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 .
Notice to Applicant
Limitations appearing inside of {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Claims 1, and 3-22 are pending in the application.
Response to Amendments
The Amendment filled 07/13/2026 in response to Non-Final Office Action mailed 02/12/2026 has been entered.
Claims 1, 11-13, 16, 17, and 19 have been amended.
Claim 2 has been canceled.
Claim 22 is newly added.
Rejections under 35 USC §§112(b), 102, and 103 on 02/12/2026 have been withdrawn in light of amended claims.
Response to Arguments/Remarks
Applicant’s arguments/remarks (See Remarks, filed on 07/13/2026, pages 7-8) with respect to references of record have been considered and persuasive with regards to the 35 USC §112(b) rejections of claims 17 and 21. The rejection of 02/12/2026 is withdrawn.
Applicant’s arguments with respect to claims 1-22 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.
Regarding amended claim 1, it is not clear if "a detection task" of line 15 detects the first unit region or second unit region. Examiner is interpreting this limitation as the first unit region and second unit region may be detected in the same detection task or may be from separate detection tasks. If applicant wants to tie detection to a specific unit region, then examiner respectfully recommends amending the claim to make that connection.
Information Disclosure Statement
Information Disclosure Statement(s) filed on 03/03/2026 and 08/11/2026 have been considered.
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.
Claims 1 and 3-22 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.
Regarding claims 1 and 19, they recite “the performed segmentation results”. It lacks antecedent basis. The previous recited step does not detail whether the segmentation yields multiple results and which of the multiple results the limitation is referring to.
The claims further recite “setting a first weight based on a result of the determination”. It appears that the determination maybe actually performed on a plurality of patch images. If so, it is not clear what “the determination” is referring to. Perhaps, the claim should be amended to recite “each of the determination for the each patch image of the one or more patch images”.
Similar explanation/clarification is required of the “update step”.
The claim further recites “a mix of detection targets of different sizes”. It is confusing as to what it means by “detection targets”. Is it referring to the “patch images” or some sort of determination/segmentation targets? Clarification/explanation is respectfully requested.
The term “relatively large” in claim 22 is a relative term which renders the claim indefinite. The term “relatively large” 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.
Claim 22 further recites “wherein for relatively large detection targets among the detection targets, the first weight for determining that the relatively large detection target is undetected is set to zero, in accordance with detection of at least a portion of the relatively large detection target”. It is unclear how the weight of “a relatively large detection target is undetected is set to zero”, and also be “in accordance with “detection of at least a portion of the relatively large detection target”. For the purpose of examination, the claim is interpreted according to specification of the instant application regarding a large area that cannot be detected: “with the method according to the embodiment of the present invention, the learning of such a large defect 922 ends (weight is set to zero) at an early stage” (¶[0079]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over “Chu” (Chu, Wen-Hsuan, and Kris M. Kitani. "Neural batch sampling with reinforcement learning for semi-supervised anomaly detection." European conference on computer vision. Cham: Springer International Publishing, 2020.) in view of “Guo” (Guo, Jingjing, et al. "Façade defects classification from imbalanced dataset using meta learning‐based convolutional neural network." Computer‐Aided Civil and Infrastructure Engineering 35.12 (2020): 1403-1418.).
