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 .
Response to Arguments
Applicant's arguments filed 7/6/2026 have been fully considered but they are not persuasive.
Applicant’s representative argues that the claims as amended are not directed to an abstract idea.
Applicant’s representative particularly states:
The claimed limitations recite technical operations involving machine learning model training and execution to produce more accurate computer models and outputs, rather than certain methods of organizing human activity.
In response, it has been clearly enumerated that claims directed to an abstract idea are patent-ineligible. Abstract ideas are characterized as concepts identified by the courts which include (1) mathematical concepts, (2) mental processes and (3) certain methods of organizing human activity.
Among those concepts performed as being identified in the category of “Certain Methods of Organizing Human Activity” are “commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
Here, it is noted that the remaining limitations, under their broadest reasonable interpretation, fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas, enumerated in MPEP 2106. 04(a}(2), such as managing commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
The BRI of these limitations describes steps or functions of “generate a plurality of risk scores, each associated with use of one of the plurality of transportation modes, generate an optimal pricing model and execute the optimal pricing model to generate an offering output (i) associated with the use of at least one of the plurality of transportation modes and (ii) based upon a corresponding risk score associated with each of the at least one transportation mode”.
Therefore the claims recite a commercial or legal interaction as such is an abstract concept.
Each of these independent claims uses generic computer technology (such as a processor coupled to a memory device and a communication interface) for receiving, generating, determining and transmitting data. system, as such do not recite an improvement to a particular computer technology. See, e.g., McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F .3 d 1299, 1314-1315 (Fed. Cir. 2016) ( finding claims not abstract because they "focused on a specific asserted improvement in computer animation").
As such, claims 1, 9 and 16 recite receiving, determining, training, determining, generating and transmitting data or information, as these functions are not a technological implementation or improvement of a technological field.
Applicant is to be reminded that a system, apparatus, machine or method for performing business, however, novel, useful, or commercially successful, is not patentable apart from the means for making the system practically useful or carrying it out. The applicant is making use of generic devices to finally provide from a user input received via a screen display and to a computing system, a second portion of identifying information in response to the request to correct the one or more errors, wherein the second portion of identifying information corrects the one or more errors in the first portion of identifying information.
Accordingly, the additional elements (such as a generic computing system with a processor and memory coupled with a communication interface) do not improve (1) the processor or database and user interface, or (2) another technology or technical field. See Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(a)). Rather, the above-noted additional elements merely (1) apply the abstract idea on a computer; (2) include instructions to implement the abstract idea on a computer (computing device or system) ; or (3) use the computer as a tool to perform the abstract idea. See Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05. Therefore, the recited additional elements do not integrate the abstract idea into a practical application when reading the claims.
None of the steps, functions and/or elements recited in the claims provide, and nowhere in the applicant’s shows any description or explanation as to how the claimed computing device, processor with memory are intended to provide: (1) a “solution . . . necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks,” as explained by the Federal Circuit in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257 (Fed. Cir. 2014); (2) “a specific improvement to the way computers operate,” as explained in Enfish, 822 F.3d at 1336; or (3) an “unconventional technological solution ... to a technological problem” that “improve[s] the performance of the system itself,” as explained in Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1299-1300 (Fed. Cir. 2016).
Accordingly, claims 1-20 are directed to an abstract idea.
Applicant’s representative then argues “the claims recite computer-implemented machine learning training and inference operations to generate computer models that are custom-made for each user and the transportation mode used for a particular trip. Such operations do not fall within any of the abstract idea categories identified in the eligibility framework” and concludes that the claims do not recite an abstract idea under the first prong of Step 2A.
In response, the claims as amended still fail to recite technological implementation details
of how the claimed functions are being realized. Claims of this nature are almost always found to be ineligible for patenting under Section 101." Beteiro, LLC V. DraftKings Inc., 104 F.4th 1350, 1356 (Fed. Cir. 2024). The specification does not even provide details of a specific architecture or means or structures or specific computer executed modules for performing the claimed functions.
