DETAILED ACTION
Status of Claims
Applicant has amended claims 1, 4, 6, 11, 12, 15, 17 and 20. No claims have been added or canceled. Thus, claims 1-20 remain pending in this application. 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 and amendments filed on 23 April 2026 with respect to:
rejections of claims 4 and 5 under U.S.C. § 112(a),
rejections of claims 1-20 under U.S.C. § 112(b),
rejection to claims 1-20 under U.S.C. § 101,
rejections of claims 1, 2, 4-9, 12, 13 and 15-18 under 35 U.S.C. § 103 as being unpatentable over Juban et al (US Pub. No. 20210224922 A1) in view of Guo (US Pub. No. 20200394707 A1), and
rejections of claims 3 and 14 under 35 U.S.C. § 103 as being unpatentable over Juban in view of Guo, in further view of Shah et al (US Pub. No. 20220399132 A1)
have been fully considered. Amendments to claims have been entered.
Examiner acknowledges amendments to claims to overcome 35 U.S.C. § 112(a) and 35 U.S.C. § 112(b) rejections. However, amendments are not totally effective. Examiner notes that the Applicant has not made any specific arguments regarding revised claim language. See revised § 112(b) rejections below.
Examiner acknowledges amendments to, and arguments regarding claims to overcome 35 U.S.C. § 101 rejection. However, arguments are not persuasive.
Applicant argues subject matter eligibility under Step 2A – Prong Two citing the decision in Ex parte Desjardins, Appeal No. 2024-000567 (ARP Sept. 26, 2025, designated precedential Nov. 4, 2025) and from Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) when evaluating whether claims directed to improvements to the functioning of a computer or other technology are patent eligible [remarks pages 8 and 9]. Examiner respectfully disagrees.
In view of Memorandum titled “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101” (“Reminder Memo”) issued on August 4, 2025.which recites:
When evaluating these two considerations, examiners may consider the following:
1 Whether the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome.
2. Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, or whether the claim purports to improve computer capabilities or to improve an existing technology.
Examiner maintains that the claims fails to recite details of how a solution to a problem is accomplished in view of highly generalized terms, such as potential feature variable, customer in an area of interest, and one or more laundering scenarios. Accordingly, the claims do not convey an improvement to computer capabilities or to improvement to an existing technology.
Examiner maintains that the Applicant’s claims are not similar to the Enfish claims in that Enfish’s claim 17 is specifically directed to a self-referential table for a computer database. For claim 17, this is reflected in step three of the “means for configuring” algorithm. The necessity of describing the claims in such a way is underscored by the specification’s emphasis that “the present invention comprises a flexible, self-referential table that stores data”. Applicant’s claims are unlike those of Enfish in that they do not directly reflect the specification using a “means for configuring” algorithm (emphasis on “means for”). Applicant’s claims merely recite manipulating data and, as such, amount to an abstract idea of mitigating risk which is a judicial exception of a method of organizing human activity.
Applicant argues subject matter eligibility under Step 2A – Prong Two In that
The Iterative Feature Selection Process Is a Specific Technical Improvement to How the TM Index Model Is Built
[remarks page 9]. Examiner respectfully disagrees.
Applicant’s “selection” limitation in representative claim 1 recites:
selecting a set of training samples on which to train the TM Index model, wherein selecting the set of training samples includes identifying, based at least in part on transaction data of the customers, customers in an area of interest for one or more money-laundering scenarios;
Applicant’s recitation of “identifying” “areas if interest’ and “money-laundering scenarios” are recited at aa high level of generality such that the Examiner cannot identify “details” of how a solution to a problem.
Applicant argues subject matter eligibility under Step 2A – Prong Two In that
The Scenario-Based Training Sample Selection Is a Specific Technical Solution to the Imbalanced Data Problem in Financial Crime ML
[remarks page 10]. Examiner respectfully disagrees.
