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 .
This communication is responsive to the correspondence filled on 06/06/2025.
Claims 1-20 are presented for examination.
IDS Considerations
The information disclosure statement (IDS) submitted on 11/25/25 and 11/20/2025 is/are being considered by the examiner as the submission is in compliance with the provisions of 37 CFR 1.97.
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 1-5, 8-14 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sridhara (Point Cloud Attribute Compression Via Chroma Subsampling - Published in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Date of Conference: 23-27 May 2022 - DOI: 10.1109/ICASSP43922.2022.9746352), in view of Chou (U.S. Pub. No. 20170347100 A1).
Examiner’s note: Encoding and decoding are done using same opposite algorithm.
Regarding to claim 1 and 16:
1. Sridhara teach a method comprising: (Sridhara page 1 col 1 para 3: Geometry-based point cloud compression (G-PCC) and video based point cloud compression (V-PCC) are the two approaches standarized by MPEG) decoding, by a computing device and from a bitstream, chroma coefficients and luma coefficients; (Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC encoders with chroma subsampling. The luminance signal (Y) will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. page 3 col 1 para 5 In all the experiments, we perform uniform quantization and entropy code the coefficients using the adaptive run-length Golomb-Rice algorithm (RLGR) [19]. For the RAGFT encoder, the block size of 16 was used in all the experiments [5].) determining chroma information (Sridhara page 2 col 2 para 2 2.4. Proposed interpolation: After the sampled points of chroma signal are encoded, we use interpolation to reconstruct chroma signal at the decoder end. Note that the full resolution geometry has to be encoded, since it is needed to represent the full resolution luma information) by inverse transforming, (Sridhara page 2 col 2 para 2 2.4. Proposed interpolation:
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Sridhara treats transforming as black box process and does not name it as inverse transform) at a first level of spatial precision and based on a geometry of a colored point cloud associated with content, the decoded chroma coefficients; (Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC [Geometry-based point cloud compression] encoders with chroma subsampling. The luminance signal (Y) will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled [first level of spatial precision] at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC encoders with chroma subsampling. The luminance signal (Y) will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. page 3 col 1 para 5 In all the experiments, we perform uniform quantization and entropy code the coefficients using the adaptive run-length Golomb-Rice algorithm (RLGR) [19]. For the RAGFT encoder, the block size of 16 was used in all the experiments [5].)
determining luma information (Sridhara page 2 col 2 para 2 2.4. Proposed interpolation: After the sampled points of chroma signal are encoded, we use interpolation to reconstruct chroma signal at the decoder end. Note that the full resolution geometry has to be encoded, since it is needed to represent the full resolution luma information) by inverse transforming, (Sridhara page 2 col 2 para 2 2.4. Proposed interpolation:
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Sridhara treats transforming as black box process and does not name it as inverse transform) at a second level of spatial precision (Sridhara Table 1
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) and based on the geometry of the colored point cloud, the decoded luma coefficients; (Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC [Geometry-based point cloud compression] encoders with chroma subsampling. The luminance signal (Y) will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled [second level of spatial precision before downsampling] at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC encoders with chroma subsampling. The luminance signal (Y) will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. page 3 col 1 para 5 In all the experiments, we perform uniform quantization and entropy code the coefficients using the adaptive run-length Golomb-Rice algorithm (RLGR) [19]. For the RAGFT encoder, the block size of 16 was used in all the experiments [5].) and determining, based on the luma information and the chroma information, color attributes associated with the geometry of the colored point cloud. (Sridhara page 2 Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC encoders with chroma subsampling. The luminance signal (Y) will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud.
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)
Sridhara do not explicitly teach inverse transforming.
However Chou teach inverse transforming. (Chou [0103] The decoder decompresses (810) geometry for the point cloud data, which includes indicators of which points of the point cloud data are occupied. Examples of ways to decompress geometry data are described in section V.C. The decoder decodes (820) quantized transform coefficients. The decoding (820) includes entropy decoding of the quantized transform coefficients (e.g., arithmetic decoding, RLGR decoding), which may be adaptive or non-adaptive. The decoder also inverse quantizes (830) the quantized transform coefficients, which reconstructs the transform coefficients for attributes of occupied points. Section V.E describes examples of decoding and inverse quantization of transform coefficients. The decoder performs (840) the inverse RAHT on the transform coefficients for attributes of occupied points. In doing so, the decoder uses the indicators (geometry data) to determine which of the points of the point cloud data are occupied. Examples of inverse RAHT are described in section V.D. Alternatively, the decoder performs the decoding (720) in some other way (still applying an inverse RAHT to transform coefficients for attributes of occupied points))
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Sridhara, further incorporating Chou in video/camera technology. One would be motivated to do so, to incorporate inverse transforming. This functionality will improve efficiency with predictable results.
