Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Oct. 13, 2023.
Claims 1-20 are pending in the case. Claims 1, 8 and 15 are independent claims.
is mere insignificant extra solution activity and something the courts have recognized as being well-understood, routine and conventional.
Claim Rejections - 35 U.S.C. § 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.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “receiving a problem definition comprising a set of variables, an objective function defined over the set of variables, and one or more constraint functions, each of the constraint functions defined by at least one variable of the set of variables” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “initializing an optimization algorithm, a sample solution to the objective function, and one or more penalty parameters corresponding to each of the constraint functions” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “iteratively until a termination criteria is met: incrementing the optimization algorithm; for each variable in the set of variables: sampling an updated value for the variable; evaluating a feasibility result of each constraint function defined by the variable; updating a problem feasibility result, the problem feasibility result comprising the feasibility result of each of the constraint functions; and for each constraint function defined by the variable where the constraint function was not feasible, increasing the penalty parameter by a first rate; after updating each variable in the set of variables, evaluating the problem feasibility result; when the problem feasibility result indicates feasibility was encountered for all constraint functions, decreasing all penalty parameters by a second rate; storing each updated penalty parameter; evaluating the termination criteria” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “when the termination criteria is met, outputting a solution comprising an updated set of variables” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “operation of a computing system to direct a search space towards feasibility to improve performance of the computing system, the computing system comprising one or more processors” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “operation of a computing system to direct a search space towards feasibility to improve performance of the computing system, the computing system comprising one or more processors” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Claims 2, 6, 7 are dependent on claim 1 and includes all the limitations of claim 1. Therefore, claims 2-7 recite the same abstract idea. The claims recite additional limitations regarding quantum processing, but do not otherwise add any meaningful limits beyond the abstract idea. The quantum processing is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Claims 3, 4, 5 are dependent on claim 1 and includes all the limitations of claim 1. Therefore, claims 3, 4, 5 recite the same abstract idea. The claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “receives a problem definition comprising a set of variables, an objective function defined over the set of variables, and one or more constraint functions, each of the constraint functions defined by at least one variable of the set of variables” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “initializes an optimization algorithm, a sample solution to the objective function, and one or more penalty parameters corresponding to each of the constraint functions” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “iteratively until a termination criteria is met: increments … when the problem feasibility result indicates feasibility was encountered for all constraint functions, decreases all penalty parameters by a second rate; stores each updated penalty parameter” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “evaluates the termination criteria; and when the termination criteria is met, outputs a solution comprising an updated set of variables” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “least one non-transitory processor-readable medium that stores at least one of processor executable instructions and data; and at least one processor communicatively coupled to the least one non-transitory processor-readable medium, which, in response to execution of the at least one of processor executable instructions and data, the processor” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “least one non-transitory processor-readable medium that stores at least one of processor executable instructions and data; and at least one processor communicatively coupled to the least one non-transitory processor-readable medium, which, in response to execution of the at least one of processor executable instructions and data, the processor” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Claims 9, 13, 14 are dependent on claim 8 and includes all the limitations of claim 8. Therefore, claims 9, 13, 14 recite the same abstract idea. The claims recite additional limitations regarding quantum processing, but do not otherwise add any meaningful limits beyond the abstract idea. The quantum processing is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Claims 10, 11, 12 are dependent on claim 8 and includes all the limitations of claim 8. Therefore, claims 10, 11, 12 recite the same abstract idea. The claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea.
As to claim 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “initializing an optimization algorithm” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “iteratively until a termination criteria is met: receiving a sample solution from the optimization algorithm; evaluating quality and feasibility of the sample solution; where the sample solution from the optimization is feasible and has a best quality so far, freezing one or more penalty parameters for a set number of iterations; where the sample solution is not feasible or does not have the best quality so far, updating the one or more penalty parameters based on a finite state machine; returning the updated one or more penalty parameters to the optimization algorithm” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “iteratively until a termination criteria is met: incrementing the optimization algorithm; for each variable in the set of variables: sampling an updated value for the variable; evaluating a feasibility result of each constraint function defined by the variable; updating a problem feasibility result, the problem feasibility result comprising the feasibility result of each of the constraint functions; and for each constraint function defined by the variable where the constraint function was not feasible, increasing the penalty parameter by a first rate; after updating each variable in the set of variables, evaluating the problem feasibility result; when the problem feasibility result indicates feasibility was encountered for all constraint functions, decreasing all penalty parameters by a second rate; storing each updated penalty parameter; evaluating the termination criteria” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “ incrementing the optimization algorithm; and evaluating the termination criteria; and in response to the termination criteria being met, returning one or more sample solutions to the optimization proble” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a computing system to direct a search space for an optimization problem towards feasibility to improve performance of the computing system, the computing system comprising one or more processors” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a computing system to direct a search space for an optimization problem towards feasibility to improve performance of the computing system, the computing system comprising one or more processors” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Claim 20 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 20 recites the same abstract idea. The claims recite additional limitations regarding quantum processing, but do not otherwise add any meaningful limits beyond the abstract idea. The quantum processing is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). Claims 16-19 are dependent on claim 1 and includes all the limitations of claim 1. Therefore, claims 16-19 recite the same abstract idea. The claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea.
