DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is in response to the amendments and remarks filed on 11/12/2025. The amendments filed on 11/12/2025 have been entered. Accordingly Claims 1-4, 6-14 and 16-22 are pending. Claims 5 and15 are canceled. Claims 21-22 are new.
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 1-4, 6-14 and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed
to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without
significantly more.
Independent claim 1 recites, for example, the following abstract ideas: “…establish, based on the ultrasound data, a data model of the fetus…” and “…generate, based on the data model, a visualization of the fetus…” fall within mental processes. That is to say, under broadest reasonable interpretation the recited limitations are typical of the functions of a physician or radiologist performed in the mind, thus the limitations fall within “Mental Processes” grouping of abstract ideas. Further, “…using a generative artificial intelligence…” in the generate limitation uses a generic model that has no specifics to the algorithmic foundation or dimensionality associated with the data model generated and as such can be considered a vector of minimal dimensions that can be “used” by mental process or simple pen and paper computations. Analogous limitations are found in claim 11.
The dependent claims 2-4, 6-10, 12-14 and 16-22 do not sufficiently link the subject matter to a practical application or recite element(s) which constitute significantly more than the abstract ideas identified. The depending claims are directed to additional limitations which encompass abstract ideas consistent with those identified above that are well-understood, routine and/or conventional activity. Further, dependent Claims 2-4, 6-10, 12-14 and 16-22 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 8-9, 11-14 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie et. al. (U.S. 20220047241, February 17, 2022)(hereinafter, “Dickie”) in view of Alomar et. al. (“Reconstruction of the fetus face from three-dimensional ultrasound using a newborn face statistical shape model.” 2022)(hereinafter, “Alomar”).
Regarding Claim 1, Dickie teaches: A circuity for generating a visualization of a fetus (Figs. 1 and 4, [0029][0042]), the circuitry being configured to:
obtain ultrasound data representing a fetus in a uterus of its mother (“…a number of ultrasound frames of the fetus may be acquired using an ultrasound scanner…” [0029]);
establish, based on the ultrasound data, a data model of the fetus (“The AI model 56 may generally be trained by ultrasound frames that each have a labeled cut line that is positioned relative to an imaged fetus 20 in the training ultrasound frame. The training ultrasound frames may include ultrasound frames 52a with cut lines that are tagged as acceptable, and/or ultrasound frames 52b with cut lines that are tagged as unacceptable.” [0043]); and
generate, based on the data model, a visualization of the fetus using a generative artificial intelligence (“…the various training ultrasound frames 52a, 52b, may be inputted into a deep neural network that can learn how to predict a correct cut line on new ultrasound images.” [0048]; “The ultrasound frames 62 with predicted cut lines may then be used for the generation of a 3D representation 64 of the fetus.” [0066]. See Fig. 4).
Dickie does not teach: including reconstructing a missing portion of the fetus, the missing portion being a portion that is not indicated by the ultrasound data.
Alomar in the field of ultrasound fetal imaging reconstruction teaches: “The reconstructions obtained for each view are shown at the bottom of Fig. 4 . We can observe how the reconstructions adapt to the US information while being guided by the facial geometry encoded in the BabyFM. Indeed, for the face regions visible in the US meshes, the reconstruction clearly resembles the US meshes while the model provides an informed guess (statistical estimate) for regions with occlusions and large missing areas.” Section 4.1 US fitting - 4.1.2. Multiple model reconstructions error; “Fig. 9 shows three examples of pre-natal and post-natal reconstructions, the latter being obtained as described in Section 3.4. As in previous examples, we observe that the pre-natal reconstructions resemble the US meshes in those regions where the face is visible, while providing plausible facial features in the missing parts. As for the post-natal reconstructions, we see that they capture well the facial features (nose, chin and chicks) and expression (mouth and eyes position).” Section 4.1 US fitting – 4.2. Pre-natal vs post-natal reconstruction.
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the visualization in Dickie to include reconstructing a missing portion of the fetus, the missing portion being a portion that is not indicated by the ultrasound data as taught in Alomar for “…assessment of congenital malformations and neurological anomalies.” And “…to aid in-utero diagnosis for conditions that in- volve facial dysmorphology.” (Alomar, Abstract).
