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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/27/2026 has been entered.
Response to Amendment
Applicant’s Amendments filed on 04/27/2026 has been entered and made of record.
Currently pending Claim(s):
Independent Claim(s):
Amended Claim(s):
Cancelled Claim(s):
1–4, 6–18 and 20
1 and 17
1 and 17
5 and 19
Response to Applicant’s Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on 04/27/2026.
In view of applicant Arguments/Remarks and amendment filed on 04/27/2026 with respect to independent claims 1 and 17 under 35 U.S.C 103, claim rejection has been fully considered and the arguments are found to be not persuasive (See Page(s) 8–12), therefore the claim rejection with respect to 35 U.S.C. 103 still applies.
Applicant argues, in summary the applied prior art (Miller) and (Ono) does not disclose or suggest (see page 9 and 10):
“receiving a third image with a third property from the first sensor and a fourth image with a fourth property from the second image sensor, wherein the third property and the fourth property are different from each other; and performing image alignment on the third image and the fourth image based on the first feature correspondence stored in the warping map; wherein the first property and the second property are similar, and the first feature correspondence calculated based on the similar first and second properties is applied to align the third and fourth images whose properties are different from each other”
The examiner respectfully disagrees, Ono recites in ¶ [0071], “The imaging unit 100 comprises a first imaging unit 110, a second imaging unit 120, and a wireless communication unit 130. The first imaging unit 110 and the second imaging unit 120 are independent imaging units that capture images in wavelength ranges described below”. Ono’s imaging structure uses a binocular camera that uses two image sensors to capture images. Both sensors are independent of each other. Ono teaches ¶ [0088], “In Step S130 (correspondence point detection step), feature points are detected by the correspondence point detection unit 210E based on a component of a wavelength range of a plurality of image signals corresponding to a plurality of images common among the images, and correspondence points are detected based on the feature points. As described above, for example, the point of the edge or the corner portion is detected as the feature point of the reference image, and the correspondence point can be detected in another image through matching between the images”. Ono teaches comparing and aligning two images based on the a common wavelength ranges that are taken in the images. Which the examiner is interpreting as aligning the first and second image based on similar properties.
Miller teaches ¶ [0051], “After obtaining a first scan of the sample using the epi-filter set that is represented by the spectra shown in FIG. 1, and a second scan using the epi-filter set represented by the spectra shown in FIG. 2, as described above, each of the two scans includes three images corresponding to three different spectral bands. The two scans are registered using the image corresponding to the blue spectral band (i.e., the shared spectral band)”. Miller registers two images from the scan of the blue spectral band to get the best alignment and then applies the alignments to the other spectral bands which the examiner is interpreting as the different properties. Examiner is incorporating the new reference Banerjee (US 20190082103 A1) to teach using two independent cameras to align two image and storing the feature correspondence in a warping map.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim(s) 1–4, 6 and 10, 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 20140193061 A1, hereafter, "Miller") in view of Ono (US 20190273862 A1, hereafter, "Ono") in further view of Banerjee et al. (US 20190082103 A1, hereafter, “Banerjee”).
Regarding claim 1, Miller teaches a method for image alignment (See Miller, [Abstract], Aligning the first and second pluralities of images based on information from a first image from the first plurality of images and a second image from the second plurality of images), comprising:
[receiving a first image with a first property from a first sensor;
receiving a second image with a second property from a second sensor, wherein the first property is similar to the second property;
calculating a first feature correspondence between the first image and the second image;
storing the first feature correspondence between the first image and the second image into a warping map];
[receiving a third image with a third property from the first sensor and a fourth image with a fourth property from the second image sensor]; wherein the third property and the fourth property are different from each other; and (See Miller, ¶ [0038], Thus, for example, the first set of images can include an image corresponding to emission from DAPI (in a first wavelength band) and also, e.g., two or three other images corresponding to emission from the sample in two or three other wavelength bands. Similarly, the second set of images can include an image corresponding to emission from DAPI in the first wavelength band, and also, e.g., two or three other images corresponding to sample emission in two or three other wavelength bands. In certain embodiments, it can be advantageous for the two or three other wavelength bands in the first set of images to be completely distinct from the two or three other wavelength bands in the second set of images. Note: The plurality other images are being interpreted as the third and fourth image and the different spectral bands are being interpreted as the different properties); and
performing image alignment on the third image and the fourth image (See Miller, ¶ [0040], To correct this problem, the images from the two scans can be aligned to a common registration using the images corresponding to the wavelength band that is shared among the first and second scans (e.g., the image that corresponds to emission from DAPI in the example above). The same shift or image transformation that yields the best alignment in this shared band is applied to all images in the scan, after which the two scans can be combined into an image cube. Note: Examiner is interpreting the best alignment of the shared band as the first and second image feature correspondence and the aligning of the other images as aligning a 3rd and 4th image) [based on the first feature correspondence stored in the warping map]; and
[wherein the first property and the second property are similar, and the first feature correspondence calculated based on the similar first and second properties] is applied to align the third and fourth images whose properties are different from each other (See Miller, ¶ [0038], Thus, for example, the first set of images can include an image corresponding to emission from DAPI (in a first wavelength band) and also, e.g., two or three other images corresponding to emission from the sample in two or three other wavelength bands. Similarly, the second set of images can include an image corresponding to emission from DAPI in the first wavelength band, and also, e.g., two or three other images corresponding to sample emission in two or three other wavelength bands. In certain embodiments, it can be advantageous for the two or three other wavelength bands in the first set of images to be completely distinct from the two or three other wavelength bands in the second set of images. Note: The plurality other images are being interpreted as the third and fourth image and the different spectral bands are being interpreted as the different properties).
