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 .
Response to Amendment/Status of Claims
Claims 1, 3-6, 13, and 15-18 were amended.
Claims 2, 7-12, and 14 were cancelled.
Claims 1, 3-6, 13, and 15-18 are pending and examined herein.
Claims 6 and 18 are objected to.
Claims 1, 3-6, 13, and 15-18 are rejected under 35 U.S.C. 112(a).
Claims 1, 3-6, 13, and 15-18 are rejected under 35 U.S.C. 101.
Claims 1, 5, 13, and 17 are rejected under 35 U.S.C. 102.
Claims 3-4, 6, 15-16 and 18 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments, see pages 6-7, filed 5/11/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 3, 6, 15, and 18 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection of claims 3, 6, 15, and 18 has been withdrawn.
Applicant's arguments filed 5/11/2026 regarding the 35 U.S.C. 101 rejection of claims 1, 3-6, 13, and 15-18 have been fully considered but they are not persuasive.
Applicant argues "The amended independent claims do not recite any "mental process" because neither "generating an updated low-rank tensor factor by inputting the initial low-rank tensor factor into a corresponding neural network" nor "updating parameters of the corresponding neural networks using a loss function between the tensor and a product of the updated low-rank tensor factors" can practically be performed in the human mind. The generating and updating steps are outside of what the human mind can practically perform because of the large number of parameters of the corresponding neural networks that are updated and used to generate an updated low-rank tensor factor."
Examiner agrees with the Applicant that neither "generating an updated low-rank tensor factor by inputting the initial low-rank tensor factor into a corresponding neural network" nor "updating parameters of the corresponding neural networks using a loss function between the tensor and a product of the updated low-rank tensor factors" can practically be performed in the human mind. However, Examiner respectfully disagrees that the amended independent claims do not recite any mental process. The limitation "decomposing the tensor into a plurality of initial low-rank tensor factors" is a mental process, as one could estimate low-rank tensors that, when multiplied together, estimate the tensor practically in the human mind.
Applicant argues "Further, the amended independent claims do not recite any "mathematical concepts" but merely involve mathematical concepts similar to the claim of Example 39 of the Office's Subject Matter Eligibility Examples: Abstract Ideas which provides an analogous analysis for reaching the conclusion that the claim is patent eligible under Step 2A, Prong 1."
Examiner respectfully disagrees. Example 39 of the SME examples simply recites the training of a neural network without reciting a mathematical concept. In contrast, the instant application include the limitation "updating parameters of the corresponding neural networks using a loss function between the tensor and a product of the updated low-rank tensor factors." MPEP 2106.04(a) states "A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation." The use of the loss function to calculate the parameter update is a recitation of the mathematical concept of a mathematical calculation. Applicant argues "The amended independent claims are patent eligible under Step 2A, Prong 2 because they recite "improvements as to how the machine learning model itself operates." Memorandum, U.S. Pat. & Trademark Off, Advance Notice of Change to the MPEP in Light of Ex Parte Desjardins (Dec. 5, 2025)3. Namely, the amended independent claims recite an improvement in how the corresponding neural networks themselves operate by producing useful outputs (i.e., updated low-rank tensor factors) following self-supervision, which contrasts with how neural networks are conventionally trained and used."
Examiner respectfully disagrees. Providing useful outputs itself does not improve the corresponding neural networks, as the usefulness of the outputs does not affect the functioning of the neural network. MPEP 2106.05(a) states "An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration." “Self-supervision”, as stated in the claims, is recited with a high-level of generality, amounting to mere instructions to apply an exception. Thus, the claims do not represent an improvement to technology.
Applicant argues "Though the amended independent claims are patent eligible under Step 2A and the analysis need not proceed to Step 2B, the amended independent claims are nonetheless patent eligible under Step 2B because the specific ordered combination of limitations is not well-understood, routine, or conventional. Because conventional decomposition techniques, such as singular value decomposition (SYD) and principal component analysis (PCA), are linear, these conventional decompositions do not adequately decompose tensors where nonlinear assumptions are needed. See Applicant's specification, para. [0042]. Accordingly, using self-supervised neural networks as claimed to nonlinearly decompose the tensor is both unconventional with respect to both tensor decomposition and how the neural networks are trained (i.e., in a self-supervised manner)."
