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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2022-0046091, filed on 14th April, 2022.
Claim Objections
Claims 19-20 are objected to because of the following informalities:
Claim 19 recites “The computer-readable recording medium” on PG(s). 39, Line(s) 23; examiner suggests amending this to “The non-transitory computer-readable recording medium” to be consistent with Claim 18; and
Claim 20 recites “The computer-readable recording medium” on PG(s). 40, Line(s) 3; examiner suggests amending this to “The non-transitory computer-readable recording medium” to be consistent with Claim 18.Appropriate correction is required.
Claim Rejections - 35 USC § 103
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, 5, 12-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US 20210074061 A1), hereinafter referenced as Brown, in view of Nie et al. (US 20210334978 A1), hereinafter referenced as Nie.
Regarding Claim 1, Brown discloses an image processing method performed by an electronic device (Brown, [0027]: teaches a method <read on image processing method> of segmenting a 3D model based on obtained 2D images from an image capture device <read on electronic device>), the method comprising:
identifying a tooth region in a two-dimensional image of a target oral cavity (Brown, [0091]: teaches a 2D image identification engine 254 that receives input from a tooth numbering machine, where tooth types (i.e., tooth regions) in an oral cavity are estimated and identified based on location and height map from various 2D images);
identifying, in the two-dimensional image, a first neighboring region [[located within a predetermined distance]] from a boundary of the tooth region (Brown, [0053]: teaches using segmentation prediction based on a height map to differentiate between a tooth area <read on tooth region> and a non-tooth area <read on first neighboring region> (e.g., gingiva and excess materials); [0102]: teaches a segmentation engine applying one or more rules to determine the boundaries of each tooth, such as the boundaries between the teeth and the gingiva <read on identified first neighboring region>);
determining, based on a difference in depth between the tooth region and the first neighboring region, [[whether to include the first neighboring region in]] a region of interest (Brown, [0114]: teaches using 2D height map <read on depth difference> projections to differentiate and segment between different components <read on ROI>, such as teeth <read on tooth region> and gingiva <read on first neighboring region>); and
generating a three-dimensional image of the target oral cavity from the two-dimensional image which includes the region of interest (Brown, [0105]: teaches generating a growing 3D model <read on 3D image>).
However, Brown does not expressly disclose
identifying, in the two-dimensional image, a first neighboring region located within a predetermined distance from a boundary of the tooth region; and
determining, based on a difference in depth between the tooth region and the first neighboring region, whether to include the first neighboring region in a region of interest.
Nie discloses
identifying, in the two-dimensional image, a first neighboring region located within a predetermined distance from a boundary of the tooth region (Nie, [0105]: teaches selecting a point close to the boundaries <read on predetermined distance from boundary> of the target region <read on tooth region> as an initial point <read on first neighboring region> in a 2D image, where an ROI region is generated with a contour line/curve surrounding said ROI region); and
determining, based on a difference in depth between the tooth region and the first neighboring region, whether to include the first neighboring region in a region of interest (Nie, [0104]: teaches using a region growing algorithm <read on determining whether to include first neighboring region in ROI> that starts at one pixel/voxel within a certain small region <read on ROI>; [0158]: teaches a growing criteria being related to the gray level of voxels, which is the absolute value of the difference in the gray level values <read on depth difference> of the seed point <read on tooth region> and a neighborhood voxel <read on first neighboring region> being less than a pre-determined threshold, where both regions are then combined; Note: it should be noted that a "region growing algorithm" is known in the art to perform ROI expansion based on a given criteria).
Nie is analogous art with respect to Brown because they are from the same field of endeavor, namely determining regions of interest in images. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a region growing algorithm based on nearby pixel characteristics as taught by Nie into the teaching of Brown. The suggestion for doing so would allow the system to determine which areas of the image to add to the growing region of interest, thereby yielding similar results. Therefore, it would have been obvious to combine Nie with Brown.
Regarding Claim 14, it recites the limitations that are similar in scope to Claim 1, but in an electronic device. As shown in the rejection, the combination of Brown and Nie discloses the limitations of Claim 1. Additionally, Brown discloses an electronic device (Brown, [0025]: teaches a system <read on electronic device>) comprising:
a processor (Brown, [0025]: teaches the system including one or more processors);
a network interface communicatively connected to an intraoral scanner (Brown, [0069]: teaches a scanner/camera 1904 <read on intraoral scanner> being wirelessly connected to a network <read on network interface>);
a display (Brown, [0080]: teaches displaying <read on display> a 3D model);
a memory (Brown, [0041]: teaches memory storing computer-program instructions); and
a computer program loaded onto the memory and executed by the processor (Brown, [0074]: teaches the computing device, which includes a processor, that is capable of reading computer-executable instructions <read on computer program> stored in memory), wherein
the computer program comprises instructions for (Brown, [0041]: teaches memory storing computer-program instructions):…
Thus, Claim 14 is met by Brown according to the mapping presented in the rejection of Claim 1, given the image processing method corresponds to an electronic device.