Regarding to claim 2, 10 and 17:
2. Sridhara teach the method of claim 1, Sridhara do not explicitly teach wherein an inverse hierarchical transform, for inverse transforming the decoded chroma coefficients and the decoded luma coefficients, comprises: a first level of hierarchy corresponding to the first level of spatial precision; and a second level of hierarchy corresponding to the second level of spatial precision.
However Chou teach wherein an inverse hierarchical transform, for inverse transforming the decoded chroma coefficients and the decoded luma coefficients, comprises: a first level of hierarchy corresponding to the first level of spatial precision; and a second level of hierarchy corresponding to the second level of spatial precision. (Chou Fig. 9, 11-14 and 18, para [0124-0125] The RAHT and inverse RAHT are “hierarchical” in that results from applying the transform (or inverse transform) at one level of hierarchically organized data are selectively passed to another level of the hierarchically organized data for successive application of the transform (or inverse transform).)
Regarding to claim 3:
3. Sridhara teach the method of claim 1, wherein the second level of spatial precision is: a refinement of the first level of spatial precision; or a reduced level of precision than the first level of spatial precision. (Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC [Geometry-based point cloud compression] encoders with chroma subsampling [first level resolution]. The luminance signal (Y) [second level finer resolution] will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled [first level of spatial precision] at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud)
Regarding to claim 4:
4. Sridhara teach the method of claim 3, Sridhara do not explicitly teach wherein the second level of spatial precision corresponds to a voxel precision of the geometry of the colored point cloud, and wherein the first level of spatial precision corresponds to 2x2x2 cubes of voxels.
However Chou teach wherein the second level of spatial precision corresponds to a voxel precision of the geometry of the colored point cloud, and wherein the first level of spatial precision corresponds to 2x2x2 cubes of voxels. (Chou Fig. 9 and Fig. 10 shows 2X2X2 cube [0108] At a given level 1, a group of points of the point cloud data can be represented as a W×W×W unit cube for a voxel. At level l+1, the W×W×W unit cube is partitioned into eight smaller sub-cubes for voxels with dimensions W/2×W/2×W/2 by splitting each dimension by half. Similarly, at level l+2, each W/2×W/2×W/2 sub-cube can be partitioned into eight smaller sub-cubes for voxels with dimensions W/4×W/4×W/4. If all W/2×W/2×W/2 sub-cubes are split, there are 64 W/4×W/4×W/4 sub-cubes for voxels. The partitioning process can be repeated for L levels, yielding 2.sup.3L voxels each having dimensions of 2.sup.−LW×2.sup.−LW×2.sup.−LW)
Regarding to claim 5:
5. Sridhara teach the method of claim 3, Sridhara do not explicitly teach wherein the second level of spatial precision corresponds to a maximum depth of a tree representing the geometry of the colored point cloud, and wherein the first level of spatial precision corresponds to a depth preceding the maximum depth of the tree.
However Chou teach wherein the second level of spatial precision corresponds to a maximum depth of a tree representing the geometry of the colored point cloud, and wherein the first level of spatial precision corresponds to a depth preceding the maximum depth of the tree. (Chou Fig. 9 and Fig. 10 shows 2X2X2 cube. As per combined teaching of claim 3 and Chou [0108] teach maximum depth as W/4 and preceding depth as W/2. Obviously this structure will work for chroma [first level of spatial precision])
Regarding to claim 8:
8. Sridhara teach the method of claim 1, wherein the determining the color attributes associated with the geometry of the colored point cloud comprises: resampling the chroma information to the second level of spatial precision; and determining the color attributes based on the resampled chroma information and the luma information. (Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC [Geometry-based point cloud compression] encoders with chroma subsampling [first level resolution]. The luminance signal (Y) [second level finer resolution] will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled [first level of spatial precision] at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. Sridhara page 2 col 2 para 2 2.4. Proposed interpolation:
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This teach interpolating low resolution chroma to the full luma resolution)
Regarding to claim 9:
9. Sridhara teach the method of claim 1, wherein the determining the color attributes associated with the geometry of the colored point cloud comprises projecting the color attributes to points of the geometry of the colored point cloud. (Sridhara Fig. 1 page 1 col 1 para 2-4 Attributes can be color, intensity or surface normals. In order to effectively store and transmit point clouds, both geometry and attributes have to be compressed. In this work, we focus on compression of point cloud color attributes.
