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 .
Applicant(s) Response to Official Action
The response filed on 07/15/2026 has been entered and made of record.
Response to Arguments/Amendments
Presented arguments have been fully considered, but are rendered moot in view of the new ground(s) of rejection necessitated by amendment(s) initiated by the applicant(s).
Claim Objections
Claim 21 is objected to because of the following informalities: The claim recites: “… a first a first neural network layer …” (emphasis added). Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4-5, 7-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation “… wherein a first integer p and a second integer q are associated with the first neural network layer … there are defined p and q for the respective layer so that the number of output channels Cout of the respective layer … and p and q …” (emphasis added to accentuate insufficient antecedent basis).
The variables p and q are explicitly defined as exact integers, that correspond specifically to the first layer. The final sub-net clause reuses these exact variables generically for any respective layer, creating an antecedent basis conflict. They must be newly defined in the final clause as “respective” variables.
For the purposes of examination, limitation is interpreted as the following:
“… there are defined a respective integer p and a respective integer q for the respective layer so that the number of output channels Cout of the respective layer equals p* Cin/q, the number of input channels Cin of the respective layer is a multiple of q, and the number of input channels Cin, the number of output channels Cout, and the respective integer p and the respective integer q of the respective layer are integers.”.
Claim 14 is analogous to claim 1 and is interpreted similarly.
Claims 4 and 5 depend on claim 3, however claim 3 is canceled.
For the purposes of examination, claims 4 and 5 are interpreted to depend on claim 1.
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 1-2, 4-5, 7-8, 11-12, 14, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Howard et al., hereinafter referred to as Howard (“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”; Pub.: April 2017; Pgs. 1-9; URL: https://arxiv.org/pdf/1704.04861 [already of record]) in view of Ronneberger et al., hereinafter referred to as Ronneberger (U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI); Pub.: May. 2015; Pgs. 8; URL: https://arxiv.org/abs/1505.04597 [already of record]).
As per claim 1, Howard discloses a neural network (Howard: Abstract.), comprising:
a first neural network layer configured to obtain a first number of channels Cin as input, and output a second number of channels Cout, wherein the first number of channels is different from the second number of channels (Howard: Pg. 4, Section 3.3 discloses “For a given layer and width multiplier α, the number of input channels M [claimed a first neural network layer configured to obtain a first number of channels Cin as input] becomes αM and the number of output channels N [claimed and output a second number of channels Cout] becomes αN,” which inherently alters the channel depth [claimed wherein the first number of channels is different from the second number of channels].); and
a second neural network layer configured to obtain the second number of channels Cout as input (Howard: Pg. 4, Table 1 discloses a sequential architecture of “Conv” layers where the output channels of one layer feed directly into the subsequent layer [claimed and a second neural network layer configured to obtain the second number of channels Cout as input].),
wherein a first integer p and a second integer q are associated with the first neural network layer, and the second number of channels satisfies Cout = p* Cin/q, wherein Cin is a multiple of q, and Cin, Cout, p, and q are integers (Howard: Pg. 4, Section 3.3 discloses applying a fractional width multiplier of 0.75, which calculates output channels as N = 0.75 × M, mathematically equating to N = 3 × M / 4 [claimed Cout=p*Cin/q]. Further, Howard: Pg. 4, Table 1 discloses base input channels M as 32, 64, and 128, which are explicitly divisible by 4 [claimed wherein Cin is a multiple of q], ensuring the resulting structural channel capacities are strictly whole numbers [claimed Cin, Cout, p, and q are integers].), and
at least one neural network sub-net comprising consecutive neural network layers including the first neural network layer, (Howard: Table 1, Pg. 4 disclose a neural network sub-net (the body architecture) formed by consecutive layers including the first layer, where all input channels Cin, output channels Cout, and associated scaling integers p and q are integers.).
However, Howard does not explicitly disclose “… wherein for each one of the consecutive neural network layers for which a number of output channels of a respective layer differs from a number of input channels of the respective layer there are defined p and q for the respective layer so that the number of output channels Cout of the respective layer equals p* Cin/q, the number of input channels Cin of the respective layer is a multiple of q …”.