Claim Rejections - 35 USC § 112
Claims 6, 7, 13 and 14 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 6, 7, 13 and 14 recites the limitation "the sample solutions”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8-12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (hereinafter Kumar) U.S. Patent No. 8,290,892 in view of Kropaczek et al. (hereinafter Kropaczek) U.S. Patent Publication No. 2004/0059696.
With respect to independent claim 1, Kumar teaches a method of operation of a computing system to direct a search space towards feasibility to improve performance of the computing system, the computing system comprising one or more processors, the method being performed by at least one of the one or more processors (see e.g., col. 4 lines 10-25), the method comprising:
receiving a problem definition comprising a set of variables, an objective function defined over the set of variables, and one or more constraint functions, each of the constraint functions defined by at least one variable of the set of variables (see e.g., Col. 1 lines 25-65 - “Constrained non-linear optimization problems are composed of a non-linear objective function and may be subject to linear, bound and non-linear constraints. The constrained non-linear optimization problem to be solved may be represented as Minimize f(x) x Such that C.sub.i(x).ltoreq.0,i=1 . . . m C.sub.i(x)=0,i=m+1 . . . mt Ax.ltoreq.b A.sub.eqx=b.sub.eq LB.ltoreq.x.ltoreq.UB (A) where, C.sub.i(x) is non-linear inequality and equality constraint. The integer "m" is the number of non-linear inequality constraints, and "mt" is the total number of non-linear constraints. Ax.ltoreq.b and A.sub.eqx=b.sub.eq are linear constraints, LB and UB are lower and upper bounds on the decision variable x.”);
initializing an optimization algorithm, a sample solution to the objective function, and one or more penalty parameters corresponding to each of the constraint functions (see e.g., co. 3 lines 14-40 - “providing an optimization problem that includes non-linear constraints, linear constraints and bound constraints. The method formulates a sub-problem that is an approximation of the original optimization problem. The sub-problem excludes the linear and bound constraints and includes the non-linear constraints. The sub-problem includes a penalty parameter and at least one Lagrange parameter estimate. The method solves the sub-problem using a pattern search and generates a solution to the optimization problem using the solution to the sub-problem. Additionally, the method stores the solution to the optimization problem in a location accessible from the computing device. (13) In another aspect of the present invention, a computing apparatus includes a pattern search algorithm that formulates a sub-problem from an optimization problem that includes non-linear constraints, linear constraints and bound constraints. The sub-problem includes the non-linear constraints and excludes the linear and bound constraints.”);
iteratively until a termination criteria is met (see e.g., col. 5 lines 25-40 and claim 4 - “These steps are repeated until one or more stopping criteria are met. The penalty and Lagrange parameters update formulae are based on the well-known algorithms in the continuous optimization theory.”):
incrementing the optimization algorithm; for each variable in the set of variables: sampling an updated value for the variable; evaluating a feasibility result of each constraint function defined by the variable (see e.g., col. 5 lines 5-50); updating a problem feasibility result, the problem feasibility result comprising the feasibility result of each of the constraint functions (see e.g., col. 5 lines 5-50 - “A pattern search minimizes a sequence of the sub-problem, which is an approximation to the original problem (A). When the sub-problem is minimized to a required accuracy, and the solution satisfies the feasibility conditions, the Lagrange parameter estimates are updated. Otherwise, the penalty parameter is multiplied by the penalty factor. This results in a new sub-problem formulation and a new minimization problem. These steps are repeated until one or more stopping criteria are met. The penalty and Lagrange parameters update formulae are based on the well-known algorithms in the continuous optimization theory.”); and
for each constraint function defined by the variable where the constraint function was not feasible, increasing the penalty parameter by a first rate; after updating each variable in the set of variables, evaluating the problem feasibility result (see e.g., col. 5 lines 25-40 - “the Lagrange parameter estimates are updated. Otherwise, the penalty parameter is multiplied by the penalty factor. This results in a new sub-problem formulation and a new minimization problem.”);
storing each updated penalty parameter; evaluating the termination criteria; and
when the termination criteria is met, outputting a solution comprising an updated set of variables (see e.g., col. 5 lines 5-50).