The claims do not recite or contain an improvement in computer-related technology which is allowing computers to produce in a manner similar to Finjan which provides "accurate and realistic lips synchronization and facial expressions in animated characters' that previously could only be produced by human animators. The court in Finjan further reviewed the related applicant's specification and relied on the specification to conclude that humans did not use the claimed rules as were used to improve on computer animation technology on a specific manner to solve the problem of producing more accurate and realistic lip synchronization and facial expressions. Such specific rules "improved [the] existing technological process, unlike cases such as Alice in which a computer was merely used as a tool to perform an existing process.
Similarly, here, the claimed processor being used merely for “receiving”, “determining”, “generating” and “transmitting” data as claimed, involves no technological advancement. The claimed processor merely acts as a tool to effect the abstract idea. Each of the independent claims uses generic computer technology (such as a generic processor or device with a memory) for receiving data or information, and determining of a score, as such do not recite an improvement to a particular computer technology. See, e.g., McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F .3 d 1299, 1314-1315 (Fed. Cir. 2016) ( finding claims not abstract because they "focused on a specific asserted improvement in computer animation").
Accordingly, the applicant’s argument is not persuasive.
Applicant’s representative then argues that “Applicant's Claims Are Directed to "Significantly More" Than the Abstract Idea With that said, even assuming for the sake of argument that the pending claims are directed to an abstract idea (which Applicant does not concede), the claims are directed to something "significantly more" than the idea itself”.
Applicant’s representative also argues that the claims at issue here are not "directed to" a judicial exception”, and requests the Section 101 rejection be withdrawn because the pending claims are not directed to an abstract idea.
In response, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because :
The reliance of a computer or computing system with a processor and memory to perform its routine tasks even more accurately is not sufficient to transform a claim into patent eligible subject matter as noted in Alice 134 S. Ct. at 2359. As indicated by the court "use of a computer to create electronic records, track multiple transactions and issue simultaneous instructions" was not an inventive incept. The claims or even the applicant's specification does not support or provide or claim any specifically inventive technology or algorithm for performing the claimed functions.
As noted in the applicant’s specification, there is not a specific structure or computer components to perform the claimed functions. The generic computing system can be any known server or computer processor or software or hardware components. However, there is not a specific or new algorithm noted in the applicant’s specification to generate the claimed functions. The claimed computing system noted in the applicant's specification are routine computer processors or computers performing generic computer functions.
Furthermore, there is not a showing or description of receiving, generating, determining, training and transmitting data or information, and providing the data to a computing system to effect specific improvements to the computing system o processor. Furthermore there is a lacking of evidence that the claims improve the manner in which the computing system receives, determines, and transmits data or information, and provides data to the computing system, as the claims in Enfish had performed their claimed invention via a “self-referential table” for a computer database. Applicant is being referred to Enfish, 822, F.3d at 1327, 1337.
The instant claims merely receive, determine, train, generate and transmit data or information, and provide the data to a computing system. These are routine and generic computer functions for processing or effecting the abstract idea. Hence, there is not a significant improvement of the computing system or the architecture of the overall system.
The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, are merely the combined and coordinated execution of generic computer functionalities which are well-understood, routine and conventional activities previously known to the industry.
Accordingly, the applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (1e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Specifically, claims 10 and 19 are directed to a method. Claim 1 is directed to a system. Each of the claims falls under one of the four statutory classes of invention.
If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A, Prong One: the claims recite the following limitations that are understood to recite an abstract idea absent the bolded limitations
Claim 1 recites:
A computer system for generating dynamic outputs using trained machine learning tools, the computer system comprising at least one processor coupled to a memory device and a communication interface, the at least one processor operable to execute an assessment module and a pricing module, the at least one processor configured to:
receive, using the communication interface, and from one or more computing devices, real-time contextual data associated with a user and a plurality of trips taken by the user;
train one or more machine learning tools by inputting into the one or more machine learning tools, the real-time contextual data;
determine, based upon a user profile, a plurality of transportation modes likely to be used by the user for a trip;
generate, using the assessment module and based upon at least one of the real-time contextual data or the user profile, a plurality of risk scores, each associated with use of one of the plurality of transportation modes;
generate an optimal pricing model by executing the pricing module, and the trained one or more machine learning tools;
execute the optimal pricing model to generate an offering output (i) associated with the use of at least one of the plurality of transportation modes and (ii) based upon a corresponding risk score associated with each of the at least one transportation mode; and
transmit, using the communication interface, the offering output in real time to a user computing device associated with the user.
