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 of Claims
This action is in reply to the amendment filed on 09/23/2025.
Claims 2 -5, 15, 16 and 21 are previously cancelled.
Claims 1, and 11, are amended.
Claims 34 and 35 are newly added.
Claims 1, 6-14, 17-20, and 22-35 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed 09/23/2025 regarding the 35 U.S.C. § 101 have been fully considered but they are not persuasive.
Applicant argues “the amended claims cover "a particular solution" ("normalizing, by the multi- tenant computing system, the response codes generated by the multiple payment gateways of different payment gateway types in accordance with the different schemas into a uniform failure code format," "evaluating, by the multi-tenant computing system, the response codes in the unform failure code format to determine a failure type" and "training, by the multi-tenant computing system, a retry optimization machine learning model based on using a respective common failure type") to "a problem" (lack of computing resource utilization efficiency in machine learning model training), Applicant respectfully submits that, as explained by the 2024 Guidance Update, the amended claims as a whole integrate any recited judicial exception into a practical application of the exception, and thus are not directed to the judicial exception.” (Response at 16).
Examiner respectfully disagrees. The claims do not describe how training, by the multi-tenant computing system, a retry optimization machine learning model based on using a respective common failure type. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)).
Applicant further argues example 42 starting on page 16 of the response. Specifically, applicant argues that the normalizing of the response codes is equivalent to example 42’s patient management method that collects, converts and consolidates patient information from various physicians and health-care providers into a standardized format. Examiner respectfully disagrees. The timing schedule and format inconsistencies are not analogues to example 42.
Applicant argues example 47 starting on page 17 of the response and argues specifically “under Step 2A, Prong 2 analysis, "using, by the multi- tenant computing system, the trained retry optimization machine learning model to determine a timing schedule and a specific payment gateway type for retrying a plurality of failed payment transactions of the second subset," and "sending, by the multi-tenant computing system, commands for retrying the plurality of failed payment transactions on a particular payment gateway of the specific payment gateway type in accordance with the determined timing schedule, thereby causing the particular payment gateway to initiate payment attempts in accordance with the determined timing schedule," as recited in the amended independent claims, are indistinguishable for eligibility purposes from "(d) detecting a source address associated with the one or more malicious network packets in real time; (e) dropping the one or more malicious network packets in real time; and (f) blocking future traffic from the source address," as recited in the patent eligible claim 3 provided in Example 47.” (response at 17-18).
Examiner respectfully disagrees, the example 47 performed protection from malicious packets on a compute while the current claims perform retired payments which is fundamental economic practice. Example 47 does not apply.
For at least the reasons stated above applicant’s 101 arguments are unpersuasive.
Claim Interpretation
Applicant uses the term multi-tenant computing environment in the claims. As best understood by examiner in view of the specification, with reference to at least paragraphs 64 and 65, it appears that the multi-tenant computing environment may be any combination of hardware, software, and/or firmware for hosting cloud-based services for tenants. For purposes of examination, examiner will be interpreting this term as related to a cloud implementation of services for tenants.
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, 6-14, 17-20, and 22-35 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 and 11 are directed to a method and system.
