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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/12/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 recites the limitation " wherein the lesion is placed in the frame with the predetermined size ". There is insufficient antecedent basis for this limitation in the claim. The claim dependency only speaks of a frame with a predetermined size in claim 2. Claim 6 depends on claim 3. Claim 3 in turn depends on claim 1.
Claim 7 recites the limitation of “endoscopic images” Claim 1 speaks of images in singular form. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 7 recites “endoscopic images”. There is uncertainty about what the plurality of these images are. Are they two separate endoscopic images? Two images separated by time/acquisition? The original endoscopic image and the segmented lesion region? The endoscopic image and the image of the displayed legion guide? For this, the claim is deemed indefinite.
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.
Claim 1, 3-5, 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Tsujimoto et al (Tsujimoto hereinafter US 20230215003 A1) Chen et al (Chen hereinafter US 20240242345 A1)
As per claim 1
Tsujimoto teaches An endoscopy support device comprising (Figure 10): at least one memory configured to store instructions (Figure 11) at least one processor (Figure 11) configured to execute the instructions to: superimpose and display a lesion guide for specifying a lesion in an endoscopic image (Paragraph [0220] “examples of the result of identification of a lesion region include highlighted display of a lesion region in the endoscopic image, such as superimposed display of a bounding box that indicates a lesion region in the endoscopic”) acquire the endoscopic image (Figure 12, Paragraph [0218] The image acquisition unit 222 can acquire the moving image 38A formed of the frame images 38B in time series. The image acquisition unit 222 can acquire the still image 39 when still-image capturing is performed in the middle of capturing of the moving image 38A.) and output the lesion region segmented (Figure 14)
Although Tsujimoto makes a link between the identified target and the segmentation: Paragraph [0112] “For the second learning model 580, the second learning is performed as learning for the CNN for segmentation of an identification target image, by using the learning data set described above. Note that the identification target image described in the embodiment is an example of identification target data. Paragraph [0220] “The image identification unit 224 stores the result of identification of a lesion region in the storage unit 226. Examples of the result of identification of a lesion region include highlighted display of a lesion region in the endoscopic image, such as superimposed display of a bounding box that indicates a lesion region in the endoscopic image.” It is unclear if the segmentation is based off both the endoscopic region and the lesion guide.
Therefore, Tsujimoto does not explicitly teach segment a lesion region from the endoscopic image based on the endoscopic image and the lesion guide
Chen teaches segment a lesion region from the endoscopic image based on the endoscopic image and the lesion guide (Figure 5A, Figure 5 B, Paragraph [0016] “When an abnormal feature 22 appears on the real-time image 18, a selection box 24 is automatically or manually marked for a position of the abnormal feature 22 on the real-time image “ Paragraph [0017] “Referring to FIG. 1 and FIG. 2 together, as shown in step S14, the segmentation model 121 in the computing device 12 performs computation according to the real-time image 18 and the selection box 24 to segment a correct position of the abnormal feature 22, to generate a bounding box 26 and position information corresponding to the abnormal feature 22 through calculation” Paragraph [0020] “he segmentation model 121 uses an endoscopic color image as the real-time image…A contour of the image of the abnormal feature 22 is obtained through threshold calculation, and segmented to obtain the image of the abnormal feature 22. A feature vector of the contour and its oblique variance matrix are calculated by a principal component analysis (PCA) algorithm using contour bumps. A bounding box 26 is obtained after the original contour is transformed. Major and minor axes of the abnormal feature 22 are calculated through corners of the bounding box 26. In a segmentation part of the abnormal feature 22, the difference between the image of the abnormal feature 22 and a background intestinal wall image is distinguished by a hybrid convolution network and a self-attention mechanism. “) and output the lesion region segmented. (Paragraph [0020] As shown in FIG. 5A, after the selection box 24 is detected by the segmentation model 121, an exact contour of the abnormal feature 22 is segmented by the segmentation mode)
Accordingly, a person of ordinary skill in the art would have found it obvious to modify Tsujimoto’s workflow with Chen’s concept of segmenting the lesion region via the endoscopic image and lesion guide. A person of ordinary skill in the art is aware that Chen’s automatic selection box around the legion is virtually a bounding box. A person of ordinary skill in the art can see that the modification of Tsujimoto in view of Chen allows Tsujimoto to use the superimposed lesion guide (bounding box) as a template/guide for segmentation. This allows for a more efficient and exact representation of a legion and its borders. Chen states that computation according to the image and selection box allows the computation “to segment a correct position of the abnormal feature “Chen’s hybrid convolutional network and self attention mechanism allows “the difference between the image of the abnormal feature 22 and a background intestinal wall image” to be clearly addressed. Efficiency and clarity of what is being segmented is essential in endoscopic imagery and can be advantageous in Tsujimoto’s workflow.