Here again. Applicant’s recitations of ”financial crime monitoring scenarios”, customers who are above or below “an alert threshold“, scenario-based population targeting, and “ranking orders customers in the unknown population” by “likelihood of interestingness” are recited at aa high level of generality such that the Examiner cannot identify “details” of how a solution to a problem.
Applicant argues subject matter eligibility under Step 2A – Prong Two In that
The Scenario-Specific Alerting Architecture Integrates the ML Output Into a Concrete Practical Application With Defined Operational Logic
[remarks pages 10 and 11]. Examiner respectfully disagrees.
Here again. Applicant’s recitations of ” determining whether a financial crime alert should be issued based on transaction data” is recited at aa high level of generality such that the Examiner cannot identify a “concrete practical application”.
Applicant argues subject matter eligibility in that
The Office Action Failed to Adequately Engage With the Specific Technical Elements of the Claims
[remarks pages 11 and 12]. Examiner respectfully disagrees as discussed supra.
Applicant argues subject matter eligibility in that:
B. The Pending Claims Are Not Directed to an Abstract Idea Under Step 2A, Prong 1
[remarks pages 12 and 13]. Examiner respectfully disagrees and maintains that the claims are directed to the abstract idea of determining an alert for a financial crime which is a fundamental business practice of mitigating risk.
Applicant argues subject matter eligibility under Step 2B in that:
C. The Pending Claims Amount to Significantly More Under Step 2B
[remarks page 13]. Examiner respectfully disagrees as discussed supra as related to subject matter eligibility under Step 2A – Prong Two.
As with determining a practical application to an abstract idea - Step 2A – Prong Two, types of limitations indicative of an inventive concept (aka “significantly more”) – subject matter eligibility under Step 2B - include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b),
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c),
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 - see MPEP 2106.05(e) and Vanda Memo
Further, limitations also indicative of an inventive concept include:
Adding a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d).
Examiner maintains that the claimed invention does not contain any of these “types” of aforementioned limitations.
Limitations that are not indicative of an inventive concept include:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f),
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g),
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h),
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo.
Examiner maintains that the claimed invention merely appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
Examiner cites [0019] of US Pub. No 20250348880 which discloses financial crime scenarios as including:
“anomalies in ATM or bank card for foreign transactions; address associated with multiple, recurring external entities”
And [0028] discloses”
“Example scenarios that can be monitored include, but are not limited to: patterns of sequentially numbered checks or other monetary instruments (CHECK MI); change in behavior for foreign activity (CIB-FA); significant change in behavior from previous average activity (CIB-PAA); patterns of funds transfers between customers and external accounts for ACH or wire transactions (FTN-EXTERNAL-ACH and FTN-EXTERNAL-WIRE); patterns of funds transfers between internal accounts and customers (FTN-INTERNAL);”
Examiner suggests that Applicant add some subject matter from paragraphs such as [0019] and [0028] to provide a practical application or an inventive concept.
Rejections have been clarified herein in view of the claim amendments and the current MPEP 2106 Patent Subject Matter Eligibility Requirements.
Applicant's arguments filed with respect to claims regarding the 35 U.S.C. § 103 rejections have been fully considered but they are moot in view of new ground(s) of rejection.
If, in the opinion of the Applicant, a telephone conference would expedite the prosecution of the subject application, the Applicant is encouraged to contact the undersigned Examiner at the phone number listed below.
Priority
This application, filed on 08 May 2024 is given priority from 08 May 2024.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1 and 12 are vague and indefinite in that they are structured such that it is hard to determine what Applicant is claiming as his invention. For example:
Regarding claims 1 and 12, in the representative limitation:
determining a set of feature variables for the TM Index model, wherein determining the set of feature variables comprises iteratively determining, for each of a plurality of potential feature variables, whether performance of the model improves, relative to a prior iteration of the TM Index model and as measured by a performance metric, due to inclusion of the potential feature variable;
the term "improves" is a relative term which renders the claim indefinite. The term “improves” (performance of the model) is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Moreover, the Examiner finds that because particular claims are rejected as being indefinite under 35 U.S.C. § 112(b), it is impossible to properly construe claim scope at this time (See Honeywell International Inc. v. ITC, 68 USPQ2d 1023, 1030 (Fed. Cir. 2003) “Because the claims are indefinite, the claims, by definition, cannot be construed.”). However, in accordance with MPEP § 2173.06 and the USPTO’s policy of trying to advance prosecution by providing art rejections even though the claims are indefinite, the claims are construed and the art is applied as much as practically possible.
