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 Arguments
Applicant's arguments filed 4/22/2026 have been fully considered but they are not persuasive.
Applicant argues, “The Office Action does not discuss Step 2 and it is respectfully submitted that the Office Action fails to show that the claims are directed to non-statutory subject matter for at least this reason.” Remarks 8. This argument is under the step 1 heading, and Applicant probably meant step 1, not step 2. There is no rule that says the office has to affirmatively find that claims are in a statutory category in order to apply the rest of the 101 analysis for claims that are in a statutory category, but are directed to a judicial exception. Therefore, 101 rejection is proper.
Applicant argues,
a person could not, as a practical mental process, perform any of the following steps, as recited in claim 18, with similar steps in claim 23: "obtaining a codebook including a codebook size for quantizing parameters of a tensor associated with at least one layer of a Deep Neural Network, the codebook size obtained according to a mean square error value determined between the tensor and a quantized version of the tensor" and "quantizing the parameters of the tensor using the obtained codebook to represent the parameters with at least a determined size".
Remarks 9.
Examiner found that the claims are directed to “an abstract idea of a mental concept and mathematical relationship…” The rule is that mental concepts need to be capable of being performed in the mind. This requirement is not the rule for mathematical relationships. Mean squared error (MSE) is something that can be performed with pen and paper and in the mind. For instance, the following MSE calculation would be eight subtraction problems, one addition problem and one division problem, see below.
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For vectors, the subtraction is usually replaced with a dot product divided by scalars, which is also something that can be done in the head or on paper. Quantizing based on a scalar codebook size can be as simple as a truncation operation, which is also a mental operation. As to the fact that this abstract idea is also a mathematical operation, arguments about what can be performed by the human mind don’t rebut the finding that the claims are directed to a mathematical relationship. Therefore, even if MSE and quantizing weren’t something that could be done in the human mind, the claims would still be directed to a mathematical relationship without significantly more.
Applicant argues that the claims amount to significantly more because the additional elements in the claim “improves the art by utilizing the same as codebook quantization may be more efficient with large tensors, for example as overhead may be significant compared to tensor size for a small tensor (see paragraph 0065 of the specification).” Remarks 11. The rule is that “A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional element(s) demonstrating that the claim as a whole integrates the exception into a practical application.” MPEP 2106.04(d)(2). Using the same codebook size for all tensors is part of the mathematical relationship in the claims. Using the same codebook size is not an additional element that could integrate the abstract idea into a practical application, because the codebook size is part of the abstract idea. For the reasons above, the 101 rejection was proper.
The arguments regarding the prior art rejection does not apply to the new art necessitated by Applicant’s claim amendments.
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 18-27, 38 and 39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to an abstract idea of a mental concept and mathematical relationship without significantly more. The claims recite the mental concept and mathematical relationship of obtaining a codebook based on a tensor; quantizing parameters; encoding the quantized parameters obtaining a codebook size from a search; quantizing a probability density function (PDF); and selecting a PDF bounding factor. This judicial exception is not integrated into a practical application because the steps of transmitting information is insignificant extra solution activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of processors are generic computer parts.
Claim Rejections - 35 USC § 112
All 112 rejections are withdrawn, thank you.
Claim Rejections - 35 USC § 103
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 18, 22, 23, 27, 38 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions by Wu et al and Quantization by Gray et al.
Claims 19 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions by Wu et al, Quantization by Gray et al and US 20190327479 A1 to Chen et al.
Claims 20 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions by Wu et al, Quantization by Gray et al and US 20120170830 A1 to Blanton et al.
Claims 21 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions by Wu et al, Quantization by Gray et al and Weighted-Entropy-based Quantization for Deep Neural Networks by Park et al.
Wu teaches claims 18 and 23. A method comprising:
obtaining a codebook including a codebook size for quantizing parameters of a tensor associated with at least one layer of a Deep Neural Network, (Wu sec. 2.1 “a convolutional layer RsXsXcXm … we reshape it as a matrix W…. we treat all columns of W as N samples, and apply k-means to assign them with K clusters…. we need only to store the cluster indexes and codebooks after k-means.” The clusters are the codebook. K is the codebook size.) the codebook size obtained according to a (Wu sec. 2.1 “cluster rate for each layer as K/N here… each convolutional layer chooses its K value such that all layers have the same cluster rate, expect for the first layer whose cluster rate is often set higher.” The value is the cluster rate because the cluster rate is the number of cluster centers K divided by the number of weights N – so the cluster rate is determined based on quantize tensors (cluster centers) and tensors (weights). Wu, above, teaches that K (codebook size) for each layer is based on the cluster rate for the layer.)
quantizing the parameters of the tensor using the obtained codebook to represent the parameters with at least a determined size. (Wu sec. 1.1 “compression is performed via weight-sharing, by only recording cluster centers and weight assignment indexes…” Wu footnote 1 p. 7 “we only quantize weight and activation to 8 bit and to 16 bit in fully-connected layers, respectively…” The compression is the quantizing. The obtained codebook is the cluster centers. The cluster centers represent the parameters (weights) with at leas a determined size of 8bit or 16 bit.)