Further, Ronneberger is in the same field of endeavor and teaches wherein for each one of the consecutive neural network layers for which a number of output channels of a respective layer differs from a number of input channels of the respective layer there are defined p and q for the respective layer so that the number of output channels Cout of the respective layer equals p* Cin/q, the number of input channels Cin of the respective layer is a multiple of q (Ronneberger: Section 2, Pg. 4 discloses “Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels” and Ronneberger: Fig. 1, Pg. 3 discloses “Fig. 1. U-net architecture … The number of channels is denoted on top of the box.” [i.e., for each consecutive layer in the expansive path that changes channels 1024 [Wingdings font/0xE0] 512 [Wingdings font/0xE0] 256 [Wingdings font/0xE0] 128 [Wingdings font/0xE0] 64, p and q are defined as p = 1 and q = 2 (halving). The input channel count Cin of each respective layer (1024, 512, 256, 128) is an even number and explicitly a multiple of q=2, yielding integer output channels Cout = 1*Cin/2.].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard and Ronneberger before him or her, to modify the neural network architecture of Howard to include the proportional channel-halving topology feature as described in Ronneberger. The motivation for doing so would have been to improve the propagation of context information to higher resolution layers and enable precise localization in deep neural networks by providing a configuration that expands the neural network architecture.
As per claim 2, Howard discloses the neural network of claim 1, wherein q equals to 2 (Howard: Pg. 4, Section 3.3 discloses width multipliers of “1, 0.75, 0.5 and 0.25,” where the multiplier 0.5 applies a fractional scaling ratio of 1/2 [claimed wherein q equals to 2].).
As per claim 4, Howard-Ronneberger disclose the neural network of claim 3, wherein the third number of channels is smaller than the second number of channels, and the second number of channels is smaller than the first number of channels;
or the third number of channels is smaller than the second number of channels, and the second number of channels is larger than the first number of channels;
or the third number of channels is larger than the second number of channels, and the second number of channels is larger than the first number of channels (Ronneberger: Pg. 2 & Fig. 1 visually disclose an expansive path that progressively steps down feature channels in a continuous reduction sequence of 1024 [Wingdings font/0xE0] 512 [Wingdings font/0xE0] 256 [Wingdings font/0xE0] 128 [Wingdings font/0xE0] 64 [claimed the third number of channels is smaller than the second number of channels, and the second number of channels is smaller than the first number of channels].).
As per claim 5, Howard-Ronneberger disclose the neural network of claim 3, wherein the neural network does not allow that the third number of channels is larger than the second number of channels, and wherein the second number of channels is smaller than the first number of channels (Ronneberger: Pg. 3; Fig. 1; Section 1 disclose a strict “u-shaped architecture” consisting of a “contracting path” that monotonically doubles the feature channels and an “expansive path” that monotonically halves them, axiomatically prohibiting an internal topology that compresses and then immediately expands channels across sequential layers [claimed wherein the neural network does not allow that the third number of channels is larger than the second number of channels, and wherein the second number of channels is smaller than the first number of channels].).
As per claim 7, Howard discloses the neural network of claim 1, wherein the first number of channels Cin and the second number of channels Cout are multiples of a chunk size (Howard: Pg. 4, Table 1 discloses base network channel dimensions of 32, 64, 128, 256, and 512, all of which are cleanly divisible by computing power-of-two data chunks [claimed multiples of a chunk size].).
As per claim 8, Howard discloses the neural network of claim 7, wherein the chunk size is 16 or 32 (Howard: Pg. 4, Table 1 discloses channel dimensions such as 32, 64, and 128, which are mathematical multiples of 16 and 32 [claimed wherein the chunk size is 16 or 32].).
As per claim 11, Howard discloses the neural network of claim 1, wherein
for the at least one neural network sub-net at least one of the following conditions is fulfilled for the consecutive neural network layers for which the number of channels changes from one neural network layer to another:
a) each of the consecutive neural network layers is configured to output only a number of channels that is smaller or larger than a number of channels it receives from a previous one of the consecutive neural network layers in processing order (Howard: Pg. 4, Table 1 discloses sequential layers progressing with channel dimensions of 32, 64, 128, 256, and 512, where each subsequent layer outputs a larger number of channels than it receives [claimed configured to output only a number of channels that is smaller or larger than a number of channels it receives from a previous one of the consecutive neural network layers in processing order].);
b) the consecutive neural network layers consist of a first sub-set of consecutive neural network layers followed in processing order by a second sub-set of consecutive neural network layers, and wherein
i) each of the consecutive neural network layers of the first sub-set is configured to output only a number of channels that is larger than a number of channels it receives from a previous one of the consecutive neural network layers of the first sub-set in processing order, and
ii) each of the consecutive neural network layers of the second sub-set is configured to output only a number of channels that is smaller than a number of channels it receives from a previous one of the consecutive neural network layers of the second sub-set in processing order; and
c) the consecutive neural network layers consist of a first sub-set of consecutive neural network layers followed in processing order by a second sub-set of consecutive neural network layers, and wherein
i) each of the consecutive neural network layers of the first sub-set is configured to output only a number of channels that is smaller than a number of channels it receives from a previous one of the consecutive neural network layers of the first sub-set in processing order,
ii) none of the consecutive neural network layers of the second sub-set is configured to output a number of channels that is larger than a number of channels it receives from a previous one of the consecutive neural network layers of the second sub-set in processing order, and
iii) a first one of the consecutive neural network layers of the second sub-set in processing order is configured to only output a number of channels that is smaller than a number of channels it receives from a last one of the consecutive neural network layers of the first sub-set in processing order.
