for various health reasons.)
“generating a parent population for the GA, wherein the parent population comprises a collection of potential solutions to the scheduling problem;” (Selection and replacement of individuals within the population, pp. 2072, “The algorithm uses a technique for direct inclusion, where the new generation among new parents and offspring is selected. When the parents improve descendants, it replaces these entirely by the parents. Otherwise, the descendants are taken within the next generation, adding the best parents to complete the population.” An initial set of parent schedules are initialized and processed by the system. After processing, sorting, reproduction and selection of the next generation the process is repeated, and a new set of parents are selected to generate the next iteration. Further, see Fig. 4 which discloses the generation of parent populations.)
“creating a child population via evolution using current probabilistic parameters comprising crossover and mutation operators, wherein new candidate solutions are produced by combining or modifying solutions of the collection of potential solutions from the parent population;” (Reproduction, pp. 2072; “The crossover probability (Tc) determines the number of individuals in the next generation. Figure 2 shows the reproduction process. The crossover point is chosen at random by crossing point size content of rotations, and to share information between two parents’ individuals to generate two descendants that represent feasible solutions.” Sana discloses a GA which takes the parent population and performs crossover and mutation to reproduce.) and (Mutation, pp. 2072; “The mutation probability (Tm) is related to the number of individuals which would be modified. The process consists of selecting at random one rotation and two workers, and exchanging the jobs assigned to the workers in that rotation (Fig. 3). The mutation intensity (Im) is the number of modifications on an individual who has been selected at random within the population.” This is the disclosed Mutation process in Sana.)
“utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate the child population, wherein the new candidate solutions are organized into hierarchical groups, and duplicates from the new candidates are removed;” (Algorithm 2. Pseudo code improved NSGA II, pp. 2074; Sana discloses the pseudo-code for their algorithm. As seen at line 5. a, the system will evaluate the generated children, individuals, and remove duplicate instances. Next, at lines 5. b-c the system will select individuals from a sorted cluster of parent nodes for comparison and sort the newly generated children. This process is also disclosed in fig. 4 where the parent and child populations are sorted into groups of different granularities and then the populations are combined to generate a new populations for further system actions.)
“determining a new population from a total population comprising the parent population and the child population, using a custom multi-objective sorting technique, wherein the new population comprises top-performing solutions;” (Parameterization algorithm, pp. 2073; “Before finalizing a generation algorithm, pre-selection process and preservation of elite solutions are involved by getting the set of solutions of Parents and descendants obtained by operator’s selection, crossover and mutation. Thus the current population increases at twice individuals of the initial population. This requires sorting of the complete set in their respective front’s dominance and preserves individuals belonging to the fronts of better quality. If it is not possible to enter all the alternatives of a particular front, then those individuals are eliminated with a smaller crowding distance. The sequence of algorithm 2 of improved NSGA-II is schematized in Fig. 4.” As seen in fig. 4, the system will combine the parent and child population. After this the populations are processed and they are further sorted. This is seen in Algorithm 2 line 5.)
Sana fails to explicitly disclose:
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;”
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.”
However, Cai discloses, “receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” (Model, pp. 361; “There are two types of jobs, requiring respectively
D
1
t
and
D
2
t
staff at time t, where t = 1; 2; ...; T, and T is the scheduling horizon. Meanwhile, there are three types of staff. The first type of staff having skill 1, can be assigned to do type-1 job, and the second type of staff having skill 2, can be assigned to do type-2 job. The third type of staff having a mixed skill, can be assigned to do either type-1 or type-2 job. The general case with more than two types of jobs and/or three types of staff can be tackled similarly by generalizing the ideas we describe in this paper. The demands D1t and D2t are deterministically known in advance.” Cai discloses a GA which they apply to a similar scheduling problem. Their network contains initialization and input of constraints for the different schedules, such as jobs, types of staff, and time.)
