DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
IDS filed 7/10/25, 8/13/25, 12/30/25 and 2/17/26 are acknowledged, the references therein relating to the general background of applicant’s invention, with the exception of “MUXConv: Information Multiplexing in Convolutional Neural Networks” by Lu, which has particular relevance as noted below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) 1, 9 and 17 recite a mathematical concept of neural network architecture. This judicial exception is not integrated into a practical application because the generated output feature map is not claimed to be utilized by any application and appears to not even be the end output of the neural network but merely an intermediate mathematical step. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “obtaining” and “processing” an input image does not add a meaningful limitation to the abstract idea and the additional element (for claims 9 and 17) of a machine-readable storage device is a well-understood, routine, conventional computer function. Claims 2-8, 10-16 and 18-20 add further mathematical concept steps but do not illuminate a practical application.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
1) Claim(s) 1 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “MUXConv: Information Multiplexing in Convolutional Neural Networks” by Lu et al. (from IDS filed 7/10/25).
2) Regarding claim 1, Lu teaches a method performed by one or more computers, the method comprising: obtaining an input image; and processing the input image using a convolutional neural network, the convolutional neural network comprising a sequence of layer blocks, and wherein each of a first subset of the layer blocks in the sequence is configured to perform operations comprising: receiving an input feature map for the layer block, the input feature map for the layer block being an h x w feature map with c1 channels (page 10; figure 11; input feature map is received at a layer block with a certain number of channels); generating an expanded feature map from the input feature map using a group convolution, the expanded feature map being an h x w feature map with c2 channels, where c2 is greater than c1 (page 9, column 1, last two lines – column 2, the first two lines; channels are expanded with a 1x1 convolution); generating a reduced feature map from the expanded feature map, the reduced feature map being an h x w feature map with c1 channels; and generating an output feature map for the layer block from the reduced feature map (page 9, column 2; MUXConv block expands the number of channels while maintaining a constant feature map size [i.e. a 1x1 convolution] with a group convolution and then compresses the number channels back to the original as the output of the layer block).
3) Regarding claim 2, Lu teaches the method of claim 1, wherein generating an expanded feature map comprises: generating an initial expanded feature map from the input feature map by applying a 1 x 1 convolution to the input feature map, the initial expanded feature map being an h x w feature map with c2 channels; and generating the expanded feature map from the initial expanded feature map by applying the group convolution to the initial expanded feature map (page 9, column 2; 1x1 convolution is applied prior to the group convolution).
4) Regarding claim 3, Lu teaches the method of claim 2, wherein the 1 x1 convolution has a larger number of output filters than input filters (page 9, column 2; “expansion” through the 1x1 convolution requires more output filters than input [as that is what is being expanded]).
5) Regarding claim 4, Lu teaches the method of claim 2, wherein the group convolution has the same total number of input filters and output filters (page 9, column 2; expansion and compression occur in the 1x1 convolutions prior to and after the group convolution, thus the group convolution does not alter the number of filters).
6) Regarding claim 5, Lu teaches the method of claim 1, wherein the sequence of layer blocks comprises: a group convolution layer block that is interleaved with a non-group convolution layer block, and wherein the group convolution layer block is used to implement the group convolution (page 4, figure 4; other blocks are interleaved with group convolution layer blocks as shown).
7) Regarding claim 6, Lu teaches the method of claim 1, wherein: the group convolution is a fused-group convolution implemented using a fused-grouped inverted bottleneck (IBN) layer that is included among the sequence of layer blocks (page 10, figure 11 caption; inverted bottleneck layers are utilized).
8) Regarding claim 7, Lu teaches the method of claim 1, wherein generating an expanded feature map comprises: generating the expanded feature map from the input feature map by applying the group convolution to the input feature map (page 9, column 2; group convolution is applied, the output being the expanded feature map).
9) Claims 9-15 are taught in the same manner as described in the rejections of claims 1-7 above, respectively.
10) Claims 17-19 are taught in the same manner as described in the rejections of claims 1, 2 and 5 above, respectively.
11) Claims 20 is taught in the same manner as described in the rejections of claims 6 and 7 above.
Allowable Subject Matter
Claims 8 and 16 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, as well as overcoming the above 101 rejection.
Conclusion
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BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/ Primary Examiner, Art Unit 2683
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