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 .
DETAILED ACTION
This action is in response to the application filed 29 June 2026. Claims 1, 5, 6, and 10 are amended. Claim 4 is cancelled. Claims 1-3, 5-7, 9, and 10 are pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 29 June 2026 has been entered.
Response to Arguments
Applicant' s arguments, see page 6, filed 29 June 2026, with respect to the objection to Claim 1 have been fully considered and are persuasive. The objection to Claim 1 has been withdrawn.
APPLICANT'S ARGUMENT: Applicant argues (page 6, paragraph 1) that "The claim has been amended to correct the error."
EXAMINER'S RESPONSE: Examiner agrees. The objection to Claim 1 is withdrawn in light of arguments and/or amendments.
Applicant's arguments, see pages 6-9, filed 29 June 2026, with respect to the rejections to Claims 1-7, 9, and 10 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
APPLICANT'S ARGUMENT: Applicant argues (page 8, paragraph 1) that "The claimed invention provides a solution to this problem in the technical field of training a neural network to process sensor values. ... As a result, the training method is able to converge faster (par. 0013; 0025). As amended, the neural network is trained to determine a change-point time of the injector valve, which is a concrete technical result tied to a particular machine within a fuel injection system. ¶ In this way, the claimed invention provides an improvement to the training process and to the resulting trained neural network."
Applicant argues (page 8, paragraph 3) that "it should be understood that, even if the processes for determining the distribution interval and initializing model parameters are understood to be abstract (as alleged by the Office), these features are integrated in a practical application by the non-abstract process of training the neural network to determine a change-point time of the injector valve based on sensor values, using the training data sets."
Applicant argues (page 9, paragraph 1) that "the claimed invention provides a clear improvement to a technology or to a technical field because the claimed invention solves a technological problem. Accordingly, the limitations of the claims amount to significantly more than the abstract idea."
EXAMINER'S RESPONSE: Examiner respectfully disagrees that amended Claim 1 recites a solution to a technical problem. As currently claimed, amended Claim 1 current recites several mental process steps, including determining a distribution interval and a transformation function, or initializing a model, which appear to be performable in the human mind, or with the aid of pen and paper. Per MPEP 2106.04(a)(2)(III)(c), a claim that requires a computer may still recite a mental process.
Amended Claim 1 recites several additional elements, but they are not sufficient to integrate the mental process steps into a practical application or provide significantly more. The additional elements reciting sensor values and an injector valve appear to generally link the existing mental process steps to a particular field of use. The additional element of training a neural network appears to recite mere application of a computer or computing machinery to an existing mental process step. In the absence of additional elements that integrate or provide significantly more, amended Claim 1 is directed to the recited mental process steps.
Applicant' s arguments, see pages 9-14, filed 29 June 2026, with respect to the rejections of Claims 1-7, 9, and 10 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
APPLICANT'S ARGUMENT: Applicant argues (page 9, paragraph 2) that "the proposed combinations do not arrive at the limitations of the respective claims because the references do not teach at least one limitation of each claim."
Applicant argues (page 11, paragraph 1) that "[Khellal's] 'distribution interval' is not determined with respect to the training data set. Instead, the mean (i.e., 0) and/or standard deviation (i.e., 1) is a defined distribution and the training data is transformed to fit the defined distribution. ... A predetermined normalization specification that remains the same regardless of the underlying data cannot constitute the claimed 'distribution interval of the respective sensor values of all of the training data sets,' which must be determined from those very values. Khellal never determines or calculates the distribution interval of the normalized input
X
N
."
EXAMINER'S RESPONSE: Examiner notes that Applicant's arguments are now moot. Amended Claims 1-3, 5-7, 9, and 10 are now rejected under 35 U.S.C. 103 as being unpatentable over Sun in view of Janakiraman.
As a second matter, Examiner respectfully disagrees that amended Claim 1 recites a determination of a distribution interval that must be interpreted, under BRI in light of the specification, strictly as a calculation of a distribution interval. Examiner further notes that amended Claim 1 recites the steps of "determining a distribution interval of the respective sensor values" and "initializing ... as a function of the distribution interval," but then recites "map ... onto an actual distribution of the respective sensor values" (emphasis added), without reference to the previously defined distribution interval of sensor values.
In the 35 U.S.C. 103 rejection below, Sun is relied on to teach the step of determining a distribution interval of training data sets.
