DETAILED ACTION
This final Office action is responsive to amendments filed September 30th, 2025. Claims 1, 9, and 17 have been amended. Claims 1-24 are presented for examination.
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 .
Response to Arguments
Applicant's arguments regarding claim rejections under 35 USC 101 filed 9/30/25 have been fully considered but they are not persuasive.
On pages 12-16 of the provided remarks, Applicant argues that the claims are not directed to an abstract idea. Beginning on page 13 of the provided remarks, Applicant argues, citing the entirety of independent claim 1, that “the claims provide a specific machine implementation, not an abstract mathematical concept, mental process, or method of organizing human activity”. Examiner respectfully disagrees and asserts the extraction of coefficients; defining of a symbolic representation of the revenue model; applying Lagrangian constraints to identify a limited set of stationary points; and evaluating those stationary points to select the one yielding a maximum predicted revenue are directed to certain methods of organizing human activity; mental processes; and mathematical concepts. Applicant’s arguments are not persuasive.
On pages 13-14 of the provided remarks, Applicant argues “the claims involve communication with external allocation targets and adjusting the resources contributed to those targets, which are network-based and cannot be performed mentally.” Examiner respectfully disagrees and asserts Applicant’s argument regarding the above limitations is moot as the communication with external allocation targets is not directed to the abstract idea but would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Additionally, the adjustment of resources contributed to allocation targets could be determined as an evaluation of the human mind. While Applicant claims this function is “network based”, the present claims do not recite the argued network but a mere “processor” which would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Applicant’s arguments are not persuasive.
On page 14 of the provided remarks, Applicant argues “the claims are directed to a practical application in a networked computing environment, in which resources are automatically reallocated across external platforms in real time.” Examiner respectfully disagrees and begins by asserting, as stated above, While Applicant claims this function is “network based”, the present claims do not recite the argued network but a mere “A server for resource allocation optimization amongst a plurality of allocation targets, the server comprising: a memory and a communications interface; and a processor interconnected to the memory and the communications interface; a client device; A system for resource allocation optimization amongst a plurality of allocation targets, the system comprising: a revenue database; a resource allocation database; a model database; and a server” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Additionally, as stated above, the adjustment of resources contributed to allocation targets could be determined as an evaluation of the human mind. Therefore, the claims are directed to the abstract idea. Applicant’s arguments are not persuasive.
Under Step 2A Prong 2, Applicant argues, beginning on page 14 of the provided remarks, “they require the server to communicate with the external allocation targets, effect real-time adjustment of resources at those targets, and output the allocation results to client devices. The claims therefore produce a real-world result by automatically adjusting resource allocations across third-party platforms and providing a concrete output that allows a user to view, monitor, and interact with the optimized allocations.” Examiner respectfully disagrees and asserts, as stated above, the ability of a server to “communicate with the external allocation targets” and “output the allocation results to client devices” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Additionally, the argued “effect real-time adjustment of resource at those targets” is directed to the abstract idea of Certain Methods of Organizing Human Activity managing personal behavior and interactions as well as mental processes. The argued “server” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Applicant’s arguments are not persuasive.
Citing paragraphs [0083-0085] as outlining the technical problem, Applicant argues “The claims address this problem by configuring the processor to represent models and constraints symbolically and apply optimization in a way that reduces the number of allocations requiring evaluation. This is not simply a matter of "doing fewer simulations," but a technical solution that enables the system to function more efficiently in real time, compared to conventional systems. In other words, the present claims improve the functioning of the computer system by enabling real- time optimization with reduced computational requirements.” Examiner respectfully disagrees and asserts that the argued ability of the processor to “represent models and constraints symbolically and apply optimization” is directed to the abstract idea of mathematical concepts as the Langrangian optimization method to the symbolic representation of the revenue model and the target constraints to obtain a set of stationary points is a mathematical calculation. The argued application by a “processor” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Applicant’s arguments are not persuasive.