Regarding Claim 18, it recites the limitations that are similar in scope to Claim 1, but in a non-transitory computer-readable recording medium. As shown in the rejection, the combination of Brown and Nie discloses the limitations of Claim 1. Additionally, Brown discloses a non-transitory computer-readable recording medium in which a computer program to be executed by a processor is recorded (Brown, [0069]: teaches a non-transitory computer-readable medium 1902, which is a type of memory; [0041]: teaches memory storing computer-program instructions; [0074]: teaches a computing device, which includes a processor, that is capable of reading computer-executable instructions stored in memory), wherein
the computer program comprises instructions for (Brown, [0041]: teaches memory storing computer-program instructions):…
Thus, Claim 18 is met by Brown according to the mapping presented in the rejection of Claim 1, given the image processing method corresponds to a non-transitory computer-readable recording medium.
Regarding Claim 3, the combination of Brown and Nie discloses the method of Claim 1. Additionally, Brown further discloses wherein the determining whether to include the first neighboring region in the region of interest comprises
comparing a first coordinate corresponding to the tooth region with a second coordinate corresponding to the first neighboring region to calculate the difference in depth (Brown, [0053]: teaches a segmentation prediction based on a height map <read on calculated depth difference>, which is used to differentiate between parts <read on first coordinate> of a tooth region (shown in white) and parts <read on second coordinate> of a non-tooth region <read on first neighboring region> (e.g., gingiva and excess materials in black and gray respectively) as shown in FIGS. 6A-6B), and wherein
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each of the first coordinate and the second coordinate corresponds to a coordinate obtained using an intraoral scanner linked to the electronic device (Brown, [0108]: teaches a 3D model being segmented by applying a segmentation engine that uses machine learning to process 2D images making up this mesh and using resulting tooth segmentation predictions to label the 3D points <read on first coordinate> and remove non-tooth points <read on second coordinate>; [0110]: teaches an intraoral scanner capturing additional information that corresponds to one or more properties of the scanned structure (e.g., teeth, gingiva, etc.), which includes color inputs from a camera, and counts of how many raw scans contribute to each height map pixel <read on coordinate>).
Regarding Claim 5, the combination of Brown and Nie discloses the method of Claim 3. Additionally, Brown further discloses wherein
each of the first coordinate and the second coordinate is a coordinate obtained through monocular depth estimation of the two-dimensional image captured from the intraoral scanner (Brown, [0070]: teaches an intraoral scanner/camera 1904 capturing 2D images of an area of interest along with height map data, where the process of the intraoral scanner/camera obtaining said height map data is being interpreted as monocular depth estimation due to only using a single camera; [0053]: teaches a segmentation prediction based on a height map, which is used to differentiate between parts <read on first coordinate> of a tooth region (shown in white) and parts <read on second coordinate> of a non-tooth region (e.g., gingiva and excess materials in black and gray respectively) as shown in FIGS. 6A-6B).
Regarding Claims 12, 16, and 19, the combination of Brown and Nie discloses the method, the electronic device, and the computer-readable recording medium of Claims 1, 14, and 18 respectively. Additionally, Brown further discloses
displaying the region of interest by highlighting the region of interest on the two-dimensional image (Brown, FIG. 8B teaches the numbered tooth types <read on ROI> being in different shades <read on highlighted ROI>).
Regarding Claims 13, 17, and 20, the combination of Brown and Nie discloses the method, the electronic device, and the computer-readable recording medium of Claims 1, 14, and 18 respectively. Additionally, Brown further discloses
displaying the region of interest by highlighting the region of interest on the three-dimensional image (Brown, [0129]: teaches providing 3D shapes of a 3D model with color <read on highlighted ROI> that improves further analysis, such as a color that is sufficiently segmented to allow for more accurate modeling).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US 20210074061 A1), hereinafter referenced as Brown, in view of Nie et al. (US 20210334978 A1), hereinafter referenced as Nie as applied to Claim 1 above respectively, and further in view of Nishimura et al. (US 20210104048 A1), hereinafter referenced as Nishimura.
