DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to amended application filed on 01/26/2026.
Claim 7 has been canceled.
Claim 21 has been added.
Claims 1-6, and 8 21 are presented for examination.
Response to Arguments
Applicant’s amendments with respect to the Title have been fully considered and are persuasive. The objection of the Title has been withdrawn.
Applicant’s amendments with respect to claims 1, 11, and 15 have been fully considered and are persuasive. The rejection of 35 USC 101 has been withdrawn.
Applicant’s arguments/amendments, see Remarks pg. 11, filed 01/26/2026, with respect to the rejection(s) of claims 1, 11, and 15 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US Publication No. 2011/0029942 A1 issued to LIU et al.
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.
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.
Claims 1-6, 8, 9, and 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 7, 530, 036 issued to Nahir et al in view of US Publication No. 2022/0137961 A1 issued to Quirynen et al, and further in view of US Publication No. 2011/0029942 A1 issued to LIU et al, and further in view of US Publication No. 2017/0228473 A1 issued to Kang et al.
Claim 1. Nahir et al discloses a method for developing a product using a model that satisfies a region constraint, the method comprising:
receiving, by a device, data (See: Col. 3 lines 24-29, test generator 20 accepts a design specification 28 and a set of test requirements 32 as input. Specification 28 defines the architecture and functionality of the verified design, typically expressed as logical rules or formulas. Test requirements 32 define directives, preferences and/or requirements regarding the generation of test cases);
identifying, by the device, the region constraint (See: Abstract, an optimization process is repeatedly invoked over an input, which includes the set of constraints and the objective function. The input of each invocation is randomly modified, so as to cause the optimization process to produce multiple different solutions that satisfy the set of constraints. Multiple random test cases for verifying a compliance of the design with the specifications are generated, based on the multiple different solutions produced by the optimization process; Col. 7 lines 54-64, FIGS. 5D and 5E show the solution spaces at this stage. In FIG. 5D, a line 142, which is parallel with lines 118 and 126 and passes through mid-point 138, represents the new constraint. In FIG. 5E, a region 146 shows a reduced solution space, which represents the collection of all valid solutions to the new set of constraints (i.e., the original set plus the newly-added constraint). In the present example, the new constraint is of the form f.ltoreq.f(MP), therefore region 146 is located below line 142. If a constraint of the form f.gtoreq.f(MP) were added instead, the reduced solution space would contain the part of space 110 that is located above line 142); and
identifying, by the device, the model that fits the data and satisfies the region constraint using a objective function and a set of point constraints derived iteratively from the region constraint (See: Col. 2 lines 1-8, An optimization process is repeatedly invoked over an input, which includes the set of constraints and the objective function. The input of each invocation is randomly modified, so as to cause the optimization process to produce multiple different solutions that satisfy the set of constraints. Multiple random test cases for verifying a compliance of the design with the specifications are generated, based on the multiple different solutions produced by the optimization process; Col. 2 lines 55-64, The embodiments of the present invention that are described hereinbelow provide methods and systems for random test case generation using optimization solving methods. The methods and systems described herein modify the set of constraints and/or the objective function provided to an optimization solver in a random manner, thus causing the optimization solver to produce multiple different solutions to a given constraint problem. Similar methods and systems may be used, mutatis mutandis, in other applications of constraint satisfaction and optimization; Fig. 2 and corresponding texts).
Nahir et al discloses objective function but not specify quadratic objective function. Quirynen et al discloses quadratic objective function (See: par [0115] When solving a constrained mixed-integer quadratic program (MIQP), in some embodiments, the primal formulation for a convex relaxation 320 in the branch-and-bound method corresponds to a convex quadratic program (QP) 400 with a linear-quadratic objective function 401, affine inequality constraints 402 and affine equality constraints 403).
It would have been obvious before the effective filing date to combine the mixed-integer optimal control optimization as taught by Quirynen et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to determine the optimal solution with low cost (time and memory space) and high accuracy by use of the branch and bound optimization (Quirynen et al, par [0126]).
