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 .
Status
This action is in response to the amendment filed on 3/16/2026. Claims 1, 3-15, 17-20, 22-30 are pending. No claims are amended. No claims are currently added. No claims are currently have been cancelled.
Response to Arguments
Applicant's arguments filed 3/16/2026 have been fully considered but they are not persuasive. The applicant has argued the 101 rejection. The applicant has argued “First, referring to the first step enumerated in the amended MPEP, Applicant respectfully submits that the specification of the pending application here discloses specific technological improvements to computer systems that generate advertising elasticity by allowing them to do so without requiring historical time-series data. The specification explains this problem: Traditionally, the process of allocating marketing budget based on known advertising elasticity requires historical time-series data from a user seeking to allocate their marketing budget about previous marketing tactics and their outcomes. In many cases, such historical time-series data either does not yet exist or, if it does exist, would be too burdensome to input such that it can be analyzed in a meaningful way. As-filed Spec. at [5] (emphasis added).” The examiner respectfully disagrees. The applicant quotes the specification arguing a technical improvement. However, the quoted paragraph does not describe a technical problem. It describes a limitation of data availability and data collection burden faced by business users. The problem of data not yet existing is not a technical problem it is a factual issue within the history of a business. The problem of data being “too burdensome to input” is a practical inconvenience of data collection, not a failure of computer functionality. The cited specification fails to identify anything about how prior computer systems operated that prevented them from generating adverting elasticity estimates. This is a business workflow problem, not a technical one. This is different from Desjardins. In Desjardins the specification identified a problem internal to how machine-learning systems compute, specifically that updating model parameters to learn a new task degraded the models performance. The solution was a new computation architecture that changed how the model updates its own parameters. The problem as argued appears to be outside the computer and more part of the environment. It is not directed to an improvement to the functioning of a computer or to a technical field.
The applicant continues by pointing to specification paragraphs 11-12 arguing “The specification then goes on to explain the solution to this problem of unavailable or expensive historical time-series data.” The examiner respectfully disagrees. Specification paragraph 11 discloses the situation where a user wants a quick estimate without having to gather or input historical time-series data and paragraph 12 discloses that the data need not be excluded from the analysis and that the demand model may later be further updated with historical time-series data and survey data if and when the data is available. It appears as though the applicant in view of the specification is arguing that the system is running the same computations with different inputs while remaining fully compatible with the original inputs. This would not be an improvement to the technology. This is not analogous with Desjardins. Desjardins claimed training architecture that fundamentally changed how the model updated its parameters. The new and old approach were computationally distinct. Here, the specification describes a system that can use either the new data or traditional ones confirming there is not change to the computer or system. Any improvement would be to the abstract idea not to the technology.
The applicant has argued “Second, referring to the second step enumerated in the amended MPEP, Applicant respectfully submits that the pending claims reflect the disclosed improvement. In particular, the pending claims recite a number of steps that define the input required (as well as the input not required) and the specific ways that input is used to be able to generate the advertising elasticity without requiring historical time-series data…” The examiner respectfully disagrees. Reciting what data is and is not required as an input is not the same as reciting a mechanism, architecture, or technical step that causes a computer system to function in a different way. Desjardins disclosed adjusting model parameters to optimize performance on a new task while protecting performance on a prior task. The improvement was embedded in how the steps were performed. The steps of applicant’s claims appear to be claiming how a method is applied not a technical step that causes the computer to operate differently. The limitation of “specific ways that input is used to be able to generate the advertising elasticity without requiring historical time-series data” is a negative input limitation stating what data is not needed but does not reflect a technological improvement to how the system operates. A claim that performs the same computations regardless of whether the data is there or not is not integrated in a technological improvement, merely just an abstract computational method.
