DETAILED ACTION
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 .
This action is in response to the application filed 04 January 2023. Claims 1-18 are pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04 January 2023 and 27 January 2025 are being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 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.
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.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 limitations are "processing unit that executes" and “pre-processing unit that transforms” in Claim 14 and its dependents. Note that the “processing unit that executes” in Claim 13, for the analysis of Claim 13 only, is not interpreted as a means-plus-function limitation, as described in the 112(a) section below, as Claim 13 recites a single-means claim.
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.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 13 and 18 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim. Claim 18 is rejected for inheriting the deficiencies of its parent claim without curing them.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 5, 9, 10, and 14-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term "close to zero" in Claims 4, 5, 9, and 10 is a relative term which renders the claim indefinite. The term "close to zero" is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The degree or extent to which the sum of coefficients of convolutional filters must approach zero is rendered indefinite by the use of "close." Appropriate clarification of correction is required.
The term "previous stage" in Claim 14 renders the claim indefinite, because the claim does not indicate anything to which the stage could be “previous.” Note that, as the “processing unit” is interpreted as having hardware structure via the means-plus-function 35 USC 112(f) interpretation, it is unclear to what “in a previous stage” of the hardware could be referring. Appropriate clarification or correction is required.
Dependent claims are rejected for inheriting the indefiniteness of a parent claim.
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.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because "a program for causing a computer to function as a processing unit" does not have a physical or tangible form and has been interpreted to recite software per se.
For the purposes of examination, Claim 12 has been interpreted to recite a software program product comprising a structural recitation of a physical or tangible form, such as a program distributed on a non-transitory medium.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 7-9, 11-14, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou, et al., "Less is More: Towards Compact CNNs" (hereinafter "Zhou").
Regarding Claim 1, Zhou teaches:
A method for generating a learning model (Zhou, p. 1, Abstract: "we aim at optimizing the number of neurons in a network, thus the number of parameters. We show that, by incorporating sparse constraints into the objective function, it is possible to decimate the number of neurons during the training stage. ... We evaluated our method on several well-known CNN structures"), the method comprising:
training the learning model (Zhou, p. 5, 3.1 Training a Sparse Constrained CNN: "Let
{
X
,
Y
*
}
be the training samples and corresponding ground-truth label. Then, a CNN can be represented as a function
Y
=
f
W
^
,
X
, where
Y
is the output of the network.
W
^
is learned through minimizing an objective function: [Eq. 1] ... ¶ Our goal is adding sparse constraints on the neurons of a CNN. Therefore, the optimization problem of (1) can be written as:
min
W
^
ψ
W
^
+
g
W
^
(2)
where
g
W
^
represents the set of constraints added to
W
^
") such that a total sum of coefficients in one or more channels ... approaches zero (Zhou, p. 6, 4 Sparse Constraints: "Our goal is removing neurons
w
^
l
j
, each of which is a tensor. To this end, we consider ... sparse constraints for
g
W
^
in Equation (2) ... group sparsity" and p. 7, 4.2 Group Sparse Constraints: "These are defined as
l
2,1
regularizer. Applying
l
2,1
to our objective function, we have:
g
W
^
=
λ
∑
j
,
l
∈
Ω
w
^
l
j
(7)
" where p. 8, 5 Importance of Rectified Linear Units in Sparse Constrained CNNs: "we show that
w
^
l
j
=
0
is a local minimum in sparse constrained CNNs") ... of at least one or more convolution filters among the convolution filters (Zhou, p. 4, 3 Sparse Constrained Convolutional Neural Networks: "Notation: In the following discussion .... We use
W
and
b
to denote all the parameters in filters and bias terms in a CNN, respectively.
w
l
and
b
l
represent the filter and bias parameters in the
l
-th layer.
w
l
is a tensor whose size is
w
×
h
×
m
×
n
, where
w
and
h
are the width and the height of a 2D filter,
m
represents the number of channels for the input feature and
n
is the number of channels for the output feature. ...
w
l
j
represents a
w
×
h
×
m
filter that creates the
j
-th channel of the output feature for layer
l
") for a first layer of a neural network including a plurality of the convolution filters (Zhou, p. 6, Figure 4(a), "Results of learning the number of neurons in MLP," depicting optimization of the first/input layer of a multi-layer perceptron, from 784 to 649 or to 434 neurons as a result of optimization), the neural network being applied to the learning model that performs recognition processing on input data (Zhou, p. 3, 1 Introduction: "our experimental results on four well-known CNN architectures demonstrate a significant reduction in the number of neurons and the memory footprint of the testing phase without affecting the classification accuracy" and p. 8, 6 Experiments: "We test our method on four well-known convolutional neural networks on three well-known datasets: LeNet on MNIST ... ," where Zhou's image classification corresponds to the instant input recognition processing).
