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 Amendment
The following Office action in response to communications received December 2, 2025. Claims 1, 8 and 9 have been amended. Therefore, claims 1-9 are pending and addressed below.
Applicant’s amendments to the claims are sufficient to overcome the 35 USC § 112 second paragraph, rejections set forth in the previous office action dated September 3, 2025.
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 1-9 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1 and 8-9 are directed to an abstract idea comprising methods for training a deep reinforcement learning model includes performing a training process.
Independent Claim 1 recites “acquiring initial dose distribution state data of an objective target volume; determining, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads; and updating the current policy data of the actor network and the critic network based on the output data of each sub-thread of the plurality sub-threads in sequence, so as to complete a current training for the deep reinforcement learning model; and iterating the training process until a count of training the deep reinforcement learning model reaches a preset count, so as to obtain the deep reinforcement learning model that has been trained; wherein the output data of the sub-thread comprises: a final dose distribution of the objective target volume, multiple dose distribution state data of the objective target volume, a plurality of target parameters corresponding to the multiple dose distribution state data respectively, a plurality of predicted values corresponding to the plurality of target parameters respectively, a plurality of actual rewards corresponding to the plurality of target parameters respectively, a predicted value of the objective target volume, and an actual reward of the objective target volume.”
Independent Claim 8 recites “acquiring image data of a to-be-treated target volume and contour data of the to-be-treated target volume; determining, based on the image data and the contour data, dose distribution state data of the to-be-treated target volume; inputting the dose distribution state data of the to-be-treated target volume into a deep reinforcement learning model, so as to obtain a target parameter of the to-be- treated target volume, wherein the deep reinforcement learning model is configured to comprise an actor network and a critic network, and the deep reinforcement learning model is trained by the following operations: performing a training process, the training process including the following operations: acquiring initial dose distribution state data of an objective target volume; determining, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads; and updating the current policy data of the actor network and the critic network based on the output data of each sub-thread of the plurality sub-threads in sequence, so as to complete a current training for the deep reinforcement learning model; and iterating the training process until a count of training the deep reinforcement learning model reaches a preset count, so as to obtain the deep reinforcement learning model that has been trained; wherein the output data of the sub-thread comprises: a final dose distribution of the objective target volume, multiple dose distribution state data of the objective target volume, a plurality of target parameters corresponding to the multiple dose distribution state data respectively, a plurality of predicted values corresponding to the plurality of target parameters respectively, a plurality of actual rewards corresponding to the plurality of target parameters respectively, a predicted value of the objective target volume, and an actual reward of the objective target volume; generating the treatment plan for the to-be-treated target volume based on the target parameter.”
Independent Claim 9 recites “performing a training process, the training process including the following operations: acquiring initial dose distribution state data of an objective target volume; determining, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads; and updating the current policy data of the actor network and the critic network based on the output data of each sub-thread of the plurality sub-threads in sequence, so as to complete a current training for the deep reinforcement learning model; and iterating the training process until a count of training the deep reinforcement learning model reaches a preset count, so as to obtain the deep reinforcement learning model that has been trained; wherein the output data of the sub-thread comprises: a final dose distribution of the objective target volume, multiple dose distribution state data of the objective target volume, a plurality of target parameters corresponding to the multiple dose distribution state data respectively, a plurality of predicted values corresponding to the plurality of target parameters respectively, a plurality of actual rewards corresponding to the plurality of target parameters respectively, a predicted value of the objective target volume, and an actual reward of the objective target volume.”
