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 .
Application Status
Present office action is in response to amendment filed 06/03/2026. Claims 3-4, 8, 10 and 17-18 are cancelled. Claims 1-2, 5-7, 9, 11-16 and 19-20 are currently pending in the application.
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 1-2, 5-7, 9, 11-16 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: Statutory Category?
Independent claims 1 and 15 respectively recites “a computer-based method” (i.e. a process), and “a computer-based system” (i.e., a “machine”). As such, independent claims 1 and 15 are each directed to a statutory category of invention within § 101, i.e., process, and machine. (Step 1: YES).
Step 2A – Prong 1: Judicial Exception Recited?
Independent claim 15, analyzed as representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are shown in italics. The additional element(s) recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
A computer-based system for extending a user’s healthspan and decreasing the user’s rate of aging, the system comprising:
[L1] processing circuitry including one or more processors, memory and a communication interface, the communication interface configured to receive user information including fixed factors including genetic information and the user's current and past medical information, non-fixed factors including lifestyle information and epigenetic factors, radiological and/or diagnostic imaging reports and one or more images of the user;
[L2] the one or more processors configured to process and analyze the received information using at least one of AI technology, machine learning and algorithms; and
[L3] the communication interface configured to, based on the one or more images of the user, implement image preprocessing and silhouette segmentation using computer vision techniques and machine learning to provide high- fidelity human body tracking in real time and a front and/or side imaging preprocessing pipeline to predict main body parameters; and
[L4] the one or more processors configured to process and analyze the received information using at least one of AI technology, machine learning and algorithms; and
[L5] the machine learning model configured to: utilize the fixed factors and the medical information to output a first health span score; and
[L6] the one or more processors further configured to utilize the modified health span score, information from the imaging reports, and the user's body type to predict health, wellness and longevity information and recommendations; and
[L7] the communication interface configured to provide the health, wellness and longevity information and recommendations to the user, including one or more of the user's rate of aging, how to decrease the user's rate of aging, and the user's estimated future physical appearance.
It is common practice for a human, such as a healthcare provider, interacting with another human, such as a patient, to receive patient information via paper questionnaire and/or verbal interaction and subsequently provide diagnosis and care plan to the patient. Thus, other than reciting the “processing circuitry including one or more processors, memory and a communication interface”, “AI technology”, and “machine learning” additional non-abstract elements in representative independent claim 15 above, under the broadest reasonable interpretation, at least the italicized claim limitations may be performed in the human mind, including observations, evaluations, and judgments and may also be characterized as a certain method of organizing human activity, i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea under Step 2A: Prong 1. (Step 2A – Prong 1: YES).
Step 2A – Prong 2: Integrated into a Practical Application?
The computer element(s), namely the “processing circuitry including one or more processors, memory and a communication interface”, “AI technology”, and “machine learning” is/are recited at a high level of generality (see originally filed Specification, at least ¶ 14: … blockchain, personalized telemedicine, generative Artificial Intelligence ("AI"), machine learning, population health and other technologies to help extend human life and longevity and reduce the user's rate of aging …; ¶ 27: … The system includes processing circuitry including one or more processors, memory and a communication interface. The communication interface is configured to receive user information including identification information, medical information, and at least one of biometric information, lab information, medical imaging reports and images of the user…; ¶ 43: … user device 12 may refer to one or more of a computer, laptop, mobile phone, tablet, handheld electronic device, etc. User device 12 is configured to interface and/or communicate with computing environment 14 via network 15, as described herein. User device 12 may include one or more user interfaces (e.g., buttons, touch screen, input devices, etc.) to facilitate a user interacting with user device 12; ¶ 45: … computing environment 14 may include lifestyle recommendation system (also referred to as "lifestyle system") 20 and data store(s) 22 for storing one or more models and/or input data, among other data that may be used by computing environment 14 to perform one or more functions described herein …lifestyle recommendation platform (also referred to as "lifestyle platform," "application," or "platform/application") 24 is part of and/or a sub-component of lifestyle recommendation system 20 …; ¶ 46: … lifestyle recommendation system 20 and lifestyle recommendation platform 24 use Artificial Intelligence ("AI") to analyze some or all of the data received…; ¶ 48: … The processing circuitry 28 may include one or more processors 