DETAILED ACTION
This communication is in response to the after-final amendment filed 6/30/26 in which claims 1-5, 7-12, 15-18 were amended. Claims 1-21 are currently pending.
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 .
Continued Examination Under 37 CFR 1.114
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 final rejection. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/30/26 has been entered.
Response to Arguments
The 103 art rejection over Yoon and Busato is withdrawn.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
A processor comprising:
one or more circuits to:
determine that respective dimensions of one or more tensors have different lengths with respect to a shape that can be processed using a structured sparsity of one or more processing resources; and
modify the different lengths of the different respective dimensions of the one or more tensors to fit the shape of the structured sparsity of one or more processing resources.
Step 1: YES. The claim is directed to a processor and, therefore, is a statutory category.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Step 2A Prong 2/Step 2B: NO. Recitation of generic computer components (e.g., processor, circuits, processing resources) is mere instruction to apply the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Accordingly, claim 1 is ineligible.
Claim 2
The processor of claim 1, wherein the one or more circuits are further to cause the one or more tensors to be compatible with the one or more processing resources and based, at least in part, on the modified different lengths of the different respective dimensions of the one or more tensors.
Step 2A Prong 1: YES. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Step 2A Prong 2/Step 2B: NO. Recitation of generic computer components (e.g., processor, circuits, processing resources) is mere instruction to apply the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Claim 3
The processor of claim 1, wherein:
the one or more tensors include one or more input tensors and one or more weight tensors; and
the one or more circuits are further to cause the different lengths of the different respective dimensions of the one or more tensors to be modified to be compatible with the one or more processing resources.
Step 2A Prong 1: YES. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Step 2A Prong 2/Step 2B: NO. Recitation of generic computer components (e.g., processor, circuits, processing resources) is mere instruction to apply the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Claim 4
The processor of claim 1, wherein:
modification of the different lengths of the different respective dimensions of the one or more tensors is to add a number of elements to the one or more tensors.
Step 2A Prong 1: YES. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Claim 5
The processor of claim 1, wherein:
the one or more tensors are one or more sparse tensors; and the different lengths of different respective dimensions of the one or more sparse tensors are modified to have one or more respective shapes identical to shapes of one or more input tensors used in training a neural network.
Step 2A Prong 1: YES. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly, claim 5 is ineligible.
Claim 6
The processor of claim 1, wherein:
the one or more tensors include two tensors; and
one or more circuits are further to cause the two tensors to be fused to be compatible with one or more sparse weight tensors.
Step 2A Prong 1: YES. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Step 2A Prong 2/Step 2B: NO. Recitation of generic computer components (e.g., processor, circuits, processing resources) is mere instruction to apply the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Accordingly, claim 6 is ineligible.
Claim 7
The processor of claim 1, wherein:
the one or more tensors include one or more input tensors; and
the modification of the different lengths of the different respective dimensions of the one or more dimensions includes coalescing the one or more input tensors to be compatible with a sparse weight tensor.
Step 2A Prong 1: YES. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly claim 7 is ineligible.
Claim 8
A system, comprising:
one or more processors to cause:
a determination that respective dimensions of one or more tensors have different lengths with respect to a shape that can be processed using a structured sparsity of one or more processing resources; and
modification of the different lengths of the different respective dimensions of the one or more tensors to fit the shape of the structured sparsity of one or more processing resources.
Step 1: YES. The claim is directed to a system and, therefore, a statutory category.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Step 2A Prong 2/Step 2B: NO. Recitation of generic computer components (e.g., processor, circuits, processing resources) is mere instruction to apply the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Accordingly, claim 8 is ineligible.
Claim 9
The system of claim 8, wherein:
the one or more tensors include one or more weight tensors and one or more input tensors;
first different lengths of different respective dimensions of the one or more weight tensors and second lengths of different respective dimensions of the one or more input tensors are modified; and
the second lengths of the different respective dimensions of the one or more weight tensors are modified to become sparse weight tensors.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly, claim 9 is ineligible.
Claim 10
The system of claim 8, wherein:
the one or more tensors include one or more input tensors and one or more weight tensors; and
modification of the different lengths of different respective dimensions of the one or more input tensors is based, at least in part, on a modification other lengths of different respective dimensions of the one or more weight tensors.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly, claim 10 is ineligible.
Claim 11
The system of claim 8, wherein:
the one or more tensors include one or more weight tensors and one or more input tensors, and further lengths of different respective dimensions of each of the one or more weight tensors are modified based further, at least in part, on dimensions of the one or more input tensors.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly, claim 11 is ineligible.
Claim 12
The system of claim 8, wherein:
the one or more tensors are one or more sparse tensors compatible with the one or more processing resources; and
further lengths of different respective dimensions of the one or more sparse tensors are modified to have one or more respective shapes identical to shapes of one or more input tensors used in training a neural network.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly, claim 12 is ineligible.
