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
The amendment filed on 01/20/2026 has been entered. Claims 1 and 13 have been amended. Claims 1-3, 6-7 and 9-18 remain pending.
Response to Arguments
In Page 7 of Remarks, regarding amended Claims 1 and 13, Applicant argues that reference Giataganas does not teach or suggest adapting a 3D representation as recited in the claims. Examiner respectfully disagrees. Giataganas explicitly discloses using machine learning model to reconstruct “on-the-fly 3D models … of the anatomical structures” (Para 0091), processing “images obtained from a live video feed of the surgical procedure in real-time …” (Para 0103), during a surgery adjusting graphical overlays and thus tracking three-dimensional structures that are operated upon (Para 0119-0122), and other relevant disclosures in the reference. These disclosures clearly indicate that 3D model of anatomical structure is displayed and is tracked in real time in surgery.
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, 7, 9-17 are rejected under 35 U.S.C. 103 as being unpatentable over Giataganas (WO2022/195304 A1; hereafter Giataganas), in view of Day et al (US 20140368526 A1; hereafter Day) and Lv et al (MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge. Accessed: Aug. 11, 2020; hereafter Lv).
With regard to Claim 1, Giataganas discloses a method for providing a real-time augmented-reality image of a medical procedure, the method comprising the steps of:
capturing an image of the medical procedure (Giataganas, Abstract; “a surgical action to be performed during a surgical procedure […] An image/video capture device such as an endoscope, a wearable camera, a stationary camera, etc., can be used to capture the image(s)”);
automatically identifying and segmenting non-organic objects in said captured image, wherein the segmenting of the non-organic objects in the captured image is by a pretrained machine learning model (Giataganas, Para 0083; “the second machine learning model 400 further includes a decoder 406 that predicts and outputs scene segmentation 408 based on the feature space 404. The scene segmentation 408 provides semantic segmentation annotations […] to delineate with contours, masks, etc. one or more anatomical structures, surgical instruments, or other items (e.g., surgical staples, sutures, etc.) identified in the input window”);
creating a first mask comprising only the segmented non-organic objects from the captured images wherein only the non-organic objects are represented, wherein said first mask is created by using the pretrained machine learning model to automatically identify said non-organic objects in said captured image and segment the identified non-organic objects in the captured images (Giataganas, Para 0087; “the segmentation masks 505 can represent separate parts of the surgical instruments and/or anatomical structures”) (Giataganas, Para 0088; “the method 200 further includes identifying anatomical structures and surgical instruments in the field of view, i.e., the input window 320, at block 206.”);
creating a second mask or overlay comprising medical information relating to the medical procedure (Giataganas, Para 0099; “the graphic overlay 502 can include annotations. […] the annotation can include a note, a sensor measurement, or other such information for the user”) and/or relating to one or more body parts visible in said captured image (Giataganas, Para 0087; “the segmentation masks 505 can represent separate parts of the surgical instruments and/or anatomical structures”), wherein the second mask includes at least one 3D representation of the one or more body parts (Giataganas, Para 0091; “The machine learning model(s) track the provided input across the multiple images (e.g., surgical video) of the surgical procedure … Additionally, separate machine learning models are used to reconstruct real-world measures of the anatomical structures, or on-the-fly 3D “models” (or partial surfaces) of the anatomical structures.”);
combining said captured image with said first mask and said second mask or overlay (Giataganas, Para 0004; “generating a visualization comprising the video of the surgical procedure, and a first graphical overlay at a location of the anatomical structure, a second graphical overlay at a location of the surgical instrument”);
applying the first mask over the second mask or overlay, wherein the first mask and the second mask or overlay are applied over the captured image (Giataganas, Para 0094; “In the augmented visualization 507, the stomach that is to be operated on is marked as anatomical structures using a graphical overlay 502. Other anatomical structures, which are also seen, are not marked. In the augmented visualization 507, the surgical instruments are marked using graphic overlays 502.”. This disclosure describes Fig. 6, in which both the masks of anatomical structures (the stomach) and of surgical instruments are overlaid on a captured image, and the mask of surgical instrument is on the top of the anatomical-structure mask.); and
