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 .
This communication is in response to the amendment received on 06/08/2026. Claims 19-38 remain pending in this application.
The objection to claims 28 and 29 has been withdrawn in light of the amendments.
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 19-38 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 19-38 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
The claims 19, 30 and 33 are directed to an abstract idea of “certain methods of organizing human activity” as explained below:
Claim 19 recites:
“… receiving, via a user interface, a generated graphical representation comprising both a predicted effectiveness of the first treatment for the patient and the predicted effectiveness of a 2nd treatment for the patient, wherein in the graphical representation is generated by:
determining, using a trained clinical model analyzing one or more patient characteristics of the patient, at least one indicator of an outcome of a first treatment for the patient;
predicting, based on the determined at least one indicator, an effectiveness of the first treatment;
predicting, based on the determined at least one indicator, and effectiveness of a second treatment, wherein the second treatment is different than the first treatment;
…
determining, by comparing the height, width, and/or area of the first portion of the graphical representation to the height, width, and/or area of the second portion of the graphical representation, which of the first treatment in the second treatment is predicted to provide more effectiveness;…”
Claim 30 recites:
“… receiving, via a user interface, a generated graphical representation comprising both a predicted effectiveness of a first treatment for the patient and the predicted effectiveness of the 2nd treatment for the patient, wherein the graphical representation is generated by:
determining, using a trained clinical model analyzing one or more patient characteristics of the patient, at least one indicator of an outcome of a first treatment for the patient;
predicting, using the trained clinical model analyzing the determined at least one indicator, and effectiveness of the first treatment, wherein the predicted effectiveness of the first treatment comprises one or more of the predicted survival outcome, the predicted pathological outcome, and a predicted functional outcome;
predicting, using the trained clinical model analyzing the determined at least one indicator, an effectiveness of a second treatment, wherein the second treatment is different than the first treatment, wherein the predicted effectiveness of the second treatment comprises one or more of the predicted survival outcome, a predicted pathological outcome, and a predicted functional outcome;
…
determining, by comparing the height, width, and/or area of the first portion of the graphical representation to the height, width, and/or area of the second portion of the graphical representation, which of the first treatment in the second treatment is predicted to provide more effectiveness;…”
Claim 33 recites:
“… determining, using a trained clinical model analyzing one or more patient characteristics of the patient, at least one indicator of an outcome of the first treatment for the patient;
predicting, using the trained clinical model analyzing the determined at least one indicator, and effectiveness of the first treatment, wherein the predicted effectiveness of the first treatment comprises one or more of a predicted survival outcome, a predicted pathological outcome, and the predicted functional outcome;
predicting, using the trained clinical model analyzing the determined at least one indicator, and effectiveness of a second treatment, wherein the second treatment is different than the first treatment, and wherein the predicted effectiveness of the second treatment comprises one or more of a predicted survival outcome, the predicted pathological outcome, and the predicted functional outcome;…”
These limitations of “predicting effectiveness of a first and a second treatments”, “determining by comparing the height, width, and/or area of the first portion of the graphical representation to the height, width, and/or area of the second portion of the graphical representation, which of the first treatment in the second treatment is predicted to provide more effectiveness;” correspond to an abstract idea of “certain methods of organizing human activity”, since this is a method of managing interactions between people, such as user following rules and instructions. The mere nominal recitation of a generic user interface does not take the claims out of the methods of organizing human interactions grouping. The “user interface” is described as “a computer display screen” in the current specification, on page 8, lines 30-32, therefore corresponds to a generic computing component. Thus, the claims recite an abstract idea.
