DETAILED ACTION
Status of the Claims
This office action is submitted in response to the amendment filed on 5/22/26.
Examiner notes that this application is a continuation of 18/316784, which is now US Patent #11966949.
Examiner further notes that 18/316784 is a continuation of three other cases, which are now US Patent Nos. 11687969, 11288704, and 10937058.
Examiner further notes Applicant’s priority date of 7/2/19, which stems from the aforementioned parent applications.
Examiner further notes the previous withdrawal of prior art on 7/16/25.
Claims 1, 15, and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Double Patenting
Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 11,966,949. Although the claims at issue are not identical, they are not patentably distinct from each other because they both disclose a method for targeting bid and position for a keyword, comprising: receiving keyword performance information from a first search engine based on user interaction; retrieving historical keyword information associating an application, an account, a conversion, or a value with the keyword performance information; joining the keyword performance information and the historical keyword information to generate a keyword dataset; generating a feature dataset comprising position-value-time sets for the keyword; applying a Gaussian process machine learning model to the position-value-time sets to obtain a non-linear prediction function and associated uncertainties; applying a Thompson sampling reinforcement machine learning model to obtain a target position; applying a backwards filtering model to historical bid position data to form a bid-to-position function using a monotonically decreasing function and a valid-or-not algorithm; determining bid information based on the target position and the bid-to-position function; transmitting a bid message to a second search engine; receiving a result of the bid message; and updating the reward function and the monotonically decreasing function based on the result.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1–20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1–20 are directed to patent-eligible subject matter categories under 35 U.S.C. § 101. Specifically, claim 1 recites a computer-implemented method, and thus falls within the "process" category. Claim 15 recites a system comprising a memory and a processor, and thus falls within the "machine" category. Claim 20 recites a non-transitory computer-readable medium, and thus falls within the "article of manufacture" category. Claims 2–14 depend from claim 1, and thus likewise fall within the "process" category. Claims 16–19 depend from claim 15, and thus likewise fall within the "machine" category. Accordingly, the claims satisfy Step 1.
Step 2A, Prong One: Independent claims 1, 15, and 20, in part, describe an invention comprising: (1) joining … the keyword performance information and the historical keyword information to generate a keyword dataset; (2) generating … a feature dataset in response to generating the keyword dataset, the feature dataset comprising a plurality of position-value-time sets for the keyword, wherein each position-value-time set of the plurality of position-value-time sets includes a respective profit-per-impression; (3) filtering … the plurality of position-value-time sets for the keyword to identify one or more position-value-time sets for the keyword … associated with a period of time; (4) determining … bid information based on the target position for the keyword and the bid-to-position function. As such, the invention is directed to the abstract idea of optimizing keyword bids for targeted ads based on historical performance and bid data, which is aptly categorized as a method of organizing human activity (managing and optimizing a commercial keyword bidding transaction). See MPEP § 2106.04(a)(2). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional elements that are associated with the judicial exception, including: receiving, via a network interface of a computing device, keyword performance information from a first search engine based on interactions with webpages associated with the first search engine; in response to receiving the keyword performance information, retrieving, from a data store of the computing device, historical keyword information associating an application, an account, a conversion, or a value with the keyword performance information; transmitting, via the network interface of the computing device, a bid message to a second search engine, the bid message including the bid information; and receiving, by the computing device and after transmitting the bid message, a result of the bid message for the keyword. The Examiner understands these limitations to be insignificant extra-solution activity. See Accenture Global Servs., GmbH v. Guidewire Software, Inc., 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191–192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle into a patentable process.").
The aforementioned claims also recite additional elements including: a "computing device" with a "network interface" and "data store" (claims 1, 15, 20); a "join information section" and "feature generation section" of the computing device (claims 1, 15, 20); a "Gaussian process model section" of the computing device (claims 1, 15, 20); a "bid model" of the computing device (claims 1, 15, 20); a "memory storing instructions" and "processor" (claim 15); and a "non-transitory computer-readable medium" and "processor" (claim 20). These limitations are recited at a high level of generality and appear to be nothing more than generic computer components (including functionally-labeled software sections of a networked computing device) that are used to apply the abstract idea. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 223 (2014), 110 USPQ2d 1977, 1983 (2014).