Regarding Step 2B analysis, Applicant argues on page 15 of the provided remarks that when viewed as an ordered combination, the claims recite significantly more than an abstract idea. Citing the entirety of the independent claims, Applicant argues “The combination of these elements provides a non-conventional and non-generic way to ensure that resource allocations are optimized and applied in real time across multiple third-party platforms, which is an improvement over conventional systems. The Examiner's assertion that the reduction of time and computation complexity is an inherent feature of conducting less simulations overlooks that the claims require a distinct technical process that enables capabilities that previous systems were unable to achieve.” Examiner respectfully disagrees and asserts as Applicant has cited the entire claim, it is unclear the argued which aspects of the “distinct technical process” are being claimed as providing “capabilities that previous systems were unable to achieve”. Examiner asserts, under Step 2B, when analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Applicant’s arguments are not persuasive.
Citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016), Applicant continues on page 16 of the provided remarks to argue, “the claim elements amount to significantly more than any alleged judicial exception. They define a particular technological solution that improves computer functioning (enabling real-time optimization) and integrates the optimization into a practical application (automatically adjusting allocations at third- party platforms and providing the results to client devices so that users can interact with the optimized allocation).” Examiner respectfully disagrees and asserts, as stated above that the claimed optimization method and adjustment of allocations is directed to the abstract idea of Certain Methods of Organizing Human Activity managing personal behavior and interactions as well as mental processes as well as mathematical concepts in the form of mathematical calculations. Additionally, the argued “providing the results to client device so that users can interact with the optimized allocation” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. The 35 U.S.C. 101 rejection is maintained. Applicant’s arguments are not persuasive.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Step 1: Independent claims 1 (server), 9 (method), and 17 (system) and dependent claims 2-8, 10-16, and 18-24, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a server (i.e. machine), claim 9 is directed to a method (i.e. process), and claim 17 is directed to a system (i.e. machine).
Step 2A Prong 1: The independent claims recite a server for resource allocation optimization amongst a plurality of allocation targets, the server comprising: a memory and a communications interface; and a processor interconnected to the memory and the communications interface, the processor configured to: compute an effectiveness metric for a selected resource allocation based on revenue data and resource allocation data; obtain a revenue model associated with an account, the revenue model trained based on a historical resource allocation and revenue for the account; extract, from the revenue model, coefficients corresponding to each of the allocation targets; define, using the extracted coefficients, a symbolic representation of the revenue model; define target constraints for each of the allocation targets based on resource allocation constraints, the target constraints expressed as symbolic representations; apply Lagrangian optimization to the symbolic representation of the revenue model and the target constraints to obtain a set of stationary points; evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computation complexity of running simulations over the set of stationary points is 0(2n) time, where n corresponds to a number of the allocation targets; allocate, for each of the allocation targets, resources according to the resource allocation defined by the stationary point, wherein allocating the resources comprises communicating with each of the allocation targets and adjusting the resources contributed to the allocation target; and output the resource allocation for each of the allocation targets to at least one client device (Certain Method of Organizing Human Activity, Mental Process, & Mathematical Concepts), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above are directed to the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are allocating for each of the allocation targets resources according to resource allocation defined by the stationary point, which is commercial interactions in the form of sales activities or behaviors. The Applicant’s claimed limitations are performing resource allocation optimization amongst a plurality of allocation targets, which recite the abstract idea of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are computing an effectiveness metric for a selected resource allocation based on revenue data and resource allocation data; defining, using the extracted coefficients, a symbolic representation of the revenue model; defining target constraints for each of the allocation targets based on resource allocation constraints, the target constraints expressed as symbolic representations; applying Lagrangian optimization to the symbolic representation of the revenue model and the target constraints to obtain a set of stationary points; and evaluating each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computational complexity of running simulations over the set of stationary points is 0(2n) time, when n corresponds to a number of the allocation targets, which are observations, judgements, and evaluations of the human mind. Additionally, the above limitations could be performed utilizing pen and paper. The Applicant’s claimed limitations are evaluating the stationary points to optimize the revenue model, which recite the abstract idea of Mental Process.