adapting the 3D representation in the second mask in real-time to a body reaction, a body function, or a response interaction with the non-organic objects (Giataganas, Para 0091; “The machine learning model(s) track the provided input across the multiple images (e.g., surgical video) of the surgical procedure … Additionally, separate machine learning models are used to reconstruct real-world measures of the anatomical structures, or on-the-fly 3D “models” (or partial surfaces) of the anatomical structures.”; Para 0122; “Further, machine learning is used for precise contour reconstruction and tracking of structures that are operated upon in the current step/phase of the surgical procedure.” These disclosures clearly indicate that 3D model of anatomical structure is reconstructed from surgical video, and is tracked so that when the structure is moved due to either interaction with surgical tool or physiologic movement such as respiration, the 3D model moves accordingly) (Giataganas, Para 0117; “to facilitate a real-time performance, the input window 320 is analyzed at a predetermined frequency, such as 5 times per second …”; Para 0119; “The graphical overlays 502 that are used to overlay the images 302 to represent the predicted and/or calculated features (surgical instruments, anatomical structures, calculated trajectories, etc.) are accordingly adjusted”; these disclosures explicitly disclose that the processing is performed in real time).
Giataganas does not clearly and explicitly disclose wherein:
the captured image contains alpha-channel information, and
said preprocessing comprises the steps of:
removing alpha-channel information from the captured image; and
performing normalization of color channels based on training set data of the pretrained machine learning model thereby reducing color variability.
Day in the same field of endeavor discloses wherein:
the captured image contains alpha-channel information (Day, Para 0032; “The volume rendered image data consists of an array of pixels, each having a coordinate value and a value of colour and/or opacity, with opacity for example represented by an alpha channel.”), and
said preprocessing comprises the step of performing normalization of color channels based on training set data of the pretrained machine learning model thereby reducing color variability (Day, Para 0056; “In some cases, depending on the shape of the foreground image, and the nature of the blur or other position-dependent transformation, the variation in colour … can produce concentrations of colour … that some users may find distracting. The position-dependent transformation can be selected in order to avoid such distracting concentrations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Giataganas, as suggested by Day, in order to perform color normalization for the image before image segmentation and to apply the masks over an image with alpha-channel information. One of ordinary skill in the art would have been motivated to make the modification of performing color normalization for the benefit of more balanced distribution of colors for more accurate post-processing by automatic method or interpretation by clinicians, and make the modification of using an image with alpha-channel information for the benefit of allowing surgeons to freely adjust transparency of image background to better visualize the various objects at the surgical site (Day, Para 0032; “In FIG. 4, areas where the opacity pixel values are below a threshold, and are effectively transparent, are represented with a background chequerboard pattern, for the purposes of illustration.”).
Giataganas and Day do not clearly and explicitly disclose wherein said preprocessing comprises the step of removing alpha-channel information from the captured image.
Lv in the same field of endeavor discloses wherein said preprocessing comprises the step of removing alpha-channel information from the captured image (Lv, page 7207, first paragraph, 4th line; “… the alpha channel was discarded in our experimental settings”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Giataganas and Day, as suggested by Lv, in order to preprocess the captured images by removing alpha-channel information from the images, if the captured images contain such information. One of ordinary skill in the art would have been motivated to make the modification for the benefit of reducing size of the captured images therefore increasing the speed of training machine learning model for image analysis.
With regard to Claim 2, Giataganas, Day and Lv disclose all the limitations of claim 1 as discussed above. Giataganas further discloses wherein the pretrained machine learning model is trained to identify and segment at least one or more surgical instruments as the non-organic objects (Giataganas, Para 0083; “the second machine learning model 400 further includes a decoder 406 that predicts and outputs scene segmentation 408 based on the feature space 404. The scene segmentation 408 provides semantic segmentation annotations […] to delineate with contours, masks, etc. one or more anatomical structures, surgical instruments, or other items (e.g., surgical staples, sutures, etc.) identified in the input window”).