Claim Objections
The objection to Claim 1 for informalities is withdrawn in light of arguments and/or amendments.
Claim Rejections - 35 USC § 112
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.
Claim 6 is 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.
Claim 6 recites the limitation "neuron functions of a layer of the layers of artificial neurons." There is insufficient antecedent basis for this limitation in the claim. Neither Claim 6 nor Claim 1, upon which Claim 6 depends, previously introduces "layers of artificial neurons." For the purpose of examination, Claim 6 is interpreted to recite "neuron functions of a layer of the neural network." Appropriate correction is required.
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 1-3, 5-7, 9, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1
Step 1
Claim 1 recites a method for training a neural network to process sensor values from a sensor arranged within an injector valve of a fuel injection system, and thus the claimed process falls within a statutory category of invention.
Step 2A Prong 1
The claim recites determining a distribution interval of the respective ... values of all of the training data sets, which is a mental process. The claim recites initializing model parameters of the neural network as a function of the distribution interval, which is a mental process. The claim recites determining a transformation function configured to map a normalized input data set with a predetermined normalized distribution onto an actual distribution of the respective ... values of all of the training data sets, which is a mental process. The claim recites specifying preliminary model parameters, which is a mental process. The claim recites applying the transformation function to the preliminary model parameters in order to obtain transformed model parameters, which is a mental process. The claim recites initializing the neural network with the transformed model parameters, which is a mathematical concept. The claim recites determine a change-point time ... based on ... values, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element providing training data sets, each training data set including respective ... values and one or more labels assigned to the respective ... values amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element sensor values does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element training the neural network ... using the training data sets by further adaptation of the model parameters simply recites a judicial exception with the words "apply it" (see MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished," as the limitations do not provide meaningful restriction with respect to how it is accomplished or provide steps by which an improvement in technology may be achieved). The additional element injector valve does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
Step 2B
The additional element providing training data sets, each training data set including respective ... values and one or more labels assigned to the respective ... values is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element sensor values does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element training the neural network ... using the training data sets by further adaptation of the model parameters simply recites a judicial exception with the words "apply it" (see MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished," as the limitations do not provide meaningful restriction with respect to how it is accomplished or provide steps by which an improvement in technology may be achieved). The additional element injector valve does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 2
Step 1
Regarding Claim 2, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites the determination of the distribution interval is determined as a function of a minimum value and a maximum value of all of the respective ... values of the training data sets, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element sensor values does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 3
Step 1
Regarding Claim 3, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites the model parameters for each layer of artificial neurons of the neural network are provided as elements of a weighting matrix and of a bias vector, which is a mental process and mathematical concept.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 5
Step 1
Regarding Claim 5, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites determining a transformation function configured to map a normalized input data set with a predetermined normalized distribution onto an actual distribution of the respective ... values of all of the training data sets (as recited by Claim 1), wherein the predetermined normalized distribution of the input data sets has a distribution interval between -1 and 1, which is a mental process and a mathematical concept.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 6
Step 1
Regarding Claim 6, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites applying the transformation function to the preliminary model parameters in order to obtain transformed model parameters (as recited by Claim 4), wherein neuron functions of a layer of the layers of artificial neurons are subjected to an inverted version of the transformation function to obtain the transformed model parameters, which is a mathematical concept and mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 7
Step 1
Claim 7 recites a computer comprising: a processor configured to carry out the method according to claim 1, and thus the claimed machine falls within a statutory category of invention.
Step 2A Prong 1
Claim 7 recites the abstract ideas recited by Claim 1.
Step 2A Prong 2, Step 2B
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 9
Step 1
Claim 9 recites a non-transitory machine-readable storage medium comprising instructions, and thus the claimed manufacture falls within a statutory category of invention.
Step 2A Prong 1
Claim 9 recites the abstract ideas recited by Claim 1.
Step 2A Prong 2, Step 2B
The additional element instructions which, when executed by a computer, cause the computer to execute the method according to claim 1 invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 10
Step 1
Regarding Claim 10, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites determining a distribution interval of the respective ... values of all of the training data sets (as recited by Claim 1), wherein the distribution interval is determined as a function of an average value and a standard deviation of the values of all of the respective ... values of the training data sets, which is a mental process and mathematical concept.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element sensor values does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-7, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Sun, et al., "Return of Frustratingly Easy Domain Adaptation" (hereinafter "Sun") in view of Janakiraman, et al., "An ELM based predictive control method for HCCI engines" (hereinafter "Janakiraman").