The applicant has argued “Under Desjardins and the revised MPEP, the claim need not explicitly recite the improvement (e.g., "thereby eliminating the need for time-series data"); rather, it must simply include the components or steps that provide the improvement described in the specification. In Desjardins, the ARP found that the limitation "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task" was the structural claim element that reflected the improvement. Here, similarly, the pending claims contain multiple such structural elements that directly correspond to the technological improvement disclosed in the specification. For example, the pending claims recite "computationally inferring a second probability distribution . . . subject to mathematical constraints derived from the profit and loss statement data, wherein the computational inferring generates the second probability distribution without requiring historical time-series data or survey data inputs," which refers to computational inference used to generate the probability distribution using the P&L data without requiring the data that has traditionally been required. The pending claims further recite "combining the first probability distribution and the second probability distribution using conjugate Bayesian computational methods to generate a unified expected advertising elasticity distribution," which refers to computational Bayesian inference. The two limitations above set up the computational pipeline that is then used to build the demand model, which is how the claims solve the technical problem described above. In particular, the pending claims recite "building a computational demand model using the unified expected advertising elasticity distribution and the financial data." This built demand model is computational component similar to the model in Desjardins-that allows the advertising elasticity to be estimated without the data that has traditionally been required.” The examiner respectfully disagrees. The improvement found in Desjardins was found to reflect a technological improvement because it recited a specific computational operation that caused the system to function differently. Applicant’s claimed adjustment of parameters is not analogous with catastrophic forgetting. Deriving constraints from financial data rather than time-series data does not describe a new computational operation, it merely describes an additional data source. Further, combining a first probably distribution and a second probability distribution using a Bayesian computational method recites a known statistical technique. The claim applies this standard technique to adverting elasticity distributions, but does not recite any improvement to how Bayesian computation itself is performed. In Desjardins the model was significant not because it produced a useful output, but because its training architecture was itself the technological improvement. Each step is conventional in its own domain. Performing them in sequence on marking data does not supply a technological improvement analogous to Desjardins. It appears as though the applicant is merely taking a known probability distribution to infer and output data based on determined elasticity values. Mathematical formulas and algorithms are considered abstract ideas or fundamental truths that exist independently of human invention. Further, Bayesian modeling is a mathematical concept the computations and/or the function of the process, can be accomplished by the human mind using a pen and paper. The model is not a device limitation, but rather a collection of mathematical concepts recited as instructions on a generically recited computer with functions and associated data directed to mathematical and mental processes. The basic functions of a model entails comparing data to other data, and making calculations or computations, which is akin to a mental process. Bancorp Services, L.L.C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he use of a computer in an otherwise patent-ineligible process for no more than its most basic function– making calculations or computations– fails to circumvent the prohibition against patenting abstract ideas and mental processes.”) Thus, a system, like the claimed system, “that employs mathematical algorithms [e.g., machine learning risk detection models] to manipulate existing information to generate additional information is not patent eligible.” See Digitech Image Techs, LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). The computer in claim 1 does not improve the computer operation, but merely generates a model with expected advertising elasticity (data) and the input financial data the overall process of building and generating data in the abstract idea remains the same. The process may be done quicker or more accurately? However, the process is still the same process, and the claim does not improve the steps themself, but makes the overall sequence of steps faster which is an inherit benefit from the use of a computer. The applicant is merely using a computer as a tool to perform the steps of the invention. The improvement the applicant argued concerns an improvement to the data itself, and not an improvement to computer functionality or a technological improvement. Under Revised Step 2A, Prong Two of the Revised Guidance, the claims can be determined to (and more specifically the additional limitations beyond the judicial exception) integrate the judicial exception into a practical application. However, no additional element (or combination of elements) recited in claim 1 that integrates the judicial exception into a practical application. See 2019 Revised Guidance, 84 Fed. Reg. at 54–55 (“Prong Two”). For example, applicant’s additional elements (i.e., a processor, backend server, and a database) recited in claim 1, (1) do not improve the functioning of a computer or other technology, (2) are not applied with any particular machine (except for generic computer components), (3) do not effect a transformation of a particular article to a different state, and (4) are not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP §§ 2106.05(a)–(c), (e)–(h).
The applicant has argued “Finally, the pending claims recite "generating, using the built computational demand model, optimal investment allocation outputs .. when historical time-series data or survey data is unavailable," meaning that it is this built demand model that is actually used to generate the desired output data. When taking these claims as a whole, as required, it becomes clear that they offer a concrete technical solution to a problem, thereby integrating the alleged abstract idea into a practical application under Step 2A, Prong Two. In Desjardins, the specification explained specific technological improvements to how the machine learning model itself operates, namely, training a model to learn new tasks while protecting knowledge about previous tasks, thereby overcoming the "catastrophic forgetting" problem in continual learning systems. Desjardins emphasized that these improvements were "tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation." Here, similarly, the specification discloses an analogous, concrete technological improvement to the field of computational marketing budget allocation. Specifically, the specification at 11-12 (shown above) identifies a previously unsolvable technical problem in existing systems. Just as the existing ML systems referred to in Desjardins could not learn new tasks without destroying prior knowledge, existing marketing budget allocation computer systems "lack the ability to provide these types of quick estimates" when historical time-series data or survey data is unavailable. This is not simply a business inconvenience; it is a limitation of the prior computational methods.” The examiner respectfully disagrees. The generating step specifies a circumstance under which the output is generated and identifies the demand model as the tool for generating it. It does not recite any technology or mechanism by which the generating occurs. It appears as though the invention is merely generating outputs for a demand model. The applicant has not identified a technical improvement, merely mathematical techniques, a data choice, a deployment condition, or an output of mathematical methods. The current application is not analogous with Desjardins as the catastrophic forgetting problem was a limitation to how machine learning systems compute. This was not a data availability problem in Desjardins this was a consequence of how the computational process functioned. The prior marketing system didn’t fail to function or suffer from a computational deficiency. The invention does not claim a technical solution or an improvement to the operation of a computer system, limitations in the claim that amount to a technical solution to a technical problem or lead to such improvements in the operation of a computer. Rather, the claim limitations are recited as the combination of mathematical computations. Although it might be an improvement to the abstract idea the limitations appear to only require a generic processor functionally and without any specific technical requirements. A probability distribution is a mathematical function or graph that shows all possible outcomes of a random event and their likelihood of occurring. To infer a probability distribution means using observed data (or a sample) to understand, model, and make educated guesses about the characteristics of the larger, underlying process or population that generated that data, essentially figuring out the rule that governs how likely different outcomes are. It's about moving from specific data points to a general function (the distribution) that describes the whole random phenomenon, using statistical techniques to quantify uncertainty in these guesses. The limitations of claim 1 include inputting data, performing computations, receiving financial data, inferring a distribution, combining the probability distributions, busing a computation demand model, and generating investment allocation output. The only step in the method that requires the use of processor is the performing meta-analysis computations. The rest as claimed can be done by a human. A human can use received data to generate a measure of advertising elasticity, receive input, infer a value, combine data, build a model, generate an output value. As such, the claim limitations do not provide any technical solutions or result in improvements to a computer system.