Regarding Claim 2, the combination of Brown and Nie discloses the method of Claim 1. The combination of Brown and Nie does not expressly disclose the limitations of Claim 2; however, Nishimura discloses wherein the identifying the tooth region comprises
identifying the tooth region in the two-dimensional image by using a tooth segmentation model constructed according to a machine learning algorithm (Nishimura, [0106]: teaches the learning model 36 including a segmentation-based learning model SM <read on tooth segmentation model> as shown in FIG. 7B; [0110]: teaches a reconfigured (reconstructed) image <read on 2D image> being used to extract a highly x-ray absorbent material region that corresponds to a tooth region <read on identified tooth region>; [0191]: teaches a learning model using algorithms <read on machine learning algorithm>), and wherein
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the tooth segmentation model corresponds to a model trained by modeling a correlation between a training image set of a tooth and a segmentation result image set corresponding to the training image set (Nishimura, [0111]: teaches training data of two image sets that are associated <read on correlation> with each other and that is used by a learning model as shown in FIGS. 9A and 9B, where "FIG. 9A illustrates an image of a tooth region <read on training image set of a tooth> and a surrounding region thereof when seen from a substantially horizontal direction" and "FIG. 9B illustrates an image <read on segmentation result image set> in which only a metallic region in the image illustrated in FIG. 9A is masked and left (annotated)").
Nishimura is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely performing segmentation processes on images of teeth. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a segmentation-based learning model for identified tooth regions as taught by Nishimura into the teaching of Brown, in view of Nie. The suggestion for doing so would allow for the neural network to automate the segmentation process, thereby providing convenience to the user. Therefore, it would have been obvious to combine Nishimura with Brown, in view of Nie.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US 20210074061 A1), hereinafter referenced as Brown, in view of Nie et al. (US 20210334978 A1), hereinafter referenced as Nie as applied to Claim 3 above respectively, and further in view of Elbaz et al. (US 20180028064 A1), hereinafter referenced as Elbaz.
Regarding Claim 4, the combination of Brown and Nie discloses the method of Claim 3. The combination of Brown and Nie does not expressly disclose the limitations of Claim 4; however, Elbaz discloses wherein
each of the first coordinate and the second coordinate is a coordinate calculated (Elbaz, [0172]: teaches captured data being stored and saved in a common coordinate system, where surface data (including 3D surface model data) uses coordinate system
S
(
x
,
y
,
z
)
<read on first coordinate> and the internal feature data uses
I
(
x
,
y
,
z
)
<read on second coordinate>) based on
a position of a first camera (Elbaz, [0167]: teaches an intraoral scanner for generating a 3D model including multiple image sensors (i.e., cameras); [0168]: teaches the cameras being adjacent <read on first camera position> to each other),
a position of a second camera distinguished from the first camera (Elbaz, [0168]: teaches the cameras being adjacent <read on second camera position> to each other as shown in FIG. 4A),
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an image captured by the first camera (Elbaz, [0167]: teaches the intraoral scanner for generating a 3D model including multiple camera sensors, where each sensor captures images <read on image captured by first camera>), and
an image captured by the second camera (Elbaz, [0167]: teaches the intraoral scanner for generating a 3D model including multiple camera sensors, where each sensor captures images <read on image captured by second camera>), and wherein
the first camera and the second camera are cameras provided in the intraoral scanner (Elbaz, [0167]: teaches the intraoral scanner for generating a 3D model including multiple camera sensors).
Elbaz is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing intraoral 2D images. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to have an intraoral scanner include a plurality of camera sensors as taught by Elbaz into the teaching of Brown, in view of Nie. The suggestion for doing so would allow for a multiview of a scanned object, such as multiple perspectives of a tooth region, thereby improving the accuracy of the reconstructed 3D model. Therefore, it would have been obvious to combine Elbaz with Brown, in view of Nie.
Claims 6-11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US 20210074061 A1), hereinafter referenced as Brown, in view of Nie et al. (US 20210334978 A1), hereinafter referenced as Nie as applied to Claims 1 and 14 above respectively, and further in view of Feng et al. (US 20240249423 A1), hereinafter referenced as Feng.
Regarding Claim 6, the combination of Brown and Nie discloses the method of Claim 1. The combination of Brown and Nie does not expressly disclose the limitations of Claim 6; however, Feng discloses wherein the determining whether to include the first neighboring region in the region of interest comprises
excluding the first neighboring region from the region of interest when the difference in depth is equal to or greater than a threshold (Feng, [0128]: teaches excluding a second element <read on first neighboring region> from the first extended region of interest based on determining the difference between the second depth and the first depth is greater than a threshold difference).