As per claim 3
Tsujimoto and Chen teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Tsujimoto teaches wherein the processor acquires a mask image which masks the lesion region in the endoscopic image, as a result of segmentation. (Figure 14)
Chen also teaches wherein the processor acquires a mask image which masks the lesion region in the endoscopic image, as a result of segmentation (Paragraph [0020] “Through analysis of the local features and global features, features of pixels are calculated and results of different sizes are fused to predict a possibility that each pixel in the real-time image 18 is the abnormal feature 22. A contour of the image of the abnormal feature 22 is obtained through threshold calculation and segmented to obtain the image of the abnormal feature 22. A feature vector of the contour and its oblique variance matrix are calculated by a principal component analysis (PCA) algorithm using contour bumps. A bounding box 26 is obtained after the original contour is transformed. Major and minor axes of the abnormal feature 22 are calculated through corners of the bounding box 26. In a segmentation part of the abnormal feature 22, the difference between the image of the abnormal feature 22 and a background intestinal wall image is distinguished by a hybrid convolution network and a self-attention mechanism. A layered size fusion architecture is adopted to scale and mix abnormal features 22 of different sizes to obtain a more accurate segmentation result, as shown in FIG. 4. Referring to FIG. 5A and FIG. 5B together, the selection box 24 is marked on the periphery of the abnormal feature 22. As shown in FIG. 5A, after the selection box 24 is detected by the segmentation model 121, an exact contour of the abnormal feature 22 is segmented by the segmentation model 121, as shown in FIG. 5B, to mark the bounding box 26. Then, position information of the abnormal feature 22, including (x.sub.1, y.sub.1), (x.sub.2, y.sub.2), (x.sub.3, y.sub.3), and (x.sub.4, y.sub.4), is obtained by calculating the bounding box 26.” In computer vision these are essentially the steps in creating a mask. System calculates a probability score for each pixel in abnormal feature. Then a threshold calculation to isolate the target pixels from background. Then hybrid convolution network/self-attention mechanism and a layered size fusion separate the image of the abnormal feature from the intestinal background. Once this mask/exact contour is made system puts a bounding box around it for specific coordinate positions.)
As per claim 4
Tsujimoto and Chen teach all claim limitations previously rejected in claim 3’s 103 rejection. See claim 3’s 103 rejection
Tsujimoto, in view of Chen Teaches wherein the processor generates and outputs display data for superimposing and displaying the lesion region masked in the endoscopic image. (Tsujimoto: Figure 13, Figure 14, Paragraph [0220] “The image identification unit 224 stores the result of identification of a lesion region in the storage unit 226. Examples of the result of identification of a lesion region include highlighted display of a lesion region in the endoscopic image, such as superimposed display of a bounding box that indicates a lesion region in the endoscopic image.” Chen: Figure 4, Figure 5B)
As per claim 5
Tsujimoto and Chen teach all claim limitations previously rejected in claim 3’s 103 rejection. See claim 3’s 103 rejection.
Tsujimoto teaches wherein the processor is configured to generate a boundary area image in which the lesion region masked is extracted from the endoscopic image (Figure 13, Figure 14 Paragraph [160] “FIG. 13 is a diagram illustrating an example lesion image. FIG. 13 is an enlarged view of the lesion image 520 illustrated in FIG. 2. The lesion image 520 illustrated in FIG. 13 has a lesion region 521A and a normal mucous membrane region 521B.” Paragraph [0161] FIG. 14 is a schematic diagram of a mask image corresponding to the lesion image illustrated in FIG. 13. A mask image 530 illustrated in FIG. 14 is generated on the basis of the lesion image 520 illustrated in FIG. 13 and is a binary image in which the pixel values of the pixels of a mask region 531 corresponding to the lesion region 521A are set to 1 and the pixel values of the pixels of a non-mask region 532 corresponding to the normal mucous membrane region 521B is set to 0. Although FIG. 14 illustrates the mask region 531 having a shape acquired by closely tracing the shape of the lesion, the mask region 531 may be, for example, a circle that circumscribes the lesion or a quadrangle that circumscribes the lesion or may have any shape”) identify the boundary area image ([Paragraph [0161] “binary image in which the pixel values of the pixels of a mask region 531 corresponding to the lesion region 521A are set to 1 and the pixel values of the pixels of a non-mask region 532 corresponding to the normal mucous membrane region 521B is set to “ the “boundary area image” is identified by the binary designation of pixel value 1.) wherein the processor generates and outputs display data including a result of identification. (Paragraph [0220] The image identification unit 224 stores the result of identification of a lesion region in the storage unit 226. Examples of the result of identification of a lesion region include highlighted display of a lesion region in the endoscopic image, such as superimposed display of a bounding box that indicates a lesion region in the endoscopic image.