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.” (Heuristic to ensure feasibility, pp. 366; “In the scheduling problem, there are numerous constraints. The procedure firstly selects the one that is violated the most. It then chooses a schedule capable of reducing the violation, and then increases the number of workers assigned to the schedule by one. If this makes the total number of type-3 staff exceed the bound, it will give up the schedule and choose the next schedule accordingly. The process repeats until all constraints are satisfied. If there are multiple schedules that can be altered to make the chromosome feasible, the heuristic will choose the best one, as determined by examining the three criteria successively.” Cai discloses an iterative process that will update different schedules to meet the given demands. This system will adjust the different schedules and staff accordingly.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Sana and Cai. Sana teaches a genetic algorithm system that is applied to job scheduling. This system uses GA concepts such as population generation, crossover/mutation and development to optimize a healthy job schedule based on the strain different jobs apply to workers. Cai teaches a genetic algorithm that is also used for job scheduling. Cai teaches a method that uses mixed constraints for scheduling staff including difficulty of tasks and ability of the staff. One of ordinary skill would have motivation to combine the architectures of genetic algorithms to optimize different schedules based on multiple outside constraints to optimize a workflow or schedule for sets of works, including for hospital staff, “We must point out that, in order to successfully apply the GA approach proposed, a careful examination on which schedules are practically feasible is important. In other words, it is important to carefully identify the sets of feasible schedules Sk , k = 1, 2, 3, before the GA is applied, since apparently the GA will be slowed down if the sets Sk are very big (note that the number of genes in a chromosome of the GA is equal to
n
=
S
1
+
S
2
+
2
S
3
;
see Eq. (8)). Usually, by utilizing the practical operational rules/constraints, one may eliminate many schedules from consideration and therefore reduce the sizes of the sets Sk. For example, in the scheduling problem as discussed in Section 4, there are only 504 feasible schedules left after considering the operational constraints of the company, in contrast with the total number 4032 of possible schedules without considering these constraints (with no constraints, a worker may start his/her shift at any one hour on each working day, and have his/her day-o on any day of a week. Thus, for a period of one week, he/she may have 24 x 6 x 7 = 1008 possible schedules. For three types of workers, the total number of schedules n = 4 x 1008 = 4032).”(Cai, Concluding remarks, pp. 368).
Regarding claim 2, Cai discloses, “determining if the pre-determined iterations are attained to determine whether the optimization has achieved pre-defined results or further iterations are required, wherein the determination is one of a successful determination or an unsuccessful determination.” (Remarks, pp. 364; “Note that when the population size tends to infinity, the sum of
q
(
1
-
q
)
i
-
1
over all i is equal to
q
/
(
1
-
1
-
q
)
=
1
. For considerably large population size, the sum is also nearly equal to 1. The actual value for q may be determined by a trial-and-error approach using the actual data of the problem to be solved. A too large value for q implies that those good individuals will have a large chance to generate more offspring which will dominate the population rapidly, and thus result in the so-called premature convergence of the GA approach. However, a too small value q will make the convergence very slow. According to our computational experience, we suggest to choose q in a range [0.05, 0.15].” Cai discloses stopping criteria for their algorithm. This system will iterate x number of times until convergence is defined to be within a defined set of values. This process is disclosed in Fig. 1 starting at line 1 with “WHILE NOT finished DO” and iteratively loops until “IF the population has converged”. Successful convergence value is interpreted to be within the given range and unsuccessful convergence is interpreted to be outside the given range.)
Regarding claim 3, Cai discloses,” at least one of: upon the successful determination, rendering final schedules with optimal statistics and trade-offs as output; and” (Introduction, pp. 359; “The main purpose of this article is to develop a model to tackle the staff scheduling problem, to take into account some practically important factors. Specifically, we will formulate the problem as a multi-criteria model, where the primary objective is to minimize the overall cost for assigning staff to meet the manpower demands, the secondary objective is to maximize the surplus of staff when assigning cost is almost same, and the tertiary objective seeks to minimize the variation of surplus staff over different time periods.” As seen in Algorithm 1, the overall output of this system is optimized schedules for individuals. This occurs when the population has converged, and the while loop is broken and the final result can be output.)
“upon the unsuccessful determination, updating the probabilistic parameters of the GA during runtime using the runtime adapter.” (Heuristic to ensure feasibility, pp. 366; “The procedure firstly selects the one that is violated the most. It then chooses a schedule capable of reducing the violation, and then increases the number of workers assigned to the schedule by one. If this makes the total number of type-3 staff exceed the bound, it will give up the schedule and choose the next schedule accordingly. The process repeats until all constraints are satisfied. If there are multiple schedules that can be altered to make the chromosome feasible, the heuristic will choose the best one, as determined by examining the three criteria successively.” As seen in Algorithm 1, the system will initiate a while loop that will break when the population has converged.)
Regarding claim 5, Sana discloses, “wherein the custom multi-objective sorting technique is used to: combine the parent population and the child population;” (Fig. 4, 2073; As seen in the figure, the parent and child populations are combined.)