The limitations of “using a trained clinical model to: analyze one or more patient characteristics of the patient,…”, “determine an indicator of an outcome of a first treatment of the patient”, “predicting an effectiveness of the first treatment”, “predicting an effectiveness of a second treatment” correspond to mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Dependent claims also correspond to an abstract idea of “certain methods of organizing human activity”, such as:
Claims 25 recites: “…determining, …, at least a second indicator of an outcome of the first treatment for the patient; combining the determined at least one indicator and the determined at least second indicator to create a combined indicator…”
Claims 26 recites: “determining, …, an updated at least one indicator of the outcome of the first treatment for the patient;
predicting, based on the determined updated at least one indicator, an updated effectiveness of the first treatment;
predicting, based on the determined updated at least one indicator, an updated effectiveness of the second treatment, wherein the second treatment is different than the first treatment;…”
Claims 32 recites: “… determining, … at of the first treatment for the patient of the first treatment for the patient; combining the determined at least one indicator and the determined this second indicator to create a combined indicator;…”
Claims 37 recites: “… determining, …, at least a second indicator of an outcome of the first treatment for the patient; combining the determined at least one indicator and the determined that the second indicator to create a combined indicator;…”
Dependent claims also correspond to an abstract idea of “mathematical concepts”, such as:
Claims 22 recites: “…predicting the effectiveness of the first treatment comprises predicting, using the trained clinical model analyzing the at least one indicator, effectiveness of the first treatment”,
Claims 23 recites: “… predicting the effectiveness of the second treatment comprises predicting, using the trained clinical model analyzing the at least one indicator, the effectiveness of the second treatment”,
Claims 24 recites: “…providing training data comprising the plurality of indicators each associated with the ground truth outcome for a treatment for a previous patient; and training, using the training data, the clinical model to determine, by analyzing one or more patient characteristics of the patient, the at least one indicator of the outcome of the first treatment for the patient”,
Claims 25 recites: “…determining, using a second trained clinical model analyzing the one or more patient characteristics of the patient…”
Claims 26 recites: “…determining, using the trained clinical model analyzing the updated one or more patient characteristics of the patient…”
Claims 31 recites: “…providing training data comprising a plurality of indicators each associated with a ground truth outcome for a treatment for a previous patient; and training, using the training data, the clinical model to determine, by analyzing one or more patient characteristics of the patient, the least one indicator of the outcome of the first treatment for the patient”
Claims 32 recites: “… determining, using a second trained clinical model analyzing the one or more patient characteristics of the patient…”
Claims 36 recites: “…providing training data comprising a plurality of indicators each associated with the ground truth outcome for a previous patient; and training, using the training data, the clinical model to determine, by analyzing one or more patient characteristics of the patient, to at least one indicator of the outcome of the first treatment for the patient”
Claims 37 recites: “… determining, using a second trained clinical model analyzing the one or more patient characteristics of the patient, at least a second indicator of an outcome of the first treatment for the patient”
Claims 20-29, 31-32 and 34-38 are ultimately dependent from claims 19, 30, 33 and include all the limitations of claims 19, 30, 33. Therefore, claims 20-29, 31-32 and 34-38 recite the same abstract idea. Claims 20-29, 31-32 and 34-38 describe a further limitation regarding the basis for determining the effectiveness of the treatments. These are all just further describing the abstract idea recited in claims 19, 30, 33, without adding significantly more.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements that are shown in bolded style below:
Claim 19. A method for implementing a treatment for a patient, comprising:
receiving, via a user interface, a generated graphical representation comprising both a predicted effectiveness of the first treatment for the patient and the predicted effectiveness of a second treatment for the patient, wherein in the graphical representation is generated by:
determining, using a trained clinical model analyzing one or more patient characteristics of the patient, at least one indicator of an outcome of a first treatment for the patient;
predicting, based on the determined at least one indicator, an effectiveness of the first treatment;
predicting, based on the determined at least one indicator, and effectiveness of a second treatment, wherein the second treatment is different than the first treatment;
generating a graphical representation comprising both the predicted effectiveness of the first treatment and predicted effectiveness of the second treatment, wherein a height, width, and/or area of the first portion of the graphical representation is determined by the predicted effectiveness of the first treatment, and wherein the height, width, and/or area of the second portion of the graphical representation is determined by a relative value of the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment, such that a comparison of the first portion of the graphical representation relative to the second portion of the graphical representation indicates which of the first treatment and the second treatment is predicted to provide more effectiveness; and
providing, via a user interface, the generated graphical representation;
determining, by comparing the height, width, and/or area of the first portion of the graphical representation to the height, width, and/or area of the second portion of the graphical representation, which of the first treatment in the second treatment is predicted to provide more effectiveness; and
administering, to the patient based on the comparing, the reach of the first treatment in the second treatment is determined to be predicted to provide more effectiveness.
Claim 20. The method of claim 19, wherein the first treatment in the second treatment are one or more of the surgery, brachytherapy, or external beam radiotherapy, and nerve sparing plan, and pelvic lymph node this section.