Chen also teaches wherein the processor generates and outputs display data including a result of identification (Paragraph [0017] “outputs the real-time image 18 to the display device 14, so that the display device 14 displays the real-time image 18, the bounding box 26, and the size”)
As per claim 9
Claim 9 is the parallel system claim of device claim 1 and will be rejected under the same premise.
As per claim 10
Claim 10 is the parallel non transitory computer readable recording medium claim of device claim 1 and will be rejected under the same premise.
Tsujimoto utilizes non transitory computer readable recording medium (Paragraph [0232] “The program that causes a computer to implement the learning functions described above can be stored in a computer-readable information storage medium that is a tangible non-transitory information storage medium and can be provided from the information storage medium.”)
Claims 2 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Tsujimoto et al (Tsujimoto hereinafter US 20230215003 A1) in view of Chen et al (Chen hereinafter US 20240242345 A1) in further view of Karino et al (Karino hereinafter US 20210153722 A1)
As per claim 2
Tsujimoto and Chen teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Tsujimoto nor Chen teach wherein the lesion guide includes a frame with a predetermined size.
Karino teaches wherein the lesion guide includes a frame with a predetermined size (Figure 3, Paragraph [0043] “The display style determination unit 42 determines the display style of the detection result of the region of interest on the basis of the detection position of the region of interest. The display style determination unit 42 determines the display style of the detection result of the region of interest on the basis of the detection position of the region of interest.” Paragraph [0045] “When a bounding box is used as the specific geometric shape and the detection accuracy of the region of interest is represented by the thickness of bounding box, as illustrated in FIG. 5, it is preferable that the frame thickness of a bounding box 46 used to provide a notification of the region of interest detected in the center region 43 be larger than the frame thickness of a bounding box “ Paragraph [0046] “On the other hand, when a bounding box is used as the specific geometric shape and the likelihood of oversight of the region of interest is represented by the frame thickness of the bounding box, as illustrated in FIG. 6, it is preferable that the frame thickness of a bounding box 48 used to provide a notification of the region of interest detected in the center region 43 be smaller than the frame thickness of a bounding box” Paragraph [0047] “In this case, as illustrated in FIG. 7, when a bounding box is used as the specific geometric shape and the detection accuracy of the region of interest is represented by the size of the bounding box, it is preferable that the size of a bounding box 50 used to provide a notification of the region of interest detected in the center region 43 be larger than the size of a bounding box 51 used to provide a notification of the region of interest detected in the peripheral region 44. This allows the user to grasp the detection accuracy using the size of bounding boxes 50 and 51” Paragraph [0048] “when a bounding box is used as the specific geometric shape and the likelihood of oversight of the region of interest is represented by the size of the bounding box, as illustrated in FIG. 8, it is preferable that the size of a bounding box 52 used to provide a notification of the region of interest detected in the center region 43 be smaller than the size of a bounding box 53 used to provide a notification of the region of interest detected in the peripheral region 44. This can prevent the region of interest detected in the peripheral region 44 from being overlooked since the bounding box 53 is displayed large.” It is evident that the display style determination unit. Karino makes it evident that the bounding box (lesion guide) has a predetermined size controlled by the medical image analysis processing unit (which includes region of interest detection unit and display style determination unit)) an interest point which is a point within the frame (Figure 5, Figure 6, Paragraph [0045] “it is preferable that the frame thickness of a bounding box 46 used to provide a notification of the region of interest detected in the center region 43 be larger than the frame thickness of a bounding box 47 used to provide a notification of the region of interest detected in the peripheral region “ ) the lesion is specified to be in the frame with the predetermined size (Figure 5-15) the processor generates prompts including the frame with the predetermined size and the interest point (Figure 5-15)
The combined teachings of Tsujimoto, in view of Chen and further view of Karino teach claimed limitation “segments the lesion region from the endoscopic image by inputting the endoscopic image and the prompts to a segmentation model.” Chen already segments based on the selection box and the endoscopic image (Paragraph [0020] “The segmentation model 121 uses an endoscopic color image as the real-time image 18…the selection box 24 is marked on the periphery of the abnormal feature 22. As shown in FIG. 5A, after the selection box 24 is detected by the segmentation model 121, an exact contour of the abnormal feature 22 is segmented by the segmentation model 121, as shown in FIG. 5B, to mark the bounding box 26.”) The claimed “prompt” is a frame with a predetermined size with a point of interest within. Chen discloses this as aforementioned.