Regarding claim 1, Chu teaches a learning method executed by a learning apparatus including a processor, the learning method comprising:
{causing the processor to execute}:
a data acquisition step of acquiring learning data consisting of one or more patch images and correct answer data of a class label for a first unit region of the one or more patch images (Fig 1 exhibits Sample Patches and Patch Labels; the figure exhibits that there are multiple patches, and neural batch sampler produces a sequence of patches, the patches are grouped into multiples of minibatches; Chu, [§3.2 Training, Autoencoder, page 9]);
a determination step of performing segmentation of the one or more patch images by using a learning model and the learning data (a classifier performing object segmentation; Chu, [§3.1 Overview, Predictor, page 6]), and
determining, for each patch image of the one or more patch images, whether or not a second unit region is correctly detected by the learning model (In Rclone, the neural batch sampler is not concerned about the ultimate goal of improving the contrast between the loss profiles of anomalous and non- anomalous regions. This results in a peculiar strategy: the batch sampler will repeatedly sample on regions near the first non-anomalous patch to minimize the risk of sampling an anomaly. To prevent this, we encourage the neural batch sampler to cover different portion of the data by including a small coverage bonus Rcover. This also preserves incentive for exploration and prevents the policy from collapsing to a single mode of action prematurely; the model is rewarded for sampling multiple regions, such as anomalous and non-anomalous regions, which is interpreted as first unit region and second unit region. The weighting parameter “[Symbol font/0x62] controls the weighting between the behavior cloning reward and the true optimization goal” Chu, [§3.2 Training, Neural Batch Sampler, page 8, ¶2-5], shown below;:
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), based on the performed segmentation results on the one or more patch images (Fig 1 exhibits Predicted Labels; and an anomaly classifier to detect and segment anomalies using the loss profiles of the data from training an autoencoder Chu, [§1 Introduction, page 2, ¶4]);
a weighting step of setting a first weight based on a result of the determination (the weighted binary cross entropy loss is calculated as described in Eq. 2 to update the predictor; Chu, [§3.2 Training, Predictor, page 10, ¶1]); and
an update step of updating the learning model based on a result of the weighting,
wherein a detection task is performed by the learning model (the predictor is a classifier; Chu, [§3.1 Overview, Predictor, page 6, ¶1]) {based on a mix of detection targets of different sizes}, and
in the weighting step, the processor sets the first weight, in a unit of the one or more patch images, for a first loss that is a loss for each patch image of the one or more patch images (Here K represents the batch size, is the empirically calculated re-weighting factor between the anomalous and non-anomalous pixels, y represents the ground truth annotations in the small labeled subset Dl, and ^y is the predicted labels obtained from the predictor at the end of the framework; Chu, [§3.2 Training, page 8, ¶1]).
Chu does not explicitly disclose a detection task based on a mix of detection targets of different sizes.
However, Guo, a similar field of endeavor of defects classification from imbalanced dataset using meta learning-based CNN model, teaches
causing the processor to execute (All the experiments were carried out with Intel® Core™ i5-8500 CPU on Ubuntu 16.04 and accelerated using an NVIDIA Titan RTX GPU with 24GB video memory; Guo, [§4.2, Model development, ¶1]); and
based on a mix of detection targets of different sizes (The size of cropping box was determined by the size of target defect on the image. It should be noted that the size of cropping box should be at least 500 × 500 to ensure sufficient features of the defect. In this study, the minimum and maximum sizes of cropping box were 776 × 776 and 5,120 × 3,840; Guo, [§4.1.2 Data preprocessing, ¶1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a processor as taught by Guo to the invention of Chu. The motivation to do so would be because a processor is needed to execute training and operating classification and detection learning models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include detection targets of different size as taught by Guo to the invention of Chu. The motivation to do so would be to reduce the influences of irrelevant objects during the training process and increase the size of the dataset, to train on more than one type of defect in an image, or to separate overlapping defects as individual defects as much as possible and label the image patch with the primary defect type.
Regarding claim 4, the combination of Chu and Guo teaches the learning method according to claim 1. Chu teaches wherein, in the determination step, the learning model detects {the second unit region belonging to} a specific class (Chu teaches training a model for detection and classification of different types of anomalies, where each class is run separately: explore semi-supervised methods for anomaly detection and segmentation in images; Chu, [§1 Introduction, p 4, ¶1]; The dataset includes image samples from 5 texture classes and 10 object classes; [§4.1 Datasets, page 11, ¶2]. “The experiments were run separately for each class”; §4.1 Datasets, page 11, ¶2).),
Chu does not explicitly disclose “detects the second unit region belonging to a specific class”.
However, Guo teaches wherein, in the determination step, the learning model detects the second unit region belonging to a specific class (A [class activation map] CAM for a particular category can indicate the discriminative image regions (i.e., first and second unit regions) used by the CNN model to identify that category; Guo, [§5.1, p1412, col 1, ¶2].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include detecting regional categories as taught by Guo to the invention of Chu. The motivation to do so would be to the visualize the and localization the target defect on an image.
Regarding claim 11, the combination of Chu and Guo teaches the learning method according to claim 1. Chu further teaches wherein, in the weighting step, the processor performs the weighting on a cross-entropy loss of the patch image (prediction loss lpred is defined as the weighted binary cross entropy loss to account of the inherent imbalance in the data; Chu 2020, [§3.2 Training, Neural Batch Sampler, ¶1]).
