DETAILED ACTION
Introduction
This Office action is responsive to the communications filed February 7, 2024. Claims 1-20 are 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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, claims 1-7 are directed to a method. Claims 8-14 are directed to a system comprising a processor. Claims 15-20 are directed to a non-transitory computer-readable medium. Therefore, these claims fall within the four statutory categories of invention.
For example, claim 1 recites an abstract idea of organizing and manipulating information through mathematical correlations. The claim under its broadest reasonable interpretation recites limitations grouped within the “mathematical concepts” grouping of abstract ideas. Mathematical concepts abstract idea grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. See MPEP § 2106.04(a)(2), subsection I. The claim limitations reciting the abstract idea are grouped within the “mathematical concepts” grouping of abstract ideas as they relate to organizing and manipulating information through mathematical correlations. More specifically, the following the bolded claim elements recite additional elements while the other claim elements recite the abstract idea. according to MPEP 2106.04(a).
1. A method of compressing a neural network model (NNM), the method comprising: receiving, by a computing device, a predefined number of filters to be removed in each of a plurality of layers of the NNM, wherein each of the plurality of layers comprises a first set of filters in a first sequence;
receiving, by the computing device, a plurality of predefined evaluation criteria ; for each of a plurality of layers of the NNM:
determining, by the computing device, a set of criteria based filters, in a corresponding layer, based on each of the plurality of predefined evaluation criteria by:
for each of the first set of filters:
determining, by the computing device, a weighted criteria score for each of the plurality of predefined evaluation criteria based on criterion-based scores of each of the first set of filters and weights assigned to each of the plurality of predefined evaluation criteria; and
determining, by the computing device, a cumulative weighted score based on a summation of each of the weighted criterion score corresponding to each of the plurality of predefined evaluation criteria, sorting the set of criteria based filters in a second sequence based on the cumulative weighted score;
determining, by the computing device, a second set of filters equal to the predefined number of filters to be removed, from the sorted set of criteria based filters; and compressing, by the computing device, the NNM based on the second set of filters for each of the plurality of layers.
Independent claims 8 and 15 recite similar language.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See MPEP 2106.04(d)), the additional element(s) of the claim(s) such as the computing device and NNM is merely used as tools to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, these additional elements perform the steps or functions of organizing and manipulating information through mathematical correlations. Viewed as a whole, the use of computing device and NNM as tools to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer or computer networks performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See MPEP 2106.05), the additional element(s) of the computing device and NNM to perform the steps amounts to no more than using generic hardware or software to automate and/or implement the abstract idea of organizing and manipulating information through mathematical correlations. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of organizing and manipulating information through mathematical correlations. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05 (f) & (h)). Therefore, the claim is not patent eligible.
The dependent claims further describe the abstract idea such as wherein the optimization of the predefined weights comprises: determining, by the computing device, a normalized cumulative weighted score for each of the first set of filters based on the cumulative weighted score; and adding, by the computing device, the normalized cumulative weighted score to a bias of a corresponding layer.
The dependent claims do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication No. 2021/0073644 to Lin et al. (“Lin”) in view of U.S. Publication No. 2022/0253708 to Tamada et al. (“Tamada”).
As per claim 1, Lin discloses receiving, by a computing device, a predefined number of filters to be removed in each of a plurality of layers of the NNM, wherein each of the plurality of layers comprises a first set of filters in a first sequence (paragraph [0049]; Fig. 5C and 5D and related text)
receiving, by the computing device, a plurality of predefined evaluation criteria
for each of a plurality of layers of the NNM: determining, by the computing device, a set of criteria based filters, in a corresponding layer, based on each of the plurality of predefined evaluation criteria by: (paragraph [0047] – compression process if filter meets certain criteria);
Additionally, Lin teaches determining weighted score, compressing the NNM based on filters for each layer (Fig. 3; Fig. 5C).
Lin does not expressly disclose
for each of the first set of filters:
determining, by the computing device, a weighted criteria score for each of the plurality of predefined evaluation criteria based on criterion-based scores of each of the first set of filters and weights assigned to each of the plurality of predefined evaluation criteria; and
determining, by the computing device, a cumulative weighted score based on a summation of each of the weighted criterion score corresponding to each of the plurality of predefined evaluation criteria, sorting the set of criteria based filters in a second sequence based on the cumulative weighted score; determining, by the computing device, a second set of filters equal to the predefined number of filters to be removed, from the sorted set of criteria based filters; and compressing, by the computing device, the NNM based on the second set of filters for each of the plurality of layers.
Tamada discloses determining, by the computing device, a weighted criteria score for each of the plurality of predefined evaluation criteria based on criterion-based scores of each of the first set of filters and weights assigned to each of the plurality of predefined evaluation criteria; and
determining, by the computing device, a cumulative weighted score based on a summation of each of the weighted criterion score corresponding to each of the plurality of predefined evaluation criteria, sorting the set of criteria based filters in a second sequence based on the cumulative weighted score; determining, by the computing device, a second set of filters equal to the predefined number of filters to be removed, from the sorted set of criteria based filters; and compressing, by the computing device, the NNM based on the second set of filters for each of the plurality of layers (abstract, Fig. 4 and related text).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Lin by including the features of Tamada as these concepts are conventional processes in machine learning . Applying the known technique of Tamada to Lin would have been recognized by those of ordinary skill in the art as resulting in an improved system that would have yielded predictable results.
As per claim 2, Tamada discloses wherein each of the plurality of predefined evaluation criteria correspond to at least one type of feature extracted by the NNM, wherein for each filter from the first set of filters: the criterion-based scores are determined based on the at least one type of feature extracted by the NNM corresponding to each of the plurality of predefined evaluation criteria, and the weighted criteria score is determined for each of the plurality of predefined evaluation criteria is determined based on a dot product of the criterion-based scores of the corresponding filter and the weights assigned to each of the corresponding predefined evaluation criteria (abstract, Fig.4 and related text).
As per claim 3, Lin in combination with Tamada discloses wherein the weights assigned to each of the plurality of predefined evaluation detection criteria are learned based on: optimization of predefined weights, assigned to each of the corresponding predefined evaluation criteria, based on determination a validation loss of the NNM using a gradient-descent back propagation technique during a model prediction of the NNM, wherein the predefined weights of each of the plurality of predefined evaluation criteria are determined based on one or more initialization techniques (Lin- paragraphs [0034], [0066]; Tamada – Fig. 3, 302 and related text).
As per claim 4, Lin discloses wherein the optimization of the predefined weights comprises: determining, by the computing device, a normalized cumulative weighted score for each of the first set of filters based on the cumulative weighted score (paragraph [0065]-[0067]); and
adding, by the computing device, the normalized cumulative weighted score to a bias of a corresponding layer (Lin at paragraphs [0032] and [0033] – forward pass)
As per claim 5, Lin discloses wherein the weights assigned to each of the plurality of predefined evaluation criteria is in a range of 0 to 1 (paragraph [0047]).
As per claim 6, Tamada discloses wherein the set of criteria based filters are sorted in the second sequence based on one of an ascending order or a descending order of the cumulative weighted score of each of the first set of filters (paragraphs [0009], [0038], and [0064]).
As per claim 7, Lin discloses wherein the first set of filters in each of the plurality of layers are determined based on a training of the NNM (paragraphs [0006] and [00078]).
Claims 8-14 are rejected on the same rationale as claims 1-7.
Claims 15-20 are rejected on the same rationale as claims 1-6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publication No. 20024/02896 to Chen et al.
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/Jalatee Worjloh/ Primary Examiner, Art Unit 3697
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