However, Miller fail(s) to teach receiving a first image with a first property from a first sensor; receiving a second image with a second property from a second sensor, wherein the first property is similar to the second property; calculating a first feature correspondence between the first image and the second image; storing the first feature correspondence between the first image and the second image into a warping map; receiving a third image with a third property from the first sensor and a fourth image with a fourth property from the second image sensor; based on the first feature correspondence stored in the warping map; wherein the first property and the second property are similar, and the first feature correspondence calculated based on the similar first and second properties.
Ono, working in the same field of endeavor, teaches: receiving a first image with a first property from a first sensor (See Ono, ¶ [0080], The optical filter 112 of the first imaging unit 110 is an optical filter that transmits light having a plurality of wavelength ranges, and transmits different wavelength ranges depending on regions. Specifically, as shown in FIG. 10, ¾ of the entire region is a region 112A (single wavelength range optical filter) through which light having a first wavelength range is transmitted at 100%, and ¼ of the entire region is a region 112B (single wavelength range optical filter) through which light having a second wavelength range is transmitted at 100% (it is assumed that the shapes and sizes of the regions 112A and 112B are fixed));
receiving a second image with a second property from a second sensor, wherein the first property is similar to the second property (See Ono, ¶ [0080], The optical filter 122 of the second imaging unit 120 is an optical filter that transmits light having a single wavelength range. As shown in FIG. 10, ¼ of the entire region is a region 122A (single wavelength range optical filter) through which light having the first wavelength range is transmitted at 100%, and ¾ of the entire region is a region 122B (single wavelength range optical filter) through which light having the second wavelength range is transmitted at 100% (it is assumed that the shapes and sizes of the regions 122A and 122B are fixed). Note: Examiner is interpreting the first wavelength and second wavelength as the common property);
calculating a first feature correspondence between the first image and the second image (See Ono, ¶ [0088], In Step S130 (correspondence point detection step), feature points are detected by the correspondence point detection unit 210E based on a component of a wavelength range of a plurality of image signals corresponding to a plurality of images common among the images, and correspondence points are detected based on the feature points. As described above, for example, the point of the edge or the corner portion is detected as the feature point of the reference image, and the correspondence point can be detected in another image through matching between the images);
wherein the first property and the second property are similar, and the first feature correspondence calculated based on the similar first and second properties (See Ono, ¶ [0088], In Step S130 (correspondence point detection step), feature points are detected by the correspondence point detection unit 210E based on a component of a wavelength range of a plurality of image signals corresponding to a plurality of images common among the images, and correspondence points are detected based on the feature points. As described above, for example, the point of the edge or the corner portion is detected as the feature point of the reference image, and the correspondence point can be detected in another image through matching between the images).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to receiving a first image with a first property from a first sensor; receiving a second image with a second property from a second sensor, wherein the first property is similar to the second property; calculating a first feature correspondence between the first image and the second image based on the method of Ono’s reference. The suggestion/motivation would have been to register multiple images having different wavelengths with high accuracy (See Ono, ¶ [0005–0007]).
However, Miller and Ono fail(s) to teach storing the first feature correspondence between the first image and the second image into a warping map receiving a third image with a third property from the first sensor and a fourth image with a fourth property from the second image sensor; based on the first feature correspondence stored in the warping map.
Banerjee, working in the same field of endeavor, teaches: storing the first feature correspondence between the first image and the second image into a warping map (See Banerjee, ¶ [0073], In some configurations (where a region transform is performed, for example), the electronic device 102 (e.g., processor 112, image obtainer 114, warper 118, image stitcher 122, etc.) may determine a mapping between the images (e.g., input images, wide-angle images, normal images, telephoto images, etc.) and the transformed region(s). The content analysis, warp prediction, and/or smoothing may be carried out based on the transformed images (e.g., transformed overlapping regions). The mapping may indicate a correspondence between the transformed images and the original images (and/or the equi-rectangular domain));
receiving a third image with a third property from the first sensor and a fourth image with a fourth property from the second image sensor (See Banerjee, ¶ [0100], The electronic device 102 may receive 202 images (e.g., two or more images). This may be accomplished as described in relation to FIG. 1. For example, the electronic device 102 may capture wide-angle images (with multiple wide-angle lenses, for instance) and/or may receive wide-angle images from another device);
based on the first feature correspondence stored in the warping map (See Banerjee, ¶ [0073], In some configurations (where a region transform is performed, for example), the electronic device 102 (e.g., processor 112, image obtainer 114, warper 118, image stitcher 122, etc.) may determine a mapping between the images (e.g., input images, wide-angle images, normal images, telephoto images, etc.) and the transformed region(s). The content analysis, warp prediction, and/or smoothing may be carried out based on the transformed images (e.g., transformed overlapping regions). The mapping may indicate a correspondence between the transformed images and the original images (and/or the equi-rectangular domain)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference storing the first feature correspondence between the first image and the second image into a warping map receiving a third image with a third property from the first sensor and a fourth image with a fourth property from the second image sensor; based on the first feature correspondence stored in the warping map based on the method of Banerjee’s reference. The suggestion/motivation would have been to image processing to reduce the complexity, reduce the processing resources and increase the quality (See Banerjee, ¶ [0002-0012]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Ono and Banerjee with Miller to obtain the invention as specified in claim 1.