Regarding Claim 2, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Dickie further teaches: wherein the circuitry is further configured to: obtain image data of a portion of a belly of the mother (“…the training data (e.g., as discussed above with respect to FIG. 4) may include inserting such an additional cut line 40a, so that the two cut lines together 40, 40a, delineate the fetus 20 from non-fetal anatomy on both the proximate and distal sides of the fetus 20 relative to the probe head. The AI model 56 may correspondingly be trained to identify the multiple cut lines 40, 40a on new fetal ultrasound images.” [0093]. See Figs. 4 and 7; and
determine a position of the fetus relative to the mother (“…FIG. 2, the image areas of the ultrasound frame that are on a distal side 42 of the predicted cut line 40, relative to the fetus 20, may be removed prior to generating the 3D fetal representation.” [0038]; “…FIG. 2, the AI model may aim to predict the cut line 40 so that it is exterior to the imaged fetus 20, fetal head 22, and fetal abdomen 26, and so that it is exterior to (e.g., does not lie within) any imaged placenta, uterine wall 24, amniotic sac, umbilical cord, cervix and bladder.” [0039]; “This second cut line 40a may delineate the fetus 20 from the non-fetal anatomy on the distal side of the fetus 20 relative to the probe head.” [0093]. See Figs. 2 and 8); and
wherein the generating of the visualization of the fetus includes generating the visualization according to the determined position (“By removing the portions of the ultrasound image on the distal side 42 of each ultrasound frame that compose the various slices forming the 3D fetal representation, portions of non-fetal anatomy will be excluded from the resulting 3D fetal representation. Alternately, in some embodiments, the portions of the ultrasound image on the distal side 42 of the ultrasound frame may simply be ignored when generating the 3D fetal representation.” [0038]; “When multiple cut lines 40, 40a are identified by the AI model 56 on each ultrasound frame, this may allow only the imaged fetus 20 between the cut lines 40, 40a to be retained when generating the 3D fetal representation. When this fetal ultrasound information is used as slices across various ultrasound image frames, the generated 3D fetal representation may be a 3D fetal volume 64a that shows the 3D contours of the fetus 20 both on the proximate side of the fetus 20 relative to the probe head, and also on the distal side of the fetus 20 relative to the probe head.” [0094]. See Figs. 2 and 8).
Regarding Claim 3, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Dickie further teaches: wherein the determining of the position of the fetus includes: identifying, in the ultrasound data, a structure of a body of the mother (“…FIG. 2, the image areas of the ultrasound frame that are on a distal side 42 of the predicted cut line 40, relative to the fetus 20, may be removed prior to generating the 3D fetal representation.” [0038]; “…FIG. 2, the AI model may aim to predict the cut line 40 so that it is exterior to the imaged fetus 20, fetal head 22, and fetal abdomen 26, and so that it is exterior to (e.g., does not lie within) any imaged placenta, uterine wall 24, amniotic sac, umbilical cord, cervix and bladder.” [0039]; “This second cut line 40a may delineate the fetus 20 from the non-fetal anatomy on the distal side of the fetus 20 relative to the probe head.” [0093]. See Figs. 2 and 8); and
determining, in an image represented by the image data, a position that corresponds to the structure (“When multiple cut lines 40, 40a are identified by the AI model 56 on each ultrasound frame, this may allow only the imaged fetus 20 between the cut lines 40, 40a to be retained when generating the 3D fetal representation. When this fetal ultrasound information is used as slices across various ultrasound image frames, the generated 3D fetal representation may be a 3D fetal volume 64a that shows the 3D contours of the fetus 20 both on the proximate side of the fetus 20 relative to the probe head, and also on the distal side of the fetus 20 relative to the probe head.” [0094]. See Figs. 2 and 8).
