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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/29/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 8-14 are objected to because of the following informalities:
In claim 8, line 4, “a nominal version an uncertain optimization problem” should “a nominal version of an uncertain optimization problem”
Dependent claims 9-14 are objected based on being directly or indirectly dependent on objected claim 8.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the uncertain constraint” in line 8. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the uncertain constraint” has been interpreted as “an uncertain constraint”.
Claim 4 recites the limitation “the initial problem” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the initial problem” has been interpreted as “an initial problem”.
Claim 4 recites the limitation “the updated nominal solution” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the updated nominal solution” has been interpreted as “an updated nominal solution”.
Claim 8 recites the limitation “the uncertain constraint” in line 7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the uncertain constraint” has been interpreted as “an uncertain constraint”.
Claim 11 recites the limitation “the initial problem” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the initial problem” has been interpreted as “an initial problem”.
Claim 11 recites the limitation “the updated nominal solution” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the updated nominal solution” has been interpreted as “an updated nominal solution”.
Claim 15 recites the limitation “the uncertain constraint” in line 9. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the uncertain constraint” has been interpreted as “an uncertain constraint”.
Claim 18 recites the limitation “the initial problem” in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the initial problem” has been interpreted as “an initial problem”.
Claim 18 recites the limitation “the updated nominal solution” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the updated nominal solution” has been interpreted as “an updated nominal solution”.
Dependent claims 2-7 are rejected based on being directly or indirectly dependent on rejected claim 1.
Dependent claims 9-14 are rejected based on being directly or indirectly dependent on rejected claim 8.
Dependent claims 16-20 are rejected based on being directly or indirectly dependent on rejected claim 15.
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.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution”
“adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem”
“finding an optimal solution to the updated nominal problem”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining an unsatisfied constraint based on determining that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution to determine an unsatisfied constraint); adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate an updated nominal problem by adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem); and finding an optimal solution to the updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can find an optimal solution to the updated nominal problem).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a processor system”
“a memory”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitation:
“accessing an initial optimal solution to a nominal version of an uncertain optimization problem”
As drafted, is an additional element that corresponds to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“performing one or more additional iterations of the processor system operations”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass performing one or more additional iterations of the determining, adding, and finding operations (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can perform additional iterations of the determining, adding, and finding operations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic processor system and memory, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that all robust counterparts of constraints of the updated nominal problem are satisfied).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 2 of a generic processor system and memory, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 2 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass based on determining that all robust counterparts of constraints of the initial problem are satisfied, determining that the updated nominal solution is optimal (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that the updated nominal solution is optimal based on determining that all robust counterparts of constraints of the initial problem are satisfied).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 3 of a generic processor system and memory, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 3 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 3. The limitations of claim 5 are only additional elements to the abstract ideas of claim 3.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a linear problem solver (LPS) system”
“the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS)”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 3 of a generic processor system and memory, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 3 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system, LPS system, problem solvers, and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 5. The limitations of claim 6 are only additional elements to the abstract ideas of claim 5.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“wherein the RCPS comprises a robust counterpart computation (RCC) reduction algorithm”
As drafted, is an additional element that amounts to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 5 of a generic processor system, LPS system, problem solvers, and memory, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 5 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system, LPS system, problem solvers, and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“wherein the updated nominal problem is generated using a simplex analysis technique”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass using a simplex analysis technique to generate the updated nominal problem (corresponds to mathematical calculations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic processor system and memory, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution”
“adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem”
“finding an optimal solution to the updated nominal problem”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining an unsatisfied constraint based on determining that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution to determine an unsatisfied constraint); adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate an updated nominal problem by adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem); and finding an optimal solution to the updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can find an optimal solution to the updated nominal problem).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“computer”
As drafted, is an additional element that amounts to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitation:
“accessing an initial optimal solution to a nominal version an uncertain optimization problem”
As drafted, is an additional element that corresponds to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“performing one or more additional iterations of the processor system operations”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass performing one or more additional iterations of the determining, adding, and finding operations (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can perform additional iterations of the determining, adding, and finding operations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 8 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 8 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that all robust counterparts of constraints of the updated nominal problem are satisfied).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 9 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 9 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass based on determining that all robust counterparts of constraints of the initial problem are satisfied, determining that the updated nominal solution is optimal (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that the updated nominal solution is optimal based on determining that all robust counterparts of constraints of the initial problem are satisfied).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 10 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 10 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 10. The limitations of claim 12 are only additional elements to the abstract ideas of claim 10.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“using a linear problem solver (LPS) system”
