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 12/23/2025. Claims 1-11 and 13-15 remain pending in this application.
The claim objection for claims 4 and 5 has been withdrawn in light of the amendments.
The 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph rejection of the claims has been withdrawn in light of the amendments.
The 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph rejection of the claims has been withdrawn in light of the amendments.
The 35 U.S.C. 101 rejection of claims 9, 12 and 13, for not falling within at least one of the four categories of patent eligible subject matter, has been withdrawn in light of cancelation of claim 12 and amendments made to claims 1 and 13.
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 1-11 and 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-9 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 10-11 are drawn to a method which is within the four statutory categories (i.e. process). Claims 13-15 are drawn to a non-transitory medium which is within the four statutory categories (i.e. manufacture).
Step 2A, Prong 1:
Claims have been amended to recite:
Claims 1 and 8: “…a processor, memory communicatively coupled to the processor; and a user input interface configured to allow a user to provide training data, the training data including: i) a plurality of previous at least partial radiation treatment plans, and ii) a user defined ranking or scoring of the plurality of previous at least partial radiation treatment plans; wherein the memory stores instructions that, when executed by the processor, cause the processor to: train a machine learning model based on the training data to generate an objective function; and provide the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to computer a radiation treatment plan….
Claim 10: “…generating an objective function for radiation treatment planning; and receiving training data via user input interface, the training data including: i) a plurality of previous at least partial radiation treatment plans; and ii) a user defined ranking or scoring of the plurality of previous at least partial radiation treatment plans; wherein the computer implemented method further comprises: training a machine learning model based on training data to generate the objective function; and providing the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan.
Claim 13: “…receive training data provided by a user via a user interface; train a machine learning model to generate an objective function; and provide the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan.
The limitations of (using a processor to) “…training a machine learning model based on training data to generate the objective function;” correspond to mathematical relationships which fall within the “mathematical concepts” grouping of abstract ideas.
The limitations of “receiving training data… ; …providing the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan” correspond to mere data gathering and outputting (see the section below).
The “processor” is described in the current specification as a generic computing device. The specification recites “he trained model M may be deployed as a machine learning component that may be run on the computing device PU, such as a desktop computer, a workstation, a laptop, etc. or plural such devices in a distributed computing architecture. Preferably, to achieve good throughput, the computing device PU includes one or more processors (CPU) that support parallel computing, such as those of multi-core design.” on page 15, lines 9-14.
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.
Claims 2-7, 9, 11 and 14-15 are ultimately dependent from claims 1, 8, 10 and 13 and include all the limitations of claims 1, 8, 10 and 13. Therefore, claims 2-7, 9, 11 and 14-15 recite the same abstract idea. Claims 2-7, 9, 11 and 14-15 describe a further limitation regarding the basis for generating an objective function for radiation planning. These are all just further describing the abstract idea recited in claims 1, 8, 10 and 13, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “a processor”, “memory communicatively coupled to the processor”, “machine learning model”, “user input interface”, “the memory stores instructions that, when executed by the processor, cause the processor to: train machine learning mode based on the training data to generate an objective function”, which are 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)).
The limitations of “receiving training data… ; …providing the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan” correspond to mere data gathering and outputting, which correspond to insignificant extra-solution activities (see MPEP 2106.05 (g)). The insignificant extra solution activities do not provide a practical application for the abstract idea.
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 processor to train the machine learning model based on the training data 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.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-11, 13-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Purdie et al. (hereinafter Purdie) (US 11,735,309 B2).