The aforementioned claims further recite additional elements directed to the use of specific machine learning and mathematical models as tools to carry out the abstract idea of algorithmic data analysis and bid optimization. Specifically, the claims recite: inputting the identified one or more position-value-time sets for the keyword to a Gaussian process machine learning model … the Gaussian process machine learning model being trained to output a non-linear prediction function and associated uncertainties based on the identified one or more position-value-time sets for the keyword; receiving … the non-linear prediction function and the associated uncertainties from the Gaussian process machine learning model; inputting the received non-linear prediction function and the associated uncertainties to a Thompson sampling reinforcement machine learning model … the Thompson sampling reinforcement machine learning model being trained to output a target position for the keyword based on the received non-linear prediction function, the associated uncertainties, and a reward function; receiving … the target position for the keyword from the Thompson sampling reinforcement machine learning model; applying … a backwards filtering model of a bid model to historical bid position data to form a bid-to-position function by starting with a most recent value-position-time set of a plurality of value-position-time sets of the historical bid position data and progressing backwards in time to form a monotonically decreasing function of values and positions of the value-position-time sets of the historical bid position data selected according to a valid-or-not algorithm, wherein the valid-or-not algorithm determines whether each value-position-time set is valid based on whether a bid-position of the value-position-time set is within a threshold distance of the monotonically decreasing function; updating … the reward function of the Thompson sampling reinforcement machine learning model … based on the result; and updating … the monotonically decreasing function based on the result, subsequent to updating the reward function. These represent pre-existing mathematical and machine learning techniques — Gaussian process regression for generating non-linear prediction functions with uncertainty estimates, Thompson sampling for balancing exploitation and exploration in decision-making under uncertainty, and backwards temporal filtering for constructing monotonically decreasing functions from historical data. The claims do not recite any improvement to how these models operate, any novel training methodology, or any modification to their architecture or internal mathematical operations. Rather, the models are invoked at a high level of generality as vehicles for carrying out the abstract idea of optimizing a commercial keyword bidding transaction. The newly-amended detail specifying that the valid-or-not algorithm determines validity based on whether a bid-position of the value-position-time set is within a threshold distance of the monotonically decreasing function merely specifies the mathematical criterion by which the backwards filtering model filters data — i.e., a threshold comparison against a function — which does not transform the models into anything beyond known tools applied to the abstract idea. Applying known mathematical and machine learning models as tools to implement an abstract idea — without reciting any improvement to the models themselves or to the technology underlying them — is the paradigmatic "apply it" scenario and does not render the claims eligible. See MPEP § 2106.05(f); Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983.
Step 2A, Prong Two: Looking at the elements individually and in combination, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field; apply the judicial exception in the treatment or prophylaxis of a disease; apply the judicial exception with a particular machine; effect a transformation or reduction of a particular article to a different state or thing; or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use generic computer components — including off-the-shelf mathematical and machine learning models applied as tools — to collect keyword performance and bid history data, apply mathematical modeling and reward-based optimization to select a target position, determine a bid amount, and iteratively update model parameters based on bid results. Accordingly, the claims do not integrate the judicial exception into a practical application, and the analysis proceeds to Step 2B.
Step 2B: The claims do not include additional elements sufficient to amount to significantly more than the judicial exception. The additional elements, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or any other technology, and their collective functions are merely facilitated by generic computer implementation.
Additionally, pursuant to the requirement under Berkheimer v. HP Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), the following citations are provided to demonstrate that the additional elements, identified above as extra-solution activity, are well-understood, routine, and conventional. See MPEP § 2106.05(d).
Receiving data from and transmitting bid messages to external network services via a generic network interface. buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); MPEP § 2106.05(g)(3).
Retrieving stored data from a data store. Versata Dev. Grp., Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015).
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception, and claims 1–20 are ineligible under 35 U.S.C. § 101.
Next, claims 2–14 depend from claim 1 and claims 16–19 depend from claim 15, and include all limitations contained therein. These claims do not recite any additional elements sufficient to transform the abstract idea into patent-eligible subject matter. Specifically:
Claims 2 and 16 further specify that the profit-per-impression is determined using a profit-by-impression algorithm. This limitation specifies a particular mathematical algorithm for computing profit-per-impression, adding further detail to the abstract data analysis without meaningfully limiting the abstract idea.
Claims 3 and 17 further specify that the profit-per-impression is determined using a moving average algorithm. This limitation specifies an alternative mathematical algorithm for computing profit-per-impression, similarly adding further detail to the abstract data analysis.
Claims 4 and 18 further specify that each position-value-time set includes a respective position associated with the application, the account, the conversion, or the value of the keyword dataset and a respective time associated with the application, the account, the conversion, or the value of the keyword dataset. These limitations describe the structure of the data being processed and do not recite any improvement to computer functionality or any other technical advance beyond the abstract idea.
Claims 5 and 19 further specify that the trained Thompson sampling reinforcement machine learning model outputs the target position based on an exploit-explore ratio. This limitation specifies a well-known parameter of Thompson sampling reinforcement learning and adds further mathematical detail to the abstract optimization process.
Claim 6 further specifies that the trained Thompson sampling reinforcement machine learning model includes an agent configured to select an exploit action or an explore action, based on the exploit-explore ratio. This limitation further describes the internal operation of the Thompson sampling model and adds algorithmic detail without meaningfully limiting the abstract idea.