“the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS)”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The recitation of additional elements in claim 10 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 10 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, LPS system, and problem solvers for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 12. The limitations of claim 13 are only additional elements to the abstract ideas of claim 12.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitation:
“wherein the RCPS comprises a robust counterpart computation (RCC) reduction algorithm”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The recitation of additional elements in claim 12 of a generic computer, LPS system, and problem solvers, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 12 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer, LPS system, and problem solvers for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“wherein the updated nominal problem is generated using a simplex analysis technique”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass using a simplex analysis technique to generate the updated nominal problem (corresponds to mathematical calculations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 8 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 8 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a computer program product stored on a computer readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution”
“adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem”
“finding an optimal solution to the updated nominal problem”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining an unsatisfied constraint based on determining that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution to determine an unsatisfied constraint); adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate an updated nominal problem by adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem); and finding an optimal solution to the updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can find an optimal solution to the updated nominal problem).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a computer readable storage medium”
“a processor system”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“accessing an initial optimal solution to a nominal version of an uncertain optimization problem”
As drafted, is an additional element that corresponds to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and computer readable storage medium for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a computer program product stored on a computer readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“performing one or more additional iterations of the processor system operations”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass performing one or more additional iterations of the determining, adding, and finding operations (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can perform additional iterations of the determining, adding, and finding operations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 15 of a generic processor system and computer readable storage medium, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 15 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and computer readable storage medium for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a computer program product stored on a computer readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that all robust counterparts of constraints of the updated nominal problem are satisfied).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 16 of a generic processor system and computer readable storage medium, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 16 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and computer readable storage medium for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a computer program product stored on a computer readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass based on determining that all robust counterparts of constraints of the initial problem are satisfied, determining that the updated nominal solution is optimal (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can determine that the updated nominal solution is optimal based on determining that all robust counterparts of constraints of the initial problem are satisfied).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 17 of a generic processor system and computer readable storage medium, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 17 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and computer readable storage medium for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a computer program product stored on a computer readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 17. The limitations of claim 19 are only additional elements to the abstract ideas of claim 17.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“the processor system comprises a linear problem solver (LPS) system”
“the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS)”
“the RCPS comprises a robust counterpart computation (RCC) reduction algorithm”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The recitation of additional elements in claim 17 of a generic processor system and computer readable storage medium, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 17 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system, LPS system, problem solvers, and computer readable storage medium for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a computer program product stored on a computer readable storage medium, which is directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitation:
“wherein the updated nominal problem is generated using a simplex analysis technique”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass using a simplex analysis technique to generate the updated nominal problem (corresponds to mathematical calculations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 15 of a generic processor system and computer readable storage medium, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “accessing …” limitation of claim 15 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processor system and computer readable storage medium for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “accessing …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fujimaki et al. (US 2018/0267934 A1) in view of Zhang et al. ("A robust counterpart approach to the bi-objective emergency medical service design problem") and further in view of Bertsimas et al. ("Reformulation versus cutting-planes for robust optimization").
Regarding Claim 1,
Fujimaki et al. teaches a computer system for solving an optimization problem having uncertainty, the computer system comprising a processor system electronically coupled to a memory, wherein the processor system performs processor system operations ([0018]: "an information processing device, an information processing system, an information processing method, and a recording medium which can find out an optimum solution to the optimization problem including the high-dimensional uncertain data" teaches an information processing device (computer system) for solving an optimization problem having uncertainty. [0104]: "the information processing device 10 may be realized as a computer device which includes a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory)" teaches that the information processing device (computer system) comprises a CPU (processing system) coupled to a memory) comprising:
accessing an initial optimal solution to a nominal version of an uncertain optimization problem ([0033]: "In order to solve a predetermined optimization problem, the information processing device 10 computes an optimum solution (for example, an optimum solution of a control variable) to the optimization problem based on input information, and outputs the computed optimum solution to a predetermined device. The input information will be explained later in detail. Note that the control variable is a variable which is operable (controllable) in order to optimize an objective function which is an object in the optimization problem" teaches accessing an initial optimum solution (initial optimal solution) for a control variable of the optimization problem based on input information (nominal version of an uncertain optimization problem). [0037]: "Definition of the optimization problem (hereinafter, denoted as “O”) which includes, at least, uncertain data in a parameter" teaches that the optimization problem has uncertainty (uncertain optimization problem)).