Regarding Claim 4, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Dickie further teaches: wherein the circuitry is further configured to determine a position of a display device relative to the mother (“…the 3D fetal volume may be manipulated during visualization (e.g., rotated along various axis) so that different aspects of the fetus 20 can be seen on a display device.” [0095]); and
wherein the generating of the visualization of the fetus includes generating the visualization of the fetus in a perspective as seen from the position of the display device (“…since the AI model 56 is trained to detect cut lines on a wide variety of views of the fetus, the AI model 56 can immediately be applied when at least a portion of the fetus is scanned by the ultrasound probe—without the operator having to first identify the frontal midsagittal view of the fetus. In this manner, when the probe being placed in an arbitrary orientation with respect to the fetus 20, a 3D fetal representation of that portion of the anatomy can generally be generated and visualized—regardless of whether it is a frontal, rear, side, superior, or posterior view of the fetus 20, or any blend of these views.” [0082]; “since the AI model 56 can work to immediately apply a cut line in real-time, it is possible to generate a 3D representation of the fetus as the probe moves over the surface of the skin. In this manner, the resultant generated 3D fetus representation may provide visualization that works similar to what may be expected if a flashlight was able to “see” through the surface of the skin. E.g., the 3D representation of the fetus may be in line with the center of the probe head, so as to give an operator a live 3D representation of the fetus that is in the direct field of view of the center of the ultrasound probe.” [0089]).
Regarding Claim 8, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Dickie further teaches: wherein the data model includes a three-dimensional representation of the fetus (“…ultrasound frames of a fetus to be acquired using an ultrasound scanner, which may be oriented arbitrarily with respect to the fetus during the acquisition. The ultrasound frames may be processed against an artificial intelligence (AI) model to predict a suitable cut line on each of the ultrasound frames, where each cut line is positioned exterior to an image of the fetus appearing on the ultrasound frame.” [0028]).
Regarding Claim 9, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Dickie further teaches: wherein the ultrasound data represent the fetus in respective perspectives as seen from multiple directions (“When acquiring the number of ultrasound frames, it is possible, but not necessary, to initially place the scanner in the midsagittal plane of the fetus.” [0029]; “…these different views may include coronal and/or transverse plane views of the fetus, including views from different angles that combine any of a sagittal plane view, a coronal plane view, or a transverse plane view.” [0030]).
Regarding Claim 10, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Dickie further teaches: wherein the circuitry is further configured to generate an instruction for positioning an ultrasound sensor at the belly of the mother (“In step 10, a number of ultrasound frames of the fetus may be acquired using an ultrasound scanner…During the scanning, the scanner may be held steady by an operator of the scanner while a motor in the head of the scanner tilts the ultrasonic transducer to acquire ultrasound frames at different angles.” [0029].
Since Dickie is performing fetal ultrasound imaging is obvious to one of ordinary skill in the art that position information of the ultrasound sensor would be generated for a user taking the scan(s).
Regarding Claim 11, The method in this limitation is the intended usage of the system. Accordingly, the previously disclosed rejections cited for Claim 1 limitations are applied.
Dickie teaches: A method for generating a visualization of a fetus (Figs. 1 and 4, [0029][0042]), the method comprising:
obtaining ultrasound data representing a fetus in a uterus of its mother (“…a number of ultrasound frames of the fetus may be acquired using an ultrasound scanner…” [0029]);
establishing, based on the ultrasound data, a data model of the fetus (“The AI model 56 may generally be trained by ultrasound frames that each have a labeled cut line that is positioned relative to an imaged fetus 20 in the training ultrasound frame. The training ultrasound frames may include ultrasound frames 52a with cut lines that are tagged as acceptable, and/or ultrasound frames 52b with cut lines that are tagged as unacceptable.” [0043]); and
generating, based on the data model, a visualization of the fetus using a generative artificial intelligence (“…the various training ultrasound frames 52a, 52b, may be inputted into a deep neural network that can learn how to predict a correct cut line on new ultrasound images.” [0048]; “The ultrasound frames 62 with predicted cut lines may then be used for the generation of a 3D representation 64 of the fetus.” [0066]. See Fig. 4).
Dickie does not teach: including reconstructing a missing portion of the fetus, the missing portion being a portion that is not indicated by the ultrasound data.
Alomar in the field of ultrasound fetal imaging reconstruction teaches: “The reconstructions obtained for each view are shown at the bottom of Fig. 4 . We can observe how the reconstructions adapt to the US information while being guided by the facial geometry encoded in the BabyFM. Indeed, for the face regions visible in the US meshes, the reconstruction clearly resembles the US meshes while the model provides an informed guess (statistical estimate) for regions with occlusions and large missing areas.” Section 4.1 US fitting - 4.1.2. Multiple model reconstructions error; “Fig. 9 shows three examples of pre-natal and post-natal reconstructions, the latter being obtained as described in Section 3.4. As in previous examples, we observe that the pre-natal reconstructions resemble the US meshes in those regions where the face is visible, while providing plausible facial features in the missing parts. As for the post-natal reconstructions, we see that they capture well the facial features (nose, chin and chicks) and expression (mouth and eyes position).” Section 4.1 US fitting – 4.2. Pre-natal vs post-natal reconstruction.