Claim 21. The method of claim 19, wherein the predicted effectiveness of the first treatment comprises one or more of a predicted survival outcome, the predicted pathological outcome, and the predicted functional outcome, and wherein the predicted effectiveness of the second treatment comprises one or more of a predicted survival outcome, the predicted pathological outcome, and the predicted functional outcome.
Claim 22. The method of claim 19, wherein predicting the effectiveness of the first treatment comprises predicting, using the trained clinical model analyzing the at least one indicator, the effectiveness of the first treatment.
Claim 23. The method of claim 19, wherein predicting the effectiveness of the second treatment comprises predicting, using the trained clinical model analyzing the at least one indicator, the effectiveness of the second treatment.
Claim 24. The method of claim 19, further comprising training the clinical model, comprising:
providing training data comprising the plurality of indicators each associated with the ground truth outcome for a treatment for a previous patient; and
training, using the training data, the clinical model to determine, by analyzing one or more patient characteristics of the patient, the at least one indicator of the outcome of the first treatment for the patient.
Claim 25. The method of claim 19, further comprising:
determining, using a second trained clinical model analyzing the one or more patient characteristics of the patient, at least a second indicator of an outcome of the first treatment for the patient;
combining the determined at least one indicator and the determined at least second indicator to create a combined indicator; and
wherein predicting the effectiveness of the first treatment is based on the combined indicator.
Claim 26. The method of claim 19, further comprising:
receiving, via the user interface after the generated graphical representation is presented, an updated one or more patient characteristics of the patient; and
receiving, via the user interface, an updated generated graphical representation comprising both an updated predicted effectiveness of the first treatment for the patient and an updated predicted effectiveness of the second treatment for the patient, wherein the updated graphical representation is generated by:
determining, using the trained clinical model analyzing the updated one or more patient characteristics of the patient, an updated at least one indicator of the outcome of the first treatment for the patient;
predicting, based on the determined updated at least one indicator, an updated effectiveness of the first treatment;
predicting, based on the determined updated at least one indicator, an updated effectiveness of the second treatment, wherein the second treatment is different than the first treatment;
generating an updated graphical representation comprising both the updated predicted effectiveness of the first treatment and the updated predicted effectiveness of the second treatment; and
providing, via the user interface, the updated generated graphical representation;
wherein determining which of the first treatment in the second treatment is predicted to provide more effectiveness further comprises analysis of the generated graphical representation compared to the updated generated graphical representation.
Claim 27. The method of claim 26, wherein receiving, via the user interface after the generated graphical representation is presented, an updated one or more patient characteristics of the patient comprises removing or deleting one or more patient characteristics of the patient.
Claim 28. The method of claim 19, wherein the generated graphical representation is interactive via the user interface, and wherein the method further comprises:
receiving, via the user interface, an instruction to display one or more details regarding one or more of the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment; and
providing, via the user interface in response to receiving the instruction, an updated generated graphical representation comprising a display of the one or more details;
wherein determining which of the first treatment and the second treatment is predicted to provide more effectiveness further comprises analysis of the display of the one or details.
Claim 29. The method of claim 28, wherein the displayed one or more details are interactive, and wherein the method further comprises:
receiving, via the user interface, an instruction to display one or more further details regarding the displayed one or more details; and
providing, via the user interface in response to receiving the instruction to display one or more further details, a further updated generated graphical representation comprising a display of the further one or more details;
wherein determining which of the first treatment in the second treatment is predicted to provide more effectiveness further comprises analysis of the display of the further one or details.