Wu doesn’t teach a distortion value like the distortion value described in paragraph 90 of the instant specification.
However, Gray teaches determining a K based on mean square error value. (Gray sec. I p. 2325 “The most common distortion measure is the squared error… and the average distortion becomes an expectation…” Average distortion is the claimed mean square error.)
The claims, Gray and Wu all quantize data. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use MSE for distortion because “The most common distortion measure is the squared error…” Id.
Wu teaches claims 19 and 24. The method of claim 18, further comprising:
encoding the quantized parameters
(Wu sec. 2.1 “a convolutional layer RsXsXcXm … we reshape it as a matrix W…. we treat all columns of W as N samples, and apply k-means to assign them with K clusters…. we need only to store the cluster indexes and codebooks after k-means.” Assigning a weights to clusters is encoding the parameters/weights. Wu sec. 1 teaches that the point of the compression is to “bring CNN into resource-constrained mobile devices…” brining quantized NN into another device requires encoding the quantized NN to transfer the quantized NN to the mobile device.)
Wu doesn’t teach transmitting the compressed neural network.
However, Chen teaches encoding the quantized parameters in a bitstream for transmission; and
transmitting the encoded quantized parameters in the bitstream to a decoder. (Chen para 11 “FIG. 1 is a block diagram illustrating an example process for compressing, transmitting, and decompressing neural network data…”)
The claims, Chen and Wu are all directed to compressing neural networks. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to send a compressed neural network to decrease “demands on storage performance and memory access bandwidth.” Chen para 5.
Wu teaches claims 20 and 25. The method of claim 18, further comprising obtaining the codebook size from a (Wu sec. 2.1 “a convolutional layer RsXsXcXm … we reshape it as a matrix W…. we treat all columns of W as N samples, and apply k-means to assign them with K clusters…. we need only to store the cluster indexes and codebooks after k-means.” The clusters are the codebook. K is the codebook size.)
Wu doesn’t teach the binary search for K.
However, Blanton teaches a binary search for K. (Blanton para 45 “as long as each data point (i.e., snippet image) is reasonably close to its cluster center, K is not increased to avoid fragmentation of the snippet images. To improve efficiency, a binary search may be used to search for K.” K is the codebook size.)
The claims, Wu, and Blanton all use K-means clustering. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to do a binary search for K to “improve efficiency…” Blanton para 45.
Wu teaches claims 21 and 26. The method of claim 18, further comprising:
quantizing based on a (Wu sec. 2.1 “a convolutional layer RsXsXcXm … we reshape it as a matrix W…. we treat all columns of W as N samples, and apply k-means to assign them with K clusters…. we need only to store the cluster indexes and codebooks after k-means.” Assigning a weights to clusters is quantizing.)
selecting from a (Wu sec. 2.1 “cluster rate for each layer as K/N here… each convolutional layer chooses its K value such that all layers have the same cluster rate…” choosing K values is selecting.)
Wu doesn’t teach quantizing based on a selected PDF factor based on entropy.
However, Park teaches quantizing based on a probability density function (pdf)-based initialization bounded according to a first pdf factor; and (Park sec. 4.1 “Based on this importance value of each weight, we derive a metric for evaluating the quality of a clustering result (i.e., quantization result) based on weighted entropy…” The quantizing of the weights is based on a weighted entropy S which is a function of relative frequency. Relative frequency is a PDF-based initialization bounded according to a first pdf factor.)
selecting from a candidate bounding pdf factor, the candidate bounding pdf factor based on an entropy obtained from candidate quantized parameters. (Park sec. 4.1 p. 5459 right column teaches the selection of a relative frequency (pdf factor) based on entropy of the weights “Starting from the initial cluster boundaries, we iteratively perform incremental search on the new cluster boundaries... for each cluster boundary candidate c’i, we recalculate the weighted entropy... and update the boundary to c'i only if the new overall weighted entropy S' is higher than the current one.”)
The claims, Wu and Park all quantize a set of weights. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to quantize using a pdf factor selected based on entropy of the quantized parameters in order to “greatly reduc[] the design-time effort to quantize the network.” Park abs.
Wu teaches claims 22 and 27. The method of claim 18, further comprising encoding information representative of a codebook type corresponding to the codebook. (Wu sec. 1 teaches that the point of the compression is to “bring CNN into resource-constrained mobile devices…” brining quantized NN into another device requires encoding the quantized NN to transfer the quantized NN to the mobile device. Wu sec. 2.1 “When K << N, we need only to store the cluster indexes and codebooks after k-means.” The codebook type is K-means, the codebook is the k clusters representing the weights of the quantized network.)
Gray teaches claims 38 and 39. (New) The method of claim 21, wherein the mean square error value determined between the tensor and the quantized version of the tensor comprises a maximum mean square error value determined between the tensor and the quantized version of the tensor. (Gray p. 2329 “Second, based on his optimality conditions, Lloyd developed an iterative descent algorithm for designing quantizers for a given source distribution: begin with an initial collection of reproduction levels; optimize the partition for these levels by using a minimum distortion mapping…” The minimum distortion mapping is a maximum squared error that can be mapped to.)
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/AUSTIN HICKS/ Primary Examiner, Art Unit 2142
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