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 Arguments
Applicant's arguments filed 16 December 2025 with respect to the rejections of claims 1, 3-4, 8-11, 13, and 17-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant’s arguments, as best understood and interpreted, are paraphrased below.
Rejection under 35 § U.S.C. 101
Applicant asserts the following:
Annotating an ROI could not be done practically in the human mind, especially because the instant application requires a processor to determine the outline of the ROI based on control points; more specifically, a machine learning model is configured to take two anchor points (one inner control and one outer control) and the medical image to generate the outline.
Even if Examiner’s interpretation of the abstract idea is correct, the instant application’s claimed invention presents enough of an improvement within the technical field to constitute a practical application within the field.
Respectfully, Examiner disagrees. A full rationale for rejection under 35 U.S.C. § 101 is included later within this Office Action, but Examiner responds to Applicant’s specific assertions below, taking independent claim 1 as representative of all independents as reciting identical steps despite being directed to different statutory matter:
Regarding Applicant’s first argument and the assertion that the limitations of the independent claims could not practically be performed using the human mind, Examiner reminds Applicant that “[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” (MPEP § 2106.04(a)(2)) Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). In the case of the instant application, despite reciting a processor and a machine learning model, the limitations of claim 1 directed to determining anchor points, connecting lines between the anchor points, selecting inner and outer control points, and determining and adjusting an ROI outline and an updated ROI segmentation can all practically be performed in the human mind with the assistance of pen and paper (for example, a clinician having ordinary skill would be able to identify all these keypoints and proceed to update a predetermined ROI with a new one). Thus, the recited processor and machine learning model serve simply as using a computer as a tool to perform a mental process.
Regarding Applicant’s second argument, the additional elements of the claim do not recite significantly more than the judicial exception. According to the broadest reasonable interpretation of both original and amended claim 1, all recited steps may be performed mentally. See MPEP § 2106.05(a). “It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016). Additionally, “a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology”. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017). The instant application, similarly to the recitations of the claims within both Synopsys and RecogniCorp, recites an abstract mental process, which an ordinarily skilled artisan would be able to perform mentally with pen and paper. Furthermore, claim 1 does not recite “significantly more” than the judicial exception, with the generic computer feature of a processor and the use of a machine learning model as the use of a computer to perform the mental process.
Rejection under 35 U.S.C. § 102 and 103
Applicant’s arguments filed 16 December 2025, with respect to the rejection of amended claims 1, 3-4, 8-11, 13, and 17-20 under 35 U.S.C. § 102 (a)(1) in view of Chono have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. 103 in view of Chono and in further view of Ijiri.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without integration into a practical application or recitation of significantly more.
In the analysis below, the apparatus/processor of independent claim 1 is considered representative of independent claims 1 and 11, since all of the independent claims recite identical steps despite being directed to different statutory matter. Furthermore, each of independent claims 1 and 11 are directed to one of the four statutory categories of eligible subject matter; thus, the claims pass Step 1 of the Subject Matter Eligibility Test (See flowchart in MPEP 2106).
Step 2A, prong 1 analysis
The independent claims are directed to “an apparatus, comprising: a processor configured to: determine, based on a current segmentation of a region of interest (ROI) in a medical image, multiple anchor points; determine a connecting line that runs through two or more of the multiple anchor points; select a point on the connecting line that is within the ROI as an inner control point; select a point on the connecting line that is outside of the ROI as an outer control point; determine an outline of the ROI based at least on the inner control point and the outer control point, wherein the outline is determined using a machine learning (ML) model that is configured to take the inner control point, the outer control point, and the medical image as inputs and generate, as an output, the outline of the ROI based on image features associated with the inner control point, the outer control point, and the medical image; adjust the outline of the ROI based on a user input, wherein the user input indicates a selection within the outline or outside of the outline, or a change to a distance between the outline and at least one of the inner control point or the outer control point; and determine an updated segmentation of the ROI based on the adjusted outline of the ROI.”
The consideration of each of the limitations is as follows:
The limitations of
“Determin[ing], based on a current segmentation of a region of interest (ROI) in a medical image, multiple anchor points” can be considered to be an observation in the human mind. Specifically, one having ordinary skill in the art would be able to view a medical image with a pre-segmented ROI and determine key points of interest within the image (ROI vertices, crucial edge points, intersections, changes in image features, etc.) which are clearly evident within the image.