Claim 2 recites: wherein the at least one processor is further configured to generate the user profile of the user by processing telematics data received using the communication interface from the user computing device.
Claim 3 recites: wherein the at least one processor is further configured to receive, using the communication interface, additional real-time contextual data associated with the trip and the user profile.
Claim 4 recites: wherein the at least one processor is further configured to generate, using the plurality of risk scores, at least one travel route to be taken by the user during the trip.
Claim 5 recites: wherein the offering output includes an insurance offering for purchase by the user.
Claim 6 recites: wherein the real-time contextual data includes at least one of weather data, age of the user, data associated with a start location of the plurality of trips, or data associated with an end location of the plurality of trips.
Claim 7 recites: wherein the at least one processor is further configured to determine, based upon the user profile, a plurality of available transportation modes for the trip to be taken by the user.
Claim 8 recites: wherein the at least one processor is further configured to generate, based upon the applying the trained the one or more machine learning tools to the optimal pricing model, the offering output.
Claim 9 recites: wherein the at least one processor is further configured to:
determine the plurality of transportation modes available for the trip by accessing transportation data associated with the trip;
compare the generated plurality of risk scores to one another;
rank, based upon the comparison, the plurality of transportation modes based upon the generated plurality of risk scores;
generate, based upon an associated rank, a plurality of offering outputs, each generated for each of the plurality of transportation modes, wherein each offering output includes a price corresponding to the associated rank; and
transmit the plurality of offering outputs to the user computing device for selection by the user.
Claim 10 recites: A computer-implemented method for generating dynamic outputs using trained machine learning tools, the method implemented by computer system including at least one processor coupled to a memory device and a communication interface, the at least one processor operable to execute an assessment module and a pricing module, the method comprising:
receiving, using the communication interface and from one or more computing devices, real-time contextual data associated with a user and a plurality of trips taken by the user;
training one or more machine learning tools by inputting, into the one or more machine learning tools, the real-time contextual data;
determining, based upon a user profile, a plurality of transportation modes likely to used by the user for a trip;
generating, using the assessment module and based upon at least one of the real-time contextual data or the user profile, a plurality of risk scores, each associated with use of one of the plurality of transportation modes;
determining, based upon a user profile, a plurality of transportation modes likely to be user for a trip;
generating an optimal pricing model by executing the pricing module, the trained one or more machine learning tools;
executing the optimal pricing model to generate an offering output (i) associated with the use of at least one of the plurality of transportation modes and (ii) based upon a corresponding risk score associated with each of the at least one transportation mode; and
transmitting, using the communication interface, the offering output in real time to a user computing device associated with the user.
Claim 11 recites: generating the user profile of the user by processing telematics data
received using the communication interface from the user computing device.
Claim 12 recites: receiving, using the communication interface, additional real-time contextual data associated with the trip and the user profile.
Claim 13 recites: generating, using the plurality of risk scores, at least one travel route to be taken by the user during the trip.
Claim 14 recites: wherein the offering output includes an insurance offering for purchase by the user.
Claim 15 recites: wherein the real-time contextual data includes at least one of weather data, age of the user, data associated with a start location of the plurality of trips, or data associated with an end location of the plurality of trips.
Claim 16 recites: determining, based upon the user profile, a plurality of available transportation modes for the trip to be taken by the user.
Claim 17 recites: generating, based upon applying the trained the one or more machine learning tools to the optimal pricing model, the offering output.
Claim 18 recites:
determining the plurality of transportation modes available for the trip by accessing transportation data associated with the trip;
comparing the generated plurality of risk scores to one another;
ranking, based upon the comparison, the plurality of transportation modes based upon the generated plurality of risk scores;
generating, based upon an associated rank, a plurality of offering outputs, each generated for each of the plurality of transportation modes, wherein each offering output includes a price corresponding to the associated rank; and
transmitting the plurality of offering outputs to the user computing device for selection by the user.