“sort the combination based on multiple optimization objectives; and” (Parameterization algorithm, pp. 2073; “Before finalizing a generation algorithm, pre-selection process and preservation of elite solutions are involved by getting the set of solutions of Parents and descendants obtained by operator’s selection, crossover and mutation. Thus the current population increases at twice individuals of the initial population. This requires sorting of the complete set in their respective front’s dominance and preserves individuals belonging to the fronts of better quality. If it is not possible to enter all the alternatives of a particular front, then those individuals are eliminated with a smaller crowding distance. The sequence of algorithm 2 of improved NSGA-II is schematized in Fig. 4.” Sana discloses a system will sort the parent and child possibilities using non-dominated sorting and their “crowd distancing” metric.)
“select the top-performing solutions for a next generation.” (Solution method, pp. 2070; “For each generation, the fitness of individuals in the population is estimated by an objective function. The fittest individuals are selected to form the next generation. This process repeats for several cycles (generations) individuals to provide better solutions to the problem. This section proposes a methodology for developing job rotation schedules considering simultaneously ergonomic constraints in repetitive works, lifting tasks and awkward postures. Next, the proposed MOEA is described by using a multi-objective NSGA-II.” After the child and parent populations are combined and sorted, the fittest possibilities are selected and used in the next generation.)
Regarding claim 6, Sana discloses, “classifying the new population into groups based on whether a crossover or a mutation is performed, facilitating dynamic adaptation of GA parameters to different operators.” (Reproduction, pp. 2072; “The crossover probability ( Tc) determines the number of individuals in the next generation. Figure 2 shows the reproduction process. The crossover point is chosen at random by crossing point size content of rotations, and to share information between two parents’ individuals to generate two descendants that represent feasible solutions.”) and (Mutation, pp. 2072; “The mutation probability (Tm) is related to the number of individuals which would be modified. The process consists of selecting at random one rotation and two workers, and exchanging the jobs assigned to the workers in that rotation (Fig. 3). The mutation intensity (Im) is the number of modification on an individual who has been selected at random within the population.” This system will perform crossover and mutation on certain parent/children when generating new children. As stated, randomly two workers are selected and classified to be mutated. They are separate process from the crossover process.)
Regarding claim 7, Sana discloses, “wherein summary statistics are computed for each group of the groups, and wherein computation of the summary statistics provides insight into performance of different subsets of solutions, and guides adjustments for the probabilistic parameters.” (Parameterization algorithm, pp. 2073; “Now, the improved process is being described in the new NSGA- II, where the diversification of the population is sought, after the conformation of the Pareto fronts and to relate the crowding distance. The algorithm develops a search on the entire population and on each of the fronts avoiding similar results. Thus, results are changed by Pareto front previous generation or random (best complying with the crowding distance) result. This strategy allows a better diversification of the population and avoids the premature convergence of the algorithm. The improved pseudo code is presented below.” This system will evaluate the new population and using that evaluation the overall global parameters are updated using information from the previous generation.)
Regarding claim 8, Cai discloses, “wherein the summary statistics comprises a mean objective value.” (Rank-based parent selection, pp. 363; “We introduce a parameter
ε
j
in our ranking conditions, with the consideration that two solutions (chromosomes) are usually regarded, in practice, as almost equally good under an objective if their objective values fall within a small range
ε
j
. In such a case, we will further evaluate the chromosomes using the condition at the lower level if the current condition is at level j = 1 or 2, or arbitrarily declare one chromosome to have higher rank than the other, if the current level j = 3 (this corresponds to the case where the performance of the two chromosomes are basically the same after evaluation under the three criteria” Cai uses a ranking system for parents values, this is used for determine reproduction of new generations.)
Regarding claim 9, Cai discloses, “wherein adjustments to be made in each probabilistic parameter are calculated based on the summary statistics obtained in a previous step, allowing for informed adjustments to GA's behavior.” (Heuristic to ensure feasibility, pp. 366; “In the scheduling problem, there are numerous constraints. The procedure firstly selects the one that is violated the most. It then chooses a schedule capable of reducing the violation, and then increases the number of workers assigned to the schedule by one. If this makes the total number of type-3 staff exceed the bound, it will give up the schedule and choose the next schedule accordingly. The process repeats until all constraints are satisfied. If there are multiple schedules that can be altered to make the chromosome feasible, the heuristic will choose the best one, as determined by examining the three criteria successively.” This system will adjust parameters for the next generation based on what schedules are needed or what tasks are remaining. This allows the system to adjust the nature of tasks.)