Further, none of the references disclose but LIU et al discloses a least squares penalty function (See: [0027] A penalty term p.sub.i(r.sub.i) is also added in the objective function to represent the cost if r.sub.i is not 0. Therefore, p.sub.i(x) is supposed to be 0 if x is 0, and is a monotonically non-decreasing function of x when x.gtoreq.0, but not necessarily. The form of the penalty function can be very flexible, and in an embodiment the form of the penalty function can be linear, piece-wise linear, quadratic, hyperbolic, exponential, to name a few. Use of the penalty term transforms the problem into….where c.sub.i is a positive constant, which is selected according to the cost of violating a constraint and can be estimated either automatically based on some decision system or by the designer. FIG. 3 is a plot of a quadratic penalty term for constraint violation, under an embodiment).
It would have been obvious before the effective filing date to combine soft constraints in scheduling as taught by LIU et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to increase the number of operations to gate as well as the duration of shutting down (LIU et al, par [0034]).
Neither the references discloses but Kang et al discloses manufacturing the product based on the model (See: par [0088] embodiments of the disclosure may be described in the context of an aircraft manufacturing and service method 1600 as shown in FIG. 16 and an aircraft 1602 as shown in FIG. 17. During pre-production, exemplary method 1600 may include specification and design 1604 of the aircraft 1602 and material procurement 1606. During production, component and subassembly manufacturing 1608 and system integration 1610 of the aircraft 1602 takes place; par [0092] composite design system 110 is utilized during specification and design of composite parts for a portion of airframe 118. This allows composite part 150 to be manufactured in component and subassembly manufacturing 1608, and then be assembled into an aircraft in system integration 1610).
It would have been obvious before the effective filing date to combine optimization of ply orientation for multi-layer composite parts as taught by Kang et al to random test generation using an optimization solver of Nahir et al would be to ensure not only that the part is designed with desired strength, but also ensure the part may be manufactured efficiently by AFP machine, ATL machine, pick-and-place techniques, or even hand layup (Kang et al, par [0004]).
Claim 2. Nahir et al discloses the method of claim 1, wherein identifying the model comprises: forming a set of constraints using the set of point constraints identified from the region constraint (See: Col. 4 lines 57-65, the set of constraints has multiple solutions, i.e., multiple value assignments of the variables that satisfy the constraints. The collection of all valid solutions of a certain set of constraints is referred to as the solution space. When N variables are defined, the solution space can be represented graphically as occupying a volume in the N-dimensional space spanned by the variables. The optimization solver returns a solution within the solution space, which minimizes or maximizes the objective function).
Claim 3. Nahir et al discloses the method of claim 2, wherein identifying the model further comprises: computing a candidate model solution to the quadratic objective function such that the candidate model solution satisfies the set of constraints (See: Col. 2 lines 1-8, An optimization process is repeatedly invoked over an input, which includes the set of constraints and the objective function. The input of each invocation is randomly modified, so as to cause the optimization process to produce multiple different solutions that satisfy the set of constraints); and determining whether the candidate model solution satisfies the region constraint (See: Col. 4 lines 57-65, the set of constraints has multiple solutions, i.e., multiple value assignments of the variables that satisfy the constraints. The collection of all valid solutions of a certain set of constraints is referred to as the solution space. When N variables are defined, the solution space can be represented graphically as occupying a volume in the N-dimensional space spanned by the variables. The optimization solver returns a solution within the solution space, which minimizes or maximizes the objective function).
