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 .
DETAILED ACTION
This is a Non-Final Office Action, in responses to Applicants’ RCE, amendments and remarks field 03/31/2026. It is noted, the current patent application was filed 09/29/2022; Claims Priority from Provisional Application 63289035, filed 12/13/2021; Claims Priority from Provisional Application 63257014, filed 10/18/2021 . Claim(s) 1-8, 10-20 and new claim 21 are pending. Claim(s) 1, 10 and 17 are independent claims. Claim(s) 1, 8, 10 and 15-17 have been amended. Claim 21 is new. Claim(s) 2-5, 11-12 and 18-19 were original. Claim(s) 6-7, 12-14, 16 and 20 were previously presented. Claim 9 is cancelled.
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 allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). 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, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 03/31/2026 has been entered.
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.
Claim(s) 1-8 and 10-21 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because:
The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... processes (claim 1) device (claim 10) computer readable storage medium (claim 17) ... “accelerating” a neural network model, performed by at least one processor and comprising: “obtaining” an original weight matrix corresponding to a trained neural network model; and “accelerating an inference operation” of the neural network model by operation of:
“pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix;
“retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and
“performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model ....
and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recites processes/device/medium... for accelerating a neural network model, ... and... “obtaining” an original weight matrix corresponding to a trained neural network model; wherein “accelerating an inference operation” of the neural network model by operation of: “pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix; and “retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and “performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model ....
These limitation(s), said, “obtaining”, “accelerating an inference operation” of the neural network model by operation of: “pruning”, “retraining”, “compressing”, “performing a matrix multiplication operation”. It is noted, the DNNs (Deep Neural Networks) have high prediction performance but have high costs in terms of storage, computation power and energy consumption [Specs in Para 0003]... to accelerate inference (e.g., classification), without sacrificing much performance (e.g., classification accuracy)... [Specs in Para 0004]. Moreover, the NN (neural Network) compression techniques using unstructured weight pruning techniques achieve a high compression rate with little prediction loss, but they typically cannot improve inference operations, and sometimes even increase the prediction loss [Specs 0037 and 00038].
Also, see the current specification in USPGPUB 20230118058 A1 Para(s) 51-60..i.e., ..the weight matrix is pruned to generate a pruned weight matrix, wherein the weight matrix be pruned using a fine-grained structured sparsity technique with a 2:4 sparse pattern, it is noted, the coefficient values in the pruned weight matrix may be retrained to generate a retrained weight matrix. For example, an absolute value of each nonzero coefficient in each group of four coefficients in the pruned weight matrix may be retrained to be a power-of-two of the smallest nonzero coefficient in the group.... The sparse tensor core may select input activation values based on the nonzero flags, and calculate a dot product with the selected activations.... The dot product result may be obtained as an output activation coefficient value, and stored in an output activation matrix.) ...[high level mathematical calculation]. moreover, the claim(s) recite only the idea of a solution or outcome i.e., “obtaining” an original weight matrix corresponding “to a trained neural network model”; and “pruning” the original weight matrix to “generated” a pruned weight matrix... [“APPLY IT”] ).
Thus, independent Claims 1, 10 and 17, recite abstract idea and "covers performance of the limitations in the mind (i.e. Mathematical concepts- mathematical relationships, mathematical formulas or equations, mathematical calculations)” .
Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “processor”, “device” and/or “computer readable storage medium “...for obtaining an original weight matrix corresponding to a trained neural network model; wherein “accelerating an inference operation” of the neural network model by operation of: “pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix; and “retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and “performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model ....
These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) of “performed by at least one processor”, “device” and/or “computer readable storage medium “.... This limitation only recites a generic computer component that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, does not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.05(f)).
As to the dependent claim(s) 2-8, 11-16 and 18-21, further recite, addition limitation(s) such as, 2:4 sparse pattern, retraining an absolute value of each nonzero coefficient, power-of-two of the smallest nonzero coefficient, compressing the retrained weight matrix to be a quarter size, nonzero flag array, a sign flag array, left-shift flag array, nonzero flag array is a one-bit array, keep track of a sign of the nonzero coefficients, the left-shift flag array is a two-bit array, input activations to the trained neural network model, only input activations that correspond to the nonzero flag array are selected, unselected input activations are skipped, converting multiple independent operations to a single operation, performed simultaneously, and general matrix-matrix multiplication (GEMM) operations...etc.,
These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Accordingly, claims 1-20 fail to recite statutory subject matter, as defined in 35 U.S.C. 101.