Claim 13
The system of claim 8, wherein:
the one or more tensors include two tensors that share an input; and
the one or more processors are further to cause the two tensors to be fused to be compatible with a sparse weight tensor.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly, claim 13 is ineligible.
Claim 14
The system of claim 8, wherein:
the one or more tensors include two tensors that share an input; and
the one or more processors are further to cause the two tensors to be fused using concatenation, stack, padding, or some combination thereof.
Step 2A Prong 1: YES. Causing two tensors to be fused using concatenation, stack, or padding can be performed mentally or with the aid of a pen and paper and, therefore, falls under the Mental Processes grouping of abstract ideas.
Step 2A Prong 2: NO. Data inputting is considered insignificant extra-solution activity. Thus, sharing an input does not cause the exception to be integrated into a practical application. Recitation of generic computer components (e.g., processor, circuits, processing resources) is mere instruction to apply the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Step 2B: NO. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 14 is ineligible.
Claim 15
A method, comprising:
determining that respective dimensions of one or more tensors have different lengths with respect to a shape that can be processed using a structured sparsity of one or more processing resources; and
modifying the different lengths of the different respective dimensions of the one or more tensors to fit the shape of the structured sparsity of one or more processing resources.
Step 1: YES. The claim is for a method and, therefore, falls under a statutory category.
Step 2A Prong 1: YES. Determining that the dimension of a tensor has a different length with respect to a shape that can be processed using a structured sparsity of a processor, can be performed mentally and, therefore, falls under the Mental Processes category of abstract ideas. Modifying the length of a dimension of a tensor to fit the shape of the structured sparsity can also be performed mentally or with the aid of pen and paper given that a “tensor” under a broadest reasonable interpretation encompasses a mathematical object.
Accordingly, claim 15 is ineligible.
Claim 16
The method of claim 15, wherein:
the one or more tensors include one or more input tensors; and
the different lengths of different respective dimensions of the one or more input tensors are modified to be expanded or coalesced.
Step 2A Prong 1: YES. Causing two tensors to be fused using concatenation, stack, or padding can be performed mentally or with the aid of a pen and paper and, therefore, falls under the Mental Processes grouping of abstract ideas.
Step 2A Prong 2: NO. Data inputting is considered insignificant extra-solution activity. Thus, sharing an input does not cause the exception to be integrated into a practical application. Recitation of generic computer components (e.g., processor, circuits, processing resources) is mere instruction to apply the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Step 2B: NO. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 16 is ineligible.
Claim 17
The method of claim 15, wherein:
the one or more tensors include one or more weight tensors and one or more input tensors; and
further lengths of different respective dimensions of the one or more weight tensors and the one or more input tensors are modified to output one or more sparse tensors.
Step 2A Prong 1: YES. Characterizing tensors as weight or input tensors is a mental process. Modifying lengths of the tensors to produce sparse tensors encompasses a mental process that can be performed in the mind or with the aid of pen and paper.
Accordingly, claim 17 is ineligible.
Claim 18
The method of claim 15, wherein:
the one or more tensors include one or more sparse tensors based, at least in part, on one or more input tensors used to train a neural network; and
the different lengths of the different respective dimensions of the one or more tensors are modified to have one or more respective shapes identical to shapes of the one or more input tensors.
Step 2A Prong 1: YES. Characterizing tensors as input tensors used to train a neural network is a mental process. Modifying lengths of the tensors to produce tensors identical in shape to input tensors encompasses a mental process that can be performed in the mind or with the aid of pen and paper.
Accordingly, claim 18 is ineligible.
Claim 19
The method of claim 15, further comprising:
identifying which of the one or more tensors share an input.
Step 2A Prong 1: YES. Identifying a tensor as sharing an input encompasses a mental process.
Accordingly, claim 19 is ineligible.
Claim 20
The method of claim 15, further comprising:
analyzing which of the one or more tensors require one or more modifications, wherein the one or more tensors are between two or more layers of a neural network.
Step 2A Prong 1: YES. Analyzing which tensor requires modification encompasses a mental process. Characterizing the tensors as between two layers of a neural network may also be performed mentally.
Accordingly, claim 20 is ineligible.
Claim 21
The method of claim 15, further comprising:
analyzing the one or more tensors, wherein the one or more tensors are input tensors; and
fusing together two or more input tensors that share an input and are not compatible for the one or more processing resources.
Step 2A Prong 1: YES. Analyzing tensors encompasses a mental process. Fusing, under a broadest reasonable interpretation, encompasses a mental process that can be performed purely mentally or with the aid of pen and paper.
Accordingly, claim 21 is ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wu, Zhaofeng, et al. "Dynamic sparsity neural networks for automatic speech recognition." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.
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/SHAHID K KHAN/Primary Examiner, Art Unit 2146
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