Examiner respectfully disagrees. As explained above, the claim does not recite an improvement to technology. The decomposition of tensors is, as explained above, an abstract idea. As stated by MPEP 2106.05(a), "However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." Additionally, self-supervision is claimed with a high-level of generality, and as used in the claims, is not unconventional.
Applicant’s arguments regarding the 35 U.S.C. 102/35 U.S.C. 103 rejections with respect to claim(s) 1, 3-6, 13, and 15-18 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.
Claim Objections
Claims 6 and 18 are objected to because of the following informalities:
“a product” should be “the product of the updated low-rank tensor factors”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 3-6 and 15-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 13 state “generating, an updated low-rank tensor factor by inputting the initial low-rank tensor factor into a corresponding neural network”. However, the specification does not support inputting the initial low-rank tensor factor into the neural network. Applicant states, in the remarks dated 5/11/2026, that "The ‘initial low-rank tensor factors’ map to the input tensors
n
u
and
n
v
101, 102 that the neural networks 103, 104 update." However, the specification, see paragraphs [0048] and [0049], directly contradicts this. The specification states that the initial low-rank factors, decomposed in block 202 of Fig. 2, correspond to 105 and 106. In Fig. 1A, 105 and 106 are the output of the neural network, not the input. Therefore, this limitation is “new matter”. For purposes of examination, the limitation will be interpreted as “generating, an updated low-rank tensor factor by inputting a value into a corresponding neural network.”
Dependent claims 3-6 and 15-18 fail to resolve the issue and are rejected with the same rationale.
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, 3-6, 13, and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject
matter. The analysis of claims 1, 3-6, 13, and 15-18, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine,
manufacture, or composition of matter. Claims 1 and 3-6 are directed to a process, and claims 13 and 15-18 are directed to an article of manufacture. All claims are directed to statutory categories and analysis proceeds.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following are abstract ideas:
decomposing the tensor into a plurality of initial low-rank tensor factors; (Decomposing a tensor into initial (estimated) tensor factors can be practically performed in the human mind. This is a mental process.)
updating parameters of the corresponding neural networks using a loss function between the tensor and a product of the updated low-rank tensor factors. (Updating parameters using a loss function is a mathematical calculation, which is a mathematical concept.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A method for low-rank decomposition of a tensor, the method comprising: (This is an intended use statement which does not limit the claim.)
for each of the initial low-rank tensor factors, iteratively, in a self-supervised manner: (Self-supervision is a generic machine learning concept. This amounts to mere instructions to apply an exception.)
generating an updated low-rank tensor factor by inputting the initial low-rank tensor factor into a corresponding neural network; and (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, the following is an abstract idea:
minimizing a mean-squared approximation error using the tensor and the product of the updated low-rank tensor factors. (This recites the mathematical calculation of minimizing a function. This is a mathematical concept.)
Regarding claim 4, the rejection of claim 1 is incorporated herein. Further, the following is an abstract idea:
using a corresponding stochastic gradient descent (This recites the mathematical calculation of stochastic gradient descent. This is a mathematical concept.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, the following is an abstract idea:
wherein decomposing the tensor comprises decomposing the tensor as a product of the low-rank tensor factors. (This describes a mathematical equation, which is a mathematical concept.)
Regarding claim 6, the rejection of claim 4 is incorporated herein. The following is an abstract idea:
wherein the tensor is a matrix, the low-rank tensor factors are vectors, and the product is the outer product of the vectors. (As in claim 1, the decomposition of the tensor/matrix into low-rank tensor factors is a mathematical concept; specifying a matrix and vectors is a continuation of the abstract idea. A product is a mathematical calculation, which is a mathematical concept.)
Regarding claim 13, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for: (This limitation recites generic computer components and functions; this amounts to mere instructions to apply an exception.)
The remainder of claim 13 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claims 15-18 recite substantially similar subject matter to claims 3-6 respectively and are rejected with the same rationale, mutatis mutandis.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claim(s) 1, 5, 13, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aittala (“Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization”, 2019).