Regarding claim 2, Miller teaches the method as claimed in claim 1, wherein the first property represents a first spectrum range of the first image, and the second property represents a second spectrum range of the second image, wherein the second spectrum range is similar to the first spectrum range (See Miller, ¶ [0036], To achieve improve scanning speeds and image registration among images corresponding to different wavelength bands, the methods and systems disclosed herein are configured to perform a scan of a sample using M signal bands (e.g., at M different spectral bands), then scan it again using N signal bands, in which the first set of M signal bands and the second set of N signal bands have a spectral band that is shared. ¶ [0040], To correct this problem, the images from the two scans can be aligned to a common registration using the images corresponding to the wavelength band that is shared among the first and second scans (e.g., the image that corresponds to emission from DAPI in the example above). The same shift or image transformation that yields the best alignment in this shared band is applied to all images in the scan. Note: Examiner is interpreting the best alignment is the first and second image and the rest of the aligned images are being interpreted as the third and fourth. The set of images have a shared band that are similar to each other).
Regarding claim 3, Miller teaches the method as claimed in claim 2, wherein the third property represents a third spectrum range of the third image, and the fourth property represents a fourth spectrum range of the fourth image, wherein the third spectrum range is similar to the first spectrum range and different from the fourth spectrum range (See Miller, ¶ [0038], Thus, for example, the first set of images can include an image corresponding to emission from DAPI (in a first wavelength band) and also, e.g., two or three other images corresponding to emission from the sample in two or three other wavelength bands. Similarly, the second set of images can include an image corresponding to emission from DAPI in the first wavelength band, and also, e.g., two or three other images corresponding to sample emission in two or three other wavelength bands. Note: The plurality of images in being interpreted as the third and fourth image. Note: Examiner is interpreting the other wavelength images that are not the first wavelength as different wavelengths).
Regarding claim 4, Miller teaches the method as claimed in claim 3, wherein the first image and the second image are received earlier than the third image and the fourth image (See Miller, ¶ [0039], While the M bands acquired during the first scan are registered among themselves, and the N bands acquired during the second scan are registered among themselves, images of the first scan and images of the second scan are, in general, misaligned due to the limited mechanical repeatability of the scanner. Note: Examiner is interpreting the first scan as the first and second images that are earlier and the second scan as the third and fourth images that are later).
Regarding claim 6, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 1, [wherein the warping map records a first displacement vector of each pixel between the first image and the second image].
However, Miller and Ono fail(s) to teach wherein the warping map records a first displacement vector of each pixel between the first image and the second image.
Banerjee, working in the same field of endeavor, teaches: wherein the warping map records a first displacement vector of each pixel between the first image and the second image (See Banerjee, ¶ [0093], For example, the warper 118 may perform dynamic warping based on the warp vector(s). In some approaches, the warper 118 may warp one or more vertex points based on the warp vector(s) (and/or disparity vector(s)). In some configurations, warping the vertex point(s) may produce a warped vertex (e.g., vertices) map).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to wherein the warping map records a first displacement vector of each pixel between the first image and the second image based on the method of Banerjee’s reference. The suggestion/motivation would have been to image processing to reduce the complexity, reduce the processing resources and increase the quality (See Banerjee, ¶ [0002-0012]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Banerjee with Miller and Ono to obtain the invention as specified in claim 6.
Regarding claim 10, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 6, [wherein the step of calculating the first feature correspondence between the first image and the second image comprises: performing feature extraction on each pixel in the first image and the second image to obtain respective pixel features; and
performing feature matching between each pixel in the first image and each pixel in the second image to obtain the first displacement vector].
However, Miller and Ono fail(s) to teach wherein the step of calculating the first feature correspondence between the first image and the second image comprises: performing feature extraction on each pixel in the first image and the second image to obtain respective pixel features; and performing feature matching between each pixel in the first image and each pixel in the second image to obtain the first displacement vector.
Banerjee, working in the same field of endeavor, teaches: wherein the step of calculating the first feature correspondence between the first image and the second image comprises: performing feature extraction on each pixel in the first image and the second image to obtain respective pixel features (See Banerjee, ¶ [0077], Warping may be performed in order to align image data (e.g., align features of the images) between images and/or to reduce or avoid artifacts (e.g., temporal artifacts, parallax artifacts, motion artifacts, structure deformation artifacts, and/or ghosting artifacts, etc.) in a stitched image); and
performing feature matching between each pixel in the first image and each pixel in the second image to obtain the first displacement vector (See Banerjee, ¶ [0077], The processor 112 may include and/or implement a warper 118. The warper 118 may determine and/or perform warping (e.g., one or more warp vectors) for one or more images. A warp vector may indicate an amount and/or direction of warping for image data (e.g., one or more pixels). For example, warping may spatially warp (e.g., stretch, shift, bend, flex, and/or compress, etc.) image data. Warping may be performed in order to align image data (e.g., align features of the images) between images and/or to reduce or avoid artifacts (e.g., temporal artifacts, parallax artifacts, motion artifacts, structure deformation artifacts, and/or ghosting artifacts, etc.) in a stitched image).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to wherein the step of calculating the first feature correspondence between the first image and the second image comprises: performing feature extraction on each pixel in the first image and the second image to obtain respective pixel features; and performing feature matching between each pixel in the first image and each pixel in the second image to obtain the first displacement vector based on the method of Banerjee’s reference. The suggestion/motivation would have been to image processing to reduce the complexity, reduce the processing resources and increase the quality (See Banerjee, ¶ [0002-0012]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Banerjee with Miller and Ono to obtain the invention as specified in claim 10.