Regarding claim 10, Cai discloses, “wherein the probabilistic parameters are adjusted based on contribution of hyper-parameters in previous iterations to improve convergence and solution quality, leveraging past performance to inform future parameter adjustments.” (Fig. 1, 363; As stated above, the population in the article will undergo crossover and mutation to generate and alter members of the population. After this the fitness of the offspring is evaluated and rolled into the new generation. This system will learn from prior generations and further iterate until convergence value is within a threshold.)
Regarding claim 11, Sana discloses, “A computer-implemented system for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the computer system comprising:” (Computational study, pp. 2074; “In the algorithm, there are many factors and parameters which are highly configurable. The algorithm is implemented in software using java programming language with the aim of obtaining the maximum flexibility in the introduction of data and visualization of results. The rotations of production scheduling in 32 workstations with 32 employees with 22 jobs (Table 4) are selected in a plastic industry. The workers employed in the job rotation have sufficient training and the workstations are located in the same area and each rotation causes no interruptions in the process.” Sana discloses using java to encode their algorithm and execute on a computing system. They disclose their own proposed Genetic Algorithm, GA, to solve a scheduling problem.)
“one or more computer processors, one or more computer readable memories, one or more computer readable storage devices, and program instructions stored on the one or more computer readable storage devices for execution by the one or more computer processors via the one or more computer readable memories, the program instructions comprising:” (Computational study, pp. 2074; “Multi-objective genetic algorithm is set up and evaluated on an Intel Core i5-2430M 2.40 GHz and 4 GB memory RAM. The algorithm is executed 5 times with the aim of giving greater coverage to the results generated under the parameters considered. In Table 10, values are given the fitness of the best individual obtained in relation to the objectives for load handling (NIOSH), repeatability (OCRA) and awkward postures (RULA). The average fitness for the Risk RULA is 32.34, for the risk NIOSH is 3.64 and 5.74 is for the risk OCRA. The algorithm takes 4 min to reach a solution after running 10,000 generations.” This is the stated computing system used by Sana for their experiments. They disclose a generic computing system containing a processor coupled to memory. They also disclose in the article they implemented the machine instructions in c programming language.)
“initializing a set of schedules using an initializer that sets an initial set of solutions for the GA to start the optimization;” (Generation the initial population of solutions, pp. 2070; “The algorithm initiates a set of solutions with a matrix scheme, where the size of the array is determined by the number of workers involved in the rotation (J) and the span of rotation is considered by intervals (T).” Sana discloses a random generation of the initial population, i.e. schedules. This is the start of an iterative process to optimize workers schedules for various health reasons.)
“generating a parent population for the GA, wherein the parent population comprises a collection of potential solutions to the scheduling problem;” (Selection and replacement of individuals within the population, pp. 2072, “The algorithm uses a technique for direct inclusion, where the new generation among new parents and offspring is selected. When the parents improve descendants, it replaces these entirely by the parents. Otherwise, the descendants are taken within the next generation, adding the best parents to complete the population.” An initial set of parent schedules are initialized and processed by the system. After processing, sorting, reproduction and selection of the next generation the process is repeated, and a new set of parents are selected to generate the next iteration. Further, see Fig. 4 which discloses the generation of parent populations.)
“creating a child population via evolution using current probabilistic parameters comprising crossover and mutation operators, wherein new candidate solutions are produced by combining or modifying solutions of the collection of potential solutions from the parent population;” (Reproduction, pp. 2072; “The crossover probability (Tc) determines the number of individuals in the next generation. Figure 2 shows the reproduction process. The crossover point is chosen at random by crossing point size content of rotations, and to share information between two parents’ individuals to generate two descendants that represent feasible solutions.” Sana discloses a GA which takes the parent population and performs crossover and mutation to reproduce.) and (Mutation, pp. 2072; “The mutation probability (Tm) is related to the number of individuals which would be modified. The process consists of selecting at random one rotation and two workers, and exchanging the jobs assigned to the workers in that rotation (Fig. 3). The mutation intensity (Im) is the number of modifications on an individual who has been selected at random within the population.” This is the disclosed Mutation process in Sana.)
“utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate the child population, wherein the new candidate solutions are organized into hierarchical groups, and duplicates from the new candidates are removed;” (Algorithm 2. Pseudo code improved NSGA II, pp. 2074; Sana discloses the pseudo-code for their algorithm. As seen at line 5. a, the system will evaluate the generated children, individuals, and remove duplicate instances. Next, at lines 5. b-c the system will select individuals from a sorted cluster of parent nodes for comparison and sort the newly generated children. This process is also disclosed in fig. 4 where the parent and child populations are sorted into groups of different granularities and then the populations are combined to generate a new populations for further system actions.)
“determining a new population from a total population comprising the parent population and the child population, using a custom multi-objective sorting technique, wherein the new population comprises top-performing solutions;” (Parameterization algorithm, pp. 2073; “Before finalizing a generation algorithm, pre-selection process and preservation of elite solutions are involved by getting the set of solutions of Parents and descendants obtained by operator’s selection, crossover and mutation. Thus the current population increases at twice individuals of the initial population. This requires sorting of the complete set in their respective front’s dominance and preserves individuals belonging to the fronts of better quality. If it is not possible to enter all the alternatives of a particular front, then those individuals are eliminated with a smaller crowding distance. The sequence of algorithm 2 of improved NSGA-II is schematized in Fig. 4.” As seen in fig. 4, the system will combine the parent and child population. After this the populations are processed and they are further sorted. This is seen in Algorithm 2 line 5.)
Sana fails to explicitly disclose:
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem;”
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.”
However, Cai discloses, “receiving inputs constraints associated with supply and demand sides, for the scheduling problem;” (Model, pp. 361; “There are two types of jobs, requiring respectively
D
1
t
and
D
2
t
staff at time t, where t = 1; 2; ...; T, and T is the scheduling horizon. Meanwhile, there are three types of staff. The first type of staff having skill 1, can be assigned to do type-1 job, and the second type of staff having skill 2, can be assigned to do type-2 job. The third type of staff having a mixed skill, can be assigned to do either type-1 or type-2 job. The general case with more than two types of jobs and/or three types of staff can be tackled similarly by generalizing the ideas we describe in this paper. The demands D1t and D2t are deterministically known in advance.” Cai discloses a GA which they apply to a similar scheduling problem. Their network contains initialization and input of constraints for the different schedules, such as jobs, types of staff, and time.)
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.” (Heuristic to ensure feasibility, pp. 366; “In the scheduling problem, there are numerous constraints. The procedure firstly selects the one that is violated the most. It then chooses a schedule capable of reducing the violation, and then increases the number of workers assigned to the schedule by one. If this makes the total number of type-3 staff exceed the bound, it will give up the schedule and choose the next schedule accordingly. The process repeats until all constraints are satisfied. If there are multiple schedules that can be altered to make the chromosome feasible, the heuristic will choose the best one, as determined by examining the three criteria successively.” Cai discloses an iterative process that will update different schedules to meet the given demands. This system will adjust the different schedules and staff accordingly.)
Regarding claim 12, Cai discloses, “further comprising determining if the pre-determined iterations are attained to determine whether the optimization has achieved pre-defined results or further iterations are required, wherein the determination is one of a successful determination or an unsuccessful determination.” (Remarks, pp. 364; “Note that when the population size tends to infinity, the sum of
q
(
1
-
q
)
i
-
1
over all i is equal to
q
/
(
1
-
1
-
q
)
=
1
. For considerably large population size, the sum is also nearly equal to 1. The actual value for q may be determined by a trial-and-error approach using the actual data of the problem to be solved. A too large value for q implies that those good individuals will have a large chance to generate more offspring which will dominate the population rapidly, and thus result in the so-called premature convergence of the GA approach. However, a too small value q will make the convergence very slow. According to our computational experience, we suggest to choose q in a range [0.05, 0.15].” Cai discloses stopping criteria for their algorithm. This system will iterate x number of times until convergence is defined to be within a defined set of values. This process is disclosed in Fig. 1 starting at line 1 with “WHILE NOT finished DO” and iteratively loops until “IF the population has converged”. Successful convergence value is interpreted to be within the given range and unsuccessful convergence is interpreted to be outside the given range.)
Regarding claim 13, Cai discloses, “at least one of: upon the successful determination, rendering final schedules with optimal statistics and trade-offs as output; and” (Introduction, pp. 359; “The main purpose of this article is to develop a model to tackle the staff scheduling problem, to take into account some practically important factors. Specifically, we will formulate the problem as a multi-criteria model, where the primary objective is to minimize the overall cost for assigning staff to meet the manpower demands, the secondary objective is to maximize the surplus of staff when assigning cost is almost same, and the tertiary objective seeks to minimize the variation of surplus staff over different time periods.” As seen in Algorithm 1, the overall output of this system is optimized schedules for individuals. This occurs when the population has converged, and the while loop is broken and the final result can be output.)