With regard to Claim 3, Giataganas, Day and Lv disclose all the limitations of claim 1 as discussed above. Giataganas further discloses wherein the pretrained machine learning model is trained (Giataganas, Para 0051; “Machine-learning processing system includes a data generator, […] to train a machine-learning model. […] Data generator can access (read/write) a data store with recorded data”) with models of a plurality of types of non-organic objects used in the medical procedure, said types comprising at least one or more of needles, gauze, wires, clamps, wires, trocars, forceps, scissors, catheter, drain, endograph elements, fibrillar, foam, clips, needle drivers, suction, hemostasis elements, vessel loops, gloves or patches (Giataganas, Para 0051; “Each of the images and/or videos included in the recorded data can be defined as a base image and can be associated with other data […] the other data can include image-segmentation data that identifies and/or characterizes one or more objects (e.g. tools, anatomical objects, etc) that are depicted in the image or video”).
With regard to Claim 7, Giataganas, Day and Lv disclose all the limitations of claim 1 as discussed above. Giataganas further discloses wherein the captured image is a stereoscopic image (Giataganas, Para 0109; “The machine learning model is trained using a pair of stereo frames that are captured using a stereo image capture device (not shown)”).
With regard to Claim 9, Giataganas, Day and Lv disclose all the limitations of claim 1 as discussed above. Giataganas further discloses wherein the second mask or overlay is an overlay and is applied on the captured image (Giataganas, Para 0098; “"marking" an anatomical structure, surgical instrument, or other features in the surgical data includes visually highlighting that feature for the surgeon or any other user by using a graphical overlay.”) (Giataganas, Para 0099; “Various visual attributes of the graphical overlay, such as colors, transparency, visual-pattern, line thickness, etc., can be adjusted”).
With regard to Claim 10, Giataganas, Day and Lv disclose all the limitations of claim 9 as discussed above. Giataganas further discloses the method comprising a step of resizing the overlay before applying the resized overlay on the captured image (Giataganas, Para 0099; “a user can adjust the attributes of the graphic overlays. For example, the user can select a type of highlighting, a color, a line thickness, a transparency, a shading pattern, a label, an outline, or any other such attributes to be used to generate and display the graphical overlay on the images”).
With regard to Claim 11, Giataganas, Day and Lv disclose all the limitations of claim 1 as discussed above. Giataganas further discloses wherein the step of applying the first mask comprises substituting pixels of the combination of the captured image and the second mask or overlay with pixels from the captured image at a position of to be substituted pixels, wherein said substitution is performed for pixels at whose position the pretrained machine learning model identified and segmented the non-organic objects (Giataganas, Para 0004; “… generating a visualization comprising the video of the surgical procedure, and a first graphical overlay at a location of the anatomical structure, a second graphical overlay at a location of the surgical instrument”. The current claim presents in a fundamental way how overlaying the masks on an image is implemented, which is the usual approach to one of ordinary skill in the field. The cited disclosure of Giataganas inherently uses the same approach for mask overlaying).
With regard to Claim 12, Giataganas, Day and Lv disclose all the limitations of claim 1 as discussed above. Giataganas further discloses wherein the captured image is provided to a capture card of a processing unit as a digital signal, and converted into an encoded bitstream by said processing unit, wherein said processing unit performs the steps of creating the first mask, the second mask or overlay and combining the first mask, the second mask or overlay and the captured image (Giataganas, Fig. 1 shows a system that contains machine learning training system (125), model execution system (140), augmentor (175), and real-time data collection system (145). This system is capable of collecting video stream, creating masks, combining masks and image for augmentation. More details are in 0050-0067).