Claim 4. Nahir et al discloses the method of claim 3, further comprising: generating an output that identifies the candidate model solution as the model that fits the data subject to the region constraint in response to a determination that the candidate model solution satisfies the region constraint (See: Col. 3 line 55 through Col. 4 line 7, Generator 20 further comprises an optimization solver 40, which is invoked by processor 36. When invoked, solver 40 accepts the set of constraints and the objective function from processor 36, and returns a single solution it considers to be optimal, assuming such a solution exists. The optimal solution is a value assignment of the variables, which satisfies the set of constraints and optimizes (i.e., maximizes or minimizes) the objective function. In order to generate multiple different solutions, processor 36 modifies the constraints and/or the objective function in a random manner, using methods that are explained in detail hereinbelow. As a result of the random modifications, solver 40 produces, with high probability, a different random solution at each iteration. Processor 36 creates and outputs random test cases based on the solutions produced by solver 40.Test generator 20 accepts the specification and outputs the generated test cases using an interface 44. Interface 44 may comprise a file interface, a network connection, a direct connection to a test setup or to another computerized system or user terminal, or any other suitable interface type).
Claim 5. Nahir et al discloses the method of claim 3, wherein identifying the model further comprises: adding a new set of point constraints identified from the region constraint to the set of constraints in response to a determination that the candidate model solution does not satisfy the region constraint (See: See: Col. 7 lines 46-53, Processor 36 now adds a new constraint to the set of constraints, at a constraint addition step 98. The added constraint compares the objective function to the value of the objective function at mid-point 138. In some embodiments, the added constraint has the form f.ltoreq.f(MP) or f.gtoreq.f(MP), wherein f denotes the objective function and MP denotes the mid-point. The choice whether to use the .ltoreq. or the .gtoreq. operator is usually random; Fig. 2 and corresponding texts); and repeating the step of computing the candidate model solution to the quadratic objective function such that the candidate model solution satisfies the set of constraints using the set of constraints that includes the new set of point constraints (See: Col. 3 line 63 through Col. 4 line 2, In order to cause the optimization solver to reach different solutions, processor 36 modifies the set of constraints and/or the objective function, at a solver input modification step 66. Several alternative methods for modifying the constraint problem are described further below. Having modified the constraints and/or the objective function, the method loops back to optimization step 58 above. Since the optimization solver is provided with a modified set of constraints and/or a modified objective function at each iteration, the returned optimal solutions are likely to be different from one another. As a result, the test cases generated by processor 36 will also differ from one another with high probability).
Claim 6. Quirynen et al discloses the method of claim 1, wherein the quadratic objective function is convex (See: par [0117] he dual objective function is always concave 430 such that the dual QP formulation 410 is always convex. When solving a primal QP 400 that is convex, the primal objective is also a convex function 420).
Claim 7. Canceled.
Claim 8. Nahir et al discloses the method of claim 1, further comprising: analyzing a performance of the product using the model (See: Abstract, Multiple random test cases for verifying a compliance of the design with the specifications are generated, based on the multiple different solutions produced by the optimization process).
Claim 9. Kang et al discloses the method of claim 1, wherein the model represents a geometry for a shim and further comprising: manufacturing the shim having the geometry based on the model (See: par [0088] embodiments of the disclosure may be described in the context of an aircraft manufacturing and service method 1600 as shown in FIG. 16 and an aircraft 1602 as shown in FIG. 17. During pre-production, exemplary method 1600 may include specification and design 1604 of the aircraft 1602 and material procurement 1606. During production, component and subassembly manufacturing 1608 and system integration 1610 of the aircraft 1602 takes place; par [0092] composite design system 110 is utilized during specification and design of composite parts for a portion of airframe 118. This allows composite part 150 to be manufactured in component and subassembly manufacturing 1608, and then be assembled into an aircraft in system integration 1610).
It would have been obvious before the effective filing date to combine optimization of ply orientation for multi-layer composite parts as taught by Kang et al to random test generation using an optimization solver of Nahir et al would be to ensure not only that the part is designed with desired strength, but also ensure the part may be manufactured efficiently by AFP machine, ATL machine, pick-and-place techniques, or even hand layup (Kang et al, par [0004]).
As per claims 15-20, independent claims 15-20 recite limitations analogous in scope to those of independent claims1-6, and as such are similar rejected.