Claim 1 has been amended to recite a system configured for generating an objective function for radiation treatment planning, the system comprising:
a processor (Purdie; col. 4, lines 51-57);
memory communicatively coupled to the processor (Purdie; col. 4, lines 51-57); and
a user input interface configured to allow a user to provide training data, the training data including:
a plurality of previous at least partial radiation treatment plans (Purdie discloses “…previously approved treatment plans may be used to train an algorithm for automated treatment planning…” in col. 24, lines 25-35 and col. 20, line 50 to col. 21, line 9); and
a user defined ranking or scoring of the plurality of previous at least partial radiation treatment plans (Purdie discloses “…previous outcomes data, including treatment related toxicity, survival data and recurrence data; data related to treatment delivery such as specifications of the treatment delivery unit; or any other data pertinent to patient treatment. Any patient information or plan feature useful for a physician to make an accurate decision about the treatment plan may be included, and that information may be considered jointly as opposed to independently” in col. 11, lines 8-16, “The historical data of historical treatment plans may include treatment outcome data” in col. 14, lines 33-35 and “The outputted visualization may be provided with a user interface to enable the user to manually modify proposed 50 dosages and/or image features, such as delineated ROI(s), used to calculate the proposed dose map…. After any changes are made, the user may confirm the changes and the method may return to one or more of the above-mentioned steps…to recalculate the proposed dose map” in col. 21, lines 49-60);
wherein the memory stores instructions that, when executed by the processor, cause the processor to (Purdie; col. 4, lines 51-57):
train a machine learning model based on the training data to generate an objective function (Purdie; col. 11, lines 27-37); and
provide the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan (Purdie discloses “At 2071, the expected dose distribution for the proposed treatment plan is calculated. Verification may be performed at this point. Verification may include, for example, checking whether the expected dose distribution matches with the proposed dose map, checking whether the expected dose distribution falls within acceptable dose guidelines, and/or checking whether the expected dose distribution is similar to historical dose distributions. At 2072, if verification fails, the proposed dose map may be changed at 2073 ( e.g., manually or automatically, such as using an iterative method), and the method returns to 2068 without generating the proposed dose map.” in col. 22, lines 41-52).
Claim 2 has been amended to recite the system as claimed in claim 1, wherein the objective function is, or comprises, the trained machine learning model (Purdie; col. 27, lines 5-31).
Claim 3 has been amended to recite the system as claimed in claim 1, wherein the user inface includes a graphical user interface (Purdie; col. 21, lines 49-60).
Claim 4 has been amended to recite the system as claimed in claim 3, wherein at least one of the user input interface or a second user input interface is configured to allow the user to store a representation of the generated objective function in a data storage in association with a user selectable identifier that identifies an objective criterion (Purdie; col. 21, lines 49-60).
Claim 5 has been amended to recite the system as claimed in claim 4, wherein at least one of the user input interface or a second user input interface is configured to allow the user to select the objective criterion, wherein, upon such selection, the instructions further cause the processor to:
run the iterative optimization procedure to compute a new treatment plan (Purdie; col. 21, lines 49-60).
Claim 6 has been amended to recite the system as claimed in claim 5, wherein the iterative optimization procedure is an inverse planning type (Purdie; col. 7, lines 31-67).
Claim 7 has been amended to recite the system as claimed in claim 1, wherein the machine learning model is a regression type model or is a generative type (Purdie; col. 42, line 67 to col. 25, line 2).
Claim 8 has been amended to recite a system for computer-assisted radiation treatment planning, the system comprising:
a processor (Purdie; col. 4, lines 51-57);
memory communicatively coupled to the processor (Purdie; col. 4, lines 51-57); and
a user input interface configured to allow a user to provide training data, the training data including:
a plurality of previous at least partial radiation treatment plans (Purdie discloses “…previously approved treatment plans may be used to train an algorithm for automated treatment planning…” in col. 24, lines 25-35 and col. 20, line 50 to col. 21, line 9); and
a user defined ranking or scoring of the plurality of previous at least partial radiation treatment plans (Purdie discloses “…previous outcomes data, including treatment related toxicity, survival data and recurrence data; data related to treatment delivery such as specifications of the treatment delivery unit; or any other data pertinent to patient treatment. Any patient information or plan feature useful for a physician to make an accurate decision about the treatment plan may be included, and that information may be considered jointly as opposed to independently” in col. 11, lines 8-16, “The historical data of historical treatment plans may include treatment outcome data” in col. 14, lines 33-35 and “The outputted visualization may be provided with a user interface to enable the user to manually modify proposed 50 dosages and/or image features, such as delineated ROI(s), used to calculate the proposed dose map…. After any changes are made, the user may confirm the changes and the method may return to one or more of the above-mentioned steps…to recalculate the proposed dose map” in col. 21, lines 49-60);
wherein the memory stores instructions that, when executed by the processor, cause the processor to (Purdie; col. 4, lines 51-57):
train a machine learning model based on the training data to generate an objective function (Purdie; col. 11, lines 27-37);
provide the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan (Purdie discloses “At 2071, the expected dose distribution for the proposed treatment plan is calculated. Verification may be performed at this point. Verification may include, for example, checking whether the expected dose distribution matches with the proposed dose map, checking whether the expected dose distribution falls within acceptable dose guidelines, and/or checking whether the expected dose distribution is similar to historical dose distributions. At 2072, if verification fails, the proposed dose map may be changed at 2073 (e.g., manually or automatically, such as using an iterative method), and the method returns to 2068 without generating the proposed dose map.” in col. 22, lines 41-52).