Claim 1 recites the abstract idea of “retrying failed payment transactions” which is a grouped under “Certain methods of organizing human activity — fundamental economic practices” in prong one of step 2A (MPEP 2106.04(a)). Claim 1 recites “A method of modifying a payment transaction timing schedule and payment gateway selection … each tenant of the multiple tenants offering subscription services to subscribers, the tenant data of each tenant of the multiple tenants including billing information of the subscribers associated with the tenant, the method of modifying the payment transaction timing schedule and payment gateway selection capable of supporting each tenant of the multiple tenants while maintaining segregation of the tenant data of the tenant, the method comprising”, “based on the particular tenant data, … submitting multiple payment transaction requests to multiple payment gateways of different payment gateway types, the multiple payment transaction requests involving multiple subscribers of the particular tenant of the multiple tenants, the submitting the multiple payment transactions resulting in a first subset of successful payment transactions and a second subset of failed payment transactions”, “receiving … response codes for the failed payment transactions of the second subset from the multiple payment gateways of different payment gateway types, wherein the multiple payment gateways of different payment gateway types generate the response codes for the failed payment transactions of the second subset in accordance with different schemas the receiving the response codes including receiving a first response code in a first failure code format from a first payment gateway of a first payment gateway type and receiving a second response code in a different second failure code format from a second payment gateway of second multiple payment gateway type, the second failure code format being different than the first failure code format”, “normalizing …the response codes generated by the multiple payment gateways of the different payment gateway types in accordance with the different schemas into a uniform failure code format associated with a common set of failure types, the normalizing the response codes including normalizing the first response code in the first failure code format and the second response code in the second different failure code format into a same uniform response code in the uniform failure code format”, “evaluating … the response codes in the unform failure code format to determine a failure type associated with each failed payment transaction of the second subset, the first response code and the second response code being determined to have a same failure type in the common set of failure types after having been normalized to the uniform code format”, “… determine a timing schedule and a specific payment gateway type for retrying a plurality of failed payment transaction of the second subset, each failed payment transaction being associated with a particular respective subscriber for a of the particular tenant of the multiple tenants, the timing schedule being based on predictions of success generated by the trained entry optimization machine learning model. ”, and “…retrying the plurality failed payment transaction on a particular payment gateway of the specific payment gateway type in accordance with the determined timing schedule thereby causing the particular payment gateway to initiate payment attempts in accordance with the determined schedule.” Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of the claim 1 such as “a multi-tenant computing system, the multi-tenant computing system storing tenant data of multiple tenants in a segregated manner”, “on behalf of a particular tenant of the multiple tenants”, “retrieving by the multi-tenant computing system particular tenant data of the particular tenant while maintaining the segregation of the particular tenant data from the other tenant data of the other tenants of the multiple tenants”, “…by the multi-tenant computing system…”, “training, by the multi-tenant computing system, a retry optimization machine learning model based on a respective common failure type and a respective payment gateway type of each failed payment transaction of the second subset as training data to generate a trained retry optimization machine learning model, the trained retry optimization machine learning model being configured through training to generate a prediction of success for a retry attempt having a particular common failure type on a particular gateway type during a particular time window” and “using the trained retry optimization machine learning model to…” represent the use of a computer as a tool to perform an. abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Furthermore, “retrieving by the multi-tenant computing system particular tenant data of the particular tenant while maintaining the segregation of the particular tenant data from the other tenant data of the other tenants of the multiple tenants”, represent the use of computer functions to perform an economic task of “retrying failed payment transactions” (MPEP 2106.05(f)(2)). 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 banknote suspension and reinstatement.
And, as the additional elements do no more than serve as a tool to perform an abstract idea and/or generally link the abstract idea to a particular field of use, they do not the functioning of a computer, or to any other technology or technical field.
Hence, claim 1 is not patent eligible.
Claim 11 recites the abstract idea of “retrying failed payment transactions” which is a grouped under “Certain methods of organizing human activity — fundamental economic practices” in prong one of step 2A (MPEP 2106.04(a)). Claim 1 recites “retrieve, … particular tenant data of the particular tenant while maintaining the segregation of the particular tenant data from the other tenant data of the other tenants of the multiple tenants”, “based on the particular tenant data, … submit multiple payment transaction requests to multiple payment gateways of different payment gateway types, the multiple payment transaction requests involving multiple subscribers of the particular tenant of the multiple tenants, the submitting the multiple payment transactions resulting in a first subset of successful payment transactions and a second subset of failed payment transactions”, “receive… response codes for the failed payment transactions of the second subset from the multiple payment gateways of different payment gateway types, wherein the multiple payment gateways of different payment gateway types