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 .
Response to Amendment
The amendments filed 03/25/2026 have been entered.
Claims 1, 4-6, 8-9, 11-12, and 14-20 remain pending within the application.
The amendments filed 03/25/2026 are sufficient to overcome the 112(b) rejections previously set forth in the Non-Final Office Action mailed 11/25/2025. The rejections have been withdrawn.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1, 4, 8, 9, 14, 19 and 20 are rejected under AIA 35 U.S.C. 102(a)(1) as being anticipated by Gao et al. (Pub. No.: US 2020/0082269 A1), hereafter Gao.
Regarding claim 1, Gao discloses:
A method of training a neural network comprising using at least one hardware processor to (Fig. 1A and Fig. 2),
for each layer to be quantized in the neural network, for each of a plurality of iterations, until all of a neurons of the layer are quantized (Fig. 2, ¶[0029] and ¶[0031-0032] teaches iteratively quantizing layers of neurons until all are quantized),
select a single neuron, wherein (Examiner notes: for prior art purposes, it is noted that a) selecting a layer of neurons includes selecting each neuron, b) filters in a convolution are interpreted as the convolutions in a CNN that filter data, and c) activation functions of neural networks are always nonlinear, as the purpose of activation functions are to introduce nonlinearity) (Fig. 2, ¶[0034], ¶[0038-0039], and ¶[0080] teaches selecting neurons of layers, where the neurons are a combination of linear operations of CNN convolution filters with non-linear activation of their output, with the activation of a single neuron in a layer producing an output to a subsequent layer),
quantize(Fig. 2, Fig. 4, ¶[0039] and ¶[0041] teaches quantizing the weights and the activations outputs to the subsequent layer),
retrain the neural network (Fig. 2, Fig. 4, ¶[0039] teaches retraining the neural network),
freeze the selected neuron, such that the selected neuron is not subsequently modified during training (Fig. 2, Fig. 4, and ¶[0039] and ¶[0053] teaches freezing selected neurons in layers of the neural network such that they are not modified during training).
iterate through each neuron in the current layer being quantized before proceeding to any subsequent layer (Fig. 4 teaches iterating through neurons of a current first layer being quantized before proceeding to a subsequent second layer).
Regarding claim 4, Gao discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Gao further discloses:
wherein freezing the neuron comprises freezing weights of the one or more filters (Fig. 4, ¶[0039], and ¶[0053] teaches freezing the neurons comprises freezing weights of the convolutional block filters).
Regarding claim 8, Gao discloses the method of claim 2 (and thus the rejection of claim 2 is incorporated). Gao further discloses:
wherein retraining the network comprises back- propagating gradients to all quantized layers (Fig. 2, ¶[0033] and ¶[0035]).
Regarding claim 9, Gao discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Gao further discloses:
wherein retraining the network comprises back- propagating a gradient from the layer to another layer that immediately precedes the layer in forward order of the neural network (Fig. 2, ¶[0033] and ¶[0035]).
Regarding claim 14, Gao discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Gao further discloses:
further comprising using the at least one hardware processor to, for each of the plurality of iterations, prior to retraining the neural network, quantize an input to the one or more filters in the neuron (¶[0053] teaches quantizing inputs, near the input layer, to one of more filters and doing so prior to retraining, i.e. tuning).
Claims 19 and 20 are substantially similar to claim 1 and are rejected on the same basis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (Pub. No.: US 2020/0082269 A1), hereafter Gao, in view of Yang et al. (Pub. No.: US 2021/0019606 A1), hereafter Yang.
Regarding claim 11, Gao discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Gao does not disclose:
wherein a number of the plurality of iterations is predefined as N, and selecting the neuron comprises selecting 1/N filters within the layer, such that all filters in the layer are selected over the plurality of iterations.
Yang discloses:
wherein a number of the plurality of iterations is predefined as N, and selecting the neuron comprises selecting 1/N filters within the layer, such that all filters in the layer are selected over the plurality of iterations (¶[0100] teaches selecting a subset of filters iteratively in batches such that all the filters in the layer are selected over the plurality of iterations).