With regard to Claim 13, Giataganas discloses a system for robot-assisted medical operations supported by real-time augmented reality support, the system comprising:
a. a surgical robot comprising at least one arm equipped with at least one surgical instrument (Giataganas, Para 0102; “The user feedback can further include generating and displaying a graphical overlay 502 on the surgical view […] can be integrated into a robotic workflow in response to the predictions described herein. For example, operating parameters of one or more surgical instruments are adjusted”) (Giataganas, Para 0131; “The surgical procedure support system 902 can also interface with a plurality of sensors 906 and effectors 908. […] The effectors 908 can be robotic components”. In surgical robotics, sensors and effectors are typically attached to end of robotic arms);
b. an image capturing device for capturing an image of the area affected by said at least one surgical instrument (Giataganas, Para 0131; “The surgical procedure support system 902 can acquire image data, such as images 302, using one or more cameras 904”); and
c. an image processing unit comprising at least an image feed input for receiving the image captured by the image capturing device, a processing element for processing the received image, and a memory element (Giataganas, Para 0131; “The surgical procedure support system 902 can include or may be coupled to the system 100 … The surgical procedure support system 902 can store, access, and/or update surgical data 914 associated with a training dataset and/or live data as a surgical procedure is being performed.”) (Giataganas, Para 0059; “the surgical data can include data streams (e.g., an array of intensity, depth, and/or RGB values) for a single image or …” The disclosed system can be configured to process a single image);
characterized in that, said memory element comprises:
medical information relating to a medical procedure and/or relating to one or more body parts (Giataganas, Para 0131; “The surgical procedure support system 902 can store, access, and/or update surgical objectives 916 …”),
a trained machine learning model (Giataganas, Para 0131; “The surgical procedure support system 902 can include or may be coupled to the system 100.” As shown in Fig. 1, the system 100 includes “trained machine learning model”), and
instructions for detecting and segmenting any non-organic objects from said captured image (Giataganas, Para 0050; “the machine learning training system 125 can be a separate device, ( e.g., server) that stores its output as the one or more trained machine learning models 130, which are accessible by the model execution system 140”),
the processing unit (Giataganas, Para 0131; “The surgical procedure support system 902 can include or may be coupled to the system 100.”) further configured for:
automatically identifying and segmenting the non-organic objects in the captured image, wherein the segmenting of the non-organic objects in the captured image is by the trained machine learning model (Giataganas, Para 0083; “the second machine learning model 400 further includes a decoder 406 that predicts and outputs scene segmentation 408 based on the feature space 404. The scene segmentation 408 provides semantic segmentation annotations […] to delineate with contours, masks, etc. one or more anatomical structures, surgical instruments, or other items (e.g., surgical staples, sutures, etc.) identified in the input window”);
generating a first mask from the segmented non-organic objects (Giataganas, Fig. 2 shows the method 200 comprising the step 204 of “Predict semantic segmentation of visual data in surgical data”, and the step 206 of “Determine surgical instrument(s) and anatomical structure(s) in the surgical data”);
generating a second mask or overlay comprising information relating to the medical procedure and/or relating to the one or more body parts visible in the captured image (Giataganas, Fig. 2 shows the method 200 comprising the step 204 of “Predict semantic segmentation of visual data in surgical data”, the step 206 of “Determine surgical instrument(s) and anatomical structure(s) in the surgical data”, and the step 208 of “Predict next surgical action to be performed”.), wherein the second mask includes at least one 3D representation of the one or more body parts (Giataganas, Para 0091; “The machine learning model(s) track the provided input across the multiple images (e.g., surgical video) of the surgical procedure … Additionally, separate machine learning models are used to reconstruct real-world measures of the anatomical structures, or on-the-fly 3D “models” (or partial surfaces) of the anatomical structures.”);
overlaying the second mask or overlay on the captured image (Giataganas, Fig. 2 shows the method 200 comprising the step 210 of “Generate augment visualization of the surgical view”; Fig. 6 shows an example of such augment visualization, in which the second mask (either “anatomical structure” or “trajectory” as dashed line) is overlaid on the image); and