generate the response codes for the failed payment transactions of the second subset in accordance with different schemas the receiving the response codes including receiving a first response code in a first failure code format from a first payment gateway of a first payment gateway type and receiving a second response code in a different second failure code format from a second payment gateway of second multiple payment gateway type, the second failure code format being different than the first failure code format”, “normalize …the response codes generated by the multiple payment gateways of the different payment gateway types in accordance with the different schemas into a uniform failure code format associated with a common set of failure types, the normalizing the response codes including normalizing the first response code in the first failure code format and the second response code in the second different failure code format into a same uniform response code in the uniform failure code format”, “evaluate … the response codes in the unform failure code format to determine a failure type associated with each failed payment transaction of the second subset, the first response code and the second response code being determined to have a same failure type in the common set of failure types after having been normalized to the uniform code format”, “… determine a timing schedule and a specific payment gateway type for retrying a plurality of failed payment transaction of the second subset, each failed payment transaction being associated with a particular respective subscriber for a of the particular tenant of the multiple tenants, the timing schedule being based on predictions of success generated by the trained entry optimization machine learning model. ”, and “…retry the plurality failed payment transaction on a particular payment gateway of the specific payment gateway type in accordance with the determined timing schedule thereby causing the particular payment gateway to initiate payment attempts in accordance with the determined schedule.” Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (MPEP 2106.04 II), the additional elements of the claim 11 such as “A system for modifying a payment transaction timing schedule and payment gateway selection by a multi-tenant computing system, the multi-tenant computing system storing tenant data of multiple tenants in a segregated manner, each tenant of the multiple tenants offering subscription services to subscribers, the tenant data of each tenant of the multiple tenants including billing information of the subscribers associated with the tenant, the system capable of supporting each tenant of the multiple tenants while maintaining segregation of the tenant data of the tenant”, “at least one memory storing computer-executable instructions”, “at least one hardware processor configured to execute the computer-executable instructions to”, “on behalf of a particular tenant of the multiple tenants, retrieve particular tenant data of the particular tenant while maintaining the segregation of the particular tenant data from the other tenant data of the other tenants of the multiple tenants”, “use the failure type and payment gateway type of each failed payment transaction of the second subset as training data to train a retry optimization machine learning model, the retry optimization machine learning model identifying one or more subscriber segments having similar outcome trends based on the failures type and on the respective payment gateway type” and “use the trained retry optimization machine learning model to …” represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Furthermore, “retrieve particular tenant data of the particular tenant while maintaining the segregation of the particular tenant data from the other tenant data of the other tenants of the multiple tenants s”, represent the use of computer functions to perform an economic task of “retrying failed payment transactions” (MPEP 2106.05(f)(2)). 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 banknote suspension and reinstatement.
And, as the additional elements do no more than serve as a tool to perform an abstract idea and/or generally link the abstract idea to a particular field of use, they do not the functioning of a computer, or to any other technology or technical field.
Hence, claim 11 is not patent eligible.
Dependent claim 6 merely further describe the abstract idea of fundamental economic practice, as it recites “determining an overall payment retry period for retrying the particular failed payment transaction one or more times”, “and scheduling a last retry of the particular failed payment transaction within a last payment retry window immediately preceding an end of the overall payment retry period.”
Dependent claim 7 merely further describe the abstract idea of fundamental economic practice, as it recites “determining a time that the particular failed payment transaction initially failed”, “determining that a maximum number of retries of the particular failed payment transaction has not been met”, “determining that a duration of the overall payment retry period accommodates an initial payment retry window that begins after expiration of a first waiting period following the time that the particular failed payment transaction initially failed such that a time period between an end of the initial payment retry window and a beginning of a first payment retry window subsequent to the initial payment retry window is at least as long as a second waiting period”, “scheduling an initial retry of the particular failed payment transaction within the initial payment retry window”
Dependent claim 8 merely further describe the abstract idea of fundamental economic practice, as it recites “wherein the first waiting period has a longer duration than the second waiting period.”
Dependent claim 9 merely further describe the abstract idea of fundamental economic practice, as it recites “determining that the maximum number of retries of the particular failed payment transaction has not been met”, “determining that the duration of the overall payment retry period accommodates an intermediate payment retry window that begins after expiration of the second waiting period following the initial payment retry window such that a time period between an end of the intermediate payment retry window and a beginning of a first payment retry window subsequent to the intermediate payment retry window is at least as long as the second waiting period” and “scheduling an intermediate retry of the particular failed payment transaction within the intermediate payment retry window.”