Gao and Yang are analogous art because they are from the same field of endeavor, CNN quantization.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao to include wherein a number of the plurality of iterations is predefined as N, and selecting the neuron comprises selecting 1/N filters within the layer, such that all filters in the layer are selected over the plurality of iterations, based on the teachings of Yang. One of ordinary skill in the art would have been motivated to make this modification for performance improvement in CNN processing, as suggested by Yang (¶[0060]).
Regarding claim 12, Gao, in view of Yang, discloses the method of claim 11 (and thus the rejection of claim 11 is incorporated). Yang further discloses:
wherein N >4 (¶[0123] teaches iteration numbers to be 100, 200, 100, and etc.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao to include wherein N >4, based on the teachings of Yang. One of ordinary skill in the art would have been motivated to make this modification for performance improvement in CNN processing, as suggested by Yang (¶[0060]).
Claims 5 - 6, and 15 - 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (Pub. No.: US 2020/0082269 A1), hereafter Gao, in view of Lee et al. ("Quantune: Post-training quantization of convolutional neural networks using extreme gradient boosting for fast deployment"), hereafter Lee, in further view of Nagel et al. ("A White Paper on Neural Network Quantization "), as cited in the IDS dated 01/31/2023, hereafter Nagel.
Regarding claim 5, Gao discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Gao does not discloses:
further comprising using the at least one hardware processor to: construct a histogram of a data distribution of inputs to the subsequent layer,
solve a minimum mean-square error problem to obtain one or more parameters of quantization based on the constructed histogram,
wherein the one or more parameters are used to quantize the input to the subsequent layer.
Lee discloses:
further comprising using the at least one hardware processor to: construct a histogram of a data distribution of inputs to the subsequent layer … to obtain one or more parameters of quantization based on the constructed histogram, wherein the one or more parameters are used to quantize the input to the subsequent layer (Fig. 1, and page 126, right column, paragraph 2, lines 2-4 “the histogram of possible numeric ranges in each layer of the neural network is captured for the activation of the quantization” and last 4 lines “the histogram of the tensor values is generated by observing the execution during the inference to capture the possible numeric ranges of activations in each layer of the neural network.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao to include construct a histogram of a data distribution of inputs to the subsequent layer … to obtain one or more parameters of quantization based on the constructed histogram, wherein the one or more parameters are used to quantize the input to the subsequent layer, based on the teachings of Lee. One of ordinary skill in the art would have been motivated to make this modification in order to generate optimal quantized models, considering accuracy, as suggested by Lee (page 126, left column, paragraph 2, last 4 lines).
While Lee discloses obtaining one or more parameters of quantization, they do not disclose solving a minimum mean-square error problem to do so.
Nagel discloses:
solve a minimum mean-square error problem to obtain one or more parameters of quantization … (page 9, equation 16 and final paragraph, lines 1-3 “Mean squared error (MSE) … in this range setting method we find qmin and qmax that minimize the MSE between the original and the quantized tensor”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao, in view of Lee, to include solve a minimum mean-square error problem to obtain one or more parameters of quantization, based on the teachings of Nagel. One of ordinary skill in the art would have been motivated to make this modification in order to alleviate the issue of large outliers, as suggested by Nagel (page 9, final paragraph, line 1).
Regarding claim 6, Gao, in view of Lee, in further view of Nagel, discloses the method of claim 5 (and thus the rejection of claim 5 is incorporated). Lee further discloses:
wherein the one or more parameters comprise a scale S having a real value and an offset 0 having an integer value (equation (2) and page 128, left column, section 4.2, paragraph “Asymmetric” teaches scale as scale S and zero point as offset value O),
wherein quantizing the input comprises performing a quantization operation on each real-valued element r in an input array as follows:
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, wherein q(r) is a quantized value for the real-valued element r (equation (2) and page 128, left column, section 4.2, paragraph “Asymmetric” teaches a quantization scheme that performs equation (2) on each real valued element xfp32).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao to include wherein the one or more parameters comprise a scale S having a real value and an offset 0 having an integer value, wherein quantizing the input comprises performing a quantization operation on each real-valued element r in an input array as follows:
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, wherein q(r) is a quantized value for the real-valued element r, based on the teachings of Lee. One of ordinary skill in the art would have been motivated to make this modification in order to generate optimal quantized models, considering accuracy, as suggested by Lee (page 126, left column, paragraph 2, last 4 lines).