Claim 19 recites:
At least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by computer system including at least one processor coupled to a memory device and a communication interface, the at least one processor operable to execute an assessment module and a pricing module, the computer-executable instructions cause the at least one processor to:
receive, using the communication interface and from one or more computing devices, real-time contextual data associated with a user and a plurality of trips taken by the user;
train one or more machine learning tools by inputting, into the one or more machine learning tools, the real-time contextual data;
determine, based upon a user profile, a plurality of transportation modes likely to be used by the user for a trip;
generate, using the assessment module and based upon at least one of the real-time contextual data or the user profile, a plurality of risk scores, each associated with use of one of the plurality of transportation modes;
generate an optimal pricing model by executing the pricing module and the trained one or more machine learning tools;
execute the optimal pricing model to generate an offering output (i) associated with the use of at least one of the plurality of transportation modes and (ii) based upon a corresponding risk score associated with each of the at least one transportation mode; and
transmit, using the communication interface, the offering output in real time to a user computing device associated with the user.
Claim 20 recites: wherein the computer-executable instructions further cause the at least one processor to generate the user profile of the user by processing telematics data received using the communication interface from the user computing device.
In removing the bolded additional elements, it is noted that the remaining limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106. 04(a}(2), such as
managing commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
Step 2A, Prong two: This judicial exception is not integrated into a practical application, In particular, the claims recite the above noted bolded limitations understood to be additional limitations.
These limitations performing functions using a computer system comprising at least a processor coupled to a memory device and a communication device, an assessment module, a pricing module, and a machine learning tool to generate an offering output associated with the use of at least one of a plurality of transportation modes, merely amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f), also see applicant's specification for guiding interpretation of these claim features, describing implementation with generic commercially available devices or any machine capable of executing a set of instructions. The computer system and machine learning model are similarly understood in light of applicant's specification as mere usage of any arrangement of generic computers and hardware intermediate components potentially using networks to communicate between systems.
Performance of a receiving step by a computer processor or machine learning tool amounts to performing steps which amount io insignificant extra-solution activity of data gathering - see MPEP 2106.05(g).
Performing steps by computer processor hardware with scores for insurance comparison purposes using a machine learning model limit the abstraction to computer field by execution by generic computers - see MPEP 2106.05(h).
As noted in MPEP 2106.04(d), limitations which amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool, limitations which amount to insignificant extra-solution activity, and limitations which amount generally linking to a particular technological environment do not integrate a judicial exception into a practical application.
The breadth of the limitations reasonably includes generating, based upon at least one of the real-time contextual data or the user profile, a plurality of risk scores, each associated with use of one of the plurality of transportation modes, generating an optimal pricing model; and executing the optimal pricing model to generate an offering output (i) associated with the use of at least one of the plurality of transportation modes and (ii) based upon a corresponding risk score associated with each of the at least one transportation mode;
Reciting "a computer system”, a “computing device” and a “communication interface” is understood to be similar to Alappat, which as noted in MPEP 2106. 05(b)(I), is superseded, and the correct analysis is to look whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception.
The claims in the instant application are performed by one or computer system
comprising at least one processor coupled to a memory device and a communication device using a machine learning tool.
Consideration of these steps or functions as a combination does not change the analysis
as they do not add anything compared to when the steps are considered separately. The claims recite a particular sequence of determining scores for an insurance of at least one risk score of a plurality of transportation modes. Performance of these steps or functions technologically does present a meaningful limit to the scope of the claim which would reasonably integrate the abstraction into a practical application.
Step 2B: The elements discussed above with respect to the practical application in Step 2A, prong 2 are equally applicable to consideration of whether the claims amount to significantly more. Accordingly, the clams fail to recite additional elements which, when considered individually and in combination, amount to significantly more. Reconsideration of these elements identified as insignificant extra-solution activity as part of Step 2B does not change the analysis.
Independent claims 10 and 19: Independent claims 10 and 19 recite the same limitations as independent claim 1. The same reasons discussed above with respect to claim 1 are equally applicable to claims 10 and 19.
These claimed elements also as found in the dependent claims are also recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component.