Regarding claim 1, Wu teaches
A method for low-rank decomposition of a tensor, the method comprising: (Page 4 states "Our goal is to recover the latent factors when we do not know the light transport matrix. In this section, we describe a novel matrix factorization method that uses the Deep Image Prior [38] to encourage natural-image-like structure in the factor matrices.")
decomposing, by a computer processor, a tensor into a plurality of initial low-rank tensor factors; and (Page 5 states "We start with two randomly initialized CNNs, each one outputting a respective matrix L and T. Similarly to [38], these CNNs are not trained from pairs of input/output labeled data, but are trained only once and specifically to the one target matrix. The optimization adjusts the weights of these networks with the objective of making the product of their output matrices identical to the target matrix being factorized." The matrices L and T are interpreted as the low-rank tensor factors, as the product of the matrices is intended to approximate the input matrix. It is low-rank because the rank of the factors is lower than the rank of the input matrix.)
for each of the initial low-rank tensor factors, iteratively, in a self-supervised manner: (Page 5 states "Similarly to [38], these CNNs are not trained from pairs of input/output labeled data, but are trained only once and specifically to the one target matrix. The optimization adjusts the weights of these networks with the objective of making the product of their output matrices identical to the target matrix being factorized." Therefore, as labels are not used, and loss is between the product of estimated factors to the target matrix (see Figure 3), the training/optimization is self-supervised.)
generating an updated low-rank tensor factor by inputting the initial low-rank tensor factor into a corresponding neural network, (See 112(b) rejection for interpretation. Page 5 states "The inputs
N
T
∈
R
n
T
and
N
L
∈
R
n
L
to the networks are typically fixed vectors of random noise." Fig. 3 shows that the input to the generator CNNs result in estimated factor matrices. Page 12 states "We typically run the network for 100 000 iterations, which takes approximately four hours on an NVIDIA Titan Xp GPU. Typically we see coarse results at a few thousand iterations, and details become filled in over the remaining iterations." Thus, during training, the low-rank tensor factors are updated for each iteration of training.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Aittala teaches
wherein decomposing the tensor comprises decomposing the tensor as the product of the low-rank tensor factors. (Page 5 states "The optimization adjusts the weights of these networks with the objective of making the product of their output matrices identical to the target matrix being factorized." Thus, the tensor decomposition decomposes the tensor as a product of the output matrices (low-rank tensor factors).)
Regarding claim 13, Wu teaches
A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for: (Page 12 states "We typically run the network for 100 000 iterations, which takes approximately four hours on an NVIDIA Titan Xp GPU. Typically we see coarse results at a few thousand iterations, and details become filled in over the remaining iterations. We implemented our model using PyTorch [25]." In order for the model to be implemented using PyTorch and run on the GPU, a non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for the method must be present.)
The remainder of claim 13 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claim 17 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
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.
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.
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aittala (“Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization”, 2019) as applied to claim 1 above, and further in view of Goodfellow (“Chapter 5: Machine Learning Basics”, 2016), hereinafter “Goodfellow-5”.
Regarding claim 3, the rejection of claim 1 is incorporated herein. Wu teaches
[minimizing a loss] using the tensor and the product of the updated low-rank tensor factors (Page 5 states "The optimization adjusts the weights of these networks with the objective of making the product of their output matrices identical to the target matrix being factorized." Additionally, Figure 3 shows that the loss is between the input matrix (tensor) and the product of the estimated factors (updated low-rank tensor factors).)
Wu does not appear to explicitly teach
minimizing a mean-squared approximation error.
However, Goodfellow-5, directed to analogous art—teaches
minimizing a mean-squared approximation error. (Page 127, Section 5.4.4 ‘Trading off Bias and Variance to Minimize Mean Squared Error’ states "For example, imagine that we are interested in approximating the function shown in figure 5.2 and we are only offered the choice between a model with large bias and one that suffers from large variance. How do we choose between them? The most common way to negotiate this trade-off is to use cross-validation. Empirically, cross-validation is highly successful on many real-world tasks. Alternatively, we can also compare the mean squared error (MSE) of the estimates:”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Aittala and Goodfellow-5 because, as Goodfellow-5 states on page 127, "The MSE measures the overall expected deviation—in a squared error sense— between the estimator and the true value of the parameter θ. As is clear from equation 5.54, evaluating the MSE incorporates both the bias and the variance. Desirable estimators are those with small MSE and these are estimators that manage to keep both their bias and variance somewhat in check."