30 and one or more memories 32. Each processor 30 may include and/or be associated with one or more central processing units, data buses, buffers, and interfaces to facilitate operation. In addition to or instead of a processor 30 and memory 32, the processing circuitry 28 may comprise other types of integrated circuitry that perform various functionality. Integrated circuitry may include one or more processors 30, processor cores, FPGAs, ASICs, GPUs, SoCs, or other components configured to execute instructions. The processor 30 may be configured to access (e.g., write to and/or read from) the memory 32, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache, buffer memory, RAM, ROM, optical memory, and/or EPROM …; ¶ 49: Hardware 26 may include communication interface 34 facilitating communication between one or more elements in system 10. For example, communication interface 34 may be configured for establishing and maintaining at least a wireless or wired connection with one or more elements of system 10 such as network 15, data store 16, user device 12, etc; ¶ 50: The processing circuitry 28 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., in computing environment 14. Processor 30 corresponds to one or more processors 30 for performing computing device 18 functions described herein; ¶ 51: … Although FIG. 2 shows the lifestyle recommendation system 20 and lifestyle recommendation platform 24 being in a single computing device 18, the lifestyle recommendation system 20 and lifestyle recommendation platform 24 may execute in multiple computing devices 18 of the computing environment 14…; ¶ 58: … Lifestyle recommendation system 20, using software and generative artificial intelligence technology analyzes all available data within each category and provide certain recommendations with regards to diet and longevity…; ¶ 172: … the platform/application 24 of the present disclosure can utilize AI or machine learning to provide predictions and/or suggestions to the user, based on the user's provided input …; ¶ 193: … Using AI and/or machine learning … lifestyle system 20, via hardware 26, performs … Integrates a prediction pipeline via, for example, an application program interface; (API) such as by using FastAPI endpoints … Utilizes rule-based body type classification logic … using computer vision techniques and machine learning, such as, for example MediaPipe Pose, etc.… The lack of details about the “processing circuitry including one or more processors, memory and a communication interface, the communication interface”, “AI technology”, and “machine learning and algorithms” indicates that the additional element(s) is/are generic, or part of generic computer elements performing or being used in performing the generic functions claimed. The claim does not change the way in which each of the recited “processing circuitry including one or more processors, memory and a communication interface”, “AI technology”, and “machine learning” performs its tasks, the claim simply uses each component for its ordinary purpose to carry out the abstract idea of extending a user’s healthspan and decreasing the user’s rate of aging. The claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP § 2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP § 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP § 2106.05(c)); or (iv) any other meaningful limitation (see MPEP § 2106.05(e)). See Guidance, 84 Fed. Reg. at 55. The claimed invention merely implements the abstract idea using instructions executed on generic computer components, as shown in bold above, and as supported in the above noted pertinent portions of the Specification. The instant claim merely uses a programmed computer as a tool to perform an abstract idea. See MPEP § 2106.05(f). The additional limitations [L1] (“receive user information”, i.e., data gathering), [L3] (“implement image preprocessing and silhouette segmentation using computer vision techniques and machine learning” , i.e., data gathering), [L5] (“output a first health span score”, i.e., data presentation) and [L7] (“provide the health, wellness and longevity information and recommendations to the user”, i.e., data presentation) reflect the type of extra-solution activity (i.e., in addition to the judicial exception) the courts have determined insufficient to transform judicially excepted subject matter into a patent-eligible application when they are claimed in a merely generic manner. See MPEP § 2106.05(g); see In re Bilski, 545 F.3d at 963 (characterizing data gathering steps as insignificant extra-solution activity); see also In re Killian, 45 F.4th 1373, 1380 (Fed. Cir. 2022) (claims “directed to collection of information, comprehending the meaning of that collected information, and indication of the results, all on a generic computer network operating in its normal, expected manner,” fail step one of the Alice framework), Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1368 (Fed. Cir. 2019) (claims “directed to the abstract idea of ‘collecting, analyzing, manipulating, and displaying data’”), FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093–94 (Fed. Cir. 2016) (determining “that the ‘realm of abstract ideas’ includes ‘collecting information, including when limited to particular content’” as well as analyzing and presenting information). The instant claim as a whole merely uses computer instructions to implement the abstract idea on a computer or, alternatively, merely uses a computer as a tool to perform the abstract idea. The claim limitations amount to merely indicating a field of use or technological environment (a computer) in which to apply a judicial exception and, as such, cannot integrate the judicial exception into a practical application. See MPEP § 2106.05(h). Hence, as per MPEP §§ 2106.05(a)–(c), (e)–(h), the additional element in representative claim 15, namely the “processing circuitry including one or more processors, memory and a communication interface, the communication interface”, “AI technology”, and “machine learning and algorithms” does not, either individually or in combination, integrate the abstract idea into a practical application. Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong 2: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the originally filed Specification, as noted above (¶¶ 14, 27, 43, 45, 46, 48, 49, 50, 51, 58, 172, 193) describes the “processing circuitry including one or more processors, memory and a communication interface”, “AI technology”, and “machine learning” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the originally filed Specification sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional elements “in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.” The generic description of the “processing circuitry including one or more processors, memory and a communication interface”, “AI technology”, and “machine learning” indicates the steps are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, the recited data gathering (i.e., [L1] “receive user information” and [L3] “implement image preprocessing and silhouette segmentation using computer vision techniques and machine learning) and data presentation (i.e., [L5] “output a first health span score” and [L7] “provide the health, wellness and longevity information and recommendations to the user”) are nothing more than well-understood, routine, and conventional activity because it is not distinguished from the generic, conventional data gathering and data presentation with a computer. See Elec. Power Grp., 830 F.3d at 1356 (claims to gathering, analyzing, and displaying data in real time using conventional, generic technology do not have an inventive concept). Hence, the additional elements are generic, well-known, and conventional computing elements. The use of the additional elements either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to independent Claim 1:
Independent claim 1 recites a computer-based method for extending a user’s healthspan and decreasing the user’s rate of aging, the method comprising steps comparable to those performed by the computer components of representative claim 15. Accordingly, independent claim 1 is rejected similarly to representative claim 15.
In regard to the dependent claims:
Dependents claims 2, 5-7, 9, 11-14, 16 and 19-20 include all the limitations of corresponding independent claims 1 and 15 from which they depend and, as such, recite the same abstract idea(s) noted above for corresponding independent claims 1 and 15. The dependent claims do not appear to remedy the issues noted above. Any additional claim element is recited as being used according to its conventional purpose in a conventional manner. The Examiner fails to see any claim activity used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2, 5-7, 9, 11-14, 16 and 19-20 integrates the judicial exception into a practical application. While dependent claims 2, 5-7, 9, 11-14, 16 and 19-20 may have a narrower scope than the representative claims, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2, 5-7, 9, 11-14, 16 and 19-20 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Examiner's Note
As per Applicant’s disclosure, the use of questionnaires and/or Artificial intelligence is interpreted to imply providing “accurate” information. See originally filed Specification, ¶ 185: … our artificial intelligence will be able to analyze you more accurately to provide much more accurate information; ¶ 186: …. Query screens.
Response to Arguments
Claim Rejections - 35 U.S.C. § 101
Applicant’s arguments have been fully considered but they are not persuasive.
Step 2A: Prong 1
Contrary to Applicant’s arguments, representative claim 15 is readily distinguishable from Example 39 at least in the fact that, unlike the hypothetical claim in Example 39, which the Office determined does not recite any judicial exception, claim 15 recites subject matter that falls with the method of organizing human activity, and mental processes concept groupings of abstract ideas as noted above. In particular, the instant application “relates to a method and system for assisting patients in improving their diet, prevent or control diseases, extend their healthy lifespans ("healthspans") and reduce their rate of aging”. It is common practice for a human, such as a healthcare provider, interacting with another human, such as a patient, to receive patient information via paper questionnaire and/or verbal interaction and subsequently provide diagnosis and care plan to the patient.
Reviewing courts have found claims to be directed to abstract ideas when they recited similar subject matter. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1208 (Fed. Cir. 2025) (using a generic machine learning technique in a particular environment). Thus, reciting training and execution of a machine learning model does not take representative claim 15 out of the abstract realm. See id; see also SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1169-70 (Fed. Cir. 2018) (holding that the additional limitations do not integrate the abstract idea into a practical application where they require “already available computers, with their already available basic functions, to use as tools in executing the claimed process”). As the Federal Circuit explained in Recentive Analytics, using existing machine learning technology to speed up a task does not render a claim patent eligible. See Recentive Analytics, 134 F.4th at 1214.