run the iterative optimization procedure to compute a new treatment plan (Purdie; col. 9, lines 4-12, col. 22, lines 41-52).
Claim 9 has been amended to recite the system as claimed in claim 8, further comprising: a radiation delivery apparatus controllable by the new treatment plan (Purdie; col. 4, lines 19-40 and col. 9, lines44-50).
Claim 10 has been amended to recite a computer implemented method for supporting radiation treatment planning, the computer implemented method comprising:
generating an objective function for radiation treatment planning; and
receiving training data via a user input interface, the training data including:
a plurality of previous at least partial radiation treatment plans (Purdie discloses “…previously approved treatment plans may be used to train an algorithm for automated treatment planning…” in col. 24, lines 25-35 and col. 20, line 50 to col. 21, line 9); and
a user defined ranking or scoring of the plurality of previous at least partial radiation treatment plans (Purdie discloses “…previous outcomes data, including treatment related toxicity, survival data and recurrence data; data related to treatment delivery such as specifications of the treatment delivery unit; or any other data pertinent to patient treatment. Any patient information or plan feature useful for a physician to make an accurate decision about the treatment plan may be included, and that information may be considered jointly as opposed to independently” in col. 11, lines 8-16, “The historical data of historical treatment plans may include treatment outcome data” in col. 14, lines 33-35 and “The outputted visualization may be provided with a user interface to enable the user to manually modify proposed 50 dosages and/or image features, such as delineated ROI(s), used to calculate the proposed dose map…. After any changes are made, the user may confirm the changes and the method may return to one or more of the above-mentioned steps…to recalculate the proposed dose map” in col. 21, lines 49-60);
wherein the computer implemented method further comprises:
training a machine learning model based on training data to generate the objective function (Purdie discloses “…previous outcomes data, including treatment related toxicity, survival data and recurrence data; data related to treatment delivery such as specifications of the treatment delivery unit; or any other data pertinent to patient treatment. Any patient information or plan feature useful for a physician to make an accurate decision about the treatment plan may be included, and that information may be considered jointly as opposed to independently” in col. 11, lines 8-16 and “The historical data of historical treatment plans may include treatment outcome data” in col. 14, lines 33-35, “…Features may be extracted from historical training data, and used to construct the learning algorithms and train the treatment planning model…” in col. 23, lines 57-62 and col. 11, lines 27-37); and
providing the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan (Purdie discloses “At 2071, the expected dose distribution for the proposed treatment plan is calculated. Verification may be performed at this point. Verification may include, for example, checking whether the expected dose distribution matches with the proposed dose map, checking whether the expected dose distribution falls within acceptable dose guidelines, and/or checking whether the expected dose distribution is similar to historical dose distributions. At 2072, if verification fails, the proposed dose map may be changed at 2073 (e.g., manually or automatically, such as using an iterative method), and the method returns to 2068 without generating the proposed dose map.” in col. 22, lines 41-52).
Claim 11 has been amended to recite the computer implemented method as claimed in claim 10, further comprising: running the iterative optimization procedure to compute a new based on the objective function (Purdie; col. 7, lines 45-55).
