DETAILED ACTION
This action is in response to the original filing of 9-29-2023. Claims 1-20 are pending and have been considered below:
Claim Objections
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: hardware unit configured to in claims 7, 9, 10 and 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
In paragraph 171 the unit can be represented as a plurality of functionalities including software. The claim should explicitly recite a processor.
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 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 1-9, 16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khailany et al. (“Khailany” 20220067512 A1) in view of Nagel et al. (“Nagel” 20200302299 A1).
Claim 1: Khalianya discloses a method of processing data in accordance with a neural network, the neural network comprising a sequence of layers comprising a first convolution layer, a second convolution layer, and none, one or more than one middle layer between the first and second convolution layers, the method comprising:
scaling, using hardware logic, a tensor in the neural network, after the first convolution layer and before the second convolution layer (Paragraphs 4, 6; per layer, therefore tensor between layer),
on a per channel basis by a set of per channel activation scaling factors(Paragraph 37-38; per channel scaling); and implementing, using the hardware logic, the second convolution layer with weights that have been scaled on a per input channel basis (Paragraph 37-38; per channel scaling);
Khalianya may not explicitly disclose by the inverses of the set of per channel activation scaling factors.
Nagel is provided because it discloses a scaling factor for layers and further discloses the inverse of the scaling factor (abstract and Paragraph 33).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide the inverse capability in the input channels of Khalianya. One would have been motivated to provide this functionality as method for minimizing error in scaling (Nagel: abstract).
Claim 2: Khalianya and Nagel disclose a method of claim 1, wherein the tensor that is scaled on a per channel basis by the set of per channel activation scaling factors is an output tensor of the first convolution layer (Khalianya: Paragraphs 26-27 and 37-38; per channel activation).
Claim 3: Khalianya and Nagel disclose a method of claim 2, wherein the sequence comprises a middle layer and an output tensor of the middle layer feeds a first branch comprising the second convolution layer and a second branch, and the method further comprises scaling, using the hardware logic, a tensor in the second branch on a per channel basis by the inverses of the set of per channel activation scaling factors (Khalianya: Paragraphs 4 and 26-28; per layer (includes branches) with tensor Nagel: abstract and Paragraph 33; inverse functionality).
Claim 4: Khalianya and Nagel disclose a method of claim 2, wherein the output tensor of the first convolution layer feeds a first branch comprising the second convolution layer and a second branch, and the method further comprises scaling, using the hardware logic, a tensor in the second branch on a per channel basis by the inverses of the set of per channel activation scaling factors (Khalianya: Paragraphs 4 and 26-28; per layer (includes branches)and 37-38 (per-channel function) and Nagel: abstract and Paragraph 33; inverse functionality).
Claim 5: Khalianya and Nagel disclose a method of claim 1, wherein the sequence comprises a middle layer and the tensor that is scaled on a per channel basis by the set of per channel activation scaling factors is an output tensor of the middle layer (Khalianya: Paragraphs 4 and 26-28; per layer (includes branches) and 37-38; per-channel function).
Claim 6: Khalianya and Nagel disclose a method of claim 1, wherein the first convolution layer forms part of a first branch and an output tensor of the first branch is combined with a tensor of a second branch to generate an input tensor to the second convolution layer, and the method further comprises scaling the tensor of the second branch on a per channel basis by the set of per channel activation scaling factors (Khalianya: Paragraphs 4 and 26-28; per layer (includes branches) and 37-38; per channel function).
Claim 7: Khalianya and Nagel disclose a method of claim 6, wherein the hardware logic comprises a neural network accelerator that includes a hardware unit configured to receive a first tensor and a second tensor, rescale the second tensor, and perform a per tensel operation between the first tensor and the rescaled second tensor, and (i) the combining of the output tensor of the first branch and the tensor of the second branch, and (ii) the scaling of the tensor in the second branch on a per channel basis by the set of per channel activation scaling factors are performed by the hardware unit (Khalianya: Figure 1A: first and second tensor, Paragraphs 4, tensor scaling 24, 27-28 and 37 per channel scaling).
Claim 8: Khalianya and Nagel disclose a method of claim 1, wherein the first convolution layer forms part of a first branch and an output tensor of the first branch is combined with a tensor of a second branch to generate an input tensor to the second convolution layer, and the tensor that is scaled on a per channel basis by the set of per channel activation scaling
factors is the input tensor to the second convolution layer (Khalianya: Figure 1A: convolutional operations Paragraph 28).
Claim 9: Khalianya and Nagel disclose a method of claim 8, wherein the combination and the scaling by the set of per channel activation scaling factors are performed by a single hardware unit of the hardware logic (Khalianya: Figure 1A: convolutional operations Paragraphs 28 and 32(hardware to perform operations).
Claim 16: Khalianya and Nagel disclose a method of claim 1, wherein the sequence comprises a middle layer that is one of an activation layer implementing a ReLU function, an activation layer implementing an LReLU function, and a pooling layer(Khalianya: Paragraph 26; pooling ).