Fujimaki et al. does not appear to explicitly teach determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution; adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem.
However, Zhang et al. teaches determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution (Section 2, first paragraph: "The robust counterpart approach [12] is a popular means to increase the robustness of a model. We present a brief introduction on this approach in this section. Consider the following problem subject to uncertain coefficients:
PNG
media_image1.png
88
892
media_image1.png
Greyscale
where ai = [ai1; ai2; ...; ain]T, bi ∈ R. In a typical deterministic model, we assume the exact values of C, ai and bi are known. The RC approach considers uncertain parameters in the model. Without loss of generality, we assume the uncertainty affect vector ai. According to [12], the RC of constraint (1) is derived by addressing the following mathematical programs:
PNG
media_image2.png
44
160
media_image2.png
Greyscale
where U represents an uncertainty set for ai. In this way, the solution is robust under uncertainty if the maximum value of the left-hand side of constraint (1) is still less than the right-hand side of the constraint" teaches determining if a robust counterpart (RC) is satisfied (e.g. solution is robust for that RC/constraint) for the solution (initial optimal solution), meaning that unsatisfied constraints are determined based on the RC not being robust/satisfied for the solution).
Fujimaki et al. and Zhang et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution as taught by Zhang et al. to the disclosed invention of Fujimaki et al.
One of ordinary skill in the art would have been motivated to make this modification because "the major advantages of the RC approach are: (1) We are not required to have the probability information about the uncertain parameters; and (2) A computational tractable formulation can be derived from the well-defined uncertainty sets" (Zhang et al. Section 2, last paragraph).
Fujimaki et al. in view of Zhang et al. does not appear to explicitly teach adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem.
However, Bertsimas et al. teaches adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches adding constraints and variables associated with the unsatisfied robust counterpart of the uncertain constrain to the master problem (nominal version of the uncertain optimization problem) to update the master problem (generate an updated nominal problem)); and
finding an optimal solution to the updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches finding the optimal robust solution (optimal solution) to the updated master problem (updated nominal problem)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 2,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer system of claim 1.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise performing one or more additional iterations of the processor system operations (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until no more constraints are added and an optimal robust solution (optimal solution) is found).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise performing one or more additional iterations of the processor system operations as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 3,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer system of claim 2.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise, during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until all robust counterparts of constraints of the updated master problem (updated nominal problem) are satisfied (e.g. no new constraints are added)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise, during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 4,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer system of claim 3.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise, based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until all robust counterparts of constraints of the master problem (initial problem) are satisfied (e.g. no new constraints are added) and determine that the updated solution x* (updated nominal solution) for the master problem is the optimal robust solution (updated nominal solution is optimal)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise, based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 5,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer system of claim 3.
In addition, Bertsimas et al. further teaches wherein: the computer system for solving the optimization problem having uncertainty comprises a linear problem solver (LPS) system (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches a linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty)); and
the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS) (Eq. 1; Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches the linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty) includes solving an optimization problem as shown in Eq. 1 (optimization problem solver (OPS)) and solving robust counterparts of constraints of the master problem (robust counterpart problem solver (RCPS))).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein: the computer system for solving the optimization problem having uncertainty comprises a linear problem solver (LPS) system; and the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS) as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 6,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer system of claim 5.
In addition, Bertsimas et al. further teaches wherein the RCPS comprises a robust counterpart computation (RCC) reduction algorithm (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches the linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty) includes solving robust counterparts of constraints of the master problem (robust counterpart problem solver (RCPS)) by only generating constraints as they are needed to ensure robustness of the solution during the algorithm (RCC reduction algorithm) (e.g. the number of robust counterparts of constraints for the algorithm are reduced because constraints are only generated as needed during the algorithm)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the RCPS comprises a robust counterpart computation (RCC) reduction algorithm as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 7,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer system of claim 1.