“Determin[ing] a connecting line that runs through two or more of the multiple anchor points” can be considered to be an observation in the human mind paired with the use of a pen and paper. Specifically, the determination of two or more collinear points can be accomplished using a writing implement and a straight edge as a result of a mental observation by an ordinarily skilled artisan.
“Select[ing] a point on the connecting line that is within the ROI as an inner control point” can be considered to be an observation in the human mind paired with the use of a pen and paper. Specifically, identifying a point on the line within the ROI can be accomplished through observation, and actual selection can be accomplished through manual annotation.
“Select[ing] a point on the connecting line that is outside of the ROI as an outer control point” can be considered to an observation in the human mind paired with the use of a pen and paper. Specifically, identifying a point on the line outside the ROI can be accomplished through observation, and actual selection can be accomplished through manual annotation.
“Determin[ing] an outline of the ROI based at least on the inner control point and the outer control point, wherein the outline is determined using the inner control point, the outer control point, and the medical image to generate, as an output, the outline of the ROI based on image features associated with the inner control point, the outer control point, and the medical image”* can be considered to be a mental process paired with the use of a pen and paper. Specifically, determining regions desired to be included and excluded within the ROI outline based on the connecting line, the identified inner and outer control points, and the prior segmentation of the ROI within the medical image would be easily determinable by the ordinarily skilled artisan as the result of a mental process (for example, an original ROI segmentation within a brain image may include a lesion; having identified critical points within the original segmentation corresponding to a lesion, interior and exterior control points would be easily identifiable by an ordinarily skilled artisan and the lesion outline could, thus, be traced using a pen and paper).
“Adjust[ing] the outline of the ROI based on a user input, wherein the user input indicates a selection within the outline or outside of the outline, or a change to a distance between the outline and at least one of the inner control point or the outer control point” can be considered to be a mental process paired with the use of a pen and paper. Specifically, a user input indication may be any user decision with respect to ROI modification, wherein the traced outline may be altered to include or exclude sections based on identifying either of the control points; furthermore, the outline’s adjustment would be accomplishable by drawing an altered ROI in place of/in addition to the original outline.
“Determin[ing] an updated segmentation of the ROI based on the adjusted outline of the ROI” can be considered to be an observation in the human mind paired with the use of a pen and paper. Specifically, the result of the aforementioned mental process of adjusting the ROI outline based on the user identification of the control points or distance would provide actionable information to the ordinarily-skilled artisan, enabling a pen-and-paper updated ROI segmentation based on the updated outline.
As such, the analysis under prong one of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Additional elements
* The italicized limitation of claim 1 was directed to the usage of an ML model. The step 2A prong 1 analysis was performed originally considering the claim limitation as an abstract idea/mental process, before evaluating the additional elements here.
Independent claim 1 recites the additional elements of:
a processor
a machine learning model configured to take in the inner control point, outer control point, and medical image as inputs and generate, as an output, the outline of the ROI based on image features associated with the inner control point, the outer control point, and the medical image.
Step 2A prong 2 analysis
The above-identified additional elements do not integrate the judicial exception into a practical application.
The use of a processor amounts to merely using a computer as a tool to perform the claimed mental process. Implementing an abstract idea on a computer does not integrate a judicial exception into a practical application (See MPEP 2106.05(f)).
The use of a “machine learning model, configured to take in the inner control point, outer control point, and medical image as inputs and generate, as an output, the outline of the ROI based on image features associated with the inner control point, the outer control point, and the medical image” amounts to merely using a computer as a tool to perform the claimed mental process. Implementing an abstract idea on a computer using a mathematical model does not integrate a judicial exception into a practical application (See MPEP 2106.05(f)). The claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment.
Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility since the additional steps do not integrate the abstract idea into a practical application.
Step 2B
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As noted above, the processor is a generic computer feature which performs generic computer functions that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system. Furthermore, the use of the machine learning model constitutes using a computer as a tool to perform a mental process. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
For all of the foregoing reasons, independent claims 1 and 11 do not recite eligible subject matter under 35 USC 101.