Claims 2-11 and 13-20 are rejected by way of dependency on a rejected independent claim.
The art rejections below are in view of the 112(b) rejections stated above.
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.
In the instant case, claims 1-11 are directed to a “method” which is one of the four statutory categories of invention.
Claims are directed to the abstract idea of determining an alert for a financial crime which is a fundamental business practice of mitigating risk, grouped under Methods Of Organizing Human Activity
in prong one of step 2A (See MPEP 2106 Patent Subject Matter Eligibility [R 10.2019]). Claims recite:
determining the TM Index score for the customer comprises summing of an output of each of the series of multiple decision trees; and
determining a set of feature variables for a “model”, wherein determining the set of feature variables comprises iteratively determining, for each of a plurality of potential feature variables, whether performance of the model improves, relative to a prior iteration of the TM Index model and as measured by a performance metric, due to inclusion of the potential feature variable; and
selecting a set of training samples on which to train the “model”, wherein selecting the set of training samples includes identifying, based at least in part on transaction data of the customers, customers in an area of interest for one or more money-laundering scenarios; and
computing TM Index scores for the customers using the trained “model”;
identifying one or more scenario alerts of potential financial crime activity by customers of the financial institution based on transaction data for the customers; and
for each of the one more scenario alerts, determining, whether a financial crime alert should be issued based on, at least, transaction data for a respective customer pertaining to the scenario alert and the TM Index score for the respective customer.
Limitations such as:
the TM Index scores are numerical values over a range, where higher scores indicate more interestingness of a customer in terms of potential money-laundering activities
are merely descriptions of data and do not impose any meaningful limit on the computer implementation of the abstract idea.
Accordingly, the claim recites an abstract idea (See MPEP 2106 Patent Subject Matter Eligibility [R-10.2019]).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (See MPEP 2106.04(d) Integration of a Judicial Exception Into A Practical Application [R-07.2022]), the additional elements of the claim such as
a computer system,
a TM Index model comprising a series of multiple decision trees;
a TM Index computation system, a scenario generation system, and an alerting computer system; and
training, by a TM Index model training computer system, via machine learning, a TM Index model to compute “score” for customers of the financial institution,
represent the use of a computer as a tool to perform an abstract idea and do no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e. automate) the acts of “collecting information, analyzing the information and providing the results of the analysis”.
When analyzed under step 2B (See MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More [R-07.2022]), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself because the ordered combination does not offer substantially more than the sum of the functions of the elements when each is taken alone.
The computer and computer program instructions are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generalized terms include:
a TM Index model training
a TM Index computation system
a scenario generation system,
an alerting computer system
which are applied to:
potential feature variable,
customer in an area of interest, and
one or more laundering scenarios
However, no specific computerized functions are recited. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities. These functionalities are well-understood, routine and conventional activities previously known to the industry. Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
Thus, viewed as a whole, the combination of elements recited in the claims merely describe the concept of determining an alert for a financial crime using computer technology (e.g. the processor).
Hence, claims are not patent eligible.
Dependent claims 2-11 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to a judicial exception (Step 2A- Prong One). Nor are the claims directed to a practical application to a judicial exception (Step 2A- Prong Two).
For example, claims 5-11 are silent as to “additional elements” which integrate the abstract idea into a practical application of a judicial exception, or that are sufficient to amount to significantly more than the judicial exception. They merely further describe the abstract idea of determining an alert for a financial crime.