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the visualization in Dickie to include reconstructing a missing portion of the fetus, the missing portion being a portion that is not indicated by the ultrasound data as taught in Alomar for “…assessment of congenital malformations and neurological anomalies.” And “…to aid in-utero diagnosis for conditions that in- volve facial dysmorphology.” (Alomar, Abstract).
Regarding Claim 12, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 12 further recites limitations: wherein the method further comprises: obtaining image data of a portion of a belly of the mother; and determining a position of the fetus relative to the mother; and wherein the generating of the visualization of the fetus includes generating the visualization according to the determined position. These limitations are present in claim 2 and are therefore, rejected under the same rationale.
Regarding Claim 13, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 13 further recites limitations: wherein the determining of the position of the fetus includes: identifying, in the ultrasound data, a structure of a body of the mother; and determining, in an image represented by the image data, a position that corresponds to the structure. These limitations are present in claim 3 and are therefore, rejected under the same rationale.
Regarding Claim 14, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 14 further recites limitations: determining a position of a display device relative to the mother; and wherein the generating of the visualization of the fetus includes generating the visualization of the fetus in a perspective as seen from the position of the display device. These limitations are present in claim 4 and are therefore, rejected under the same rationale.
Regarding Claim 18, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 18 further recites limitations: wherein the data model includes a three-dimensional representation of the fetus. These limitations are present in claim 8 and are therefore, rejected under the same rationale.
Regarding Claim 19, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 19 further recites limitations: wherein the ultrasound data represent the fetus in respective perspectives as seen from multiple directions. These limitations are present in claim 9 and are therefore, rejected under the same rationale.
Regarding Claim 20, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 20 further recites limitations: wherein the method further comprises generating an instruction for positioning an ultrasound sensor at the belly of the mother. These limitations are present in claim 10 and are therefore, rejected under the same rationale.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie and Alomar as applied to claims 1 and 12, respectively above, and further in view of Kahn et. al. (U.S. 20040260178,December 23, 2004)(hereinafter, “Kahn”).
Regarding Claim 7, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Dickie does not teach: wherein the ultrasound data represent the fetus at two different dates; and wherein the circuitry is configured to interpolate the fetus for a date between the two different dates.
Kahn in the field of obstetric ultrasound systems teaches: “…FIG. 4, the graphical format of FIG. 3 can be modified to allow the representation of fetal growth trends during the gestation by including data from multiple examinations throughout the gestation. In the graphical display format shown in FIG. 4, each dimension of fetal growth data contains a set of points representing data acquired throughout pregnancy, with the right-most point in each bar representing the data collected at the noted gestational age (30 weeks, 4 days).” [0104]; “In the graphical display format of FIG. 5, biparietal diameter, head circumference, and abdominal circumference are all plotted on the same graph. This graph illustrates expected value (mean) and standard deviations for each of the dimensions of fetal growth data versus gestational age. The "dots" (squares, triangles, and circles) on the graph indicate measurements obtained during the examination at various gestational ages, and each of the dimensions of fetal growth data is normalized with respect to the mean.” [0105].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the ultrasound data in Dickie to represent the fetus at two different dates; and wherein the circuitry is configured to interpolate the fetus for a date between the two different dates as taught in Kahn “…to determine whether the growth of a fetus is consistent with a best estimate of the fetus' age and to determine whether the relative sizes of various anatomical components are in correct proportion.” (Kahn, [0001]).
Regarding Claim 17, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 17 further recites limitations: wherein the ultrasound data represent the fetus at two different dates; and wherein the method comprises interpolating the fetus for a date between the two different dates. These limitations are present in claim 7 and are therefore, rejected under the same rationale.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Dickie and Alomar as applied to claims 12 and 1, respectively above, and further in view of Mahfouz (U.S. 20230165482, June 1, 2023)(hereinafter, “Mahfouz”).
Regarding Claim 21, the combination of Dickie and Alomar teaches the claim limitations as noted above.