Regarding claim 12, the combination of Chu and Guo teaches the learning method according to claim 1. Chu further teaches wherein the processor further executes a loss function derivation step of deriving a loss function for a batch composed of the patch images (See Chu, Equation 2, shown below, exhibits loss function for a batch, where K represents the batch size
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), and
updates the learning model by using the loss function in the update step (The loss function is understood by one of ordinary skill in the art to update the model during backpropagation, and See Algorithm 1: Training exhibits “Update”; [page 7]).
Regarding claim 13, the combination of Chu and Guo teaches the learning method according to claim 12. Chu further discloses wherein, in the loss function derivation step, the processor derives, as the loss function, a first loss function obtained by averaging the result of the weighting over an entire batch composed of the patch images (See Equation 2, provided with claim 12, exhibits the averaging over K-batches).
Regarding claim 14, the combination of Chu and Guo teaches the learning method according to claim 13. Chu further teaches wherein, in the loss function derivation step, the processor uses, as the loss function, a function in which the first loss function and a second loss function, which is a loss function for the batch and is different from the first loss function, are combined (Chu teaches loss functions
l
p
r
e
d
, a loss function for the batch, and
l
a
e
, a loss function that is different, and where the two losses are combined in the overall architecture, as shown in Fig 1).
Regarding claim 15, the combination of Chu and Guo teaches the learning method according to claim 1. Guo further teaches wherein, in the update step, the processor updates a parameter of the learning model to minimize the loss function (the main idea in this paper is to reassign weights to input training data to minimize the loss function without bias; Guo, [§3.2, page 1408, col 2, ¶1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include updating parameters to minimize a loss function as taught by Guo to the invention of Chu. The motivation to do so would be because a person of ordinary skill in the art understands that a learning model is trained by minimizing a loss function.
Regarding claim 16, the combination of Chu and Guo teaches the learning method according to claim 1. Chu further teaches wherein, in the data acquisition step, the processor inputs an image to acquire a divided image of the input image as the one or more patch images (Chu, Fig 1 exhibits “Sampled Patches”; and the neural batch sampler produces a sequence of patches; Chu, [§3.2 Training, Autoencoder, page 9, ¶1]).
Regarding claim 17, the combination of Chu and Guo teaches the learning method according to claim 1. Guo further teaches wherein, in the data acquisition step, the processor acquires one or more patch images of a size corresponding to a size of a scratch and/or a defect of a subject to be detected (the image patch was cropped to focus on a specific type of defect; Guo, [§4.1.2, page 1410, col 2, ¶1]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include patch sizes according to defect size as taught by Guo to the invention of Chu. The motivation to do so would be to focus on a specific type of defect.
Regarding claim 18, the combination of Chu and Guo teaches the learning method according to claim 1. Chu further teaches wherein the learning model includes a neural network that performs the segmentation (the predictor is a classifier performing object segmentation in the loss space. The predictor is implemented as a fully convolutional network; Chu [§3.1, Predictor, page 6, ¶1]).
Claim 19 is similarly analyzed as analogous claim 1.
Regarding claim 20, Guo teaches a non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, a processor provided to the computer to execute the learning method according to claim 1 is recorded (All the experiments were carried out with Intel® CoreTM i5-8500 CPU on Ubuntu 16.04 and accelerated using an NVIDIA Titan RTX GPU with 24GB video memory; Guo, [§4.2, page 1411, col 1, ¶1]).
Claim 21 is similarly analyzed as claim 1. Chu further teaches wherein the trained model is used to detect a scratch and/or a defect of a subject from an input image (anomaly detection and segmentation in images; Chu, [§1 Introduction, page 2, ¶4]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Chu, in view of Guo and further in view of, Suzuki, US20200117991A1, as cited in the IDS.
Regarding claim 3, Chu teaches the learning method according to claim 1. The combination does not explicitly disclose wherein, in the weighting step, the processor sets, as the first weight, a larger weight in a case in which it is determined that the second unit region is not correctly detected than in a case in which it is determined that the second unit region is correctly detected.