Geometry-based point cloud compression (G-PCC) and video based point cloud compression (V-PCC) are the two approaches standarized by MPEG. G-PCC methods exploit geometry information to encode the associated attributes, while V-PCC is a projection based method wherein 3D points are projected onto a 2D image so
that the existing image/video encoding methods can be used [2]. The region adaptive hierarchical transform (RAHT) [3], a multiresolution orthogonal transform, has been evaluated as a G-PCC codec by MPEG [1, 2]. In addition to RAHT, various alternative
transforms have been proposed for attribute compression [4, 5, 6, 7]. For color attribute compression, the RGB values are first transformed to a YUV space)
Regarding to claim 11:
11. Sridhara teach the method of claim 10, further comprising: decoding, from a bitstream, the chroma coefficients and the luma coefficients; (Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC encoders with chroma subsampling. The luminance signal (Y) will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. page 3 col 1 para 5 In all the experiments, we perform uniform quantization and entropy code the coefficients using the adaptive run-length Golomb-Rice algorithm (RLGR) [19]. For the RAGFT encoder, the block size of 16 was used in all the experiments [5])
and determining, based on the luma information and the chroma information, color attributes associated with the geometry of the colored point cloud. (Sridhara Fig. 1 page 1 col 1 para 2-4 Attributes can be color, intensity or surface normals. In order to effectively store and transmit point clouds, both geometry and attributes have to be compressed. In this work, we focus on compression of point cloud color attributes.
Geometry-based point cloud compression (G-PCC) and video based point cloud compression (V-PCC) are the two approaches standarized by MPEG. G-PCC methods exploit geometry information to encode the associated attributes, while V-PCC is a projection based method wherein 3D points are projected onto a 2D image so
that the existing image/video encoding methods can be used [2]. The region adaptive hierarchical transform (RAHT) [3], a multiresolution orthogonal transform, has been evaluated as a G-PCC codec by MPEG [1, 2]. In addition to RAHT, various alternative
transforms have been proposed for attribute compression [4, 5, 6, 7]. For color attribute compression, the RGB values are first transformed to a YUV space)
Regarding to claim 12:
12. Sridhara teach the method of claim 10, wherein determining the luma information defined at the second level of spatial precision further comprises: (Sridhara Fig. 1 Point cloud attribute compression pipeline using G-PCC [Geometry-based point cloud compression] encoders with chroma subsampling [first level resolution]. The luminance signal (Y) [second level finer resolution] will be directly passed to the G-PCC encoder, whereas chrominance signals (U and V) will be downsampled [first level of spatial precision] at a given sampling rate before encoding. At the decoder side, we interpolate the downsampled chrominance signal values and reconstruct the attributes of the full resolution point cloud. Sridhara page 2 col 2 para 2 2.4. Proposed interpolation:
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This teach interpolating low resolution chroma to the full luma resolution)
Sridhara do not explicitly teach using a first level inverse transform to inverse transform second luma coefficients of the luma coefficients into intermediate luma coefficients; and using a first to second level inverse transform to inverse transform first luma coefficients of the luma coefficients and the intermediate luma coefficients.