Claim 18: Khalianya and Nagel disclose a method of claim 1, wherein the hardware logic comprises a neural network accelerator (Khalianya: Paragraph 32; accelerator).
Claim 19: Khalianya and Nagel disclose a method of claim 18, wherein the neural network accelerator comprises a hardware unit configured to perform per channel multiplication and the scaling of the tensor by the set of per channel activation scaling factors is performed by the hardware unit (Khalianya: Paragraph 27, 37 (per channel scaling) and 43 multiply accumulation).
Claim 20 is similar in scope and therefore rejected under the same rationale.
Non transitory readable medium (Khalianya: Paragraphs 140-141)
Claims 11 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khailany et al. (“Khailany” 20220067512 A1) and Nagel et al. (“Nagel” 20200302299 A1) in further view of Condurache et al. (“Condurache” 20230206063 A1).
Claim 11: Khalianya and Nagel disclose a method of claim 1, however may not explicitly disclose wherein the sequence comprises a middle layer that is non-scale invariant.
Condurache is provided because it discloses layers with invariant integration (Paragraph 14).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide the invariant integration in Khalianya. One would have been motivated to provide this functionality as a method for improving accuracy (Condurache: Paragraph 14).
Claim 15: Khalianya and Nagel disclose a method of claim 1, however may not explicitly disclose wherein the sequence comprises a middle layer that is scale invariant.
Condurache is provided because it discloses layers with invariant integration (Paragraph 14).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide the invariant integration in Khalianya. One would have been motivated to provide this functionality as a method for improving accuracy (Condurache: Paragraph 14).
Claims 12-14 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khailany et al. (“Khailany” 20220067512 A1) and Nagel et al. (“Nagel” 20200302299 A1) in further view of Rosewarne et al. (“Rosewarne” 20250310548 A1).
Claim 12: Khalianya and Nagel disclose a method of claim 1, wherein the method further comprises: implementing the first convolution layer with weights that have been scaled on a per output channel basis by a set of per channel weight scaling factors; and scaling an output tensor of the first convolution layer on a per channel basis by the inverses of the set of per channel weight scaling factors (Khalianya: Figure 1A: convolutional operations Paragraphs 28 and 32(hardware to perform operations Nagel: abstract and Paragraph 33; inverse functionality).
Rosewarne is further provided because it discloses an output of a scaling tensor, while also utilizing inverse scaling in the convolutional layers (Paragraphs 98 and 58 (inverse quantisation/scaling defined in Paragraph 148)).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide tensor scaling in Khalianya. One would have been motivated to provide this functionality as method of improving efficiency (Rosewarne: Paragraph 183).
Claim 13: Khalianya, Nagel and Rosewarne disclose a method of claim 12, wherein the tensor that is scaled on a per channel basis by the set of per channel activation scaling factors is the output tensor of the first convolution layer, and the set of per channel activation scaling factors and the inverses of the set of per channel weight scaling factors are applied to the output tensor of the first convolution layer by a same operation (Khalianya: Figure 1A: convolutional operations Paragraphs 28 and 32(hardware to perform operations Nagel: abstract and Paragraph 33; inverse functionality and
Rosewarne: Paragraphs 98 and 58 (inverse quantisation/scaling defined in Paragraph 148)).
Claim 14: Khalianya, Nagel and Rosewarne disclose a method of claim 12, wherein the set of per channel activation scaling factors are applied to the tensor by a first operation, and the inverses of the set of per channel weight scaling factors are applied to the output tensor of the first convolution layer by a second, different operation (Khalianya: Figure 1A: convolutional operations Paragraphs 28 and 32(hardware to perform operations Nagel: abstract and Paragraph 33; inverse functionality and Rosewarne: Paragraphs 98 and 58 (inverse quantisation/scaling defined in Paragraph 148).
Claim 17: Khalianya and Nagel disclose a method of claim 1, wherein the neural network comprises a second sequence of layers comprising a third convolution layer, a fourth convolution layer, and none, one or more than one middle layer between the third and fourth convolution layers, and the method further comprises: scaling a tensor in the neural network, after the third convolution layer and before the fourth convolution layer, on a per channel basis by a second set of per channel activation scaling factors; and implementing the fourth convolution layer with weights that have been scaled on a per input channel basis by the inverses of the second set of per channel activation scaling factors (Khalianya: Figure 1A: convolutional operations Paragraphs 28 and 32(hardware to perform operations Nagel: abstract and Paragraph 33; inverse functionality).
Rosewarne is further provided because it discloses an output of a scaling tensor, while also utilizing inverse scaling in the convolutional layers (Paragraphs 98 and 58 (inverse quantisation/scaling defined in Paragraph 148)).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide tensor scaling in Khalianya. One would have been motivated to provide this functionality as method of improving efficiency (Rosewarne: Paragraph 183).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20240354570 A1 0032
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERROD L KEATON whose telephone number is (571)270-1697. The examiner can normally be reached on MONDAY -FRIDAY 9:30-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steve Hong can be reached on 571-272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148
6-20-2026
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