The limitations of Claims 1 and 8-9, as drafted, under its broadest reasonable interpretation, covers the performance of a Mental Process which are concepts performed in the human mind (including an observation, evaluation, judgment, opinion). That is, other than reciting, “processor, non-transitory CRSM” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “processor, non-transitory CRSM” language, “acquiring” in the context of this claim encompasses the user manually retrieving initial dose distribution state data of an objective target volume. Similarly, the updating the current policy data, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “processor, non-transitory CRSM” to perform all of the “obtaining, transforming, parsing, determining, transforming, selecting and storing” steps. The “processor, non-transitory CRSM” is/are recited at a high-level of generality associated with the use (i.e., as a generic processor performing a generic computer function) of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 has the following additional elements (i.e., processor). Claim 8 has the following additional elements (i.e., processor). Claim 9 has the following additional elements (i.e., processor, non-transitory CRSM). Looking to the specification, these components are described at a high level of generality (¶ 34 and 217; In some embodiments, the electronic device a2 may be at least one of the following devices: a smartphone, a smart watch, a desktop computer, a portable computer, a virtual reality terminal, an augmented reality terminal, a wireless terminal, or a laptop portable computer.). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, although the claims add “[storage]” steps, it is only considered as insignificant extrasolution activity. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no 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.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-7). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Mental Processes,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 1-9 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
Applicant’s arguments filed December 2, 2025 have been fully considered but they are not persuasive. In the remarks applicant argues (1) Claims 1-9 were rejected under 35 U.S.C. § 101.
Step 2A -Prong One:
The Office Action indicated that the claims fall within the "Mental Processes" grouping of abstract ideas.
Under the Revised Guidelines, Prong One of Revised Step 2A recites: "to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (a) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (b) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance." The groupings of abstract ideas in the Revised Guidelines are:
(a) Mathematical concepts-mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity, fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
(c) Mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The Office Action contends that the claimed method covers the performance of mental processes, but the claims recite steps that cannot practically be performed in a human mind. For example, the steps of "determining, by the processor, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads" cannot be practically applied in a human mind, because the human mind lacks the capacity for parallel processing of output data. Therefore, the human mind is not equipped for "determining based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads" as recited by the claims. Even if with the use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step, the human mind is still not able to process in parallel (i.e., simultaneous). In addition, sub-threads constitute the fundamental unit of scheduling for a computer processor, executed by the processor (emphasis added). Therefore, this limitation does not fall under any of "observation, evaluation, judgment, or opinion", and the combination of this limitation with other steps ensures that the method as a whole is not an abstract idea. As described above, a human mind is capable of an observation, evaluation, judgment, opinion, but the above claimed subject matter does not include any of these.
Moreover, Applicants respectfully submits that the subject matter of independent claim 1 has been amended to recite limitations that do not fall under any "Mental Processes". For example, the method for training a deep reinforcement learning model for generating a treatment plan in claim 1 is applied to an electronic device, and the electronic device including a processor, the steps of "acquiring, by the processor, initial dose distribution state data of an objective target volume", "determining, by the processor, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub- threads", "updating, by the processor, the current policy data of the actor network and the critic network based on the output data of each sub-thread of the plurality sub-threads in sequence, so as to complete a current training for the deep reinforcement learning model" and "iterating, by the processor, the training process until a count of training the deep reinforcement learning model reaches a preset count, so as to obtain the deep reinforcement learning model that has been trained." The steps involve a series of operations performed by a processor on a model, whereas the human mind cannot generate such a model, execute, nor process according to the steps as recited by the claims. Consequently, these steps do not constitute a concept executed in the human mind.
According to the Revised USPTO Guidelines, "claims that do not recite matter that falls within these enumerated groupings of abstract ideas should not be treated as reciting abstract ideas", "claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations" (emphasis added). Applicants respectfully submit that none of the claimed subject matter falls into any of the three subject matter groupings of abstract ideas as set forth in the Revised Guidelines. For this reason alone, the claims of the present application are not directed to an abstract idea and constitute patent eligible subject matter.
Step 2A -Prong Two:
The Office Action indicated that the claimed method does not provide integration into a practical application or significantly more than the abstract idea.