“upon the unsuccessful determination, updating the probabilistic parameters of the GA during runtime using the runtime adapter.” (Heuristic to ensure feasibility, pp. 366; “The procedure firstly selects the one that is violated the most. It then chooses a schedule capable of reducing the violation, and then increases the number of workers assigned to the schedule by one. If this makes the total number of type-3 staff exceed the bound, it will give up the schedule and choose the next schedule accordingly. The process repeats until all constraints are satisfied. If there are multiple schedules that can be altered to make the chromosome feasible, the heuristic will choose the best one, as determined by examining the three criteria successively.” As seen in Algorithm 1, the system will initiate a while loop that will break when the population has converged.)
Regarding claim 15, Sana discloses, “wherein the custom multi-objective sorting technique is used to: combine the parent population and the child population;” (Fig. 4, 2073; As seen in the figure, the parent and child populations are combined.)
“sort the combination based on multiple optimization objectives; and” (Parameterization algorithm, pp. 2073; “Before finalizing a generation algorithm, pre-selection process and preservation of elite solutions are involved by getting the set of solutions of Parents and descendants obtained by operator’s selection, crossover and mutation. Thus the current population increases at twice individuals of the initial population. This requires sorting of the complete set in their respective front’s dominance and preserves individuals belonging to the fronts of better quality. If it is not possible to enter all the alternatives of a particular front, then those individuals are eliminated with a smaller crowding distance. The sequence of algorithm 2 of improved NSGA-II is schematized in Fig. 4.” Sana discloses a system will sort the parent and child possibilities using non-dominated sorting and their “crowd distancing” metric.)
“select the top-performing solutions for a next generation.” (Solution method, pp. 2070; “For each generation, the fitness of individuals in the population is estimated by an objective function. The fittest individuals are selected to form the next generation. This process repeats for several cycles (generations) individuals to provide better solutions to the problem. This section proposes a methodology for developing job rotation schedules considering simultaneously ergonomic constraints in repetitive works, lifting tasks and awkward postures. Next, the proposed MOEA is described by using a multi-objective NSGA-II.” After the child and parent populations are combined and sorted, the fittest possibilities are selected and used in the next generation.)
Regarding claim 16, Sana discloses, “further comprising classifying the new population into groups based on whether a crossover or a mutation is performed, facilitating dynamic adaptation of GA parameters to different operators.” (Reproduction, pp. 2072; “The crossover probability ( Tc) determines the number of individuals in the next generation. Figure 2 shows the reproduction process. The crossover point is chosen at random by crossing point size content of rotations, and to share information between two parents’ individuals to generate two descendants that represent feasible solutions.”) and (Mutation, pp. 2072; “The mutation probability (Tm) is related to the number of individuals which would be modified. The process consists of selecting at random one rotation and two workers, and exchanging the jobs assigned to the workers in that rotation (Fig. 3). The mutation intensity (Im) is the number of modification on an individual who has been selected at random within the population.” This system will perform crossover and mutation on certain parent/children when generating new children. As stated, randomly two workers are selected and classified to be mutated. They are separate process from the crossover process.)
Regarding claim 17, Sana discloses, “wherein summary statistics are computed for each group of the groups, and wherein computation of the summary statistics provides insight into performance of different subsets of solutions, and guides adjustments for the probabilistic parameters.” (Parameterization algorithm, pp. 2073; “Now, the improved process is being described in the new NSGA- II, where the diversification of the population is sought, after the conformation of the Pareto fronts and to relate the crowding distance. The algorithm develops a search on the entire population and on each of the fronts avoiding similar results. Thus, results are changed by Pareto front previous generation or random (best complying with the crowding distance) result. This strategy allows a better diversification of the population and avoids the premature convergence of the algorithm. The improved pseudo code is presented below.” This system will evaluate the new population and using that evaluation the overall global parameters are updated using information from the previous generation.)
