DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is Final and is in response to the claims filed 12/18/2025. Claims 1-2 and 4-16 are currently pending, of which claims 1-2 and 4-16 are currently rejected. Claims 3 and 17-20 have been cancelled by applicant.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/07/2026 has been entered.
Response to Arguments
Applicant’s arguments filed 05/07/2026 have been fully considered.
Drawing Objection: Objection to drawings has been withdrawn necessitated by amendments.
35 U.S.C. 101: Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but they are not persuasive.
Applicant argues in pages 8-9 that claims are not directed to a mathematical concept or mental process. Applicant specifically argues “A shift operation is a binary- representation-specific machine operation that has no pen-and-paper analog. When a human performs arithmetic with pen and paper, the human performs addition and multiplication using decimal representations. The human does not perform "shift" operations. A shift operation (left shift or right shift of binary digits) is inherently a machine-level operation that takes advantage of the binary data representation used by digital hardware. Accordingly, a method step that requires performing "shift operations" on data cannot practically be performed in the human mind.”
Examiner respectfully disagrees. A shifting operation can be performed in the human mind using pen and paper, and is not inherently a machine-level operation. For example, an engineering student in a computer architecture class could be assigned an assignment requiring showing a shifting operation of binary data using pen and paper. Additionally, claims limit matrix sizes to at most 3x3 and 4x4, which are sizes small enough to illustrate a shifting operation on paper. Even if not considered a mental process, Applicant does not argue how a “shifting operation” is not considered a mathematical concept.
Applicant additionally argues in pages 9-10 how the claim limitations specifying forward transformation matrices are not abstract mathematical concepts. Specifically, Applicant argues “Furthermore, the claim limitations specifying that the forward transformation matrices have element values limited to 0 and +1 are not merely mathematical observations. Rather, they are design constraints that are caused by the preceding size-constraint limitations (kernel <3 X 3 and input data <4x4) and that enable the elimination of multiplication operations. This causal chain, from specific size constraints to integer-only transformation matrices to multiplication- free computation via shift-and add, represents a concrete technical design, not an abstract mathematical relationship. For at least these reasons, amended claim 1, taken as a whole, is not directed to a mental process or a mathematical concept under Prong One of Step 2A.”
Examiner respectfully disagrees. The claim limitations specifying that the forward transformation matrices have element values limited to 0 and +1 are merely mathematical limitations specifying numerical element values of matrices at most. There is no claim limitations specifying how these element values are directed to any specific hardware structure in claim 1. The enablement of the elimination of multiplication operations are merely transforming the mathematical computation to be performed based on the numerical values representing the matrix elements.
Applicant further argues in pages 10-11 that amended claim 1 recites a specific and concrete improvement to convolution processing technology. Applicant argues that size contains of matrices are not arbitrary numerical limits that result in shift and summation computations instead of multiplication computations. Applicant further argues transforming input data and kernel data requires additions, subtractions, and sign inversions implemented in hardware without any multiplier circuits. Applicant explains “This means that transforming input data and kernel data requires only additions, subtractions, and sign inversions, all of which can be implemented in hardware without any multiplier circuits. This is a specific technological improvement that directly reduces chip area, power consumption, and latency.”
Examiner respectfully disagrees. Amended claim 1 merely describes performing shift and summation operation instead of multiplication computations. The alleged improvement of reducing chip area, power consumption, and latency is not reflected in the claim language. Amended claim 1 merely describes the computations, and does not claim any hardware element performing these computations. See 35 U.S.C. 101 rejection below.
Applicant further argues in pages 11-12 that amended claim 1 is analogous to McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-16, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016), and that claims are directed to a technological improvement and not an abstract idea.
Examiner respectfully disagrees. As per MPEP 2106.05(a): “in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans”. The instant application recites limitations related to performing shift and addition computations instead of multiplication computations. Therefore, McRO is not analogous to amended claim 1.
Applicant further argues in pages 12-13 that amended claim 1 does not recite well-understood, routine, or conventional limitations.
Examiner did not categorize amended claim 1 as well-understood, routine, or conventional. Therefore, no argument is presented.
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 1-6 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1, at Step 1 the claim is directed to method, which is a statutory category of invention.