Regarding Claim 1, Sun teaches:
A method for training a neural network to process ... values ... (Sun, p. 2058, 1 Introduction: "we present a 'frustratingly easy' unsupervised domain adaptation method called CORrelation ALignment (CORAL). CORAL aligns the input feature distributions of the source and target domains by exploring their second-order statistics. ... Then, supervised learning proceeds as usual -- training a classifier on the transformed source features"), the method comprising:
providing training data sets (Sun, p. 2062, 4.1 Object Recognition, Object Recognition with Shallow Features: "We ... conduct experiments on the Office-Caltech10 dataset with shallow features (SURF). The SURF features were encoded with 800-bin bag-of-words histograms and normalized to have zero mean and unit standard deviation in each dimension. ... In each trial, we use the standard setting ... and randomly sample the same number ... of labelled images in the source domain as training set, and use all the unlabelled data in the target domain as the test set"), each training data set including respective ... values and one or more labels assigned to the respective ... values (Sun, p. 2060, 3.1 Formulation and Derivation: "We describe our method by taking a multi-class classification problem as the running example. Suppose we are given source-domain training examples
D
S
=
{
x
→
i
}
,
x
→
∈
R
D
with labels
L
S
=
{
y
i
}
,
y
∈
{
1
,
…
,
L
}
, and target data
D
T
=
{
u
→
i
}
,
u
→
∈
R
D
");
determining a distribution interval of the respective ... values of all of the training data sets (Sun, p. 2062, 4.1 Object Recognition, Object Recognition with Shallow Features: "The SURF features were ... normalized to have zero mean and unit standard deviation in each dimension," where Sun's unit standard deviation corresponds to the instant distribution interval);
initializing model parameters of the neural network as a function of the distribution interval, the initializing including (i) determining a transformation function configured to map a normalized input data set with a predetermined normalized distribution onto an actual distribution of the respective ... values of all of the training data sets (Sun, p. 2060, 3.1 Formulation and Derivation: "To minimize the distance between the second-order statistics (covariance) of the source and target features, we apply a linear transformation A to the original source features and use the Frobenius norm as the matrix distance metric," Sun's covariances of source and target features correspond to the instant distributions of input and training sets, respectively, and where the input features are normalized, as in p. 2060, 3.1 Formulation and Derivation: "Suppose
μ
s
,
μ
t
and
C
S
,
C
T
are the feature vector means and covariance matrices. As illustrated in Figure 2,
μ
t
=
μ
s
=
0
after feature normalization while
C
S
≠
C
T
"), (ii) specifying preliminary model parameters (Sun, Figure 2(a-c), "Illustration of CORrelation ALignment (CORAL) for Domain Adaptation: (a) The original source and target domains have different distribution covariances, despite the features being normalized to zero mean and unit standard deviation. This presents a problem for transferring classifiers trained on source to target," and p. 2061, 3.2 Algorithm: "After CORAL transforms the source features to the target space, a classifier
f
w
→
parametrized by
w
→
can be trained on the adjusted source features," where Sun's source classifier parameters correspond to the instant preliminary parameters), (iii) applying the transformation function to the preliminary model parameters in order to obtain transformed model parameters (Sun, p. 2061, 3.2 Algorithm: "After CORAL transforms the source features to the target space, a classifier
f
w
→
parametrized by
w
→
can be trained on the adjusted source features and directly applied to target features. For a linear classifier
f
w
→
I
=
w
→
T
ϕ
I
, we can apply an equivalent transformation to the parameter vector
w
→
instead of the features
u
," where Sun's equivalent transformation to
w
→
corresponds to the instant applying the transformation to the model parameters) and (iv) initializing the neural network with the transformed model parameters (Sun, p. 2061, 3.2 Algorithm: "After CORAL transforms the source features to the target space, a classifier
f
w
→
parametrized by
w
→
can be trained on the adjusted source features and directly applied to target features. For a linear classifier
f
w
→
I
=
w
→
T
ϕ
I
, we can apply an equivalent transformation to the parameter vector
w
→
instead of the features
u
," where Sun's transformed classifier parameters before training correspond to the instant initializing); and
training the neural network to determine ... based on ...values (Sun, p. 2061, 3.2 Algorithm: "After CORAL transforms the source features to the target space, a classifier
f
w
→
parametrized by
w
→
can be trained on the adjusted source features and directly applied to target features. For a linear classifier
f
w
→
I
=
w
→
T
ϕ
I
, we can apply an equivalent transformation to the parameter vector
w
→
instead of the features
u
," where Sun's classification of input features corresponds to the instant determining based on values), using the training data sets by further adaptation of the model parameters (Sun, p. 2058, 1 Introduction: "CORAL aligns the distributions by re-coloring whitened source features with the covariance of the target distribution. CORAL is simple and efficient, as the only computations it needs are (1) computing covariance statistics in each domain and (2) applying the whitening and re-coloring linear transformation to the source features. Then, supervised learning proceeds as usual -- training a classifier on the transformed source features").