Kumar does not expressly show when the problem feasibility result indicates feasibility was encountered for all constraint functions, decreasing all penalty parameters by a second rate. However, Kumar expressly teaches adjustment-based feasibility (see e.g., col. 5 lines 5-50). Furthermore, Kropaczek teaches the decreasing feature (see e.g., Para [13] and claim 8- “the adjustments to the weighting factors are performed during the course of the optimization search. In the presence of constraint violations, the magnitude of the penalty weight for the `worst` penalty component is increased while simultaneously decreasing the weight for the remaining penalty and credit components. The worst penalty component is calculated from the product of the penalty weight and the penalty term, where the penalty weights are the initial static values”).
Both Kumar and Kropaczek are directed to optimization functions. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Kumar and Kropaczek in front of them to modify the system of Kumar to include the above feature. The motivation to combine Kumar and Kropaczek comes from Kropaczek. Kropaczek discloses the motivation to decrease weight to optimize the function (see e.g. Para [13] and claim 8). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to dependent claim 2, the modified Kumar teaches incrementing the optimization algorithm comprises incrementing one of simulated annealing, parallel tempering, and quantum annealing (see e.g., Kropaczek Para [57]- “the optimization iteration count is increased and processing returns to step S12. The generation, convergence assessment and modification operations of steps S12, S18 and S26 are performed according to any well-known optimization algorithm such as Genetic Algorithms, Simulated Annealing, and Tabu Search. When the optimization problem is boiler water reactor core design, the optimization algorithm can be, for example, one of the optimization processes as disclosed in U.S. application Ser. No. 09/475,309, titled SYSTEM AND METHOD FOR OPTIMIZATION OF MULTIPLE OPERATIONAL CONTROL VARIABLES FOR A NUCLEAR REACTOR filed Dec. 30, 1999 or U.S. application Ser. No. 09/683,004, tilted SYSTEM AND METHOD FOR CONTINUOUS OPTIMIZATION OF CONTROL-VARIABLES DURING OPERATION OF A NUCLEAR REACTOR, filed Nov. 7, 2001.”).
With respect to dependent claim 3, the modified Kumar teaches increasing the penalty parameter by a first rate comprises increasing the penalty parameter by a first rate that depends on the termination criteria and a number of variables that participate in the respective constraint function (see e.g., col. 5 lines 5-50 and claim 1 - “multiplying the penalty parameter, included in the sub-problem, by a penalty factor when a solution, to the sub-problem, is not minimized to a pre-determined accuracy or when the solution to the sub-problem does not satisfy a feasibility condition; generating a solution to the single function optimization problem using the solution to the sub-problem; and storing the solution to the single function optimization problem in a storage location accessible to a computing device.” Increase Penalty parameter when feasibility condition is not met).
With respect to dependent claim 4, the modified Kumar teaches decreasing all penalty parameters by a second rate comprises decreasing all penalty parameters by a second rate that depends on the termination criteria (see e.g., col. 5 lines 5-50 and Kropaczek Para [13] and claim 8).
With respect to dependent claim 5, the modified Kumar teaches evaluating the termination criteria comprises evaluating a number of iterations (see e.g., col. 5 line 25-50 and claim 4 “repeating until a stopping criteria is achieved” Kumar does not expressly show “a number,” however, it would have been obvious to include this feature because the “criteria” can be any criteria that user desires).
Claim 8 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 9 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 10 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 11 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 12 is rejected for the similar reasons discussed above with respect to claim 5.