Regarding claim 15, Gao discloses the method of claim 14 (and thus the rejection of claim 14 is incorporated). Gao does not disclose:
further comprising using the at least one hardware processor to: construct a histogram of a data distribution of inputs to the neuron; and
solve a minimum mean-square error problem to obtain one or more parameters of quantization based on the constructed histogram,
wherein the one or more parameters are used to quantize the input.
Lee discloses:
construct a histogram of a data distribution of inputs to the neuron … to obtain one or more parameters of quantization based on the constructed histogram, wherein the one or more parameters are used to quantize the input (Fig. 1, and page 126, right column, paragraph 2, lines 2-4 “the histogram of possible numeric ranges in each layer of the neural network is captured for the activation of the quantization” and last 4 lines “the histogram of the tensor values is generated by observing the execution during the inference to capture the possible numeric ranges of activations in each layer of the neural network.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao to include construct a histogram of a data distribution of inputs to the neuron … to obtain one or more parameters of quantization based on the constructed histogram, wherein the one or more parameters are used to quantize the input, based on the teachings of Lee. One of ordinary skill in the art would have been motivated to make this modification in order to generate optimal quantized models, considering accuracy, as suggested by Lee (page 126, left column, paragraph 2, last 4 lines).
While Lee discloses obtaining one or more parameters of quantization, they do not disclose solving a minimum mean-square error problem to do so.
Nagel discloses:
solve a minimum mean-square error problem to obtain one or more parameters of quantization … (page 9, equation 16 and final paragraph, lines 1-3 “Mean squared error (MSE) … in this range setting method we find qmin and qmax that minimize the MSE between the original and the quantized tensor”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao, in view of Lee, to include solve a minimum mean-square error problem to obtain one or more parameters of quantization, based on the teachings of Nagel. One of ordinary skill in the art would have been motivated to make this modification in order to alleviate the issue of large outliers, as suggested by Nagel (page 9, final paragraph, line 1).
Regarding claim 16, Gao, in view of Lee, in further view of Nagel, discloses the method of claim 15 (and thus the rejection of claim 15 is incorporated). Lee further discloses:
wherein the one or more parameters comprise a scale S having a real value and an offset 0 having an integer value (equation (2) and page 128, left column, section 4.2, paragraph “Asymmetric” teaches scale as scale S and zero point as offset value),
wherein quantizing the input comprises performing a quantization operation on each real-valued element r in an input array as follows:
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, wherein q(r) is a quantized value for the real-valued element r (equation (2) and page 128, left column, section 4.2, paragraph “Asymmetric” teaches a quantization scheme that performs equation (2) on each real valued element xfp32).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao to include wherein the one or more parameters comprise a scale S having a real value and an offset 0 having an integer value, wherein quantizing the input comprises performing a quantization operation on each real-valued element r in an input array as follows:
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, wherein q(r) is a quantized value for the real-valued element r, based on the teachings of Lee. One of ordinary skill in the art would have been motivated to make this modification in order to generate optimal quantized models, considering accuracy, as suggested by Lee (page 126, left column, paragraph 2, last 4 lines).
Regarding claim 17, Gao discloses the method of claim 1 (and thus the rejection of claim 1 is incorporated). Gao does not disclose:
wherein quantizing the weights comprises solving: S,0: argmin (E(r - qs,o(r))2)
wherein S is a scale having a real value, 0 is an offset having an integer value, and
E(r - qs,o(r))2 is a mean-square error function that calculates an error between real values r of the weights and quantized values qso(r),
wherein
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.
Lee discloses:
wherein quantizing the weights comprises solving: S,0… wherein S is a scale having a real value, 0 is an offset having an integer value (equation (2) and page 128, left column, section 4.2, paragraph “Asymmetric” teaches scale as scale S and zero point as offset value),
… a mean-square error function that calculates an error between real values r of the weights and quantized values qso(r), wherein
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(equation (2) and page 128, left column, section 4.2, paragraph “Asymmetric” and page 130, left column, paragraph 1, lines 8-9 “The differentiable convex functions are mean square error”).