Dependent claim 10 merely further describe the abstract idea of fundamental economic practice, as it recites “determining that the maximum number of retries of the particular failed payment transaction has not been met”, “determining that the duration of the overall payment retry period cannot accommodate an intermediate payment retry window that begins after expiration of the second waiting period following the initial payment retry window because a period of time between an end of the intermediate payment retry window and a beginning of a first payment retry window subsequent to the intermediate payment retry window would be shorter in duration than the second waiting period”, and “excluding the intermediate payment retry window from the determined timing schedule.”
Dependent claim 12, recite additional elements such as “obtain payment outcome data for retries of the particular failed payment transaction”, and “provide the payment outcome data as feedback training data to the trained retry optimization machine learning model to enhance the trained retry optimization machine learning model” 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. And, therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 13 merely further describe the abstract idea of fundamental economic practice, as it recites “…obtain a set of one or more tenant- specific optimal payment retry times that account for the payment outcome trends observed in the subscribers of the specific tenant who belong to the subscriber segment”, and “retry the second failed payment transaction at the set of one or more tenant-specific optimal payment retry times.” The additional elements such as “apply the enhanced retry optimization machine learning model to a second failed payment transaction relating to the subscriber segment” 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. And, therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 14, recite additional elements such as “obtain second payment outcome data for one or more retries of the second failed payment transaction at the set of one or more tenant-specific optimal payment retry times”, and “provide the second payment outcome data as additional feedback training data to the enhanced retry optimization machine learning model to further enhance the payment retry optimization machine learning model to output a new set of optimal payment retry times” 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. Specifically, the claim does not provide technical details regarding how the “further enhance the payment retry optimization machine learning model” is performed. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)). Therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 17, recite additional elements such as “learn parameters of the retry optimization machine learning model using a function evaluated across each payment attempt represented in historical payment outcome data” 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. And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 18, recite additional elements such as “wherein the trained retry optimization machine learning model approximates a conditional probability distribution for whether an input payment transaction succeeds based on a timing of the input payment transaction and a set of vector attributes associated with the input payment transaction.” 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. Specifically, the claim does not provide technical details regarding how the “the trained retry optimization machine learning model approximates a conditional probability distribution” is performed. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)). And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 19, recite additional elements such as “receive a maximum number of payment retries and an overall payment retry period as input constraints”, “wherein the trained retry optimization machine learning model is configured to output a set of optimal payment retry times for the particular failed payment transaction based on the input constraints” 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. Specifically, the claim does not provide technical details regarding how the “configured to output a set of optimal payment retry times…” is performed. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)). And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 20, recite additional elements such as “wherein the maximum number of payment retries and the overall payment retry period are user-configurable for the subscriber segment.” 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. And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claims 22 and 26 merely further describe the abstract idea of fundamental economic practice, as it recites “wherein the response codes include a code associated with insufficient funds, a code associated with suspected fraud, a code associated with a lost credit card, and a code associated with a system error.”
Dependent claims 23 and 27 merely further describe the abstract idea of fundamental economic practice, as it recites “further comprising enabling the particular tenant to set a retry constraint on the timing schedule.”
Dependent claims 24 and 28 merely further describe the abstract idea of fundamental economic practice, as it recites “wherein the retry constraint includes one of a maximum number of retries or a maximum retry time period.”