The steps/functions discloses above and in the independent claims recite the abstract idea of Mathematical Concepts because the claimed limitations are applying Lagrangian optimization to the symbolic representation of the revenue model and the target constraints to obtain a set of stationary points, which is a mathematical calculation. The Applicant’s claimed limitations are applying Lagrangian optimization to the symbolic representation of the revenue model and target constraints to obtain a set of stationary points, which recite the abstract idea of Mathematical Concepts.
In addition, dependent claims 2-8, 10-16, and 18-24 further narrow the abstract idea and further recite the initiation of resource allocation optimization; determine whether the effectiveness metric is below a determined threshold score, and initiating the resource allocation optimization; determining correlation coefficients between revenue data and the resource allocation data; recomputing the effectiveness metric after a predetermined period of time; selecting the stationary point optimizing the revenue model; defining increments of the resource allocation to allocate to the allocation target based on the defined time period and the defined time interval; and allocating the resource allocation to the allocation target in the defined increments at the defined time interval over the defined time period. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include commercial and legal interactions such as sales activities or behaviors and mental process. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, even if not directed toward the abstract idea, the above “obtain a revenue model associated with an account; extract, from the revenue model, coefficients corresponding to each of the allocation targets; storing revenue data for at least one account; storing resource allocation data for the account; storing revenue models for the account, the revenue models trained based on the revenue data and the resource allocation data; communicating with each of the allocation targets and adjusting the resources contributed to the allocation target; and output the resource allocation for each of the allocation targets to at least one client device” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “A server for resource allocation optimization amongst a plurality of allocation targets, the server comprising: a memory and a communications interface; and a processor interconnected to the memory and the communications interface; a client device; A system for resource allocation optimization amongst a plurality of allocation targets, the system comprising: a revenue database; a resource allocation database; a model database; and a server” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 2-8, 10-16, and 18-24 further narrow the abstract idea and dependent claims 2, 4, 5, 8, 10, 12, 13, 16, 18, 20, 21 and 24 additionally recite “receive an optimization request from the at least one client device via the communications interface”, “obtain the revenue data based on the selected resource allocation”, “obtain the resource allocation data based on the selected resource allocation”, “extract contextual features from the resource allocation data”, “obtain a defined time period over which to distribute the resources”, and “obtain a defined time interval over which to distribute the resources” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “processor” and “server” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “A server for resource allocation optimization amongst a plurality of allocation targets, the server comprising: a memory and a communications interface; and a processor interconnected to the memory and the communications interface; a client device; A system for resource allocation optimization amongst a plurality of allocation targets, the system comprising: a revenue database; a resource allocation database; a model database; and a server” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, server claims 1-8; method claims 9-16; and system claims 17-24 recite “A server for resource allocation optimization amongst a plurality of allocation targets, the server comprising: a memory and a communications interface; and a processor interconnected to the memory and the communications interface; a client device; A system for resource allocation optimization amongst a plurality of allocation targets, the system comprising: a revenue database; a resource allocation database; a model database; and a server”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0029 and 0039 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “obtain a revenue model associated with an account; extract, from the revenue model, coefficients corresponding to each of the allocation targets; storing revenue data for at least one account; storing resource allocation data for the account; storing revenue models for the account, the revenue models trained based on the revenue data and the resource allocation data; communicating with each of the allocation targets and adjusting the resources contributed to the allocation target; and output the resource allocation for each of the allocation targets to at least one client device” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-8, 10-16, and 18-24 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 2, 4, 5, 8, 10, 12, 13, 16, 18, 20, 21 and 24 additionally recite “receive an optimization request from the at least one client device via the communications interface”, “obtain the revenue data based on the selected resource allocation”, “obtain the resource allocation data based on the selected resource allocation”, “extract contextual features from the resource allocation data”, “obtain a defined time period over which to distribute the resources”, and “obtain a defined time interval over which to distribute the resources” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “processor” and “server” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: Claims 1, 9, and 17 recite a combination of claim limitations that, as drafted, under considerations of the broadest reasonable interpretation of the claimed invention, are rendered neither obvious nor anticipated by the available field of prior art. The prior art of the record fails to explicitly teach, disclose, or suggest the combination of claim limitations, including at least: evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computation complexity of running simulations over the set of stationary points is 0(2n) time, where n corresponds to a number of the allocation targets. The closest prior art of the record discloses:
Beloi (U.S 2018/0047039 A1) discloses obtain a revenue model associated with an account, the revenue model trained based on a historical resource allocation and revenue for the account; extract, from the revenue model, allocation targets; define target constraints for each of the allocation targets based on resource allocation constraints; apply optimization to the target constraints; and allocate, for each of the allocation targets, resources according to the resource allocation. However, Beloi fails to explicitly disclose, teach, or suggest, extract, from the revenue model, coefficients corresponding to each of the allocation targets; define, using the extracted coefficients, a symbolic representation of the revenue model; define target constraints for each of the allocation targets based on resource allocation constraints, the target constraints expressed as symbolic representations; apply Lagrangian optimization to the symbolic representation of the revenue model and the target constraints to obtain a set of stationary points; evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computation complexity of running simulations over the set of stationary points is 0(2n) time, where n corresponds to a number of the allocation targets; allocate, for each of the allocation targets, resources according to the resource allocation defined by the stationary point; and compute an effectiveness metric for a selected resource allocation based on revenue data and resource allocation data.
Yang (U.S 2010/0082392 A1) discloses extract, from the revenue model, coefficients corresponding to each of the allocation targets; define, using the extracted coefficients, a symbolic representation of the revenue model; define target constraints for each of the allocation targets based on resource allocation constraints, the target constraints expressed as symbolic representations; apply Lagrangian optimization to the symbolic representation of the revenue model and the target constraints to obtain a set of stationary points; evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets; and allocate, for each of the allocation targets, resources according to the resource allocation defined by the stationary point. However, Yang fails to explicitly disclose, teach, or suggest, evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computation complexity of running simulations over the set of stationary points is 0(2n) time, where n corresponds to a number of the allocation targets and compute an effectiveness metric for a selected resource allocation based on revenue data and resource allocation data.
Dasdan (U.S 2016/0117736 A1) discloses compute an effectiveness metric for a selected resource allocation based on revenue data and resource allocation data. However, Dasdan fails to explicitly disclose, teach, or suggest, evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computation complexity of running simulations over the set of stationary points is 0(2n) time, where n corresponds to a number of the allocation targets.
Huang (CN110557287A) discloses a resource allocation method based on Lyapunov optimization. However, Huang fails to explicitly disclose, teach, or suggest, evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computation complexity of running simulations over the set of stationary points is 0(2n) time, where n corresponds to a number of the allocation targets.
Hatano (‘Lagrangian decomposition algorithm for allocating marketing channels’) discloses the use of Lagrangian decomposition to influence maximization in the context of computational advertising. However, Hatano fails to explicitly disclose, teach, or suggest, evaluate each of the stationary points from the set to select a stationary point optimizing the revenue model, the selected stationary point defining a resource allocation for each of the allocation targets, wherein a computation complexity of running simulations over the set of stationary points is 0(2n) time, where n corresponds to a number of the allocation targets.
Therefore, the combination of claim limitations, when considered in view of the available field of prior art, are rendered neither obvious nor anticipated.
However, the present claims are not in condition for allowance because the claims are rejected under 35 USC 101, as set forth in the current office action. Therefore, the claims are not in condition for allowance at this time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Feng, Guofu, et al. "Revenue maximization using adaptive resource provisioning in cloud computing environments." 2012 ACM/IEEE 13th International Conference on Grid Computing. IEEE, 2012.
DOCUMENT ID
INVENTOR(S)
TITLE
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Kagan et al.
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US 2014/0278622 A1
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ITERATIVE PROCESS FOR LARGE SCALE MARKETING SPEND OPTIMIZATION
US 2021/0232433 A1
Sun et al.
Operations cost aware resource allocation optimization systems and methods
THIS ACTION IS MADE FINAL. 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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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3625