The applicant has argued “Desjardins describes the solution as a new computational architecture that overcomes the "catastrophic forgetting" problem by adjusting some of the parameters of the machine-learning model to optimize its performance on a second task while protecting performance of the model on the first task. Here, similarly, the specification describes a computational architecture that solves the "lack of historical time-series data" problem by combining two distinct computational inputs (i.e., normative database meta-analysis and financial/P&L-derived inference) and fusing them through conjugate Bayesian methods to build a demand model that can both be re-used and can later be further updated. It is important to note here that both Desjardins and the pending claims focus on generating a model that can be used to accomplish specific computing tasks. In the case of Desjardins, the parameters of the model are updated in specific ways to allow the model to remember context across computing tasks. Here, the model is built in specific ways to allow the model to accomplish computing tasks without traditionally required data..” The examiner respectfully disagrees. When looking at the applicant’s specification there is nothing supporting the same analogous computation architecture of Desjardin. Desjardins parameter structure and updating rules is the technological improvement. The model was built different than prior models. In applicant’s claims the demand model is not itself a noble computational construct but a demand model specifically a mathematical relationship between advertising elasticity, investment and revenue. In Desjardins the invention involved a novel parameter adjustment mechanism that changed the models internal computational behavior. Claim 1 does not recite an improvement to a technical field or an improvement in technology, such as an improved memory or processor. Rather, claim 1 is directed to a fundamental economic practice generating a determined optimal investment amount and utilizes generic technology to implement that business method. Generating statistical outputs does not expand computational capabilities. Any improvement in generating of an optimal allocation and generating new data is in the abstract idea itself, i.e., the determination of an optimal investment amount, and not to any improvement to the claimed technology. The computer must provide a specific, non-generic technical benefit for instance enhancing the computer's own functionality rather than just automating an abstract idea (like calculating menus or processing data) using standard functions. Applicant’s arguments are not found persuasive. The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h). Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2); Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e). Applicant’s invention merely reciting the words “apply it” (or an equivalent) with the judicial exception, and merely including instructions to implement an abstract idea on a computer/ merely using a computer as a tool to perform an abstract idea, as well as generally links the use of a judicial exception to a particular technological environment.
Merely stating that an alternative algorithm was developed is not enough. The claims and specification must disclose how the new algorithm works differently at a technical level to achieve the described improvement, distinguishing it from conventional methods. Functional claiming alone, describing only what the invention does without the technical how, is often insufficient for patentability. Further, the arguments must clearly show that the invention is not merely implementing a statistical method on a generic computer. The "improvements" must be tied to the functioning of the computer system itself and not just the application of an abstract concept to a new field. A “claim for a new abstract idea is still an abstract idea.” For example, recitations directed to providing “for future determinations of advertising elasticity without the use of time-series or survey data” are, even if novel, insufficient to provide an inventive concept that transforms claim 1 into a patent-eligible invention. Although the second step in the Alice/Mayo framework is termed a search for an “inventive concept,” the analysis is not an evaluation of novelty or non-obviousness, but rather, a search for “an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.’” Alice, 134 S. Ct. at 2355. A novel and nonobvious claim directed to a purely abstract idea is, nonetheless, patent ineligible. See Mayo, 132 S. Ct. at 1304. The applicant has not demonstrated any particular arrangement in the claim as providing an inventive concept. See e.g., buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.”); In re Katz Interactive Call Processing Patent Litig., 639 F.3d 1303, 1316 (Fed. Cir. 2011) (“Absent a possible narrower construction of the terms ‘processing’, ‘receiving’, and ‘storing’… functions can be achieved by any general purpose computer without special programming”). Applicant’s arguments are not persuasive and the previous 101 rejection is maintained.