Regarding claim 12, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 10, [wherein the pixel features comprise brightness, color, and texture].
However, Miller fail(s) to teach wherein the pixel features comprise brightness, color, and texture.
Ono, working in the same field of endeavor, teaches: wherein the pixel features comprise brightness, color, and texture (See Ono, ¶ [0056], As a method of correspondence point detection and registration, various known methods (for example, a point of an edge or a corner portion is detected as a feature point of a reference image, a correspondence point is detected in another image through matching between images, and the images are moved, rotated, enlarged, and/or reduced such that the positions of the feature point and the correspondence point coincide with each other). Note: the edge and corner portion represent changes in intensity/brightness which is brightness and color. The edges show the texture of the image).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to wherein the pixel features comprise brightness, color, and texture based on the method of Ono’s reference. The suggestion/motivation would have been to register multiple images having different wavelengths with high accuracy (See Ono, ¶ [0005–0007]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Ono with Miller and Banerjee to obtain the invention as specified in claim 12.
Regarding claim 14, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 10, [wherein the step of performing image alignment on the third image and the fourth image based on the first feature correspondence between the first image and the second image comprises: converting the position of each pixel in the third image to the position of each pixel in the fourth image through the first displacement vector to perform image alignment between the third image and the fourth image].
However, Miller and Ono fail(s) to teach wherein the step of performing image alignment on the third image and the fourth image based on the first feature correspondence between the first image and the second image comprises: converting the position of each pixel in the third image to the position of each pixel in the fourth image through the first displacement vector to perform image alignment between the third image and the fourth image.
Banerjee, working in the same field of endeavor, teaches: wherein the step of performing image alignment on the third image and the fourth image based on the first feature correspondence between the first image and the second image comprises (See Banerjee, ¶ [0077], Warping may be performed in order to align image data (e.g., align features of the images) between images and/or to reduce or avoid artifacts (e.g., temporal artifacts, parallax artifacts, motion artifacts, structure deformation artifacts, and/or ghosting artifacts, etc.) in a stitched image): converting the position of each pixel in the third image to the position of each pixel in the fourth image through the first displacement vector to perform image alignment between the third image and the fourth image (See Banerjee, ¶ [0077], The processor 112 may include and/or implement a warper 118. The warper 118 may determine and/or perform warping (e.g., one or more warp vectors) for one or more images. A warp vector may indicate an amount and/or direction of warping for image data (e.g., one or more pixels). For example, warping may spatially warp (e.g., stretch, shift, bend, flex, and/or compress, etc.) image data. Warping may be performed in order to align image data (e.g., align features of the images) between images and/or to reduce or avoid artifacts (e.g., temporal artifacts, parallax artifacts, motion artifacts, structure deformation artifacts, and/or ghosting artifacts, etc.) in a stitched image).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to wherein the step of performing image alignment on the third image and the fourth image based on the first feature correspondence between the first image and the second image comprises: converting the position of each pixel in the third image to the position of each pixel in the fourth image through the first displacement vector to perform image alignment between the third image and the fourth image based on the method of Banerjee’s reference. The suggestion/motivation would have been to image processing to reduce the complexity, reduce the processing resources and increase the quality (See Banerjee, ¶ [0002-0012]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Banerjee with Miller and Ono to obtain the invention as specified in claim 14.
Claim(s) 7–9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 20140193061 A1, hereafter, "Miller") in view of Ono (US 20190273862 A1, hereafter, "Ono") further view of Banerjee et al. (US 20190082103 A1, hereafter, “Banerjee”) and further in view of Riley et al. (US 20100189363 A1, hereafter, "Riley").
Regarding claim 7, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 3, further comprising: [comparing the third image with the first image to obtain a comparison result]; and
determining whether to perform image alignment on the third image and the fourth image based on the first feature correspondence according to the comparison result (See Miller, ¶ [0040], To correct this problem, the images from the two scans can be aligned to a common registration using the images corresponding to the wavelength band that is shared among the first and second scans (e.g., the image that corresponds to emission from DAPI in the example above). Note: The second plurality of scan is being interpreted as the third and fourth image and the alignment between the first scan and the second scan is being interpreted as aligning the third image (second scan) and first image (first scan)).
However, Miller, Ono and Banerjee fail(s) to teach comparing the third image with the first image to obtain a comparison result.
Riley, working in the same field of endeavor, teaches: comparing the third image with the first image to obtain a comparison result (See Riley, ¶ [0007], The processing element is further configured for generating an alternate mapping between the first and second sets of imagery data based on the comparing between at least a first area of the pixels in the first image and at least a first area of the pixels in the third image that are non-corresponding according to the first mapping function).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to comparing the third image with the first image to obtain a comparison result based on the method of Riley’s reference. The suggestion/motivation would have been to accurately register images taking into account moving objects (See Riley, ¶ [0004–0006]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Riley with Miller, Ono and Banerjee to obtain the invention as specified in claim 7.