In claims 2-4, the features:
a gradient boosting machine learning framework;
a Light Gradient Boosted Tree model; and
iteratively adding decision trees.
add technology to the abstract idea of the independent claim. However, a these components amount to no more than standard Boolean tree analysis and convey generic technological components. Their use is in their normal, expected, and routine manner. The components are recited at a high level of generality which do not improve another technology or technical field nor the functioning of the computer itself.
Accordingly, none of the dependent claims add a technological solution to the fundamental business practice in the independent claim.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of claims 12-20 otherwise styled as a system, would be subject to the same analysis.
Conclusion
The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-9, 12, 13 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Juban et al (US Pub. No. 20210224922 A1) in view of Shachar et al (US Pub. No. 20210342847 A1).
Regarding claims 1 and 12, Juban teaches systems and methods that may advantageously apply machine learning to accurately manage and predict accounts and account holders with money laundering risk [0003]. Such systems and methods may allow accurate predictions of money laundering risk based on analysis of account variables based on aggregated data from multiple disparate data source systems, identification of suspicious accounts or account holders for investigation, and identification of actionable recommendations to users, all in real time, near real-time, just-in-time, at regular intervals (e.g., every week, every day, every four hours, etc.), upon the request of a user, or the like. He teaches:
training, by a TM Index model training computer system, via machine learning, a TM Index model to compute an TM Index score for customers of the financial institution – [0006], [0020], [0064] “Classification of illegal activity can be improved through machine learning training on a set of confirmed money laundering cases and associated transaction and account information or account holder information”, and [0100], wherein:the TM Index scores are numerical values over a range, where higher scores indicate more interestingness of the customer in terms of potential money-laundering activities – [0014];the TM Index model comprises a series of multiple decision trees, and determining the TM Index score for the customer comprises summing of an output of each of the series of multiple decision trees – [0105]; and
training the TM Index model includes:
determining a set of feature variables for the TM Index model, wherein determining the set of feature variables comprises iteratively determining, for each of a plurality of potential feature variables, whether performance of the model improves, relative to a prior iteration of the TM Index model and as measured by a performance metric, due to inclusion of the potential feature variable – [0025] “implements a method for anti-money laundering (AML) analysis”, [0062] “the AML (anti-money laundering) application can track key performance metrics of AML activity to ensure operational improvement over time and provide summary-level information about recent verified illegal activity and current suspicious case” and [0074]; and
periodically, after training the TM Index model – [0003] “at regular intervals (e.g., every week, every day, every four hours, etc.)”, [0017] and [0024]:
computing, by a TM Index computation system, TM Index scores for the customers using the trained TM Index model – [0006], [0007], [0008], [0015], [0017], [0018] and [0024] “a money laundering risk score”;
identifying, by a scenario generation system, one or more scenario alerts of potential financial crime activity by customers of the financial institution based on transaction data for the customers – [0079] and [0100] “training the classification model using the features of prior confirmed illegal activity cases (e.g., known financial crimes)”; and
for each of the one more scenario alerts, determining, by an alerting computer system, whether a financial crime alert should be issued based on, at least, transaction data for the customer pertaining to the scenario alert and the TM Index score for the customer – [0046], [0075], [0078], [0091], [0092] and [0107].
Juban teaches training a classification model using the features of prior confirmed illegal activity cases (e.g., known financial crimes) [0100]. Juban does not explicitly disclose:
selecting a set of training samples on which to train the TM Index model, wherein selecting the set of training samples includes identifying, based at least in part on transaction data of the customers, customers in an area of interest for one or more money-laundering scenarios.
However, Shachar teaches a system and method for training machine learning models to identify the anomalous transactions, such as money laundering, in transaction data sets using federated transfer learning [0002]. He teaches that in order to provide for anomaly detection in data sets for an entity, such as money laundering, fraud, or noncompliant transactions in transaction data sets for a financial entity, an artificial intelligence (AI) system may first require micromodels trained on other data sets and/or using different supervised machine learning (ML) algorithms and techniques [0015].