The closest pertinent prior art of record references include Cavander (US 20110010211 A1) which discloses automated budgeting tools. Shapiro (US 20070239542 A1) which discloses automated or assisted creation, analysis and control of online advertisements. Turner (US 20200279198 A1) which discloses determining a cash position for a company based on at least financial transactions and ERP system financial data. McConnell et al. (US 20160110661 A1) which discloses optimizing marketing budgets based upon budget limitations, forecasted revenue. Montero et al. (US 10915912 B2) which discloses testing of promotions and base pricing within brick and mortar retailers to determine an optimal price for goods. The prior art does not specifically teach computationally inferring a probability distribution of advertising elasticity by determining elasticity values that maximize net present value calculations subject to mathematical constraints derived from the profit and loss statement data, wherein the computational inferring generates the second probability distribution without requiring historical time-series data or survey data inputs, building a computational demand model using an unified expected advertising elasticity distribution and the financial data; and generating, using the built computational demand model, an optimal investment allocation outputs that represent solutions to advertising elasticity probability distribution problems when historical time-series data or survey data is unavailable.
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, 3-15, 17-20, 22-30 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1, 3-14, 29, are directed to a method and claims 15, 17-20, 22-28, 30 are directed to a system. Therefore, claims 1, 3-15, 17-20, 22-30 are directed to patent eligible categories of invention.
Step 2A Prong 1:
The claim(s) recite(s) (mathematical relationships/formulas, mental process or certain methods of organizing human activity). Specifically the independent claims recite:
(a) mental process: as drafted, the claim recites the limitations of input data specifying marketing variable, performing computations on probabilistic estimates, receiving financial data, inferring a probability distribution, combining probability distributions, busing a demand model, and generating optimal investment allocation outputs which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processor” in the system claim nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the processor language, the claims encompass a user manually calculating an investment amount. The applicant does not claim a type of technology, merely just technologic concepts in the method claim. The mere nominal recitation of a generic processor in the system claim does not take the claim limitation out of the mental processes grouping. This limitation is a mental process.
(b) mathematical formula: The claim recites a mathematical concept (which can include a mathematical relationships, mathematical formulas or equations, and mathematical calculations), and in this case calculates Thus, the claim recites mathematical concepts and mathematical relationships. The claims are calculating an advertising elasticity and combining measures of advertising elasticity and generating probability distributions. “Mathematical Relationships” A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A. Examples of mathematical relationships recited in a claim include: a relationship between reaction rate and temperature, which relationship can be expressed in the form of a formula called the Arrhenius equation, Diamond v. Diehr; a conversion between binary-coded decimal and pure binary numerals, Gottschalk v. Benson; and a mathematical relationship between enhanced directional radio activity and antenna conductor arrangement (i.e., the length of the conductors with respect to the operating wave length and the angle between the conductors), Mackay Radio & Tel. Co. v. Radio Corp. of Am. “Mathematical Calculations” A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation.
(c) certain methods of organizing human activity: The claim as a whole recites a method of organizing human activity. The claimed invention is a method that allows for when data is missing, generating data and storing a model related to a elasticity probability and a marketing investment which is a commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). Thus, the claim recites an abstract idea. According to the 2019 PEG, commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations.
5. Dependent claims 3-14, 17-20, 22-30, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Step 2A, Prong 2: Independent claims 1, 15, do not integrate the judicial exception into a practical application. Claim 1 is a computational method for generating probability distributions “performing by at least one processor, meta-analysis computations… a normative database… computationally inferring a second probability distribution… Bayesian computational methods…” Claim 15 is a computational system for generating probability distributions that recites “a normative database… a back-end server communicatively coupled to the normative database, the back-end server comprising a processor… computationally inferring a second probability distribution… Bayesian computational methods…” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to process, generate, manipulate, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity, mathematical formula) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3-14, 17-20, 22-30, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 15, do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, claim 1 is a computational method for generating probability distributions “performing by at least one processor, meta-analysis computations… a normative database… computationally inferring a second probability distribution… Bayesian computational methods…” Claim 15 is a computational system for generating probability distributions that recites “a normative database… a back-end server communicatively coupled to the normative database, the back-end server comprising a processor… computationally inferring a second probability distribution… Bayesian computational methods…” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, and merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). See MPEP 2106.05(h).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 3-14, 17-20, 22-30, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Therefore based on the above analysis as conducted based on MPEP 2106 from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, does not provide significantly more, and does not provide an inventive concept, therefore the claims are ineligible.
Accordingly, claims 1, 3-15, 17-20, 22-30 are rejected under 35 USC 101.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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