In addition, Bertsimas et al. further teaches wherein the updated nominal problem is generated using a simplex analysis technique (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method" teaches that the updated master problem (updated nominal problem) is generated using the dual simplex method (simplex analysis)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the updated nominal problem is generated using a simplex analysis technique as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 8,
Fujimaki et al. teaches a computer-implemented method for solving an optimization problem having uncertainty, wherein the computer-implemented method performs processor system operations ([0018]: "an information processing method, and a recording medium which can find out an optimum solution to the optimization problem including the high-dimensional uncertain data" teaches an information processing method (computer-implemented method) for solving an optimization problem having uncertainty) comprising:
accessing an initial optimal solution to a nominal version an uncertain optimization problem ([0033]: "In order to solve a predetermined optimization problem, the information processing device 10 computes an optimum solution (for example, an optimum solution of a control variable) to the optimization problem based on input information, and outputs the computed optimum solution to a predetermined device. The input information will be explained later in detail. Note that the control variable is a variable which is operable (controllable) in order to optimize an objective function which is an object in the optimization problem" teaches accessing an initial optimum solution (initial optimal solution) for a control variable of the optimization problem based on input information (nominal version of an uncertain optimization problem). [0037]: "Definition of the optimization problem (hereinafter, denoted as “O”) which includes, at least, uncertain data in a parameter" teaches that the optimization problem has uncertainty (uncertain optimization problem)).
Fujimaki et al. does not appear to explicitly teach determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution; adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem.
However, Zhang et al. teaches determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution (Section 2, first paragraph: "The robust counterpart approach [12] is a popular means to increase the robustness of a model. We present a brief introduction on this approach in this section. Consider the following problem subject to uncertain coefficients:
PNG
media_image1.png
88
892
media_image1.png
Greyscale
where ai = [ai1; ai2; ...; ain]T, bi ∈ R. In a typical deterministic model, we assume the exact values of C, ai and bi are known. The RC approach considers uncertain parameters in the model. Without loss of generality, we assume the uncertainty affect vector ai. According to [12], the RC of constraint (1) is derived by addressing the following mathematical programs:
PNG
media_image2.png
44
160
media_image2.png
Greyscale
where U represents an uncertainty set for ai. In this way, the solution is robust under uncertainty if the maximum value of the left-hand side of constraint (1) is still less than the right-hand side of the constraint" teaches determining if a robust counterpart (RC) is satisfied (e.g. solution is robust for that RC/constraint) for the solution (initial optimal solution), meaning that unsatisfied constraints are determined based on the RC not being robust/satisfied for the solution).
Fujimaki et al. and Zhang et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution as taught by Zhang et al. to the disclosed invention of Fujimaki et al.
One of ordinary skill in the art would have been motivated to make this modification because "the major advantages of the RC approach are: (1) We are not required to have the probability information about the uncertain parameters; and (2) A computational tractable formulation can be derived from the well-defined uncertainty sets" (Zhang et al. Section 2, last paragraph).
Fujimaki et al. in view of Zhang et al. does not appear to explicitly teach adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem.
However, Bertsimas et al. teaches adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches adding constraints and variables associated with the unsatisfied robust counterpart of the uncertain constrain to the master problem (nominal version of the uncertain optimization problem) to update the master problem (generate an updated nominal problem)); and
finding an optimal solution to the updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches finding the optimal robust solution (optimal solution) to the updated master problem (updated nominal problem)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 9,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer-implemented method of claim 8.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise performing one or more additional iterations of the processor system operations (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until no more constraints are added and an optimal robust solution (optimal solution) is found).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise performing one or more additional iterations of the processor system operations as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 10,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer-implemented method of claim 9.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise, during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until all robust counterparts of constraints of the updated master problem (updated nominal problem) are satisfied (e.g. no new constraints are added)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise, during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 11,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer-implemented method of claim 10.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise, based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until all robust counterparts of constraints of the master problem (initial problem) are satisfied (e.g. no new constraints are added) and determine that the updated solution x* (updated nominal solution) for the master problem is the optimal robust solution (updated nominal solution is optimal)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise, based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 12,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer-implemented method of claim 10.
In addition, Bertsimas et al. further teaches wherein: the computer-implemented method for solving the optimization problem having uncertainty comprises using a linear problem solver (LPS) system (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches a linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty)); and
the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS) (Eq. 1; Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches the linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty) includes solving an optimization problem as shown in Eq. 1 (optimization problem solver (OPS)) and solving robust counterparts of constraints of the master problem (robust counterpart problem solver (RCPS))).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein: the computer-implemented method for solving the optimization problem having uncertainty comprises using a linear problem solver (LPS) system; and the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS) as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 13,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer-implemented method of claim 12.