Regarding claim 18, Cai discloses, “wherein adjustments to be made in each probabilistic parameter are calculated based on the summary statistics obtained in a previous step, allowing for informed adjustments to GA's behavior.” (Heuristic to ensure feasibility, pp. 366; “In the scheduling problem, there are numerous constraints. The procedure firstly selects the one that is violated the most. It then chooses a schedule capable of reducing the violation, and then increases the number of workers assigned to the schedule by one. If this makes the total number of type-3 staff exceed the bound, it will give up the schedule and choose the next schedule accordingly. The process repeats until all constraints are satisfied. If there are multiple schedules that can be altered to make the chromosome feasible, the heuristic will choose the best one, as determined by examining the three criteria successively.” This system will adjust parameters for the next generation based on what schedules are needed or what tasks are remaining. This allows the system to adjust the nature of tasks.)
Regarding claim 19, Cai discloses, “wherein the probabilistic parameters are adjusted based on contribution of hyper-parameters in previous iterations to improve convergence and solution quality, leveraging past performance to inform future parameter adjustments.” (Fig. 1, 363; As stated above, the population in the article will undergo crossover and mutation to generate and alter members of the population. After this the fitness of the offspring is evaluated and rolled into the new generation. This system will learn from prior generations and further iterate until convergence value is within a threshold.)
Regarding claim 20, Sana discloses, “A non-transitory computer-readable storage medium having stored thereon computer executable instruction which when executed by one or more processors, cause the one or more processors to carry out operations for optimizing performance of a Genetic Algorithm (GA) in solving a scheduling problem, the operations comprising perform the operations comprising:” (Generation the initial population of solutions, pp. 2070; “The algorithm initiates a set of solutions with a matrix scheme, where the size of the array is determined by the number of workers involved in the rotation (J) and the span of rotation is considered by intervals (T).” Sana discloses a random generation of the initial population, i.e. schedules. This is the start of an iterative process to optimize workers schedules for various health reasons.)
“generating a parent population for the GA, wherein the parent population comprises a collection of potential solutions to the scheduling problem;” (Selection and replacement of individuals within the population, pp. 2072, “The algorithm uses a technique for direct inclusion, where the new generation among new parents and offspring is selected. When the parents improve descendants, it replaces these entirely by the parents. Otherwise, the descendants are taken within the next generation, adding the best parents to complete the population.” An initial set of parent schedules are initialized and processed by the system. After processing, sorting, reproduction and selection of the next generation the process is repeated, and a new set of parents are selected to generate the next iteration. Further, see Fig. 4 which discloses the generation of parent populations.)
“creating a child population via evolution using current probabilistic parameters comprising crossover and mutation operators, wherein new candidate solutions are produced by combining or modifying solutions of the collection of potential solutions from the parent population;” (Reproduction, pp. 2072; “The crossover probability (Tc) determines the number of individuals in the next generation. Figure 2 shows the reproduction process. The crossover point is chosen at random by crossing point size content of rotations, and to share information between two parents’ individuals to generate two descendants that represent feasible solutions.” Sana discloses a GA which takes the parent population and performs crossover and mutation to reproduce.) and (Mutation, pp. 2072; “The mutation probability (Tm) is related to the number of individuals which would be modified. The process consists of selecting at random one rotation and two workers, and exchanging the jobs assigned to the workers in that rotation (Fig. 3). The mutation intensity (Im) is the number of modifications on an individual who has been selected at random within the population.” This is the disclosed Mutation process in Sana.)
“utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate the child population, wherein the new candidate solutions are organized into hierarchical groups, and duplicates from the new candidates are removed;” (Algorithm 2. Pseudo code improved NSGA II, pp. 2074; Sana discloses the pseudo-code for their algorithm. As seen at line 5. a, the system will evaluate the generated children, individuals, and remove duplicate instances. Next, at lines 5. b-c the system will select individuals from a sorted cluster of parent nodes for comparison and sort the newly generated children. This process is also disclosed in fig. 4 where the parent and child populations are sorted into groups of different granularities and then the populations are combined to generate a new populations for further system actions.)
“determining a new population from a total population comprising the parent population and the child population, using a custom multi-objective sorting technique, wherein the new population comprises top-performing solutions;” (Parameterization algorithm, pp. 2073; “Before finalizing a generation algorithm, pre-selection process and preservation of elite solutions are involved by getting the set of solutions of Parents and descendants obtained by operator’s selection, crossover and mutation. Thus the current population increases at twice individuals of the initial population. This requires sorting of the complete set in their respective front’s dominance and preserves individuals belonging to the fronts of better quality. If it is not possible to enter all the alternatives of a particular front, then those individuals are eliminated with a smaller crowding distance. The sequence of algorithm 2 of improved NSGA-II is schematized in Fig. 4.” As seen in fig. 4, the system will combine the parent and child population. After this the populations are processed and they are further sorted. This is seen in Algorithm 2 line 5.)
