DETAILED ACTION
[1] Remarks
I. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
II. This Office Action is in response to the RCE filed on 03/16/2026.
III. Claims 1-20 are pending and have been examined, where claims 1, 4-12, 15-18, 20 is/are rejected and claim 2-3, 13-14 and 19 is/are objected. Explanations will be provided below.
IV. Inventor and/or assignee search were performed and determined no double patenting rejection(s) is/are necessary.
V. Patent eligibility (updated in 2019) shown by the following: Claims 1-20 pass patent eligibility test because there is/are no limitation or a combination of limitations amounting to an abstract idea. Also, the following limitation or the combinations of the limitations: “apply, by utilizing a neural network, a Fast Fourier Transform to each of the equally-sized partially overlapping image patches; and compute, for each pixel in each image patch of the equally-size partially overlapping image patches, a matrix-vector product between at least one channel of each image patch and a matrix from a corresponding pixel location in an image filter” effect a transformation or a reduction of a particular article to a different state or thing / adds a specific limitation(s) other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application and providing improvements to the technical field of deep learning, which recite additional elements that integrate the judicial exception into a practical application and amounting significant more.
VI. There are no PCT associated with the current application.
[2] Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function. Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim(s) 1-17 are not interpreted under 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph because of the following reason(s): limitations are modified by sufficient structure or material for performing the claimed function.
Claim(s) 18-20 do not require 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph interpretation because they are method claims and / or they are CRM claims.
Upon examination of the specification and claims, the examiner has determined, under the best understanding of the scope of the claim(s), rejection(s) under 35 U.S.C. 112(a)/(b) is not necessitated because of the following reasons: sufficient support are provided in the written description / drawings of the invention.
[3] Grounds of Rejection
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) person shall be entitled to a patent unless—
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claim 1, 4-12, 15-18, 20 is rejected under 35 U.S.C. 102(b) as being anticipated by Chitsaz et al. Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions, arXiv, 27 Mar 2020.
Regarding claim 1, Chitsaz discloses a system, comprising: a memory; and a deep learning accelerator configured to execute instructions from the memory, wherein the deep learning accelerator (see 4. Experimental Results first paragraph, FGPA is employed as the deep learning accelerator) is configured to;
divide an input image associated with an artificial intelligence task into equally-sized partially overlapping image patches (see figure 2 illustration below, partially overlapping, dotted lines);
apply, by utilizing a neural network, a Fast Fourier Transform to each of the equally-sized partially overlapping image patches (see figure 2, is a FFT-based processing of CNN using splitting); and
compute, for each pixel in each image patch of the equally-size partially overlapping image patches, a matrix-vector product between at least one channel of each image patch and a matrix from a corresponding pixel location in an image filter (see figure 1 illustration below, matrix dot product multiplication):
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Regarding claim 4, Abtahi discloses the system of claim 1, wherein the deep learning accelerator is further configured to output the convolved version of the input image (see figure 1, multiplication in the frequency domain with two Fourier transformed images is equivalent with the convolution of two image matrix).
Regarding claim 5, Abtahi discloses the system of claim 4, wherein the deep learning accelerator is further configured to perform the artificial intelligence task using the convolved version of the input image that is outputted (see figure 1 where the one marked output is the convolve version of the input image in the frequency domain).
Regarding claim 6, Abtahi discloses the system of claim 1, wherein the equally-sized partially overlapping image patches correspond to a frequency domain after application of the Fast Fourier Transform to each of the equally-sized partially overlapping image patches (see figure 1 illustration below):
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Regarding claim 7, Abtahi discloses the system of claim 1, wherein the input image received by utilizing the neural network corresponds to the spatial domain (see figure 1, input d is in the spatial domain which is fed into the deep learning network, also see figure 2, the input is an image in the spatial domain).
Regards to claim 8, Abtahi discloses the system of claim 1, wherein the deep learning accelerator is further configured to determine a size of the overlapping portions based on an image filter size of the image filter (see figure 1 illustration below):
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Regarding claim 9, Abtahi discloses the system of claim 1, wherein the deep learning accelerator is further configured to generate mappings between first channels of the input image onto second channels of the convolved version of the input image (see figure 1 illustration below):
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Regarding claim 10, Abtahi discloses the system of claim 1, wherein the deep learning accelerator is further configured to cache frequency domain versions of the equally-sized partially overlapping image patches after application of the Fast Fourier Transform to the equally-sized partially overlapping image patches (see figure 1, output is the application of the FFT of equally sized partially overlapping image patches).