The combination of references does not teach: wherein the method further comprises applying a visual indicator to the reconstructed missing portion in the visualization, the visual indicator distinguishing the reconstructed missing portion from portions of the fetus indicated by the ultrasound data.
Mahfouz in the field of reconstruction-based systems teaches: “the reconstructed 3D model is compared to the patient-specific 3D model to identify and record bone missing from the patient-specific 3D model that is present in the reconstructed 3D model. Localization may be carried out in a multitude of fashions including, without limitation, curvature comparison, surface area comparisons, and point cloud area comparisons. Ultimately, in exemplary form, the missing/abnormal bone is localized and the output comprises two lists: (a) a first list identifying vertices corresponding to bone of the reconstructed 3D model that is absent or deformed in the patient-specific 3D model; and, (b) a second list identifying vertices corresponding to bone of the reconstructed 3D model that is also present and normal in the patient-specific 3D model.” [0250]. See fig. 21
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to further modify the combination to apply a visual indicator to the reconstructed missing portion in the visualization, the visual indicator distinguishing the reconstructed missing portion from portions of the fetus indicated by the ultrasound data as taught in Mahfouz for organ, tissue, etc. reconstruction and measurement utilization during procedures (Mahfouz, [0002]).
Regarding Claim 22, the combination of Dickie and Alomar teaches the claim limitations as noted above.
Claim 22 further recites limitations: wherein the circuitry is further configured to apply a visual indicator to the reconstructed missing portion in the visualization, the visual indicator distinguishing the reconstructed missing portion from portions of the fetus indicated by the ultrasound data. These limitations are present in claim 21 and are therefore, rejected under the same rationale.
Response to Arguments
With regards to Applicant’s arguments regarding 35 U.S.C. 101: “Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed. Claims 1 and 11 have been amended to further particularize the claimed invention and more clearly recite statutory subject matter. These amendments introduce a specific function performed by the generative artificial intelligence that provides a tangible improvement over prior art imaging techniques. The claims, both as originally filed and as amended, are not directed to a judicial exception. The Office Action asserts that establishing a data model and generating a visualization are abstract "mental processes.' This characterization misconstrues the nature and complexity of the claimed steps and overlooks the specific technological improvements provided by the invention. As the recent USPTO memorandum on subject matter eligibility reminds, Examiners should not expand the mental process grouping to encompass limitations that cannot "practically be performed in the human mind." (Remarks, pgs. 1-2).
Examiner respectfully disagrees and maintains the 35 U.S.C. 101 rejections. As provided in the MPEP § 2106.05(f) “using a computer as a tool to perform the abstract idea” is not sufficient to integrate a judicial exception into a practical application as interpreted by the court(s). Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972) “held that simply implementing a mathematical principle on a physical machine, namely a computer, was not a patentable application of that principle and Intellectual Ventures LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016) established that mental processes encompass acts which, absent anything beyond generic computer components, may be “performed by a human, mentally or with pen and paper.” The recited claims are broad and general with no specifics to the data dimensions for the generating step and can be considered simple vector mathematical computations performed in the mind and at most using pen and paper.
Applicant argues amended claims 1 and 11 are eligible according to the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG). Examiner, again respectfully disagrees. Applicant’s recited claims differ from both examples 3 and 39 for example, specifically with regards to Example 39 which is better correlated to Applicant the claim set of Example 39 as deemed eligible recites the particulars of how the convolutional neural network/artificial intelligence approach works, the claim is directed to making the computer work and how the model is being trained and designed. In contrast Applicant’s recited claims simply uses generative artificial intelligence and provides claims that can be considered collections of intangible data that mathematical operations are performed on to obtain a result.
With regards to Applicant’s arguments regarding amended claims 1 and 11 35 U.S.C. 102 rejection is moot as the rejection in the above action is a 35 U.S.C. 103 rejection Dickie in view of Alomar.
With regards to Applicant’s arguments regarding amended claims 1 and 11 35 U.S.C. 103 rejection Dickie in view of Alomar, arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Applicant respectfully is interpreting the recited claims more narrowly with respect to the generate step and what the claim limitations is actually requiring.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMAL FARAG whose telephone number is (571)270-3432. The examiner can normally be reached 8:30 - 5:30 M-F.
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/AMAL ALY FARAG/ Primary Examiner, Art Unit 3798