Claim 7 further specifies that the keyword performance information includes information about at least one of a number of clicks, a number of impressions, or an average position for the keyword. This limitation describes the types of data collected and does not recite any improvement to computer functionality.
Claim 8 further specifies that determining the non-linear prediction function and the associated uncertainties includes obtaining the feature dataset … and processing the feature dataset and a prior function, using Bayesian inference, to make a posterior inference to determine the non-linear prediction function and the associated uncertainties. This limitation specifies that the Gaussian process model uses Bayesian inference — a well-known mathematical technique — adding further mathematical detail to the abstract idea.
Claim 9 further specifies that the prior function is initiated as one or more of linear functions, quadratic functions, or exponential functions relating to kernels. This limitation specifies the mathematical form of the prior function, adding further mathematical detail.
Claim 10 further specifies that the prior function is initiated as one or more of functions relating to kernels, including constant, squared exponential, matern, periodic, or linear functions. This limitation specifies particular kernel types — all well-known in Gaussian process literature — adding further mathematical detail.
Claim 11 further specifies storing the non-linear prediction function for the keyword as the prior function to be used in subsequent iterations of determining the non-linear prediction function and the associated uncertainties. Storing a computed function for reuse in subsequent iterations is a well-known data management step inherent to iterative mathematical modeling and does not confer patent eligibility.
Claim 12 further specifies that the exploit-explore ratio is based on an exploit range of positions when an exploit action is chosen or an explore range of positions when an explore action is chosen according to the exploit-explore ratio. This limitation further defines the mathematical exploit-explore framework of the Thompson sampling model.
Claim 13 further specifies that the exploit range of positions and the explore range of positions are determined based on expected values and the positions for the keyword. This limitation further defines how the exploit and explore ranges are mathematically determined.
Claim 14 further specifies that the trained Thompson sampling reinforcement machine learning model avoids selecting the target position from a no-bid range of positions, and wherein the no-bid range of positions is determined based on positions corresponding to expected values below a threshold value. This limitation specifies a threshold-based exclusion zone — a mathematical constraint on the optimization — adding further algorithmic detail to the abstract idea.
Therefore, claims 1–20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Relevant Prior Art
The following references are deemed to be relevant to Applicant’s disclosures:
Knapp (20150066639) discloses a method for bidding for ad impressions in an online setting.
Libby (20100004974) discloses a method for determining weighted average success probabilities of internet ads.
Mathew et al. (20090327083) discloses a method for automating online ad placement optimization.
Response to Arguments
I. Double Patenting
Applicant argues that the claims as amended are patentably distinct from claims 1–20 of U.S. Patent No. 11,966,949. This argument is not persuasive. Applicant provides no substantive analysis explaining how the amended claims are patentably distinct from the reference patent claims, and no terminal disclaimer has been filed. The amended claims and the reference patent claims both disclose a method for targeting bid and position for a keyword comprising collecting keyword performance and historical keyword information, generating position-value-time sets, applying a Gaussian process model to obtain a prediction function, applying a selection operation to obtain a target position, applying a backwards filtering model to historical bid position data to form a bid-to-position function, determining bid information, and transmitting a bid message to a search engine. The nonstatutory double patenting rejection is therefore maintained.
II. Section 101 Rejection
A. The "Particular Way" / Practical Application Argument
Applicant argues that the amended claims recite "specific technical mechanisms for ensuring bid-to-position predictions remain accurate in changing environments," and that these mechanisms constitute a "particular way to achieve a desired outcome" under MPEP § 2106.05(a), thereby integrating the abstract idea into a practical application. Specifically, Applicant points to the backwards filtering model with its valid-or-not algorithm — newly amended to specify that the algorithm determines validity based on whether a bid-position of the value-position-time set is within a threshold distance of the monotonically decreasing function — and to the sequential updating mechanism whereby the reward function is updated based on the bid result, followed by an update to the monotonically decreasing function. Applicant cites paragraphs [0003]–[0004] and [0053] of the specification in support. These arguments are not persuasive.
The newly-amended valid-or-not algorithm detail does not transform the claims into a practical application. Comparing a data point's position against a threshold distance from a monotonically decreasing function to determine whether that data point should be retained or discarded is a mathematical filtering operation — it is a threshold comparison applied to numerical data, which is itself part of the abstract mathematical process of constructing a bid-to-position function from historical bid data. The fact that the claims now specify the particular mathematical criterion used by the filtering algorithm does not convert the abstract mathematical process into a technical improvement; it merely adds further mathematical detail to the abstract idea. As the Federal Circuit has repeatedly held, the specificity or complexity of the mathematical operations does not determine eligibility. See SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018) ("[T]hat the claimed techniques are '[g]roundbreaking, innovative, or even brilliant' is not enough for eligibility . . . . [T]he claims here are ineligible because their innovation is an innovation in ineligible subject matter.").