However Chou teach using a first level inverse transform to inverse transform (Chou Fig. 13 [0103] The decoder decompresses (810) geometry for the point cloud data, which includes indicators of which points of the point cloud data are occupied. Examples of ways to decompress geometry data are described in section V.C. The decoder decodes (820) quantized transform coefficients. The decoding (820) includes entropy decoding of the quantized transform coefficients (e.g., arithmetic decoding, RLGR decoding), which may be adaptive or non-adaptive. The decoder also inverse quantizes (830) the quantized transform coefficients, which reconstructs the transform coefficients for attributes of occupied points. Section V.E describes examples of decoding and inverse quantization of transform coefficients. The decoder performs (840) the inverse RAHT on the transform coefficients for attributes of occupied points. In doing so, the decoder uses the indicators (geometry data) to determine which of the points of the point cloud data are occupied. Examples of inverse RAHT are described in section V.D. Alternatively, the decoder performs the decoding (720) in some other way (still applying an inverse RAHT to transform coefficients for attributes of occupied points)) second luma coefficients of the luma coefficients into intermediate luma coefficients; and using a first to second level inverse transform to inverse transform first luma coefficients of the luma coefficients and the intermediate luma coefficients. (Chou Fig. 13 [0189] In this way, the encoder applies the transform to successively lower levels of the hierarchy of point cloud data. Suppose the hierarchy includes a bottom level, zero or more intermediate levels, and a top level. For each of the top level and zero or more intermediate levels (as the given level), the encoder can perform the transforming and the reserving (of lowpass coefficients) for each of the group(s) of points at the given level. This provides at least one reserved value for the next lower level. It also provides, as output, any of the transform coefficients that are associated with the given level (that is, highpass coefficients). At the bottom level of the hierarchy, for a group of points at the bottom level, the encoder can transform any attributes of occupied points of the group at the bottom level. This produces one or more of the transform coefficients (that is, lowpass coefficient and highpass coefficient(s) for the bottom level).)
Regarding to claim 13:
13. Sridhara teach the method of claim 12, Sridhara do not explicitly teach wherein the first to second level transform is an inverse region adaptive hierarchical transform (RAHT) transformation, and wherein the first level transform is an inverse RAHT iterative transformation.
However Chou teach wherein the first to second level transform is an inverse region adaptive hierarchical transform (RAHT) transformation, and wherein the first level transform is an inverse RAHT iterative transformation. (Chou Fig. 13 [0103] The decoder decompresses (810) geometry for the point cloud data, which includes indicators of which points of the point cloud data are occupied. Examples of ways to decompress geometry data are described in section V.C. The decoder decodes (820) quantized transform coefficients. The decoding (820) includes entropy decoding of the quantized transform coefficients (e.g., arithmetic decoding, RLGR decoding), which may be adaptive or non-adaptive. The decoder also inverse quantizes (830) the quantized transform coefficients, which reconstructs the transform coefficients for attributes of occupied points. Section V.E describes examples of decoding and inverse quantization of transform coefficients. The decoder performs (840) the inverse RAHT on the transform coefficients for attributes of occupied points. In doing so, the decoder uses the indicators (geometry data) to determine which of the points of the point cloud data are occupied. Examples of inverse RAHT are described in section V.D. Alternatively, the decoder performs the decoding (720) in some other way (still applying an inverse RAHT to transform coefficients for attributes of occupied points).)
Regarding to claim 14:
14. Sridhara teach the method of claim 13, Sridhara do not explicitly teach wherein the inverse RAHT iterative transformation comprises a same quantity of iterations as a quantity of depths in a tree representing the geometry of the colored point cloud, and wherein the tree is an occupancy tree representing occupancy of nodes of the tree.
However Chou teach wherein the inverse RAHT iterative transformation comprises a same quantity of iterations as a quantity of depths in a tree representing the geometry of the colored point cloud, and wherein the tree is an occupancy tree representing occupancy of nodes of the tree. (Chou Fig. 9 [0128] The inverse RAHT proceeds in the opposite direction. The inverse RAHT uses the attributes of a level l of a hierarchy of point cloud data to predict the attributes of a higher level l+1 of the hierarchy. The inverse RAHT reconstructs the attributes of occupied points (voxels) in a given level l of the hierarchy and passes the reconstructed attributes to the next higher level l+1 of the hierarchy. To reconstruct attributes at the given level 1, the inverse RAHT uses highpass coefficients for the given level l along with lowpass coefficients (for the bottom level) or attributes passed from the lower level l−1 of the hierarchy (for another level). At the next level l+1 of the hierarchy, the inverse RAHT repeats the process of reconstructing attributes (using highpass coefficients and passed attributes) and passing reconstructed attributes to the next higher level l+2. In this way, for example, the inverse RAHT can follow an octtree representation from level l to level L, splitting the root for the entire 3D space into successively smaller voxels until the inverse RAHT reaches the smallest voxels at level L.)
Regarding to claim 18:
18. Sridhara teach the method of claim 17, Sridhara do not explicitly teach wherein the second level of hierarchy comprises a second to first level transform and a first level transform.