Regarding claim 8, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 7, [wherein the comparison result indicates that the third image matches the first image, or the third image does not match the first image].
However, Miller, Ono and Banerjee fail(s) to teach wherein the comparison result indicates that the third image matches the first image, or the third image does not match the first image.
Riley, working in the same field of endeavor, teaches: wherein the comparison result indicates that the third image matches the first image, or the third image does not match the first image (See Riley, ¶ [0007], The processing element is further configured for generating an alternate mapping between the first and second sets of imagery data based on the comparing between at least a first area of the pixels in the first image and at least a first area of the pixels in the third image that are non-corresponding according to the first mapping function).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to wherein the comparison result indicates that the third image matches the first image, or the third image does not match the first image based on the method of Riley’s reference. The suggestion/motivation would have been to accurately register images taking into account moving objects (See Riley, ¶ [0004–0006]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Riley with Miller, Ono and Banerjee to obtain the invention as specified in claim 8.
Regarding claim 9, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 7, [wherein if the third image does not match the first image, the method further comprises: calculating a second feature correspondence between the third image and the fourth image; and performing image alignment on the third image and the fourth image based on the second feature correspondence between the third image and the fourth image].
However, Miller, Ono and Banerjee fail(s) to teach wherein if the third image does not match the first image, the method further comprises: calculating a second feature correspondence between the third image and the fourth image; and performing image alignment on the third image and the fourth image based on the second feature correspondence between the third image and the fourth image.
Riley, working in the same field of endeavor, teaches: wherein if the third image does not match the first image, the method further comprises: calculating a second feature correspondence between the third image and the fourth image; and performing image alignment on the third image and the fourth image based on the second feature correspondence between the third image and the fourth image (See Riley, ¶ [0007], The processing element is further configured for generating an alternate mapping between the first and second sets of imagery data based on the comparing between at least a first area of the pixels in the first image and at least a first area of the pixels in the third image that are non-corresponding according to the first mapping function).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference wherein if the third image does not match the first image, the method further comprises: calculating a second feature correspondence between the third image and the fourth image; and performing image alignment on the third image and the fourth image based on the second feature correspondence between the third image and the fourth image based on the method of Riley’s reference. The suggestion/motivation would have been to accurately register images taking into account moving objects (See Riley, ¶ [0004–0006]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Riley with Miller, Ono and Banerjee to obtain the invention as specified in claim 9.
Regarding claim 15, Miller in view of Ono further in view of Banerjee and further in view of Riley teaches the method as claimed in claim 9, further comprising: [storing the second feature correspondence between the third image and the fourth image into a warping map].
However, Miller, Ono fail(s) to teach storing the second feature correspondence between the third image and the fourth image into a warping map.
Banerjee, working in the same field of endeavor, teaches: storing the second feature correspondence between the third image and the fourth image into a warping map (See Banerjee, ¶ [0073], In some configurations (where a region transform is performed, for example), the electronic device 102 (e.g., processor 112, image obtainer 114, warper 118, image stitcher 122, etc.) may determine a mapping between the images (e.g., input images, wide-angle images, normal images, telephoto images, etc.) and the transformed region(s). The content analysis, warp prediction, and/or smoothing may be carried out based on the transformed images (e.g., transformed overlapping regions). The mapping may indicate a correspondence between the transformed images and the original images (and/or the equi-rectangular domain)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference storing the second feature correspondence between the third image and the fourth image into a warping map based on the method of Banerjee’s reference. The suggestion/motivation would have been to image processing to reduce the complexity, reduce the processing resources and increase the quality (See Banerjee, ¶ [0002-0012]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Banerjee with Miller, Ono and Riley to obtain the invention as specified in claim 15.
Claim(s) 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 20140193061 A1, hereafter, "Miller") in view of Ono (US 20190273862 A1, hereafter, "Ono") in further view of Banerjee et al. (US 20190082103 A1, hereafter, “Banerjee”) and further in view of Han et al. (See NPL attached, "An Approach to Fine Coregistration Between Very High Resolution Multispectral Images Based on Registration Noise Distribution", hereafter, "Han").
Regarding claim 11, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 10, [wherein the step of performing feature matching between each pixel in the first image and each pixel in the second image comprises: searching for and recording a position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image; and
generating the first displacement vector according to the position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image].
However, Miller, Ono and Banerjee fail(s) to teach wherein the step of performing feature matching between each pixel in the first image and each pixel in the second image comprises: searching for and recording a position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image; and generating the first displacement vector according to the position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image.
Han, working in the same field of endeavor, teaches: wherein the step of performing feature matching between each pixel in the first image and each pixel in the second image comprises: searching for and recording a position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image (See Han, [Pg. 3, A. CPs Extraction Based on RN Distribution], In a general feature-based matching process for VHR images, CPs are extracted on each image and matched themselves by directly using intensity values of their neighboring pixels or by generating description vectors to estimate similarity); and
generating the first displacement vector according to the position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image (See Han, [Pg. 5, C. Generation of the Deformation Map and Image Warping], Finally, cubic spline interpolation method is applied to the deformation grid to generate the deformation map. The last step consists in the warping of the slave image to the master one according to the obtained deformation map DM).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to wherein the step of performing feature matching between each pixel in the first image and each pixel in the second image comprises: searching for and recording a position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image; and generating the first displacement vector according to the position of the pixel features in the first image corresponding to the pixel features with the highest similarity in the second image based on the method of Han’s reference. The suggestion/motivation would have been to improve the registration accuracy (See Han, [Pg. 9–10, A. Results: Simulated Data Set]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Han with Miller, Ono and Banerjee to obtain the invention as specified in claim 11.