As per claim 12, Howard discloses the neural network of claim 11, wherein each of the consecutive neural network layers is configured to output a number of channels that is a multiple of 16 or 32 (Howard: Table 1, Pg. 4 discloses sequential channel outputs of 32, 64, 128, 256, and 512, which are all mathematical multiples of 16 and 32 [claimed configured to output a number of channels that is a multiple of 16 or 32].).
As per claim 14, Howard discloses a computer-implemented method of operating a neural network with a variable number of channels of neural network layers, comprising:
obtaining, by a first neural network layer, a first number of channels Cin, as input, outputting, by the first neural network layer, a second number of channels Cout, wherein the first number of channels is different from the second number of channels (Howard: Pg. 4, Section 3.3 discloses “For a given layer and width multiplier α, the number of input channels M [claimed a first neural network layer configured to obtain a first number of channels Cin as input] becomes αM and the number of output channels N [claimed and output a second number of channels Cout] becomes αN,” which inherently alters the channel depth [claimed wherein the first number of channels is different from the second number of channels].), Cout = p*Cin/q, Cin is a multiple of q, and Cin, Cout, p, and q are integers (Howard: Pg. 4, Section 3.3 discloses applying a fractional width multiplier of 0.75, which calculates output channels as N = 0.75 × M, mathematically equating to N = 3 × M / 4 [claimed Cout=p*Cin/q]. Further, Howard: Pg. 4, Table 1 discloses base input channels M as 32, 64, and 128, which are explicitly divisible by 4 [claimed wherein Cin is a multiple of q], ensuring the resulting structural channel capacities are strictly whole numbers [claimed Cin, Cout, p, and q are integers].); and
obtaining, by a second neural network layer, the second number of channels as input (Howard: Pg. 4, Table 1 discloses a sequential architecture of “Conv” layers where the output channels of one layer feed directly into the subsequent layer [claimed obtaining, by a second neural network layer, the second number of channels as input].).
As per claim 17, Howard discloses a computer program product comprising a program code stored on a non- transitory medium, wherein the program, when executed on one or more processors, performs the computer-implemented method according to claim 14 (Howard: Pg. 1, Section 1, disclose software models running on mobile devices (e.g., “small, low latency models that easily match the design requirements for mobile and embedded vision applications”).).
Claims 9-10, 13, 15-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Howard in view of Ronneberger in further view of Minnen et al., hereinafter referred to as Minnen (Joint Autoregressive and Hierarchical Priors for Learned Image Compression – Advances in Neural Information Processing Systems, 31; Pub.: Sep. 2018; Pgs. 22; URL: https://arxiv.org/pdf/1809.02736).
As per claim 9, Howard-Ronneberger disclose the neural network of claim 1 (Howard: Abstract.),
However, Howard-Ronneberger do not explicitly disclose “… wherein the first neural network layer or a sub-net of the neural network is one of a hyper scale decoder sub-net and a prediction fusion sub-net.”
Further, Minnen is in the same field of endeavor and teaches wherein the first neural network layer or a sub-net of the neural network is one of a hyper scale decoder sub-net and a prediction fusion sub-net (Minnen: Fig. 1, Pg. 2, Section 2 disclose an image compression framework comprising a “Hyper-Decoder” that predicts the scale of the latent representation [claimed hyper scale decoder sub-net] and an “Entropy Parameters Network” that combines the predictions of the context model and hyper-decoder [claimed prediction fusion sub-net].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the mobile network architecture of Howard-Ronneberger to include the hyper scale decoder and prediction fusion sub-nets feature as described in Minnen. The motivation for doing so would have been to improve efficient data transmission and performance by providing a configuration that effectively minimizes the cross entropy of the data.
As per claim 10, Howard-Ronneberger disclose the neural network of claim 1 (Howard: Abstract.),
However, Howard-Ronneberger do not explicitly disclose “… wherein the first neural network layer comprises data paths for at least one of a primary component and a secondary component.”
