DETAILED ACTION
This Action is responsive to claims filed 07/06/2026.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/04/2026 was filed after the mailing date of the Non-Final Rejection mailed 04/08/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claim 11 has been amended. Claims 1-19 are currently pending.
Response to Arguments
Applicant’s arguments, see Pages 6-8, filed 07/06/2026, regarding the 35 U.S.C. 102(a)(1) Rejection of Claims 1-19 have been fully considered and are persuasive. The 102(a)(1) Rejection of Claims 1-19 has been withdrawn.
Applicant's arguments, see Pages 8-10, filed 07/06/2026, regarding the 35 U.S.C. Rejection(s) of Claims 1-19 have been fully considered but they are not persuasive.
The Applicant argues the applicant of references Pope and Parashar. The Examiner respectfully disagrees with the Applicant, based on the generality of the claim limitations.
Based on the highly general recitation of the claims, the Examiner contends the operations of decompressed data by Pope and/or Parashar indicates the density must be predetermined. Nowhere in the claims is a determination of the sparsity density(s) made, or their values specified in a specification fashion. Based on the BRI of such generic limitation or elements, the Examiner contends any operation decompressing data, and/or recompressing it inherently involves pre-sparsified data, which broadly reads on “predetermined” densities. As was discussed in the Examiner Interview dated 07/06/2026, if this area is where the Applicant feels their novelty is best expressed, the Examiner encourages further detail or implementation be amended into the claim in order to overcome the broad application of prior art. See the reiterated 103 Rejection(s) below.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4, 6, 8-15, and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pope (US 11,728,826 B2), hereinafter Pope and Parashar et al. (SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks, 2017), hereinafter Parashar.
In regards to claim 1: The present invention claims: “A memory system for training a neural network model, comprising: a decompressor unit configured to decompress an activation tensor…based on the activation tensor being compressed, and to decompress a weight tensor…based on the weight tensor being compressed;” Pope teaches “The data can be stored and sent by the memory device 130 according to a variety of different formats. In examples in which the workload for the system 100 is to execute or train a neural network, data can be stored and sent as tensors. A tensor is a multi-dimensional array. For example, a zero-dimensional tensor is a scalar value, a one-dimensional tensor is a vector, and a two-dimensional tensor is a matrix. Elements of a tensor can be, for example values for different model parameters [weight tensor] for a given layer of a neural network, or input or output between layers [activation tensor] of a neural network or of the neural network itself.” (Column 7, Lines 11-21) See Pope Figure 1 for the memory device sending the compressed data to a decompression unit (Item 110). Figure 1 also illustrates the data sent to the decompressor is already compressed [based on the…tensor being compressed]
“a buffer unit configured to receive the activation tensor…and the weight tensor…” (Examiner’s Note: The Examiner interprets this limitation to broadly point to a buffer or memory unit for storing the decompressed data) Pope teaches the memory and processing device can be any of multiple, commonplace memory and processing technologies (Column 6, Lines 55-63).
“and a neural processing unit configured to receive the activation tensor and the weight tensor from the buffer unit and to compute a result for the activation tensor and the weight tensor …the activation tensor and based on …the weight tensor.” (Examiner’s Note: The Examiner interprets this limitation to broadly point to executing or training the neural processing unit on the decompressed data). Pope teaches “The decompressor device 120 receives and processes the compressed data 112A to generate uncompressed data 114A. The processor 140 receives the uncompressed data 114A and performs one or more operations on the data. For example, the processor 140 can be configured for performing logical or arithmetic operations on the uncompressed data 114A, and generate output uncompressed data 114B.” See above where Pope teaches the system can be for training or executing a neural network.
Pope fails to explicitly teach “a first predetermined sparsity density” and “a second predetermined sparsity density” explicitly; however, Parashar, in a similar field of endeavor, teaches the benefits of exploiting sparsity in weight and activation tensors, particularly in Section 6.1 (Page 36), where Figure 8 demonstrates improved performance as sparsity increases.
Parashar indicates the improved performance of models like SCNN over the state of the art at the time of their writing as sparsity increases (Page 36). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to incorporate sparsity densities requisite to achieve their performance desires when implemented in a compression/decompression system such as Pope’s, especially as Pope operates on compressed/decompressed data in its operation (Figure 1).
In regards to claim 2: The present invention claims: “wherein the first predetermined sparsity density is based on a structured-sparsity arrangement or a random-sparsity arrangement.” Pope utilizes entropy encoding for compression and decompression (Brief Summary, Columns 1-2). A cursory search shows entropy encoding is a structure-sparsity arrangement. Parashar also teaches “Figure 7 shows an example of SCNN’s compressed sparse encoding for R = S = 3 and K = 2 with 6 non-zero elements. The encoding includes a data vector consisting of the non-zero values and an index vector that includes the number of non-zero values followed by the number of zeros before each value.” (Pages 33-34).