Regarding claim 13, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 10, wherein the step of performing image alignment on the third image and the fourth image based on the first feature correspondence between the first image and the second image (See Miller, ¶ [0040], To correct this problem, the images from the two scans can be aligned to a common registration using the images corresponding to the wavelength band that is shared among the first and second scans (e.g., the image that corresponds to emission from DAPI in the example above). The same shift or image transformation that yields the best alignment in this shared band is applied to all images in the scan, after which the two scans can be combined into an image cube. Note: Examiner is interpreting the best alignment of the shared band as the first and second feature correspondence and the aligning of the other images as aligning a 3rd and 4th image) comprises: [generating a warping function according to the pixel features in both the first image and the second image with the highest discrimination and the highest similarity]; and
inputting the third image or the fourth image into the warping function to perform image alignment between the third image and the fourth image (See Miller, ¶ [0054], More generally, any one or more transformations, including translations, rotations, magnifications, and/or image warping, can be used to register images of the first and second scans to correct for imaging variations between scans).
However, Miller, Ono and Banerjee fail(s) to teach generating a warping function according to the pixel features in both the first image and the second image with the highest discrimination and the highest similarity.
Han, working in the same field of endeavor, teaches: generating a warping function according to the pixel features in both the first image and the second image with the highest discrimination and the highest similarity (See Han, [Pg. 1, I. INTRODUCTION], Most of the coregistration procedures between multitemporal images consist of four steps. First, control points (CPs), which are the objects that correspond to distinctive and representative points of the investigated scene, are extracted from each image independently. [A. CPs Extraction Based on RN Distribution], In a general feature-based matching process for VHR images, CPs are extracted on each image and matched themselves by directly using intensity values of their neighboring pixels or by generating description vectors to estimate similarity Note: The CPs are points represent distinct/unique points in the image); and
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to generating a warping function according to the pixel features in both the first image and the second image with the highest discrimination and the highest similarity based on the method of Han’s reference. The suggestion/motivation would have been to improve the registration accuracy (See Han, [Pg. 9–10, A. Results: Simulated Data Set]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Han with Miller, Ono and Banerjee to obtain the invention as specified in claim 13.
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 20140193061 A1, hereafter, "Miller") in view of Ono (US 20190273862 A1, hereafter, "Ono") further view of Banerjee et al. (US 20190082103 A1, hereafter, “Banerjee”) and further in view of Wei et al. (See NPL attached, "Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation", hereafter, "Wei").
Regarding claim 16, Miller in view of Ono and further in view of Banerjee teaches the method as claimed in claim 1, further comprising: [performing image fusion on the third image and the fourth image after the image alignment to output a fusion image].
However, Miller, Ono and Banerjee fail(s) to teach performing image fusion on the third image and the fourth image after the image alignment to output a fusion image.
Wei, working in the same field of endeavor, teaches: performing image fusion on the third image and the fourth image after the image alignment to output a fusion image (See Wei, [Pg. 1, Introduction], In this paper, we propose to fuse HS and MS images within a constrained optimization framework, by incorporating a sparse regularization using dictionaries learned from the observed images).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to performing image fusion on the third image and the fourth image after the image alignment to output a fusion image based on the method of Wei’s reference. The suggestion/motivation would have been to increase the performance of the fusion and quality, (See Wei, [Pg. 7–8, C. Fusion Quality Metrics] and [Pg. 8, TABLE 1]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Wei with Miller, Ono and Banerjee to obtain the invention as specified in claim 16.
Claim(s) 17–18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Miller (US 20140193061 A1, hereafter, "Miller") in view of Hamaguchi et al. (US 11,099,008 B2, hereafter, “Hamaguchi”) further in view of Ono (US 20190273862 A1, hereafter, "Ono") and further in view of Banerjee et al. (US 20190082103 A1, hereafter, “Banerjee”).
Regarding claim 17, Miller teaches an electronic system, comprising:
[a first sensor, configured to output a first image and a third image according to a first property;
a second sensor, configured to output a second image according to the first property and output a fourth image according to a second property, wherein the second property is different from the first property;
a processor, configured to perform the following steps: receiving the first image from the first sensor;
receiving the second image from the second sensor;
calculating a first feature correspondence between the first image and the second image;
storing the first feature correspondence between the first image and the second image into a warping map;
receiving the third image from the first sensor and the fourth image from the second sensor, wherein the third property and the fourth property are different from each other]; and
[performing image alignment on the third image and the fourth image based on the first feature correspondence stored in the warping map;
wherein the first property and the second property are similar], and the first feature correspondence calculated based on the similar first and second properties is applied to align the third and fourth images whose properties are different from each other (See Miller, ¶ [0038], Thus, for example, the first set of images can include an image corresponding to emission from DAPI (in a first wavelength band) and also, e.g., two or three other images corresponding to emission from the sample in two or three other wavelength bands. Similarly, the second set of images can include an image corresponding to emission from DAPI in the first wavelength band, and also, e.g., two or three other images corresponding to sample emission in two or three other wavelength bands. In certain embodiments, it can be advantageous for the two or three other wavelength bands in the first set of images to be completely distinct from the two or three other wavelength bands in the second set of images. ¶ [0040], the images from the two scans can be aligned to a common registration using the images corresponding to the wavelength band that is shared among the first and second scans (e.g., the image that corresponds to emission from DAPI in the example above). The same shift or image transformation that yields the best alignment in this shared band is applied to all images in the scan. Note: The plurality other images are being interpreted as the third and fourth image and the different spectral bands are being interpreted as the different properties).