In addition, Bertsimas et al. further teaches wherein the RCPS comprises a robust counterpart computation (RCC) reduction algorithm (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches the linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty) includes solving robust counterparts of constraints of the master problem (robust counterpart problem solver (RCPS)) by only generating constraints as they are needed to ensure robustness of the solution during the algorithm (RCC reduction algorithm) (e.g. the number of robust counterparts of constraints for the algorithm are reduced because constraints are only generated as needed during the algorithm)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the RCPS comprises a robust counterpart computation (RCC) reduction algorithm as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 14,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer-implemented method of claim 8.
In addition, Bertsimas et al. further teaches wherein the updated nominal problem is generated using a simplex analysis technique (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method" teaches that the updated master problem (updated nominal problem) is generated using the dual simplex method (simplex analysis)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the updated nominal problem is generated using a simplex analysis technique as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 15,
Fujimaki et al. teaches a computer program product for solving an optimization problem having uncertainty, wherein the computer program product comprises a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor system to perform processor system operations ([0022]: "A non-transitory computer-readable recording medium according to one aspect of the present invention records a program. The program makes a computer execute" teaches a recording medium (computer readable storage medium) that stores a program (computer program product) for execution by a computer (processor system) for performing aspects of the invention. [0018]: "a recording medium which can find out an optimum solution to the optimization problem including the high-dimensional uncertain data" teaches the recording medium (computer readable storage medium) for solving an optimization problem having uncertainty.) comprising:
accessing an initial optimal solution to a nominal version of an uncertain optimization problem ([0033]: "In order to solve a predetermined optimization problem, the information processing device 10 computes an optimum solution (for example, an optimum solution of a control variable) to the optimization problem based on input information, and outputs the computed optimum solution to a predetermined device. The input information will be explained later in detail. Note that the control variable is a variable which is operable (controllable) in order to optimize an objective function which is an object in the optimization problem" teaches accessing an initial optimum solution (initial optimal solution) for a control variable of the optimization problem based on input information (nominal version of an uncertain optimization problem). [0037]: "Definition of the optimization problem (hereinafter, denoted as “O”) which includes, at least, uncertain data in a parameter" teaches that the optimization problem has uncertainty (uncertain optimization problem)).
Fujimaki et al. does not appear to explicitly teach determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution; adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem.
However, Zhang et al. teaches determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution (Section 2, first paragraph: "The robust counterpart approach [12] is a popular means to increase the robustness of a model. We present a brief introduction on this approach in this section. Consider the following problem subject to uncertain coefficients:
PNG
media_image1.png
88
892
media_image1.png
Greyscale
where ai = [ai1; ai2; ...; ain]T, bi ∈ R. In a typical deterministic model, we assume the exact values of C, ai and bi are known. The RC approach considers uncertain parameters in the model. Without loss of generality, we assume the uncertainty affect vector ai. According to [12], the RC of constraint (1) is derived by addressing the following mathematical programs:
PNG
media_image2.png
44
160
media_image2.png
Greyscale
where U represents an uncertainty set for ai. In this way, the solution is robust under uncertainty if the maximum value of the left-hand side of constraint (1) is still less than the right-hand side of the constraint" teaches determining if a robust counterpart (RC) is satisfied (e.g. solution is robust for that RC/constraint) for the solution (initial optimal solution), meaning that unsatisfied constraints are determined based on the RC not being robust/satisfied for the solution).
Fujimaki et al. and Zhang et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining an unsatisfied constraint based at least in part on a determination that a robust counterpart of the uncertain constraint is not satisfied for the initial optimal solution as taught by Zhang et al. to the disclosed invention of Fujimaki et al.
One of ordinary skill in the art would have been motivated to make this modification because "the major advantages of the RC approach are: (1) We are not required to have the probability information about the uncertain parameters; and (2) A computational tractable formulation can be derived from the well-defined uncertainty sets" (Zhang et al. Section 2, last paragraph).
Fujimaki et al. in view of Zhang et al. does not appear to explicitly teach adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem.