Dependent claims 3-4 and 8-10 are all dependent on claim 1. Dependent claims 13 and 17-20 are all dependent on claim 11. Therefore, claims 3-4, 8-10, 13, and 17-20 all recite the same abstract idea of a mental process which can be performed in the mind with the aid of pen and paper. For the sake of clarity, the following dependent claims, reciting identical steps despite being directed to different statutory matter, are grouped for analysis:
3, 4, and 13
8 and 17
9 and 18
10 and 19
Claims 3, 4, and 13 recite “selecting the multiple anchor points from a contour of the current segmentation, wherein the current segmentation is obtained based on human annotation or another ML model”. A “segmentation” is simply a portion of an image that can be extracted from the remainder of the image; this can therefore be performed through a pairing of a mental process and a pen and paper. The extraction of anchor points or a general region associated with an ROI can be a mental process physically performed using a pen and paper, wherein the selection of anchor points is part of the same mental process, and wherein the segmentation is explicitly recited to be based on a human annotation. Furthermore, the usage of a machine learning model amounts to the application of a computer to perform claimed mental processes, and does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)).
Claims 8 and 17 recite “wherein adjusting the outline of the ROI based on the user input comprises: determining whether the selection indicated by the user input is within the outline or outside of the outline; based on a determination that the selection is within the outline, adjusting the outline to exclude an area corresponding to the selection; and based on a determination that the selected area or spot is outside of the outline, adjusting the outline to include an area corresponding to the selection”. Adjusting the ROI outline based on user input, comprising determining selection location with respect to the ROI, adjusting the ROI if the selection is within the ROI, and adjusting the ROI to include an area outside of the original outline area to include the selection, can be performed as a mental process combined with a pen and paper. Selecting a modification control point based on a user’s criteria can be accomplished by observing and selecting a particular control point on an image based on known criteria mentally, and moving the bounds of the outline to either expand or constrict to accommodate the new control point can be done mentally or, with the aid of pen and paper, visually.
Claims 9 and 18 recite “[wherein the apparatus of claim 1 and the method of claim 11 further comprise] providing a graphical user interface (GUI) element for changing an area surrounded by the outline and the one or more control points, wherein adjusting the outline based on the user input comprises: receiving the user input via the GUI element; determining, based on the user input, an adjustment to a value of the area surrounded by the outline based on the user input; and adjusting the outline of the ROI based on the determined adjustment, wherein the user input includes a click or a tap by the user”. Adjusting the outline of the ROI based on user input, determining an area value surrounded by the outline based on user input, and adjusting the outline of the ROI based on the determined value can all be performed as a mental process paired with the use of pen and paper. Specifically, determining an area value based on user input of the outline can be accomplished as a simple area calculation given a size modification of the ROI. Additionally, modifying the ROI based on the determined adjustment to match areas can be accomplished as a mental process (expansion or constriction of the ROI), paired with a pen and paper for easier visualization of the prior and new ROI boundaries. The usage of a GUI element for area selection, and the related user input being a click or a tap in user interaction, amount to the application of a computer to perform claimed mental processes, and do not integrate the judicial exception into a practical application (See MPEP 2106.05(f)).
Claims 10 and 19 recite “wherein adjusting the outline based on the user input comprises providing a preview of the adjustment to be made to the outline and adjusting the outline in response to receiving a confirmation of the adjustment”. Providing a preview of the adjustment to be made can constitute a visualization in the human mind (pre-viewing the potential chance), while confirmation and adjusting can be accomplished as a mental process and as a final modification by drawing a new ROI physically using pen and paper.
Claim 20 recites “A non-transitory computer-readable medium comprising instructions that, when executed by a processor included in a computing device, cause the processor to implement the method of claim 11”. This amounts to the application of a computer to perform claimed mental processes, and does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)).
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, 3-4, 8-11, 13, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chono et al. (US Pat. No. 9,072,489, hereinafter “Chono”) in view of Ijiri et al. (US Pat. No. 8,351,670, hereinafter “Ijiri”) and in further view of Bai et al. (“A Proof-of-Concept Study of Artificial Intelligence assisted Contour Editing”, Radiology: Artificial Intelligence 2022; 4(5):e210214, hereinafter “Bai”) .