subsequently applying the first mask over the second mask or overlay and the captured image, wherein the first mask and the second mask or overlay are applied over the captured image (Giataganas, Para 0094; “In the augmented visualization 507, the stomach that is to be operated on is marked as anatomical structures using a graphical overlay 502. Other anatomical structures, which are also seen, are not marked. In the augmented visualization 507, the surgical instruments are marked using graphic overlays 502.”. This disclosure describes Fig. 6, in which both the masks of anatomical structures (the stomach) and of surgical instruments are overlaid on a captured image, and the mask of surgical instrument is on the top of the anatomical-structure mask.); and
adapting the 3D representation in the second mask in real-time to a body reaction, a body function, or a response interaction with the non-organic objects (Giataganas, Para 0091; “The machine learning model(s) track the provided input across the multiple images (e.g., surgical video) of the surgical procedure … Additionally, separate machine learning models are used to reconstruct real-world measures of the anatomical structures, or on-the-fly 3D “models” (or partial surfaces) of the anatomical structures.”; Para 0122; “Further, machine learning is used for precise contour reconstruction and tracking of structures that are operated upon in the current step/phase of the surgical procedure.” These disclosures clearly indicate that 3D model of anatomical structure is reconstructed from surgical video, and is tracked so that when the structure is moved due to either interaction with surgical tool or physiologic movement such as respiration, the 3D model moves accordingly) (Giataganas, Para 0117; “to facilitate a real-time performance, the input window 320 is analyzed at a predetermined frequency, such as 5 times per second …”; Para 0119; “The graphical overlays 502 that are used to overlay the images 302 to represent the predicted and/or calculated features (surgical instruments, anatomical structures, calculated trajectories, etc.) are accordingly adjusted”; these disclosures explicitly disclose that the processing is performed in real time).
Giataganas does not clearly and explicitly disclose wherein:
the captured image contains alpha-channel information, and
the processing unit is configured for:
removing alpha-channel information from the captured image; and
performing normalization of color channels based on training set data of the pretrained machine learning model thereby reducing color variability.
Day in the same field of endeavor discloses wherein the captured image contains alpha-channel information (Day, Para 0032; “The volume rendered image data consists of an array of pixels, each having a coordinate value and a value of colour and/or opacity, with opacity for example represented by an alpha channel.”), and
the processing unit is configured for performing normalization of color channels based on training set data of the pretrained machine learning model thereby reducing color variability (Day, Para 0056; “In some cases, depending on the shape of the foreground image, and the nature of the blur or other position-dependent transformation, the variation in colour … can produce concentrations of colour … that some users may find distracting. The position-dependent transformation can be selected in order to avoid such distracting concentrations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Giataganas, as suggested by Day, in order to perform color normalization for the image before image segmentation and to apply the masks over an image with alpha-channel information. One of ordinary skill in the art would have been motivated to make the modification of performing color normalization for the benefit of more balanced distribution of colors for more accurate post-processing by automatic method or interpretation by clinicians, and make the modification of using an image with alpha-channel information for the benefit of allowing surgeons to freely adjust transparency of image background to better visualize the various objects at the surgical site (Day, Para 0032; “In FIG. 4, areas where the opacity pixel values are below a threshold, and are effectively transparent, are represented with a background chequerboard pattern, for the purposes of illustration.”).
Giataganas and Day do not clearly and explicitly disclose wherein the processing unit is configured for removing alpha-channel information from the captured image.
Lv in the same field of endeavor discloses wherein the processing unit is configured for removing alpha-channel information from the captured image (Lv, page 7207, first paragraph, 4th line; “… the alpha channel was discarded in our experimental settings”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Giataganas and Day, as suggested by Lv, in order to preprocess the captured images by removing alpha-channel information from the images, if the captured images contain such information. One of ordinary skill in the art would have been motivated to make the modification for the benefit of reducing size of the captured images therefore increasing the speed of training machine learning model for image analysis.