Shachar teaches generating and determining one or more micromodels using an ML algorithm and technique [0023]. Multiple different types of ML algorithms may be used to generate different micromodels. Micromodels 121 are trained to have multiple hyper-parameter settings where, instead of optimizing certain hyper-parameters that would be tailored to a data set (e.g., second transaction data sets 131), multiple micromodels are instead trained and selected based on the data set and scenario. These models are generated instead to provide risk scores on the data set at stake (e.g., second transaction data sets 131) for ML modeling for anomaly detection [Id.].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Juban’s disclosure to include training micromodels based on selected data sets and scenarios as taught by Shachar in order to provide federated transfer learning by training models with different federated data sets that are transferred to a “data set at stake” for ML modeling - Shachar [0024].
Regarding claims 2 and 13, Juban teaches the TM Index model as comprising a gradient boosting machine learning framework – [0006] and [0020].
Regarding claims 4 and 15, Juban teaches the TM Index model as comprising iteratively adding decision trees until a next tree in the iteration reduces a validation loss of the TM Index model by less than a threshold – [0118].
Regarding claims 5 and 16, Juban does not explicitly disclose each decision node in the series of multiple decision trees as comprising a test on one of the feature variables.
However, Shachar teaches testing the ML model after the ML model is trained and the hyper-parameters are optimized [0043].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Juban’s disclosure to include testing the ML model after the ML model is trained as taught by Shachar because such testing is old and well known in the art of using machine learning.
Regarding claims 6 and 17, Juban teaches training the TM index score being the sum of an output from each of the series of decision trees – [0105].
Regarding claim 7, Juban teaches periodically computing the TM Index scores as comprising computing the TM Index scores weekly – [0017] and [0024].
Regarding claim 8, Juban teaches the customers as comprising persons – [0006]-[0008] “given account holder”, [0020] and [0022].
Regarding claim 9 and 18, Juban does not explicitly disclose:
training the TM index model with training samples; and
selecting the training samples for the training, wherein selecting the training samples comprises selecting training samples that are within an area of interest for one or more predefined suspicious financial crime scenarios.
However, Shachar teaches steps of data cleaning, sampling, normalizing, determining intersecting columns between data sets [0036]. Data cleaning may include removing columns which are characterized as zero-variance (meaning, have no more than one unique value), as those may not contribute to the model. Segment-specific feature and row selection may be performed, for example, based on small to mid-sized enterprise (SME) knowledge [Id.].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Juban’s disclosure to include segment-specific feature as taught by Shachar in order to focusing on, say, retail transactions, and removing non-monetary transactions that are not relevant to the specific segment - Shachar [0036].
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Juban in view of Shachar, in further view of Shah et al (US Pub. No. 20220399132 A1).
Regarding claims 3 and 14, neither Juban nor Shachar explicitly discloses the TM Index model as comprising a Light Gradient Boosted Tree model.
However, Shah teaches training a machine learning model as including training a light gradient boosted tree model [0009].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Juban’s disclosure to include a light gradient boosted tree model as taught by Shah since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Additional Comments
Regarding claims 10, 11, 19 and 20, in view of pending rejections, the Examiner is unable to locate prior art references that anticipate the claimed invention or renders it obvious.
Conclusion
The prior art of record and not relied upon is considered pertinent to Applicant’s disclosure:
Pomeroy et al: “Method And System For Reloading Prepaid Card”, (US Pub. No. 20160086166 A1).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD J BAIRD whose telephone number is (571)270-3330. The examiner can normally be reached 7 am to 3:30 pm M-F.
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If Applicant wishes to correspond to the Examiner via email, Applicant needs to file an AUTHORIZATION FOR INTERNET COMMUNICATIONS IN A PATENT APPLICATION form. The form may be downloaded at:
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon can be reached at 571-270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDWARD J BAIRD/Primary Examiner, Art Unit 3692
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