Claim 21. Kang et al discloses the method of claim 1, wherein the region constraint specified that a part maintains a certain thickness over a defined region (See: par [0005] The method also includes identifying sublaminates comprising consecutive ply sequences for the block that are compatible with the guide for the block and comply with the rules, subdividing the part into panels that each comprise a fraction of the area of the composite part, and selecting one of the compatible sublaminates for one of the panels of the block, based on compatible sublaminates for neighboring panels; par [0035] Controller 112 initiates by receiving input via I/F 114 indicating a geometry of a multi-layer part (part 150). This information may indicate which panels are adjacent/neighboring within part 150, and may further include an expected number of plies for each different fiber orientation to be laid at each panel (e.g., a final depth/thickness and composition of each completed panel in part 150)).
Claims 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Nahir et al, Quirynen et al, LIU et al, and further in view of US Publication No. 2016/0076968 A1 issued to Bosetti et al.
Claim 10. Neither the references disclose but Bosetti et al discloses performing a flight test using the model of the ice surface to verify performance of the aircraft (See: Abstract, systems and methods for the manufacture and use of artificial ice shapes for aircraft certification, including methods of manufacturing artificial ice shapes, artificial ice component systems for attachment to aircraft, methods of flight testing aircraft having artificial ice sections attached thereto, and artificial ice testing systems).
It would have been obvious before the effective filing date to combine icing flight tests as taught by Bosetti et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to remove from wing surfaces with little or no damage to the aircraft (Bosetti et al, par [0146]).
Claim 11. Nahir et al discloses a method for generating a model that satisfies a set of predefined constraints, the method comprising:
receiving data and the set of predefined constraints, the set of predefined constraints including a set of region constraints (See: Col. 3 lines 24-29, test generator 20 accepts a design specification 28 and a set of test requirements 32 as input. Specification 28 defines the architecture and functionality of the verified design, typically expressed as logical rules or formulas. Test requirements 32 define directives, preferences and/or requirements regarding the generation of test cases);
forming a set of constraints corresponding to objective function using any predefined point constraints in the set of predefined constraints and at least one point constraint identified from each region constraint in the set of region constraints (See: Col. 4 lines 57-65, the set of constraints has multiple solutions, i.e., multiple value assignments of the variables that satisfy the constraints. The collection of all valid solutions of a certain set of constraints is referred to as the solution space. When N variables are defined, the solution space can be represented graphically as occupying a volume in the N-dimensional space spanned by the variables. The optimization solver returns a solution within the solution space, which minimizes or maximizes the objective function);
computing a candidate model solution to the objective function such that the candidate model solution satisfies the set of constraints (See: Col. 2 lines 1-8, An optimization process is repeatedly invoked over an input, which includes the set of constraints and the objective function. The input of each invocation is randomly modified, so as to cause the optimization process to produce multiple different solutions that satisfy the set of constraints); and
determining whether the candidate model solution satisfies the set of predefined constraints, including the set of region constraints (See: Col. 4 lines 57-65, the set of constraints has multiple solutions, i.e., multiple value assignments of the variables that satisfy the constraints. The collection of all valid solutions of a certain set of constraints is referred to as the solution space. When N variables are defined, the solution space can be represented graphically as occupying a volume in the N-dimensional space spanned by the variables. The optimization solver returns a solution within the solution space, which minimizes or maximizes the objective function);
adding a new set of point constraints identified from the region constraint to the set of constraints in response to a determination that the model does not satisfy the region constraint (See: Col. 7 lines 46-53, Processor 36 now adds a new constraint to the set of constraints, at a constraint addition step 98. The added constraint compares the objective function to the value of the objective function at mid-point 138. In some embodiments, the added constraint has the form f.ltoreq.f(MP) or f.gtoreq.f(MP), wherein f denotes the objective function and MP denotes the mid-point. The choice whether to use the .ltoreq. or the .gtoreq. operator is usually random; Fig. 2 and corresponding texts); and
repeating the steps of computing the candidate model solution to the objective function and determining whether the candidate model solution satisfies the set of predefined constraints based on the set of constraints that includes the new set of point constraints (See: Col. 3 line 63 through Col. 4 line 2, In order to cause the optimization solver to reach different solutions, processor 36 modifies the set of constraints and/or the objective function, at a solver input modification step 66. Several alternative methods for modifying the constraint problem are described further below. Having modified the constraints and/or the objective function, the method loops back to optimization step 58 above. Since the optimization solver is provided with a modified set of constraints and/or a modified objective function at each iteration, the returned optimal solutions are likely to be different from one another. As a result, the test cases generated by processor 36 will also differ from one another with high probability).