In response, claim 1 has been amended. Specifically, amended claim 1 recites the elements such as "electronic device", "processor", "deep reinforcement learning model", "actor network", "critic network" and "sub-threads", which are integrated into a practical application, including "A method for training a deep reinforcement learning model for generating a treatment plan, applied to an electronic device", "performing, by the processor, a training process”, “acquiring, by the processor, initial dose distribution state data of an objective target volume", "determining, by the processor, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads", "updating, by the processor, the current policy data of the actor network and the critic network based on the output data of each sub-thread of the plurality sub-threads in sequence, so as to complete a current training for the deep reinforcement learning model", "iterating, by the processor, the training process until a count of training the deep reinforcement learning model reaches a preset count, so as to obtain the deep reinforcement learning model that has been trained", "wherein the output data of the sub-thread comprises: a final dose distribution of the objective target volume, multiple dose distribution state data of the objective target volume, a plurality of target parameters corresponding to the multiple dose distribution state data respectively, a plurality of predicted values corresponding to the plurality of target parameters respectively, a plurality of actual rewards corresponding to the plurality of target parameters respectively, a predicted value of the objective target volume, and an actual reward of the objective target volume" in combination with other steps in claim 1.
The problem solved by amended claim 1 is that "at present, the design for a more reasonable treatment plan usually relies on a physician to repeatedly adjust the treatment plan manually based on their own experience and professional skills. Therefore, the existing methods for generating a treatment plan require a high level of clinical experience from the physician, and the treatment plan requires continuous trial and error, which is time-consuming, laborious, and inefficient".
The claimed subject matter relates to an improvement to medical technology field, wherein the method ensures that the deep reinforcement learning model for generating a treatment plan is trained by a plurality of sub-threads in parallel in the present disclosure. Due to the parallel trial and error capability of the plurality of sub-threads, the speed of training the model is improved. Due to the fact that the deep reinforcement learning model conforms to the characteristics of the design for the treatment plan with Gamma knife, and a processor in a computer system is able to repeat the trial and error process, target parameters with better performance may be automatically generated through the deep reinforcement learning model, resulting in generating a better treatment plan based on the target parameters with better performance. Thus, the dependence on clinical experience may be reduced, the effect of the treatment plan may be improved without the need for manual setting of target parameters, and further, the efficiency for generating a treatment plan is improved.
Further, the claim is not merely applied to the judicial exception through highly generic recitation as indicated in MPEP 2106.05(f). For example, the limitation "output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads" not only requires the processor to apply this judicial exception, but it further requires that it be executed in parallel through the plurality of sub-threads. That is, the processor of the present application is not merely applied to judicial exceptions, but also executes the details of implementing such judicial exceptions.
In order for the judicial exception to be "integrated into a practical application", an additional element or a combination of additional elements in the claim "will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." PEG, 84 Fed. Reg. 54 (Jan. 7, 20 19).
Applicants respectfully submit that in amended claim 1, at least the additional elements "sub-thread" uses the judicial exception in a manner (i.e. in parallel) that imposes a meaningful limit on the judicial exception.
Therefore, the above judicial exception is used with an "electronic device", "processor", "deep reinforcement learning model", "actor network", "critic network", and "sub-thread", which constitutes an improvement to the medical technology field, thereby solving the above problem and imposing a meaningful restriction on the judicial exception considered by the Examiner (Applicants do not believe that the elements listed by the Examiner are directed to the judicial exception according to the judgment under Prong One of Revised Step 2A described above).
Therefore, even when the claims are considered under Prong Two of Revised Step 2A, Applicants respectfully submit that the claims are patent eligible under this test as well.
Accordingly, in view of the Revised Guidelines, Applicants respectfully request that the rejection of amended claims 1-9 under 35 U.S.C. § 101 be withdrawn because these claims are directed to an improvement to medical technology field, do not fall into any of subject matter groupings of abstract ideas and is integrated into a practical application.
Step 2B:
The Office Action indicated that the claims do not amount to significantly more than the abstract idea.
In response, the pending claims recite specific steps for a data processing method.