Accordingly, a person of ordinary skill in the art would have found it obvious to further modify the Tsujimoto/Chen methodology to include Karino’s concept of having the lesion guide include a frame with a predetermined size with a point of interest inside. A person of ordinary skill understands that having a predetermined frame as your lesion guide is beneficial when identifying lesions of various dimensions and physiologies. Karino states that “the detection accuracy differs depending on the detection position of the region of interest detected from the medical image” in paragraph [0043]. Having a module predetermine a frame’s size depending on what is being identified allows flexibility when “the user is likely to overlook the region of interest and a region where the user is less likely to overlook the region of interest” as Karino states in paragraph [0043]. A person of ordinary skill in the art sees this as advantageous.
As per claim 6
Tsujimoto and Chen teach all previously rejected claim limitations of claim 3 in claim 3’s 103 rejection. See claim 3’s 103 rejection.
Tsujimoto in view of Chen in further view of Karino teaches wherein the lesion is placed in the frame with the predetermined size based on manipulations of a user (Chen: Paragraph [0016] “As shown in step S12, when an abnormal feature 22 appears on the real-time image 18, a selection box 24 is automatically or manually marked for a position of the abnormal feature 22 on the real-time image 18, as shown in FIG. 3” Karino: Figure 3, Paragraph [0043] “The display style determination unit 42 determines the display style of the detection result of the region of interest on the basis of the detection position of the region of interest. The display style determination unit 42 determines the display style of the detection result of the region of interest on the basis of the detection position of the region of interest.” Paragraph [0045] “When a bounding box is used as the specific geometric shape and the detection accuracy of the region of interest is represented by the thickness of bounding box, as illustrated in FIG. 5, it is preferable that the frame thickness of a bounding box 46 used to provide a notification of the region of interest detected in the center region 43 be larger than the frame thickness of a bounding box”) the processor acquires the endoscopic image based on the manipulations of the user. (Tsujimoto: Figure 10)
In a combined teaching the Tsujimoto/Chen/Karino pipeline shows flexibility when it comes to automatic determination of bounding boxes (frames) and manual entry of bounding boxes by the user to encompasses the legion and or region of interest. Tsujimoto supplies the endoscopic image acquisition through manipulation of the user
Claim 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Tsujimoto et al (Tsujimoto hereinafter US 20230215003 A1) in view of Chen et al (Chen hereinafter US 20240242345 A1) in further view of Juergens et al (Juergens hereinafter US 20260253212 A1)
As per claim 7
Tsujimoto and Chen teach all claim limitations previously rejected in claim 1’s 103 rejection. See claim 1’s 103 rejection.
Juergens teaches the processor is further configured to determine whether or not a user is observing (Figure 5, Figure 3 Paragraph [0051] “The gaze location detection algorithm 406 can be ran by the controller 336 or any other processor of, or in communication with…can be run once the endoscopist detection algorithm 404 finds the location of the endoscope operator…to analyze the operator video stream 320 and determine a gaze location 426 (FIG. 3) of the medical professional performing the medical procedure. For example, the endoscopist detection algorithm 404 can be used to find an area of attention 422, which can be a location on the display 4”) based on a movement amount between endoscopic images [Paragraph [0023] “configured by the instructions to determine a gaze location of the doctor during the procedure using a gaze algorithm, determine whether the doctor looked at the detected abnormality by comparing the signal from the computer-aided diagnostic module and the gaze location of the doctor, and trigger a countermeasure based on determining that the doctor did not look at the detected abnormality.” Paragraph [0021] “a system that can generate signals for an operator of a CAD system based on an amount of time the operator spends examining individual CAD-identified tissue anomalies.” Paragraph [0092] “The image 1210 can be a continuous feed from the video stream captured by the camera during the endoscopic procedure. The image 1210 can be from a timestamp that corresponds a timestamp of an indicator of an abnormality found by the computer aided diagnostic system (e.g., the computer-aided diagnostic module 312 (FIG. 3)” This shows the observation detection is based on movement. The practitioner must use the endoscopes real time live video feed and if within the feed, the practitioner misses a detectable lesion because their gaze is not within the threshold criteria, the system responds to this. The movement amount through live images is based on the continuous procedure of the endoscope as well as the practitioners eye movement as they observe the live images. wherein the processor displays the lesion guide in a case where the user is observing. (Paragraph [0090] “As shown in FIG. 11, the abnormality 314C can be once again shown on the screen as the endoscopist moves the camera toward the abnormality 314C after the visual graphic 904 drew the endoscopists attention to the abnormality 314C. In such an implementation, the bounding box (e.g., the bounding box 702 (FIG. 7)) can turn from red to green once the gaze duration of the operator toward abnormality 314C reaches the appropriate gaze duration threshold (e.g., gaze duration threshold 708).”)