However, Bertsimas et al. teaches adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches adding constraints and variables associated with the unsatisfied robust counterpart of the uncertain constrain to the master problem (nominal version of the uncertain optimization problem) to update the master problem (generate an updated nominal problem)); and
finding an optimal solution to the updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches finding the optimal robust solution (optimal solution) to the updated master problem (updated nominal problem)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate adding variables and constraints associated with the robust counterpart of the uncertain constraint that is not satisfied to the nominal version of the uncertain optimization problem to generate an updated nominal problem; and finding an optimal solution to the updated nominal problem as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 16,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer program product of claim 15.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise performing one or more additional iterations of the processor system operations (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until no more constraints are added and an optimal robust solution (optimal solution) is found).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise performing one or more additional iterations of the processor system operations as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 17,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer program product of claim 16.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise, during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until all robust counterparts of constraints of the updated master problem (updated nominal problem) are satisfied (e.g. no new constraints are added)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise, during one of the one or more additional iterations of the processor system operations, determining that all robust counterparts of constraints of the updated nominal problem are satisfied for the updated nominal problem as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 18,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer program product of claim 17.
In addition, Bertsimas et al. further teaches wherein the processor system operations further comprise, based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal (Section 2.1, first paragraph: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2" teaches that the algorithm steps (processor system operations) perform additional iterations until all robust counterparts of constraints of the master problem (initial problem) are satisfied (e.g. no new constraints are added) and determine that the updated solution x* (updated nominal solution) for the master problem is the optimal robust solution (updated nominal solution is optimal)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the processor system operations further comprise, based at least in part on determining that all robust counterparts of constraints of the initial problem are satisfied, further determining that the updated nominal solution is optimal as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 19,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer program product of claim 17.
In addition, Bertsimas et al. further teaches wherein: the processor system comprises a linear problem solver (LPS) system (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches a linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty));
the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS) (Eq. 1; Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches the linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty) includes solving an optimization problem as shown in Eq. 1 (optimization problem solver (OPS)) and solving robust counterparts of constraints of the master problem (robust counterpart problem solver (RCPS))); and
the RCPS comprises a robust counterpart computation (RCC) reduction algorithm (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method. As a result almost all commercial and open-source LO solvers can be used to solve the master problem" teaches the linear optimization (LO) solver (linear problem solver) for solving the master problem (optimization problem having uncertainty) includes solving robust counterparts of constraints of the master problem (robust counterpart problem solver (RCPS)) by only generating constraints as they are needed to ensure robustness of the solution during the algorithm (RCC reduction algorithm) (e.g. the number of robust counterparts of constraints for the algorithm are reduced because constraints are only generated as needed during the algorithm)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein: the processor system comprises a linear problem solver (LPS) system; the LPS system comprises an optimization problem solver (OPS) and a robust counterpart problem solver (RCPS); and the RCPS comprises a robust counterpart computation (RCC) reduction algorithm as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Regarding Claim 20,
Fujimaki et al. in view of Zhang et al. and further in view of Bertsimas et al. teaches the computer program product of claim 15.
In addition, Bertsimas et al. further teaches wherein the updated nominal problem is generated using a simplex analysis technique (Section 2.1, first-second paragraphs: "While there is a constraint for every
a
~
in the relevant uncertainty set, only a small subset of these constraints is binding for a robust optimal solution. This suggests that only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique. Given this motivation, the algorithm for RLO problems is as follows:
1. Initialize the master problem to be the nominal problem, that is the problem described in Eq. (1) where all
a
~
ij are replaced with their nominal values aij.
2. Solve the master problem, obtaining a solution x*.
3. For each uncertain row i (that is, rows i such that Ji ≠ ∅)
(a) Compute
a
-
= argmaxa˜∈ Ui
a
~
Tx*.
(b) If
a
-
Tx* > bi + ϵ, add the constraint
a
-
Tx ≤ bi to the master problem.
4. If no constraints were added then we declare that x* is the optimal robust solution to the RO and terminate. If any constraints were added we return to Step 2. The computational practicality of this method relies on the fact that while adding constraints will make the current master problem solution infeasible, we are able to hot-start the optimization (in Step 2) by using the dual simplex method" teaches that the updated master problem (updated nominal problem) is generated using the dual simplex method (simplex analysis)).
Fujimaki et al., Zhang et al., and Bertsimas et al. are analogous to the claimed invention because they are directed towards solving robust optimization problems having uncertainty.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the updated nominal problem is generated using a simplex analysis technique as taught by Bertsimas et al. to the disclosed invention of Fujimaki et al. in view of Zhang et al.
One of ordinary skill in the art would have been motivated to make this modification because "only generating constraints as they are needed to ensure robustness of the solution would be an efficient technique" (Bertsimas et al. Section 2.1 first paragraph).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN J HALES whose telephone number is (571)272-0878. The examiner can normally be reached M-F 9:00am - 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRIAN J HALES/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
Prosecution Insights