However, Miller fail(s) to teach a first sensor, configured to output a first image and a third image according to a first property; a second sensor, configured to output a second image according to the first property and output a fourth image according to a second property, wherein the second property is different from the first property; a processor, configured to perform the following steps: receiving the first image from the first sensor; receiving the second image from the second sensor; calculating a first feature correspondence between the first image and the second image; storing the first feature correspondence between the first image and the second image into a warping map; receiving the third image from the first sensor and the fourth image from the second sensor, wherein the third property and the fourth property are different from each other; performing image alignment on the third image and the fourth image based on the first feature correspondence stored in the warping map; wherein the first property and the second property are similar.
Hamaguchi, working in the same field of endeavor, teaches: a first sensor, configured to output a first image and a third image according to a first property (See Hamaguchi, [Col. 15, ln. 36–40], See Hamaguchi, [Col. 15, ln. 40–44], The first image signal 40 obtained from the first image capture device 120A is referred to as a "first signal-A", and the first image signal obtained from the second image capture device 120B is referred to as a "first image signal-B''. [Col. 15, ln. 44–47], Further, each of the image capture devices 120A and 120B captures the image of at least the subject 140 in the low brightness irradiation state to obtain the second image signal. [Col. 15, ln. 50–54], The second image signal obtained from the first image capture device 120A is referred to as a "second signal-A", and the second image signal obtained from the second image capture device 120B is referred to as a "second image signal-B'. [Col. 16, ln. 25–27], Ambient light is included in any signal among the first image signal-A and first image signal-B, and the second image signal-A and the second image signal-B. Note: Examiner is interpreting the first image as the first signal-A and the third image as the second signal-A. The first property is being interpreted as the ambient light);
a second sensor, configured to output a second image according to the first property and output a fourth image according to a second property, wherein the second property is different from the first property (See Hamaguchi, [Col. 15, ln. 40–44], The first image signal 40 obtained from the first image capture device 120A is referred to as a "first signal-A", and the first image signal obtained from the second image capture device 120B is referred to as a "first image signal-B''. [Col. 15, ln. 44–47], Further, each of the image capture devices 120A and 120B captures the image of at least the subject 140 in the low brightness irradiation state to obtain the second image signal. [Col. 15, ln. 50–54], The second image signal obtained from the first image capture device 120A is referred to as a "second signal-A", and the second image signal obtained from the second image capture device 120B is referred to as a "second image signal-B'. [Col. 16, ln. 25–27], Ambient light is included in any signal among the first image signal-A and first image signal-B, and the second image signal-A and the second image signal-B. Note: Examiner is interpreting the second image as the first signal-B and the fourth image as the second signal-B and the second property as the low brightness irradiation state);
a processor, configured to perform the following steps: receiving the first image from the first sensor; receiving the second image from the second sensor (See Hamaguchi, [Col. 15, ln. 40–44], The first image signal 40 obtained from the first image capture device 120A is referred to as a "first signal-A", and the first image signal obtained from the second image capture device 120B is referred to as a "first image signal-B''. [Col. 15, ln. 50–54], The second image signal obtained from the first image capture device 120A is referred to as a "second signal-A", and the second image signal obtained from the second image capture device 120B is referred to as a "second image signal-B'. Note: Examiner is interpreting the first image as the first signal-A and the second image as the first signal-B);
receiving the third image from the first sensor and the fourth image from the second sensor (See Hamaguchi, [Col. 15, ln. 40–44], The first image signal 40 obtained from the first image capture device 120A is referred to as a "first signal-A", and the first image signal obtained from the second image capture device 120B is referred to as a "first image signal-B''. [Col. 15, ln. 50–54], The second image signal obtained from the first image capture device 120A is referred to as a "second signal-A", and the second image signal obtained from the second image capture device 120B is referred to as a "second image signal-B'. Note: Examiner is interpreting the third image as the second signal-A and the fourth image as the second signal-B).
wherein the first property and the second property are similar (See Hamaguchi, [Col. 15, ln. 40–44], The first image signal 40 obtained from the first image capture device 120A is referred to as a "first signal-A", and the first image signal obtained from the second image capture device 120B is referred to as a "first image signal-B''. [Col. 16, ln. 25–27], Ambient light is included in any signal among the first image signal-A and first image signal-B, and the second image signal-A and the second image signal-B).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to a first sensor, configured to output a first image and a third image according to a first property; a second sensor, configured to output a second image according to the first property and output a fourth image according to a second property, wherein the second property is different from the first property; a processor, configured to perform the following steps: receiving the first image from the first sensor; receiving the second image from the second sensor; receiving the third image from the first sensor and the fourth image from the second sensor; wherein the first property and the second property are similar based on the method of Hamaguchi’s reference. The suggestion/motivation would have been to remove the influence of ambient light to enhance the detection of reference light (See Hamaguchi, [Col. 2, ln. 5–32]).