Claim 30. A method for implementing the prostate cancer treatment for a prostate cancer patient, comprising:
receiving, via a user interface, a generated graphical representation comprising both a predicted effectiveness of a first treatment for the patient and the predicted effectiveness of the second treatment for the patient, wherein the graphical representation is generated by:
determining, using a trained clinical model analyzing one or more patient characteristics of the patient, at least one indicator of an outcome of a first treatment for the patient;
predicting, using the trained clinical model analyzing the determined at least one indicator, and effectiveness of the first treatment, wherein the predicted effectiveness of the first treatment comprises one or more of the predicted survival outcome, the predicted pathological outcome, and a predicted functional outcome;
predicting, using the trained clinical model analyzing the determined at least one indicator, an effectiveness of a second treatment, wherein the second treatment is different than the first treatment, wherein the predicted effectiveness of the second treatment comprises one or more of the predicted survival outcome, a predicted pathological outcome, and a predicted functional outcome;
generating a graphical representation comprising both the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment, wherein the height, width, and/or area of a first portion of the graphical representation is determined by the predicted effectiveness of the first treatment, and wherein the height, width, and/or area of the second portion the graphical representation is determined by a relative value of the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment, such that a comparison of the first portion of the graphical representation relative to the second portion of the graphical representation indicates which of the first treatment in the second treatment is predicted to provide more effectiveness; and
providing, via a user interface, the generated graphical representation;
determining, by comparing the height, width, and/or area of the first portion of the graphical representation to the height, width, and/or area of the second portion of the graphical representation, which of the first treatment in the second treatment is predicted to provide more effectiveness; and
administering, to the patient based on the comparing, the which of the first treatment in the second treatment is determined to be predicted to provide more effectiveness, wherein the first treatment in the second treatment are one or more of robotic surgery of the patient’s prostate, retropubic prostate surgery of the patient’s prostate, brachytherapy of the patient’s prostate, external beam radiotherapy of the patient’s prostate, and pelvic lymph node dissection.
Claims 31 and 32 repeat the same limitations as claims 24 and 25.
Claim 33. A method for generating and providing an interactive graphical representation of an effectiveness of a first treatment for a patient versus an effectiveness of a second treatment for the patient, comprising:
determining, using a trained clinical model analyzing one or more patient characteristics of the patient, at least one indicator of an outcome of the first treatment for the patient;
predicting, using the trained clinical model analyzing the determined at least one indicator, and effectiveness of the first treatment, wherein the predicted effectiveness of the first treatment comprises one or more of a predicted survival outcome, a predicted pathological outcome, and the predicted functional outcome;
predicting, using the trained clinical model analyzing the determined at least one indicator, and effectiveness of a second treatment, wherein the second treatment is different than the first treatment, and wherein the predicted effectiveness of the second treatment comprises one or more of a predicted survival outcome, the predicted pathological outcome, and the predicted functional outcome;
generating a graphical representation comprising both the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment, wherein the height, width, and/or area of a first portion of the graphical representation is determined by the predicted effectiveness of the first treatment, and wherein the height, width, and/or area of the second portion the graphical representation is determined by a relative value of the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment, such that a comparison of the first portion of the graphical representation relative to the second portion of the graphical representation indicates which of the first treatment in the second treatment is predicted to provide more effectiveness; and
providing, via the user interface in response to receiving the instruction, an updated generated graphical representation comprising a display of the one or more details.
Claim 34 repeats the same limitations as claim 29.
Claim 35 repeats the same limitations as claim 20.
Claims 36 and 37 repeat the same limitations as claims 24 and 25.
Claim 38. The method of claim 33, wherein the generated graphical representation comprises a graph or chart with one of the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment overlaid on the other of the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment.
These additional elements correspond to hardware and software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment, and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Claims 19 and 30 recite “administering, to the patient based on the comparing, the reach of the first treatment in the second treatment is determined to be predicted to provide more effectiveness” and “administering, to the patient based on the comparing, the which of the first treatment in the second treatment is determined to be predicted to provide more effectiveness, wherein the first treatment in the second treatment are one or more of robotic surgery of the patient’s prostate, retropubic prostate surgery of the patient’s prostate, brachytherapy of the patient’s prostate, external beam radiotherapy of the patient’s prostate, and pelvic lymph node dissection”, and these limitations correspond to insignificant limitations, which are directed to insignificant application (see MPEP 2106.05(g)).
Accordingly, these 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 claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a trained clinical model to perform both the predicting and determining steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 06/08/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed in the order in which they appear.
Arguments about step 2A, prong one rejection (Remarks, pages 12-19):
Applicant argues that the claims as a whole are not directed to certain methods of organizing human activity, such as following rules and instructions, since claims recite “for use in training a convolutional neural network using a training data set”, which is not similar to the examples given in the MPEP.
In response, Examiner submits that the MPEP recites “…the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception…”. The limitations of “predicting effectiveness of a first and a second treatments”, “determining by comparing the height, width, and/or area of the first portion of the graphical representation to the height, width, and/or area of the second portion of the graphical representation, which of the first treatment in the second treatment is predicted to provide more effectiveness;” correspond to an abstract idea of “certain methods of organizing human activity”, since this is a method of managing interactions between people, such as user following rules and instructions.