Sana fails to explicitly disclose:
“receiving inputs constraints associated with supply and demand sides, for the scheduling problem; initializing a set of schedules using an initializer that sets an initial set of solutions for the GA to start the optimization;”
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.”
However, Cai discloses, “receiving inputs constraints associated with supply and demand sides, for the scheduling problem; initializing a set of schedules using an initializer that sets an initial set of solutions for the GA to start the optimization;” (Model, pp. 361; “There are two types of jobs, requiring respectively
D
1
t
and
D
2
t
staff at time t, where t = 1; 2; ...; T, and T is the scheduling horizon. Meanwhile, there are three types of staff. The first type of staff having skill 1, can be assigned to do type-1 job, and the second type of staff having skill 2, can be assigned to do type-2 job. The third type of staff having a mixed skill, can be assigned to do either type-1 or type-2 job. The general case with more than two types of jobs and/or three types of staff can be tackled similarly by generalizing the ideas we describe in this paper. The demands D1t and D2t are deterministically known in advance.” Cai discloses a GA which they apply to a similar scheduling problem. Their network contains initialization and input of constraints for the different schedules, such as jobs, types of staff, and time.)
“updating probabilistic parameters of the GA during runtime using a runtime adapter, when pre-determined iterations unattained, wherein the probabilistic parameters are updated iteratively until an optimized schedule is attained.” (Heuristic to ensure feasibility, pp. 366; “In the scheduling problem, there are numerous constraints. The procedure firstly selects the one that is violated the most. It then chooses a schedule capable of reducing the violation, and then increases the number of workers assigned to the schedule by one. If this makes the total number of type-3 staff exceed the bound, it will give up the schedule and choose the next schedule accordingly. The process repeats until all constraints are satisfied. If there are multiple schedules that can be altered to make the chromosome feasible, the heuristic will choose the best one, as determined by examining the three criteria successively.” Cai discloses an iterative process that will update different schedules to meet the given demands. This system will adjust the different schedules and staff accordingly.)
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sana and Cai in view of Kung et al, (Kung et al, “An improved Monte Carlo Tree Search approach to workflow scheduling”, Apr. 7th, 2022, hereinafter “Kung”).
Regarding claim 4, Kung discloses, “wherein the initializer is a Monte Carlo Tree Search (MCTS) initializer.” (Algorithm 2: Improved MCTS for workflow scheduling, pp. 1233; The system in Kung utilizes a Monte Carlo Tree Search algorithm and combines it with genetic algorithm architecture. The algorithm discloses a process of initializing a set of workflows and over many iterations an optimized workflow is generated in lines 5-10.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Sana, Cai, and Kung. Sana teaches a genetic algorithm system that is applied to job scheduling. This system uses GA concepts such as population generation, crossover/mutation and development to optimize a healthy job schedule based on the strain different jobs apply to workers. Cai teaches a genetic algorithm that is also used for job scheduling. Cai teaches a method that uses mixed constraints for scheduling staff including difficulty of tasks and ability of the staff. Kung teaches the use of a Monte Carlo Tree Search system in conjunction with genetic and evolutionary algorithms. One of ordinary skill would have motivation to research common subjects such as the genetic algorithms for optimization of schedules based on multiple outside constraints and systems that use special search algorithms to introduce randomness into genetic algorithms, “Monte Carlo Tree Search (MCTS) is a promising direction for workflow scheduling but was less explored in previous studies. In this paper, we present and evaluate several new mechanisms to further improve the effectiveness of MCTS when applied to workflow scheduling, including a new pruning algorithm and new heuristics for guiding the selection, expansion and simulation steps in MCTS. Performance evaluation was conducted with simulation experiments based on both synthetic and real-world workflow application structures. The experimental results show that our new approaches can achieve better workflow execution schedules than the previous method MCTS-BB in (Liu et al., 2019).” (Kung, Conclusion and future work, pp. 1249)
Regarding claim 14, Kung discloses, “wherein the initializer is a Monte Carlo Tree Search (MCTS) initializer.” (Algorithm 2: Improved MCTS for workflow scheduling, pp. 1233; The system in Kung utilizes a Monte Carlo Tree Search algorithm and combines it with genetic algorithm architecture. The algorithm discloses a process of initializing a set of workflows and over many iterations an optimized workflow is generated in lines 5-10.)
Conclusion
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/HASSAN MRABI/Primary Examiner, Art Unit 2147
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