The hypothetical claim of Example 39 recites steps of collecting digital facial images; applying transformations to each digital facial image; creating a first training set; training the neural network in a first stage; and training the neural networks in a second stage. The combination of features recited in the claim of Example 39 provided an improved facial detection model which, unlike prior models, could detect faces in distorted images while limiting the number of false positives. Eligibility Examples at 8. In particular, prior neural network models used for detecting facial images suffered from an inability to detect human faces in images having shifts, distortions, and variations in scale and rotation of the face pattern. Id. To address this problem, the claim applied mathematical transformations to an acquired set of facial images (thereby introducing shifts, distortions, and variations in scale and rotation of the face pattern) to develop an expanded training set, and trained the neural network using this expanded set. Id. While training with the expanded set better detects human faces in images having shifts, distortions, and variations in scale and rotation of the face pattern, it also suffers from increased false positives when classifying non-facial images. Id. To reduce these false positives, the claim retrains the neural network with an updated training set containing the false positives produced after face detection has been performed on non-facial images. Id.
Representative claim 15 recites receiving “user information including fixed factors including genetic information and the user's current and past medical information, non-fixed factors including lifestyle information and epigenetic factors, radiological and/or diagnostic imaging reports and one or more images of the user” and further recites “based on the one or more images of the user, implement image preprocessing”, “a front and/or side imaging preprocessing pipeline to predict main body parameters”, and “utilize the modified health span score, information from the imaging reports”. Assuming arguendo, the claim steps to (1) “utilize the fixed factors and the medical information to output a first health span score” and (2) “utilize the non-fixed factors to modify the first health score to output a modified health span score” represent a “first stage” and a “second stage”, it is apparent that, in view of the above claim recitations of “images/imaging”, the use of “fixed factors” and “non-fixed factors” is unlike the hypothetical claim in Example 39, where the second stage used the first training set with digital non-facial images detected incorrectly as facial images in the first stage to train the neural network. No analogous technological improvement is apparent in representative claim 15 and there is no evidence or improvement to the claimed additional elements, namely the “processing circuitry including one or more processors, memory and a communication interface”, “AI technology”, and “machine learning”. In contrast, the instant application utilizes the “user information including fixed factors including genetic information and the user's current and past medical information, non-fixed factors including lifestyle information and epigenetic factors, radiological and/or diagnostic imaging reports and one or more images of the user” to “provide the health, wellness and longevity information and recommendations to the user” which is an abstract idea in the form of a method of organizing human activity and a mental process. The recited process, at best, affects an improvement to the abstract idea itself. It is well-established, however, that improvements in the abstract idea are insufficient to confer eligibility on an otherwise ineligible claim. SAP Am. Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Hence, the fact pattern of representative claim 15 does not match the fact pattern of the claim of Example 39.
Step 2A: Prong 2
Applicant further argues “a method that cannot be performed entirely in a human's mind” by asserting “Claim 15, as amended recites additional elements that are not generic, and do not perform generic functions” and analogy to Ex parte Donovan, Appeal No. 2017-005993 (PTAB Mar. 5, 2019). As a panel of the Patent Trial and Appeal Board (PTAB) noted in Ex parte Shurgot, Donovan is “not precedential and [is] not binding [on our panel].” Ex parte Shurgot, Appeal No. 2023-003404, 2024 WL 3739348, at *8 (PTAB Aug. 9, 2024). Applicant’s arguments are nonetheless considered and found not analogous to the claims in Ex parte Donovan, as shown below.