Regarding claim 1, Chono discloses an apparatus, comprising:
a processor (col. 5, lines 51-58) configured to:
determine, based on a current segmentation of a region of interest (ROI) in a medical image, multiple anchor points (Col. 4, lines 26-36, wherein the determination of the contour of the heart is the region of interest being outlined (initially), and wherein the key points are the set of apexes which make up the myocardium);
determine a connecting line that runs through two or more of the multiple anchor points (Col. 9 line 61-col. 10 line 16, wherein the contour edge position can be determined from reference points, wherein the reference points are configured such that two points are made collinear as part of the contour extraction process);
select a point on the connecting line that is within the ROI as an inner control point (Col. 10 lines 1-16, wherein the point on the interior of the contour is detected by the contour extraction unit, and the contour can be deformed to make the point on the interior of the contour become a contour boundary point);
determine an outline of the ROI based at least on the inner control point (Col. 8, lines 17-28 for the inner control point, wherein the inner control point is the center of gravity, and col. 8 line 17 – col. 9 line 3 for the full disclosure of using inner control points to calculate the center of gravity to use as a contour construction guide point).
adjust the outline of the ROI based on a user input, wherein the user input indicates a selection within the outline or outside of the outline, or a change to a distance between the outline and at least one of the inner control point or the outer control point (Col. 10, lines 39-49 and fig. 7, wherein the user input consists of modifying the contour extraction process and ensuring key feature points within and associated with/close to the contour are properly extracted); and determine an updated segmentation of the ROI based on the adjusted outline of the ROI (Col. 10, lines 61-67, wherein the process of extracting or “segmenting” contours around general organs can be performed based on the adjusted ROI outline).
Specifically, Chono discloses a method and system for extracting organ contours from medical images based on measurement of feature points on and within a regional outline within the image.
Chono does not disclose selection of a point on the connecting line that is outside of the ROI as an outer control point; or determining an outline of the ROI based at least on an outer control point, wherein the outline is determined using a machine learning (ML) model that is configured to take the inner control point, the outer control point, and the medical image as inputs and generate, as an output, the outline of the ROI based on image features associated with the inner control point, the outer control point, and the medical image.
However, Ijiri discloses processing a medical image collecting and utilizing points outside of the estimated ROI as crucial feature points within contour generation (Col. 5 lines 10-27, and col. 9 lines 20-40, wherein both excerpts disclose collecting data from outside a 3D region of interest).
Specifically, Ijiri discloses a method and a system for region estimation within surfaces, wherein deforming region contours is performed as a result of updating feature points from local data.
Therefore, both Chono and Ijiri disclose local feature point-based region of interest segmentation, enabling user-controlled shifting and deformation of regions based on local feature recognition.
Thus, it would have been obvious for one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized feature points outside of the region of interest as disclosed by Ijiri in conjunction with the inner points and segmentation mask estimation and editing method and system of Chono as the application of a known method in the art to a known method or device ready for improvement to yield the predictable improvement of more feature points for better, more accurate region of interest creation and allowing for the inclusion of feature points which the initial estimate might have missed.
The combination of Chono in view of Ijiri thus teaches the generation of an updated ROI outline based on the collection of an original ROI segmentation, inner control points, and outer control points collected as the result of collinear anchor points used for control point determination according to the above rationale. This combination, nevertheless, fails to disclose using a machine learning (ML) model for determining an outline of the ROI.
However, Bai discloses using a machine learning model (Abstract, Materials and Methods, “a trained deep learning model uses [points indicated as critical within the ROI] to update the contour”) given an initial input segmentation contour (Abstract, Materials and Methods, “Given an initial contour that requires clinician editing”), the initial image, and indicated points by a user for deep learning-based contour updating (Materials and Methods section, para. 2). More specifically, the indicated user points are analogous to the inner control points of the combined disclosures of Chono and Ijiri, wherein they are critical points within an ROI used by the ML model for updating the contour.
The disclosure of Bai overall is directed to limiting the need for clinicians to intensively edit determined contours of regions of interest within medical images using deep learning. Therefore, the disclosures of Bai and of Chono in view of Ijiri both disclose methods and systems for medical image contour updating based on detected key points within and outside of a new region of interest, given an initial segmentation and medical image. Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the machine learning based method of contour updating disclosed by Bai within the apparatus of Chono in view of Ijiri as the application of a known technique to a known device ready for improvement, yielding the predictable improvement of an efficient, clinician input-minimizing, feature-optimized (both interior and exterior control points) method of ROI contour updating and fine-tuning (Materials and Methods section of Bai, para. 1, “The goal of AIA CE is to minimize clinician input at each iteration and the number of iterations required”).
Claim 11 is rejected, mutatis mutandis, for reasons similar to claim 1.