Claim 13 recite a non-transitory computer readable medium with instructions stored thereon which, when executed by a processor, cause the processor to:
receive training data provided by a user via user (Purdie; col. 24, lines 25-35 and col. 20, line 50 to col. 21, line 9);
train a machine learning model to generate an objective function (Purdie; col. 23, lines 57-62 and col. 11, lines 8-16, 27-37); and
provide the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan (Purdie; col. 22, lines 41-52).
Claim 14 has been amended to recite the non-transitory computer readable medium of claim 13, wherein the training data comprises:
a plurality of previous at least partial radiation treatment plans (Purdie discloses “…previously approved treatment plans may be used to train an algorithm for automated treatment planning…” in col. 24, lines 25-35 and col. 20, line 50 to col. 21, line 9), and
a user defined ranking or scoring of the plurality of previous at least partial radiation treatment plans (Purdie discloses “…previous outcomes data, including treatment related toxicity, survival data and recurrence data; data related to treatment delivery such as specifications of the treatment delivery unit; or any other data pertinent to patient treatment. Any patient information or plan feature useful for a physician to make an accurate decision about the treatment plan may be included, and that information may be considered jointly as opposed to independently” in col. 11, lines 8-16, “The historical data of historical treatment plans may include treatment outcome data” in col. 14, lines 33-35 and “The outputted visualization may be provided with a user interface to enable the user to manually modify proposed 50 dosages and/or image features, such as delineated ROI(s), used to calculate the proposed dose map…. After any changes are made, the user may confirm the changes and the method may return to one or more of the above-mentioned steps…to recalculate the proposed dose map” in col. 21, lines 49-60).
Claim 15 has been amended to recite the non-transitory computer readable medium of claim 14, wherein user weighting inside and outside a delineated region of the at least partial radiation treatment plans is applied to the training of the machine learning model (Purdie; abstract).
Response to Arguments
Applicant's arguments filed 12/23/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claim have been amended to recite limitations that are not directed to an abstract idea. In particular, claim limitations of “memory storing instructions that cause the processor to train a machine learning model based on training data to generate an objective function and provide the objective function for controlling an iterative optimization procedure that adjusts treatment plan parameters to compute a radiation treatment plan” do not recite mathematical relationships.
In response, Examiner submits that the limitations of “receiving training data… ; …providing the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan” are not part of the abstract idea, but correspond to mere data gathering and outputting. The limitations of “…training a machine learning model based on training data to generate the objective function;” correspond to mathematical relationships which fall within the “mathematical concepts” grouping of abstract ideas.
Applicant argues that claims do not recite mathematical relationships, formulas or calculations, so they can’t be directed to an abstract idea of mathematical concepts.
In response, Examiner submits that the current specification describes the machine learning model as “The model or the function to derived may be made available as a user-selectable objective function for an optimization procedure to compute a treatment plan, such as inverse planning algorithm.” on page 4, lines 5-7, “In general, the term "machine learning" includes a computerized arrangement (or module) that implements a machine learning ("ML") algorithm.” on page 4, lines 26-27. MPEP recites “…a mathematical concept need not be expressed in mathematical symbols…” in § 2106.04(a)(2). Therefore, based on the USPTO’s 2024 Guidance, these limitations correspond to an abstract idea of mathematical concepts.
Applicant argues that claims recite a practical application, since they recite “receiving specific training data, through a user input interface, training a machine learning model to generate an objective function and providing that objective function for controlling an iterative optimization procedure that adjusts treatment plan parameters to compute a radiation treatment plan”, which impose meaningful limits.
In response, Examiner submits that the limitations of “receiving training data… ; …providing the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan” correspond to mere data gathering and outputting, which correspond to insignificant extra-solution activities (see MPEP 2106.05 (g)). The insignificant extra solution activities do not provide a practical application for the abstract idea.
Applicant argues that the claims apply any alleged judicial exception with a particular machine, the claims require specific hardware configured to perform specific functions in the context of radiation treatment planning.
In response, Examiner submits that the “processor” is described in the current specification as a generic computing device. The specification recites “he trained model M may be deployed as a machine learning component that may be run on the computing device PU, such as a desktop computer, a workstation, a laptop, etc. or plural such devices in a distributed computing architecture. Preferably, to achieve good throughput, the computing device PU includes one or more processors (CPU) that support parallel computing, such as those of multi-core design.” on page 15, lines 9-14. MPEP recites “…a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine…” in § 2106.05(b).