Accordingly, a person of ordinary skill in the art at the time this invention was effectively filed would have found it obvious to further modify the Tsujimoto/Chen methodology with Juergens concept of attention tracking of the user. A person of ordinary skill in the art knows that this modification prevents distraction of the practitioner during active movement and to highlight lesions when the doctor is actively and carefully inspecting the tissue. This also removes the amount of noise the doctor gets during the endoscopy procedure. The movement of the endoscope in real-time can be blurry and the system may be constantly detecting regions of interest. Juergen states in paragraph [0077] that “Such an implementation can mitigate negative operator experience that results from the bounding boxes 702 being placed around false positives. For example, some gastroenterologists have indicated that bounding boxes being placed over false positives (i.e., tissue areas being falsely identified by a CADe/CADx system as being of interest) causes distraction and annoyance during medical examinations.” A person of ordinary skill in the art understands this can bring about cognitive overload to the practitioner. Furthermore, this modification ensures focused attention and minimizes false positives caused by motion artifacts. The system may choose a reflection or some fluid distortion as a lesion. The advantage of this modification to the system allows attentive gaze caused by a slowing of the procedure on a fixed point to activate the identification.
As per claim 8
Tsujimoto and Chen teach all claim limitations previously rejected in claim 3’s 103 rejection. See claim 3’s 103 rejection.
Tsujimoto teaches wherein the processor is further configured to detect the lesion from the endoscopic image by using a machine learning model, (Paragraph [0042] “second learning model that identifies identification target data” Paragraph [0056] “ highly accurate identification of a lesion based on segmentation of an abnormal part for an endoscopic image can be performed by using the second learning model that has been trained.” Paragraph [0219] “The image identification unit 224 includes the learning apparatus 600 described with reference to FIG. 1 to FIG. 9. “) wherein the processor displays the lesion guide in a case where a lesion detection AI detects the lesion. (Paragraph [0219] “The image identification unit 224 includes the learning apparatus 600 described with reference to FIG. 1 to FIG. 9. “Paragraph [0220] “Examples of the result of identification of a lesion region include highlighted display of a lesion region in the endoscopic image, such as superimposed display of a bounding box that indicates a lesion region in the endoscopic image.”)
Chen wherein the processor is further configured to detect the lesion from the endoscopic image by using a machine learning model (Paragraph [0016] “As shown in step S12, when an abnormal feature 22 appears on the real-time image 18, a selection box 24 is automatically or manually marked for a position of the abnormal feature 22 on the real-time image” Paragraph [0020] “…the segmentation model 121 is a neural network model… As shown in FIG. 5A, after the selection box 24 is detected by the segmentation model 121, an exact contour of the abnormal feature 22 is segmented by the segmentation model”
Juergen wherein the processor is further configured to detect the lesion from the endoscopic image by using a machine learning model (Figure 3, Paragraph [0044] “the CAD algorithm 402 can determine a location of the detected abnormality 314 and can label the location of the detected abnormality 314” Paragraph [0052] “As such, the operator video stream 320 can then be stored and used for further machine learning of the CAD algorithm 402 or any other algorithm or system that can benefit from seeing where the doctor is gazing during the medical procedure”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANE WRENSFORD CODRINGTON whose telephone number is (571)272-8130. The examiner can normally be reached 8:00am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHANE WRENSFORD CODRINGTON/ Examiner, Art Unit 2667
/MATTHEW C BELLA/ Supervisory Patent Examiner, Art Unit 2667
Prosecution Insights