Nahir et al discloses objective function but not specify quadratic objective function. Quirynen et al discloses quadratic objective function (See: par [0115] When solving a constrained mixed-integer quadratic program (MIQP), in some embodiments, the primal formulation for a convex relaxation 320 in the branch-and-bound method corresponds to a convex quadratic program (QP) 400 with a linear-quadratic objective function 401, affine inequality constraints 402 and affine equality constraints 403).
It would have been obvious before the effective filing date to combine the mixed-integer optimal control optimization as taught by Quirynen et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to determine the optimal solution with low cost (time and memory space) and high accuracy by use of the branch and bound optimization (Quirynen et al, par [0126]).
Further, none of the references disclose but LIU et al discloses a least squares penalty function (See: [0027] A penalty term p.sub.i (r.sub.i) is also added in the objective function to represent the cost if r.sub.i is not 0. Therefore, p.sub.i(x) is supposed to be 0 if x is 0, and is a monotonically non-decreasing function of x when x.gtoreq.0, but not necessarily. The form of the penalty function can be very flexible, and in an embodiment the form of the penalty function can be linear, piece-wise linear, quadratic, hyperbolic, exponential, to name a few. Use of the penalty term transforms the problem into….where c.sub.i is a positive constant, which is selected according to the cost of violating a constraint and can be estimated either automatically based on some decision system or by the designer. FIG. 3 is a plot of a quadratic penalty term for constraint violation, under an embodiment).
It would have been obvious before the effective filing date to combine soft constraints in scheduling as taught by LIU et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to increase the number of operations to gate as well as the duration of shutting down (LIU et al, par [0034]).
Neither the references disclose but Bosetti et al discloses performing a flight test using the model of the ice surface to verify performance of the aircraft (See: Abstract, systems and methods for the manufacture and use of artificial ice shapes for aircraft certification, including methods of manufacturing artificial ice shapes, artificial ice component systems for attachment to aircraft, methods of flight testing aircraft having artificial ice sections attached thereto, and artificial ice testing systems).
It would have been obvious before the effective filing date to combine icing flight tests as taught by Bosetti et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to remove from wing surfaces with little or no damage to the aircraft (Bosetti et al, par [0146]).
Claim 12. Quirynen et al discloses the method of claim 11, wherein determining whether the candidate model solution satisfies the set of predefined constraints comprises: dividing a spline of the candidate model solution into a selected number of sections to form a plurality of sections (See: par [0102] the main idea of a branch-and-bound (B&B) method is to sequentially create partitions of the original problem and then attempt to solve those partitions, where each partition corresponds to a particular region of the discrete control variable search space. In some embodiments, a branch-and-bound method selects a partition or node and selects a discrete control variable to branch this partition into smaller partitions or search regions, resulting in a nested tree of partitions or search regions.); identifying a convex set for each section of the plurality of sections (See: par [0011] to evaluate a lower bound for the instance representing a partition of a search space, the solution of a convex relaxation for the instance needs to be determined; par [0115] he primal formulation for a convex relaxation 320 in the branch-and-bound method corresponds to a convex quadratic program (QP) 400 with a linear-quadratic objective function 401, affine inequality constraints 402 and affine equality constraints 403. Some embodiments of the present disclosure are based on the realization that a partitioning can be performed of the primal optimization variables z=[x, y] between the variables x and y 404 that enter the objective function 401 in a linear-quadratic or purely linear form, respectively).