Regarding claim 11, Abtahi discloses the system of claim 10, wherein the deep learning accelerator is further configured to reuse the cached frequency domain versions of the equally-sized partially overlapping image patches in a convolutional layer (see figure 1 where the one marked output is the convolve version of the input image in the frequency domain).
Regarding claims 12 and 18, see the rationale and rejection for claim 1.
Regarding claim 15, Abtahi discloses the device of claim 14, wherein the deep learning accelerator is further configured to output the reconstructed convolved image (see figure 1 illustration in claim 14).
Regarding claim 16, Abtahi discloses the device of claim 12, wherein the deep learning accelerator is further configured to adjust the image filter size by padding the image filter with zeroes until the image filter size corresponds to the size of each of the equally-sized partially overlapping image patches (see figure 2 illustration below, the overlaps are shown in figure 1, output):
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Regarding claim 17, Abtahi discloses the device of claim 12, wherein the deep learning accelerator is further configured to receive, by utilizing a neural network, the input image for use for an artificial intelligence task (see section 2. CNN COMPUTING BASED ON FFT, the CNN constitutes AI task).
Regarding claim 20, Abtahi discloses the method of claim 18, further comprising reconstructing the convolved version of the input image by discarding overlapping regions of the image patches and combining retained portions of the image patches, or by summing the overlapping regions of the image patches together (see figure 1, illustration below):
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[4] Claim Objections
Claim(s) 2-3, 13-14 and 19 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.
With regards to claim 2, the examiner cannot find any applicable prior art providing teachings for the following limitation(s): “the system of claim 1, wherein the deep learning accelerator is further configured to: apply an inverse Fast Fourier Transform to the matrix-vector product for each pixel in each image patch to convert each image patch to a spatial domain; and reconstruct, after application of the inverse Fast Fourier Transform, a convolved version of the input image by summing overlapping portions of each image patch together; and pad the image filter with zeroes until an image filter size of the image filter is adjusted to correspond to the size of each of the equally-sized partially overlapping image patches” in combination with the rest of the limitations of claim 1.
Abtahi (T. Abtahi, C. Shea, A. Kulkarni and T. Mohsenin, "Accelerating Convolutional Neural Network with FFT on Embedded Hardware," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 26, no. 9, pp. 1737-1749, Sept. 2018) discloses a system, comprising:
a memory; and a deep learning accelerator configured to execute instructions from the memory (see A. PENC Many-core Overview and Key Features), wherein the deep learning accelerator is configured to;
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divide an input image associated with an artificial intelligence task into equally-sized
apply, by utilizing a neural network, a Fast Fourier Transform to each of the equally-sized
compute, for each pixel in each image patch of the equally-size
apply an inverse Fast Fourier Transform to the matrix-vector product for each pixel in each image patch to convert each image patch to a spatial domain (see figure 3, 2D IFFT); and
reconstruct, after application of the inverse Fast Fourier Transform, a convolved version of the input image by multiplying
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Primary reference, Srinivasan (US 7751482) discloses
divide an input image associated with an artificial intelligence task into equally-sized partially overlapping image patches (see figure 4 illustration below);
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apply,
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compute, for each pixel in each image patch of the equally-size partially overlapping image patches, a matrix-vector product between at least one channel of each image patch and a matrix from a corresponding pixel location in an image filter (see column 5, lines 55-63, where F1 is read as the channel of each image patch and F2 is read as a matrix from a corresponding pixel location):
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apply an inverse Fast Fourier Transform to the matrix-vector product for each pixel in each image patch to convert each image patch to a spatial domain (see equation 6 illustration below):
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Srinivasan is silent in disclosing reconstruct, after application of the inverse Fast Fourier Transform, a convolved version of the input image by summing overlapping portions of each image patch together (see equation 6 where there are no summations to obtain the output).
With regards to claims 13 and 19, see the rationale for claims 2.
Claim(s) 3 and 14 is/are objected as well because it is dependent on a claim with allowable subject matter.
The combination of Abtahi and Srinivasan as a whole do not disclose apply, by utilizing a neural network, a Fast Fourier Transform to each of the equally-sized partially overlapping image patches. Such a combination would result in piecemealing
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX LIEW (duty station is located in New York City) whose telephone number is (571)272-8623 (FAX 571-273-8623), cell (917)763-1192 or email alexa.liew@uspto.gov. Please note the examiner cannot reply through email unless an internet communication authorization is provided by the applicant. The examiner can be reached anytime.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MISTRY ONEAL R, can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEX KOK S LIEW/Primary Examiner, Art Unit 2674 Telephone: 571-272-8623
Date: 3/20/26