However, Suzuki, a similar field of endeavor of detection and misclassification of objects, teaches as the first weight, a larger weight in a case in which it is determined that the second unit region is not correctly detected than in a case in which it is determined that the second unit region is correctly detected (The machine learning apparatus 100 compares the obtained results against correct regions and classes indicated by training information attached to the training dataset,. The machine learning apparatus 100 updates the synaptic weights in the detection model to reduce the error; Suzuki, ¶[0118]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include determining if second region is correctly detected as taught by Suzuki to the combined invention of Chu and Guo. The motivation to do so would be to thereby calculate error over all the detected region proposals.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Chu in view of Guo, and further in view of Ikeda et al., US 20190147586 A1.
Regarding claim 5, the combination of Chu and Guo teaches the learning method according to claim 4. Chu further teaches wherein, in the determination step, the processor determines that the second unit region is not correctly detected in a first case in which the second unit region belonging to the specific class is erroneously detected by the learning model (false positives that are not in the ground truth; Chu, [§4.2 Experimental Results, page 13, ¶1]. Similarly, Guo further teaches (The false-categorized cases can be divided as (a) false location and false label and (b) correct location and false label; Guo, [§5.1 Experimental results, page 1412, col 2, ¶2]) and
{in a second case in which the second unit region belonging to the specific class is not detectable by the learning model}.
The combination does not explicitly disclose in a second case in which the second unit region belonging to the specific class is not detectable by the learning model.
However, Ikeda, a similar field of endeavor, teaches in a second case in which the second unit region belonging to the specific class is not detectable by the learning model (in the case where an undetected defect has occurred a predetermined number of times, the filter parameter of the aforesaid preprocessing filter may be updated based on an image (hereinafter also “non-detection image”) including the undetected defect; Ikeda, ¶[0117]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include an undetected defect case as taught by Ikeda to the combined invention of Chu and Guo. The motivation to do so would be to update the processing filter with false negative cases.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Chu in view of Guo, in view of Ikeda and further in view of “He” (H. He and E. A. Garcia, "Learning from Imbalanced Data," in IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263-1284, Sept. 2009, doi: 10.1109/TKDE.2008.239.).
Regarding claim 6, the combination of Chu, Guo, and Ikeda teach the learning method according to claim 5. The combination does not explicitly disclose wherein, in the weighting step, the processor sets a larger weight in the second case than in the first case.
However, He, a similar field of endeavor of learning from imbalanced data, teaches wherein, in the weighting step, the processor sets a larger weight in the second case than in the first case (He [p 1270, col 2, §3.2.1, ¶1]; in a binary classification scenario, we define CðMin;MajÞ as the cost of misclassifying a majority class example as a minority class example and let CðMaj;MinÞ represents the cost of the contrary case).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a larger weight in the second case than the first as taught by He to the combined invention Chu, Guo, and Ikeda. The motivation to do so would be to minimize the overall cost on a data set based on probabilities/risk and select the best training data for induction.
Regarding claim 7, the combination of Chu, Guo, and Ikeda teaches The learning method according to claim 5. The combination does not explicitly disclose wherein, in the determination step, the processor determines that a result of the detection is correct in a third case in which the result of the detection is neither the first case nor the second case.
However He teaches wherein, in the determination step, the processor determines that a result of the detection is correct in a third case in which the result of the detection is neither the first case nor the second case (He [p 1270, col 2, §3.2.1, ¶1]; Typically, there is no cost for correct classification of either class and the cost of misclassifying minority examples is higher than the contrary case, i.e., CðMaj;MinÞ > CðMin;MajÞ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include correct detection as taught by He to the combined invention of Chu, Guo, and Ikeda. The motivation to do so would be to not penalize correct cases.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Chu in view of Guo, and further in view of “Bhatt” (Bhatt, P. M., Malhan, R. K., Rajendran, P., Shah, B. C., Thakar, S., Yoon, Y. J., and Gupta, S. K. (February 9, 2021). "Image-Based Surface Defect Detection Using Deep Learning: A Review." ASME. J. Comput. Inf. Sci. Eng. August 2021; 21(4): 040801. https://doi.org/10.1115/1.4049535).
Regarding claim 8, the combination of Chu and Guo teaches the learning method according to claim 4. Guo further teaches wherein, in the determination step, the processor performs the determination on {a scratch and} a defect of a subject (See Guo, Table 1, lists the categories of defects the detection model is trained on, including: “Blistering, Peeling, Crack, Delamination, Spalling, Biological growth”). The combination does not explicitly teach determination on scratch and a defect.