However Chou teach wherein the second level of hierarchy comprises a second to first level transform and a first level transform. (Chou Fig. 11, 12, 19, 20)
Regarding to claim 19:
19. Sridhara teach the method of claim 16, Sridhara do not explicitly teach wherein the chroma information represents two chroma components of a three-component color space, and wherein the luma information represents one luma component of the three-component color space.
However Chou teach wherein the chroma information represents two chroma components of a three-component color space, and wherein the luma information represents one luma component of the three-component color space. (Chou [0049] the attribute(s) for an occupied point can include: (1) one or more sample values each defining, at least in part, a color associated with the occupied point (e.g., YUV sample values, RGB sample values, or sample values in some other color space); (2) an opacity value defining, at least in part, an opacity associated with the occupied point; (3) a specularity value defining, at least in part, a specularity coefficient associated with the occupied point; (4) one or more surface normal values defining, at least in part, direction of a flat surface associated with the occupied point; (5) a light field defining, at least in part, a set of light rays passing through or reflected from the occupied point; and/or (6) a motion vector defining, at least in part, motion associated with the occupied point. Alternatively, attribute(s) for an occupied point include other and/or additional types of information. During later stages of encoding with the encoder (302) of FIG. 3b, the transformed value(s) for an occupied point can also include: (7) one or more sample values each defining, at least in part, a residual associated with the occupied point. [0050] An arriving point cloud frame is stored in the input buffer (310). The input buffer (310) can include multiple frame storage areas. After one or more of the frames have been stored in input buffer (310), a selector (not shown) selects an individual point cloud frame to encode as the current point cloud frame. The order in which frames are selected by the selector for input to the encoder (301, 302) may differ from the order in which the frames are produced by the capture components, e.g., the encoding of some frames may be delayed in order, so as to allow some later frames to be encoded first and to thus facilitate temporally backward prediction. Before the encoder (301, 302), the system can include a pre-processor (not shown) that performs pre-processing (e.g., filtering) of the current point cloud frame before encoding. The pre-processing can include color space conversion into primary (e.g., luma) and secondary (e.g., chroma differences toward red and toward blue) components, resampling, and/or other filtering)
Claims 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sridhara (Point Cloud Attribute Compression Via Chroma Subsampling - Published in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Date of Conference: 23-27 May 2022 - DOI: 10.1109/ICASSP43922.2022.9746352), in view of Chou (U.S. Pub. No. 20170347100 A1), further in view of Ray (U.S. Pub. No. 20210385303 A1).
Regarding to claim 20:
20. Sridhara teach the method of claim 16, Sridhara do not explicitly teach wherein the encoding further comprises: encoding a syntax element that signals activation of the transforming the chroma information and the transforming the luma information.
However Ray teach wherein the encoding further comprises: encoding a syntax element that signals activation of the transforming the chroma information and the transforming the luma information. (Ray [0027] In some instances, the residual values for components of an attribute can be zero. For instance, there may be a string of reflectance attributes for points in a consecutive order for which the residual values are zero. In this case, rather than encoding and signaling each zero residual value, the G-PCC encoder may signal and the G-PCC decoder may receive a syntax element (e.g., “zerorun” syntax element) that indicates a number of points in the order for which the residual values are zero. [0028] However, for multicomponent attributes, there may be a situation where the residual values for some of the components are zero, but the residual value for another component is non-zero. For example, for the color attribute, the color components may be a luma component and two chroma components. As one example, the residual values for the chroma components may be zero, but the residual value for the luma component may be non-zero. In this case, the zerorun syntax element would indicate that there is no zerorun (i.e., there is a non-zero residual value for at least one component of the attribute))
The motivation for combining Sridhara and Chou as set forth in claim 1 is equally applicable to claim 20. It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Sridhara, further incorporating Chou and Ray in video/camera technology. One would be motivated to do so, to incorporate the encoding further comprises: encoding a syntax element that signals activation of the transforming the chroma information and the transforming the luma information. This functionality will improve user experience with predictable results.
Allowable subject matter
Regarding to claim 6-7 and 15:
Claims 6-7 and 15 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims because the limitations of these dependent claims are not obvious from the prior art search when all the limitations of independent and intervening claims are taken into account.
Closely related prior art
Examiner notes teaching of U.S. Pub. No. 10694210 B2 is/are pertinent to the independent claim(s), however is not used because dependent claims are covered by references used in rejection.
Conclusion
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/NASIM N NIRJHAR/Primary Examiner, Art Unit 2896
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