Feng is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing images that contain depth data using ROI algorithms. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an ROI extension process based on depth data between an ROI and nearby elements, as well as the characteristics of said elements as taught by Feng into the teaching of Brown, in view of Nie. The suggestion for doing so would allow the system to determine which parts of the image belongs to the same subject/object, which can be used for scanned data (i.e., intraoral images), thus yielding similar results. Therefore, it would have been obvious to combine Feng with Brown, in view of Nie.
Regarding Claim 7, the combination of Brown, Nie, and Feng discloses the method of Claim 6. The combination of Brown and Nie does not expressly disclose the limitations of Claim 7; however, Feng discloses wherein the excluding the first neighboring region from the region of interest comprises
even when a difference in depth between the first neighboring region and a second neighboring region located within the predetermined distance from the boundary of the first neighboring region is less than the threshold, excluding the second neighboring region from the region of interest (Feng, [0126]: teaches determining a difference between a first depth <read on second neighboring region> and a depth of at least one element <read on first neighboring region> associated with the first region of interest being less than a threshold difference; [0103]: teaches the ROI controller 616 only extending particular ROIs (i.e., special ROIs), such as ROIs associated with particular objects, where the ROI controller 616 does not extend a general ROI that is set to a default position (e.g., center position) within an image; Note: this process is being interpreted as excluding a neighboring element <read on second neighboring region> from the ROI).
Feng is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing images that contain depth data using ROI algorithms. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an ROI extension process based on depth data between an ROI and nearby elements, as well as the characteristics of said elements as taught by Feng into the teaching of Brown, in view of Nie. The suggestion for doing so would allow the system to determine which parts of the image belongs to the same subject/object, which can be used for scanned data (i.e., intraoral images), thus yielding similar results. Therefore, it would have been obvious to combine Feng with Brown, in view of Nie.
Regarding Claim 8, the combination of Brown and Nie discloses the method of Claim 1. The combination of Brown and Nie does not expressly disclose the limitations of Claim 8; however, Feng discloses wherein the determining whether to include the first neighboring region in the region of interest comprises
when the difference in depth is less than a threshold, including the first neighboring region in the region of interest (Feng, [0108]: teaches an ROI controller 616 extending the ROI to include a neighboring element <read on first neighboring region> if the difference in depth values is within the threshold difference (i.e., less than a threshold difference) and the confidence of the neighboring element depth value is greater than the confidence threshold).
Feng is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing images that contain depth data using ROI algorithms. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an ROI extension process based on depth data between an ROI and nearby elements, as well as the characteristics of said elements as taught by Feng into the teaching of Brown, in view of Nie. The suggestion for doing so would allow the system to determine which parts of the image belongs to the same subject/object, which can be used for scanned data (i.e., intraoral images), thus yielding similar results. Therefore, it would have been obvious to combine Feng with Brown, in view of Nie.
Regarding Claim 9, the combination of Brown, Nie, and Feng discloses the method of Claim 8. The combination of Brown and Nie does not expressly disclose the limitations of Claim 9; however, Feng discloses wherein the including the first neighboring region in the region of interest comprises
repeatedly expanding the region of interest until the difference in depth between the region of interest and a third neighboring region located within the predetermined distance from the boundary of the region of interest becomes equal to or greater than the threshold (Feng, [0108]: teaches the ROI controller 616 extending the ROI to include a neighboring element <read on third neighboring region> if the difference in depth values is within the threshold difference (i.e., less than a threshold difference) and the confidence of the neighboring element depth value is greater than the confidence threshold; Note: it should be noted that although "repeatedly expanding the region of interest" is not expressly stated, it would be obvious to one of ordinary skill in the art to perform an ROI extension/expansion process repeatedly).
Feng is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing images that contain depth data using ROI algorithms. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an ROI extension process based on depth data between an ROI and nearby elements, as well as the characteristics of said elements as taught by Feng into the teaching of Brown, in view of Nie. The suggestion for doing so would allow the system to determine which parts of the image belongs to the same subject/object, which can be used for scanned data (i.e., intraoral images), thus yielding similar results. Therefore, it would have been obvious to combine Feng with Brown, in view of Nie.