In the interest of compact prosecution, Examiner recommends to incorporate the limitations as recites in Para(s) 37 and 38 of the current specification(s) (i.e. USPGPUB 20230118058)... [0038] and limitations from dependent claim(s) 2 and 3 into independent claim(s) 1, 10 and 17 ...(i.e.... neural network compression using unstructured weight pruning techniques may achieve a high compression rate with little prediction loss, .... A fine-grained structured sparsity technique may be manifest as a 2:4 pattern, where out of every four coefficients, at least two must be zero. This technique may reduce a data footprint and bandwidth of a weight tensor by half, and double an inference throughput by skipping computation of zero-value coefficients. ... for a unified sparse tensor core operation. The unified sparse tensor core operation combines a fine-grained structured sparsity technique with a weight unification technique, to achieve higher inference throughput.)... may overcome the above stated rejections under 35 U.S.C. 101. (MPEP 2106.04(d), 2106.05(f)).
Also, After the examiner has consulted the specification and determined that the disclosed invention improves technology or a technical field, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp.,838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016)... The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology or a technical field (e.g., the improvement described in the specification). Also (see MPEP § 2106.05(f))
Allowable Subject Matter
Claim(s) 1-8 and 10-21 would be allowable if rewritten and/or amending to remedy the 101 rejection(s).
Reason for Allowance
Under the broadest reasonable interpretation of the claimed limitation which is consistence with the Applicant's Specification, the prior arts of recorded when taken individually or in combination do not expressly teach or render obvious the limitations recited in claim(s) 1, 10 and 17 when taken in the context of the claims as a whole, especially the concept of, “accelerating” a neural network model, performed by at least one processor and comprising: “obtaining” an original weight matrix corresponding to a trained neural network model; and “accelerating an inference operation” of the neural network model by operation of:
“pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix;
“retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and
“performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model..... As claimed and further supports in the specifications PGPUB 220230118058 A1- Para(s) 51-60 and 67 for details.
In addition, neither a reference uncovered that would have provided a basis of evidence for asserting a motivation, nor one of ordinary skilled in the art before the effective filing date of the claimed invention, would have combined them to arrive at the present invention as recited in the context of independent claim(s) 1, 10 and 17 as a whole.
Thus, Claim(s) 1-8 and 10-21 would be allowable if rewritten and/or amending to remedy the 101 rejection(s).
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.”
Response to Arguments/Remarks
This is Non-Final Office Action, in responses Applicants’ RCE, amendments and remarks field 03/31/2026. Moreover, Applicants’ arguments with respect to claim(s) 1-8 and 10-21 have been considered but are not persuasive. Because:
Applicant argues, that 35 U.S.C. § 101 as being direct to nonpatentable subject matter should be “traversed” because:
The Applicants argue, “it is not clear how the rejection continues to conclude that the claims do not represent an improvement to computer technology. Nonetheless, in addition to the preamble, the body of the claim 1, and similarly of the other independent claims, is further amended to recite such acceleration, and so, it is again respectfully submitted that the claims satisfy the criteria for patent eligibility under 35 USC 101 as set out in MPEP 2106.04(d)(1) - the specification describes an improvement, an acceleration to a neural network model, and the claims reflect that improvement..” [see the remarks page 10 second paragraph]. Also, based on the “Official Guidance” and Example 39 of the guidance; what appears the claims are not direct to any “Abstract Idea” and further, even if the claim features involve mathematical concepts, the claims are not directed to those concepts but instead to, overall, acceleration to a neural network model. And so, that point of the traversal is also maintained as the claims are also not directed to mathematical concepts even if those concepts are involved in the claims. See example claim 39 from the USPTO's guidance where those claim features also certainly involve mathematical concepts and yet are not directed to those concepts..[Emphasis Added] [see the remarks pages 11-12]
The examiner disagrees. As discussed in the rejection above, claims 1-8 and 10-21 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more. Moreover,
Step 1: YES (Claim(s) is/are process, machine, manufacture or composition of the matter). ... processes (claim 1) device (claim 10) computer readable storage medium (claim 17) ... “accelerating” a neural network model, performed by at least one processor and comprising: “obtaining” an original weight matrix corresponding to a trained neural network model; and “accelerating an inference operation” of the neural network model by operation of:
“pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix;
“retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and
“performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model ....
and therefore, fall into one of the four categories of patent eligible subject matter (process, machine, manufacture or composition of the matter).
Step 2A : Prong One: ( whether a claim recites a judicial exception ?) the claim(s) recites processes/device/medium... for accelerating a neural network model, ... and... “obtaining” an original weight matrix corresponding to a trained neural network model; wherein “accelerating an inference operation” of the neural network model by operation of: “pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix; and “retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and “performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model ....
These limitation(s), said, “obtaining”, “accelerating an inference operation” of the neural network model by operation of: “pruning”, “retraining”, “compressing”, “performing a matrix multiplication operation”. It is noted, the DNNs (Deep Neural Networks) have high prediction performance but have high costs in terms of storage, computation power and energy consumption [Specs in Para 0003]... to accelerate inference (e.g., classification), without sacrificing much performance (e.g., classification accuracy)... [Specs in Para 0004]. Moreover, the NN (neural Network) compression techniques using unstructured weight pruning techniques achieve a high compression rate with little prediction loss, but they typically cannot improve inference operations, and sometimes even increase the prediction loss [Specs 0037 and 00038].