With respect to independent claim 15, the modified Kumar teaches a method of operation of a computing system to direct a search space for an optimization problem towards feasibility to improve performance of the computing system, the computing system comprising one or more processors, the method being performed by at least one of the one or more processors (see e.g., col. 4 lines 10-25), the method comprising:
initializing an optimization algorithm; iteratively until a termination criteria is met: receiving a sample solution from the optimization algorithm (see e.g. col. 5 lines 45-60); evaluating quality and feasibility of the sample solution (see e.g., claim 1, 2 and co. 3 lines 14-40 - “providing an optimization problem that includes non-linear constraints, linear constraints and bound constraints. The method formulates a sub-problem that is an approximation of the original optimization problem. The sub-problem excludes the linear and bound constraints and includes the non-linear constraints. The sub-problem includes a penalty parameter and at least one Lagrange parameter estimate. The method solves the sub-problem using a pattern search and generates a solution to the optimization problem using the solution to the sub-problem. Additionally, the method stores the solution to the optimization problem in a location accessible from the computing device. (13) In another aspect of the present invention, a computing apparatus includes a pattern search algorithm that formulates a sub-problem from an optimization problem that includes non-linear constraints, linear constraints and bound constraints. The sub-problem includes the non-linear constraints and excludes the linear and bound constraints.”);
where the sample solution from the optimization is feasible and has a best quality so far, freezing one or more penalty parameters for a set number of iterations (see e.g., Claims 1-4 – “the determining whether the solution to the sub-problem is minimized to a pre-determined accuracy and whether the solution to the sub-problem satisfies a feasibility condition, and the adjusting, when the solution to the sub-problem is minimized to a pre-determined accuracy and when the solution to the sub-problem satisfies a feasibility condition, the at least one Lagrangian parameter estimate.” Kumar does not expressly indicate “freezing.” However, Kumar expressly teaches that penalty parameter will be updated when condition met. Therefore, it would have been obvious to “freeze” when condition not met.);
where the sample solution is not feasible or does not have the best quality so far, updating the one or more penalty parameters based on a finite state machine (see e.g., col. 5 lines 40-65 - “The use of the pattern search algorithm is depicted in greater detail in the flowchart of FIG. 4. The sequence begins when the Augmented Lagrangian pattern search algorithm 16 minimizes the sub-problem (step 220). The sub-problem is checked to see if it has been minimized to a required accuracy and satisfies feasibility conditions (step 221). If it has/does not, the penalty parameter is increased (step 222) which results in a new sub-problem which is then minimized again (step 220). If the minimization does satisfy the accuracy and feasibility requirements (step 221), the Lagrangian estimates are updated (step 224). If one or more stopping criteria are met (step 225), the sequence ends (step 226). If no stopping criteria is met, the process iterates and the Augmented Lagrangian Pattern Search algorithm 16 minimizes the new sub-problem again with new Lagrange and penalty parameters.”);
returning the updated one or more penalty parameters to the optimization algorithm (see e.g., col. 5 lines 25-40 - “A pattern search minimizes a sequence of the sub-problem, which is an approximation to the original problem (A). When the sub-problem is minimized to a required accuracy, and the solution satisfies the feasibility conditions, the Lagrange parameter estimates are updated. Otherwise, the penalty parameter is multiplied by the penalty factor. This results in a new sub-problem formulation and a new minimization problem. These steps are repeated until one or more stopping criteria are met. The penalty and Lagrange parameters update formulae are based on the well-known algorithms in the continuous optimization theory.”);
incrementing the optimization algorithm; and evaluating the termination criteria; and in response to the termination criteria being met, returning one or more sample solutions to the optimization problem (see e.g., col. 5 lines 25-60 - “These steps are repeated until one or more stopping criteria are met … The sequence begins with the providing of an optimization problem (step 200). The linear and bound constraints are then separated from non-linear constraints (step 202). A sub-problem is then formulated (step 204) and solved with the pattern search algorithm (step 206). The solution to the optimization problem is then generated and stored using the solution to the sub-problem (step 208) … The sequence begins when the Augmented Lagrangian pattern search algorithm 16 minimizes the sub-problem (step 220). The sub-problem is checked to see if it has been minimized to a required accuracy and satisfies feasibility conditions (step 221). If it has/does not, the penalty parameter is increased (step 222) which results in a new sub-problem which is then minimized again (step 220). If the minimization does satisfy the accuracy and feasibility requirements (step 221), the Lagrangian estimates are updated (step 224). If one or more stopping criteria are met (step 225), the sequence ends (step 226). If no stopping criteria is met, the process iterates and the Augmented Lagrangian Pattern Search algorithm 16 minimizes the new sub-problem again with new Lagrange and penalty parameters.”).
With respect to independent claim 20, the modified Kumar teaches initializing an optimization algorithm comprises initializing one of simulated annealing, parallel tempering, and quantum annealing (see e.g., Kropaczek Para [57]- “the optimization iteration count is increased and processing returns to step S12. The generation, convergence assessment and modification operations of steps S12, S18 and S26 are performed according to any well-known optimization algorithm such as Genetic Algorithms, Simulated Annealing, and Tabu Search. When the optimization problem is boiler water reactor core design, the optimization algorithm can be, for example, one of the optimization processes as disclosed in U.S. application Ser. No. 09/475,309, titled SYSTEM AND METHOD FOR OPTIMIZATION OF MULTIPLE OPERATIONAL CONTROL VARIABLES FOR A NUCLEAR REACTOR filed Dec. 30, 1999 or U.S. application Ser. No. 09/683,004, tilted SYSTEM AND METHOD FOR CONTINUOUS OPTIMIZATION OF CONTROL-VARIABLES DURING OPERATION OF A NUCLEAR REACTOR, filed Nov. 7, 2001.”).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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