Sun teaches a method for training a neural network to process values, the method comprising providing training data sets, each training data set including respective values and one or more labels assigned to the respective values.
Sun does not explicitly teach training a neural network to process sensor values from a sensor arranged within an injector valve of a fuel injection system, each training data set including respective sensor values and one or more labels assigned to the respective sensor values, and training the neural network to determine a change-point time of the injector valve based on sensor values.
However, Janakiraman teaches:
training (Janakiraman, p. 107, 1. Introduction: "the main contribution of this paper is an integrated control framework that applies existing ideas from ELM [Extreme Learning Machine], predictive control and convex optimization to a complex combustion engine. This includes optimal model selection (tuning model hyper-parameters), training and validation of the model to the HCCI [homogeneous charge compression ignition] engine data ") a neural network (Janakiraman, p. 110, Fig. 6. Illustration showing an extreme learning machine model," depicting neural network input, hidden, and output neurons) to process sensor values from a sensor arranged within an injector valve of a fuel injection system (Janakiraman, p. 108, Fig. 2, "A schematic of the HCCI engine showing relevant sensors and instrumentation")
each training data set including respective sensor values and one or more labels assigned to the respective sensor values (Janakiraman, p. 110, 3.1. Learning HCCI dynamics using recurrent models: "For the HCCI engine system, both the inputs and the outputs of the engine are available as sensor measurements and naturally, supervised learning methods can be used. ... We consider a nonlinear auto-regressive model with exogenous input (NARX) (Nelles, 2001) to model the engine variables as follows ... [Eq. 1] ... where
u
k
∈
R
u
d
and
y
k
∈
R
y
d
represent the inputs and outputs of the system respectively,
k
represents the discrete time index")
training the neural network to determine a change-point time of the injector valve based on sensor values (Janakiraman, p. 110, 3.1. Learning HCCI dynamics: "The HCCI model is linearized around the sampled states and is used in the online MPC [model predictive control] algorithm to determine the optimal control change from its set-point (
Δ
U
*
); i.e., the optimal fuel mass, EVC and SOI to be given to the engine to track the reference commands," where Janakiraman's optimal control change reasonably suggests the change-point time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sun regarding a method for training a neural network to process values, the method comprising providing training data sets, each training data set including respective values and one or more labels assigned to the respective values with those of Janakiraman regarding training a neural network to process sensor values from a sensor arranged within an injector valve of a fuel injection system, each training data set including respective sensor values and one or more labels assigned to the respective sensor values, and training the neural network to determine a change-point time of the injector valve based on sensor values.
The motivation to do so would be to facilitate support of MPC-based engine control by means of a neural network providing prediction for control decisions (Janakiraman, p. 107, 1. Introduction: "we propose to use a model predictive control (MPC) approach for the HCCI control problem because MPC can efficiently handle black-box type models for evaluating control candidates (Maciej, 2009). In addition, MPC is well suited for handling operation related and hardware related constraints in an elegant manner. ... In this paper, an ELM engine model is used to make predictions which are then used by the MPC module to make control decisions for tracking a given reference command").
Regarding Claim 7, Sun teaches:
A computer comprising: a processor configured to carry out the method according to claim 1 (Sun, p. 2060, 3.2 Algorithm: "we can perform the classical whitening and coloring. This is advantageous because: (1) it is faster (e.g., the whole CORAL transformation takes less than one minute on a regular laptop ... and more stable, as SVD on the original covariance matrices might not be stable and might slow to converge .... The final algorithm can be written in four lines of MATLAB code as illustrated in Algorithm 1"). Claim 7 is rejected under the same rationale as Claim 1.