While Lee teaches wherein quantizing the weights comprises solving: S,0… wherein S is a scale having a real value, 0 is an offset having an integer value … a mean-square error function that calculates an error between real values r of the weights and quantized values qso(r), wherein
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, they do not explicitly teach solving argmin (E(r - qs,o(r))2) … where E(r - qs,o(r))2 is a mean-square error function.
Nagel discloses:
solving argmin (E(r - qs,o(r))2) … where E(r - qs,o(r))2 is a mean-square error function (page 9, equation 16 and final paragraph, lines 1-3 “Mean squared error (MSE) … in this range setting method we find qmin and qmax that minimize the MSE between the original and the quantized tensor” teaches solving the mean square error function in equation 16 , where r and qs,o(r) are original and quantized tensors respectively).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao, in view of Lee, to include solving argmin (E(r - qs,o(r))2) … where E(r - qs,o(r))2 is a mean-square error function, based on the teachings of Nagel. One of ordinary skill in the art would have been motivated to make this modification in order to alleviate the issue of large outliers, as suggested by Nagel (page 9, final paragraph, line 1).
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (Pub. No.: US 2020/0082269 A1), hereafter Gao, in view of Lee et al. ("Quantune: Post-training quantization of convolutional neural networks using extreme gradient boosting for fast deployment"), hereafter Lee, in further view of Nagel et al. ("A White Paper on Neural Network Quantization "), as cited in the IDS dated 01/31/2023, hereafter Nagel, in further view of Surti et al. (Pub. No.: US 2020/0311041 A1), hereafter Surti.
Regarding claim 18, Gao, in view of Lee, in further view of Nagel, discloses the method of claim 17 (and thus the rejection of claim 17 is incorporated). Gao, in view of Lee, in further view of Nagel, does not disclose:
wherein the solving comprises a ternary search.
Surti discloses:
wherein the solving comprises a ternary search (¶[0218] and ¶[0226] teaches solving using a ternary search).
Gao, Lee, Nagel, and Surti are analogous art because they are from the same field of endeavor, data compression and neural networks.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Gao, in view of Lee, in further view of Nagel, to include solving using a ternary search, based on the teachings of Surti. One of ordinary skill in the art would have been motivated to make this modification in order to improve memory bandwidth of workloads including machine learning, allowing for increased power efficiency and performance, as suggested by Surti (¶[0203]).
Response to Arguments
Applicant's arguments filed 03/25/2026 have been fully considered with regards to the 35 U.S.C. 102/103 rejection, but they are not persuasive.
The applicant asserts on page 8 of the remarks “Gao nowhere teaches selecting one neuron within a layer, quantizing that neuron's weights and that neuron's corresponding portion of the subsequent layer's input, freezing that neuron, and repeating until all neurons of that layer are done.”. The Examiner respectfully disagrees, as the BRI of selecting, freezing, and quantizing one neuron does not exclude the selection, freezing, and quantization of multiple neurons in a layer, as taught by Gao. The amended claims recite “freeze the selected neuron, such that the selected neuron is not subsequently modified during training, and iterate through each neuron in the current layer being quantized before proceeding to any subsequent layer”, which is taught by Gao, where Fig. 2, Fig. 4, and ¶[0039] and ¶[0053] teach freezing selected neurons in layers of the neural network such that they are not modified during training, and Fig. 4 teaches iterating through neurons of a current first layer being quantized before proceeding to a subsequent second layer. If the applicant wishes to specify that there are two distinct subsets of neurons in each layer, where one subset (i.e. a single neuron) is “frozen” while the second subset of “unfrozen” neurons in the same layer are subjected to further changes, with these changes not being applied to the frozen neuron, the claims could be amended to reflect that feature.
Claims 19 and 20 are substantially similar to claim 1, and thus are rejected on the same basis.
Claims dependent on independent claims do not overcome the deficiencies of the rejected independent claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
Isikdogan et al. (“SemifreddoNets: Partially Frozen Neural Networks for Efficient Computer Vision Systems”) teaches freezing convolution neurons.
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 extension fee 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 date of this final action.
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/H.Z.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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