Regarding claims 3 and 4 (dependent on claim 1) and claim 13 (dependent on claim 11), Chono in view of Ijiri and in further view of Bai discloses all limitations of claims 1 and 11. Chono further discloses wherein the processor is further configured to select the multiple anchor points from a contour of the current segmentation, and wherein the current segmentation mask is obtained based on human annotation or another ML model (Col. 9 line 25-col. 10 line 8 and col. 10 lines 36-49, wherein the contour extraction is the step of obtaining a segmentation mask; wherein the multiple points on a contour as anchor points are the disclosed feature and apical points, either using detected distances/distance factors or specified by a user/examiner; and wherein the human annotation comprises the fine-tuning of the contour by manually dragging edges/vertices of the contour)
Regarding claims 8 and 17, Chono in view of Ijiri and in further view of Bai discloses all limitations of claims 1 and 11, respectively.
Chono does not disclose wherein the processor being configured to adjust the outline of the ROI based on the user input comprises the processor being configured to: determine whether the selection indicated by the user input is within the outline or outside of the outline; based on a determination that the selection is within the outline, adjust the outline to exclude an area corresponding to the selection; and based on a determination that the selected area or spot is outside of the outline, adjust the outline to include an area corresponding to the selection.
However, Ijiri further discloses wherein the processor being configured to adjust the outline of the ROI based on the user input comprises the processor being configured to: determine whether the selection indicated by the user input is within the outline or outside of the outline (Col. 9, lines 20-39, wherein the region updating mechanism identifies a plurality of control points, and whether they are inside or outside of the specific border region serving as a contour/ROI); based on a determination that the selection is within the outline, adjust the outline to exclude an area corresponding to the selection; and based on a determination that the selected area or spot is outside of the outline, adjust the outline to include an area corresponding to the selection (Col. 9, lines 20-39 for the inclusion-exclusion operations based on whether a selection is within or outside of a contour; col. 11 line 9 – col. 12 67, wherein the user selection of the contour and the deformation of contours based on detected voxels and user adjustments are disclosed). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the disclosures of Chono and Ijiri according to the rationale of claim 1.
Regarding claims 9 and 18, Chono in view of Ijiri and in further view of Bai discloses all limitations of claims 1 and 11, respectively. Chono further discloses wherein the processor is further configured to provide a graphical user interface (GUI) element for changing an area surrounded by the outline, and wherein the processor being configured to adjust the outline based on the user input comprises the processor being configured to receive the user input via the GUI element, determine, based on the user input, an adjustment to a value of the area surrounded by the outline based on the user input, and adjust the outline of the ROI based on the determined adjustment, wherein the user input includes a click or tap by a user (Col. 10, lines 17-67, wherein the GUI element is a user interface on a display unit configured with a control unit to provide views of the medical image in question as well as the ability of the user to adjust, with metrics such as area and length displayed). Additionally, Bai explicitly discloses a click for user interaction with the disclosed method and system (pg. 2, Materials and Methods, Model Training and Testing, “In the testing phase, we simulated the clinician’s clicking at each iteration by choosing the boundary point corresponding to the largest error and quantified the performance with the popular Dice similarity coefficient”, wherein the testing and validation workflows were reflective of clinicians using the method and system of Bai); the rationale of such a combination is the same as that of claim 1 with Chono in view of Ijiri and in further view of Bai teaching all limitations.
Regarding claims 10 and 19, Chono in view of Ijiri and in further view of Bai discloses all limitations of claims 1 and 11, respectively. Chono further discloses wherein the processor being configured to adjust the outline based on the user input comprises the processor being configured to provide a preview of the adjustment to be made to the outline and adjust the outline in response to receiving a confirmation of the adjustment (Col. 10, lines 36-67, wherein the coarse and fine adjustments are made at different scales by the user
using a mouse within the UI displayed by the display unit, and wherein the display unit also displays the finalized, post-adjustment figure and a composite figure combining the post-adjustment contour and the original medical image).
Regarding claim 20, Chono in view of Ijiri and in further view of Bai discloses all limitations of claim 11. Chono further discloses a non-transitory computer-readable medium comprising instructions that, when executed by a processor included in a computing device, cause the processor to implement the method of claim 11 (Col. 10, lines 10-41).
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 ROHAN TEJAS MUKUNDHAN whose telephone number is (571)272-2368. The examiner can normally be reached Monday - Friday 9AM - 6PM.
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/ROHAN TEJAS MUKUNDHAN/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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