Applicants respectfully submit that the additional elements, such as "electronic device", "processor", "deep reinforcement learning model", "actor network", "critic network" and "sub-threads", which are associated with "A method for training a deep reinforcement learning model for generating a treatment plan, applied to an electronic device", "performing, by the processor, a training process", "acquiring, by the processor, initial dose distribution state data of an objective target volume", "determining, by the processor, based on the initial dose distribution state data of the objective target volume and current policy data of the actor network and the critic network, output data of each sub-thread of a plurality of sub-threads in parallel by using the plurality of sub-threads", "updating, by the processor, the current policy data of the actor network and the critic network based on the output data of each sub-thread of the plurality sub-threads in sequence, so as to complete a current training for the deep reinforcement learning model", "iterating, by the processor, the training process until a count of training the deep reinforcement learning model reaches a preset count, so as to obtain the deep reinforcement learning model that has been trained", "wherein the output data of the sub-thread comprises: a final dose distribution of the objective target volume, multiple dose distribution state data of the objective target volume, a plurality of target parameters corresponding to the multiple dose distribution state data respectively, a plurality of predicted values corresponding to the plurality of target parameters respectively, a plurality of actual rewards corresponding to the plurality of target parameters respectively, a predicted value of the objective target volume, and an actual reward of the objective target volume" in combination with other steps in claim 1. Additionally, the claimed subject matter relates to an improvement to a method for generating a treatment plan in the radiotherapy, wherein the deep reinforcement learning model for generating a treatment plan is trained by a plurality of sub-threads in parallel in the present disclosure.
Due to the parallel trial and error capability of the plurality of sub-threads, the speed of training the model is improved.
Due to the fact that the deep reinforcement learning model conforms to the characteristics of the design for the treatment plan with Gamma knife, and a processor in a computer system is able to repeat the trial and error process, target parameters with better performance may be automatically generated through the deep reinforcement learning model, resulting in generating a better treatment plan based on the target parameters with better performance. Thus, the dependence on clinical experience may be reduced, the effect of the treatment plan may be improved without the need for manual setting of target parameters, and further, the efficiency for generating a treatment plan is improved.
Further, the processor included in the electronic device can use "a plurality of sub- thread" to determine output data of each sub-thread of a plurality of sub-threads in parallel, thereby the deep reinforcement learning model used for generating a treatment plan. In combination with the remaining limitations, this constitutes a technical improvement. Therefore, these operations are not well-understood, routine, or conventional and are significantly more than any law of nature, natural phenomenon, or an abstract idea. (Applicants do not believe that the judicial exception is not integrated into the practical application according to the judgment under Prong Two of Revised Step 2A described above).
Even when the claims are considered under Revised Step 2B, Applicants respectfully submit that the claims are patent eligible as well.
Accordingly, in view of the Revised Guidelines, Applicants respectfully request that the rejection of claims 1-9 under 35 U.S.C. § 101 be withdrawn because these claims do include additional elements that are sufficient to amount to significantly more than the judicial exception.
Applicants respectfully request that the rejections be withdrawn.
In response to argument (1), The Examiner has considered Applicant's arguments but maintains the rejection of claims 1, 8, and 9 under 35 U.S.C. § 101. While Applicant presents detailed analysis under the Revised Patent Subject Matter Eligibility Guidance, the claims remain directed to abstract ideas with only conventional computer implementation.
Regarding Step 2A Prong One: The claims are directed to the abstract idea of mathematical optimization through reinforcement learning for treatment planning. The core concept involves iteratively training a model to optimize treatment parameters, a mathematical process that could be performed mentally or with conventional computing tools. The fact that the claims recite "sub-threads" and "parallel processing" does not remove them from the category of mathematical concepts and mental processes; it merely describes a conventional implementation detail of how computers perform mathematical optimization. The human mind is indeed capable of performing reinforcement learning conceptually, even if not at the scale or speed of a computer. The claims' focus remains on mathematical relationships, optimization algorithms, and data processing, all falling within the enumerated abstract idea groupings.