Dependent claim 25, recite additional elements such as “further comprising training the retry optimization machine learning model to select a different payment gateway or different payment gateway type for the retrying of the particular of the particular failed payment transaction.” 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. Specifically, the claim does not provide technical details regarding how the “training the retry optimization machine learning model to select a different payment gateway …” is performed. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)). And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 29, recite additional elements such as “wherein the at least one hardware processor is further configured to execute the computer-executable instructions to train the retry optimization machine learning model to select a different payment gateway or different payment gateway type for the retrying of the particular of the particular failed payment transaction.” 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. Specifically, the claim does not provide technical details regarding how the “train the retry optimization machine learning model to select a different payment gateway …” is performed. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)). And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 30 merely further describe the abstract idea of fundamental economic practice, as it recites “normalizing the first response code and the second response code according to a uniform schema, wherein the uniform schema defines categories and subcategories within each of the categories, each of the categories being mapped to a different range of probabilities of success upon retrying and each of the subcategories being mapped to a different subrange of probabilities of success upon retrying, wherein a probability of success upon retrying is mapped to a particular timing schedule”, and “wherein the evaluating of the response codes comprises evaluating the response codes according to the uniform schema.” The claim recite additional elements such as “wherein the receiving of the response codes comprises receiving the first response code according to a first schema from the first payment gateway of the multiple payment gateways and receiving the second response code according to a second schema from the second payment gateway of the multiple payment gateways, the second schema being different than the first schema.” 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. And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 31 merely further describe the abstract idea of fundamental economic practice, as it recites “normalize the first response code and the second response code according to a uniform schema, wherein the uniform schema defines categories and subcategories within each of the categories, each of the categories being mapped to a different range of probabilities of success upon retrying and each of the subcategories being mapped to a different subrange of probabilities of success upon retrying, wherein a probability of success upon retrying is mapped to a particular timing schedule”, and “wherein the evaluating of the response codes comprises evaluating the response codes according to the uniform schema.” The claim recite additional elements such as “wherein the receiving of the response codes comprises receiving the first response code according to a first schema from the first SMRH:4866-9963-1864 -8-payment gateway of the multiple payment gateways and receiving the second response code according to a second schema from the second payment gateway of the multiple payment gateways, the second schema being different than the first schema.” 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. And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 32, recite additional elements such as “during use of the trained retry optimization machine learning model, and in response to training the retry optimization machine learning model”, “generating a testing dataset corresponding to payment transactions over a second range of times after the first range of times, the testing dataset comprising response codes normalized according to the uniform failure code format and a uniform schema”, “using the testing dataset to evaluate the training of the retry optimization machine learning model”, “according to the evaluation of the training of the retry optimization machine learning model, generating additional feedback training data”, and “training the retry optimization machine learning model based on the additional feedback training data.” 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. Specifically, the claim does not provide technical details regarding how the “generating a testing dataset corresponding to payment transactions …”, and “using the testing dataset to evaluate the training…” are performed. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)). And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claim 33, recite additional elements such as “during use of the trained retry optimization machine learning model, and in response to training the retry optimization machine learning model”, “generate a testing dataset corresponding to payment transactions over a second range of times after the first range of times, the testing dataset comprising response codes normalized according to the uniform failure code format and a uniform schema”, “using the testing dataset to evaluate the training of the retry optimization machine learning model”, “according to the evaluation of the training of the retry optimization machine learning model, generating additional feedback training data”, and “train the retry optimization machine learning model based on the additional feedback training data.” 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. Specifically, the claim does not provide technical details regarding how the “generate a testing dataset corresponding to payment transactions …”, and “using the testing dataset to evaluate the training…” are performed. As a result, it is no more than apply it. (MPEP 2106.05(f)(1)). And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
Dependent claims 34 and 35, recite additional elements such as “wherein using the trained retry optimization machine learning model to determine the timing schedule comprises: determining a plurality of time windows”, “for each of the plurality of time windows: generating, using the trained retry optimization machine learning model, a prediction of success for a retry attempt of each failed payment transaction of the second subset in the time window”, “ranking the retry attempts of the failed payment transactions of the second subset according to the predictions of success to identify one or more failed payment transactions that have highest likelihood of success for retry within the time window”, “generating the timing schedule that, within each of the plurality of time windows, retries the one or more failed payment transactions identified for the time window.” 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. And therefore, does not improve the functioning of a computer, or to any other technology or technical field.
he 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.
Prior Art of Record Not Currently Relied Upon
Jayanthi et al (US 2021/0216509 A1) teaches: database replication error recovery based on supervises learning.
Chaturvedi (US 2021/0406896 A1) teaches: transaction periodically forecast using machine learning trained classifier.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY MARK JAMES whose telephone number is (571)272-5155. The examiner can normally be reached M-F 8:30am - 5:00pm EST.
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/GREGORY M JAMES/Examiner, Art Unit 3692 /DAVID P SHARVIN/Primary Examiner, Art Unit 3692