Also, see the current specification in USPGPUB 20230118058 A1 Para(s) 51-60..i.e., ..the weight matrix is pruned to generate a pruned weight matrix, wherein the weight matrix be pruned using a fine-grained structured sparsity technique with a 2:4 sparse pattern, it is noted, the coefficient values in the pruned weight matrix may be retrained to generate a retrained weight matrix. For example, an absolute value of each nonzero coefficient in each group of four coefficients in the pruned weight matrix may be retrained to be a power-of-two of the smallest nonzero coefficient in the group.... The sparse tensor core may select input activation values based on the nonzero flags, and calculate a dot product with the selected activations.... The dot product result may be obtained as an output activation coefficient value, and stored in an output activation matrix.) ...[high level mathematical calculation]. moreover, the claim(s) recite only the idea of a solution or outcome i.e., “obtaining” an original weight matrix corresponding “to a trained neural network model”; and “pruning” the original weight matrix to “generated” a pruned weight matrix... [“APPLY IT”] ).
Thus, independent Claims 1, 10 and 17, recite abstract idea and "covers performance of the limitations in the mind (i.e. Mathematical concepts- mathematical relationships, mathematical formulas or equations, mathematical calculations)” .
Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “processor”, “device” and/or “computer readable storage medium “...for obtaining an original weight matrix corresponding to a trained neural network model; wherein “accelerating an inference operation” of the neural network model by operation of: “pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix; and “retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and “performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model ....
These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Step 2B: (Whether a Claim Amounts to Significantly More) ? The claim(s) recite additional limitation(s) of “performed by at least one processor”, “device” and/or “computer readable storage medium “.... This limitation only recites a generic computer component that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, does not amount to significantly more than the abstract idea itself (MPEP 2106.05, 2106.05(f)).
As to the dependent claim(s) 2-8, 11-16 and 18-21, further recite, addition limitation(s) such as, 2:4 sparse pattern, retraining an absolute value of each nonzero coefficient, power-of-two of the smallest nonzero coefficient, compressing the retrained weight matrix to be a quarter size, nonzero flag array, a sign flag array, left-shift flag array, nonzero flag array is a one-bit array, keep track of a sign of the nonzero coefficients, the left-shift flag array is a two-bit array, input activations to the trained neural network model, only input activations that correspond to the nonzero flag array are selected, unselected input activations are skipped, converting multiple independent operations to a single operation, performed simultaneously, and general matrix-matrix multiplication (GEMM) operations...etc.,
These limitation(s) only amounts to mere instructions to implement the abstract idea ...and do not include elements that amount to significantly more than the abstract idea and are also rejected under the same rational.
Thus, in light of the Advance notice of change to the MPEP in light of Ex Parte Desjardins new (December 5, 2025) and the memorandum dated August 4, 2025 and December 4, 2025...(i.e. Examples of claims that improve technology or a technical field and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (data structure claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253, 125960, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea);
Also, After the examiner has consulted the specification and determined that the disclosed invention improves technology or a technical field, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp.,838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues)....The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology or a technical field (e.g., the improvement described in the specification). [MPEP § 2106.05(a) Fourth and Fifth Para(s)] . See also Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential) (“Examiners and panels should not evaluate claims at such a high level of generality” that potentially meaningful technical limitations).
An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.... (See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025) (Appeals Review Panel Decision)re dismissed without adequate explanation).
In this case, in Step 2A : Prong Two: (Do the claim(s) recite “additional element(s) that integrate the “Judicial Exception” into “A Practical Application” ? The claim(s) recite additional limitation(s) such as “processor”, “device” and/or “computer readable storage medium “...for obtaining an original weight matrix corresponding to a trained neural network model; wherein “accelerating an inference operation” of the neural network model by operation of: “pruning” the original weight matrix to “generate” a pruned weight matrix, wherein at least two coefficients are nonzero in each group of four coefficients in the pruned weight matrix; and “retraining” nonzero coefficients in the pruned weight matrix; “compressing” the retrained weight matrix; and “performing” a matrix operation based on inputting the compressed weight matrix and a set of input activations to the trained neural network model ....
These limitation(s) only recite a generic computer component(s) that only amounts to mere instructions to implement the abstract idea on a computer, and therefore, do not integrate the judicial exception into a practical application. (MPEP 2106.04(d), 2106.05(f)).
Accordingly, claims 1-8 and 10-21 fail to recite statutory subject matter, as defined in 35 U.S.C. 101, because: The claimed invention is/are directed to a judicial exception (i.e., abstract idea) without significantly more.
Conclusion
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/QUOC A TRAN/ Primary Examiner, Art Unit 2145
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