Regarding Step 2A Prong Two: The claims do not integrate the abstract idea into a specific, non-conventional practical application. While the specification describes applications in radiation therapy planning, the claims themselves are drafted at a high level of generality that could apply to any optimization problem. The recitation of "dose distribution" and "treatment plan" does not sufficiently limit the claims to a specific medical application, as these terms could encompass any computational treatment planning without specifying how the mathematical model interfaces with or controls actual medical equipment. The claimed parallel processing via sub-threads represents a conventional computer implementation technique, not a meaningful limitation that ties the abstract idea to a specific technological improvement in medical treatment planning.
Regarding Step 2B: The claims lack an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The elements recited, "electronic device," "processor," "deep reinforcement learning model," "actor network," "critic network," and "sub-threads," are generic computing components used in conventional ways. The use of reinforcement learning for optimization problems is well-established in the computing arts, and implementing such algorithms with parallel processing represents a routine efficiency improvement, not a non-conventional technological advancement. The claims do not recite any specific, unconventional hardware configuration, software architecture, or data processing technique that would amount to significantly more than the abstract mathematical optimization concept.
Specific to Claim 8: While this claim incorporates the trained model into a method for generating a treatment plan, it remains at a high level of generality. The claim recites acquiring image and contour data, determining dose distribution state data, inputting this into the model, and generating a treatment plan—all steps that could be performed by conventional treatment planning software. The claim does not specify how the generated treatment plan interfaces with or controls radiation delivery equipment, nor does it require actual treatment delivery. It remains directed to computational treatment planning, which courts have consistently found to be abstract when claimed at this level of generality.
Specific to Claim 9: As a computer-readable storage medium claim, it suffers from the same deficiencies as Claim 1, merely encoding the abstract mathematical optimization process on conventional storage media.
To be eligible, the claims would need to be narrowed to recite specific technological improvements in medical treatment planning equipment, specific unconventional implementations of reinforcement learning in the medical context, or specific limitations tying the computational model to the actual control of medical treatment delivery systems.
Accordingly, the rejection of claims 1, 8, and 9 under 35 U.S.C. § 101 is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12370378 B2; A machine learning-based method of generating a radiotherapy treatment plan for a patient, comprises dose prediction and dose mimicking, wherein the dose prediction step involves using a machine learning system that has been trained to consider at least one optimality criterion related to physical or technical restrictions that will affect the delivery of the treatment plan. Thus, at least one of the factors that are normally taken into account in the dose mimicking step is introduced in the dose prediction step. The invention also relates to a method of training such a machine learning system for use in radiotherapy treatment planning, a computer program product and a computer system.
WO 2020146356 A1; Techniques for determining a dosage plan for administering at least one therapeutic agent to a subject. The techniques include using at least one computer hardware processor to perform: accessing information specifying a plurality of cell population concentrations for a respective plurality of cell populations in a biological sample from a subject; and determining the dosage plan using a trained statistical model and the plurality of cell population concentrations, the dosage plan including one or more concentrations of the at least one therapeutic agent to be administered to the subject at one or more respective different times. The trained statistical model may be trained using an actor-critic reinforcement learning technique and a model of cell evolution. The trained statistical model may include a deep neural network.
WO 2022268576 A1; Embodiments described herein provide for revising radiation therapy treatment plans, and in particular, revising beam angles used during radiation therapy treatment. A computer may receive a radiation therapy treatment plan based on a particular patient's diagnosis. The computer may use a machine learning model (520) to revise radiation therapy treatment parameters (510a) such as a beam angle indicating a direction of radiation into the patient. The machine learning model (520) may use reinforcement learning to optimize an initial beam angle from the radiation therapy treatment plan, revising the beam angle. The performance of the machine learning model (520) is measured against metrics including fulfilling dosimetric clinical goals. The machine learning model (520) may present (210) the revised beam angle for display to a medical professional, or transmit (206) the revised beam angle to downstream applications to further revise the radiation therapy treatment plan.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683
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