Further, Minnen is in the same field of endeavor and teaches wherein the first neural network layer comprises data paths for at least one of a primary component and a secondary component (Minnen: Pg. 4, Section 2.1 discloses “The final layer of the decoder must have three channels to generate RGB images” [claimed data paths for at least one of a primary component and a secondary component].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the neural network architecture of Howard-Ronneberger to include the data paths for at least one of a primary component and a secondary component feature as described in Minnen. The motivation for doing so would have been to improve compression performance by providing a configuration that accurately processes full color images.
As per claim 13, Howard-Ronneberger disclose the neural network of claim 11 (Howard: Abstract.),
However, Howard-Ronneberger do not explicitly disclose “… wherein the at least one neural network sub-net is one of a hyper scale decoder sub-net and a prediction fusion sub-net.”
Further, Minnen is in the same field of endeavor and teaches wherein the at least one neural network sub-net is one of a hyper scale decoder sub-net and a prediction fusion sub-net (Minnen: Fig. 1, Pg. 2, Section 2 disclose sub-networks structured as “the Hyper Decoder blocks” and the “Entropy Parameters network” arranged consecutively [claimed wherein the at least one neural network sub-net is one of a hyper scale decoder sub-net and a prediction fusion sub-net].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the mobile network architecture of Howard-Ronneberger to include the hyper scale decoder and prediction fusion sub-nets feature as described in Minnen. The motivation for doing so would have been to improve efficient data transmission and performance by providing a configuration that effectively minimizes the cross entropy of the data.
As per claim 15, Howard-Ronneberger disclose a method (Howard: Abstract.).
However, Howard-Ronneberger do not explicitly disclose “… a method of encoding data …”.
Further, Minnen is in the same field of endeavor and teaches a method of encoding data (Minnen: Pg. 2, Section 2 discloses a learned image compression framework that performs encoding of image data into a compressed representation [claimed method of encoding data].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the neural network architecture of Howard-Ronneberger to include the method of encoding data feature as described in Minnen. The motivation for doing so would have been to improve compression performance while maintaining end-to-end optimization by providing a configuration that reduces computational complexity.
As per claim 16, Howard-Ronneberger disclose a method (Howard: Abstract.).
However, Howard-Ronneberger do not explicitly disclose “… a method of decoding encoded data …”.
Further, Minnen is in the same field of endeavor and teaches a method of decoding encoded data (Minnen: Pg. 2, Section 2 discloses decoding the compressed representation back into a reconstructed image [claimed method of decoding encoded data].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the neural network architecture of Howard-Ronneberger to include the decoding encoded data feature as described in Minnen. The motivation for doing so would have been to improve end-to-end communication by providing a configuration that reduces computational complexity.
As per claim 18, Howard-Ronneberger disclose an apparatus for (Howard: Abstract.),
However, Howard-Ronneberger do not explicitly disclose “… decoding at least a portion of an encoded image, comprising processing circuitry configured for providing an entropy model … processing a bitstream using a neural network based on the provided entropy model to obtain a latent tensor representing a component of the image, and processing the latent tensor to obtain a tensor representing the component of the image.”
Further, Minnen is in the same field of endeavor and teaches decoding at least a portion of an encoded image, comprising processing circuitry configured for providing an entropy model … processing a bitstream using a neural network based on the provided entropy model to obtain a latent tensor representing a component of the image, and processing the latent tensor to obtain a tensor representing the component of the image (Minnen: Fig. 1; Pgs. 1-3, Sections 1-2 disclose an image decoder [claimed apparatus for decoding at least a portion of an encoded image] utilizing a conditional “entropy model” [claimed configured for providing an entropy model] to decode a compressed bitstream into a spatial “latent representation ŷ” [claimed processing a bitstream using a neural network based on the provided entropy model to obtain a latent tensor representing a component of the image], and passing ŷ through a synthesis transform network to generate the “reconstructed image x̂” [claimed processing the latent tensor to obtain a tensor representing the component of the image].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the mobile vision apparatus of Howard-Ronneberger to include the entropy model obtaining latent tensor feature as described in Minnen. The motivation for doing so would have been to improve compression performance while maintaining end-to-end optimization by providing a configuration that reduces bandwidth requirements on the host device.
As per claim 19, Howard-Ronneberger disclose an apparatus for (Howard: Abstract.).
However, Howard-Ronneberger do not explicitly disclose “… encoding at least a portion of an image …”.