Regarding Claim 10, the combination of Brown, Nie, and Feng discloses the method of Claim 9. The combination of Brown and Nie does not expressly disclose the limitations of Claim 10; however, Feng discloses wherein
a distance between the boundary of the region of interest and the third neighboring region increases as an expansion count increases (Feng, [0107]: teaches an extended ROI being extended by a factor of four <read on increased expansion count> from the original size; [0109]: teaches the ROI controller 616 extending the target ROI 902 to be associated with the neighboring element (increase the target ROI 902 by a factor of one <read on increased distance between ROI boundary and third neighboring region> in the downward direction); [0105]: teaches using size thresholds to determine an amount to extend an ROI; Note: "expansion count" is being interpreted broadly).
Feng is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing images that contain depth data using ROI algorithms. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an ROI extension process based on depth data between an ROI and nearby elements, as well as the characteristics of said elements as taught by Feng into the teaching of Brown, in view of Nie. The suggestion for doing so would allow the system to determine which parts of the image belongs to the same subject/object, which can be used for scanned data (i.e., intraoral images), thus yielding similar results. Therefore, it would have been obvious to combine Feng with Brown, in view of Nie.
Regarding Claim 11, the combination of Brown, Nie, and Feng discloses the method of Claim 9. The combination of Brown and Nie does not expressly disclose the limitations of Claim 11; however, Feng discloses wherein the repeatedly expanding of the region of interest comprises
when an expansion count is equal to or greater than a reference count, suspending an expansion of the region of interest even when the difference in depth between the region of interest and the third neighboring region is less than the threshold (Feng, [0105]: teaches the size <read on expansion count> of an ROI being greater than a first size threshold <read on reference count>; [0126]: teaches determining a difference <read on depth difference> between a first depth <read on third neighboring region> and a depth of at least one element associated with the first region of interest being less than a threshold difference; [0105]: teaches using size thresholds to determine an amount to extend an ROI <read on suspend expansion of ROI>; Note: it should be noted that although "suspending an expansion of the region of interest" is not expressly stated, it would be obvious for one of ordinary skill in the art to understand that using a threshold to determine the amount to extend an ROI would imply a condition where halting the extension/expansion of the ROI is present; additionally, "expansion count" and "reference count" are being interpreted broadly).
Feng is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing images that contain depth data using ROI algorithms. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an ROI extension process based on depth data between an ROI and nearby elements, as well as the characteristics of said elements as taught by Feng into the teaching of Brown, in view of Nie. The suggestion for doing so would allow the system to determine which parts of the image belongs to the same subject/object, which can be used for scanned data (i.e., intraoral images), thus yielding similar results. Therefore, it would have been obvious to combine Feng with Brown, in view of Nie.
Regarding Claim 15, the combination of Brown and Nie discloses the electronic device of Claim 14. The combination of Brown and Nie does not expressly disclose the limitations of Claim 15; however, Feng discloses wherein the instruction for determining whether to include the first neighboring region in the region of interest comprises an instruction for
when the difference in depth is equal to or greater than a threshold, excluding the first neighboring region from the region of interest (Feng, [0128]: teaches excluding a second element <read on first neighboring region> from the first extended region of interest based on determining the difference between the second depth and the first depth is greater than a threshold difference), and
when the difference in depth is less than the threshold, including the first neighboring region in the region of interest (Feng, [0108]: teaches an ROI controller 616 extending the ROI to include a neighboring element <read on first neighboring region> if the difference in depth values is within the threshold difference (i.e., less than a threshold difference) and the confidence of the neighboring element depth value is greater than the confidence threshold).
Feng is analogous art with respect to Brown, in view of Nie because they are from the same field of endeavor, namely processing images that contain depth data using ROI algorithms. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an ROI extension process based on depth data between an ROI and nearby elements, as well as the characteristics of said elements as taught by Feng into the teaching of Brown, in view of Nie. The suggestion for doing so would allow the system to determine which parts of the image belongs to the same subject/object, which can be used for scanned data (i.e., intraoral images), thus yielding similar results. Therefore, it would have been obvious to combine Feng with Brown, in view of Nie.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (US 20130022251 A1) discloses segmenting a feature of interest from a volume image by acquiring image data elements from the image of a subject;
Ray et al. (US 20080143718 A1) discloses segmenting a lesion from normal anatomy in a 3D image;
Reynard et al. (US 20220122264 A1) discloses segmenting a 3D model image of a patient's dentition; and
Saphier et al. (US 20210353152 A1) discloses receiving a plurality of intraoral scans, where a 3D surface is determined.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARL TRUONG whose telephone number is (703)756-5915. The examiner can normally be reached 10:30 AM - 7:30 PM.
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, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.D.T./Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
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