Although the model taught by Goodfellow-5 is not a neural network, one of ordinary skill in the art would be motivated to use the optimization taught by Goodfellow-5 for a neural network as taught by Aittala.
Claim 15 recites substantially similar subject matter to claim 3 and is rejected with the same rationale, mutatis mutandis.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aittala (“Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization”, 2019) as applied to claim 1 above, and further in view of Goodfellow (“Chapter 8. Optimization for Training Deep Models”, 2016), hereinafter “Goodfellow-8”.
Regarding claim 4, the rejection of claim 1 is incorporated herein. Aittala does not appear to explicitly teach
wherein updating the parameters of the corresponding neural networks comprises using a corresponding stochastic gradient descent.
However, Goodfellow—directed to analogous art—teaches
wherein updating the parameters of the corresponding neural networks comprises using a corresponding stochastic gradient descent. (Page 290 states "Stochastic gradient descent (SGD) and its variants are probably the most used optimization algorithms for machine learning in general and for deep learning in particular." As it is for deep learning, SGD is for a neural network. Page 291, Algorithm 8.1 shows that the parameter
θ
is optimized.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Aittala and Goodfellow-8 because, as Goodfellow-8 states on page 292, "The most important property of SGD and related minibatch or online gradient-based optimization is that computation time per update does not grow with the number of training examples. This allows convergence even when the number of training examples becomes very large. For a large enough dataset, SGD may converge to within some fixed tolerance of its final test set error before it has processed the entire training set."
Claim 16 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aittala (“Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization”, 2019) as applied to claim 1 above, and further in view of Ma (“BDMF: A Biased Deep Matrix Factorization Model for Recommendation”, 2019) and Wang (“Nonnegative Matrix Factorization: A Comprehensive Review”, 2013).
Regarding claim 6, the rejection of claim 4 is incorporated herein. Aittala teaches
wherein the tensor is a matrix (Figure 3 shows that the input matrix (tensor) is a matrix).
Aittala does not appear to explicitly teach
the initial low-rank tensor factors are vectors, and
However, Ma—directed to analogous art—teaches
the initial low-rank tensor factors are vectors, and (Page 1041 states "The input section of BDMF consists of high-dimensional feature vectors for user u and item v, denoted by u and v, respectively. We first construct a user-item interaction matrix Y according to Equation (1), and then take u from matrix Y and v from matrix
Y
T
" The matrix is interpreted as the tensor. Page 1041 states "The projection section maps user vector and item vector from the input section into two dense vector spaces and trains the two networks separately. Finally,
u
and
v
are mapped to low-dimensional vectors in a latent space, as shown in Equations (9) and (10)."
u
and
v
are interpreted as the low-rank tensor factors.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Aittala and Ma because, as stated by Ma on Page 1040 "As one of the most effective CF method, matrix factorization has been widely studied." Additionally, Wu states on page 3, "In this paper, a Biased Deep Tensor Factorization Network (BDTFN) is proposed based on deep learning and tensor decomposition, inspired by the idea of a Biased Deep Matrix Factorization (BDMF) model [32]."
The combination of Aittala and Ma does not appear to explicitly teach
a product is an outer product of the vectors.
However, Wang—directed to analogous art—teaches
a product is an outer product of the vectors. (Page 1339 states "Using the bilinear model, complete NMF can be rewritten as linear combination of rank-one nonnegative matrices expressed by
X
=
∑
i
=
1
L
U
⋅
i
V
i
⋅
=
∑
i
=
1
L
U
⋅
i
∘
V
i
⋅
T
where
U
⋅
i
is the ith column vector of
U
while
V
i
⋅
is the ith row vector of
V
, and
∘
denotes the outer product of two vectors.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Aittala and Ma with the factorization of Wang because as stated by Wang on page 1336, "By contrast, a new paradigm of factorization—Nonnegative Matrix Factorization (NMF), which incorporates the nonnegativity constraint and thus obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly, was initiated by Paatero and Tapper [1], [2] together with Lee and Seung [3], [4]."
Claim 18 recites substantially similar subject matter to claim 6 and is rejected with the same rationale, mutatis mutandis.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA THUY PHAM whose telephone number is (571)272-2605. The examiner can normally be reached Monday - Friday, 9 A.M. - 5:00 P.M..
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/J.T.P./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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