However, Miller and Hamaguchi fail(s) to teach calculating a first feature correspondence between the first image and the second image; storing the first feature correspondence between the first image and the second image into a warping map; performing image alignment on the third image and the fourth image based on the first feature correspondence stored in the warping map.
Ono, working in the same field of endeavor, teaches: calculating a first feature correspondence between the first image and the second image (See Ono, ¶ [0088], In Step S130 (correspondence point detection step), feature points are detected by the correspondence point detection unit 210E based on a component of a wavelength range of a plurality of image signals corresponding to a plurality of images common among the images, and correspondence points are detected based on the feature points. As described above, for example, the point of the edge or the corner portion is detected as the feature point of the reference image, and the correspondence point can be detected in another image through matching between the images).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference to calculating a first feature correspondence between the first image and the second image based on the method of Ono’s reference. The suggestion/motivation would have been to register multiple images having different wavelengths with high accuracy (See Ono, ¶ [0005–0007]).
However, Miller, Hamaguchi and Ono fail(s) to teach storing the first feature correspondence between the first image and the second image into a warping map; performing image alignment on the third image and the fourth image based on the first feature correspondence stored in the warping map.
Banerjee, working in the same field of endeavor, teaches: storing the first feature correspondence between the first image and the second image into a warping map (See Banerjee, ¶ [0077], The processor 112 may include and/or implement a warper 118. The warper 118 may determine and/or perform warping (e.g., one or more warp vectors) for one or more images. A warp vector may indicate an amount and/or direction of warping for image data (e.g., one or more pixels). For example, warping may spatially warp (e.g., stretch, shift, bend, flex, and/or compress, etc.) image data. Warping may be performed in order to align image data (e.g., align features of the images) between images and/or to reduce or avoid artifacts (e.g., temporal artifacts, parallax artifacts, motion artifacts, structure deformation artifacts, and/or ghosting artifacts, etc.) in a stitched image);
performing image alignment on the third image and the fourth image based on the first feature correspondence stored in the warping map (See Banerjee, ¶ [0073], In some configurations (where a region transform is performed, for example), the electronic device 102 (e.g., processor 112, image obtainer 114, warper 118, image stitcher 122, etc.) may determine a mapping between the images (e.g., input images, wide-angle images, normal images, telephoto images, etc.) and the transformed region(s). The content analysis, warp prediction, and/or smoothing may be carried out based on the transformed images (e.g., transformed overlapping regions). The mapping may indicate a correspondence between the transformed images and the original images (and/or the equi-rectangular domain))
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference storing the first feature correspondence between the first image and the second image into a warping map; performing image alignment on the third image and the fourth image based on the first feature correspondence stored in the warping map based on the method of Banerjee’s reference. The suggestion/motivation would have been to image processing to reduce the complexity, reduce the processing resources and increase the quality (See Banerjee, ¶ [0002-0012]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Hamaguchi, Ono and Banerjee with Miller to obtain the invention as specified in claim 17.
Regarding claim 18, claim 18 is rejected the same as claim 4 and the arguments similar to that presented above for claim 4 are equally applicable to the claim 18, and all of the other limitations similar to claim 4 are not repeated herein, but incorporated by reference.
Regarding claim 20, Miller in view of Hamaguchi further in view of Ono and further in view of Banerjee teaches the electronic system as claimed in claim 17, [wherein the warping map records a first displacement vector of each pixel between the first image and the second image].
However, Miller, Hamaguchi and Ono fail(s) to teach wherein the warping map records a first displacement vector of each pixel between the first image and the second image.
Banerjee, working in the same field of endeavor, teaches: wherein the warping map records a first displacement vector of each pixel between the first image and the second image (See Banerjee, ¶ [0093], For example, the warper 118 may perform dynamic warping based on the warp vector(s). In some approaches, the warper 118 may warp one or more vertex points based on the warp vector(s) (and/or disparity vector(s)). In some configurations, warping the vertex point(s) may produce a warped vertex (e.g., vertices) map).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Miller’s reference wherein the warping map records a first displacement vector of each pixel between the first image and the second image based on the method of Banerjee’s reference. The suggestion/motivation would have been to image processing to reduce the complexity, reduce the processing resources and increase the quality (See Banerjee, ¶ [0002-0012]).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Banerjee with Miller, Hamaguchi and Ono to obtain the invention as specified in claim 20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yao (US 20210216811 A1) teaches the method include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequences; determining a first similarity measurement between image sequences in the set of image sequences; dividing the set of image sequences into one or more subset of image sequences based on a first similarity measurement; and determining, in each subset of image sequences, degrees of correlation between images in one image sequence of the subset of image sequences and images in other image sequences of the subset of image sequences.
Habib (US 20190147567 A1) teaches a non-transitory computer-readable medium encoded with a computer-readable program, which when executed by a processor, will cause a computer to execute a computational method, the computational method including collecting an image data, wherein the collecting the image data comprises collecting a first plurality of RGB images and a second plurality of hyperspectral images. The method further includes orthorectifying the image data to produce an RGB based orthophoto and a partially rectified hyperspectral orthophoto. The method further includes selecting tie features from each of the RGB based orthophoto and the partially rectified hyperspectral orthophoto. Lastly, the method includes registering the features of the partially rectified hyperspectral orthophoto into the tie features of the RGB based orthophoto.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at (571) 272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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