At Step 2A, Prong 1, Examiner notes that the claims are directed to mathematical concepts and/or mental processes:
A data processing method, comprising:
splitting a convolutional kernel with a size greater than 3*3 into a plurality of sub convolutional kernels with a size less than or equal to 3*3 (mathematical relationships / calculations and/or mental process);
splitting input data into a plurality of pieces of target sub input data with a size less than or equal to 4*4 according to position distributions of the plurality of sub convolutional kernels in the convolutional kernel (mathematical relationships / calculations and/or mental process), wherein
a sub convolutional kernel of the plurality of sub convolutional kernels corresponds to one or more pieces of target sub input data (mathematical relationships and/or mental process), and
the splitting of the input data into the plurality of pieces of target sub input data with the size less than or equal to 4*4 according to the position distributions of the plurality of sub convolutional kernels in the convolutional kernel includes:
splitting the input data into a plurality of pieces of first sub input data according to the position distributions of the plurality of sub convolutional kernels in the convolutional kernel, wherein one of the sub convolutional kernels has uniquely-corresponding first sub input data (mathematical relationships / calculations and/or mental process);
for one sub convolutional kernel of the plurality of sub convolutional kernels, splitting first sub input data with a size greater than 4*4 into a plurality of pieces of second sub input data with the size less than or equal to 4*4 if a size of the first sub input data corresponding to the sub convolutional kernel is greater than 4*4 (mathematical relationships / calculations and/or mental process); and
determining the plurality of pieces of second sub input data with the size less than or equal to 4*4 as the target sub input data corresponding to the sub convolutional kernel (mathematical relationships / calculations and/or mental process);
for one of the sub convolutional kernels, performing a winograd convolution operation on the sub convolutional kernel and corresponding target sub input data to obtain a convolution result corresponding to the sub convolutional kernel (mathematical relationships / calculations and/or mental process), wherein, during the winograd convolution operation, multiplication computations are not required and the convolution result corresponding to the sub convolutional kernel is obtained through a shift computation and a summation computation (mathematical relationships / calculations and/or mental process); wherein, for the target sub input data having the size less than or equal to 4*4, element values in a corresponding forward transformation left-multiply matrix and a corresponding forward transformation right-multiply matrix are 0 and +1; and wherein, for the sub convolutional kernel having the size less than or equal to 3*3, element values in a corresponding forward transformation left-multiply matrix and a corresponding forward transformation right-multiply matrix are 0 and +1 (mathematical relationships and/or mental process); and
performing a summation operation on convolution results corresponding to the plurality of sub convolutional kernels to obtain a convolution result of the convolutional kernel and the input data (mathematical relationships / calculations and/or mental process).
As stated above, claim limitations are merely reciting mathematical concepts for performing a winograd convolution operation based on numerical limitations (i.e., using at most a 3*3 and a 4*4 input matrices with 0 and +1 element values). Even if not considered mathematical concepts, the computations are performing a splitting, winograd convolution, and shift and summation operations using at most a 3*3 and a 4*4 input matrices. These operations performed on matrices of said dimension can be performed in the human mind using pen and paper.
Under Steps 2A prong 2 and 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application nor do they amount to significantly more than the judicial exception.
Claim 2 is directed to the mathematical concept of splitting the convolutional kernel into sub convolutional kernels (mathematical relationships / calculations) and/or the mental process of splitting the convolutional kernel into matrices of a size less than or equal to 3*3 (observation and evaluation). Under Steps 2A prong 2 and 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application nor do they amount to significantly more than the judicial exception.
Claim 4 is directed to the mathematical concept of splitting the input data based on the sub convolutional kernels into first sub input data for a Winograd convolution operation (mathematical relationships / calculations) and/or the mental process of splitting the input data into data of size less than or equal to 4*4 (observation and evaluation). Under Steps 2A prong 2 and 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application nor do they amount to significantly more than the judicial exception.
Claim 5 is directed to the mathematical concept of splitting the input data based on the sub convolutional kernels into first sub input data (mathematical relationships / calculations) and/or the mental process of splitting the input data into data based on the sub convolutional kernels (observation and evaluation). Under Steps 2A prong 2 and 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application nor do they amount to significantly more than the judicial exception.
Claim 6 is directed to the mathematical concept of splitting, Winograd convolution, and summation operations using target sub input data and sub convolutional kernel (mathematical relationships / calculations) and/or the mental process of splitting, Winograd convolution, and summation operations (observation and evaluation). Under Steps 2A prong 2 and 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application nor do they amount to significantly more than the judicial exception.
Allowable Subject Matter
Claims 1-2 and 4-6 would be allowable if rewritten to overcome the 35 U.S.C 101 rejections discussed above.
Claims would be allowable for the reasons indicated in Final Rejection on 02/10/2026.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS H DE LA GARZA whose telephone number is (571)272-0474. The examiner can normally be reached Monday-Friday 9:30AM-6PM.
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/C.H.D./
Carlos H. De La GarzaExaminer, Art Unit 2182 (571)272-0474
/EMILY E LAROCQUE/ Primary Examiner, Art Unit 2182
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