With regard to Claim 14, Giataganas, Day and Lv disclose all the limitations of claim 13 as discussed above. Giataganas further discloses wherein the image capturing device is an endoscope (Giataganas, Abstract; “A surgical action to be performed during a surgical procedure […] An image/video capture device such as an endoscope, a wearable camera, a stationary camera, etc., can be used to capture the image(s)”).
With regard to Claim 15, Giataganas, Day and Lv disclose all the limitations of claim 14 as discussed above. Giataganas further discloses wherein the endoscope is a stereoscopic endoscope (Giataganas, Para 0109; “The machine learning model is trained using a pair of stereo frames that are captured using a stereo image capture device”).
With regard to Claim 16, Giataganas, Day and Lv disclose all the limitations of claim 13 as discussed above. Giataganas further discloses wherein the system comprises a display unit for displaying a masked video feed produced by the image processing unit and a control unit for controlling the robotic arm, said display unit and said control unit being integrated together (Giataganas, Para 0102; “the user feedback can further include generating and displaying a graphical overlay 502 on the surgical view […] the user feedback can be integrated into a robotic workflow in response to the predictions described herein. For example, operating parameters of one or more surgical instruments are adjusted”).
With regard to Claim 17, Giataganas, Day and Lv disclose all the limitations of claim 13 as discussed above. Giataganas further discloses the system comprising a display unit for displaying the processed images from the image processing unit (Giataganas, Para 0067; “the augmented display can be presented at a non-wearable user device, such as at a computer or tablet”).
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Giataganas, Day and Lv, further in view of Allan et al (US 20230050857 A1; hereafter Allan).
With regard to Claim 6, Giataganas, Day and Lv disclose all the limitations of claim 1 as discussed above, but do not explicitly and clearly disclose wherein the second mask comprises at least one 3D representation of a body part.
Allan in the same field of endeavor discloses wherein the second mask comprises at least one 3D representation of a body part (Allan, Para 0037; “synthetic element could be implemented as a representation of an anatomical structure (e.g. a preoperatively scanned 3D model of surface anatomy, etc.)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Giataganas, Day and Lv, as suggested by Allan, in order to use at least one 3D representation of a body part as the second mask. One of ordinary skill in the art would have been motivated to make this modification for the benefit of providing a display of 3D representation of a body part for an augmented reality experience to a user (Allan, Para 0037; “members of the surgical team benefit from an augmented reality experience in which the augmentation (e.g., the 3D anatomical model) is depicted behind the surgical instruments shown in the endoscopic view.”), e.g. for a surgeon during a surgery to ensure increased precision of surgical actions and enhanced patient safety.
With regard to Claim 18, Giataganas, Day and Lv disclose all the limitations of claim 17 as discussed above, but do not explicitly and clearly disclose wherein said display unit is a stereoscopic display.
Allan in the same field of endeavor disclosed wherein said display unit can be a stereoscopic display (Allan, Para 0095; “a stereo viewer having two displays where stereoscopic images of a surgical site associated with patient and generated by a stereoscopic imaging system may be viewed by surgeon”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Giataganas, Day and Lv, as suggested by Allan, in order to use a stereoscopic display as said display unit. One of ordinary skill in the art would have been motivated to make the modification for the benefit of visualizing the depth information of a surgical site for better surgical guidance (Allan, Para 0095; “a stereoscopic image may include two or more similar images captured simultaneously from different vantage points such that depth information may be derived from differences between the images”) (Allan, 0025; “the depth map […] allow the system to track the position and orientation of the surgical instrument in space”).
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.
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/L.Z./ Examiner, Art Unit 3798
/PASCAL M BUI PHO/ Supervisory Patent Examiner, Art Unit 3798
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