Similarly, Applicant argues that claims as a whole do not recite a mathematical process or equation, and they are not directed to an abstract idea.
In response, Examiner submits that MPEP recites “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” in §2106.04(a)(2) I C.
The current specification recites “Clinical models include any model or framework that may be used to determine (e.g. predict or calculate) an indicator related to an outcome of the first treatment.” on page 5, lines 11-12. Hence the limitations of “using a trained clinical model to: analyze one or more patient characteristics of the patient,…”, “determine an indicator of an outcome of a first treatment of the patient”, “predicting an effectiveness of the first treatment”, “predicting an effectiveness of a second treatment” correspond to mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Arguments about step 2A, prong two rejection (Remarks, pages 19-23):
Administering: Applicant argues that claims 19-38 comprise numerous practical applications. Claims 19-29 recite “administering, to the patient based on the comparing, the which of the first treatment and the second treatment is determined to be predicted to provide more effectiveness (wherein the first treatment and the second treatment are one or more of surgery, brachytherapy, or external beam radiotherapy, and nerve sparing plan, and pelvic lymph node this section).
In response, Examiner submits that Claims 19 and 30 recite “administering, to the patient based on the comparing, the reach of the first treatment in the second treatment is determined to be predicted to provide more effectiveness” and “administering, to the patient based on the comparing, the which of the first treatment in the second treatment is determined to be predicted to provide more effectiveness, wherein the first treatment in the second treatment are one or more of robotic surgery of the patient’s prostate, retropubic prostate surgery of the patient’s prostate, brachytherapy of the patient’s prostate, external beam radiotherapy of the patient’s prostate, and pelvic lymph node dissection”, and these limitations correspond to insignificant limitations, which are directed to insignificant application (see MPEP 2106.05(g)).
Applicant also argues that claim limitation of “training the clinical model to generate a trained clinical model” and “interacting with an interactive graphical representation” improves a clinician’s ability to determine a best treatment for a patient, which is directed to a practical application.
In response, Examiner submits that “using a trained clinical model to: analyze one or more patient characteristics of the patient,…”, “determine an indicator of an outcome of a first treatment of the patient”, “predicting an effectiveness of the first treatment”, “predicting an effectiveness of a second treatment” correspond to mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas and “the generated graphical representation is interactive via the user interface” corresponds to mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea.
Interacting: Applicant argues that claims 26-29 and 33-38 recite methods of interacting with an interactive graphical representation, which further improves a clinician’s ability to determine a best treatment for a patient. Applicant argues that combining indicators from different clinical models into a single overall prediction allows a medical professional to quickly obtain a summary of underlying clinical models without consulting individual models directly, which is an improvement to the user interface.
In response, Examiner submits that claims recite “determining, using a trained clinical model analyzing one or more patient characteristics of the patient, at least one indicator of an outcome of a first treatment for the patient; predicting, based on the determined at least one indicator, an effectiveness of the first treatment; predicting, based on the determined at least one indicator, and effectiveness of a second treatment, wherein the second treatment is different than the first treatment; generating a graphical representation comprising both the predicted effectiveness of the first treatment and predicted effectiveness of the second treatment, wherein a height, width, and/or area of the first portion of the graphical representation is determined by the predicted effectiveness of the first treatment, and wherein the height, width, and/or area of the second portion of the graphical representation is determined by a relative value of the predicted effectiveness of the first treatment and the predicted effectiveness of the second treatment, such that a comparison of the first portion of the graphical representation relative to the second portion of the graphical representation indicates which of the first treatment and the second treatment is predicted to provide more effectiveness” and the claims do not recite “different clinical models”, “combining indicators”, or any other recitation for an improvement to the user interface. And “improving a clinician’s ability to determine a best treatment” is not recited in the claims. Furthermore, this feature would be directed to a non-functional descriptive material, which does not have any effect on the structure of the system. Also, providing data on the user interface indicating the comparison of the first and the second treatments may result in the improvement on the outcomes, but does not provide any improvement to the technology.
Therefore, the arguments are not persuasive and claims 19-38 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
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 DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Obeid Mamon can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DILEK B COBANOGLU/Primary Examiner, Art Unit 3687
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