Regarding Claim 9. Sun teaches:
A non-transitory machine-readable storage medium comprising instructions which, when executed by a computer, cause the computer to execute the method according to claim 1 (Sun, p. 2060, 3.2 Algorithm: "we can perform the classical whitening and coloring. This is advantageous because: (1) it is faster (e.g., the whole CORAL transformation takes less than one minute on a regular laptop ... and more stable, as SVD on the original covariance matrices might not be stable and might slow to converge .... The final algorithm can be written in four lines of MATLAB code as illustrated in Algorithm 1," where a non-transitory storage medium is inherent in executing code on a laptop). Claim 9 is rejected under the same rationale as Claim 1.
Regarding Claim 2, the rejection of Claim 1 is incorporated. The Sun/Janakiraman combination teaches:
the determination of the distribution interval is determined as a function of a minimum value and a maximum value of all of the respective sensor values of the training data sets (Sun, p. 2062, 4.1 Object Recognition, Object Recognition with Shallow Features: "We ... conduct experiments on the Office-Caltech10 dataset with shallow features (SURF). The SURF features were ... normalized to have zero mean and unit standard deviation in each dimension," where Sun's unit standard deviation corresponds to the instant distribution interval, inherently having minimum and maximum values, similarly to [0016] of the instant specification).
Regarding Claim 3, the rejection of Claim 1 is incorporated. Janakiraman further teaches:
wherein the model parameters for each layer of artificial neurons of the neural network are provided as elements of a weighting matrix and of a bias vector (Janakiraman, p. 11, 3.2. Extreme Learning Machines: "ELM training involves solving the following optimization problem ...
ϕ
=
H
T
=
ψ
W
r
T
x
k
+
b
r
∈
R
n
h
×
1
(7)
... ¶ The matrix
W
r
consists of randomly assigned elements that maps the input vector to a high dimensional feature space while
b
r
∈
R
n
h
is a bias component").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sun/Janakiraman combination regarding initializing model parameters of the neural network with the further teachings of Janakiraman regarding wherein the model parameters for each layer of artificial neurons of the neural network are provided as elements of a weighting matrix and of a bias vector.
The motivation to do so would be to facilitate modeling a nonlinear optimization problem as linear parameter estimation (Janakiraman, p. 11, 3.2. Extreme Learning Machines: "A prominent feature of ELM is that the nonlinear optimization problem is reduced to a linear parameter estimation problem").
Regarding Claim 5, the rejection of Claim 1 is incorporated. The Sun/Janakiraman combination teaches:
wherein the predetermined normalized distribution of the input data sets has a distribution interval between -1 and 1 (Sun, p. 2062, 4.1 Object Recognition, Object Recognition with Shallow Features: "We ... conduct experiments on the Office-Caltech10 dataset with shallow features (SURF). The SURF features were ... normalized to have zero mean and unit standard deviation in each dimension").
Regarding Claim 6, the rejection of Claim 1 is incorporated. The Sun/Janakiraman combination teaches:
wherein neuron functions of a layer of the layers of artificial neurons are subjected to an inverted version of the transformation function to obtain the transformed model parameters (Sun, p. 2061, Algorithm 1, "CORAL for Unsupervised Domain Adaptation," lines 5-6, depicting an inverse square root of the source covariance matrix, later applied by the feature transformation matrix
A
, and p. 2060, 3.2 Algorithm: "We can think of transformation
A
in this way intuitively: the first part ... whitens the source data while the second part ... re-colors it with the target covariance. ... The traditional whitening is adding a small regularization parameter λ to the diagonal elements of the covariance matrix to explicitly make it full rank and then multiply the original feature by the inverse square root (or square root for coloring) of it").
Regarding Claim 10, the rejection of Claim 1 is incorporated. The Sun/Janakiraman combination teaches:
wherein the distribution interval is determined as a function of an average value and a standard deviation of the values of all of the respective sensor values of the training data sets (Sun, p. 2062, 4.1 Object Recognition, Object Recognition with Shallow Features: "We ... conduct experiments on the Office-Caltech10 dataset with shallow features (SURF). The SURF features were ... normalized to have zero mean and unit standard deviation in each dimension").
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT N DAY whose telephone number is (703)756-1519. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.N.D./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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