In processing the claims, it is noted that the recitation of these additional elements does not impact the analysis of the claims because these elements in combination are noted only to be a general purpose computer for performing basic or routine computer functions. These claimed elements are noted to a be a generic computer for collecting data, storing data, training data, generating data and transmitting data, and performing routine and conventional functions.
These additional elements do not overcome the analysis as these elements are merely considered as additional elements which amount to instructions to be applied to the generic computer or processor.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claimed elements are also seen as generic computer components for receiving data, training data, generating data, determining data, and transmitting data, thus performing generic functions without an inventive concept as they do not amount to significantly more than the abstract idea. The claimed additional elements are interpreted as being recited at a high level of generality and even if the claims recited in the affirmative.
The type of data being manipulated does not impose meaningful limitations or renders the idea less abstract. Looking at the elements as a combination, the elements do not add anything more than the elements analyzed individually. Therefore, the claims do not amount to significantly more than the abstract idea itself.
Applicant is reminded that a statutory claim would recite an automated machine implemented method or system with specific structures for performing the claimed invention so as to provide an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Each claim as a whole, does not amount to significantly more than the abstract idea itself. This is because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
The reliance of a computer or processor to perform its routine tasks even more accurately is not sufficient to transform a claim into patent eligible subject matter as noted in Alice 134 S. Ct. at 2359. As indicated by the court "use of a computer to create electronic records, track multiple transactions and issue simultaneous instructions" was not an inventive concept. The claims or even the applicant's specification does not support or provide or claim any specifically inventive technology or algorithm for performing the claimed functions.
Therefore, the recited additional elements do not integrate the abstract idea into a
practical application when reading the claims. The independent claims 1, 10 and 19 and do not contain structures to provide a significantly more than the abstract idea.
The dependent claim(s) when analyzed and each taken as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea.
Accordingly, claims 1-20 are directed to an abstract idea.
Double Patenting
The non-statutory 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. See 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);and, 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) may be used to overcome an actual or provisional rejection based on a non-statutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 remain rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,599,951. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are directed to a similar subject matter contained in claims 1-20 of the '951 patent. The only difference between the instant application and the '951 patent is merely a labeling difference. It is noted that all the features of claims 1-20 are contained in claims 1-20 of the '951 patent.
Claims 1-20 remain rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,217,313. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are directed to a similar subject matter contained in claims 1-20 of the '313 patent. The only difference between the instant application and the '951 patent is merely a labeling difference. It is noted that all the features of claims 1-20 are contained in claims 1-20 of the '313 patent.
The prior art taken alone or in combination failed to teach or suggest:
“generate, using the assessment module and based upon at least one of the real-time contextual data or the user profile, a plurality of risk scores, each associated with use of one of the plurality of transportation modes, generate an optimal pricing model by executing the pricing module, and the trained one or more machine learning tools, and execute the optimal pricing model to generate an offering output (i) associated with the use of at least one of the plurality of transportation modes and (ii) based upon a corresponding risk score associated with each of the at least one transportation mode” as recited in independent claim 1 and as similarly recited in independent claims 10 and 19.
Kawamura ( US Pub. No. 20140052479 A1 ) discloses a method for estimating insurance risk is described. The method may include receiving, by a processor, real-time information related to a travel itinerary. The method may also include estimating, by the processor, the insurance risk by analyzing the real-time information based on a quantitative assessment of risks posed to a population by the at least one factor. The method may also include generating, by the processor, an insurance risk profile based on the estimated insurance risk.
Peak et al ( US Pub. No. 20110213628 A1 ) disclose an insurance system, method and device which include a data storage device for storing, updating and providing access to loss risk score data. A request for information associated with a user's location identified by user location data may be received over a communications network. A computer processing system may then be operated to generate a safety score associated with said use location data, the safety score being based on a plurality of loss risk factors associated with the user location data. At least one element of an insurance policy associated with the user may be modified in accordance with the safety score. A response, including the safety score, may then be transmitted to the user over the communications network.
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.
Conclusion
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/fp/
/FRANTZY POINVIL/Primary Examiner, Art Unit 3693
July 20, 2026
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