However, Bhatt, in a similar field of endeavor of model-based techniques for defect detection in images, teaches wherein, in the determination step, the processor performs the determination on a scratch and a defect of a subject (Surface defects can be of a variety of forms such as scratch, crack, inclusion, spots, dents, holes, and many more…. Concurrent identification of multiple defects problems has been studied in Ref. [34]. The entire system architecture is divided into four stages, (1) anomaly detection, (2) filtering false anomaly, (3) clustering defect pixels, and (4) defect classification; [p 4, §3.3, Col 1, ¶1 and Col 2, ¶2])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include detecting a scratch and a defect as taught by Bhatt to the invention of Chu and Guo. The motivation to do so would be because defects arise with various shapes and sizes and there is no need for a custom code needed for training different types of defects. The labeled data for different defects with the appropriate network provides a significantly flexible defect detection mechanism.
Claims 9 and 10 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Chu in view of Guo and further in view of “Dong” (Z. Dong, X. Shao and R. Zhang, "Surface Defect Segmentation with Multi-column Patch-Wise U-net," 2019 IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China, 2019, pp. 1436-1441, doi: 10.1109/ICCC47050.2019.9064246.).
Regarding claim 9, the combination of Chu and Guo teaches the learning method according to claim 4. The combination does not explicitly disclose wherein the learning model outputs a certainty of the detection in the determination step, the processor determines whether or not the second unit region belongs to the specific class based on whether or not the certainty is equal to or higher than a threshold value.
However, Dong, a similar field of endeavor of surface defect segmentation, teaches wherein the learning model outputs a certainty of the detection (We add MIL layer behind the last convolutional layer of our network, it computes probability map and each element represents the probability pij of the category in which it falls according to Eq. (1).; Dong, [p 1438, Col 1, §III.B., ¶1]), and
in the determination step, the processor determines whether or not the second unit region belongs to the specific class based on whether or not the certainty is equal to or higher than a threshold value (once the mean of the pixel level (corresponding to the instance level) probabilities pij surpasses a certain threshold, a patch level (corresponding to the bag level) probability pij is activated; Dong, [p 1438, Col 2, §III.B. MIL Layer, ¶2]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include uncertainty of classification as taught by Dong to the invention of Chu and Guo. The motivation to do so would be to activate a patch level class.
Regarding claim 10, the combination of Chu, Guo and Dong teaches the learning method according to claim 9. Dong further teaches wherein the processor changes the threshold value in a process of learning (MIL layer is used to activate a patch level class probability pij by softmax function when the mean of the instance level probabilities surpasses the setting threshold. Because we set the feature tensors [ch,w, h] to [ch,wxh], the value of fij is computed in the global scope of image patch. The parameters a and bij control the shape of sigmoid function, [Symbol font/0x20][Symbol font/0x73](abij) and [Symbol font/0x20][Symbol font/0x73](-abij) ab are added to normalize patch probability pij to [0,1]; Dong, [p 1438, Col 2, §III.B. MIL Layer, ¶3]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include uncertainty of classification as taught by Dong to the invention of Chu and Guo. The motivation to do so would be to activate a patch level class.
Claim 22 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Chu in view of Guo and further in view of “Bi” (Bi et al., US 20070110292 A1).
Regarding claim 22, the combination of Chu and Guo teaches the learning method according to claim 1.
The combination does not explicitly disclose wherein for relatively large detection targets among the detection targets, the first weight for determining that the relatively large detection target is undetected is set to zero, in accordance with detection of at least a portion of the relatively large detection target.
However, Bi, a similar field of endeavor of computer-aided detection of abnormalities in images, teaches wherein for relatively large detection targets among the detection targets, the first weight for determining that the relatively large detection target is undetected is set to zero, in accordance with detection of at least a portion of the relatively large detection target (interpreted according to specification of the instant application ¶[0079].)(only a few features receive a non-zero weight w,…, a weighted l.sub.1-norm is employed where weights are determined by the computational cost of each feature. Each linear program employs an asymmetric error measure that penalizes false negatives and false positives with different weights. An extreme case is that the penalty for a false negative is infinity, which is used in the early stage of the cascade design; Bi, ¶[0010])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include reducing false negatives in the model as taught by Bi to the combined invention of Chu and Guo. The motivation to do so would be to alleviate the skewed class distribution and preserve high detection rates.
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.
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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