The claims in Ex parte Donovan are directed to “[a] method for administering a course of treatment to a subject with a cancer type” via specific hardware and machine learning application configured to carry out specific operations in furtherance of generating an effective course of treatment for the subject with cancer. Those operations include, inter alia, calculating p95HER2 expression levels with the computer according to a particular function. The machine learning application, on the other hand, is configured to generate an effective treatment model using population data according to a specific statistical functionality (linear discriminant analysis) and relying on particular inputs (e.g., data for each member of the population taken at two different times, data values corresponding to treatment resistance and health outcomes, and both measured and derived protein expression levels for a plurality of biomarkers). The Board determined “upon evaluation of the measured and calculated subject dataset in relation to the effective treatment model generated by the machine learning application, the computing device includes programming (i.e., instructions) in the form of a treatment module to generate a course of treatment for the cancer subject----excluding ineffective treatments where resistance is probable”. Id. The Board reversed the Examiner’s rejection under 35 U.S.C. § 101 because the claims, as a whole, require orchestrated steps and specific interoperation of hardware and specially configured computing modules including machine learning that generate effective treatment models and courses of treatment for cancer.
While the instant representative claim 15 recites “machine learning model trained by experimentation, … configured to” utilize data to determine a “health span score” then a “modified health span score”, the claim limitations do not rise to the level of details shown in Ex parte Donovan requiring “the computing device and a machine learning application that are also configured to carry out specific operations in furtherance of generating an effective course of treatment for the subject with cancer. Those operations include, inter alia, calculating p95HER2 expression levels with the computer according to a particular function” for “a specific hardware and machine learning application configured to carry out specific operations in furtherance of generating an effective course of treatment for the subject with cancer”, “excluding ineffective treatments where resistance is probable”. Id. By contrast, the instant claims do not meaningfully limit how the “machine learning model” functions. Furthermore, Applicant has not provided any further definition of the “machine learning model trained by experimentation”. Humans have long used experimentation and have done so in connection with machine learning, as evidenced by Yu et al. (US 20200279180 A1) discloses “… the parameters used to train the machine learning (ML) model 230 may be manually determined by a data scientist or engineer (e.g., based on experimentation and analysis), such as which machine learning algorithms to use for training” (¶ 38). Additionally, in regard to “the one or more processors configured to apply rule-based logic to determine the user's body type” limitation, rules are embodied in computer software that is processed by general-purpose computers. Thus, contrary to Applicant’s arguments, there is no indication that the operations recited in representative claim 15 require any specialized computer hardware or other inventive computer components, invoke any allegedly inventive programming, or that each of the recited additional claim elements is other than a generic computer component operating in its ordinary capacity. As such, the additional elements, alone or in combination, do not reflect an integration of the abstract ideas into a practical application.
Step B
As noted in the rejections above, the analysis conducted in step 2A applies in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Applicant failed to indicate and the Examiner failed to find in the specification any indication of improvement to the additional claim elements (“processing circuitry including one or more processors, memory and a communication interface”, “AI technology”, and “machine learning”) in view of the functions performed. As noted earlier, the instant claims do not meaningfully limit how the “machine learning model” functions and the claimed “machine learning model” is a generic “machine learning model”.
Thus, contrary to Applicant’s arguments, there is no indication that the operations recited in representative claim 15 require any specialized computer hardware or other inventive computer components, invoke any allegedly inventive programming, or that each of the recited additional claim elements is other than a generic computer component operating in its ordinary capacity. The ordered combination of elements recites no more than the individual elements do. Killian, 45 F.4th at 1380 (gathering information, comprehending its meaning, and displaying results of the analysis on generic computers is not inventive); Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (data retrieval, analysis, modification, display, generation, and transmission on generic computers is not inventive); Gale, 856 F. App’x at 889–890 (using generic computer components operating in a conventional manner to collect and analyze information to calculate a usage pattern and determine compliance with a predetermined usage pattern by feeding data into a computer that repeatedly recalculates an output is not inventive). No element or combination of elements recited in the instant claims contains any “inventive concept” or adds anything “significantly more” to transform the abstract concept into a patent-eligible application. See Alice, 573 U.S. at 221.
For at least the above reasons and contrary to Applicant’s arguments, the combination of the features of the claims as a whole do not amount to a practical application of the abstract idea under step 2A and are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more under step 2B.
In view of the foregoing, claims 1-2, 5-7, 9, 11-16 and 19-20 remain rejected under 35 U.S.C. § 101.
Claim Rejections - 35 U.S.C. § 102 and 103
The prior art rejections of the claims are withdrawn in view of Applicant’s amendment and remarks.
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.
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
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