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 2/5/2026 have been fully considered.
With respect to claim 17’s written description. Examiner interprets the computing device as the means for accomplish all functions in claim 17. Therefore, the 112a rejection of claim 17 is withdrawn.
Applicant argues, “Applicant submits that machine learning is an established technology and that the subject matter of independent claims 1, 9, 17, and 18 provides a particular solution/improvement to machine learning…. the claimed features provide a solution to the problem that a ‘[t]rained neural network can require costly nested loop execution that can burden the computing resources of such computing hardware…” Remarks 10-11. There is no law or rule that recognizes machine learning is a technology that may be improved in order to integrate an abstract idea into a practical application. MPEP 2106.04(d)(1). Improving an algorithm, even if it is a machine learning algorithm, is not, by itself, patent eligible subject matter.
Applicant argues, “However, Taba fails to describe, at least, ‘accessing a first memory to retrieve weights of the weight tensor in a transformed order that is different than an order for retrieving the weights for a calculation at a network layer of a trained machine learning model, wherein accessing the first memory to retrieve the weights comprises retrieving the weights from the first memory according to a row-major layout,’ as recited in claim 1.” Remarks 12. Examiner agrees. New art is added to teach the added claim limitations.
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 claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
Claim 17 invokes a mean-for interpretation.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mathematical relationship abstract idea without significantly more. The claims recite accessing first data, transforming data and loading the data. This judicial exception is not integrated into a practical application because the claim is merely linked to the field of computing. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements such as a first and second memory, processing device and computing device are generic computer parts. Further, the specification makes it clear that the two memories can be virtual memories, which means that this data manipulation can be entirely inside of one memory, “the first memory and the second memory may be within the same memory device, such as different partitions within the same memory device.” Spec. 91. Therefore, the additional elements are really just one memory and a processor inside of a computer.
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 1-3, 5-11, 13-20 and 22-25 are rejected under 35 U.S.C. 103 as being unpatentable over US20200104718A1 to Taba et al and US20200285892A1 to Baum et al.
Claims 4, 12 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over US20200104718A1 to Taba et al, US20200285892A1 to Baum et al and US20190205735A1 to Smelyanskiy et al (Smely).
Taba teaches claims 1, 9, 17 and 18. (Currently Amended) A method for weight layout transformation of a weight tensor, comprising:
accessing a first memory to retrieve weights of the weight tensor in a transformed order that is different than an order for retrieving the weights for a calculation at a network layer of a trained machine learning model, wherein accessing the first memory to retrieve the weights comprises retrieving the weights from the first memory according to a (Taba para 41 “IPU 200 includes a memory 201 for the neural network model. As described above, the neural network model may include the synapse weights for a neural network to be computed.” Taba para 70 “the weight order and input order are illustrated for exemplary snake paths.” Taba para 71 “FIGS. 13A-F, the weight order and input order are illustrated for exemplary spiral paths.” The weights are stored in a matrix in model memory 301 in Taba fig. 3, and then computed in the cores 305/303.)
loading the weights to a second memory in the transformed order. (Taba para 71 “FIGS. 13A-F, the weight order and input order are illustrated for exemplary spiral paths.”)
Taba doesn’t teach row-major layout.
However, Baum teaches row-major layout. (Baum para 328 “the compiler reorders the plurality of weight by unrolling one of the weights into a vector using a row-major order.”)
Baum, Taba and the claims all organize to NN parameters in memory. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use Baum’s alternative layout limit access to memory because “[l]imiting access to memory by the compute elements using a memory windowing scheme significantly improves the available bandwidth while greatly reducing the required address and control routing.” Baum para 228.
Taba teaches claims 2, 10 and 19. (Currently Amended) The method of claim 1, wherein accessing the first memory to retrieve the weights of the weight tensor in the transformed order that is different than the order for retrieving the weights for the calculation at the network layer of the trained machine learning model comprises retrieving the weights according to a pattern of memory access iterating over a slowest changing dimension of the weight tensor. (Taba para 71 “FIGS. 13A-F, the weight order and input order are illustrated for exemplary spiral paths.” Taba fig. 6a iterates over all the dimensions, R and S, so Taba necessarily iterates over the slowest changing dimension.)
Taba teaches claims 3, 11 and 20. (Original) The method of claim 2, wherein accessing the first memory to retrieve the weights according to the pattern of memory access iterating over the slowest changing dimension of the weight tensor comprises retrieving the weights according to a pattern of memory access iterating over a height dimension of the weight tensor. (Taba para 71 “FIGS. 13A-F, the weight order and input order are illustrated for exemplary spiral paths.” Taba fig. 6a iterates over all the dimensions, R and S, so Taba necessarily iterates over the height dimension.)
Taba teaches claims 4, 12 and 21. The method of claim 1, wherein accessing the first memory to retrieve weights of the weight tensor in the transformed order that is different than the order for retrieving the weights for the calculation at the network layer of the trained machine learning model comprises accessing the first memory to retrieve weights of the weight tensor in an order specified by a first counter variable and a second counter variable of a first memory access command, wherein the first counter variable and the second counter variable are configured to represent a location in the weight tensor, and wherein the first counter variable and the second counter variable are (Taba fig. 6b shows an access and storage order that are different. The first and second counter variable is the position in the matrix, see below.) Taba doesn’t teach iterating one column at a time, which is what happens when you transpose the counters in a square 2D matrix.
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Taba fig. 6b.
However, Smely teaches traversing a column one-at-a-time. (Smely para 47 “hardware lowering operations of the instant disclosure may involve linearizing matrices or submatrices (e.g., patches of a matrix) by converting them to and/or arranging them in a single row or column of a matrix multiplication operand to allow for matrix multiplication.”)
Taba, Smely and the claims are all directed to machine implementations of linear algebra. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to traverse the columns after accessing on a row-by-row basis in order to “improve the efficiency of neural networks that rely on convolution” (Smely para 45), because flattening the matrix allows for parallel processing of all the columnar values at once.
Taba teaches claims 5, 13 and 22. The method of claim 1, wherein loading the weights to the second memory in the transformed order comprises loading the weights to the second memory in a linear layout according to a pattern of memory access iterating over a slowest changing dimension of the weight tensor. (Taba para 71 “FIGS. 13A-F, the weight order and input order are illustrated for exemplary spiral paths.” Taba fig. 6a iterates over all the dimensions, R and S, so Taba necessarily iterates over the slowest changing dimension.)
Taba teaches claims 6, 14 and 23. The method of claim 5, wherein loading the weights to the second memory in the linear layout comprises loading the weights to the second memory as a linear array. (Taba shows the linear array as the line traversing the weights in Fig. 6b, see below.)
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Taba teaches claims 7, 15 and 24. The method of claim 1, further comprising:
retrieving the weights from the second memory in the transformed order; and
reordering the weights to the order for implementing the calculation at the network layer of the trained machine learning model. (Taba para 71 “FIGS. 13A-F, the weight order and input order are illustrated for exemplary spiral paths.”)
Taba teaches claims 8, 16 and 25. The method of claim 1, wherein the first memory and the second memory are in a same memory device. (Taba para 43 “IPU 300 includes a model memory 301… IPU 300 includes a plurality of cores 303.” The IPU 300 is the memory device, and the cores 303 and model memory 301 are in the IPU 300, see below.)
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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