Similarly, the sequential updating mechanism — updating the reward function based on the bid result and subsequently updating the monotonically decreasing function — was previously addressed in the prior Office Action's Response to Arguments, and the current amendment does not alter the analysis. Receiving the result of a commercial bid transaction and feeding that result back into a reinforcement learning model's reward function is the ordinary operation of a Thompson sampling reinforcement learning feedback loop. Updating the monotonically decreasing function based on that same result is the ordinary operation of the backwards filtering model. These are not improvements to the models themselves or to any underlying technology; they are the designed, expected functions of the recited mathematical tools applied to the abstract idea of optimizing keyword bids. See MPEP § 2106.05(f); Alice Corp., 573 U.S. at 223–26.
Applicant's citation to MPEP § 2106.05(a) and the proposition that claims demonstrating "a particular way to achieve a desired outcome" integrate a judicial exception into a practical application is inapposite. The "particular way" inquiry under § 2106.05(a) asks whether the claims recite a specific means or method that improves the relevant technology — for example, an improvement to computer functionality, to a technical process, or to the operation of a particular machine. The claims here do not recite an improvement to computer functionality, to machine learning technology, or to any technical field. Rather, they recite a particular implementation of the abstract idea of bid optimization using known mathematical tools. The specification itself confirms this: the Background identifies the problem as one of commercial optimization — existing approaches to keyword bidding "may make very simple assumptions about an auction space" and "may not use reinforcement learning" (spec. paragraphs [0003]–[0004]) — and the Detailed Description frames the purported improvement as achieving better business outcomes: "The system 200 of the present disclosure may consider non-linearity of a position to profit relationship, by using a Gaussian process model" and "may use reinforcement learning to adapt to changes, e.g., caused by seasonal effects or changes in other entities bidding behavior" (spec. paragraph [0063]). The improvement identified in the specification is an improvement to the commercial outcome of the bidding process — more profitable keyword bids that adapt to changing auction environments — not an improvement to the functioning of a computer or to machine learning technology. Specifying the mathematical details of how the abstract optimization is carried out — however specifically — does not satisfy § 2106.05(a).
B. The Berkheimer / Evidence Challenge
Applicant argues that the Office Action characterized the backwards filtering model and its integration with the Thompson sampling reinforcement machine learning model as "well-known, off-the-shelf" and "invoked at a high level of generality" without providing evidence, and requests the Examiner identify evidence supporting this characterization.
This argument conflates two distinct analytical steps. The characterization of the Gaussian process model, Thompson sampling model, and backwards filtering model as "well-known, off-the-shelf" techniques applied as tools to implement the abstract idea was made under the "apply it" analysis of MPEP § 2106.05(f) at Step 2A, Prong Two — not in a Berkheimer WURC analysis at Step 2B. The Berkheimer evidentiary requirement applies specifically to the Step 2B determination of whether extra-solution activity is well-understood, routine, and conventional. See Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018). It does not require evidentiary support for a finding that known mathematical models are being applied as tools to implement an abstract idea under § 2106.05(f). The "apply it" determination is a legal conclusion based on the claim language and the specification's own description of how the models are used — not a factual finding requiring extrinsic evidence.
Here, the specification itself describes these techniques as known tools applied to the business problem of keyword bid optimization. The Background frames the problem as one of applying better mathematical tools to an existing business process (spec. paragraphs [0003]–[0004]). The Detailed Description treats Thompson sampling as one of several interchangeable known selection algorithms, listing "maximum probability of improvement (MPI), expected improvement (EI) and upper confidence bound (UCB)" as alternatives (spec. paragraph [0044]). The Gaussian process model is described using standard, well-known kernel functions — "constant, squared exponential, matern, periodic, and linear" (spec. paragraph [0041]) — without any suggestion that these are novel. And the specification expressly frames the benefit of the system in commercial terms: better profit optimization and adaptation to seasonal and competitive dynamics in the auction space (spec. paragraph [0063]). No improvement to the models themselves — no solution to a technical problem in machine learning or computer science — is described anywhere in the specification.
To the extent the Berkheimer requirement applies to the extra-solution activity identified in the rejection — namely, receiving keyword performance information from a search engine via a network interface, transmitting bid messages to a search engine, retrieving stored data from a data store, and receiving bid results — the Office Action has provided case citations demonstrating that these activities are well-understood, routine, and conventional. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); Versata Dev. Grp., Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015).
Accordingly, Applicant's arguments have been fully considered but are not persuasive. The rejection of claims 1–20 under 35 U.S.C. § 101 is therefore maintained.
Finally, the previous double patenting rejection has been revised and sustained in view of the aforementioned amendments and Applicant's failure to file a terminal disclaimer.
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.
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/CHRISTOPHER C BUSCH/Examiner, Art Unit 3621
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621
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