Claim 12 is directed to non-statutory subject matter, as it recites software per se. For the purposes of further examination, Claim 12 has been interpreted to recite a software program product comprising a structural recitation of a physical or tangible form.
Regarding Claim 12, Zhou teaches:
A program for causing a computer to function as a processing unit that trains a learning model (Zhou, p. 2, 1 Introduction: "Our method consists of imposing sparse constraints on the neurons of a CNN in the objective function during the training process. ... We conduct a comprehensive set of experiments to validate our method using four well-known models (i.e., LeNet, CIFAR-10 quick, AlexNet and VGG) on three public datasets including ImageNet," where a program is inherent in Zhou's training and validation steps) such that training occurs according to the steps recited by Claim 1. Claim 12 is rejected under the same rationale as Claim 1.
Regarding Claim 13, Zhou teaches:
An information processing apparatus comprising a processing unit that executes an operation of a learning model trained (Zhou, p. 2, 1 Introduction: "Our method consists of imposing sparse constraints on the neurons of a CNN in the objective function during the training process. ... We conduct a comprehensive set of experiments to validate our method using four well-known models (i.e., LeNet, CIFAR-10 quick, AlexNet and VGG) on three public datasets including ImageNet," where an apparatus is inherent in Zhou's training and validation steps) such that training occurs according to the steps recited by Claim 1. Claim 13 is rejected under the same rationale as Claim 1.
Regarding Claim 2, the rejection of Claim 1 is incorporated.
training the learning model such that a total sum of the coefficients approaches zero for each of all the channels of at least one or more the convolution filters (Zhou, p. 6, 4 Sparse Constraints: "Our goal is removing neurons
w
^
l
j
, each of which is a tensor" where Zhou's neuron corresponds to the channel of a filter, as in p. 4, 3 Sparse Constrained Convolutional Neural Networks: "Notation: In the following discussion ....
w
l
and
b
l
represent the filter and bias parameters in the
l
-th layer.
w
l
is a tensor whose size is
w
×
h
×
m
×
n
, where
w
and
h
are the width and the height of a 2D filter,
m
represents the number of channels for the input feature and
n
is the number of channels for the output feature. ...
w
l
j
represents a
w
×
h
×
m
filter that creates the
j
-th channel of the output feature for layer
l
") among the convolution filters for the first layer (Zhou, p. 6, Figure 4(a), "Results of learning the number of neurons in MLP," depicting optimization of the first/input layer of a multi-layer perceptron, from 784 to 649 or to 434 neurons as a result of optimization).
Regarding Claim 3, the rejection of Claim 1 is incorporated.
training the learning model such that a sum (Zhou, p. 5, 3.1 Training a Sparse Constrained CNN: "the optimization problem of (1) can be written as:
min
W
^
ψ
W
^
+
g
W
^
(2)
where
g
W
^
represents the set of constraints added to
W
^
") of an error term based on a difference between an output of the learning model when input data with a ground truth output is input to the learning model and the ground truth output (Zhou, p. 5, 3.1 Training a Sparse Constrained CNN: "Let
{
X
,
Y
*
}
be the training samples and corresponding ground-truth label. Then, a CNN can be represented as a function
Y
=
f
W
^
,
X
, where
Y
is the output of the network.
W
^
is learned through minimizing an objective function:
min
W
^
ψ
f
W
^
,
X
,
Y
*
(1)
We use
ψ
W
^
to represent the objective function for simplicity. The objective function
ψ
W
^
is usually defined as the average cross entropy of the ground truth labels with respect to the output of the network for each training image") and a regularization term based on the coefficients of the convolution filters included in the neural network (Zhou, p. 7, 4.2 Group Sparse Constraints: "These are defined as
l
2,1
regularizer. Applying
l
2,1
to our objective function, we have:
g
W
^
=
λ
∑
j
,
l
∈
Ω
w
^
l
j
(7)
") is minimized (Zhou, p. 5, 3.1 Training a Sparse Constrained CNN: "the optimization problem of (1) can be written as:
min
W
^
ψ
W
^