It would have been obvious before the effective filing date to combine the mixed-integer optimal control optimization as taught by Quirynen et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to determine the optimal solution with low cost (time and memory space) and high accuracy by use of the branch and bound optimization (Quirynen et al, par [0126]).
Claim 13. Nahir et al discloses the method of claim 12, wherein determining whether the candidate model solution satisfies the set of predefined constraints further comprises: determining that at least one section of the plurality of sections fully violates the region constraint based on the convex set corresponding to each section of the plurality of sections (See: Col. 5 lines 56-67, FIG. 3 is a diagram that schematically illustrates a solution space 70 of a constraint problem, in accordance with an embodiment of the present invention. Each point in solution space 70 (the shaded area in the figure) corresponds to a valid solution of the constraint problem. Points outside space 70 correspond to value assignments that do not satisfy the constraints. The solution space has an outer boundary 74. In the present example, the constraint problem has two variables denoted X and Y, thus the solution space is two-dimensional. Generally, however, the methods described herein can be used with any number of variables, i.e., in solution spaces having any number of dimensions); and creating at least one new point constraint for any section of the plurality of sections that violates the region constraint to form the new set of point constraints (See: Col. 7 lines 46-53, Processor 36 now adds a new constraint to the set of constraints, at a constraint addition step 98. The added constraint compares the objective function to the value of the objective function at mid-point 138. In some embodiments, the added constraint has the form f.ltoreq.f(MP) or f.gtoreq.f(MP), wherein f denotes the objective function and MP denotes the mid-point. The choice whether to use the .ltoreq. or the .gtoreq. operator is usually random; Fig. 2 and corresponding texts).
Claim 14. Nahir et al discloses the method of claim 12, wherein determining whether the candidate model solution satisfies the set of predefined constraints further comprises: determining that at least one section of the plurality of sections partially violates the region constraint based on the convex set corresponding to each section of the plurality of sections (See: Col. 5 lines 56-67, FIG. 3 is a diagram that schematically illustrates a solution space 70 of a constraint problem, in accordance with an embodiment of the present invention. Each point in solution space 70 (the shaded area in the figure) corresponds to a valid solution of the constraint problem. Points outside space 70 correspond to value assignments that do not satisfy the constraints. The solution space has an outer boundary 74. In the present example, the constraint problem has two variables denoted X and Y, thus the solution space is two-dimensional. Generally, however, the methods described herein can be used with any number of variables, i.e., in solution spaces having any number of dimensions); and repeating the step of identifying the convex set for each section of the new plurality of sections (See: Col. 3 line 63 through Col. 4 line 2, In order to cause the optimization solver to reach different solutions, processor 36 modifies the set of constraints and/or the objective function, at a solver input modification step 66. Several alternative methods for modifying the constraint problem are described further below. Having modified the constraints and/or the objective function, the method loops back to optimization step 58 above. Since the optimization solver is provided with a modified set of constraints and/or a modified objective function at each iteration, the returned optimal solutions are likely to be different from one another. As a result, the test cases generated by processor 36 will also differ from one another with high probability).
Nahir et al does not specify but Quirynen et al discloses dividing each partially violating section of the plurality of sections into the selected number of sections to form a new plurality of sections (See: par [0102] the main idea of a branch-and-bound (B&B) method is to sequentially create partitions of the original problem and then attempt to solve those partitions, where each partition corresponds to a particular region of the discrete control variable search space. In some embodiments, a branch-and-bound method selects a partition or node and selects a discrete control variable to branch this partition into smaller partitions or search regions, resulting in a nested tree of partitions or search regions).
It would have been obvious before the effective filing date to combine the mixed-integer optimal control optimization as taught by Quirynen et al to random test generation using an optimization solver of Nahir et al would be to increase efficiency of a processor that is configured to determine the optimal solution with low cost (time and memory space) and high accuracy by use of the branch and bound optimization (Quirynen et al, par [0126]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 03/03/2026