In regards to claim 4: The present invention claims: “wherein the second predetermined sparsity density is based on a structured-sparsity arrangement or a random-sparsity arrangement.” Pope utilizes entropy encoding for compression and decompression (Brief Summary, Columns 1-2). A cursory search shows entropy encoding is a structure-sparsity arrangement. Parashar also teaches “Figure 7 shows an example of SCNN’s compressed sparse encoding for R = S = 3 and K = 2 with 6 non-zero elements. The encoding includes a data vector consisting of the non-zero values and an index vector that includes the number of non-zero values followed by the number of zeros before each value.” (Pages 33-34).
In regards to claim 6: The present invention claims: “wherein the second predetermined sparsity density is based on a structured-sparsity arrangement or a random-sparsity arrangement.” Pope utilizes entropy encoding for compression and decompression (Brief Summary, Columns 1-2). A cursory search shows entropy encoding is a structure-sparsity arrangement. Parashar also teaches “Figure 7 shows an example of SCNN’s compressed sparse encoding for R = S = 3 and K = 2 with 6 non-zero elements. The encoding includes a data vector consisting of the non-zero values and an index vector that includes the number of non-zero values followed by the number of zeros before each value.” (Pages 33-34).
In regards to claim 8: The present invention claims: “wherein the decompressor unit is further configured to decompress the activation tensor to the first predetermined sparsity density using first metadata associated with the activation tensor and is further configured to decompress the weight tensor to the second predetermined sparsity density using second metadata associated with the weight tensor.” Pope Column 10, Lines 26-43 details how the compression unit may include or concatenate pertinent entropy codewords to the data it receives. The Examiner maps this to the broad recitation of “metadata” in the context of its relevance to the compression or decompression of the data stored in the memory device. Column 3, Lines 10-12 (at least) teach “The decompressor device can include a plurality of 10 entropy coders configured to decompress data using the entropy encoding.” The Examiner maps this to the broad recitation of using the metadata to decompress the data in conjunction with sparsity densities taught by Parashar.
In regards to claim 9: The present invention claims: “a compressor unit configured to receive and compress the result computed by the neural processing unit, and a memory further stores the result compressed by the compressor unit.” See above where Pope Figure 1 shows a compressor which stores compressed data into a memory unit.
In regards to claim 10: The present invention claims: “wherein the compressor unit is further configured to generate metadata associated with the result, and wherein the memory further stores the metadata.” Pope Column 10, Lines 26-43 details how the compression unit may include or concatenate pertinent entropy codewords to the data it receives. The Examiner maps this to the broad recitation of “metadata” in the context of its relevance to the compression or decompression of the data stored in the memory device in conjunction with sparsity densities taught by Parashar.
In regards to claims 11-15 and 18-19: Claims 11-15 and 18-19 recites similar limitations to claims 1-2, 4, 6, and 8-10 with the exception of “A memory system for training a neural network model, comprising:” of claim 11. Therefore, both sets of claims are similarly rejected.
Claim(s) 3, 5, 7, 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pope and Parashar as applied to claims 1 and 11 above, in further view of Zhou et al. (LEARNING N:M FINE-GRAINED STRUCTURED SPARSE NEURAL NETWORKS FROM SCRATCH, 2021), hereinafter Zhou.
In regards to claims 3, 5, 7, 16-17: The claims similarly recite a first and second predetermined sparsity density “is based on a 1:4 structured-sparsity arrangement, or a 2:8 structured-sparsity arrangement.” While Pope makes reference to compression ratios throughout their disclosure (Background, etc. at least), and Parashar Figure 8 at least references sparsity ratios as well, the combination of Pope and Parashar fails to explicitly teach ratios with the claimed specificity.
However, Zhou teaches “We also compare the performance of neural networks with different granularities of fine-grained structured sparsity (i.e., 1:4, 2:4, 2:8, 4:8) and conduct thorough experiments on several typical deep neural networks with different N:M sparsity levels, covering image classification, detection, segmentation, optical flow estimation, and machine translation.” (Page 2) and “The sparsity of 1:4 and 2:8 are both 75%. In Table 1, we observe that the 4:8 structural sparsity outperforms 2:4 with the same computational cost, and 2:8 also performs better than 1:4. the training curve in Fig. 6(a). It shows that with the same sparsity for N:M structural sparse patterns, a larger M will lead to better performance since it can provide more abundant convolution kernel shape…” (Page 8).
Zhou demonstrates that using fine grain compression ratios such as 1:4 and 2:8 would have been known in the art at the time of the Applicant’s filing, and demonstrates a benefit of using 2:8 over 1:4. It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to combine the systems of Pope and Parashar with the known methods of Zhou.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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