Further, Minnen is in the same field of endeavor and teaches encoding at least a portion of an image (Minnen: Fig. 1; Pgs. 1-3, Sections 1-2 disclose an end-to-end framework comprising an encoder [claimed encoding at least a portion of an image] utilizing neural networks.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the apparatus of Howard-Ronneberger to include the encoding feature as described in Minnen. The motivation for doing so would have been to improve image compression by providing a configuration that expands the utility of the hardware architecture.
As per claim 20, Howard-Ronneberger disclose an apparatus for (Howard: Abstract.).
However, Howard-Ronneberger do not explicitly disclose “… decoding at least a portion of an encoded image.”
Further, Minnen is in the same field of endeavor and teaches decoding at least a portion of an encoded image (Minnen: Fig. 1; Pgs. 1-3, Section 1 & Pg. 2, Section 2 discloses “components that are executed by the receiver to recover an image from a compressed bitstream” [claimed apparatus for decoding at least a portion of an encoded image] utilizing a deep learning “decoder”.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Howard-Ronneberger and Minnen before him or her, to modify the apparatus of Howard-Ronneberger to include the decoding at least a portion of an encoded image feature as described in Minnen. The motivation for doing so would have been to improve efficient decompression of images by providing a configuration that expands the utility of the hardware architecture.
Allowable Subject Matter
Prior art for was found for the claims as follows:
Re. Claim 21,
Howard et al., (“MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”; Pub.: April 2017; Pgs. 1-9; URL: https://arxiv.org/pdf/1704.04861.) disclose the following limitations:
A neural network (Howard: Abstract.), comprising:
a first a first neural network layer configured to obtain a first number of channels Cin as input, and output a second number of channels Cout, wherein the first number of channels is different from the second number of channels (Howard: Pg. 4, Section 3.3 discloses “For a given layer and width multiplier α, the number of input channels M [claimed a first neural network layer configured to obtain a first number of channels Cin as input] becomes αM and the number of output channels N [claimed and output a second number of channels Cout] becomes αN,” which inherently alters the channel depth [claimed wherein the first number of channels is different from the second number of channels].), and
wherein based on a first integer p and a second integer q, Cout = p* Cin/q , wherein Cin is a multiple of q, and Cin, Cout, are integers (Howard: Pg. 4, Section 3.3 discloses applying a fractional width multiplier of 0.75, which calculates output channels as N = 0.75 × M, mathematically equating to N = 3 × M / 4 [claimed Cout=p*Cin/q]. Further, Howard: Pg. 4, Table 1 discloses base input channels M as 32, 64, and 128, which are explicitly divisible by 4 [claimed wherein Cin is a multiple of q], ensuring the resulting structural channel capacities are strictly whole numbers [claimed Cin, Cout, p, and q are integers].);
a second neural network layer configured to obtain the second number of channels Cout as input and output a third number of channels C’ (Howard: Pg. 4, Table 1 discloses a sequential architecture of “Conv” layers where the output channels of one layer feed directly into the subsequent layer [claimed and a second neural network layer configured to obtain the second number of channels Cout as input] and output a new number of channels [i.e., output a third number of channels C’].);
a third neural network layer configured to obtain the third number of channels C’, wherein C’ = p’* Cout/q’, (Howard: Pg. 4, Table 1 discloses deep sequential networks with successive convolutional layers [claimed third neural network layer] where the width multiplier α uniformly scales sequential layers, resulting in successive whole-integer transformations where output N becomes the new input [claimed C′=p′*Cout/q′, and p′ and q′ are integers].), and
Ronneberger et al., (U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI); Pub.: May. 2015; Pgs. 8; URL: https://arxiv.org/abs/1505.04597) disclose the following limitations:
Cout is a multiple of q' and C' (Ronneberger: Fig. 1; Pgs. 2, 4; Section 2 disclose an upstream block receiving 512 channels and applies an operation that “halves the number of feature channels” down to 256 channels [claimed Cout is a multiple of q' and C'] because the upstream 512 channels (Cout) is an exact mathematical multiple of both the fractional denominator 2 (q') and the downstream 256 channels (C').).
Applicant uniquely claimed a distinct feature in the instant invention, which is not found in the prior art, either singularly or in combination. The feature is [Claim 21] “… p/q=5/2 and p’/q =2; or p/q=2 and p’/q’=5/2.”. This feature is not found or suggested in the prior art.
Claim 21 is allowable provided that the claim objections are resolved.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and can be viewed in the list of references.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEET DHILLON whose telephone number is (571)270-5647. The examiner can normally be reached M-F: 5am-1:30pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath V. Perungavoor can be reached at 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEET DHILLON/Primary Examiner
Art Unit: 2488
Date: 09-08-2026
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