Applicant argues that claims recite an unconventional combination of elements that provides a specific technological improvement on radiation treatment planning, and the limitation of “providing the objective function for controlling an iterative optimization procedure to compute a radiation treatment planning and adjust one or more treatment plan parameters is not well-understood, routine or conventional.
In response. Examiner submits that limitation of “providing the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan” corresponds to mere data gathering and outputting, which is directed to insignificant extra-solution activity. Claims are directed to receiving training data to train a machine learning model to generate an objective function, which is providing an outcome data, and for controlling an iterative optimization procedure using objective function is providing said output data to use in order to obtain an outcome for treatment plan parameters. The claim limitation is directed to insignificant extra-solution activity, and the insignificant extra solution activities do not provide a practical application for the abstract idea.
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Arguments about 35 USC 102 rejection:
Applicant argues that Purdie does not teach “a user input interface configured to allow a user to provide training data”, and Purdie discloses “previously approved treatment plans may be used to train an algorithm for automated treatment planning”.
In response, Examiner submits that Purdie discloses “The outputted visualization may be provided with a user interface to enable the user to manually modify proposed 50 dosages and/or image features, such as delineated ROI(s), used to calculate the proposed dose map…. After any changes are made, the user may confirm the changes and the method may return to one or more of the above-mentioned steps…to recalculate the proposed dose map” in col. 21, lines 49-60 and also “…the user may be prompted to manually change the proposed treatment plan at 2070, to change the input data, to change the proposed dose map and/or to change optimization parameters..” in col. 22, lines 36-40. Therefore, Purdie teaches user providing training data.
Applicant argues that Purdie does not teach “a user defined ranking or scoring of the plurality of previous at least partial radiation treatment plans”.
In response, Examiner submits that the claims recite “a user input interface…a user provide training data…the training data including…a user defined ranking or scoring of the plurality of previous…treatment plans…” and Purdie discloses “…previous outcomes data, including treatment related toxicity, survival data and recurrence data; data related to treatment delivery such as specifications of the treatment delivery unit; or any other data pertinent to patient treatment. Any patient information or plan feature useful for a physician to make an accurate decision about the treatment plan may be included, and that information may be considered jointly as opposed to independently” in col. 11, lines 8-16 and “The historical data of historical treatment plans may include treatment outcome data” in col. 14, lines 33-35, therefore Purdie teaches a user defined outcome data (a user defined ranking or scoring of the plurality of previous treatment plans). Examiner considers that “a user defined ranking or scoring” can be any previous treatment outcome obtained from previous treatment plans.
Applicant argues that Purdie does not teach “train a machine learning model based on the training data to generate an objective function” and “provide the objective function for controlling an iterative optimization procedure that adjusts one or more treatment plan parameters to compute a radiation treatment plan”.
In response, Examiner submits that Purdie discloses “…previous outcomes data, including treatment related toxicity, survival data and recurrence data; data related to treatment delivery such as specifications of the treatment delivery unit; or any other data pertinent to patient treatment. Any patient information or plan feature useful for a physician to make an accurate decision about the treatment plan may be included, and that information may be considered jointly as opposed to independently” in col. 11, lines 8-16 and “The historical data of historical treatment plans may include treatment outcome data” in col. 14, lines 33-35, “…Features may be extracted from historical training data, and used to construct the learning algorithms and train the treatment planning model…” in col. 23, lines 57-62 and col. 11, lines 27-37, and “At 2071, the expected dose distribution for the proposed treatment plan is calculated. Verification may be performed at this point. Verification may include, for example, checking whether the expected dose distribution matches with the proposed dose map, checking whether the expected dose distribution falls within acceptable dose guidelines, and/or checking whether the expected dose distribution is similar to historical dose distributions. At 2072, if verification fails, the proposed dose map may be changed at 2073 (e.g., manually or automatically, such as using an iterative method), and the method returns to 2068 without generating the proposed dose map.” in col. 22, lines 41-52.
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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